Farm-scale ponding susceptibility mapping for potential land ...

Farm-scale ponding susceptibility mapping for potential land reclamation assessment

in large flatlands

A case study in the Salado River Basin, Province of Buenos Aires, Argentina

Sebastián G. Ludueña

March, 2006

Farm-scale ponding susceptibility mapping for potential land reclamation assessment

in large flatlands

A case study in the Salado River Basin, Province of Buenos Aires, Argentina

by

Sebastián Gabriel Ludueña Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Geo-information Science and Earth Observation, Specialisation: Integrated Catchment and Water Resources Management. Thesis Assessment Board Chairman: Prof. Dr. Ir. Z. Su (Head of WRS Department – ITC, Enschede)

External Examiner: Dr. Ir. Paolo Reggiani (WL-Delft Hydraulics)

First Supervisor: Ir. MSc. Gabriel N. Parodi (ITC, Enschede)

Second Supervisor: Dr. Ir. Tom H. M. Rientjes (ITC, Enschede)

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION ENSCHEDE, THE NETHERLANDS

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.

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Abstract The Salado River Basin, located in the Argentina’s Llanura Pampeana (Pampean Plain), is an extended flatland of about 200,000 km2 characterized for suffering cyclic, extreme water-excess and drought conditions. This dynamic hydrological behaviour seriously affects farming activities carried out in the basin, the country’s most prolific region in terms of agriculture and livestock production. This study was carried out in an agriculture-livestock ranch of 6,000 Ha located in General Villegas District, over the NW region of the basin. The study aimed to determine specific relief-soil-plant-water associations existing in the area that can be used as indicators of different ponding susceptibilities; and to implement a mapping procedure for the elaboration of ponding susceptibility maps at farm-scale with application to the study region, where data scarcity limits advanced hydromodelling. The research was framed by the INTA’s Agro-hydrologic Systematization Methodology, which is a comprehensive approach that includes hydrologic, edaphic, agronomic and socioeconomic aspects to deal with the different dimensions (legal, political, technical, social and economic) of the ponding problematic at the selected level of analysis. Applied fieldwork methods included soil profile analysis, saturated hydraulic conductivity measurements, bulk density determination and vegetation/land cover identification. The developed mapping methodology combined multitemporal satellite imagery processing (NDVI, Pv); RS data qualitative interpretation; soil survey results; and expert local knowledge, altogether working in a GIS-assisted environment to delineate the spatial distribution of terrain units and soil series within the study site. This information was combined with qualitative agronomic and productivity criteria to define the land diagnosis of the study site; and to establish related ponding susceptibility and potential land reclamation categories. The results indicated that several sectors of the study site are currently affected by hydro-halomorphic conditions, derived from the superficial accumulation of water excesses and the rising of salt-bearing water table. These conditions endanger the current productive activities, while hinder the full production-development of the site. After the full implementation of the current agro-hydrologic water management project executed in the farm, a reclamation procedure on the affected lands would expand the current livestock areas up to a 25% of the total ranch area. The followed research approach has resulted useful for defining land diagnosis; and deriving ponding susceptibility and potential land reclamation categories in the study site. The applied methods demonstrated to be suitable for facing ancillary data scarcity at the selected scale of analysis.

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Acknowledgements I would like to express my sincere gratitude to the members of the Joint Japan/World Bank Graduate Scholarship Program (JJ/WBGSP) for granting me this unique opportunity to pursue a Master of Science degree. I am profoundly grateful to my countryman and first supervisor, Ir. MSc. Gabriel Norberto Parodi, for his excellent guidance during the research work. His constructive attitude and invaluable advices helped me to clear off many doubts and take this research into the right direction. Moreover, in moments of academic and personal difficulties, he was always willing to listen and give me a word of encouragement. For all these things…Gracias, Gabriel!! An immense gratitude goes to Ing. Agr. Francisco “Franco” Damiano, my dear fieldwork guide. Without his invaluable knowledge, patience and assistance during and after the field campaign, this thesis would have been impossible to realize. I specially thank my second supervisor, Dr. Ir. Tom Rientjes, for his valuable openness and suggestions for this research, but particularly for the fruitful conversations we had that helped me to broaden my basic understanding about many hydrology topics. My sincere gratitude goes to Gerald and Peter Emerson, the owners of “Estancia Los Recuerdos”, who made me feel like at home during the long days of field campaign within their property. I also wish to extend my gratitude to the Instituto Nacional de Tecnología Agropecuaria (INTA), for providing me the logistics and institutional support during the fieldwork period. Many thanks go also to all the WREM Programme staff, for imparting their valuable knowledge during past 18 months, especially to the Programme Director Ir. Arno van Lieshout for his kindly assistance throughout the study period. A special acknowledgment goes the members of “La Banda Latina”, my now “old” friends Lily, Jessica, Jenny, Laura, Jaime, Manuel, Alex and Rafael. During eighteen months I shared with them many moments of joy, and some of sorrow. From the very beginning of my staying in Enschede, their support and companionship meant much to me. I will never forget you!! Finally, there are many people in Argentina who have helped me in many ways to accomplish this journey across the Atlantic. It is impossible to name here all of them, but I am nevertheless deeply grateful. …This thesis is dedicated to the memory of my father, Horacio; to my mother, Elsa; to my loving wife, Miriam; and to my dearest son José Ignacio…

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Table of contents 1. Introduction .............................................................................................................1

1.1. Briefing flatlands hydro-systems .........................................................................................1 1.2. Salado River Basin background information .......................................................................2 1.3. Problem statement................................................................................................................3 1.4. Research relevance and goals ..............................................................................................6 1.5. Research objectives..............................................................................................................7 1.6. Research questions...............................................................................................................8 1.7. Research approach ...............................................................................................................9

1.7.1. Susceptibility approach................................................................................................................ 9 1.7.2. Research steps.............................................................................................................................. 9

1.8. Thesis structure ..................................................................................................................10

2. Description of study area .....................................................................................11 2.1. Location .............................................................................................................................12 2.2. Geology..............................................................................................................................12 2.3. Geomorphology .................................................................................................................13

2.3.1. Sub-area of longitudinal dunes .................................................................................................. 14 2.3.2. Sub-area of parabolic dunes....................................................................................................... 15

2.4. Climate ...............................................................................................................................16 2.5. Soils....................................................................................................................................18 2.6. Hydrology ..........................................................................................................................19 2.7. Vegetation ..........................................................................................................................20 2.8. Socioeconomic facts ..........................................................................................................22 2.9. Fieldwork site.....................................................................................................................24

3. Relief-soil-plant relations .....................................................................................26 3.1. Introduction........................................................................................................................26 3.2. Relief..................................................................................................................................26 3.3. Genesis and characterization of soils .................................................................................27

3.3.1. Ponding and soil salinization ..................................................................................................... 30 3.3.1.1. Soil structure and quality of ponding water ................................................................................... 33

3.3.2. Soil physical properties and their relation with hydrologic characteristics ............................... 34 3.3.2.1. Texture........................................................................................................................................... 34 3.3.2.2. Structure......................................................................................................................................... 35 3.3.2.3. Bulk density ................................................................................................................................... 36 3.3.2.4. Soil depth....................................................................................................................................... 37 3.3.2.5. Infiltration and hydraulic conductivity .......................................................................................... 37

3.4. Vegetation ..........................................................................................................................38 3.4.1. Mesophytic meadows ................................................................................................................ 39 3.4.2. Humid mesophytic meadows..................................................................................................... 39 3.4.3. Humid prairies ........................................................................................................................... 39 3.4.4. Halophytic steppes..................................................................................................................... 40

4. Land diagnosis methodology ..............................................................................41 4.1. Introduction........................................................................................................................41 4.2. Available Data....................................................................................................................41

4.2.1. Software and fieldwork equipment............................................................................................ 42 4.3. Agro-hydrologic Systematization methodology ................................................................42

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4.3.1. Agro-hydrologic feasibility module........................................................................................... 44 4.3.2. Functional design module.......................................................................................................... 45 4.3.3. Structural design module ........................................................................................................... 46 4.3.4. Project implementation module ................................................................................................. 46 4.3.5. Soil evaluation and fertilization module .................................................................................... 47

4.4. Land diagnosis procedures.................................................................................................48 4.4.1. Analysis of ancillary data .......................................................................................................... 48

4.4.1.1. Topographic maps ......................................................................................................................... 48 4.4.1.2. Aerial photographs......................................................................................................................... 50 4.4.1.3. Soil maps ....................................................................................................................................... 53

4.4.2. Satellite imagery analysis and interpretation ............................................................................. 62 4.4.3. Field methods............................................................................................................................. 73

4.4.3.1. Soil profiles description ................................................................................................................. 75 4.4.3.2. Soil hydraulic conductivity measurement...................................................................................... 83 4.4.3.3. Bulk density measurement............................................................................................................. 85 4.4.3.4. Land cover identification and analysis .......................................................................................... 89

4.4.4. Relating soil series information to terrain units......................................................................... 91

5. Ponding susceptibility and reclamation assessment ........................................97 5.1. Ponding susceptibility definition and mapping..................................................................97 5.2. Potential reclamation of affected areas ............................................................................101

6. Research results: other applications ................................................................105 6.1. Subsurface flow modelling ..............................................................................................105

6.1.1. Brief model description ........................................................................................................... 105 6.1.2. Simulated situations................................................................................................................. 106 6.1.3. Data requirements .................................................................................................................... 106

6.1.3.1. Soil information ........................................................................................................................... 106 6.1.3.2. Stress conditions .......................................................................................................................... 108 6.1.3.3. Boundary conditions.................................................................................................................... 109 6.1.3.4. Initial conditions .......................................................................................................................... 110

6.1.4. Modelling results ..................................................................................................................... 110 6.1.4.1. Saboya series ............................................................................................................................... 110 6.1.4.2. Balbín series ................................................................................................................................ 112

6.2. Land subdivision, land valuation and other socioeconomic issues..................................114

7. Conclusions and recommendations .................................................................115 7.1. Conclusions......................................................................................................................115 7.2. Recommendations............................................................................................................116

8. References...........................................................................................................118

Appendices ....................................................................................................................126 Appendix A: Geo-reference information .....................................................................................126 Appendix B: Location details of ground control points (GCPs)..................................................127 Appendix C: Soil series analytical information (from INTA’s 1:50,000 soil maps)...................129 Appendix D: Morphologic information of analyzed soil profiles................................................137 Appendix E: Hydraulic conductivity measurements ...................................................................140 Appendix F: Bulk density and soil water-storage measurements ................................................150

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List of figures Figure 1-1: Severe flood event in the study area (1998) ................................................................................... 5 Figure 1-2: Ponding conditions affecting productive lands............................................................................... 5 Figure 2-1: Location of Salado River Basin.................................................................................................... 11 Figure 2-2: Halcrow’s Project divisions: location of Northwest Region......................................................... 12 Figure 2-3: DEM of Northwest Region........................................................................................................... 14 Figure 2-4: Characteristic landscape areas in the NWR: longitudinal (A) and parabolic (B) dunes............... 15 Figure 2-5: Annual Mean Precipitation in Gral. Villegas (1898-2004)........................................................... 16 Figure 2-6: Rainfall – ET0 – Water deficit relations in Gral. Villegas (1990-1996) ....................................... 17 Figure 2-7: Distribution of dominant soil types in Northwest Region ............................................................ 18 Figure 2-8: Surface hydrology of Northwest Region ...................................................................................... 20 Figure 2-9: Typical view of Northwest Region grasslands ............................................................................. 21 Figure 2-10: Vegetation of saline conditions .................................................................................................. 21 Figure 2-11: Northwest Region districts ......................................................................................................... 22 Figure 2-12: Location of “Los Recuerdos” ranch............................................................................................ 24 Figure 3-1: Topo-morphologic sequence sketch ............................................................................................. 27 Figure 3-2: Ideal topographic distribution of subgroup of soils ...................................................................... 30 Figure 3-3: Soil structure deterioration under sodic conditions ...................................................................... 31 Figure 3-4: Diagram showing the consequences of ponding water quality on soil structure .......................... 33 Figure 3-5: Unconfined and confined groundwater rising in soils with and without a tough Bt horizon ....... 33 Figure 3-6: Particle size classification............................................................................................................. 34 Figure 3-7: Soil texture triangle (USDA) ........................................................................................................ 34 Figure 3-8: Soil structure types ....................................................................................................................... 35 Figure 3-9: Soil structure and its effects on permeability................................................................................ 36 Figure 3-10: Vegetation distribution generated from relief and soil characteristics ....................................... 40 Figure 4-1: INTA’s Agro-hydrologic Systematization Methodology flowchart............................................. 44 Figure 4-2: Topographic setting and main water movement vectors of “Los Recuerdos” ranch ................... 49 Figure 4-3: Indicative Digital elevation model (DEM) of “Los Recuerdos” ranch......................................... 50 Figure 4-4: Location of GPS measuring points ............................................................................................... 51 Figure 4-5: Ortho-mosaic of “Los Recuerdos” ranch ...................................................................................... 52 Figure 4-6: Parcel layout and internal infrastructure....................................................................................... 53 Figure 4-7: Class and Subclass components of the land use capability classification .................................... 54 Figure 4-8: 1:50,000 scale soil maps covering the study area ......................................................................... 56 Figure 4-9: Soil map of “Los Recuerdos” ranch.............................................................................................. 57 Figure 4-10: Sketch of CU 1 composition....................................................................................................... 58 Figure 4-11: Sketch of CU 2 composition....................................................................................................... 58 Figure 4-12: Sketch of CU 3 composition....................................................................................................... 59 Figure 4-13: Sketch of CU 4 composition....................................................................................................... 59 Figure 4-14: Sketch of CU 5 composition....................................................................................................... 60 Figure 4-15: Sketch of CU 6 composition....................................................................................................... 60 Figure 4-16: Sketch of CU 7 composition....................................................................................................... 61

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Figure 4-17: Monthly precipitation pattern and imagery acquisition dates..................................................... 63 Figure 4-18: Methodological flowchart for RS data analysis and interpretation ............................................ 65 Figure 4-19: NDVI and Pv products generated from Aster imagery............................................................... 68 Figure 4-20: NDVI and Pv products generated from Landsat-5 imagery ....................................................... 69 Figure 4-21: NDVI and Pv products generated from Landsat-7 imagery ....................................................... 69 Figure 4-22: Map of terrain units for “Los Recuerdos” ranch......................................................................... 72 Figure 4-23: Ideal toposequence showing the relative topographic position of each terrain unit ................... 72 Figure 4-24: Distribution of soil sampling points ........................................................................................... 74 Figure 4-25: General setting of Pit #1 ............................................................................................................. 76 Figure 4-26: Profile arrangement of Pit #1...................................................................................................... 76 Figure 4-27: Landscape characteristics of Pit #2............................................................................................. 77 Figure 4-28: Profile arrangement of Pit #2, showing massive columnar structures in Bt1 horizon................ 77 Figure 4-29: Location conditions of Pit #3...................................................................................................... 79 Figure 4-30: Profile arrangement of Pit #3, showing rounded columnar structures in Bt horizon.................. 79 Figure 4-31: Location setting of Pit #4............................................................................................................ 81 Figure 4-32: Profile arrangement of Pit #4, detailing the textural and structural discontinuity between AC and 2Bt horizons .................................................................................................................................................... 81 Figure 4-33: Location setting of Pit #5............................................................................................................ 82 Figure 4-34: Diagram of inverse auger method............................................................................................... 84 Figure 4-35: Soil samples extraction for determination of bulk density values in laboratory......................... 86 Figure 4-36: Diagram showing the relation between hydromorphic-halomorphic soil conditions and land cover types....................................................................................................................................................... 90 Figure 4-37: Different land covers in “Los Recuerdos” ranch ........................................................................ 91 Figure 4-38: Map of soil series distribution per terrain unit in “Los Recuerdos” ranch................................. 93 Figure 5-1: Flowchart of ponding susceptibility and potential reclamation maps elaboration........................ 97 Figure 5-2: Ponding susceptibility map of “Los Recuerdos” ranch .............................................................. 100 Figure 5-3: Potential reclamation map of “Los Recuerdos” ranch ................................................................ 103 Figure 6-1: Gumbel probability analysis for 24-hours rainfall at “Los Recuerdos” ranch (1952-2004)....... 108 Figure 6-2: Modelling of subsurface flow in Saboya soil series ................................................................... 111 Figure 6-3: Modelling of a through flow across a permeable dam in Balbín soil series ............................... 113

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List of tables Table 2-1: Geological sequence of Northwest Region .................................................................................... 13 Table 2-2: Average rainfall values from four locations in Northwest Region ................................................ 16 Table 2-3: Area and population of Northwest Region districts ....................................................................... 23 Table 3-1: Common salt compounds found in soils ........................................................................................ 30 Table 3-2: Characteristic values of pH, EC and ESP in salt-affected soils ..................................................... 32 Table 3-3: General relationship of soil bulk density to soil texture ................................................................ 37 Table 3-4: Soil permeability scale according to Kh values .............................................................................. 38 Table 4-1: Available aerial photographs ......................................................................................................... 41 Table 4-2: Available satellite imagery............................................................................................................. 41 Table 4-3: Available maps............................................................................................................................... 42 Table 4-4: Available meteorological data ....................................................................................................... 42 Table 4-5: Parcel’s database ............................................................................................................................ 53 Table 4-6: Land use capability (LUC) classes................................................................................................. 55 Table 4-7: Productivity Index (PI) values and related land uses ..................................................................... 55 Table 4-8: List of soil map Cartographic Units (CU)...................................................................................... 57 Table 4-9: List of soil series or taxonomic units (TU) .................................................................................... 61 Table 4-10: Hydrologic condition of available satellite imagery .................................................................... 63 Table 4-11: NIR and Red bands wavelength for Landsat TM/ETM+ and Aster sensors................................ 64 Table 4-12: Calibration coefficients used for Landsat-5 images (bands 3 and 4) ........................................... 66 Table 4-13: LMAX and LMIN values for Landsat 7 ETM+ images .............................................................. 67 Table 4-14: cosθ and d2 values used for calculation of reflectances ............................................................... 67 Table 4-15: ESUNλ values for the respective Aster and Landsat 5/7 bands................................................... 68 Table 4-16: Terrain units areas........................................................................................................................ 72 Table 4-17: Location of sampling points......................................................................................................... 74 Table 4-18: Major characteristics of horizons in Pit #1 .................................................................................. 75 Table 4-19: Major characteristics of horizons in Pit #2 .................................................................................. 78 Table 4-20: Major characteristics of horizons in Pit #3 .................................................................................. 80 Table 4-21: Principal characteristics of horizons in Pit #4.............................................................................. 82 Table 4-22: Major profile characteristics in Pit #5.......................................................................................... 83 Table 4-23: Soil data retrieved from a previous soil survey carried out in the study site ............................... 83 Table 4-24: Saturated hydraulic conductivity (Kh) results............................................................................... 85 Table 4-25: ρb, φ, Lh and moisture values for Saboya soil series (Typic Argiudoll) ....................................... 87 Table 4-26: ρb, φ, Lh and moisture values for Balbín soil series (Duric Natraquoll)....................................... 87 Table 4-27: ρb, φ, Lh and moisture values for Drabble soil series (Typic Natraquoll) .................................... 88 Table 4-28: ρb, φ, Lh and moisture values for Ortiz de Rosas soil series (Thapto-Argic Hapludoll) .............. 88 Table 4-29: ρb, φ, Lh and moisture values for Cañada Seca soil series (Thapto-Argic Hapludoll)................. 88 Table 4-30: Average soil moisture values (cm3 water / cm3 soil) along the profile in each soil series ........... 88 Table 4-31: Soil moisture and water storage conditions for the analyzed soil series ...................................... 89 Table 4-32: Representative vegetation /land cover types per terrain unit ....................................................... 90 Table 4-33: Soil series areas and corresponding percentages making up each terrain unit............................. 93

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Table 4-34: Principal characteristics of relief, vegetation, surface horizon, drainage and recovery factors per soil series ......................................................................................................................................................... 96 Table 5-1: Characteristics of ponding susceptibility categories ...................................................................... 99 Table 5-2: Ponding susceptibility areas, percentages and embraced soil series ............................................ 100 Table 5-3: Characteristics of potential reclamation categories...................................................................... 102 Table 5-4: Potential reclamation areas and percentages, involved soil series ............................................... 103 Table 6-1: Observed and calculated information for Saboya soil series ....................................................... 107 Table 6-2: Observed and calculated information for Balbín soil series ........................................................ 108 Table 6-3: Summary of the stresses selected for the modelling period (30 days in November, 2005) ......... 109

Farm-scale ponding susceptibility mapping for potential land reclamation assessment in large flatlands. A case study in the Salado River Basin, Province of Buenos Aires, Argentina

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1. Introduction Every year, coastal and inland floods adversely affect around 75 millions people worldwide. Flood episodes of a previously unremembered magnitude occurred several times in the past twenty years in places like Canada, South Africa, India and Argentina. Moreover, the trend during the last three decades shows an increase in the number of floods events and of affected populations (Smith, 2001). Although floods have always existed and are a natural and recurring phenomenon, they became a problem since humans established cities and facilities in the nearby of streams and rivers, as well as in flat and coastal areas (Dingman, 2002; Fattorelli et al., 1999). However, floods do not happen actually in all places and the nature and scale of flood events varies greatly. There are certain areas that because their particular topographic, geological or geomorphological characteristics (Fuschini Mejía, 1994; Kovacs, 1984; Tricart, 1973; Tricart, 1984) have more conditions to be prone to floods: this is the case of the flatlands.

1.1. Briefing flatlands hydro-systems

Hydrological processes in flatlands and large plains differ considerably from those characterizing sloping terrains. Flat areas’ most characteristic feature is a negligible or almost completely absence of slope, and an extremely low morphological energy. In this context, vertical components of water cycle (evaporation and infiltration) have much more relevance than the horizontal (surface and subsurface runoff) ones. This indicates that the principles of conventional hydrology, based on the catchment response to precipitation, cannot be used to characterize and analyze flatlands’ hydrology without profound adaptations (Fuschini Mejía, 1989; Kovacs, 1984). Supporting this concept, methods in conventional hillslope hydrology are based on the principle of water (mass) conservation occuring in a unique, measurable and topographically bounded area, denominated basin or watershed (Dingman, 2002), capable of receiving a sequence of inputs of a conservative quantity, storing some amounts of that quantity and discharging outputs of the same quantity. The quantification of the total water amount described in the processes happening in the basin is then established by geography. In flatlands environments, on the contrary, the basin is not defined by topographic features but for the dimensions (in time, space and intensity) of the input event. For a mild storm, small relief features (like ponds and concave, deflational sectors) would be enough to hold the water excess, limiting the drainage area to localized environments. However, for a bigger input event all these previously isolated stores become now connected, resizing the contributing area to a different conservative system. Hence, in flatlands the basin extension changes for every storm (considering prevailing moisture conditions as part of the game), spanning from few hectares to thousands of square kilometres. Inundation events in flat areas are characterized for significant water volumes that are stored and remain on top of the surface, covering vast extensions of land during relatively long periods. This is produced mainly because of the low topographic gradients and natural or man-made barriers preventing horizontal flow. Also, a commonly associated phenomenon is the rise of the phreatic level. As a consequence of both horizontal and vertical constraints, water excess cannot either flow or infiltrate, and remains at the same location during long periods (months or even years), after which water eventually begin to be removed very slowly


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by laminar flow (following the exiguous slope of the surface); by evapotranspiration; and by deep percolation (Fuschini Mejía, 1994; Kovacs, 1984; Tricart, 1973). This water excess may have its origin in the overflow of rivers, streams or canals crossing the affected area but bringing water from outside sources (external water); or it can result from the accumulation of local rainfall waters, directly precipitated over the affected area or over its nearby surroundings. Although the physical results are the same, the origins are completely dissimilar. Therefore, along this document we will distinguish both situations by using the term flooding for referring to the first case; and ponding, for alluding to the second case (Barnes et al., 1990). The Salado River Basin∗, located in the Argentina’s Llanura Pampeana (Pampean Plain) constitutes a worldwide unique case of an extensive flatland, which is regularly subjected to flooding/ponding and droughts events with the previously mentioned characteristics. Moreover, the region’s very particular topographic and hydrologic attributes (extended area having an average slope around 0.01%, no watershed definition, scarcely organized stream network without a defined outlet and poorly drained soils) and the progressive increase of rainfall depths during the last 30 years, led to a dramatic rising of the regional water table (Ferrari Buono, 2004; Halcrow & Partners, 1999; Scarpati et al., 2002). In this area, the vertical components of the hydrologic cycle (evaporation-infiltration) drain up to 94% of the water coming into the system, as proven by water balances and river discharges analysis carried out through the last three decades in different sectors of the basin (Damiano et al., 1989; Halcrow & Partners, 1999; Paoli & Giacosa, 2003; Scarpati et al., 2002).

1.2. Salado River Basin background information

Historically, the Salado River basin is characterized for suffering regularly both extreme flood and drought conditions during long periods of time. This situation had been observed and documented since the first years of Spanish colonization (Moncaut, 2003), and it became the research topic for many local and foreign naturalists since the late nineteenth century, as for example Florentino Ameghino who was the first scientist describing the alternation of wet and dry periods affecting the region, and giving some leading guidelines (still valid) for its management (Ameghino, 1886). The Salado River basin’s well-defined wet and dry cycles are found to be closely linked to larger scale (global) climatic variability (Scarpati et al., 2002; Solbrig, 1997; Spescha et al., 2004). Dry periods, with severe droughts, water scarcity, high temperatures and high potential evapotranspiration rates are followed by wet periods with high precipitation values and the rise of groundwater levels. These excess circumstances contribute to create constant ponding conditions; and (because of the high salt concentrations that are present in the groundwater all over the region) to produce generalized soil salinization processes with different degrees of severity (Barbagallo, 1984; Casas, 2003). Even though its area occupies about 3% of the Argentine territory and it is inhabited by only 1.3 million people, the Salado basin is the country’s most important region in terms of agriculture and livestock ∗ Despite that this text refers to the “Salado Basin” in the Province of Buenos Aires, the adjacent basins in the Pampean Plain have similar characteristics and problems, covering an area as twice as big as the Salado basin.


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activities, generating 25% and 30% of the national production of crops and meat respectively, 60% of the nation’s agro-industrial exports, and 9% of Argentina’s GDP (Dibbern, 2003; Garbulsky & Deregibus, 2004). According to these facts, it is noteworthy that a major portion of the national economy is supported by the basin’s farming activities. The cyclic phenomena of floods and droughts limit livestock and agricultural production in potentially usable areas, endanger regional urban centres and cause uncertainties that hinder investments, such as the adoption of new agricultural technologies and integrated development plans to exploit the area’s full potential. Therefore, it becomes clear how such a dynamic hydrologic behaviour generates serious impacts and restrictions not only in the regional but also in the national socioeconomic development (Halcrow & Partners, 1999). A wet phase (similar to the current one) was observed in the region during the late 19th and early 20th centuries, which originated overwhelming flooding events in 1884, 1900, 1913/14 and 1923. On the contrary, between 1930 and 1965 the Salado basin was affected by a severe dry phase that reduced dramatically the water availability, threatening its agricultural and livestock production activities (Carballo, 2001; Moncaut, 2003). As from the early seventies, the Salado basin started a new wet period, notably boosted during ENSO∗ years (Isla et al., 2003; Scarpati et al., 2002). Since that moment, the basin’s average annual precipitation has shifted from the historical 700/800 to the current 1000 millimetres, now mainly concentrated during the summer period. As mentioned by several authors (Carballo, 1993, 2001; Casas & Pittaluga, 1990; Kruse et al., 2001) a direct consequence related to the increment in rainfall was the continuous rising of the regional phreatic level, which was located more than 10 m depth at the end the previous dry phase but it is nowadays very close or even on surface in many sectors of the basin. The effect is a dramatic limitation in the soil water storage capacity, which can only be recovered by evapotranspiration. All these circumstances led to a series of technologic, economic and cultural adaptations in the region, in detriment of the natural behaviour of the system (Montico, 2004).

1.3. Problem statement

From the previous paragraphs it can be summarized that, as from the beginning of the new wet climatic phase affecting the Pampean Plain, a growing frequency of flood events, ponding conditions and soil salinization processes seriously threaten not only the Salado River basin’s natural conditions but also its socio-economic activities during the last 30 years (Carballo, 2001; Montico, 2004; Penning-Rowsell, 1996; Scarpati et al., 2002; Viglizzo et al., 2001). Despite the continuous increase of social and political pressure generated by flooding episodes occurred in recent years (1980, 1986/87 and 1993), the succession of provincial governments in Buenos Aires was deceptive and unresponsive to the people’s claims. It was thought that due to the scarce population living in the area, the political consequences would not pay a high price; and also that time would lead to the new dry period clearing the people’s short memories of the floods. However, this inaction came to an end when the ∗ ENSO refers to El Niño/Southern Oscillation phenomenon.


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catastrophic flood of 1998 coerced the government of the Province of Buenos Aires to contract an international consultancy project (indicated in this thesis as the Halcrow’s project or Master Plan, indistinctly) called “Plan Maestro Integral de la Cuenca del Rio Salado” (Integrated Master Plan for the Salado River Basin), with the objective of looking for a definitive amelioration for flood problems in the region, by means of elaborating an integrated and sustainable management plan for the Salado River basin and the wetlands biodiversity∗ of its region (Halcrow & Partners, 1999). Halcrow’s project is an ambitious, long-term plan which main goals are: • to reduce the negative impacts of recurrent floods and droughts on the basin’s economy and environment; • to improve the general economic condition of the area by means of a sustainable development of its

potentialities, specially the agricultural and livestock activities; • to preserve and protect the environmental value of the basin, particularly its wetlands; and • to formulate and develop recommendations for the implementation of an institutional framework that

allows the planning and management of the water resource in the long term (20 years). Halcrow’s project presents a series of institutional, economic and engineering measures related to the integral basin’s development. However, the Salado Basin’s Master Plan mainly focuses on the building of specific protection structures to mitigate urban flooding within the basin’s area. These structural measures (usually dikes) are conceived to protect cities from flooding events with a return period up to 10 years. Urban protection represents a clear political and economic objective, therefore the main focus of the project. Nevertheless, in a region where water accumulation and ponding due to the special geomorphologic and topographic conditions are remarkable, water has to inundate all rural lands before getting enough volume to reach an urban area, which is normally located in the relatively higher positions. Surprisingly, Halcrow’s project leaves the management and retention of all rural water excess to farmers, who have to cope with recurrent floods and ponding conditions individually or collectively (organized in consortiums or cooperatives). This weak aspect of the Halcrow’s project was highly criticized by experts and the media in Argentina (Urien, 2001). Despite that, the project currently constitutes the official political-technical framework adopted by the province’s government for the Salado River basin management (Zárate & Rosa, 2003). From the legal point of view, the government of the province of Buenos Aires elaborated a new law (number 12.257), called “Código de Aguas” (Water Law), which – though not yet fully implemented – was created with three main goals: • to establish a consistent legal framework for the sustainable management of watersheds and basins in the

Province; • to regulate the use and property of water; and • to stop the current uncontrolled spreading of illegal drainage channels made by the affected farmers

attempting to reclaim their productive lands from flooding.

∗ Samborombón Bay: a RAMSAR site located on the outlet area of the Salado River, over the Atlantic Ocean.


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In its article 180, the new Water Law strongly supports water control and management at farm level (individually or in consortiums), where a full local implementation plan has to be presented to the water authority before getting the approval for the construction of any water control scheme. However, an important drawback in the law is the very poor description of the procedures for the design and implementation of agro-hydrologic structures at farm level, adding to this an amount of bureaucracy that makes any initiative on management almost impractical. Moreover, the Water Law states that water-control structures must be paid exclusively by the landowners after approval of pertinent authorities, taking away previous incentives for similar reclamation works (Provincial Law 10.170). In this sense, the National Institute for Agricultural Technology – INTA – developed a methodology called “Agro-hydrologic Systematization” (Barbagallo, 1984; Barbagallo et al., 1978). This methodology for water control and management in flat areas is a judicious combination of structural and non-structural measures, where the guiding idea is the conservation of water considering not only the engineering issues but also the socioeconomic and productive aspects (farm management habits) of the affected area. After almost 30 years, this approach is still considered the only socially, economically and technically adequate to tackle the ambivalent drought and excess periods in areas like the Salado basin at farm scale (Casas, 2001; Damiano, 2004), considering the available resources and possibilities of farmers in the region. The authors of the aforementioned methodology defined, after some economical analysis, the types and dimensions of control structures that could be afforded by the farmers while recovering their investment after some years. Experience gained from several projects carried out in this way established an average of 10 – year return period as the design event for these structures.

Figure 1-1: Severe flood event in the study area (1998)

Figure 1-2: Ponding conditions affecting productive lands

From the productive and financial points of view, major flood events have an immediate, devastating effect on farmers and regional economies (Figure 1-1). Despite these episodes affect one of the most productive areas of Argentina, the huge scale and extent of the problem is far beyond the current technical and financial resources available not only at farm level but also at provincial or national organizations. On the other hand, ponding conditions (Figure 1-2) do not have such dramatic, short-term impact on farmer’s economy, although it has been recognized that the damage they produce to soils and production activities could be as important as those generated by floods, if the ponding situation remains in the farms after some months (Casas, 2003; Taboada & Damiano, 2005).


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From the previous two scenarios, the INTA’s “Agro–hydrologic Systematization” methodology deals specifically with the ponding problem, and it works under five principles, developed after almost 50 years of experience in the Salado region (Barbagallo, 1984). The principles are: • Water must be controlled in the place where it falls; and guided to nearby lower retentions areas or where

soils do not support any production. • Control structures must be designed to hold an event of 10 – year return period. • The control systems must be conservative, based on holding water more than draining it, based on the fact

that droughts are worse than excess. • Evaporation is regarded as the main draining force. • The design must consider the traditional productive scheme of the landowner, and have to prove to be

economically beneficial for the business. Any disagreement or conflict of interests with the farmer in these aspects leads to the failure of the project.

As such, the water-control project design and development is a very dedicated and detailed task (defined as an “ant-work” by their developers), being based on the recognition of features associated to the natural landscape (soils, land cover, vegetation and landforms in an integrated manner) using aerial photo-interpretation techniques and the field expertise of senior personnel. The success of this methodology was awarded with the approval of the provincial government (during the 80’s) after the Provincial Law 10.170, which benefited those farmers and landowners joining the “Agro – hydrological Systematization” with reductions in land taxes∗, in order to fund the water-control project. The INTA’s methodology has survived years of convulsion and political change in the country, it is explicitly supported in the new provincial Water Law, and it is also indicated as “appropriate” for the management of rural water in the Halcrow’s project. Therefore, the “Agro–hydrologic Systematization Methodology” will be adopted as the methodological framework for the development of this research.

1.4. Research relevance and goals

Understanding that flood events and ponding conditions are inherent to the Salado River basin dynamics and that these natural phenomena cannot be fully controlled by currently available means, it is essential to upgrade the scientific research about their knowledge and mitigation by means of structural and agricultural measures in order to reduce vulnerability and improve sustainable development of this highly productive area of Argentina. In this way, one of the most cost-effective and reliable prevention techniques is based on flood susceptibility/flood hazard mapping assisted by advanced geographic information systems (GIS) and remote sensing (RS) procedures (Klindao, 1983; OAS, 1993). GIS and RS and have brought in recent years new and different types of approaches to assess floods, particularly in mapping terrain features and later integrating them with other thematic data to delineate flood susceptibility/flood hazard zones (Asaduzzaman, 1994; Islam & Sado, 2000; Muianga, 2004; Mulando, 2002; Shamaoma, 2005).

∗ These benefits are now withdrawn from the new Water Law.


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Accordingly, several studies were conducted applying GIS and RS techniques to assess flooding events in different sectors of the Salado basin during the last years, focusing their approaches at a regional or catchment scale (Bustos et al., 2001; Varni et al., 2005; Vazquez et al., 2003). Nevertheless, research on the integration of modern GIS and RS techniques with the actual ponding susceptibility mapping procedures at farm scale was not yet carried out in the Salado basin area. Regarding the multiple environmental, socioeconomic and land-management matters involved at farm scale, the definition and mapping of ponding susceptibilities is a very specialized assignment that requires knowledge in several related scientific fields. Moreover, its results must achieve a high, unambiguous level of detail to “convince” the landowner that this approach is the proper way to reconcile their interests and concerns with the technical aspects leading to the solution of the ponding problem and the execution of an agro-hydrologic reclamation procedure in the involved farm. In the particular case of the Salado basin, this knowledge will also lead to a better understanding of the ponding conditions and their relation with the geomorphologic, topographic, soil and botanic characteristics of the study area. As a pertinent remark for the justification of this research, Schulze (2000) identifies three spatial scales at which further detailed research would be beneficial for “real world” hydrologic-related issues in developing countries during the coming years. One of these is the field-to-farm scale, be it at subsistence or commercial farming level. Thus, this document seeks a contribution for a better understanding of the causes, dynamics and management of ponding conditions at farm level in a specific sector of the Salado River basin, proposing a mapping procedure (adjusted to the area’s data availability) which accounts for the terrain attributes, the local farming-bussiness activities and the day-to-day matters of farmer’s labours. In order to do so, the author of this thesis participated in a currently ongoing agro-hydrologic project, situation that allowed getting a better insight on the actual techniques as they are used in the field.

1.5. Research objectives

The research objectives of this study are:

1) To develop and implement a GIS-assisted methodology for the elaboration of ponding susceptibility maps at farm scale with application to the study area and other similar flatland environments in the region;

2) to recognize and to assess specific relief-soil-plant-water relations as ponding susceptibility indicators at farm scale;

3) to identify and categorize areas with different ponding susceptibility, observing conservative agronomic and productive criteria;

4) to establish a criteria to classify areas that could be potentially reclaimed for productive activities; and


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5) to develop guidelines for the elaboration of the technical documentation to be submitted to the pertinent water authorities∗, in order to facilitate the process of permissions for building water control structures at farm level. This task will be limited only to the first stage of the Agro-hydrologic Methodology, which is called pre-feasibility (or “land diagnosis”), as will be described in further chapters.

1.6. Research questions

Regarding to objectives 1 and 2: assuming that a strong spatial relation exists in this low-energy region between relative topographic positions, soils, groundwater depth, native vegetation and ponding water levels and permanencies; and that these aspects could be interpreted as thematic GIS layers to create a ponding susceptibility map: − What are the topography-soil-plant-water relationships that indicate the degree of ponding susceptibility

of a given area? − Which is the set of distinctive terrain attributes or characteristics that, once surveyed and analyzed,

would assist in the identification and delineation of susceptibility areas? − What are the relevant field survey and GIS steps needed to come up with a map of ponding

susceptibility categories? Regarding objectives 3 and 4: under the guideline that any water control design must avoid erratically-distributed drainage networks according to the new water law; and explicitly must be in agreement with the landowner’s interests to have chances to be realized: − What are the guidelines (including constraints or bottlenecks), which eventually would lead to the

compromise of a pure technical solution, supported by the engineer, with the farming-production scheme defended by the landowner?

− What kind of criteria should be considered to define and map different ponding susceptibilities at farm-scale?

− What are the areas that, after the building of a water control project in the farm, could possibly be reclaimed to productive activities?

− Is it possible to make a rank of efforts related to land recuperation? − What are the economic and agronomic criteria to define the feasibility of a land-reclamation process? Regarding objective 5: understanding that as today there is no official technical procedure described in the new Water Law to obtain the approval from the responsible authorities for the building of water control projects at farm level, and after the analysis done in the field: − What are the elements that should be incorporated in a technical document to be presented to the local

water authority, in order to get the permission to build a water control project at farm level?

∗ As indicated in the Water Law.


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1.7. Research approach

1.7.1. Susceptibility approach

The objective of flood/ponding susceptibility studies is to identify areas which are likely to be subjected to water excess conditions, assigning them a certain degree (or index) of severity using a tested criteria. Such susceptibility is related to the physical environment of a given area, i.e. topography, geology, pedology, vegetation and other natural or human-made conditions, for instance groundwater fluctuations and land use. Therefore, such susceptibility varies accordingly to those physical factors (Michelena, 1990). Flood/ponding susceptibility mapping can be defined as the differentiation of areas which may become inundated and areas which will remain dry (Klindao, 1983). This differentiation can be done by using susceptibility indicators, which can be built from geomorphic features (not always present in flatlands), soil characteristics, native flora distribution and relative topographic positions in the landscape (Barbagallo et al., 1978; Damiano et al., 1989). There are several methods of classifying flood/ponding susceptible areas, although they can be grouped in three major approaches (Dibyosaputro, 1984; Klindao, 1983): Topo-morphological approach: It is based on the correlation of topographic features and relative positions, with different water levels and permanencies to determine flood susceptibility areas. Pedologic approach: This approach classifies areas susceptible to floods based on the determination of soil characteristics, such as texture, structure, permeability, infiltration capacity, hydraulic conductivity, salinity/sodicity and the appearance of other “water table behaviour” indicators in the soil profile, like carbonate concretions and mottles. Botanic/phytologic approach: This approach is based on the assumption that the occurrence of certain species is an evidence of different water levels, permanencies and recurrences, so they can function as flood susceptibility indicators. In this research, a combination of elements from the three approaches will be utilized for the identification of ponding susceptibility areas, since this method allows reaching the maximum level of detail required for susceptibility mapping at farm-scale, as recommended by INTA’s “Agro-hydrologic Systematization” methodology (Barbagallo et al., 1978; Damiano, 2004).

1.7.2. Research steps

The research work consisted of 3 main stages, namely pre-fieldwork, fieldwork and post-fieldwork. A brief description of the activities involved in each stage is presented in the coming paragraphs. During the initial (or pre-fieldwork) stage, primary data was collected. This included searching and downloading satellite imagery from the internet; collecting meteorological data; and gathering digital and hard-copy material from the study site, such as vector and raster files, topographic and soil maps, aerial photographs, etc.


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Along with these tasks, a comprehensive literature survey was carried out to become familiar with the characteristics of the study area and its water-related problems, including bibliography about soils, geomorphology, vegetation, flatlands hydrology, socioeconomic activities, etc. Also, specific references about the integration of GIS and RS techniques for mapping purposes were collected. Finally, a visual interpretation of satellite images, aerial photographs and thematic maps was carried out in order to define and arrange the activities to be executed during the fieldwork. In the second stage, a field campaign was carried out in the selected study site for ground truth data collection. During this phase, a thorough field reconnaissance was carried out in order to assess the main physical characteristics of the site, highlighting those aspects required for this research (relief, soil and vegetation); and to verify the preliminary terrain analysis made in the first stage of the study. Along with these tasks, an extensive soil survey was made, collecting samples and measuring specific properties needed for the development of the research (infiltration, water table depth, texture, bulk density, etc.). In addition, ground control points were recorded (using a hand held GPS receiver) covering the study area and the surroundings, for land cover/land use identification and geo-referencing purposes. In the third and the final stage, all the collected data was processed, integrated and analyzed within the framework provided by the INTA’s agro-hydrologic methodology. In doing so, pertinent information to be used leading to the definition of the land diagnosis of the study site and the elaboration of the related ponding susceptibility and potential land reclamation maps was generated. This also comprised the expertise of a multidisciplinary group of experts who participated in the diverse stages of the current project.

1.8. Thesis structure

In Chapter 1, a general introduction on flatlands hydrology and the background of the Salado River basin’s characteristic hydrologic conditions were presented. Then, the problem statement; research relevance; research objectives; research questions; and research approach were introduced. A description of the study area, focused on the major physical characteristics and socioeconomic activities, is offered in Chapter 2. A brief presentation of the fieldwork site is given as well. Chapter 3 explains the connections between relief development, soil characteristics and vegetation communities existing in the study area. In Chapter 4, the INTA’s Agro-hydrologic methodology is briefly explained. Subsequently, the materials and methods used for the land diagnosis procedure are presented and discussed. Chapter 5 introduces the different criteria utilized to come up with the ponding susceptibility and potential reclamation classifications. Then, their corresponding tabular information and related maps are depicted. Chapter 6 introduces some examples of important activities for which the results obtained in this research are of direct application. Finally, Chapter 7 presents the conclusions of this study, as well as some recommendations about future research prospects derived from this investigation.


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2. Description of study area Located in the heart of the Llanura Pampeana (Pampean Plain) in central-east Argentina, the Salado River Basin occupies an area of about 200,000 km2, between 33º 30’ to 38º 00’ S and 64º 20’ to 56º 40’ W, distributed among the territories of Buenos Aires, Santa Fe, Córdoba and La Pampa provinces (Figure 2-1).

Figure 2-1: Location of Salado River Basin

Regarding specifically to the Province of Buenos Aires, the natural area of the basin originally occupied around 94,000 km2 but during the last 100 years several hydraulic developments (mainly drainage canals) and other anthropic interventions connected adjacent sectors to the Salado basin, increasing its area to the current 175,600 km2 identified by Halcrow’s project (1999). Although quite homogeneous in terms of landscape physiognomy, inside this vast area several regions can be differentiated in terms of geological, geomorphological, soil, hydrological and ecological criteria. Considering these different approaches but emphasizing the hydrological criterion, Halcrow’s project (1999) established a primary partition of the basin’s area within the Province of Buenos Aires into three regions: − Northwest Region (Región Noroeste) − Salado-Vallimanca Region (Región Salado-Vallimanca) − Western-linked Lagoons Region (Región Encadenadas del Oeste)


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Since the study site for this research is located in the Northwest Region, the coming sub-sections will be focused in describing this specific region and, finally, the fieldwork site itself.

2.1. Location

The Northwest Region (NWR) is located on the extreme north-west corner of the Province of Buenos Aires, embracing an area of aprox. 66,000 km2 (Halcrow & Partners, 1999). It is limited approximately by the Salado River sub-catchment in the east, the province’s political boundary in the north and west, and the Vallimanca stream and Lagunas Encadenadas del Oeste (western-linked lagoons) sub-catchments in the south (Figure 2-2).

Figure 2-2: Halcrow’s Project divisions: location of Northwest Region

2.2. Geology

The Salado basin in an extensive region that geologically constitutes an active sedimentary basin (graben), where thick sequences (up to 6,000 metres in the centre-east of the basin) of continental and marine sediments are being accumulated. This sediments range in age from the Paleozoic to the present, and rest unconformably upon a block-faulted igneous-metamorphic basement composed of Precambrian (1,200 Million years) granites, gneiss and schists (Imbellone & Giménez, 1998).


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The existing lithologic and stratigraphic information for the NWR of the Salado basin is relatively poor and fragmented, and it is associated only to hydrogeologic activities carried out in the area. In the same way, no complete geological cross-sections of the region are available. Table 2-1 shows the geological sequence of the NWR, including formation names and representative lithologies that are present in the subsoil (Halcrow & Partners, 1999).

Thickness (m) Formation Name Age Lithology 0 - 20 Junín (o Médano Invasor) Holocene Silty, fine sands

80 - 165 Pampeano Pleistocene Sandy and clay silt (loess) 0 - 140 Araucano Plio-pleistocene Clay, calcareous and gypsum sandstones 0 - 60 Arenas Puelches Plio-pleistocene Medium to fine sands with clay matrix

10 - 100 Paraná Upper Miocene Clays, sandy clays and sands with marine fossils 80 - 230 Olivos Lower Miocene Sandstones and clays with gypsum and anhydrite

150 - 290 Las Chilcas Paleocene Siltstones and marine clays 130 - 345 Abramo Cretacic Consolidated sandstones and sandy siltstones

(Pre-Paraná??) Paleozoic Quartzites and limestones ????

Basement Precambric Granites, gneiss and schists

Table 2-1: Geological sequence of Northwest Region

The uppermost sediments of the region (which are the most important ones in terms of pedogenetic activity) comprise: - The Junín Formation (also called Post-Pampeano or Médano Invasor), which constitute a succession of eolian fine-sand accumulations formed under the semi-arid conditions prevailing in the region during the late Holocene, and currently conform the typical dune-shaped landscape characteristic of this sector of the Province of Buenos Aires. - Constituting the lithologic substratum for the previous sandy materials, a thick deposit of unconsolidated silts of Pleistocene age conform the Pampeano Formation. These sediments (reddish to brown, sandy silt deposits) distinguish for their predominantly eolian origin and their strong loessic characteristics. Along the soil profile, they frequently present well-distributed calcareous interleaves in the shape of tosca concretions (caliche), granular calcium carbonates, and have significant component of rhyolitic volcanic ash (MAA, 1987). According to several stratigraphic, mineralogic and paleoclimatic studies (Iriondo, 1999; Zárate, 2003), the main sources for the sediments that filled up the NWR during the last 10,000 years are thought to be related to the weathered outcrops located to the west and southwest (northern Patagonia), as well as to the volcanic activity occurred along the Andes Cordillera during the Holocene.

2.3. Geomorphology

The Salado basin’s NWR is almost completely located within the geomorphologic unit called Pampa Arenosa (Sandy Pampa). This is a vast plain composed of loessic and eolian sand deposits, with a gently undulating topography from south-west area levelling into a flat-lying plain towards the east and north-east sectors (Imbellone & Giménez, 1998; MAA, 1987; Malagnino, 1988).


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Although the area is located more than 400 km away from the sea, its altitudinal development is very scarce, with a topographic gradient ranging from 60 to 138 m.a.s.l. along an east-west distance of 270 km (Figure 2-3). The regional average slope is 0.25 m km-1 (Michelena, 1993).

Figure 2-3: DEM of Northwest Region

Due to its recent geomorphologic history, which strongly contributed to the development of the current topographic and sedimentary characteristics (Iriondo, 1999), no fluvial processes are observed in the NWR, being the only geoforms those related to eolian processes and to the occurrence of seasonal and annual flood events in the lower topographic positions. This impossibility that the NWR presents to develop fluvial forms is due to the lack of a minimum gradient of energy in order to carry out the erosive cutting of the landscape, conditions that cannot be reached with the current combination of very low slope and unconsolidated sandy sediments (Halcrow & Partners, 1999) The sandy uppermost sediments of this region were deposited in two subsequent eolian cycles: the oldest one (Pleistocene) with a dominance of silt and clay textures; and the latest one (Holocene) presenting a more sandy composition. Subsequently, the NWR is split into two contrasting and characteristic landscape areas (MAA, 1987), though in both of them the same geomorphic element (dunes) constitutes the main landscape feature (Figure 2-4).

2.3.1. Sub-area of longitudinal dunes

This sub-area occupies the central and northern parts of the NWR, and it is characterized by the presence of longitudinal dunes of about 100 km long, 2 to 5 km wide and up to 6 m high. They are arranged as sub-


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parallel arches in SW-N direction separated by interdune depressions of 0.5 to 5 km wide, which are waterlogged during periods of heavy rains and become the only drainage waterways since the sub-area is totally devoid of streams. The original shape and height of the dunes, which are relict forms of a climate much drier than the present, have been smoothed by the action of wind and water and now appear as flat-topped, stabilized landforms which do not have a conspicuous topographic expression (Imbellone & Giménez, 1998; MAA, 1987).

2.3.2. Sub-area of parabolic dunes

As from the south of the previous area, it expands a vast field of parabolic dunes. Although showing a lesser development, these parabolic dunes are more recent and therefore superimposed on top of the longitudinal dunes. The parabolic dunes appear as medium to large scale, curve-shaped crests that cross obliquely the underlying system of longitudinal dunes, modifying it partially. Towards the south of this sub-area the sand deposits get thicker and coarser, so the mentioned mixed pattern of longitudinal and parabolic dunes slowly disappears until being completely dominated by parabolic dunes only. Quite often, these dunes present a concave, depressed inner area which is commonly occupied by a sub-circular or elongated lagoon (perennial or ephemeral), which constitutes the only hydrological expression in this sector of the Salado basin (Halcrow & Partners, 1999; Imbellone & Giménez, 1998). In both sub-areas the characteristic dune morphologies are not pure, being normally mixed with sand fields, deflational depressions and other local, minor features with scarce topographic relevance (MAA, 1987).

Figure 2-4: Characteristic landscape areas in the NWR: longitudinal (A) and parabolic (B) dunes

Since terrain’s relief and micro-relief characteristics play a fundamental role for the identification and definition of ponding susceptibility areas, a more comprehensive and detailed explanation of such elements will be presented in respective chapters.


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2.4. Climate

The dominant climatic type in the region corresponds to the sub-humid temperate according to the Thorntwaite classification, showing a dry season in winter and some continental characteristics (MAA, 1987). The mean annual temperature is 16.2 ºC, with the highest values in January (mean maximum: 33 ºC) and the lowest in July (mean minimum: 1.6 ºC). These temperature conditions (mesothermic regime) allow a good development and production of typical forage and crop species from temperate regions. Most of the rainfall – 70 to 75% – occurs between November and April (spring to fall seasons), while the remaining 25 to 30% occurs from May to late September (mid-fall to late winter). The current annual rainfall average for the region is around 920 mm, contrasting with the average 700-750 mm produced before the beginning of the present wet phase (1972-73).

Location Latitude S Longitude W Altitude (m.a.s.l.) Year average (mm) Period Gral. Villegas 34º 55' 62º 44' 117 864 1898-2004

Pehuajó 35º 31' 61º 32' 87 858 1897-2003 9 de Julio 35º 16' 60º 31' 76 938 1897-2003 Rivadavia 35º 30' 63º 00' 105 827 1905-1999

Table 2-2: Average rainfall values from four locations in Northwest Region

Table 2-2 shows the year average values from four representative locations of this region, while Figure 2-5 depicts the behaviour of mean annual rainfall depths during the period 1898-2003 at Gral. Villegas. It is noteworthy that rainfall amounts are highly variable between years, typical climatic condition of sub-humid and semiarid regions (Díaz-Zorita et al., 2002).

Figure 2-5: Annual Mean Precipitation in Gral. Villegas (1898-2004)

Wind is more intense and more frequent during the hottest season (December to March) and, along with the high temperature levels during summer, induces the occurrence of high evapotranspiration rates. Mean monthly wind speeds rise from a minimum during fall season (March to June) of about 13.0 km h-1 with 24%


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calm to a maximum between September and February of 16.8 km h-1 with only 14% calm. Based on average rainfall amounts and the potential evapotranspiration (1092 mm per year) calculated using the Penman-Monteith equation, the water balance of the region is negative (Díaz-Zorita et al., 2002), (Figure 2-6).

Figure 2-6: Rainfall – ET0 – Water deficit relations in Gral. Villegas (1990-1996)

The relationship between monthly evaporation and rainfall values serves as a rough guide to seasonal changes in water availability. The months when rainfall exceeds evaporation are considered wet. In this western part of the Pampa Plain there is almost no wet period, and from July to September rainfall is less than one-half of predicted evapotranspiration. The greatest negative water balance occurs between November and February (Díaz-Zorita et al., 2002) and coincides with establishment and flowering of main summer crops. Soil water recharge is concentrated between March and April and coincides with the beginning of fallow practices and with the seeding of pastures species for grazing. In this region, crop and pasture productivity is strongly related to the amount and timing of available water. This region of the Pampean Plain is influenced by the South Atlantic quasi-stationary high pressure centre, with prevailing light tropical humid north-eastern winds. Intermittent polar-front irruptions from the south (“Pampero” winds) modify this pattern. Due to the prevailing circulation, most of the humidity comes from the N and the NE. Therefore, when the westerly winds reach latitudes to the north of their mean position, lower than normal rains can be expected over the region and conversely, when they are south of their mean position, prefrontal and frontal rains are more abundant. One intrinsic characteristic of this region is that rainfall regime varies abruptly in space and time (Prieto, 2000), determining recurrent extreme conditions of droughts and floods over wide areas. The available climatologic data confirms the hypothesis that the climatic conditions have changed during the last 30 years, more notoriously in this region than in the rest of the Salado basin (Minetti et al., 1995). The precipitation amounts increased about 150 mm (which means more than 10% for the Northwest Region), and the mean annual temperature of the region rose 0.5 to 0.8 ºC (Halcrow & Partners, 1999; Taboada & Damiano, 2005).


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2.5. Soils

The Salado basin presents an intricate, patchy mosaic of soils, where dominant ones in the NWR correspond to the Mollisols order (USDA classification, 1999), developed from eolian sediments deposited during the Pleistocene and Holocene periods (Barbagallo, 1984). The soil moisture regime is “udic” (soil is not dry in any part for as long as 90 cumulative days in normal years), intergrading to “ustic” (soil is dry in some or all parts for 90 or more cumulative days in normal years) towards the west. The “aquic” regime (conditions that currently undergo continuous or periodic saturation and reduction) is common in low-lying areas, and the soil temperature regime is “thermic” (mean annual soil temperature is 15 ºC or higher but lower than 22 ºC, and the difference between mean summer and mean winter soil temperatures is more than 6 ºC) throughout the region. In general terms these soils are deep, sandy to sandy-loam textured with calcareous carbonate presence, relatively well-drained, with low to medium organic content and low water storage capacity (Imbellone & Giménez, 1998; INTA, 1989; Solbrig, 1997). The higher and relatively well-drained topographic positions are mostly covered by Thapto-Argic, Typic and Entic Hapludoll soil types in the eastern and central parts of the NWR, whereas in the west and south Entic and Typic Hapludolls, associated with Udipsaments, Ustortents and Entic Haplustolls, are the predominant soil types. A small sector in the north-east corner of NWR is dominated by Typic Argiudolls. Natracualfs and Natraquolls with high sodium contents are the representative soil types of bajos (low-lying and poorly drained areas) throughout the region (Figure 2-7). In the lower positions, the presence of a fine textured subsurface horizon impedes normal root development and soil-water movement. Consequently, it diminishes the volume of soil available to the crops for water and nutrient uptake and many times leads to salinization and sodification processes where the groundwater table is near to (or in contact with) the topographic surface.

Figure 2-7: Distribution of dominant soil types in Northwest Region


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The NWR of Buenos Aires province presents many complex soil profiles which reflect a succession of sedimentary and pedogenetic events in the recent geologic history (Pleistocene-Holocene). This variability, which is visible in soils texture and structure, was originally caused by differences in the composition of the parent material, but strong erosive processes, especially at the beginning of the agricultural period, have accentuated the original differences (Quiroga et al., 1999). Due to the fact that soils play a fundamental role for the identification of ponding susceptibility areas, a more comprehensive and detailed explanation of their characteristics, as well as their relations with relief and vegetation, will be presented in chapters 3 and 4.

2.6. Hydrology

From the hydrological point of view, the most outstanding characteristic of the NWR is its condition of arheic environment. In fact, this region lacks of a hierarchically developed superficial stream network, a typical attribute from the recent geologic-geomorphologic landscape history of the Pampean Plain. In its central and western sectors, the eolian processes that occurred during the last Holocene’s dry and cold phase largely overruled the fluvial processes, generating a vast sandy flatland with an extremely low morphologic and hydraulic energy (Malagnino, 1988). In the light of traditional, hillslope hydrology analysis those areas presenting terrain characteristics like the NWR are defined as Non-typical Hydrologic Systems (Fertonani & Prendes, 1984). These systems are characterized by an almost completely flat relief, which morphogenetic energy is incompatible with the development of hydrologic boundaries (water divides) and organized river networks. As a consequence, in these areas the vertical components of the water cycle exert a more important weight than the horizontal ones. In this context, the scarce surface runoff – if exists – takes the shape of an erratic laminar flow, which pathway is determined by the micro-topographic differences, the land cover and the wind direction (Fertonani & Prendes, 1984). A set of shallow perennial and temporary water bodies – lagoons and ponds – constitutes the region’s only surface hydrology feature (Figure 2-8). These water bodies, which present different dimensions ranging from 2 to more than 5000 Ha, occupy the topographic depressions generated by eolian processes that took place in this region during recent geologic times (deflation lows and inter-dune areas) and are fed either by rainfall water or local groundwater discharge. Generically, these lagoon has been characterized by Quirós et al. (2002) as typical plain lakes, very shallow, non thermally-stratified, clear to turbid, eutrophic or hypertrophic, with water renewal time and salinity levels highly fluctuating. Among the most known lagoons in the NWR (mainly for tourism and fishing activities) can be mentioned the Laguna de Mar Chiquita, Laguna de Gómez and Laguna Los Toldos in the northern sector, and the Complejo Hinojo-Las Tunas, Laguna El Salitral and Laguna El Indio in the southern area of the region.


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Figure 2-8: Surface hydrology of Northwest Region

During wet periods, the smaller low topographic positions become ephemeral ponds and get sometimes interconnected increasing their size considerably, but during dry periods they completely evaporate generating the development of encrustations and salt deposits around their margins (Imbellone & Giménez, 1998; MAA, 1987; Paoli & Giacosa, 2003). It is important to mention that the NWR was originally an independent hydrologic region, not only in terms of physical aspects but also in the context of water management issues in the Province of Buenos Aires. However, after some important flood events (1987-88) that caused serious environmental and socioeconomic damages in the northwest region, the provincial government initiated the construction of a network of primary (urban) and secondary (rural) channels to drain directly to the Salado River the water excess affecting the region, in order to give a solution to the flooding problem. Hence, after the execution of some of the mentioned channels (Canal Jauretche, Canal Mercante and Canal San Emilio, among others) in 1991, the NWR became “officially” integrated to the Salado River basin, and so recognized in the Halcrow’s Master Plan (1999).

2.7. Vegetation

The Northwest Region, as well as the rest of the Salado Basin area, is located in the Pradera Pampeana (Pampean Grasslands) domain. The Pampean grasslands are one of the largest, but less conserved, humid temperate grassland regions in the world (Solbrig, 1997).


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The composition and structure of native vegetation has been drastically modified in the last 120 years by the invasion of foreign grasses and the introduction of pastures for cattle grazing (mainly alfalfa and rye grass) and agricultural crops (wheat, sorghum, sunflower, corn, maize and soybean). However, according to Prieto (2000) some relicts of original vegetation are still preserved in those sectors where anthropic intervention has been not so intense. The dominant vegetation community is a grassland prairie (Figure 2-9) composed by grass species such as Bothriochloa laguroides, Stipa neesiana, Stipa trichotoma, Briza subaristata, Paspalum spp. and Stipa papposa, which mainly occupy the higher topographic positions that present deeper and relatively well-drained soils (Bilenca & Miñarro, 2004; Gabellone et al., 2003). In areas that are total or partially covered by water during some part of the year, different aquatic communities adapted to ponding conditions are present, with plants such as Scirpus californicus, Solanum melacoxylon, Typha domingensis and Typha latifolia, Zizaniopsis bonariensis and Spartina spp. Common broad-leaved herbs in these areas include specimens of the genera Alternanthera (philoxeroides), Vicia spp. and Eryngium spp (Barnes, 1990b; Gabellone et al., 2003). Where the substratum presents saline or sodic conditions, important halophytic communities of Chenopodiaceae (Salicornia spp., Cressa spp., Atriplex spp.), and alkaline communities of Poaceae (Spartina spp. and Distichlis spp.) are characteristic in this western part of the Pampas (Barnes, 1990b; Bilenca & Miñarro, 2004), (Figure 2-10).

Figure 2-9: Typical view of Northwest Region grasslands

Figure 2-10: Vegetation of saline conditions

Related to the introduced weeds in the Sandy Pampean grasslands, the more common are Carduus acanthoides, Cirsium vulgare, Cynara cardunculus, Carthamus lanatus and several species of Centaurea. Also, Cynodon hirsutus, Kochia scoparia, Diplotaxis tenuifolia, Carduus nutans, Salsola kali, Mentha pulegium, Hypochaeris radicata and Lolium multiflorum are very common, and often dominate in several communities (Barnes, 1990b; Prieto, 2000). Finally, among woody species the shrubs Prosopis alpataco and Geoffroea decortican are also common in this region (Bilenca & Miñarro, 2004), but nowadays can be mostly found in overgrazed areas.


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As same as topography and soils, vegetation is a fundamental indicator of ponding conditions in this sector of the Salado basin. Hence, a more exhaustive description of its composition and its close relations with soil characteristics and topographic positions will be presented in chapters 3 and 4.

2.8. Socioeconomic facts

The Northwest Region occupies an area of about 66,000 km2 and contains – fully or partially – in its extension 24 partidos (districts), as the internal political division in the Province of Buenos Aires is named (Figure 2-11). In terms of population, the NWR comprises a total number of aprox. 390,000 inhabitants (figures projected for 2005), from which 80% are located in urban settings (Halcrow & Partners, 1999), (Table 2-3).

Figure 2-11: Northwest Region districts


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District Name Town head of district

Area (km2) Population* Pop. density

(inhab/km2) Area covered by NWR (km2)

% of district’s area

Adolfo Alsina Carhué 5960.144 16366 2.75 161.33 2.71 Bolívar Bolívar 4917.376 32336 6.58 22.19 0.45 Bragado Bragado 2197.447 43865 19.96 1008.76 45.91 Carlos Casares Carlos Casares 2519.119 20155 8.00 2284.67 90.69 Carlos Tejedor Carlos Tejedor 3903.223 11518 2.95 3903.22 100.00 Colón Colón 1013.108 23131 0.00 47.89 4.73 Daireaux Daireaux 3850.830 16099 4.18 1856.32 48.21 Florentino Ameghino Ameghino 1839.297 8255 4.49 1839.30 100.00 Gral. Arenales General Arenales 1491.771 14960 10.03 1176.58 78.87 Gral. Pinto Ranchos 2550.088 12431 4.87 2550.09 100.00 Gral. Viamonte Los Toldos 2159.101 18823 8.72 2158.47 99.97 Gral. Villegas General Villegas 7366.990 29272 3.97 7348.20 99.74 Guaminí Guaminí 4822.897 12383 2.57 998.88 20.71 Hipólito Yrigoyen Henderson 1624.461 8975 5.52 1039.40 63.98 Junín Junín 2267.254 96531 42.58 1059.40 46.73 Leandro N. Alem Vedia 1606.273 17128 10.66 1606.27 100.00 Lincoln Lincoln 5760.311 45389 7.88 5760.31 100.00 Nueve de Julio 9 de Julio 4288.501 47259 11.02 3273.06 76.32 Pehuajó Pehuajó 4517.863 38872 8.60 4517.86 100.00 Pellegrini Pellegrini 1855.815 6370 3.43 1846.61 99.50 Rivadavia America 3985.017 17267 4.33 3984.98 100.00 Salliqueló Salliqueló 806.120 10420 12.93 421.38 52.27 Trenque Lauquen Trenque Lauquen 5460.369 38252 7.01 5460.37 100.00 Tres Lomas Tres Lomas 1240.157 8297 6.69 1240.16 100.00 * Figures projected for 2005 (Halcrow & Partners, 1999)

Table 2-3: Area and population of Northwest Region districts

The main socioeconomic activity carried out in this region is the so-called producción mixta (mixed production) which comprises livestock activities (70%) and crop farming (30%), varying the percentage of each activity according to the farm size and location, the landowner’s habits and traditions, and the behaviour of local and international markets (Halcrow & Partners, 1999; Solbrig, 1997). In this region of the Salado basin the most popular and traditional land use is an intricate combination of cattle-crop rotation, with several years of continuous agriculture followed by years of grazing cattle on alfalfa. Among annual crops, soybean, maize, rye, sunflower, wheat and sorghum are the most frequently planted; the most important forage plants are alfalfa and rye. Dairy farming is also an important rural activity, often combined with crop farming and with raising of beef cattle (Garbulsky & Deregibus, 2004; Solbrig, 1997). However, the region’s dominant land use is of extensive type since flood and ponding problems constitute a constraint that currently reduces not only crop yields and land prices but also the landowner’s incentive to invest in new and more profitable technologies. Agriculture in these western pampas has undergone a progressive change in land use during the last century from natural condition to native grassland grazing, then to introduced pasture grazing, and currently to rotational-based (or even permanent) crops. In the Sandy Pampa, years with good yield conditions for some crops (i.e. maize) generally have poor conditions for other crops (i.e. sunflower) production, and vice versa. Since ecological conditions tend to be unpredictable in this region, having several crops in a given farm may help ensure its economic stability. The historical conversion from grazing areas into croplands in the NWR has provided a strong empirical evidence of trade-offs between productivity, stability and sustainability under real farming conditions.


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Following the rising of rainfall depths during the last 30 years, productivity has increased all over the region at the expense of stability and sustainability, and this effect was particularly noticeable in the most western lands where climate conditions are more variable than in the rest of the Salado basin (Ghersa et al., 2002; Viglizzo et al., 2001). Likewise in the rest of the Pampean Plain, the no-tillage system is increasingly being used for cropping activities all over the NWR. As highlighted by several authors (Díaz-Zorita et al., 2002; Fabrizzi et al., 2005; Sasal et al., 2005), this practice is growing among small and big landowners, which are implementing this technique in order to prevent land degradation and to improve soil infiltration rates and water storage capacity.

2.9. Fieldwork site

The fieldwork activities for the development of this research were carried out in “Estancia Los Recuerdos” ranch, a large-land farming establishment of around 6,000 hectares situated in the south-east sector of General Villegas district, close to its border with Florentino Ameghino district (Figure 2-12). The main access to the study site is the National Route 188; and “Los Recuerdos” ranch is about 45 km from the city of Gral. Villegas and 27 km from the town of Ameghino.

Figure 2-12: Location of “Los Recuerdos” ranch


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According to Solbrig (1997), who classified the types of farming enterprises in the Pampean region, “Los Recuerdos” ranch constitutes a typical example of a “capitalist agrarian firm”. This type of unit is the most common one in the Pampas, and it is defined as one that employs rural labour force to do the farming and cattle activities while the management is usually performed by the owner, who may or may not live in the farm. It is much larger in extent than a traditional family farm, and derives all of its income from rural enterprises. “Los Recuerdos” ranch’s main economic activity is a combination of cattle grazing and crop farming, which was explained in section 2.8 as the producción mixta, the most common and traditional activity in the NWR. In the ranch, the two aforementioned predominant activities are also accompanied by dairy production, although the latter constitute only a small fraction of the ranch’s total income.


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3. Relief-soil-plant relations

3.1. Introduction

This chapter addresses the relief position-soil characteristics-vegetation type relationships that exist in the study area. These will be used to establish a ponding susceptibility classification in coming chapters.

3.2. Relief

The term flatland signifies sensu strictu a vast area which shows a uniform levelled relief all over its extension, sometimes interrupted by smooth height differences that do not affect the general composition of such plane landscapes (Barnes, 1990a). The characteristic ponding phenomenon affecting this type of areas in the Province of Buenos Aires finds in the negligible slope of the terrain (unable to evacuate the rainfall water that remains on top of the surface) and in the soil characteristics (which limit infiltration of water excesses) its causing conditions. Apart from the main topographic relief that develops over large areas, the micro-relief acquires a particular importance in flatlands (Prego, 1993). Micro-relief is defined as those small topographic irregularities, in the order of 50 to 100 cm as mentioned by Pal et al. (2003), that come out from the apparently surface smoothness. In this sense, micro-relief has an important role regarding areas of generating runoff (micro-highs) and receiving accumulation (micro-lows). In areas presenting very small slopes (like the NWR), these irregularities can sometimes be separated by hundred or thousand of meters. Accordingly, it is clear why for the research and assessment of the ponding problematic, the knowledge of topography has a fundamental importance. Moreover, in the “Agro-hydrologic systematization methodology” (Barbagallo, 1984) a detailed study of the relief is needed since it shows the topographic characteristics of the area, imprints the landscape disposition and defines the movements (vectors) of surface water at farm level. It was mentioned in previous chapters that the general landscape of the Sandy Pampa, the geomorphologic region where the fieldwork site is located, is characterized by a succession of gently-rolling, sandy longitudinal and parabolic hillocks (dunes). These complex geomorphic features make difficult to precisely determine the water way-ins and way outs as well as the location of topographic divides, which added to the complete absence of surface stream networks generates an arheic hydrologic environment. However, for agronomic and water management issues at farm level it is possible to identify a topo-morphological sequence (Barbagallo, 1984) composed by a number of mapable sub-units in which the surface water, groundwater table and soils present specific behaviour, position and characteristics (Figure 3-1).


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The constitutive relief elements∗ of this topo-morphological sequence are, from highest to lowest positions, the following: - Small hillocks with gentle crests and some surface irregularities (micro-undulations), occupying the relatively highest positions and denominated “higher plain” (loma) and “high transitional plain” (media loma alta). In these areas, infiltration and percolation are more important than runoff processes, and the groundwater table is normally deep. - Extended lying sectors with long, gentle slopes called “low plain with reticulate drainage” (media loma baja) and “low plain foot” (pie de loma), which are located on intermediate positions between the highest and lowest sectors. These areas are characterized by a low infiltration and relatively higher runoff generation. Normally, in these areas the groundwater table is closer to the topographic surface than in the previous sub-unit. - Areas with almost no slope, smaller in size than the previous sub-units, called “hydromorphic cuvette” (bajos de acumulación). They constitute pure reception and accumulation areas, where only vertical (evaporation and a very limited infiltration) water movements are present. The groundwater table is very close or in contact with the land surface, leading to the development of hydromorphic and halomorphic characteristic in the soils and vegetation located in this sub-unit. - Concave sectors, partially or totally covered by water during most of the year, which are located in the relatively lowest topographic positions. They are called cuvettes (cubetas).

Figure 3-1: Topo-morphologic sequence sketch

The above explained topo-morphologic sub-units may present varied dimensions (Barnes, 1990a), so it is possible that any given ranch, according to its size and shape, can contain one or more of these sub-units.

3.3. Genesis and characterization of soils

As mentioned in section 2.5, the soils that were developed in the northwest of the province of Buenos Aires correspond to the Mollisols order (INTA, 1989). According to the USDA classification (USDA, 1999b), Mollisols are the very dark coloured, base-rich, mineral soils of the steppes. Their main, distinctive characteristic is the presence of a brown to black, deep superficial horizon (mollic epipedon), relatively fertile, formed under prairie or grassland vegetation in climates that have a moderate to pronounced seasonal ∗ English names are solely illustrative. The Spanish names (in italic) indicate how these units are locally identified.


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moisture deficit. However, many Mollisols also have an argillic, natric, or calcic horizon, a few have an albic horizon and some also have a duripan or a petrocalcic horizon. In the USDA soil taxonomy, the order level may not provide sufficient information for some purposes. In this sense, the Suborder level reflects the influence of topography, temperature and soil moisture conditions that permit to differentiate genetic and soil use characteristic features. Seven Suborders (USDA, 1999b) have been recognized within the Mollisols order level: Albolls: are the Mollisols that have an albic (white materials) horizon and fluctuating ground water. Most of these soils are saturated with water up to the soil surface during winter or spring in normal years. In summer, groundwater is commonly deeper than 200 cm. Below the albic horizon, there is either an argillic or, less commonly, a natric horizon. These soils developed mostly on broad, nearly level to sloping ridges, on back slopes, or in closed depressions. Aquolls: are the Mollisols that are wet and have dominant olive hues, with high contrast redox depletions in or below the epipedon. These soils commonly develop in low areas where water collects and stands, but some are on broad flats or on seepy hillsides. Aquolls have aquic (continuous or periodic saturation) conditions and need to be artificially drained for specific types of land use. Rendolls: these are the Mollisols that are of humid regions and that formed in highly calcareous parent materials, such as limestone, chalk, drift composed mainly of limestone, or shell bars. These soils have a mollic epipedon that rests on the calcareous parent materials or on a horizon that is rich in carbonates. Cryolls: are the cool or cold, more or less freely drained Mollisols which have a cryic (soil mean annual temperature lower than 8 °C but do not have permafrost) temperature regime. They are moderately extensive on plains and mountains. On the plains, they are mainly in areas of late-Pleistocene or Holocene deposits. Xerolls: these soils are Mollisols of regions that have Mediterranean climates. As their name implies, they generally have a xeric (dry most of the year) moisture regime. Characteristically, Xerolls have a relatively thick mollic epipedon, a cambic or argillic horizon, and an accumulation of carbonates in the lower part of the B horizon and are neutral in most horizons. Ustolls: are the more or less freely drained Mollisols of subhumid to semiarid climates. Rainfall occurs mainly during a growing season, but is erratic. Drought is frequent and may be severe. Without irrigation, the low supply of moisture usually limits crop yields. Ustolls formed in sediments and on surfaces of varying ages from Holocene to mid Pleistocene or earlier. Those that have a warmer temperature regime, in particular, may have formed during two or more glacial and interglacial stages. The temperature regimes of Ustolls are warmer than cryic, and moisture regimes are dominantly ustic (intermediate between wet and dry regimes). Udolls: are the more or less freely drained Mollisols of subhumid to humid climates. In addition to the mollic epipedon, these soils may have a calcic, nitric (sodic), or argillic (clay) horizon. They formed mainly in sediments and on surfaces of varying ages from Holocene to mid Pleistocene, and may have formed during two or more glacial and interglacial stages. Their temperature regime is frigid (warmer than cryic in summer) or warmer, and their moisture regime is udic (not dry in any part for as long as 90 cumulative days in normal years). Nearly all of these soils are cultivated without important limitants.


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Regardless the classification method used for soil taxonomy, the relation between the long-term climatic conditions and soils distribution over the land surface allows the recognition of a soil genetic categorization. According to this categorization, the denominated “zonal” soils (Barnes, 1990a) are defined as those soils spread over extended areas, with well developed characteristics, located on undulating, well-drained terrains, and generated from parental materials that were exposed long enough to climate and biologic effects as to reflect their complete genetic influence. However, there are some local factors that interrupt the climatic equilibrium of “zonal” soils, modifying them to several extents and, sometimes, introducing new types of soils. Such factors can be, among the most important, the local relief, the phreatic level variations and the parental material composition. In this way, “intrazonal” soils will reflect the dominant influence of one, or a combination, of the mentioned factors. In the case of the study area for this research, the occurrence of different local and micro relief, fluctuating groundwater levels and dissimilar parent materials (older fine, loessic sediments overlaid by newer eolian sandy deposits) has generated particular “intrazonal” soils with hidromorphic and halomorphic characteristics. Such morphologic characteristics, evidenced for example by ferro-manganesic concretions, mottles and clay-skins for hidromorphic conditions and salt deposits for halomorphic soils, are present not only in the surface but also in the soil profile, and reveal that these soils are affected by different ponding conditions along the year. The Udolls suborder constitutes the “zonal” soils in the study area, exposing the results of the sub-humid climate influence over Pleistocene and Holocene loessic and sandy parental materials. Typic Argiudolls and Typic Hapludolls are the dominant subgroup taxonomic level soils. These sandy clay loam soils developed from thick eolian sand and loess deposits (INTA, 1989; Michelena, 1993) are found in the relatively upper parts of the landscape, where the local relief is smooth to gently undulating, the drainage conditions are good and the phreatic level remains far from the surface all over the year. On the opposite side, when the local landscape develops into flat, low areas with difficult drainage and higher water table affecting the soil profile during long periods the Aquolls suborder, which express the dominance of local topography, constitute “intrazonal” soils. In these areas, the subgroup of Typic Natraquolls conforms the main soil type. In intermediate positions, where the topography is still high but the local relief becomes less undulated and the thickness of uppermost sandy deposits decreases gradually, it appears a polygenetic profile that shows a sudden change between horizons: a current superficial A horizon that abruptly rest on a buried B horizon formed during a previous pedogenetic process. These “intrazonal” Hapludolls soils that evidence the influence of dissimilar parental materials have been classified as Thapto-Argic subgroup where the characteristic clay B horizon shows a neutral to moderately alkaline pH reaction, and Thapto-Natric subgroup in slightly lower positions showing the clay B horizon with a predominant sodic alkalinity (INTA, 1989; Michelena, 1993). Both subgroups are localized in relatively higher local relief that emerge from a general level landscape. However, these soils present some noticeable hidromorphic characteristics due to the permeability leap that occurs in the sedimentologic discontinuity between the sandy upper sediments and the finer, clay sediments below. In this abrupt contact tends to occur a “perched” (or false phreatic) water table that difficult infiltration and root development processes and therefore may cause the appearance of ponding conditions.


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Following the above explanation, Figure 3-2 presents the ideal distribution of the mentioned subgroups in relation with their topographic positions.

Figure 3-2: Ideal topographic distribution of subgroup of soils

3.3.1. Ponding and soil salinization

The soluble salts that are normally found in soils consist mainly of varied proportions of sodium (Na), potassium (K), calcium (Ca) and magnesium (Mg) cations, and chloride, sulphate, carbonate and bicarbonate anions. The main origin of these elements is related to the mineral composition of rocks and parental materials over which soils were developed. During the pedogenetic process, chemical phenomena like hydrolysis, hydratation and oxidation release the primary minerals which, in contact with water, acquire solubility. Carbonate ions come from the atmosphere and from the biologic activity in soils, whereas bicarbonate ions are formed by the solution of CO2 in water. Table 3-1 shows the composition and common names of the main types of salt compounds found in soils.

Salt compound Cation (+) Anion (-) Common name NaCl sodium chloride halite (table salt)

Na2SO4 sodium sulphate Glauber’s salt MgSO4 magnesium sulphate epsom salts

NaHCO3 sodium bicarbonate baking soda Na2CO3 sodium carbonate sal soda CaSO4 calcium sulphate gypsum CaCO3 calcium carbonate calcite (lime)

Table 3-1: Common salt compounds found in soils

The accumulation processes of soluble salts that occur along soil profile are generically denominated salinization (USDA, 1969). In these circumstances, when the prevailing salts are Ca, Mg or Na chlorides and sulfates devoid of alkali properties and relatively easy to wash, the most adverse effects are mainly suffered by plants and crops. In such halomorphic conditions, water uptake is reduced due to differential osmotic pressure between plant water and salty soil water, altering the non-tolerant vegetation structure because water stress and causing ion-specific imbalances and toxicities which may cause plants to die (Parida & Das, 2005). In the same way, crop yields in saline conditions are reduced dramatically. However, saline soils often are in normal physical


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condition with good structure and permeability, therefore deterioration is not so severe and normally once the salts are removed by rainfall the soil is recovered. On the other hand, when the predominant salts are Na carbonates and bicarbonates, these accumulation processes exhibit completely different results, which are known as sodification or alkalinization (Piñeiro et al., 1986). Infiltration rates, hydraulic conductivity, soil structure and soil porosity are the most affected properties by the balance between salinity and exchangeable sodium, especially as salinity decreases and exchangeable sodium increases. Sodification is a more severe form of land degradation, influencing soil’s structure as well as physical and hydrologic properties by causing surface crusting, poor water infiltration, and water stagnation on top of surface. A typical example of severe soil structure deterioration under sodic conditions is the formation of a columnar structure in the clay Bt horizon: in dry periods, this horizon shrinks and vertical cracks tend to appear creating proto-prismatic structures. When rainfall occurs, water is restricted to infiltrate only through these cracks, washing down-wards the horizon’s clay components and rounding the prisms. After some time, these prisms evolve into well-defined columnar structures which, when wet, expand and close completely the cracks turning the horizon almost impermeable. At this stage, water storage and rooting development along the profile are seriously restricted; and the recovery of soil structure and physical properties is almost impossible.

Figure 3-3: Soil structure deterioration under sodic conditions

Soluble salts content of soil (salinity) is measured through its Electrical Conductivity (EC) which is the measure of the ability of a soil solution to carry electrical current; whereas the presence of sodium excess is denoted by pH when measured samples exceed the value of 8. In order to produce alkalinization, exchangeable Na concentration against the other cations must exceed 15%. This Na proportion measured in laboratory is expressed by the ESP, or Exchangeable Sodium Percentage, which is the relation between sodium and the sum of the other cations present in soil. The salt-affected soils can be categorized into saline, sodic and saline-sodic (Zamolinski, 2000) according to types of salts present and the relations between pH, EC (measured in dS m-1, or deci-siemens per metre) and ESP values. Table 3-2 summarizes these categories and presents the threshold values (USDA, 1969) of the aforementioned variables.


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Soil condition pH EC

(dS m-1) ESP (%) Predominant anions and cations

Normal 6 to 7 <2 <15 Low quantities of Na, K, Mg and Ca chlorides and sulphates

Saline <8.5 >4 <15 Chlorides, sulphates, sometimes nitrates of Na/small amount of bicarbonates

Sodic >8.5 <4 >15 Carbonates and bicarbonates of sodium

Saline-sodic >8.5 >4 >15 Chlorides, sulphates, carbonates and bicarbonates of sodium

Table 3-2: Characteristic values of pH, EC and ESP in salt-affected soils

In the study area, the nature and intensity of saline/sodic problem can be visually identified and assessed during a field survey by observing some soil and vegetation features. For example, Zamolinski (2000) found that when EC values are close to 8 dS m-1 plants show a lesser development and changes in their structural composition. When EC is above 14 dS m-1, vegetation is almost completely inhibited and spots of naked soil begin to appear all over the affected area. In the same way, saline white efflorescences (“white alkali”) on topsoil imply EC values higher than 20 dS m-1 but pH less than 8.5, whereas dark spots of soil (“black alkali”) entail the presence of “sodic humates” produced by organic matter dispersion under sodicity conditions with pH values close to 10, showing the worst soil-conditions scenario. As mentioned by Dwivedi et al. (1999), soil salinization and/or alkalization are the major land degradation processes in agricultural lands of sub-humid and semiarid regions of the world. In the case of the study area for this research, these circumstances are strongly interrelated with the additional problem of long-lasting ponding conditions, becoming the most overwhelming land and water management problems faced by local farmers. Soil salinity/sodicity problems in the study area are not associated by any means to irrigation practices. They are related only to the rising of the phreatic water table carrying high proportions of salts originated in the mineral composition of soil’s parental materials. Restricted drainage is a factor that contributes to the salinization of soils, and involves the presence of a high water table (often related to topography) and low permeability of the soil. Owing to the particular hydrologic and topographic conditions of the NWR, surface waterways are poorly developed or, most of the times, inexistent. As a consequence, the drainage of salt-bearing waters away from the relatively higher lands cause the raising of the groundwater level to the soil surface on the lower lands, causing ponding. Casas and Pittaluga (1990) established that under such conditions, after the evaporation of ponding water and the drying of soil profile begins the capillary rise of saline groundwater. This hydrologic phenomenon determines the spreading of salts all over the profile and the formation of salt-affected soils which, as mentioned before, depending on the type of dominant salt will become saline or sodic soils. This situation has been occurring in the NWR since the early seventies, so it becomes clear the tight relation that exists between increased rainfall values, rising groundwater table with high salt concentrations, ponding conditions and soil salinization/sodification processes that affect environmental and socioeconomic activities in the study area.


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3.3.1.1. Soil structure and quality of ponding water

Soil profile characteristics determine whether groundwater reaches soil surface or not. The response and evolution of soil properties and structure after ponding events mainly depends on the quality of ponding water (e.g. salinity, sodicity, type of sodium salt, etc.), so it is important to determine locally the origin of such water. The diagram depicted in Figure 3-4 shows the expected consequences of different ponding water qualities on soil structure.

Ponding water

Rain water accumulation Groundwater rising

Freshwater Saline water

•No salinization•No alkalinization•Problems related only to soil

bearing capacityTemporary adverse effects

•Salinization•Alkalinization•Soil properties and

soil structure deteriorationPermanent adverse effects

Figure 3-4: Diagram showing the consequences of ponding water quality on soil structure

Soil ponding by non-saline (rainfall) water does not cause severe consequences, only those related to the loss of soil bearing capacity required for transit of livestock and rural machinery. Oppositely, when soil is ponded by saline/sodic water this may lead to adverse and difficult to reverse consequences, such as those caused by alkali excesses on soil structure. Whether the soil is ponded by saline or non-saline water, it can be evaluated after studying the soil profile arrangement in the area. Figure 3-5 illustrates two contrasting cases.

Figure 3-5: Unconfined and confined groundwater rising in soils with and without a tough Bt horizon

Those soils not having a tough, strongly clay Bt horizon allow free downward-upward water movements throughout the profile. Thus, rising groundwater may reach the topsoil causing eventually (due to groundwater high salt contents) salt deposits in surface horizons and salinization of the soil profile. This


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situation mainly concerns to soils located in low lying areas with drainage difficulties, for example most Natraquolls. On the other hand, it can be observed a tough Bt horizon above which a perched water table is seasonally accumulated. In these soils, ponding conditions are largely caused by the accumulation of rain (non-saline) water from that perched water table. Calcium carbonates are precipitated at the bottom of the Bt horizon limiting the zone of maximum groundwater rise, so the adverse effects on soils would not be as severe as those observed when ponding water is saline.

3.3.2. Soil physical properties and their relation with hydrologic characteristics

Land susceptibility to ponding is greatly influenced by those factors affecting the infiltration-runoff relation, such as precipitation regime, regional, local and micro-relief, vegetation structure and soil physical characteristics. According to Michelena (1990), the most important soil physical properties that need to be considered and sampled regarding to the assessing of land degradation problems generated by ponding conditions are the following:

3.3.2.1. Texture

The particle-size composition of a soil, or its texture, is defined by the proportions of sand, silt and clay after particles larger than sand are removed (Dingman, 2002). Figure 3-6 shows the particle size classification according to the United States Department of Agriculture (USDA) and International Soil Science Society (ISSS) scales; while Figure 3-7 depicts the textural triangle used for the determination of texture classes based on the weight percentages of sand, silt and clay.

Figure 3-6: Particle size classification

Figure 3-7: Soil texture triangle (USDA)

Texture, by means of its influence over total porosity and pore size distribution, controls to a large extent the water storage capacity, infiltration and permeability of the soil (Urio et al., 1979).


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A correct determination and assessment of soil texture classes (both in the field and/or by laboratory tests) is an essential aspect when analysing soil characteristics as indicators of ponding conditions.

3.3.2.2. Structure

Structure is defined as the disposition and arrangement of soil particles, including the pore size, which describes the general organization and consistency of a soil (Michelena, 1990). It is one of the soil’s physical properties that most affect permeability, movement of water along the soil profile and the soil’s rainfall-infiltration rate. Based on its physical and chemical properties, soil particles tend to aggregate into natural units called peds, which are characterized by a particular shape. Most common shapes of soil structure (WRS, 2005) are granular, blocky (angular and sub-angular), platy, columnar and prismatic, whereas soils with no structure may appear as single-grain (i.e. beach sand) or massive (solid mass without any specific shape) (Figure 3-8).

Figure 3-8: Soil structure types

The main factors that control the soil structure’s formation are the type and quantity of clay, organic (humus) or inorganic (iron, aluminium and carbonates) cements, and micro-organisms activity. Likewise, soil consistency and structure type have important effects on permeability (Etchevehere, 1976). In this sense, a loamy soil with a good structure (i.e. a Typic Argiudoll soil) will have sufficient aeration, adequate root development and higher permeability, while a clay rich, poorly structured soil (a Typic Natraquoll) will show a lower permeability, scarce root penetration and poor drainage conditions. An approximate view of the effects of structure type on soil permeability is depicted in Figure 3-9.


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Figure 3-9: Soil structure and its effects on permeability

3.3.2.3. Bulk density

Bulk density (also called apparent density or dry density of the soil), bρ , is defined as the ratio of oven-dried

soil (mass) to its bulk volume, which includes the volume of particles and the pore space between the particles (USDA, 1999a):

m mb

s a w m

M MV V V V

ρ ≡ =+ +

,

where Mm is the mass of mineral grains, Vs is the soil sample volume, and Va, Vw and Vm are the volumes of air, water and mineral components of the soil, respectively. For the assessing of most hydrologic problems, bulk density is constant in time at any point; however it is a dynamic property that varies with the structural condition of the soil and commonly increases with depth due to compaction by the weight of overlying soil. Bulk density is closely related to the soil structure, porosity and level of degradation. It is also an indicator of the degree of meteorization and evolution of a soil from its parental materials; loose, structured soils with low bulk density values present normally more favourable physical conditions, root development and water movement than those soils with high bulk density values (Urio et al., 1979). Common farming practices in the study area such as overgrazing, intensive mono-cropping and burnings tend to produce a series of degradations in soil’s physical-hydrological properties (Quiroga et al., 1999) by reducing organic matter availability, altering structure and compacting soil. Soil compaction generates a decrease of porosity, restricts the movement of air and water through the soil and increase bulk density, hence augmenting the rain-infiltration rate. Therefore, bulk density can indirectly serve as an indicator of ponding susceptibility conditions of a soil, since it provides a good estimation of its degradation and the behaviour of the rain-infiltration relation along


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the soil profile. Typical soil bulk densities range from 1.0 to 1.8 g cm-3(USDA, 1999a), and they are closely related to soil textural classes (Table 3-3).

Soil texture Ideal bulk densities (g cm-3) sands, loamy sands < 1.60

sandy loams, loams < 1.40

sandy clay loams, loams, clay loams < 1.40

silts, silt loams < 1.30

silt loams, silty clay loams < 1.40

sandy clays, silty clays, some clay loams (35-45% clay) < 1.10

clays (> 45% clay) < 1.10

Table 3-3: General relationship of soil bulk density to soil texture

3.3.2.4. Soil depth

Although it is not a physical property itself, soil depth is an important pedologic characteristic since it constitutes the actual space for structure development, root penetration and water storage along the profile. A fluctuating phreatic level close to the surface or the occurrence of toughened and cemented layers (hardpans), will logically reduce the availability of space, and therefore determine the lower limit of the soil section. Michelena (1990) established that a depth of 90 to 100 cm for a soil with adequate texture and structure is fairly enough for the good development of crops and movement of water; and defined three broad soil depth categories: − Shallow soil: depth < 50 cm − Intermediate soil: depth from 50 to 100 cm − Deep soil: depth >100 cm

3.3.2.5. Infiltration and hydraulic conductivity

Infiltration rate, f(t), is the rate at which water enters the soil from the surface. However, to understand and describe the infiltration process when a water-input begins, two more related variables (Dingman, 2002) need to be defined: - Water-input rate, w(t), which is the rate at which water arrives at the surface due to rain, snow or irrigation. - Infiltration capacity – or infiltrability –, f*(t) which is the maximum rate at which infiltration can occur. Saturated hydraulic conductivity (Kh) is defined as the rate at which water moves through a saturated porous medium under a unit-potential energy gradient (Dingman, 2002). It is closely related to permeability conditions, and is affected by some soil’s characteristic such as texture, organic matter and water content, porosity and grain size. Infiltration rates, determined by factors such as rainfall rate, physical-chemical characteristics of soil, antecedent water content and surface roughness and slope, are usually high during the early stages of the input event, and gradually decrease to a nearly constant value, which is taken as the characteristic infiltration capacity for a soil at a particular point. Infiltration and saturated hydraulic conductivity are closely connected since f*(t) is considered almost equal to Kh for the near-surface soil (Dingman, 2002).


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All f(t), w(t) and Kh have dimensions [L T-1], and are normally measured in cm or mm h-1. Evaluating these factors quantitatively in areas where, due to the absence of slope only evaporation and infiltration processes govern water movements, allows the delineation of those sectors where ponding conditions could be potentially generated. According to Kh values, Etchevehere (1976) created an scale of seven grades of soil permeability, which is commonly used in Argentina for soil survey fieldworks (Table 3-4).

Kh values (cm h-1) Grade Permeability Comments

< 0.125 1 Very slow to null Soils are almost impermeable

0.125 to 0.5 2 Slow Soils are saturated, may remain ponded very long periods and show mottles all over the profile

0.5 to 2 3 Moderately slow Soils saturated during long periods, may present some mottles along the profile

2 to 6.25 4 Moderate Soils saturated only few days, ideal for most crops

6.25 to 12.5 5 Moderately rapid Soils with relatively high porosity, without mottles in any part of the profile

12.5 to 25 6 Rapid Very permeable soils, with high porosity

> 25 7 Very rapid Excessively permeable soils, with very high porosity

Table 3-4: Soil permeability scale according to Kh values

3.4. Vegetation

Native or planted vegetation communities reflect, by means of its composition, structure or even by its absence, a series of ecological, climatic, topographic and hydrologic conditions that allow detecting, by aerial photo interpretation or direct field observation, different degradation or susceptibility features (Barnes, 1990b). In this sense, vegetation is regarded as one of the most important natural indicators of the environmental conditions (i.e. ponding susceptibility) of a given site, especially in low morphogenetic areas. Although the distribution of the NWR typical vegetation communities (see section 2.7) spans the entire range of the region, all vegetation types can also be found at farm level over relatively short distances in association with the micro-relief and soil heterogeneity that characterize the area. However, the floristic composition of each community is homogeneous, and their limits are clearly recognizable (Batista et al., 2004). In this sense, a clear botanic gradation which indicates an intricate association with subtle topographic positions (micro-relief), soil water content, soil physical characteristics and saline-alkaline-hydromorphic conditions can be successively observed from regional (104 – 105 km2) to very fine spatial scales (0.1–10 km2). In the relatively higher topographic positions, the characterization and assessment of NWR grasslands natural components is very difficult due to the strong habitat modification caused by more than 100 years of agricultural activities, however there still exist areas where such modification has not been so intense, and natural vegetation can be observed. On the opposite, in the relative low-lying sectors different conditions


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such as salinity, alkalinity or hydromorphism may be developed, and each one of these conditions will show a typical vegetation presence. Several studies carried out in the Flooding Pampa – roughly equivalent to the Salado-Vallimanca Region from Halcrow’s project – have demonstrated the existence of a marked relationship between floristic composition and soil types as indicator of the physical, chemical and hydrologic conditions of such soils (Batista et al., 2004; Burkart et al., 1998; Perelman et al., 2001). Although elaborated in a different area of the Salado basin, the results obtained from these investigations can be also applied to the NWR since similarities in the soil-vegetation patterns exist between both regions (Bilenca & Miñarro, 2004; Gabellone et al., 2003; Perelman et al., 2001). Reflecting the topographic gradient from uplands to lowlands, and hence different soil characteristics and water contents, four vegetation units are distinguished in the study area:

3.4.1. Mesophytic meadows

This unit present a high diversity of species, being Paspalum quadrifarium (“paja colorada”), Stipa neesiana (“flechilla”) and Lollium multiflorum (“pasto ryegrass”) the most frequent and widely distributed. This community is typically associated with the positive and convex areas formed by recent eolian sandy deposits and located in the relatively highest sectors of the landscape. Deep, well drained, acidic to neutral, non-saline are the correspondent soils characteristics, represented by Typic Hapludolls and Typic Argiudolls. Due to its relatively elevated terrain position and associated good soil conditions, this unit has been largely altered by livestock and cropping activities.

3.4.2. Humid mesophytic meadows

This community typically appears over flat or smoothly undulating areas in intermediate topographic positions, presenting some drainage limitations and being slightly ponded, keeping only few centimetres of water during few days or weeks. Acidic, non-saline A1 horizon but natric or argillic B2 horizon is the common soil profile associated to these meadows, corresponding to buried, polygenetic Thapto-Argic and Thapto-Natric Hapludolls soils. Humid mesophitic meadows floristic composition is integrated by species of the previous community but now accompanied by typical species adapted to “intrazonal” humidity soil conditions, like Spartina montevidense (“espartillo”), Stipa papposa (“pasto puna”), Bothriochloa laguroides (“cola de zorro”), Conyza bonariensis (“rama negra”) and Ambrosia tenuifolia (“artemisia”).

3.4.3. Humid prairies

Humid prairies extend in horizontal, poorly drained low-lying sectors located in the relatively lower positions, where rainfall water tends to accumulate due to the high contents of clay in the upper profile. Soils characteristics range from acidic throughout the profile to saline in the topsoil and strongly alkaline in the deeper layers. Representative of such characteristics are the Natraquolls soils.


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Some of these positions, which may remain waterlogged most of the year but do not present superficial saline conditions, are locally known as “bajos dulces”, and their most representative plants are the Solanum malacoxylon (“duraznillo”), Alternathera phyloxeroides (“lagunilla”) and Hydrocotyle bonariensis (“redondito de agua”).

3.4.4. Halophytic steppes

The halophytic community is dominated by species that are adapted to accentuated saline, sodic or saline/sodic conditions; which grow on top of clay soils, with very shallow superficial horizon. The soil is saline or sodic along the entire profile, with the water table near or in contact with the topographic surface most of the year. Natracualf and Natraquolls soils are related to this community. Most characteristic species of alkaline conditions in such environments are Distichlis spicata (“pelo de chancho”), Distichlis scoparia (“pasto salado”) and Paspalum vaginatum (“pasto horqueta”), whereas in saline conditions Salicornia sp., Atriplex sp. and Statice sp. are the dominant species. Finally, in the lowest, concave areas that are almost permanently covered by still water depths up to 1 metre, the dominant vegetation is the Scirpus californicus (“junco”). Figure 3-10 presents a schematic distribution of most representative vegetation communities in correspondence with the relief position and soil characteristics.

Figure 3-10: Vegetation distribution generated from relief and soil characteristics


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4. Land diagnosis methodology

4.1. Introduction

The present chapter describes the development of the research steps executed, within the framework provided by the Agro-hydrologic Systematization Methodology, for the definition of the land diagnosis in “Los Recuerdos” ranch. In doing so, the ultimate objective is the generation of a ponding susceptibility classification for the identification of those areas affected by halo-hydromorphic conditions that can be potentially reclaimed to farming production activities.

4.2. Available Data

Before and during fieldwork a thorough literature review was carried out, collecting pertinent bibliography that served to construct the research framework used throughout the thesis. Likewise, different primary sources of information like aerial photos, satellite imagery, maps and meteorological data were already available or gathered during the fieldwork stage, both in digital and hardcopy format. They are presented in the subsequent tables.

Aerial Photographs Date Type Scale Source Quantity

16-Feb-1983 Panchromatic 1:20,000 Dirección Provincial de Catastro 3 11-Apr and 05-Oct-1982 Panchromatic 1:20,000 Dirección Provincial de Catastro 9

Table 4-1: Available aerial photographs

Satellite Sensor Scene info Acquisition date Spatial resolution Source

Central point: -35.11 Lat, -62.78 Lon 16 November 2000 Central point: -35.13 Lat, -62.70 Lon 19 January 2001 Central point: -34.81 Lat, -62.65 Lon 06 October 2001 Central point: -34.82 Lat, -62.58 Lon 10 November 2001

Terra ASTER

Central point: -34.82 Lat, -62.55 Lon 14 February 2002

15 m EOS / NASA

06 October 1997 09 September 1998

19 April 1999 17 February 2000

15 November 2000 02 November 2001

Landsat-5 TM Path/Row 228/084

21 May 2005

30 m INTA

28 March 2000 21 May 2002

13 November 2002 16 January 2003

Landsat-7 ETM+ Path/Row 228/084

06 April 2003

30 m INTA

Table 4-2: Available satellite imagery


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Maps Type Name Code Scale Date Source

Piedritas 3563-16-1 October 1952 Santa Eleodora 3563-16-2 June 1952

Moores 3563-16-3 November 1952 Topographic Map

Estación Volta 3563-16-4

1:50,000

November 1952

IGM

Piedritas 3563-16-1 Santa Eleodora 3563-16-2

Moores 3563-16-3 Soil Map

Estación Volta 3563-16-4

1:50,000 1993 INTA

Table 4-3: Available maps

Meteorological Data

Type Station Period Source Daily rainfall data EEA Las Velitas 04-Apr-1973 to 11-Dec-2001 INTA Daily rainfall data Estancia Los Recuerdos 01-Jan-1952 to 30-Jun-2005 Ranch owner

Monthly rainfall data Estación Gral. Villegas Jan-1898 to Dec-2004 SMN

Table 4-4: Available meteorological data

4.2.1. Software and fieldwork equipment

The major software packages used for the development of this research included ArcView 3.3 & ArcGis 9.1 for spatial analysis, GIS operations and database management, and ERDAS 8.7 & ILWIS 3.3 for the digital processing of aerial photographs and satellite imagery. During fieldwork activities, an IPaq palmtop computer provided with ArcPad 6.0.3 software for mobile GIS and real-time mapping activities, a GPS Garmin Etrex receiver for GCPs positioning, an auger infiltration set, bulk density samplers and a soil test set were used.

4.3. Agro-hydrologic Systematization methodology

It is known that one of the major constraints to achieve the full agricultural potential in the NWR fields is the recurrent water excess-deficit phenomenon, determined by specific geomorphologic, edaphic and climatic characteristics of the region. The INTA’s “Agro-hydrologic Systematization Methodology” (Barbagallo, 1984) is the most accepted and recognized approach for its control and mitigation at farm level. This methodology, specially designed for water management and land reclamation in flat areas, represents an empirical and pragmatic blending of structural and non-structural (agronomic and vegetative) measures attempting for an efficient use of rain water (also called “blue water”) by first retaining it in the place where it drops and later guide the eventual excesses (which generate runoff) to topographically lower or unproductive areas, where they can be stored and later used during periods of water scarcity. The final goals of these measures aim at improving the infiltration, reducing the risks related to water excess and deficit, reclaiming affected areas that could be potentially converted into productive areas, and increasing the farm productivity for millimetre of drop water (Damiano, 2004).


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Despite that this is not the only approach suggested for the area, the major advantages of the Agro-hydrologic systematization are: • This methodology contemplates the characteristic climatic duality (excess and deficit) occurring all over

the Pampean Plain of Argentina; • establishes an appropriate soil-productivity oriented working framework for the accumulation of water

excess in less productive areas with simple but effective structures; • allows the reclamation of affected areas having soils with farming activities potential; • includes legal, social, productive and economic aspects coming from the landowner, which are of

uttermost importance for the realization of works; • uses technology and rural machinery available in situ, preventing large costs of contracts and specialized

skills; and • includes a training program for the construction and maintenance of the structures. Modelling the surface water and groundwater behaviour in a natural flatland-environment like the NWR of the Salado basin is technically plausible. However, the balance between the enormous quantities of detailed data required, the costs involved in the whole modelling process and the benefits obtained from such an effort do not justify an investment that local government (municipalities) and farmers can not afford. A technically valid and economically manageable alternative to recognize and assess the hydrologic behaviour in flatlands consists of identifying the processes in the field by evaluating the physico-chemical evidences that the relation between soil, topography, hydrology and vegetation produce on the land surface. Finally, by incorporating advanced GIS & RS techniques combined with accurate hydraulic calculations to assist those tasks, a complete set of tools to face the problem is assembled. This is the approach introduced by the INTA’s methodology, adopted and further complemented in this thesis. Operationally, this methodology presents a “modular” flexible structure (that is why it is locally called “modular agro-hydrology”) consisting of five modules, which can be developed successively in a logical, linked sequence or some of them may be omitted according to some factors like the financial resources of the landowner, the degree of severity of the problem affecting the farm, and the needs and interests of the landowner/consortium of farms. The methodology’s sequence of modules is depicted in Figure 4-1, and described in the coming sub-sections.


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Figure 4-1: INTA’s Agro-hydrologic Systematization Methodology flowchart

4.3.1. Agro-hydrologic feasibility module

This module constitutes the first step in the execution of an Agro-hydrologic project for water control and management at farm (or consortium) level. This module is also called “land diagnosis”, where the kind of research done is mostly agronomic and conducted by specialists in soil and land degradation processes. The fundamental objective of this module is the definition of a detailed and precise Land Diagnosis for the evaluation of the halo-hydromorphic conditions affecting the farm, leading to the identification of areas that can potentially be reclaimed into production. In order to achieve the main objective, a sequence of tasks is required. These tasks can be seen as secondary objectives since their outcomes will be used during subsequent modules: − Identification of the origin and causes of the water stress conditions affecting the area where the farm is

located. − Identification and interpretation of the principal water inputs and outputs to and from the farm’s area. − Identification and description of terrain units presenting the same (or similar) surface water behaviour,

soil profile characteristics, vegetation presence, drainage conditions and ponding water quality (topo-hydrologic units).

− Prioritization of water control and management tasks. − Identification of suitable areas for the location of water control structures. − Identification of areas that can be reclaimed to production activities. − Agreement with the landowner regarding the agronomic works to be done after soil reclamation. The building up of an accurately geo-referenced cartographic base supported in a GIS environment (making use of ancillary data such as topographic maps, soil maps, aerial photographs, satellite imagery,


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tabulated information, reports and other thematic information that is available, but preferentially using free-obtainable materials to avoid increasing the costs for the farmer) constitutes a basic pre-requisite for the particular terrain assessment and spatial analysis operations that must be performed leading to the completion of the land diagnosis. This assignment is carried out by a combination of GIS & RS operations and specific field survey activities, keeping in mind that for farm-scale reclamation works every single hectare counts at the moment of assessing and defining which areas can be recovered to productive activities. Understanding this idea is of key priority when working at such scale, since the concerns and interests of the landowner are directly involved. This thesis deals specifically with the tasks related to the execution of the first module from the Agro-hydrologic methodology, hence a more detailed development and explanation of the research steps leading to the generation of such tasks (assisted by GIS & RS techniques) will be presented in coming sections. The remaining modules are mentioned below for the sake of completeness, but are not treated in this study.

4.3.2. Functional design module

In module one, the characterization and assessment of the halo-hydromorphic problematic and the consequent land diagnosis are carried out. These allow defining the actual feasibility for the execution of an agro-hydrologic project in the affected rural establishment, and setting up a sequential prioritization of assignments. In the second module, a purely conceptual hydrological diagram of the farm area and its surroundings is developed in order to understand the water system dynamics, and to define the functional design of the agro-hydrologic structures that will deal with the water problematic affecting the ranch. In order to realize the functional design, the main tasks are: − To define the (independent) hydrologic circuits for the management of farm’s water. − To establish a set of modular hydrologic structures that will produce the desirable (projected) effect in

each circuit. As such the structures are divided according to their function in: o Protection and/or guiding o Transference o Retention o Expansion o Evacuation

For every category there are a limited number of structures that can be selected. Each structure is designed for different capacities of water handling, depending on the amount of water to be controlled at each sector. The structures are designed in such a way that can be built with the available rural machinery, what makes the entire procedure economically possible.

− The plan-view dimensioning of the water structures; the approximate estimation of soil volume movement; and the size of the areas affected by the agro-hydrologic structures.

Moreover, in the functional design stage different water control strategies are conceptually compared without making a detailed hydrologic and hydraulic calculus. The most feasible one is selected considering not only the cost-benefit aspects but also the current land diagnosis, the pre-existent rural infrastructure, the topo-


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edaphic characteristics and the water-system dynamics of the site. Finally, the functional design makes compatible the mentioned aspects with the interests and suggestions of the landowner. Respecting their concerns and land management traditions brings into the project the quota of realism and sensibility that makes a difference between a purely technical approach and a reality with a good chance of success.

4.3.3. Structural design module

Once the functional design is solved by the technical staff and approved by the landowner after a careful cost-benefit and land management tradition analysis, the next step is the structural design of the agro-hydrologic structures. In this third methodological stage all the hydrologic and structure’s hydraulic calculations are done in the simplest way and using a plain language to facilitate a clear interpretation and understanding by the landowner. However, a detailed memory of calculus is attached for legal reasons. The main objectives of the structural design module are, therefore, the calculation of the water volumes and flows that should be controlled by the agro-hydrologic project for a pre-selected return period, and the hydraulic and engineering dimensioning of the structures defined and chosen in the previous (functional design) module. In order to accomplish these tasks, different models are required: − Statistical models: to estimate the return period of the design storm or water flow used for the structural

design of the different types of structures. It has been estimated that a 10-year return period is about the upper breakeven point limit.

− Hydrologic models: to determine the surface volumes and flows that the designed structures should hold.

− Hydraulic models: to hydraulically dimension each one of the selected agro-hydrologic structures. The use of these models is restricted due to the lack of a DEM with enough accuracy.

One of the critical constraints that affect the productive aptitude of this flatland is the origin and quality of the ponding water. The over-flow of permanent or ephemeral lagoons, which contain high levels of salinity, affect directly the lower sectors located close to these water bodies. Therefore, controlling the expansion of such areas by means of rural engineering structures constitutes a high priority for any agro-hydrologic project as a fundamental step towards the reclamation of potentially productive soils.

4.3.4. Project implementation module

This module consists of the final execution of the already designed and planned structures. This stage, which is a delicate and accurate assignment due to the high level of precision required in the setting up and building of the rural structures, embraces a mixture of tasks from the technical staff and the farm owner. The technical staff responsibilities are: − To set up and level in the field all the designed agro-hydrologic circuits. − To train the workforce for the building of the structures. − To supervise the pace of the construction. − To inspect the finished structures. − To conclude and test the building project.


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The landowner’s responsibilities are: − To subcontract (fully, partially or none) the project. − To provide the required machinery for the constructions of structures and the ground movement. − To provide working personnel. − To command and supervise the overall project.

4.3.5. Soil evaluation and fertilization module

The final module of the Agro-hydrologic systematization is performed once all the rural structures are finished and working properly; and signs of land recovery are visible. The latter normally takes an average of two years. This module involves the implementation of reclamation and fertilization procedures for those areas that were identified during the first module as potentially suitable for production activities after the execution of the water control measures. These tasks must be accomplished by highly specialized agronomic staff, in agreement with the landowner’s interests and priorities. The aforementioned procedures are not topics of this thesis. In order to broadly introduce them, they were divided into two groups, according to the type of soils and physical characteristics of the affected areas. For those areas having affected soils with agricultural potential, the reclamation measures include: − Soil analysis for the evaluation and diagnosis of the “new” (after reclamation) physical-chemical

properties, aiming to the definition of fertilization schemes. − Fertilization programmes under no-tillage farming practices. − Soil water analysis, seeking for supplementary irrigation (if necessary). − Crop-rotation management plans according to availability of water in the soil profile. − Integrated soil management strategies (in agreement with landowner’s interests), including the use of

chemical and eco-fertilizers. For those areas presenting soils with cattle-grazing potential, the agronomic measures comprise: − Soil managing strategies according to the origin of ponding water and the degree of halomorphism

suffered by the soil (saline or alkaline conditions). − Reduction of the animal load per pasture parcel, in order to reduce the soil compaction, physical erosion

and overgrazing. − Fertilization of natural or implanted pastures in areas with hydro-halomorphic problematic. − Nutrients re-supply in halomorphic soils. − Analysis of livestock drinking water. − Integrated management of bio-solids and dairy-production waste in order to improve the nitrogen

availability in halomorphic soils. For both types of areas, this module contemplates also a strict soil sampling scheme aimed to the monitoring of those qualitative and quantitative parameters showing the evolution of the reclaimed areas in terms of technical and productive aspects.


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4.4. Land diagnosis procedures

Barnes et al.(1990) established that, from an agronomic point of view, any given piece of land is considered affected when the interaction of climatic, topographic, hydrologic, edaphic and anthropic factors generates a problematic situation that alters negatively its normal productivity or its ability to sustain agricultural and livestock activities. In the case of the study area for this research, the most visible and significant alterations take the shape of hydromorphic (ponding) and halomorphic (saline or alkaline) conditions. A sound quali-quantitative knowledge, interpretation and assessment – as a logic sequence – of such problematic conditions will allow the definition of a trustworthy and realistic land diagnosis, as a fundamental tool for selecting the most convenient treatment to solve the problem, and the measures to achieve an integrated agro-hydrologic management of the farm. “Realistic” and “worthy” means involving technical, practical and economic aspects together. Most of the times, not to include the economical and social aspects of the problem lead to the failure of the whole reclamation scheme. Following the “modus operandi” established by the INTA’s Agro-hydrologic methodology, the sequence of procedures leading to the definition of the farm’s land diagnosis involves the following office and fieldwork steps:

4.4.1. Analysis of ancillary data

4.4.1.1. Topographic maps

The cartographic analysis allows defining the topographic settings of the study area according to the available scale. For the vast, flat areas of Argentina’s Pampean Plain, where height differences of merely 2 or 3 metres are separated by several kilometres of distance, the only affordable source of topographic information is represented by the 1:50,000 scale topographic maps from the Instituto Geográfico Militar – IGM – (Military Geographic Institute). For the purpose of surface water analysis in areas such as the NWR, these maps present severe disadvantages: they were produced, in average, more than 50 years ago (in this period the hydrologic, land use and infrastructure conditions changed drastically); they are generally considered not accurate for precise altimetry measurements in these flat areas; and present equal-distances ranging from 1.25 to 2.5 metres, which are not enough detailed to show the subtle relief characteristics of the study site. Therefore, they were just utilized as a general reference to obtain a preliminary topographic approach of the area. Sophisticated topographic survey techniques developed during the last few years (Lillesand et al., 2004), such as the radar-based Interferometry and laser-based LIDAR (Light Detection and Ranging) are available and might overcome this constraint. However, the high costs involved in obtaining these tools are far beyond the economic resources at hand for any regular farmer in the area. For this research, four topographic maps at 1:50,000 scale covering the study area and its surroundings were available in hardcopy format (see Table 4-3). These topo-sheets were scanned, geo-referenced in ILWIS to the Argentina’s Gauss-Kruger (Zone 4) coordinate system – which was defined as the “official” projection system for this research (see Appendix A for projection system details) – and finally semi-automated converted to vector (arcs and points) format using the ArcScan extension available within the ArcGis 9 software.


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Despite their age, the topo-sheets were used as a cartographic base for the initial terrain analysis consisting on locating the farm; obtaining an initial view of its main topographic characteristics and roughly tracing the regional water divide as well as identifying the local water movement vectors regarding the ranch area (Figure 4-2).

Figure 4-2: Topographic setting and main water movement vectors of “Los Recuerdos” ranch

Afterwards, using the contour lines and spot heights obtained from the mentioned maps a merely indicative 10 m grid-size digital elevation model (DEM) of the ranch was constructed (Figure 4-3). From the digital analysis of this product, it was determined that “Los Recuerdos” ranch presents a general flat topography, and a scarce altitudinal development ranging from 113.97 metres – in its northern border – to 108.05 metres – in its south-eastern border – with an average slope of 0.06%. These figures respond to the typical topo-morphological pattern of the Pampas flatlands (Barnes et al., 1990).


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Figure 4-3: Indicative Digital elevation model (DEM) of “Los Recuerdos” ranch

4.4.1.2. Aerial photographs

Aerial photo interpretation (API) involves the understanding of different textures, patterns and tones that actually represent the land surface features. Through the use of stereo-view capability of aerial photographs, an experimented professional can extract information on geomorphology and topography of an area and, implicitly, lithology, soil types, land cover, surface processes and geologic features. Normally, the core objective of this task is to complement and widely improve the information obtained from the topographic analysis carried out in a previous stage. Unfortunately, in the case of this study the photo interpretation could not be performed because the available photographs (see Table 4-1) were taken 23 and 24 years ago∗ (during a very dry hydrologic year), and since then not only the land cover/land use but also the hydrologic conditions of the area have significantly changed. Likewise, no further topo-geomorphologic analysis was considered because the almost completely flat nature of the study site. However, aerial photographs were employed for the creation of an ortho-mosaic used as an accurate cartographic base for the confection of all the thematic maps derived from the results of different lab and field analysis executed during this research. The mosaic will be also used to support hydrological modelling

∗ It must be highlighted that for this area of the Province of Buenos Aires, the acquired set of AP constitute the only available product of such type, since no further aero-photographic surveys had been executed after those dates


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during the structural design module; and to complement the data base for the remaining stages of the actual agro-hydrologic project (out of the scope of this thesis). The ortho-mosaicking is a photogrammetric technique that allows the elimination of common large geometric distortions associated with raw aerial photography (such as systematic errors associated with camera/sensor orientation, topographic displacement, earth curvature and film and scanning distortion) by solving a large array of complicated mathematical equations (Lillesand et al., 2004). In this technique multiple images can be processed at one time establishing a mathematical relationship, called triangulation, between the camera properties, the photographs and the ground surface (Leica, 2003). Once this relationship has been defined, the ortho-rectification process can be performed. As a result, all the photographs are free of distortions and misalignments, resampled to the selected pixel size and coordinates system, and ready to bring accurate and reliable planimetric/altimetric information about the land surface. The ortho-image, converted from its intrinsic central projection to a rectangular projection, has the geometric characteristics of a map and the image qualities of a photograph, and all the objects in an ortho-image are in their true geographic positions. Therefore, any measurement taken on an ortho-image reflects an actual measurement taken on the ground. To accomplish this task 156 ground control points (GCPs) were positioned during the field campaign – within and around the ranch’s area – with a regular Garmin GPS handheld receiver (GCPs location details can be found in Appendix B). In doing so, the Argentina’s Gauss-Kruger (Zone 4) projection was used, and each point was measured with the maximum expected horizontal positioning accuracy of the device, in order to assure their reliability (Mc Coy, 2005). The selected points were located over land features recognizable both in the field and in the pictures. These features, which were well spread over the ground coverage of the 12 used photographs, included road crossings, rural gateways (tranqueras), mills and other distinguishable sharp edges (Figure 4-4).

Figure 4-4: Location of GPS measuring points


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In order to achieve the maximum attainable accuracy, the photos were scanned using an advanced photogrammetric scanner to a resolution of 30 µm (equivalent to 847 dpi) representing a pixel ground-resolution of 0.67 metres. The ortho-mosaic was generated using the OrthoBASE add-on of ERDAS 8.7 image processing software (Leica, 2003). Since the mosaicking technique involves the use of topographic information in order to perform the triangulation process, the generated DEM of the area was coupled to the aerial photographs during this procedure. The final result of the overall process was a single image made of a block-mosaic of 12 ortho-rectified images (Figure 4-5), obtained with an overall accuracy error of 2.5 metres, which was considered acceptable according to the research objectives.

Figure 4-5: Ortho-mosaic of “Los Recuerdos” ranch

The first methodological task accomplished after having finished the ortho-mosaic as a base map, was the accurate delineation of the “lotes” (the inner parcel division of the ranch) by screen-digitizing, plus the building-up of their related database (area, land cover, land use, etc.) in a GIS environment. This task allowed obtaining the total area of the farm (5909.95 Ha), as well as the individual area of each parcel. In a similar way, the location of all internal infrastructures positioned during field campaign with GPS and data provided by the landowner, as well as the construction of their respective databases, was carried out. An example from these products is depicted in Figure 4-6 and Table 4-5.


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Parcel ID Area (Ha) Land cover 1 36.396 Rangeland

1 bis 9.686 Polo pitch2 23.834 Cattle hose/pitch3 46.598 Fine pasture4 71.637 Soya 5 138.380 Fine pasture6 149.987 Winter pasture/Soya/Maize

7 East 111.250 Wheat/Soya7 East bis 5.827 Cattle graveyard

7 West 114.393 Coarse pasture8 East 117.569 Fine pasture8 West 113.852 Maize 9 East 120.286 Soya 9 West 116.281 Soya 10 East 111.200 Wheat/Soya10 West 122.596 Soya 11 North 44.205 Fine pasture11 South 43.710 Winter pasture/Soya

11South bis 5.748 Rangeland12 North 46.224 Soya 12 South 46.390 Soya

13 159.863 Degraded pasture14 a 37.305 Winter pasture14 b 43.000 Winter pasture14 c 37.524 Winter pasture14 d 42.907 Winter pasture

15 East 94.611 Soya 15 West 137.252 Wheat/Soya16 East 117.472 Pasture16 West 114.167 Soya 17 East 121.155 Pasture17 West 114.441 Soya 18 East 112.457 Rangeland18 West 122.230 Rangeland19 East 128.353 Fine pasture19 West 128.031 Soya

20 80.968 Pasture21 East 115.322 Soya 21 West 116.978 Soya 22 North 151.270 Fine pasture22 South 153.547 Coarse pasture23 East 124.338 Soya 23 West 115.356 Maize 24 East 118.058 Fine pasture24 West 113.794 Winter pasture25 East 124.835 Fine pasture25 West 131.290 Maize 26 East 110.866 Pasture/Natural field26 West 120.336 Pasture/Natural field27 East 118.124 Soya 27 West 113.677 Soya

28 74.655 Winter pasture29 East 126.411 Maize 29 West 114.989 Soya 32 East 121.079 Soya 32 North 77.103 Coarse pasture32 South 105.497 Soya 34 East 115.832 Winter pasture34 West 126.338 Wheat/Soya

36 100.723 Soya House 31.749 Park

Total Area

(Ha) 5909.95 Figure 4-6: Parcel layout and internal infrastructure Table 4-5: Parcel’s database

An accurate parceling map is essential since farmers can easily recognize the position of land features by parcel identification and not by cartographic coordinates. As such, this map constitutes a common language of location.

4.4.1.3. Soil maps

The execution of a correct land diagnosis requires a very detailed assessment of the soils properties as well as their performance in front of different management practices or degradation processes. This information is gathered and offered by soil maps, which constitute an invaluable tool to infer the land productivity potential and to determine good management practices for different soil types according to their physical-chemical characteristics (Niborsky, 2002). For the study area, as same as for the rest of the Salado basin area, this information is provided by the “Carta de Suelos de la Region Pampeana” (Soil Chart for the Pampean Region), a series of 1:50,000 scale soil maps elaborated from API and field campaigns, and published by INTA from the ’70 to ’90 decades. They consist of a hardcopy chart and a descriptive memory. The level of detail and cartographic generalization these maps present is the best when evaluating soils for feasibility analysis and agricultural development plans, although when working at higher (farm to field) scales they only allow obtaining a first, general approach of soils distribution pattern and morphologic characteristics.


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The information provided by the 1:50,000 scale INTA’s soil maps consists of the delineation of distinctive, uniform areas with homogeneous characteristics, identified by a specific symbol, and denominated Cartographic Units (CU). Each CU may be composed by one or a combination of various Taxonomic Units (TU). For the mentioned scale, every TU corresponds to one Soil Series as defined in the USDA Soil Taxonomy (1999), adopted in Argentina. In this sense, a series reflects uniquely the nature of a soil and narrowly defines it by a set of external (such as climate, slope and landscape position) and morphologic (like thickness and disposition of horizons, texture, structure, colour, pH, salts and carbonates presence, percentage of humus and mineralogical composition) properties. The soil series included in the soil map of the study site will be introduced and explained later in this chapter. Regarding to the CUs, five types are recognized for INTA’s 1:50,000 scale soil maps (Niborsky, 2002): Soil Association: is composed by two or more taxonomic units that could be individually represented in a more detailed map. Within the represented area, the percentage that each unit occupies is clearly determined. Soil Complex: its components can not be separately represented due to their intricate disposition on the surface; however the percentage of each taxonomic unit can be determined. Soil Consociation: corresponds to a CU dominated by one taxonomic unit and a certain percentage (between 15 and 25) of dissimilar soils. Non-differentiated Group: is composed by soil complex or associations which extremely low productive capacity prevents the effort of determining their percentual distribution. Miscellaneous: are those areas which do not exhibit a pedogenetic soil profile, independently from their productive aptitude (bare rocks, urban areas, marshes, recent alluvial deposits, polders, etc.). Important information for the evaluation of soil’s productive characteristics, leading to the definition of the land diagnosis and reclamation prospects in the ranch, is also brought by the Land use capability (LUC) and the Productivity Index (PI), being both included in the information provided by INTA’s soil maps. The LUC is an ordered classification of the land according to specific properties (such as physical limitations, management requirements and soil conservation needs) that determine its capacity to sustain production permanently (Miaczynski, 1961). The classification has three components – a class, a subclass and a unit – although for the INTA’s map scale it is restricted to the first two components (Figure 4-7).

Figure 4-7: Class and Subclass components of the land use capability classification

The capability class is the broadest grouping of the classification. It is an assessment of how versatile the land is for sustained production taking into account its physical limitations, and it also gives the general degree of limitation to use. There are eight classes (represented by roman numerals): classes I to IV are suitable for cropping, pasture or forestry while classes V to VII are limited to livestock or forestry use. The maximum limitations correspond to class VIII which is a land not suitable for grazing or production forestry; it best serves a conservation function (Table 4-6).


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Class Cropping Suitability Livestock & Forestry Suitability General Suitability I II High

III Medium IV Low

Multiple land use

V

High

VI Medium VII Low

Livestock or Forestry land

VIII

Unsuitable

Unsuitable Protection land

Table 4-6: Land use capability (LUC) classes

The capability subclass (identified by a lower case letter in the land use capability code) divides the land within each class according to the major type of limitation to use. There are four kinds identified but only the dominant ones (one or two for each class) are recorded: e (erodibility): where susceptibility to erosion is the dominant limitation to use. w (wetness): where a high water table, slow internal drainage, and/or flooding constitutes the major limitation to use. s (soil limitation): where the major restriction to use is a limitation within the rooting zone. This can be due to a shallow soil profile, stoniness, rock outcrops, low soil moisture holding capacity, low fertility (where this is difficult to correct), salinity or toxicity. c (climate): where the climate is the major limitation to use. The PI (INTA, 1987) is a parametric method utilized to establish a unitless numerical valuation of the land’s productive capacity. This determination is obtained by using specific climatic and soil information (the latter obtained from soil survey measurements), according to the following formula:

PI H D Pe Ta Tb Sa Na Mo T E= ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ , where H is climatic condition (for water availability); D is drainage; Pe is soil’s effective depth; Ta is superficial texture; Tb is sub-superficial texture; Sa is content of soluble salts (within the first 75 cm); Na is sodic alkalinity (up to 100 cm depth); Mo is organic matter content (in the upper layer); T is cationic interchange capacity (sub-superficial layer); and E is actual and potential erosion (water and eolian). Table 4-7 presents the PI numeric scale and the related land uses.

Productivity Index (PI) Land use < 10 Not suitable for livestock or agricultural uses

10 - 30 Livestock only 30 - 50 Livestock - agriculture 50 - 70 Agriculture - livestock

> 70 Agriculture

Table 4-7: Productivity Index (PI) values and related land uses

For this research, from four available maps (see Table 4-3) the “Santa Eleodora” and “Estación Volta” sheets were used to characterize and evaluate the type of soils present in the studied area (Figure 4-8). Their information was later checked for consistency and expanded during the field campaign.


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Figure 4-8: 1:50,000 scale soil maps covering the study area

The processing sequence for the soil maps was similar to the one used for the topographic maps: scanning, geo-reference and geo-coding (but this time using the the ortho-image as reference) and conversion to vector (polygon) format using the ArcScan tool. After that, the corresponding databases were completed with the information provided in the maps’ descriptive memory. Figure 4-9 depicts the soil map from “Los Recuerdos” ranch, elaborated following the aforementioned steps, whereas Table 4-8 introduces the CUs found in the area along with their major attributes. A more detailed explanation of each CU is developed in the coming paragraphs.


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Figure 4-9: Soil map of “Los Recuerdos” ranch

# Symbol Type Landscape Taxonomic units (%) LUC PI Area (Ha)

1 Agh1 Consociation Dune bars Ameghino (80), Lincoln (20) IVs 54 115.5

2 CSe2 Complex Gently undulating ample high plains

with cuvettes

Cañada Seca (40), Pichincha (30), Balbín (20), Lincoln (10) IVw 50-53 2100.8

3 Dr1 Complex Hydromorphic plains with micro-hillocks

Drabble (40), Pichincha (40), Cañada Seca (20) VIws 35-37 854.3

4 Dr2 Complex Concave-elongated low plains

Drabble (50), Pichincha (50) VIws 29-31 160.7

5 OR48 Complex Extended undulating plains

imperfectly drained Ortiz de Rosas (50), Saboya (30), Pichincha (30) IIs 71 2305.9

6 OR49 Complex Extended undulating plains with cuvettes

Imperfectly-drained Ortiz de Rosas (40), Pichincha (30), moderately-well drained Saboya (20), Drabble (10) IIws 59 309.9

7 Sy4 Complex Extended gently undulating plains

Moderately-well drained Saboya (50), Pichincha (30), imperfectly-drained Ortiz de Rosas (20) IIIw 67 7.4

8 L Miscellaneous Permanent and ephemeral lagoons N/A N/A <10 54.2

Table 4-8: List of soil map Cartographic Units (CU)


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CU 1 (Agh1): this unit constitutes an elongated SW-NE oriented strip in the central-east portion of the ranch and a minor sector over its NW border, both embracing an area of 115.55 Ha which is 1.9% of the total ranch’s area. This CU is a consociation that represents a segment of elevated dune-bar landscape (which orientation is related to the regional pattern of the characteristic longitudinal dunes of the NWR), and it is formed by 80% of Ameghino series (Entic Hapludoll) located on the upper sectors of the bars, and 20% of Lincoln series (Typic Hapludoll) situated on intermediate (media loma) and relatively lower (pie de loma) positions (Figure 4-10). Its “IVs” land use capability class reflects the consociation’s severe restrictions for any crop except for forest or natural pastures due to physical limitations within the rooting zone. It PI is 54, indicating livestock and agricultural uses suitability.

Ameghino series

80% Lincoln series

20%

CU 1

Figure 4-10: Sketch of CU 1 composition

CU 2 (CSe2): this CU constitutes the second largest unit present in the study domain, embracing an area of 2100.77 Ha, equivalent to a 35.55% of the total area. This soil complex is the representation of a landscape formed by ample and gently undulating high plains with intercalated cuvettes, which is composed of an intricate mosaic of soils series with different characteristics: 40% of Cañada Seca series (Thapto-Argic Hapludoll) on higher positions; 30% of Pichincha series (Thapto-Natric Hapludoll) occupying micro-high (loma) and lying (pie de loma) locations; 20% of Balbín series (Duric Natraquoll) in lower sectors; and 10% of Lincoln series (Typic Hapludoll) on higher spots (Figure 4-11). The CU 2 presents an “IVw” land use capability class, meaning that this soil complex presents severe limitations for cropping activities, except natural pastures, due to water-excess and poor soil drainage conditions. The PI values ranged from 50 to 53, which indicate suitability for agricultural-livestock uses.

Cañada Seca series 40%

Pichincha series 30%

Balbín series 20%

Lincoln series 10%

CU 2


CU 3 (Dr1): this CU represents a landscape of hydromorphic plains intercalated with small lomas, which is located over the W and SW sectors of the ranch, occupying 854.29 Ha (14.45% ) from the total ranch area. This soil complex is composed by a mixture of 40% of Drabble series (Typic Natraquoll) in lowest positions; 40% of Pichincha series (Thapto-Natric Hapludoll) in intermediate, horizontal planes near to low


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sectors; and 20% of Cañada Seca series (Thapto-Argic Hapludoll) in intermediate, horizontal planes (Figure 4-12). The CU 3 presents a “VIws” land use capability class, meaning that this soil complex has severe restrictions for cropping activities due to poor soil drainage and scarce rooting zone limiting conditions. Its productivity index ranges from 35 to 37, signifying suitability for livestock-agricultural uses.

Cañada Seca series 20%


Drabble series 40%

CU 3


CU 4 (Dr2): this CU represents a plane-concave landscape of minor elongated low plains, which is found surrounded by the undulating landscape represented by the CU 2. The CU 4 presents two small polygons located on the central and central-north sectors of the ranch (parcels 7 E and 21 E), occupying 160.75 Ha (2.72%) from the total ranch area. This soil complex is composed by an equal percentage distribution of Drabble series (Typic Natraquoll) in lowest (bajo) positions; and Pichincha series (Thapto-Natric Hapludoll) in small elevated spots (Figure 4-13). As well as the CU 3, this CU presents a “VIws” land use capability class, meaning that this soil complex is unsuitable for any cropping activities due to water excess (poor soil drainage) and insufficient rooting zone (sodic alteration of soil structure) acting as limiting conditions. PI values of 29 to 31 indicate suitability for livestock uses only.

Drabble series

50% Pichincha series

50%

CU 4


CU 5 (OR48): the CU 5 constitutes the principal unit of the ranch and represents a large landscape of extended undulating plains, spread largely over the NE and central-south sectors. It occupies 2305.96 Ha, a 39.02% from the total ranch area. This soil complex is composed by 50% of an imperfectly-drained phase of Ortiz de Rosas series (Thapto-Argic Hapludoll) located in intermediate (media-loma) positions; 30% of Saboya series (Typic Argiudoll) situated in higher (loma) sectors; and 20% of Pichincha series (Thapto-Natric Hapludoll) in transitional (media-loma) and plain foot (pie de loma) positions (Figure 4-14).


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According to its “IIs” land use capability class and 71 PI value, this unit is the most capable of all to sustain both agricultural and livestock activities, having some soil restrictions only in lower sectors where alkaline conditions may appear.

Saboya series

30% Imperfectly drained Ortiz de Rosas series

50% Pichincha series

20%

CU 5


CU 6 (OR49): this soil complex unit occupies a narrow, N-S oriented strip of land over the SE sector of the ranch. It embraces 309.98 Ha corresponding to a 5.25% from the total ranch area. This CU represents a landscape of extended undulating plains with cuvettes (cubetas), being composed by an intricate mixture of soils: 40% of an imperfectly-drained phase of Ortiz de Rosas series (Thapto-Argic Hapludoll) in highest positions; 30% of Pichincha series (Thapto-Natric Hapludoll) in transitional (media loma) sectors; 20% of a moderately-well drained phase of Saboya series (Typic Argiudoll) also in higher positions; and 10% of Drabble series (Typic Natraquoll) in lowest (bajo) positions (Figure 4-15). This unit presents an “IIws” land use capability class, having use aptitudes and limitations in a similar way to the previous CU. Its PI is 59, meaning suitability for agriculture-livestock uses.

Imperfectly-drained Ortiz de Rosas series

40%


Drabble series 10%

Moderately well-drained Saboya series

20%

CU 6


CU 7 (Sy4): this soil complex unit occupies a very small area of 7.4 Ha over the SE sector of the ranch (parcel 19 E). It embraces 7.40 Ha corresponding to a mere 0.13% from the total ranch area. This CU represents a landscape of extended undulating plains, being composed by 50% of a moderately-well drained phase of Saboya series (Typic Argiudoll) in top (loma) positions; 30% of Pichincha series (Thapto-Natric Hapludoll) in micro-higher (micro-loma) sectors; and 20% of an imperfectly-drained phase of Ortiz de Rosas series (Thapto-Argic Hapludoll) in both top and micro-higher positions (Figure 4-16). According to its “IIIw” land use capability class and 67 PI value, this CU is capable to sustain both agricultural and livestock activities but to a lesser extent than previous CUs 5 and 6, mainly due to a stronger soil-drainage limiting condition.


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Moderately-well drained

Saboya series 50%

Imperfectly drained Ortiz de Rosas series

20%


CU 7


CU 8 (L): the last CU corresponds to a permanent water body (lagoon) which partially occupies the parcels 15E and 13, in the central-NE sector of the ranch. It embraces an area of 54.25 Ha, a 0.94% of the ranch total area. The soil series (taxonomic units) composing the previously explained CUs are listed in Table 4-9 and synthetically described in the upcoming paragraphs, while their detailed analytical information can be found in Appendix C.

Order Sub-order Great group Sub-group Series Udoll Hapludoll Entic Ameghino Aquoll Natraquoll Duric Balbín Udoll Hapludoll Thapto-Argic Cañada Seca Aquoll Natraquoll Typic Drabble Udoll Hapludoll Typic Lincoln Udoll Hapludoll Thapto-Argic Ortiz de Rosas Udoll Hapludoll Thapto-Natric Pichincha

Mollisol

Udoll Argiudoll Typic Saboya

Table 4-9: List of soil series or taxonomic units (TU)

Ameghino series: this series is classified as Entic Hapludoll, coarse-loamy, mixed and thermic. It is located on elevated positions (lomas) with slopes ranging from 0 to (rarely) 1%. This soil is some-excessively drained, and possesses intermediate surface runoff and moderately-fast permeability. The vegetation cover is crops, and has low water-retention capability as use limitation. Its land use capability is IIIs; and its PI ranges from 48 to 51. Balbín series: this series is classified as Duric Natraquoll, fine-silty, mixed and thermic. It is positioned on low lying (bajo) sectors with slopes smaller than 1%. This soil is imperfectly drained, with slow permeability. It is strongly alkaline and non-saline. Its vegetation cover is natural pastures; its land use capability is VIws; and its PI is 22 to 23. Cañada Seca series: this series is classified as Thapto-Argic Hapludoll, coarse-loamy, mixed and thermic. It is positioned on extended higher plains (loma) with slopes smaller than 1%. This soil is moderately-well drained, and possesses intermediate surface runoff and moderate permeability. It is alkaline in depth but non-saline. Its land use capability is IVws, with drainage and low cationic-interchange capability as limiting factors; its PI 58 to 62. Drabble series: this series is classified as Typic Natraquoll, fine, illitic and thermic. It is positioned on low plains (bajo) and low plain foot (pie de loma) sectors with slopes smaller than 1%. This soil is poorly drained, and possesses very slow surface runoff and very slow permeability. It is strongly alkaline and


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slightly saline from surface. Its land use capability is VIws, with poor drainage, strong alkalinity and salinity as limiting factors; its PI is 10. Lincoln series: this series is classified as Typic Hapludoll, coarse-loamy, mixed and thermic. It is positioned on extended higher plains with slopes smaller than 1%. This soil is well to some excessively drained, and possesses intermediate surface runoff and moderate to moderately-fast permeability. It is alkaline in depth and non-saline. Its land use capability is IIs, with moderate water-retention capability as limiting factor while its PI is 73. Ortiz de Rosas series: this series is classified as Thapto-Argic Hapludoll, fine-silty, mixed and thermic. It is positioned on higher (loma) and high transitional (media loma alta) sectors with loessic parental materials and slopes of 1%. This soil is moderately well-drained, non saline, non alkaline and possesses intermediate surface runoff and moderately slow permeability. Its normal vegetation cover is crops, and has slight water excess as use limitation. Its land use capability is IIw; and its PI ranges from 77 to 85. Pichincha series: this series is classified as Thapto-Natric Hapludoll, loamy-fine, mixed and thermic. It is positioned on low plains (media loma baja and pie de loma) sectors, with slopes ranging from 0.5 to 1%. This soil is moderately well-drained, and possesses intermediate surface runoff and moderate permeability. It is non-saline but alkaline below 50 cm depth. Its vegetation cover is natural pasture, and presents sodicity and drainage as use limitation factors. Its land use capability is IVws; and its PI is 48 to 51. Saboya series: this series is classified as Typic Argiudoll, loamy-fine, mixed and thermic. It is positioned on higher plains (loma) with loessic parental materials and slopes ranging from 0.5 to 1%. This soil is well-drained, and possesses intermediate surface runoff and moderate to moderately-fast permeability. It is neither saline nor alkaline. Its vegetation cover is crops, and its land use capability is I without significant limiting factors. Its PI is 85 to 90.

4.4.2. Satellite imagery analysis and interpretation

The application of RS techniques for the study of vegetation-terrain-water relations in wetlands and flood-affected areas has been widely used (Degioanni et al., 2001; Lunetta & Balogh, 1999; Vazquez et al., 2003). Traditionally, Landsat MSS, NOAA AVHRR, Landsat TM/ETM+, and SPOT satellite systems have been selected for these studies, although recently Aster images became available and have been employed for that purposes, also in flatlands (Tchilingurian et al., 2003). In absence of detailed vegetation information, RS analysis and interpretation techniques may be used to define areas with different vegetation vigour and coverage responses (Asner, 2004) in front of fluctuating (dry, normal and wet) hydrological conditions. Based on this idea, Damiano et al. (1997) developed a methodology – for areas of low morphogenetic energy – that makes use of multi-temporal/multi-sensor analysis and interpretation of vegetation coverage, expert knowledge and field surveys to delineate characteristic terrain units that respond differently to the ponding dynamics of the study area. The level of accuracy obtained from this methodology resulted very suitable for detailed applications, such as agro-hydrologic studies at farm level. Therefore, their approach was adopted for this research. However, the relationships existing between these terrain units and the specific characteristics of the soils which compose them can not be defined by RS


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interpretation, entailing particular soil survey and agronomic techniques to bridge this gap (explained later in this chapter). In order to identify the mentioned vegetation responses, different sources of remote sensing data consisting of multi-temporal series of Landsat TM /ETM+ and Aster satellite imagery were utilized (Table 4-2). Additionally, available meteorological (rainfall) information from “Los Recuerdos” ranch was used to associate each image to a 15, 30 and 60 day-period antecedent rainfall conditions (Table 4-10). The employed scenes were selected as being representative of the latest contrasting hydrological conditions occurred in the study area. The relation between observed monthly rainfall pattern at “Los Recuerdos” ranch (from January, 1997 to June, 2005) and selected imagery acquisition dates is depicted in Figure 4-17.

Rainfall (mm)

Sensor Acquisition date Season

Accumulated previous 15 days



Hydrologic condition

TM 06-Oct-97 Spring 85 94 99 Dry TM 07-Sep-98 Winter 12 29 56 Dry TM 19-Apr-99 Fall 24 65 350 Dry TM 17-Feb-00 Summer 8 28 212 Dry

ETM+ 28-Mar-00 Fall 62 139 167 Dry TM 15-Nov-00 Spring 146 251 329 Wet

ASTER 16-Nov-00 Spring 146 251 329 Wet ASTER 19-Jan-01 Summer 104 108 117 Wet ASTER 06-Oct-01 Spring 173 173 245 Wet

TM 02-Nov-01 Spring 115 189 355 Wet ASTER 10-Nov-01 Spring 141 194 419 Wet ASTER 14-Feb-02 Summer 0 55 287 Wet ETM+ 21-May-02 Fall 34 34 198 Wet ETM+ 13-Nov-02 Spring 70 304 369 Wet ETM+ 16-Jan-03 Summer 7 250 359 Wet ETM+ 06-Apr-03 Fall 37 102 254 Wet

TM 21-May-05 Fall 0 0 192 Normal Table 4-10: Hydrologic condition of available satellite imagery

Figure 4-17: Monthly precipitation pattern and imagery acquisition dates


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The Normalized Difference Vegetation Index (NDVI) is an appropriate measure of the amount of biomass and vigour of vegetation at the surface (Lillesand et al., 2004). In this sense, the magnitude of NDVI (ranging from -1 to 1) is related to the level of photosynthetic activity in the observed vegetation: higher values of NDVI indicate greater vigour and amounts of vegetation, whereas non-vegetated areas and water bodies show strong negative values. The reason NDVI is related to vegetation is that healthy flora reflects very well in the near infrared part of the spectrum: vigorous green leaves have a reflectance of 20% or less in the 0.5 to 0.7 µm range and about 60% in the 0.7 to 1.3 µm range (near infrared). The NDVI is obtained by the following equation (Liang, 2004):

RNIRRNIRNDVI

ρρρρ

+−

= ,

where ρ NIR and ρ R are reflectances of near infrared and red bands, respectively. For Landsat TM/ETM+ images, NIR and R correspond to bands 4 and 3, respectively whereas for Aster images, NIR corresponds to band 3N and R corresponds to band 2 (Table 4-11).

Band Wavelength (µm) Spectrum Spatial resolution 3 0.63-0.69 Visible red (R) 30 m Landsat

TM / ETM + 4 0.78-0.90 Near infrared (NIR) 30 m

2 0.63-0.69 Visible red (R) 15 m Aster

3N 0.76-0.86 Near infrared (NIR) 15 m

Table 4-11: NIR and Red bands wavelength for Landsat TM/ETM+ and Aster sensors

However, since the relation between NDVI and percentage of vegetation in a pixel is not linear, no direct information can be extracted about the vegetation coverage from a NDVI image. Therefore, the elaboration of a NDVI-based percentage of vegetation cover map (Pv) was done employing the method proposed by Valor and Caselles (1996), which accounts for these non-linearities and is applicable to areas with several soil and vegetation types and where the vegetation cover changes in time. According to these authors, the calculation of vegetation proportion Pv is as follows:

( ) 100)1()1(

)1(% ∗

−⋅−−

−=

vs

s

NDVINDVIK

NDVINDVI

NDVINDVI

Pv ,

being ss

vv

RNIRRNIR

K−−

= ,

where NDVIv is the maximum NDVI value for pure vegetation and NDVIs is the minimum NDVI value for pure bare soil in the NDVI reference image; NIRv is the fully-vegetated pixel reflectivity in the near infrared; NIRs is the bare soil pixel reflectivity in the near infrared; Rv is the fully-vegetated pixel reflectivity in the red; and Rs is the bare soil pixel reflectivity in the red. The application of this method has been suggested by Parodi (2002) specially in those situations when no vegetation map is at hand, no idea of the spatial distribution of the vegetation exists and no information on vegetation structures is available.


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The processing workflow concerning the steps performed within this sub-section to obtain the final map is depicted on Figure 4-18.

Figure 4-18: Methodological flowchart for RS data analysis and interpretation

Aster L1B images were obtained free of charge through ITC’s Geo-Information Department in their original .hdf file format and straightaway transformed to radiance values (W m-2 sr-1 µm-1) using the import command of ILWIS 3.3 software. Regarding the Landsat TM/ETM+ images, they were freely provided by INTA’s Climate and Water Institute (Instituto de Clima y Agua) as .img format files in a 4-3-5 (RGB) False Colour Composite band combination without the respective header files. As a first step in the image processing procedure, all images were geo-referenced to the project’s coordinate system with Erdas 8.7 software, using the ortho-image as reference base in order to obtain the most accurate possible image positioning and co-registration. For the areas falling outside the coverage of the ortho-image, several ground control points taken from already referenced topographic maps were properly placed around the study site. The overall RMS error achieved in the geo-reference process of the Landsat imagery ranged from 0.57 to 0.60 pixels while for Aster imagery this error was between 0.39 to 0.41 pixels. The Nearest Neighbour resampling technique was selected for best preserving the original radiometry of every single pixel (Lillesand et al., 2004).


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After being geo-referenced, Landsat-5 TM and Landsat-7 ETM+ images were radiometrically calibrated. Calibrated radiometric quantities (i.e. radiances or reflectances) are mandatory basic input for many RS applications, and this becomes most evident when multi-sensor data or multi-temporal series are being investigated. Calculation of radiance is the fundamental step in putting image data from multiple sensors and platforms into a common radiometric scale (Liang, 2004). For Aster images, this step was automatically performed in ILWIS 3.3 software before geo-referencing. Atmospheric correction techniques were neglected for all images since flux-based quantitative RS procedures were not applied in this research. Normally, the conversion coefficients are given in the radiometric record of the header file and are unique for each image, however since for this study the Landsat images header files were not available, such information was retrieved from internet and related literature review. For Landsat-5 TM images, the conversion from digital numbers (DN) to at-sensor spectral radiances was done for bands 3 and 4 following the method presented by Chander & Markham (2003), as follows:

λλλ

λ LMINQcalQcal

LMINLMAXL +∗⎟⎟

⎠

⎞⎜⎜⎝

⎛ −=

max,

where Lλ is spectral radiance at the sensor’s aperture in W m-2 sr-1 m-1; Qcal is the pixel value in DNs; Qcal max is the maximum quantized pixel value (DN=255) corresponding to LMAXλ; LMINλ is spectral radiance scaled to Qcal min (DN=0) in W m-2 sr-1 m-1; and LMAXλ is the spectral radiance scaled to Qcal max in W m-2 sr-1 m-1. Table 4-12 provides the band-specific LMINλ and LMAXλ used for Landsat-5 images radiance conversion.

Spectral radiances, Lmin and Lmax in W m-2 sr-1 µm-1 Processing

date From March 1st 1984

to May 4th 2003 After May 5th 2003

Band LMIN LMAX LMIN LMAX 1 -1.52 152.10 -1.52 193.00 2 -2.84 296.81 -2.84 365.00 3 -1.17 204.30 -1.17 264.00 4 -1.51 206.20 -1.51 221.00 5 -0.37 27.19 -0.37 30.20 6 1.2378 15.303 1.2378 15.303 7 -0.15 14.38 -0.15 16.50

Table 4-12: Calibration coefficients used for Landsat-5 images (bands 3 and 4)

For Landsat-7 ETM+ images, the conversion from DNs to spectral radiances was performed for bands 3 and 4 according to the “Landsat 7 Science User Data Handbook” (NASA, 2002) procedure, as follows:

LMINQCALMINDNQCALMINQCALMAXLMINLMAXL +−⎟⎟

⎠

⎞⎜⎜⎝

⎛−−

= )(*λ ,

where DN is the Digital Number for each pixel of the image, LMAX and LMIN are the calibration constants, and QCALMAX and QCALMIN are the highest and the lowest points of the range of rescaled radiance in DN (255 and 1, respectively).


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Although the overall scaling is very similar to the one used to calibrate Landsat-5 images, when calibrating Landsat-7 images the gain setting combination for the particular image must be clearly known for selecting the LMIN and LMAX values (Table 4-13). For non-desert, non-ice land surfaces, bands 1, 2, 3, 5, 6, 7 are normally set to high gain whereas bands 4 and 8 are set to low gain (NASA, 2000).

ETM+ Spectral Radiances in W m-2 sr-1 µm-1 Before July 1st 2000 After July 1st 2000 Processing

date Low Gain High Gain Low Gain High Gain Band LMIN LMAX LMIN LMAX LMIN LMAX LMIN LMAX

1 -6.20 297.50 -6.20 194.30 -6.20 293.70 -6.20 191.60 2 -6.00 303.40 -6.00 202.40 -6.40 300.90 -6.40 196.50 3 -4.50 235.50 -4.50 158.60 -5.00 234.40 -5.00 152.90 4 -4.50 235.00 -4.50 157.50 -5.10 241.10 -5.10 157.40 5 -1.00 47.70 -1.00 31.76 -1.00 47.57 -1.00 31.06 6 0.00 17.04 3.20 12.65 0.00 17.04 3.20 12.65 7 -0.35 16.60 -0.35 10.93 -0.35 16.54 -0.35 10.80 8 -5.00 244.00 -5.00 158.40 -4.70 243.10 -4.70 158.30

Table 4-13: LMAX and LMIN values for Landsat 7 ETM+ images

The radiance as measured by the instrument at the top of the atmosphere is dependant of the solar irradiance at that level. For relatively clear scenes and to avoid variations due to yearly variation of the solar irradiance, it is suggested to normalise the radiance measured by the satellite, as calculated above, to planetary reflectance. The obtained values will then be comparable from one date to the other. Furthermore, this procedure simplifies later the work of a photo-interpreter and allows inter-sensor comparison of images. For all Landsat and Aster images, the top-of-atmosphere (TOA) reflectance ρ for band λ was computed by the following equation:

θπ

ρλ

λλ cos

2

⋅⋅⋅

=ESUN

dL,

where Lλ is TOA spectral radiance of the band observed by the satellite, d is the Earth-Sun distance in astronomical units, ESUNλ is mean solar exoatmospheric irradiances for the wavelength band interval λ, and cosθ is the cosine of the solar zenith angle in degrees. Table 4-14 shows the values of Earth-Sun geometry variables used to compute this equation, whereas Table 4-15 presents the corresponding λESUN values.

Sensor Date cos θ d2

16-nov-2000 0.90449601 0.9769345619-jan-2001 0.87536635 0.9673784206-oct-2001 0.81124779 0.9993881110-nov-2001 0.89216296 0.98012147

Aster

14-feb-2002 0.81816267 0.9746857106-oct-1997 0.71277763 0.9993881107-sep-1998 0.58972899 1.0158614719-apr-1999 0.53552325 1.0091210517-feb-2000 0.71762451 0.9758976915-nov-2000 0.83148974 0.9773699802-nov-2001 0.8104572 0.98412082

Landsat-5

21-may-2005 0.43415158 1.0251134428-mar-2000 0.63995404 0.9968222621-may-2002 0.44534308 1.0251134413-nov-2002 0.84409226 0.9787167716-jan-2003 0.81707316 0.96694348

Landsat-7

06-apr-2003 0.60385849 1.00154552

Table 4-14: cosθ and d2 values used for calculation of reflectances


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Sensor Band ESUNλ (W m-2 µm-1)

2 1555.00 Aster 3N 1120.00 3 1554.00 Landsat-5 TM 4 1036.00 3 1551.00 Landsat-7 ETM+ 4 1044.00

Table 4-15: ESUNλ values for the respective Aster and Landsat 5/7 bands

After having all images properly calibrated to TOA reflectances, the NDVI and Pv calculations were performed according to the respective formulas, as explained in previous paragraphs of this section. These results are depicted in Figure 4-19, Figure 4-20 and Figure 4-21 for Aster, Landsat-5 and Landsat-7 imagery, respectively.

Figure 4-19: NDVI and Pv products generated from Aster imagery


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Figure 4-20: NDVI and Pv products generated from Landsat-5 imagery

Figure 4-21: NDVI and Pv products generated from Landsat-7 imagery


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The visual inspection of false colour composites (FCCs) is usually taken as a first approach to identify and analyse multi-temporal data and changes. Certain processes and changes occurring on the land surface can be visualised by the generation of FCCs and their interpretations. The representations of colour, patterns and texture, and the knowledge about the reflectance properties of the observed objects help the researcher to identify objects and processes, and interpret satellite data. After that, the elaboration of processed products (such as NDVI and Pv, in the case of this study) can further improve and widen the initial interpretations derived from FCCs. The visual analysis of multi-sensor, multi-resolution and multi-temporal data, in conjunction with ancillary data and actual ground truth data can serve to aid understanding and analysis of environmental processes and changes. As mentioned by Schmidt (2003), in most occasions visual interpretations and manual on-screen polygonization of observed or inferred processes are the most effective and precise tools in land-surface change analysis when working in a coupled RS & GIS environment. This approach is reasonably accurate if some criteria are met, i.e. if the study area is well known, not too large and the areas of interest are easy to visually distinguish. Moreover, visual analysis done by experts always incorporate into the interpretation process the existent long-term behaviour of the studied region that cannot be identified by automatic RS classification processes based on instantaneous data. In the case of this research, it would be erroneous to assign a high level of ponding susceptibility (inferring low productivity) to a farm sector that was ponded only on some extraordinary event for which, by opportunity, an image was acquired. This kind of approach is maybe adequate for basin or regional scale flood mapping, but not for reclamation studies where such an event must be seen as an exception where production is halted temporarily. This is the right context of using RS imagery in this kind of particular studies, where field knowledge still remains the deciding mapping factor. Apart from the analysis of FCCs (4-3-5 for Landsat imagery and 3N-2-1 for Aster data), the amount of information derived from NDVI and Pv interpretation constitute a fundamental input in the process of identifying and delineating areas with different vegetation vigour and coverage related to the fluctuating hydrological conditions in the study site. Following the approach that guided this study (which is based on soil-plant-relief relationships as susceptibility indicators), vegetation information can indirectly help in identifying soil physicochemical conditions and relief positions. Summarizing, a comprehensive visual analysis and interpretation of the original and processed image data was supported by expert knowledge on the area provided by INTA’s personnel. These, along with the subsequent assignments carried out during the field campaign (general field reconnaissance, verification and re-interpretation of the office tasks aided by mobile-GIS mapping, and soil survey), allowed the elaboration of a thematic map (Figure 4-22) composed by a mosaic of distinguishable topo-hydrologic terrain units (locally called “ambientes”). For the purpose of this study, each terrain unit: − describes a natural division of the land surface that can be recognized from remotely-sensed data

analysis & interpretation, and can be verified on the ground; − is an entity that groups particular interrelationships among relief, soil, drainage and vegetation; and

differs from adjoining entities because either the relief, the vegetation characteristics, the drainage conditions or the soil properties are evidently different;

− may be considered internally homogeneous and is assumed to behave uniquely; and − can be described as a GIS element (polygon-shaped) which congregates a unique set of attributes.


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From higher to lower positions, these units are denominated: High planes (“planos altos”): under this name were grouped all those areas observed free of water throughout the analyzed sequence of images, showing relatively high values of NDVI and Pv. In this category were also included the plots that presented bare soil or low vegetation coverage because of the different crop growing stages during the year. These areas correspond to relatively elevated relief positions, although some micro-depressions may appear where excess water tends to momentarily accumulate. Within this unit, soils are deep and well drained, without physical limiting conditions to infiltration and water storage along the profile, and able to support good crop development all over the year. Lying planes (“tendidos”): these areas presented intermediate to low NDVI and Pv values, related to rangelands and annual pastures with diverse percentages of vegetation coverage affected by hydromorphic conditions. These extended flat sectors, associated to intermediate topographic positions, showed saturated soils in those images preceded by periods of intense precipitation, though in normal periods they evidenced an apparent total recovery. Ponding lows (“bajos anegables”): these horizontal to slightly concave areas are found frequently ponded, even during normal periods, and most of them act as the expansion or evacuation areas for nearby lagoons. They always presented very low vegetation coverage values related to the specific physicochemical soil characteristics of these low areas, which only allow the presence of communities adapted to strong halo-hydromorphic conditions. Some of these terrain units present strong signs of salinity, even visible at the resolution of the selected images. Ponds (“cubetas”): corresponding to the lowest topographic positions, these areas are occupied by perennial or semi-perennial water bodies with a varied degree of salt contents, due to the fact that some of them are in direct contact with the phreatic table; and low to null aquatic and hydromorphic vegetation coverage. During periods of intense precipitations they normally increase their areas several times and get interconnected forming extended ponded areas, which many of them become salt-affected when dry.


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Figure 4-22: Map of terrain units for “Los Recuerdos” ranch

The figure below depicts an ideal toposequence showing the relative positions of the units mapped above, whereas Table 4-16 presents the area occupied by each terrain unit within “Los Recuerdos” ranch.

Figure 4-23: Ideal toposequence showing the relative topographic position of each terrain unit

Terrain unit Area (Ha) % High planes 3037.6 51.4 Lying planes 2171.7 36.7 Ponding lows 468.8 7.9

Ponds 231.8 3.9 Total 5909.9 100

Table 4-16: Terrain units areas


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4.4.3. Field methods

A field campaign in “Los Recuerdos” ranch was carried out from September 6th to 21st, 2005 in order to perform a general reconnaissance of the field, to understand its hydrological context, and to collect pertinent ground truth data to support the study. Additionally, during this campaign 156 GCPs were taken in order to accurately geo-reference aerial photographs and satellite imagery used for the elaboration of the cartographic base and thematic maps, respectively. The soil-terrain-vegetation related assignments during the field campaign consisted of: − observation, interpretation and mapping of field characteristics such as topography (including relief and

micro-relief shapes, relative positions and slopes), land cover and land use; − validation and/or correction of the terrain unit’s boundaries derived from the analysis and interpretations

of RS data explained in previous sub-section, verifying also that the final interpretation was correct and matched with the actual ground truth;

− soil sampling (for laboratory testing) and profile examination, in order to obtain qualitative and quantitative information about physicochemical properties (permeability, salinity, alkalinity, texture, structure, water-holding capacity, bulk density) of the soil types found in the area. Several sampling positions were made on representative sectors to corroborate the information provided by the 1:50,000 scale soil maps, and to improve and enlarge such information with porosity, bulk density, EC and pH values from the different horizons recognized in each profile;

− measurement of water table depths (wherever possible) and Kh values; − determination of bulk densities, in order to calculate related porosity and water storage capacity values; − identification of vegetation communities and species, relating them to relief positions and soil

characteristics. Figure 4-24 shows the transect followed to sample soils in representative locations within each one of the four terrain units identified (points 1 to 5). To complement this, soil information (by augering technique) from seven more sampling positions (points 6 to 12) was obtained from a previous study carried out in the ranch. Table 4-17 presents the location of all sampling points and their corresponding terrain unit.


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Figure 4-24: Distribution of soil sampling points

# X Y Parcel Terrain unit1 4529547 6148018 25W High plane 2 4529617 6145577 13 Lying plane 3 4525997 6140817 26W Ponding low 4 4528142 6142572 8W High plane 5 4526178 6139556 34W High plane 6 4526006 6147034 23W High plane 7 4525576 6145316 17W Ponding low 8 4525791 6143509 10W Lying plane 9 4527719 6141978 16W Lying plane

10 4526275 6140017 26W High plane 11 4529071 6147440 21E High plane 12 4529510 6140552 22N High plane

Table 4-17: Location of sampling points

The soil survey was carried out following a hydrological criterion, namely, trying to determine those soil characteristics and properties that may reveal hydro-halomorphic conditions related to surface water and groundwater behaviour along the profile. Following a logical sequence described in the INTA’s Soil Survey Standards (“Normas de Reconocimiento de Suelos”) (Etchevehere, 1976), the main internal and external soil attributes were described, sampled and tested with the help of a specialized professional, combining expert knowledge with direct observation, pit drillings and adequate technical equipment in order to obtain water and soil samples from the selected points for their further analysis.


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4.4.3.1. Soil profiles description

Because the analysis of a large number of soil samples is costly and labour-intensive, it is impractical and unaffordable to arbitrarily establish an intense soil-sampling scheme for a 15-day-long field campaign. Instead, five pits were made in representative sectors of the ranch’s soil types in order to analyze the profiles, as mentioned in previous paragraphs. Apart from external factors (like relief position or management practices), the soil properties/characteristics which were considered relevant factors to define ponding susceptibility conditions, and therefore measured during the soil survey campaign, are: − texture − structure − profile arrangement − presence/absence and depth of limiting horizons − depth and quality of water table − salinity/sodicity/hydromorphism − permeability − bulk density/porosity − water content All measurement/sampling positions were geo-referenced with GPS. Additionally, for each pit the general landscape configuration, the specific relief position, as well as the vegetation characteristics (species, coverage, condition) of the surroundings were analyzed and documented. The detailed morphologic information obtained in each sampling site is presented in Appendix D. Pit #1: The first hole was made in the north-eastern sector of the ranch, within the area belonging to parcel 25-west, on a “high plane” according to the terrain units map. The setting corresponded to a elevated, gently undulating landscape while the pit was located on a relatively higher relief position (loma). The vegetation coverage was represented by soya stubble, sowed under no-tillage system (Figure 4-25). The 111 cm depth excavation presented a profile composed by a clear and gradual succession of Ap/A, Bt, BC and C horizons without any important textural leap, entirely developed from a thick deposit of loamy-fine sand loess (Figure 4-26). The analyzed profile showed a deep, rich in organic matter, moderately-well drained soil, loamy to loamy-sand textured and well structured, without symptoms of salinity, alkalinity or hydromorphism either on surface or along the profile (only few clay-skins were found within the Bt horizon and weak mottles in C horizon). Also, the profile showed a good root development and evidences of biological activity (worm-holes and bio-pores). The main characteristics of these horizons are presented in Table 4-18.

Horizon Depth (cm) Texture Structure pH EC (dS m-1) Carbonates Skins Mottles Ap/A 0-29 Loam Granular 6.5 <2 no no no

Bt 29-50 Sandy clay loam Blocky 6.5 <2 no few no BC 50-82 Loam Blocky 6.5 <2 no no no C 82-111 Sandy loam Blocky 6.5 <2 no no weak

Table 4-18: Major characteristics of horizons in Pit #1


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Figure 4-25: General setting of Pit #1

Figure 4-26: Profile arrangement of Pit #1

Drilling with the auger under the pit’s bottom, at approximately 180 cm depth a shallow water table was located on top of a tough layer of caliche (“tosca”). The electrical conductivity measurement (performed all over the field campaign with an electronic conductimeter) detected a value of 1.46 dS m-1, confirming the presence of a non-saline, perched or “false phreatic” water table, in opposition to the strongly saline conditions (from 4 to 14 dS m-1) presented by the regional groundwater (Casas & Pittaluga, 1990; Halcrow & Partners, 1999).


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According to its external characteristics given by the landscape location, relief position and vegetation coverage; and its morphologic properties, derived from the profile analysis, the arrangement of horizons and their physico-chemical properties, this soil was classified as a Typic Argiudoll and associated to the Saboya series. Pit #2: it was made 2.4 km southwards from the first pit, inside the parcel 13, on a “lying plane” according to the terrain units map. In this sector, the setting corresponded to an ample, horizontal landscape without slope, being the pit located within a low position (Figure 4-27).

Figure 4-27: Landscape characteristics of Pit #2

Figure 4-28: Profile arrangement of Pit #2, showing massive columnar structures in Bt1 horizon


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The vegetation coverage was represented by an old pasture, degraded by ponding and overgrazing, composed by carduus sp. (“cardo”) and trifolium sp. (“trébol”). Related to relief position and soil characteristics, in natural conditions this coverage would have been represented by typical species from the “humid prairies” community. The pit was excavated until an impeding tosca layer was found at 110 cm depth. The exhibited profile was composed by a sequence of A, Bt1, Bt2, BCk and Ck horizons with an evident textural leap between the A and Bt1 horizons. Similarly to the previous pit, this soil was entirely developed from a thick deposit of loessic parental material (Figure 4-28). The phreatic table was found at 97 cm depth, within the Ck horizon. The EC measurement gave a value of 4.48 dS m-1, which is considered moderately saline according to INTA’s soil survey standards. The analyzed profile showed a deep but poorly drained soil, with low infiltration capacity, loam to clay-loam textured and with severe symptoms of sub-superficial alkalinity (from 20 cm depth and throughout the entire profile) and hydromorphism (abundant clay-skins and mottles). In this sense, strong columnar structures were found between 25 and 35 cm from the surface as a clear evidence of structure deterioration due to sodicity conditions along the sub-superficial solum (Figure 4-28). In accordance with this, the profile showed a restricted root development and scarce signs of biological activity. The main characteristics of this soil’s horizons are presented in Table 4-19.

Horizon Depth (cm) Texture Structure pH EC (dS m-1) Carbonates Skins Mottles A 0-20 Loam Blocky 6.5 <2 no no common

Bt1 20-35 Clay loam Columnar >8 ≅ 4 no abundant common

Bt2 35-53 Clay loam Blocky >8 ≅ 4 no abundant common

BCk 53-72 Loam Blocky >8 ≅ 4 yes abundant abundant Ck 72-110 Loam Blocky >8 >4 yes abundant abundant


According to its external characteristics given and its morphologic properties, derived from the profile analysis, the arrangement of horizons and their physico-chemical properties, this soil was classified as a Duric Natraquoll and assigned to the Balbín series. Pit #3: it was made close to the south-western border of the ranch, in the centre of the parcel 26-west and within a hydromorphic, “ponding low” sector. The setting corresponded to a landscape of extended low plains, where the pit was made on the relatively lowest position. The natural vegetation was exemplified by a degraded and scarcely covered natural rangeland, composed by typical individuals of “halophytic steppe” species adapted to strongly saline/alkaline conditions: Distichlis spicata (“pelo de chancho”), Atriplex sp. and Nostoc sp. (“alga”), (Figure 4-29).


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Figure 4-29: Location conditions of Pit #3

The pit was excavated 67 cm depth, due to the presence of a limiting tosca layer. This shallow profile presented a sequence of A, Bt, and BCk horizons developed from a transitional deposit of recent sand and older loess (Figure 4-30). The phreatic table was found at 67 cm depth, just below the calcium carbonate layer that acts as a barrier restricting – when present – the rising of the groundwater. The result of the EC measurement gave it a value corresponding to a strongly saline condition: 13.06 dS m-1.

Figure 4-30: Profile arrangement of Pit #3, showing rounded columnar structures in Bt horizon


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The appearance of white efflorescences all over the surface denoted the strong salinity conditions of this soil (more than 14 dS m-1), even in non-ponding circumstances. The A horizon presented a loam to sandy loam texture, little organic matter and elevated pH value. Bt horizon was strongly natric (pH >>8), clay loam textured and with a prismatic/columnar structure evidencing a severe degradation due to alkaline conditions. Also, this horizon presented ferro-manganesic mottles and abundant clay-skins. Finally, horizon BCk presented a blocky structure, a loam texture and signs of hydromorphism in the shape of profuse carbonate concretions, mottles and clay-skins. The main characteristics of soil horizons observed in pit #3 are presented in Table 4-20. Horizon Depth (cm) Texture Structure pH EC (dS m-1) Carbonates Skins Mottles Salt crust 0-3 ----- ------- >8 >14 no no Fe-Mn

A 3-23 Loam to sandy loam Blocky 8 >8 no no Fe-Mn Bt 23-45 Clay loam Columnar >8 ≅ 8 no abundant Fe-Mn

BCk 45-67 Loam Blocky >8 13 abundant abundant Fe-Mn


Based on physico-chemical and morphological evidence showed by the profile, this soil was characterized as shallow, moderately saline, strongly alkaline from surface, poorly drained, hydromorphic and severely limited for any vegetation growing, except for species adapted specifically to such conditions. Therefore, it was classified as Typic Natraquoll and assigned to Drabble series. Pit #4: This pit was made in the central sector of the ranch (parcel 8-west) on a “high plane” terrain unit, being the landscape constituted by an ample and gently undulating plain with intercalated cuvettes. In this setting, the pit was located on a higher position and its vegetation coverage was represented by soya stubble, sowed under no-tillage system and recently harvested (Figure 4-31). The soil profile exhibited the following sequence of horizons: Ap/A, AC, 2Bt, 2BC, 2C and 2Ck. This succession is typically related to a Thapto (buried) soil, where the 2Bt horizon corresponds to a previous soil that was eroded and lost its superficial horizon; then a posterior eolian process deposited a new layer of sediments over which the current A and AC horizons were developed. As a result, an abrupt textural and structural discontinuity occurs between AC and Bt horizons, which is depicted in Figure 4-32. The Ap/A horizon appeared well-provided of organic matter, with a sandy loam texture, neutral, well structured and deep enough (>18 cm) to allow good root development. This horizon gradually turned into a sandy loam AC horizon, with evidences of rooting and biologic activity, neutral and with few weak mottles. Changing abruptly in texture and structure, an illuvial 2Bt horizon appeared (see detail in Figure 4-32). This horizon, formed over a different parental material, presented a sandy clay loam texture and a weak prismatic structure, with abundant clay-skins and mottles. Next, the transitional 2BC horizon also showed similar characteristics, but with a sandy loam texture and slightly higher pH values. The horizon 2C presented a sandy loam texture, moderately alkaline and with abundant clay-skins and mottles, whereas the 2Ck horizon contained profuse hard carbonatic concretions. Under this horizon, at 137 cm depth a moderately saline water table appeared (10.03 dS m-1). A summary of horizon characteristics as observed in pit #4 is presented in the table below.


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Figure 4-31: Location setting of Pit #4

Figure 4-32: Profile arrangement of Pit #4, detailing the textural and structural discontinuity between AC and 2Bt horizons


82

Horizon Depth (cm) Texture Structure pH EC (dS m-1) Carbonates Skins Mottles Ap/A 0-27 Sandy loam Granular 6.5 <2 no no no AC 27-41 Sandy loam Granular 6.5 <4 no no no 2Bt 41-58 Sandy clay loam Prismatic 6.5 4 no weak weak 2BC 58-81 Sandy loam Blocky 7.5 4 no weak some 2C 81-120 Sandy loam Blocky >8 >4 no no abundant 2Ck 120-137 --------- --------- >8 10.03 abundant some abundant

Table 4-21: Principal characteristics of horizons in Pit #4

Based on its external and internal morphological features, this soil was characterized as deep, sandy loam, moderately-well drained, slightly alkaline below 80 cm depth and non saline. In consequence, it was categorized as Thapto-Argic Hapludoll and assigned to Ortiz de Rosas series. Pit #5: the last pit examined in the field campaign was made over the south-western sector of the ranch, in the centre of the parcel 34-west and within a sector belonging to a “high plane” terrain unit. The setting corresponded to a landscape of gently undulating to level plains, where the pit was made on a micro-high position. The vegetation coverage during fieldwork was represented by a wheat crop in early growing stage (Figure 4-33), although this area actually supports a rotating scheme of wheat/soya and pastures.

Figure 4-33: Location setting of Pit #5

Due to technical difficulties with the field equipment, this pit was caved only up to 56 cm. Up to that depth the soil profile exhibited a sequence of horizons composed by Ap/A, AC and 2Bt. Similarly to the profile described in the previous pit, a textural and structural jump was found in the contact between AC and 2Bt horizons, a typical characteristic corresponding to Thapto soils developed from two dissimilar parental materials (Figure 4-33). The Ap/A horizon appeared deep, well-provided of organic matter, with a sandy loam texture, neutral and well structured. This horizon gradually turned into a sandy loam AC horizon, slightly alkaline and with few weak mottles. After an abrupt textural and structural discontinuity, an illuvial 2Bt horizon appeared presenting a sandy clay loam texture and a weak prismatic structure, with abundant clay-skins and mottles. This horizon exhibited


83

higher values of pH (>8) indicating the beginning of slight alkaline conditions. The water table was localized at a depth of 120 cm. A summary of horizon characteristics as observed in pit #5 is presented in table below.

Horizon Depth (cm) Texture Structure pH EC (dS m-1) Carbonates Skins Mottles Ap/A 0-23 Sandy loam Granular 6.5 ----- no no no AC 23-39 Sandy loam Granular 7-7.5 ----- no no weak 2Bt 39-56 Sandy clay loam Prismatic >8 ----- no weak weak

Table 4-22: Major profile characteristics in Pit #5

This soil was characterized as deep, sandy loam, imperfectly drained, slightly alkaline below 50 cm depth and occasionally saline due to rising of water table. In consequence, it was categorized as Thapto-Argic Hapludoll and assigned to Cañada Seca series. Soils data regarding those sectors which were not surveyed during this field campaign was obtained from a previous study developed in “Los Recuerdos” ranch, and it is summarized in Table 4-23.

ID Parcel Landscape Terrain unit Soil type Drainage Series 6 23W Extended higher plains High plane Thapto-Argic

Hapludoll Well drained Ortiz de Rosas

7 17W Elongated low plain Ponding low Typic Natraquoll Poorly drained Drabble

8 10W Extended plain with cuvettes Lying plane Thapto-Natric Hapludoll

Moderately well drained Pichincha

9 16W Extended plain with cuvettes Lying plane Thapto-Natric Hapludoll

Imperfectly drained Pichincha

10 26W Ample transitional plain High plane Thapto-Argic Hapludoll

Imperfectly drained Cañada Seca

11 21E Gently undulating higher plain High plane Thapto-Argic Hapludoll

Moderately well drained Ortiz de Rosas

12 22 Gently undulating higher plain High plane Thapto-Argic Hapludoll

Moderately well drained Ortiz de Rosas

Table 4-23: Soil data retrieved from a previous soil survey carried out in the study site

4.4.3.2. Soil hydraulic conductivity measurement

Saturated hydraulic conductivity (Kh) is a quantitative measure of the saturated soil's ability to transmit water when subjected to a hydraulic gradient. It can be thought of as the ease with which pores of a saturated soil permit water movement, and it is related to soil properties such as texture, structure and porosity. The infiltration rate is sensitive to near-surface conditions and is subject to significant change with soil coverage and management practices. It is affected by the development of plant roots, earthworm burrows, soil aggregation, and organic matter. The hydraulic conductivity values for this study were obtained in the field using the inverse auger-hole method (Figure 4-34). Following the recommendations of the USDA Soil Quality Test Guide (1999a), that a minimum of three samples or measurements must be collected on any sampling point, 14 hydraulic conductivity tests∗ were carried out in the ranch, trying to cover the terrain units and soil types distribution in order to evidence their infiltration attributes.

∗ Three repetitions were executed in each sampling point except for the last one (pit #5), where only two repetitions were made due to technical problems.


84

D’ = Depth of the auger hole below reference HL= Depth to water level Ho = Initial water level

Figure 4-34: Diagram of inverse auger method

The technical procedure followed to execute the infiltration test is explained below (Ambayo et al., 2005): − Sample locations were selected based on the soil type distribution in the study area. − At the selected sampling points, holes of 8 cm diameter and 55 to 110 cm depth were drilled using an

auger, taking enough caution to avoid contacting the water table. − A measuring tape attached to a float was mounted on a framework so that the float is vertical and moves

freely in the hole. − Water was added into the hole until to the brim and the float let down. The initial height of the float was

measured. − The dropdown height of the float was measured at certain intervals of time depending on the speed of

the drop which in turn is determined by the soil type. − Measurements were taken until dropdown differences between four consecutive measurements remained

constant. To compute the saturated hydraulic conductivity of the different sampling points, the following formula was used:

0

1 )21log()

21log(

15.1tt

rhrhrK

o

h −

+−+= ,

where Kh is the hydraulic conductivity in cm day-1; r is radius of the auger hole in cm; ho is height of water column in cm at t = to; and h1 is height of water column in cm at t = t.


85

This experiment evaluates the saturated hydraulic conductivity of the horizon where the test is performed. In the case of this study, the test was executed across several horizons at the same time. As such, the result offered a horizon-depth-weighted average Kh, which is adequate to estimate the hydraulic conductivity of the investigated soil series. The figures resulting from the fourteen infiltration measurements conducted in “Los Recuerdos” ranch are shown in Table 4-24. The detailed infiltration curves and values for each test are presented in Appendix E.

Site # X (m) Y (m) Terrain unit

Soil type Series Land

cover Kh 1

(cm h-1) Kh 2

(cm h-1) Kh 3

(cm h-1) Infiltration

1 4529547 6148018 High plane

Typic Argiudoll Saboya Soya

stubble 0.96 0.91 0.92 Moderately slow

2 4529617 6145577 Lying plane

Duric Natraquoll Balbín Degraded

pasture 0.28 0.24 0.26 Slow

3 4525997 6140817 Ponding low

Typic Natraquoll Drabble Halophytic

rangeland 0.11 0.13 0.12 Very slow to null

4 4528142 6142572 High plane

Thapto-Argic Hapludoll

Ortiz de Rosas

Soya stubble 0.15 0.35 0.23 Slow

5 4526178 6139556 High plane

Thapto-Argic Hapludoll

Cañada Seca

Wheat crop 0.44 0.61 N/A Moderately

slow

Table 4-24: Saturated hydraulic conductivity (Kh) results

The table shows different scenarios for the hydraulic conductivity. Those soils with better textural and structural conditions (Typic Argiudoll and Thapto-Argic Hapludolls) showed infiltration values comparable to the depth of intense rainfalls occurring in the area. Thus, these soils would be less susceptible to ponding episodes, even during wet periods. On the other hand, soils occupying inferior topographic positions (Typic and Duric Natraquolls) present low to very low infiltration values (lower than the intensity of normal rainfall episodes). This combination of level relief and low hydraulic conductivity would make them more susceptible to be affected by ponding conditions even in normal climatic conditions. Although noticeable differences were found between the maximum (0.61 cm day-1) and minimum (0.15 cm day-1) infiltration values within the same soil type (Thapto-Argic Hapludoll), these differences can be attributed to the impact of the land cover, organic matter content, type of management that the soil has been subjected to and the antecedent soil moisture, plus the intrinsic limitations of the measurement method. However, the data obtained appeared to be in the range of values that can be found in standard hydraulic conductivity tables derived from different soil textures (USDA, 1999a).

4.4.3.3. Bulk density measurement

Bulk density, ρb, is a measure of the oven-dry weight of the soil per unit volume (see sub-section 3.3.2.3). It is dependent on the densities of the soil particles (sand, silt, clay, and organic matter) and their packing arrangement along the profile. Additionally, bulk density is a dynamic property that varies with the structural condition of the soil, and can be altered by cultivation; trampling by animals; agricultural machinery; and weather conditions (i.e., raindrops impact, frost). Variations in bulk density values are attributable to the relative proportion and specific gravity of solid organic and inorganic particles and to the porosity of the soil. Most mineral soils have bulk densities between 1.0 and 2.0 g cm-3, where higher values indicate growing soil compaction conditions. Compacted soils tend to restrict root growth and inhibit the movement and accumulation of water through the profile.


86

During the field campaign, apart from the measurement of water table depths, soil bulk density values were also calculated to assess the superficial water-storage capacity of the different soil types analyzed. To achieve this, three undisturbed soil samples per horizon were carefully taken in each pit using a 69 mm diameter ring (Figure 4-35), following the procedure indicated in the USDA Soil Quality Kit Test Guide. Later on, the collected soil samples were sealed and sent to laboratory for the determination of bulk densities.

Figure 4-35: Soil samples extraction for determination of bulk density values in laboratory

If the soil’s superficial water-storage capacity is larger than the rainfall depth, and the rainfall intensity is lesser than the soil’s hydraulic conductivity, then all the water will be accumulated along the profile and the soil will not be ponded. Normally, soils are composed by a combination of mineral components and interstices. The latter can be filled with air (dry soil), water (saturated soil) or a mixture of water and air (non-saturated soil). In this context, porosity (φ) can be defined as the proportion of pore spaces in a volume of soil (Dingman, 2002):

s

wa

VVV +

=φ ,

where Va is the volume of air; Vw is the volume of water; and Vs is the volume of the soil sample. In practice, ρb and φ are closely related since φ is usually determined as

m

b

ρρ

φ −=1 ,

where ρm is the particle density of the mineral grains making up the soil. This value is generally adopted as 2.45 g cm-3 for superficial horizons provided with organic matter, and 2.65 g cm-3 for deeper horizons (Etchevehere, 1976).


87

If the thickness of each horizon (Ph) is known, the depth of the water column that can be stored in each horizon (Lh_max) can be calculated as

hh PL ∗= φmax_

Then, the total depth of the water column, Lmax , that can be theoretically stored in the soil profile (up to the measurement depth) until its saturation is

∑=

=n

hhLL

1max_max

However, Lmax must be interpreted as the water column depth that a soil could store being completely dry, but only up to the depth of measurement in the profile. Additionally, two important factors might reduce drastically the soil’s water storage as calculated above: the water table height and the antecedent soil moisture. During the field campaign, the momentary state of these two factors was assessed using the available equipment. The following tables present the results of the bulk density measurements carried out for each soil series∗ during fieldwork, and the related calculation of porosity, total profile and per horizon water-storage depths and momentary moisture values (transformed to mm of water). Detailed information about these measurements is presented in Appendix F.

ρb φ Lh Moisture (mm of water) Horizon Depth (cm) Rep 1 Rep 2 Rep 3 Average (cm3/cm3) (mm) Rep 1 Rep 2 Rep 3 Average

Ap/A 29 1.17 1.33 1.29 1.26 0.484 140.4 89.3 95.6 93.1 92.7 Bt 21 1.34 1.39 1.35 1.36 0.477 100.2 64.0 66.4 66.1 65.5 BC 32 1.46 1.43 1.40 1.43 0.449 143.7 80.1 79.5 77.7 79.1 C 29 1.34 1.40 1.40 1.38 0.469 136.0 76.9 87.2 87.2 83.7 111 1.36 0.470 520.3 321

Table 4-25: ρb, φ, Lh and moisture values for Saboya soil series (Typic Argiudoll)

Maximum water storage: 4.7 mm water / cm soil. Actual water storage: 2.9 mm water / cm soil


A 20 1.44 1.38 1.47 1.43 0.451 90.2 32.9 26.4 26.7 28.7 Bt 33 1.44 1.40 1.44 1.43 0.452 149.2 114.1 105.6 94.7 104.8

BCk 19 1.40 1.45 1.33 1.39 0.464 88.2 69.0 67.4 67.9 68.1 72 1.42 0.456 327.6 201.6

Table 4-26: ρb, φ, Lh and moisture values for Balbín soil series (Duric Natraquoll)


∗ The complete profile of Cañada Seca series could not be analyzed. All calculations were made only for A/Ap horizon, so these figures must be taken only as a mere reference.


88


A 23 1.55 1.61 1.58 1.58 0.394 90.6 63.44 60.69 60.19 61.4 Bt 22 1.31 1.22 1.23 1.25 0.518 114.0 92.97 93.16 96.57 94.2

BCk 22 1.25 1.28 1.34 1.29 0.504 110.9 90.83 96.76 88.98 92.2 67 1.37 0.472 315.5 247.8

Table 4-27: ρb, φ, Lh and moisture values for Drabble soil series (Typic Natraquoll)



Ap/A 27 1.26 1.19 1.17 1.20 0.508 137.2 88.3 88.1 86.3 87.6 AC 14 1.35 1.39 1.34 1.36 0.477 66.8 44.4 44.2 44.2 44.3 2Bt 17 1.54 1.52 1.55 1.53 0.410 69.7 49.9 53.2 48.0 50.4 2BC 23 1.47 1.50 1.48 1.49 0.429 98.7 67.7 66.4 63.7 65.9 2C 39 1.42 1.44 1.46 1.44 0.446 173.9 140.8 140.7 138.1 139.9

120 1.4 0.454 546.3 388.1

Table 4-28: ρb, φ, Lh and moisture values for Ortiz de Rosas soil series (Thapto-Argic Hapludoll)



Ap/A 23 1.33 1.36 1.32 1.33 0.455 104.7 38.7 45.6 39.9 41.4

Table 4-29: ρb, φ, Lh and moisture values for Cañada Seca soil series (Thapto-Argic Hapludoll)

Maximum water storage: 4.6 mm water / cm soil. Actual water storage: 1.8 mm water / cm soil Additionally, Table 4-30 presents the average soil moisture values along the profile for each soil series at the moment of the field measurements (September, 2005).

Saboya Balbín Drabble Ortiz de Rosas Cañada Seca∗ Rep 1 0.282 0.291 0.370 0.319 0.168 Rep 2 0.299 0.269 0.376 0.321 0.198 Rep 3 0.295 0.259 0.368 0.310 0.174

Average 0.292 0.273 0.371 0.317 0.18

Table 4-30: Average soil moisture values (cm3 water / cm3 soil) along the profile in each soil series

Normally, soil moisture values are highly variable and must be taken only as an instantaneous reference. However, regarding that the hydrological situation in September 2005 was considered normal for the area of “Los Recuerdos” ranch, the following table exhibits the maximum water storage (till saturation), the current soil moisture and the actual water storage capacity for the analyzed soil series. ∗ Valid only for Ap/A horizon


89

Maximum Actual water storage water storage Depth

(cm) capacity

Soil moisture

capacity (mm water (mm water (mm water

Soil Series

Profile Water table /cm soil)

(mm) /cm soil)

(mm) /cm soil)

(mm)

Saboya 111 180 4.7 520.3 2.9 321.9 1.8 324 Balbín 72 97 4.7 327.6 2.8 201.6 1.9 184

Drabble 67 67 4.6 315.5 3.7 247.8 0.9 60 Ortiz de Rosas 120 137 4.6 546.3 3.2 388.1 1.4 192 Cañada Seca 23 23 4.6 104.7 1.8 41.4 2.75 63

Table 4-31: Soil moisture and water storage conditions for the analyzed soil series

The analysis of Table 4-31 allows evidencing the low available water storage capacity present in all profiles, except for the Saboya series (Typic Argiudoll) given its larger soil section development (180 cm).

4.4.3.4. Land cover identification and analysis

Along the different chapters it has been exposed the particular connections existing in the study area between the three main components of the ponding susceptibility approach adopted for this research: relief, soil and vegetation. These elements present so tight relationships that their separated analysis is, therefore, quite difficult and methodologically inappropriate. This is particularly evident when environmental processes occurring in the area – like ponding and salinization – are being investigated in order to find the appropriate measures to alleviate their impacts on the most important socioeconomic activity of the study area: the agricultural-livestock production. Although the vegetation characteristics of an area constitute a direct consequence of the long-term interactions between climate and soils, they are also influenced by other natural (relief position) and anthropic (management practices) factors. In any area, there is always a particular type of vegetation, natural or implanted, that narrowly defines the relief position (high, intermediate or low), the soil conditions (salinity, alkalinity, fertility), the land management practices and even the reclamation possibilities of the area. However, vegetation is a dynamic feature and it may vary when their determining factors do so; in this sense a scarce coverage could be found over a good soil with an inadequate management, or a healthy vegetation can be observed over a previously affected soil which fertility was improved by means of a proper land-water systematization work. During the first days of field campaign an exhaustive terrain reconnaissance was performed in order to identify the relief-soil-plant relations as they actually exist on the land surface, through the observation and assessment of the land cover and land use characteristics of the ranch. In doing so, a detailed vegetation analysis was carried out, trying to relate the position of the different terrain units (firstly outlined by satellite imagery analysis and interpretation) with the appearance of particular vegetation communities and natural/implanted species as typical indicators of diverse hydro-halomorphic soil conditions. Figure 4-36 depicts a diagram showing the development of diverse types of land cover according to the degree of hydromorphic/halomorphic conditions occurring in the soil. This diagram was used to categorize the different land cover types observed during the field campaign.


90

Based on specific literature review; a systematic vegetation sampling and analysis; the expert knowledge from INTA’s personnel; and information about crops, pastures and vegetation species provided by the ranch owner, the following table presents the most representative land cover type/vegetation community per terrain unit, including relative relief positions and soil conditions. Additionally, each land cover is assigned one of the types depicted in Figure 4-36, according to the degree of hydromorphic/halomorphic conditions present on the soil or the appearance of indicator species of such conditions.

Terrain unit

Relative relief

position

Land cover Type

Vegetation community

Observed species Parcel Picture

High plane

Higher Crop A Mesophytic meadows

Glycine max (Soya) 25W 1

High plane

Intermediate Pasture A Mesophytic meadows

Medicago sativa (alfalfa), Bromus unioloides (cebadilla

criolla) 16E 2

Lying plane

Higher Pasture A Mesophytic meadows

Lolium multiflorum (rye-grass)

11S 3

Lying plane

Intermediate Pasture (degraded by

ponding and overgrazing)

B Humid mesophytic

meadows Trifolium repens (trébol

blanco), Cardus sp. (cardo) 13 4

Lying plane

Intermediate Rangeland B Humid mesophytic

meadows

Bothriochloa laguroides (cola de zorro), Cyynodon dactilon

(gramilla) 26E 5

Lying plane

Lower Pasture (degraded by weeds and ponding)

B Humid mesophytic

meadows Conyza bonariensis

(rama negra) 24E 6

Ponding low

Lower Hydromorphic

rangeland C

Humid prairies

Alternathera phyloxeroides (lagunilla), Hydrocotile

bonariensis (redondito de agua)

17W 7

Ponding low

Lower Halo-hydromorphic

rangeland D

Halophytic steppes

Distichils spicata (pelo de chancho), Nostoc sp. (alga)

26W 8

Pond Lowest (concave)

Water C Aquatic Scirpus sp. (junco) 18E 9

Table 4-32: Representative vegetation /land cover types per terrain unit

Finally, Figure 4-37 illustrates the information presented in the previous table with representative pictures from the different land covers and terrain units.

Figure 4-36: Diagram showing the relation between hydromorphic-halomorphic soil conditions and land cover types


91

Figure 4-37: Different land covers in “Los Recuerdos” ranch

4.4.4. Relating soil series information to terrain units

The terrain units map constitutes a key product in the process of land diagnosis elaboration. It contains relevant information – with a suitable accuracy – about the existent relationships between relative relief positions, soil characteristics, vegetation conditions, and water-system dynamics. However, this map lacks from the necessary edaphic attributes required to determine, with the same exactness, the productive capability of soils or their degree of ponding susceptibility. Consequently, no management practices or reclamation feasibilities can be determined from this map without adding the specific soil information. These relationships can not be directly derived from the INTA’s 1:50,000 scale soil maps since they only provide soil information at cartographic unit level. From these maps, the type of soils present in the area, their use capability and limitations, and which are the taxonomic units that integrate each CU can be obtained. But they lack of actual shapes, arrangement and location on the land surface of these taxonomic


92

units (soil series). That is why this cartography has a limited use for agro-hydrologic and reclamation studies at farm scale. Therefore, this sub-section seeks to associate each terrain unit with the adequate soil series as the final step to define the land diagnosis of the ranch, the categories of soil’s ponding susceptibility; and to identify the related reclamation prospects and subsequent efforts. The assignation of the soil series to the terrain units is a detailed assignment that is achieved by entailing soil survey techniques and a sound knowledge of the soil characteristics in the study area the study area; and it can not be accomplished by RS procedures or GIS operations alone. In accomplishing this objective some issues of scaling arise, understanding scaling (Schulze, 2000) as the set of concepts and techniques that will allow information produced or obtained at one particular scale to be used at another (either finer or coarser, smaller or larger) scale. In this sense, it is essential to distinguish between: − up-scaling, as the extrapolation from the point scale at which observations are made to a coarser scale of

study (aggregation), in which processes and values observed at a point would be represented by relatively homogeneous “patches” of landscape (or terrain units); and

− down-scaling, as taking outputs from a larger-scale observation or information to deduce circumstances that would occur at a finer resolution (dis-aggregation).

As asserted by Schulze (2000), in highly-modified rural landscapes having concrete water resources problems (with consequences to actual farmers) that need realistic analysis (as this case study presents), the question of aggregation/dis-aggregation and preferred working scale in these “real world” situations is determined by the nature of the problem to be solved. Moreover, there is a key factor that goes beyond the simple aggregation (up) or dis-aggregation (down scaling) of information. It is the expert knowledge of the staff in charge, who is the best qualified to decide what adapts better for the case under analysis, and will influence the nature and final quality of the produced map. As such, for this case study, both up and down-scaling concepts were used in order to come up with the final results, supported by the expertise of INTA’s staff. The information related to relief position and percentage distribution of soil series contained in the 1:50,000 scale soil maps was down-scaled, assuming that such characteristics (assigned to each CU) could be dis-aggregated and matched to the corresponding terrain units. This was later corroborated during the fieldwork activities and post-fieldwork analysis. The data obtained from the field measurements and observations was up-scaled from its intrinsic point-scale to the larger terrain unit-scale; this was done assuming that both soil attributes and processes verified and measured at representative points can be aggregated to the full (or partial) terrain unit area. As a result of all fieldwork activities and analysis, and based on the previously mentioned scaling concepts, the final soil-series-to-terrain-units assignation map is depicted in Figure 4-38, while Table 4-33 presents the corresponding areas and distributed percentages. Related to this map, it must be emphasized that the associations between terrain units and soil series respond to a one-to-many relationship scheme. Although behave homogenously, the diverse terrain units manifested some variations in soil properties, relief/micro-relief development and vegetation characteristics, which were used to define the soil series allocation within each terrain unit area.


93

Figure 4-38: Map of soil series distribution per terrain unit in “Los Recuerdos” ranch

Soil Series

Area (Ha)

% of total area

% of HP area

% of LP area

% of PL area

% of P area

Saboya 820.7 13.9 27.0 0 0 0Ortiz de Rosas 1724.6 29.2 56.8 0 0 0Cañada Seca 492.3 8.3 16.2 0 0 0

Pichincha 2107.9 35.7 0 97.1 0 0Balbín 198.1 3.4 0 2.9 28.6 0

Drabble 334.7 5.7 0 0 71.4 0(Ponds) 231.6 3.8 0 0 0 100

Total 5909.9 100 100 100 100 100HP= High planes; LP= Lying planes; PL= Ponding lows; P= Ponds

Table 4-33: Soil series areas and corresponding percentages making up each terrain unit


94

It was explained in previous chapters the synergy existing between the relief particularities, soil series distribution, land cover/vegetation characteristics and surface water/groundwater dynamics in the study site. As a result of all the analysis carried out during this research to elucidate such relationships, a table that summarizes and links up the major properties of those thematic elements was constructed (Table 4-34), attending to the guidelines of INTA’s Soil Survey Standards (Etchevehere, 1976). Accordingly, this table presents the necessary attributes to define the land diagnosis for “Los Recuerdos” ranch, and to elaborate the related ponding susceptibility and potential reclamation maps by reclassifying the soil series distribution map, as it is described in detail in next chapter. Therefore, the aforementioned table, the utilized methods and the resulting maps constitute the ultimate products of this thesis; understanding that the whole procedure can be used as a guiding methodology for similar evaluations in other properties (or consortiums of farms) of the larger NWR area, but considering that different circumstances (i.e. soil series, terrain units’ distribution, etc.) might be involved.


95

Soil classification (USDA) Typic Argiudoll Thapto-Argic Hapludoll Thapto-Argic Hapludoll Thapto-Natric Hapludoll Soil Series Saboya Ortiz de Rosas Cañada Seca Pichincha Parental material Loess Sand/Loess Sand/Loess Sand/Loess /Pampeano Area (Ha) 821 1725 492 2108

Landscape Extended, gently undulating higher plains

Undulating, higher and transitional plains with intercalated cuvettes

Undulating transitional plains Extended horizontal plains

Terrain unit High planes High planes High planes Lying planes Relief position High High Intermediate Intermediate to low (low plain) Drainage Well drained Well to moderately-well drained Moderately to imperfectly drained Imperfectly drained Surface Horizon: -Texture -Structure -pH -Hydromorphism evidences

Loam Granular (crumby) Neutral None

Loam to sandy loam Granular (crumby) Neutral None

Sandy loam Granular/ blocky (sub-angular) Neutral None

Sandy loam Blocky (sub-angular blocks) Slightly acid Scarce, weak Fe-Mn mottles

Drainage factors: - Surface run-off - Water table - Permeability Recovery factors: - Alkalinity - Salinity

Intermediate Below 125 cm Moderate Non alkaline Non saline

Intermediate Below 125 cm Moderate to moderately slow Non alkaline Non saline

Intermediate Between 50 and 125 cm Moderately slow Alkaline from 50 to 125 cm Very eventually (phreatic rising)

Intermediate to slow Between 50 and 125 cm Moderately slow to slow Alkaline from 20 to 50 cm Eventually (phreatic rising)

Land cover/Vegetation Crops Crops Crop/Pasture Pasture Land use Agriculture Agriculture Agriculture/Livestock Livestock /Agriculture LUC I IIs IIIw / IVws IV /VIws PI 90 81 62 51

Farm

-sca

le p

ondi

ng s

usce

ptib

ility

map

ping

for p

oten

tial l

and

recl

amat

ion

asse

ssm

ent i

n la

rge

flatla

nds.

A

cas

e st

udy

in th

e S

alad

o R

iver

Bas

in, P

rovi

nce

of B

ueno

s Ai

res,

Arg

entin

a

96

Tabl

e 4-

34: P

rinci

pal c

hara

cter

istic

s of

relie

f, ve

geta

tion,

sur

face

hor

izon

, dra

inag

e an

d re

cove

ry fa

ctor

s pe

r soi

l ser

ies

Soi

l cla

ssifi

catio

n (U

SD

A)

D

uric

Nat

raqu

oll

Typi

c N

atra

quol

l N

/A

Soil

Serie

s B

albí

n D

rabb

le

N/A

P

aren

tal m

ater

ial

San

d/Lo

ess/

Pam

pean

o

San

d/Lo

ess/

Pam

pean

o

San

d/Lo

ess/

Pam

pean

o

Area

(Ha)

19

8 33

5 23

2 La

ndsc

ape

Low

pla

ins

Elon

gate

d hy

drom

orph

ic p

lain

s W

ater

bod

ies

(per

enni

al a

nd e

phem

eral

cuv

ette

s)

Terra

in u

nit

Lyin

g pl

anes

/Pon

ding

low

s Po

ndin

g lo

ws

Pond

s R

elie

f pos

ition

Lo

w (p

lain

foot

) Lo

w (h

ydro

mor

phic

cuv

ette

) Lo

wes

t (cu

vette

) So

il dr

aina

ge

Poor

ly d

rain

ed

Poor

ly d

rain

ed

Very

poo

rly d

rain

ed

Sur

face

Hor

izon

: -T

extu

re

-Stru

ctur

e -p

H

-Hyd

rom

orph

ism

evi

denc

es

Loam

B

lock

y (s

ub-a

ngul

ar b

lock

s)

Neu

tral

Com

mon

, pre

cise

Fe-

Mn

mot

tles

Sand

y lo

am

Blo

cky

(sub

-ang

ular

blo

cks)

M

oder

atel

y al

kalin

e A

bund

ant,

prec

ise

Fe-M

n m

ottle

s

N/A

N

/A

N/A

G

leyz

atio

n D

rain

age

fact

ors:

- S

urfa

ce ru

n-of

f - W

ater

tabl

e - P

erm

eabi

lity

Rec

over

y fa

ctor

s:

- Alk

alin

ity

- Sal

inity

Slo

w

Bet

wee

n 50

and

100

cm

S

low

A

lkal

ine

from

20

to 5

0 cm

Sl

ight

ly s

alin

e

Ver

y sl

ow

With

in 5

0 cm

V

ery

slow

A

lkal

ine

from

sur

face

M

oder

atel

y sa

line

Nul

l O

n su

rface

V

ery

slow

to n

ull

Non

alk

alin

e S

trong

ly s

alin

e (in

con

tact

with

wat

er ta

ble)

La

nd c

over

/Veg

etat

ion

Pas

ture

s/hy

drom

orph

ic v

eget

atio

n H

ydro

-hal

omor

phic

rang

elan

d H

ydro

mor

phic

and

Aqu

atic

veg

etat

ion

Land

use

Li

vest

ock

Live

stoc

k N

one

LU

C

VIw

s V

I/VIIw

s V

III

PI

23

10

< 10


97

5. Ponding susceptibility and reclamation assessment

In this chapter, the definition of ponding susceptibility and potential land reclamation categories is addressed. To achieve this objective, all the information generated throughout the research process was employed to establish the set of criteria utilized to define such categories. Thus, the tabulated information and final maps presented in this chapter constitute the synthesis of the research analysis and fieldwork activities. The following flowchart depicts the sequence of methodological steps executed to come up with the aforementioned results, as will be explanined in coming sub-sections.

Figure 5-1: Flowchart of ponding susceptibility and potential reclamation maps elaboration

5.1. Ponding susceptibility definition and mapping

One of the core objectives of this research is the definition and mapping of ponding susceptibility categories (for this case study, within the area of “Los Recuerdos” ranch). According to the followed methodological approach, such concept is related to farming productivity affectation, which involves an intrinsic degree of fuzziness; and not to absolute values like the ones derived from hydrologic or hydraulic models (frequency, duration, return period, water volume, water depth, etc.).


98

Consequently, ponding susceptibility is defined as the relative easiness of a given area (having a particular set of relief and soil characteristics) to get and remain ponded so that farming production becomes directly affected. This affectation is expressed diversely in different relief positions, and it is manifested through specific soil and vegetation conditions simultaneously; hence, since soil and vegetation are involved, the concept of farming productivity is inherent to this definition. From Table 4-34, the following relief, soil and vegetation attributes (characterized at soil series level of analysis) were selected to build up the set of criteria used for the ponding susceptibility categorization: − relief position − surface run-off − soil drainage − hydromorphism signs (in surface horizon) − water table depth − permeability − land cover/vegetation − land use capability (LUC) − productivity index (PI) Thus, the ponding susceptibility categories were defined as: − Extreme susceptibility: soils remain permanently covered by water. Any productive use is

impracticable. This category embraces only those areas identified as ponds, not related to any defined soil series.

− High susceptibility: this category includes the soils which are frequently ponded, where productivity may be affected at any moment of the year. They support only extensive livestock use, but due to overgrazing and poaching this use is restricted as well.

− Moderate susceptibility: this category comprises those soils which are periodically affected, mainly by seasonal severe rainfall events. These areas are used productively for livestock activities most of the year, but utilizing hydromorphic-adapted pastures.

− Low susceptibility: within this category are included the soils that are scarcely affected (only by sporadic severe events, few times per year), but long enough to prevent pure agricultural uses. In these areas, hydromorphic-adapted pastures are not necessarily required.

− Very low susceptibility: this category is constituted by soils that get ponded rarely, only under exceptional climatic circumstances or in very wet years. These areas do not remain ponded for long time in any case, so agriculture is their primary use (though livestock activities are also practised).

− None susceptibility: this category is constituted by soils that remain free of ponding conditions. Used exclusively for agriculture.

Table 5-1 establishes the existing relationships between the selected set of criteria and the ponding susceptibility categories as defined above.

Farm

-sca

le p

ondi

ng s

usce

ptib

ility

map

ping

for p

oten

tial l

and

recl

amat

ion

asse

ssm

ent i

n la

rge

flatla

nds.

A

cas

e st

udy

in th

e S

alad

o R

iver

Bas

in, P

rovi

nce

of B

ueno

s Ai

res,

Arg

entin

a

99

Tabl

e 5-

1: C

hara

cter

istic

s of

pon

ding

sus

cept

ibili

ty c

ateg

orie

s

D

efin

ing

crite

ria

Pond

ing

susc

eptib

ility

ca

tego

ry

Rel

ief

posi

tion

Soil

drai

nage

Su

rfac

e ru

n-of

f H

ydro

mor

p.

sign

s in

sur

face

ho

rizon

W

ater

tabl

e de

pth

Perm

eabi

lity

Land

cov

er/

vege

tatio

n LU

C

PI

Extr

eme

Low

est

(cuv

ette

s)

Very

poo

rly

drai

ned

Nul

l G

leyz

atio

n O

n su

rface

V

ery

slow

to

nul

l

Hyd

rom

orph

ic/

Aqu

atic

ve

geta

tion

VIII

<1

0

Hig

h Lo

w

(hyd

rom

orph

ic

cuve

ttes)

Poo

rly

drai

ned

Very

S

low

Abun

dant

Fe

-Mn

mot

tles

With

in

50 c

m

Very

sl

ow

Hyd

ro-

halo

mor

phic

ra

ngel

ands

VI t

o V

II 10

to <

30

Mod

erat

e Lo

w

(pla

in fo

ot a

nd

hydr

om. c

uvet

tes)

Poo

rly

drai

ned

Slo

w

Com

mon

Fe

-Mn

mot

tles

Bet

wee

n 50

an

d 10

0 cm

S

low

A

dapt

ed p

astu

res/

H

ydro

mor

phic

ve

geta

tion

V to

V

I 20

to <

50

Low

In

term

edia

te

(low

pla

ins)

Im

perfe

ctly

dr

aine

d In

term

edia

te

to s

low

Sca

rce

Fe-M

n m

ottle

s

Bet

wee

n 50

an

d 12

5 cm

M

oder

atel

y sl

ow to

slo

w

Pas

ture

s/cr

ops

IV to

V

I 50

to <

70

Very

low

In

term

edia

te to

hig

h (tr

ansi

tiona

l hig

her

plai

ns)

Mod

erat

e to

im

perfe

ctly

dr

aine

d In

term

edia

te

Non

e B

etw

een

50

and

125

cm

Mod

erat

ely

slow

C

rops

/pas

ture

s III

to

IV

50 to

<70

Non

e H

igh

(hig

her p

lain

s)

Wel

l to

mod

erat

ely

wel

l dr

aine

d In

term

edia

te

Non

e Be

low

12

5 cm

Mod

erat

e to

M

oder

atel

y sl

ow

Cro

ps

I to II

>70


100

Figure 5-2 and Table 5-2 show the ponding susceptibility map and the related areas and percentages, respectively. The map depicted below was elaborated by reclassifying the soil series distribution map (Figure 4-38) using the defining conditions from Table 5-1. By comparing both maps, it can be observed that one susceptibility category may fully embrace more than one soil series, but not the other way around.

Figure 5-2: Ponding susceptibility map of “Los Recuerdos” ranch

Ponding susceptibility Area (Ha) % Involved soil series None 2545.3 43.1 Saboya / Ortiz de Rosas Very low 492.3 8.3 Cañada Seca Low 2107.9 35.7 Pichincha Moderate 198.1 3.4 Balbín High 334.7 5.7 Drabble Extreme 231.7 3.9 (Ponds) 5909.9 100

Table 5-2: Ponding susceptibility areas, percentages and embraced soil series


101

5.2. Potential reclamation of affected areas

Besides the problems derived from different ponding conditions (“lack of ground” for rural machinery, harvesting losses, soil compaction by animal poaching, etc.), the deterioration of soil physicochemical conditions from halomorphic conditions – salinity and sodicity – represents the most restrictive condition for the recovery and full productive-development of the study site. Although similar in affecting farming productivity, understanding the differences existing between the above mentioned conditions is critical because this will determine how the soils should be reclaimed and managed. As such, the solution for each type of condition requires different strategies, techniques and treatments. While hydromorphic conditions can be managed by controlling the soil moisture with the use of drainage practices; halomorphic conditions require expensive recuperation practices including leaching techniques to reverse salinity, and chemical amendments to manage alkalinity (Cardon et al., 2004). For all cases, soil reclamation is technically possible but not always economically feasible. In the worst scenario, reclamation will be impossible or, at best, temporary. In the case of “Los Recuerdos” ranch, the application of these techniques can only be initiated after the implementation of the agro-hydrologic water control structures planned for. In order to establish a classification for the potentially recoverable areas in the study site, an economic-agronomic feasibility approach that considers the type of financial and technical efforts required to reclaim lands to the farm’s productive chain has been adopted. For this purpose, the following attributes (extracted from Table 4-34) constitute the set of criteria for the classification: − soil drainage − water table depth − permeability − salinity − alkalinity − land use capability (LUC) − productivity index (PI) Based on the intrinsic productive characteristics of each soils series; their ponding susceptibility categorization (as defined in the previous sub-section); and the kinds of efforts and practices involved in the recovery process (which are directly related to the nature and severity of the hydro-halomorphic problem), the following potential reclamation categories were defined for the soils present in the study site: − Type I: areas not requiring any reclamation efforts. − Type II: areas that may exceptionally require minor drainage and/or recuperation practices. − Type III: areas requiring both minor drainage and recuperation practices. − Type IV: areas that need minor drainage but major recuperation practices. − Type V: areas requiring both major drainage and recuperation practices. − Type VI: these areas present extremely degraded soils, so any reclamation prospect is unviable.

However, these areas (located in the relatively lowest positions) can be used for the placement of agro-hydrologic water control structures (dams). Some other alternative, environmentally-oriented uses for these lands (such as wild life preservation, bird colonization, bio-diversity maintenance) have been suggested by Bilenca and Miñarro (2004).


102

The relationships between the selected criteria and the potential reclamation categories as defined above are indicated in Table 5-3.

Defining criteria

Reclamation category

Soil drainage

Water table depth

Permeability Salinity Alkalinity LUC PI

Type I Well to

Moderately well drained

Below 125 cm

Moderate to moderately

slow

Non saline

Non alkaline

I to II > 70

Type II Moderate to imperfectly

drained

Between 50 and

125 cm

Moderately slow

Very eventually (due to phreatic

rising)

From 50 to 125 cm depth

III to IV 50 to < 70

Type III Imperfectly drained

Between 50 and 125 cm

Moderately slow to slow

Very eventually (due to phreatic

rising)


IV to VI 50 to < 70

Type IV Poorly drained

Between 50 and 100 cm

Slow Slightly saline


V to VI 20 to < 50

Type V Poorly drained

Within 50 cm

Very slow

Moderately saline

From surface VI to VII 10 to < 30

Type VI Very poorly drained

On surface Very slow

to null Strongly saline

Non alkaline

VIII < 10

Table 5-3: Characteristics of potential reclamation categories

Using the criteria established in Table 5-3, the soil series distribution map was reclassified to obtain the potential reclamation map for “Los Recuerdos” ranch. It is depicted in Figure 5-3, whereas Table 5-4 presents the related areas and percentages corresponding to each reclamation category.


103

Figure 5-3: Potential reclamation map of “Los Recuerdos” ranch

Potential reclamation Area (Ha) % Involved soil series Related ponding susceptibility

Type I 2545.3 43.1 Saboya / Ortiz de Rosas None Type II 492.3 8.3 Cañada Seca Very low Type III 2107.9 35.7 Pichincha Low Type IV 198.1 3.4 Balbín Moderate Type V 334.7 5.7 Drabble High Type VI 231.7 3.9 (Ponds) Extreme 5909.9 100

Table 5-4: Potential reclamation areas and percentages, involved soil series


104

Recovery and maintenance of appropriate soil physicochemical properties is a primary concern for the success of productive activities in the reclaimed areas. For the case of “Los Recuerdos” ranch, recuperation practices should be applied over those areas currently having hydro-halomorphic problems but showing suitable recovery potential conditions, as it is the case of certain portions corresponding to the “Lying planes” (Pichincha and Balbín soil series), and the “Ponding lows” (Balbín and Drabble soil series). Although a detailed explanation of reclamation techniques for each specific condition is out of the scope of this research, several recovery experiences found in local literature (Zamolinski, 2000) have demonstrated that a relatively fast recuperation of affected soils is possible. This was achieved by planting salt-tolerant forage species – such as Melilotus albus (“trébol blanco”), Elytrigia elongata (“agropiro alargado”) and Festuca sp. – in the early stages of reclamation; and allowing the plots to recover by rotational grazing schemes, mulching and fertilization practices. A detailed cost-benefit analysis is required to define the actual feasibility of a reclamation process in the study site. However, previous experiences demonstrated that the reclamation investment is recovered within a five-year-period after the recovery works are initiated∗. For the particular case of the NWR, this time is expected to be lesser since the soils in this region are of better quality than in the “Región Deprimida del Salado” (Depressed Salado Region), where previous reclamation experiences were carried out. For the case of “Los Recuerdos” ranch, it is expected that the execution of a reclamation process in the currently hydro-halomorphic affected sectors would expand the present area dedicated to livestock activities up to a 25% of the total ranch area.

∗ This information was obtained from personal communications with several INTA’s staff.


105

6. Research results: other applications This chapter presents some additional activities for which the results obtained in this research may have direct application. In this sense, both the spatial distribution of terrain units (with their related soil series, morphologic and quali-quantitative attributes) and the ponding susceptibility maps have direct relevance in at least two areas with very high demand at present: − Terrain-water interaction modelling (sub-superficial flow and crop-growth models) − Land valuation, subdivision and legal issues Whereas the first topic is related to academic, technical and land-management applications, the second is mainly concerned to civil rights, legal aspects and real-estate matters.

6.1. Subsurface flow modelling

Although this thesis does not have within its objectives the application of a subsurface model, and consequently the fieldwork campaign was not set for dealing with this purpose, it was nevertheless considered relevant to give an example on how the information derived from this research could be of use in modelling. The example also help in understanding what kind of data needs to be collected for this purpose. Among the available diversity of models with applications in the fields of groundwater hydraulics and agronomy, an example of sub-superficial model was selected; it matches the data collected, is user-friendly and fits to logical time constraints. The model for the occasion is called VS2DI 1.2 (USGS, 2004). It is a freeware version of the former DOS software VS2D, now fashioned in a modern graphic user interface based on Java for Windows. The complete model documentation and additional information can be found at http://water.usgs.gov/software/vs2di.html.

6.1.1. Brief model description

The VS2DI is a graphical software package for modelling flow and transport in variably saturated porous media, using a finite-difference model to solve Richard’s equation (Hsieh et al., 2000). This package consists of three components: VS2DTI, for simulating fluid flow and solute transport; VS2DHI, for simulating fluid flow and energy (heat) transport; and VS2POST, a standalone postprocessor, for viewing results saved from previous simulation runs. Both VS2DTI and VS2DHI combine a graphical user interface with a numerical model to create an integrated, window-based 2D modelling environment. The preprocessor is used to define the model domain, hydraulic and transport properties, initial and boundary conditions, grid spacing, and other model parameters. Data that vary spatially are entered and edited graphically with "drawing" tools that are commonly found in other graphical software. Data that are independent of space are entered into tables. Model options are selected from pull-down menus and various buttons, check boxes, and text fields. Once all required data have been entered, the postprocessor may be invoked to run the simulation. As the postprocessor is being launched, model input files are automatically generated and loaded to the numerical model. The postprocessor is linked with the numerical model in such a


106

way that as the model runs, simulation results are displayed for each time step, thus creating a simple animation. Simulation results can be displayed as contours of pressure head, moisture content, saturation, concentration (for solute transport) or temperature (for energy transport), velocity or flux. In addition to the graphical displays, ASCII (or text) output files are also generated. These files may be viewed in a text editor, printed, or imported to graphing programs.

6.1.2. Simulated situations

Many localized subsurface processes are interesting to investigate during the first design stages of a land reclamation project in flatlands. Due to time constraints, only two simulation cases were performed in order to evaluate the potential application of the software in the study area. The first case is the simulation of a subsurface flow (seepage) happening in the drainage channel projected over the Saboya series. It has been established during the fieldwork that the existence of an impervious carbonatic layer (“tosca”) within 2 meters depth, would diminish the soil water storage by creating a perched water table close to the channel bottom, extending laterally. The extension of affected area will depend on the duration of the flow in the channel; and this effect will be tested briefly. The second case is the simulation of an infiltration process occurring through some of the retention structures (designed to function as linear reservoirs) projected over one the “poorest” soil series (Balbín) and lowest positions in the ranch. The initial hypothesis is that a permeable embankment (constructed with available materials at the site) would be enough to minimize the ponding of lowlands intended to be reclaimed. The infiltration rate through the permeable embankment will saturate only a small sector in the surroundings of the retention structure, as opposite to the “no-dam scenario” where continuous and generalized ponding would adversely affect the reclaimed land. The objective of this is to exemplify this throughflow and quantify its permanence time.

6.1.3. Data requirements

The model has several options to enter diverse boundary and initial conditions, as well as stresses and soil properties. In this sub-section some procedures are developed in order to use the information generated in this thesis for feeding the model for the preliminary runs. Due to time and space limitations, only two soil series (Saboya and Balbín) were used as modelling examples.

6.1.3.1. Soil information

The required soil information for the VS2DI model is: − Soil horizons: texture and depth (eventually granulometry, if available) − Water retention curve For this purpose, some of the required information was taken from the field campaign data and INTA’s soil map analytical information, whereas some values were obtained using specific software (as will be explained later).


107

The soil information derived from fieldwork measurements and INTA’s maps was found adequate to characterize the analyzed soils according to the standards suggested by Saxton et al.(1986); and therefore it was brought into the software SPAW-Soil Water Characteristics (version 6.1.51) which is obtainable from http://hydrolab.arsusda.gov/soilwater/Index.htm. Using it, the volumetric moisture at field capacity; the volumetric moisture at wilting point; and the saturated hydraulic conductivity were estimated. As stated by Dingman (2002), the relations between pressure and moisture content [ψ (θ)] and between hydraulic conductivity and moisture content [Ks (θ)] are crucial determinants of unsaturated flows in soils. In this sense, the relation between the pressure head, ψ, and the moisture content, θ, in a given soil is called Moisture Characteristic Curve (MCC). Since this relationship is highly non-linear and very difficult to measure, it is normally expressed in the form of an equation. The chosen one for this study is the van Genuchten equation (van Genuchten, 1980), which takes the following form:

( ) ( ) 1 1/[1 ]s r

r n nh θ θθ θ

αψ −

−= +

+,

where θ(h) represents the water retention curve defining the water content, θ (cm3/cm3), as a function of the soil water pressure head ψ (cm); θr and θ s (cm3/cm3) are residual and saturated water contents, respectively; while α (1/cm) and n are curve shape parameters. The Rosetta and RETC 6.0 for Windows software (both freely available at http://www.ars.usda.gov/services/software/software.htm) allow the estimation of the van Genuchten equation parameters. Using the van Genuchten approach is the only method that allows continuous modelling in the entire range of the moisture characteristic curve. Table 6-1 and Table 6-2 present the information for Saboya and Balbín soil series, respectively.

Depth Kz** Clay Silt Sand OM ρb φ Moisture (% Volume) α** Horizon (cm) cm/h % % % %

Texture g/cm3 - θ WP* FC* θr** θs** cm-1

n

Ap/A 0-26 1.63 18.9 29.9 51.2 3.39 Loam 1.26 0.484 32 14 29.7 6.2 44.3 0.0131 1.484BA 26-33 0.98 18.5 31.8 49.7 1.84 Loam 13 26.7 6.9 43.5 0.0157 1.433BT 33-50 0.98 23.8 24.7 51.5 0.96 Sandy clay loam

1.36 0.486 3114.6 26 6.9 43.5 0.0157 1.433

BC 50-82 1.08 18.7 24.8 56.5 0.38 Sandy loam 1.43 0.46 24 12.2 22.5 5.8 40.9 0.0186 1.435C 82-111 1.83 14.4 25.3 60.3 0.14 Sandy clay loam 1.38 0.479 29 12.9 22.9 5.1 41.3 0.0207 1.452Ck 111-180 1.83 13.4 23.5 55.9 0.14 Sandy loam + K 1.38 0.479 29 12.9 22.9 5.1 41.3 0.0207 1.452

Tosca >180 Highly impervious Cemented ‘Kz’: saturated hydraulic conductivity; ‘OM’: organic matter; ‘WP’: volumetric moisture at wilting point; ‘FC’: volumetric moisture at field capacity; ‘θr’: residual soil moisture; ‘θs’: saturated soil moisture. ‘α’ and ‘n’ are the van Genuchten equation parameters. ‘ρb’: bulk density; ‘φ’: porosity and ‘θ’: actual volumetric moisture as measured from fieldwork soil samples * Parameters estimated with the software “Soil water characteristics” ** Parameters estimated with software Rosetta or RECT.

Table 6-1: Observed and calculated information for Saboya soil series


108

Depth Kz** Clay Silt Sand OM ρb φ Moisture (% Volume) α** Horizon (cm) cm/h % % % %

Texture g/cm3 - θ WP* FC* θr** θs** cm-1

n

Ap 0-20 0.64 19.3 34.5 46.2 2.72 Loam 1.43 0.461 14 11.7 23 5.9 39.9 0.0123 1.48 BT 20-53 0.37 27.4 40.3 32.3 0.76 Clay loam 1.43 0.462 31 15.4 29.3 7.5 41.7 0.0095 1.495

BCk 53-72 1.04 18 30.5 51.7 0.12 Loam 1.39 0.474 35 11.6 23.4 5.7 40.9 0.0146 1.467 Ck 72-110 1.04 17.8 30.5 51.7 0.08 Loam 1.38 0.474 29 12.9 22.9 5.1 41.3 0.0207 1.452

Tosca >110 Highly impervious cemented ‘Kz’: saturated hydraulic conductivity; ‘OM’: organic matter; ‘WP’: volumetric moisture at wilting point; ‘FC’: volumetric moisture at field capacity; ‘θr’: residual soil moisture; ‘θs’: saturated soil moisture. ‘α’ and ‘n’ are the van Genuchten equation parameters. ‘ρb’: bulk density; ‘φ’: porosity and ‘θ’: actual volumetric moisture as measured from fieldwork soil samples * Parameters estimated with the software “Soil water characteristics” ** Parameters estimated with software Rosetta or RECT.

Table 6-2: Observed and calculated information for Balbín soil series

The measured “profile-integrated” hydraulic conductivity values for the Saboya soil series ranged from 0.91 to 0.96 cm h-1 (see sub-section 4.4.3.2). The above estimated values of hydraulic conductivity fit in the order of magnitude of the values observed in the field. However, this was not the case for Balbín series.

6.1.3.2. Stress conditions

A frequency analysis of extreme-rainfall events (Gumbel procedure) was generated with the available rainfall data at “Los Recuerdos” ranch. The resulting graph is depicted in Figure 6-1.

Figure 6-1: Gumbel probability analysis for 24-hours rainfall at “Los Recuerdos” ranch (1952-2004)

Information on ponded fields is locally reported in magazines and newspapers. From them, severe discrepancies appear between the a priori expected ponding-potential of the storm (as from the previous graph) and the reality in the field. A closer look to the daily records reveals that the biggest ponding risk occurs during summertime in those years when the rainfall during winter time is higher than normal, reducing the water storage capacity along the profile.


109

March is statisitcally the month with the highest rainfall values, but a situation in November was selected for the modelling case, understanding that this is the moment when the early-summer rainfalls could produce more damage to production (long-lasting ponding). For this example, a period of 30 days in November, 2005 was selected for the simulation. The potential evaporation value used as upper limit in the stress calculator of VS2DTI was taken from an automatic station located to the north of the region, which data is available on-line. The average daily potential evaporation (Penman) for the month is 5.5 to 6 mm. VS2DTI allows cyclic evaporation periods, so every day was divided in periods of 6 hours and a proportional value of potential evaporation was adopted for these periods, in such a way that the daily total matches the potential value. Real evaporation in VS2DTI is calculated separately for soil and crops, following a resistance scheme in both cases. The parameterization for the evaporation stress is described in detail in Lappala et al. (1987). Crop evaporation was simulated for the Saboya series, assuming 1 m depth roots. Crop evaporation was not simulated in the dam situation, because Balbín series would not support crops at the initial reclamation stages. Table 6-3 summarizes the stresses selected for the model. The first five days simulate a situation of external runoff coming into the system, with no rainfall at the site. Then, a 12-hour rainfall of 100 mm occurs in the model domain (evaporation is not simulated during this period). It follows a 10-day drying up period. A similar rainfall to the previous one happen next, but no runoff is simulated this time since the intention is to observe the evaporation process from the land surface.

Days Rainfall Evaporation Transpiration Flow Dam Store 1 No Yes Yes minimum minimum 2 No Yes Yes 30 % full 30 % full

3-4 No Yes Yes full full 5 No Yes Yes 30% full 30% full

5.5 Yes (100 mm) No No 30 % full 30 % full 7 No Yes Yes minimum minimum

8-17 No Yes Yes No minimum 17.5 Yes (100 mm) No No No minimum

18-30 No Yes Yes No minimum

Table 6-3: Summary of the stresses selected for the modelling period (30 days in November, 2005)

6.1.3.3. Boundary conditions

It is well known that boundary conditions should be carefully evaluated for every modelling situation. For example, the selection of appropriate boundary conditions for a regional groundwater modelling in the Salado River basin was a major drawback in previous modelling attempts due to the vastness of the area, its flatness and limited geological structures that can serve as concrete boundary. In this case, the model is restricted to specific project situations, related to the engineering behaviour of structures and to the estimation of periods where ponding is prevented from the areas to be reclaimed. The tolerance in the ponding times and permanences forecasted by the model should be less than the ponding time that would eventually affect production. With adequate data input, this requirement can be easily


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matched. For this analysis it was assumed that the rates of the local vertical processes are faster than the regional movement of the water table. A stationary water table (fixed head) allowing lateral seepage boundary on the most permeable horizons was considered as boundary condition. The recharge was simulated with a fix pressure head (for channel seepage) and a fix inflow per unit of area (for precipitation). The ins and outs (mass balance error in the model) were controlled at every step, trying to keep the system under modelling away from the boundaries, so their influence is minimized.

6.1.3.4. Initial conditions

This term refers to the water content (moisture condition) of the system at the beginning of the modelling calculations. Within the VS2DI It can be established in three ways. Pressure heads or volumetric moisture contents can be entered into the system as contour lines. However, a very common alternative is to establish a linear variation of the pressure head with depth by setting two lines of boundaries: the water table (ψ = 0) and a specified negative pressure at depth selected by the user. The initial moisture at any point will be obtained by the software from the van Genuchten relation for the soil under this pressure. Different initial moisture conditions dramatically affect the outcome of the model in this “shallow water table” domain.

6.1.4. Modelling results

6.1.4.1. Saboya series

The simulation results obtained for Saboya soil series are depicted in Figure 6-2. The cross section of the channel (between progressives 9.25 and 14.75 m) and lateral agronomic lands are simulated in the model. The chromatic scale to the right of the pictograms shows the pressure head (ψ) in meters, where 0 means saturation condition. The time unit is days.


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Figure 6-2: Modelling of subsurface flow in Saboya soil series


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After one day of channel flow, the seepage reached the soil’s most impervious layer (“tosca”) creating a perched water table. At day six, an intense rainfall event (100 mm) occurs but it is stored in the soil profile, without runoff. This “no-runoff” effect was observed in many similar models (Scanlon et al., 2002). These authors suggested the possibility of creating a thin layer with low conductivity to avoid this effect. However, since no information on runoff was available from field campaign, this recommendation was noticed but not considered. From day seven onwards, no more water was considered to flow along the channel, and the system was left to evaporate except for day eighteen, when a second intense rainfall happens. The perched water table remained after 22 days from the end of the flow in the channel. The mass balance error for the simulation process was less than 3%; simulation elements (grid) were selected as uniform squared, with 14 cm of side.

6.1.4.2. Balbín series

The analysis here is focused on the aptitude of a permeable dam constructed to prevent long lasting ponding conditions on the area to be reclaimed. In the original “no-dam” scenario, the soil in the areas intended to protect will be saturated after the rain and immediately the runoff water will inundate all micro-relief depressions. The accumulated water will only dissipate by effect of evaporation as main dragging force. The hypothesis is that the dam can not prevent the soil to be saturated by heavy rainfall events, but it can avert the additional problem of ponding. The time to dry the excess by evaporation should be highly reduced in the latter case, what is the clue to workability, land access and agronomic activities necessary to reclaim the land to the productive chain. It would be optimal that the permeable dam (to be built with materials from the site without clay core) is able to minimize infiltration through its body. It has been estimated that due to the nature of the landscape and the regulating structure of the outlet, one full cycle (filling and emptying the reservoir) will be in the order of seven days after the rainfall event. For Balbín soil series, a through flow across a permeable dam was silmulated. The embankement is 1:2.5 and 1 meter high, constructed with the first layer of soil to preserve the structure, since alkalinity presence disrupt the stability of the embankement. The results are depicted in Figure 6-3. The chromatic and time scales are identical as in Figure 6-2. Time step 0.2 shows the initial stage where the water at the dam (1/3 capacity) infiltrates vertically untill the “tosca” layer, where a perched water table is formed. The water table was allocated somewhere deeper in the tosca. On day three, the dam is full and water stays there for two days, when starts to recede. As from day nine, water in the reservoir gets a minimum level and stays like that till the end of calculations. Like in the previous case, in days six and eighteen a heavy rainfall is simulated in the sector. The analysis of the model’s output showed that for this specific set of external stresses, it will take more than two days to seepage on the other side of the embankment, understanding that any time higher than 2 days will be enough to prevent ponding in the protected areas. However, this situation would change under different initial moisture conditions. The analysis of different alternatives is possible within the model, but out of the scope of this example.


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Figure 6-3: Modelling of a through flow across a permeable dam in Balbín soil series


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6.2. Land subdivision, land valuation and other socioeconomic issues

Based on information gathered through interviews and personal communications with many INTA and Ministry of Rural Affairs (Ministerio de Asunstos Agrarios) staff during the fieldwork stage, the following paragraphs present the main applications given to the terrain units and ponding susceptibility maps (as developed in this research) for legal and economic issues in the Province of Buenos Aires during the last years: − The Rural Affairs departments of two important public banks closely related to the farming sector

development in the province (Banco de la Nación Argentina and Banco de la Provincia de Buenos Aires) utilize these maps (among other documentation) to grant credits to buy farming elements (like tractors, harvesting machinery, etc.); and to assess the land value for mortgage warranty.

− In cases of legal disputes between farmers (and also between farmers and the local or provincial

government) due to water-related conflicts, these maps are presented to the Court as valid legal and technical evidence for the resolution of litigations. According to INTA’s staff experiences, there exist an important number of trials that were resolved based on the information provided by this kind of studies.

− The official land valuation procedure in the Province of Buenos Aires requires from the interested

landowner an important amount of detailed terrain and soil information that must be collected and incorporated into a form in order to initiate the whole process. Water quality, edaphic characteristics, land productivity, distance to urban settlements and transport infrastructure, and flooding/ponding susceptibility cartography (at 1:20,000 scale or bigger) are the most important information involved. After that, and according to a pre-existant valuation table that assigns a score to all previously mentioned aspects, an official value is assigned to the land.

− For the land subdivision, the process is similar to the previous one, but the requirements also include

information about the costs and yields of the different productive activities carried on in the involved farm. According to this aspects, the smallest land subdivision must have the all the conditions (in terms of size and productivity) as to provide a minimum yearly income for a typical family group.

− Regarding to the private sector, several insurance companies require the presentation of this kind of

maps as an important input for the definition of the land insurance policy and assurance costs; and for money refund in cases of climatic damages, productive emergencies, etc.

− In a similar way, the most important regional land-trade firms solicit detailed information as the one

generated in this research when they have to buy or sale a ranch. After that, they assign a price to the hectare, based on the physical characteristics and the business-potential of the site. At the moment of doing this research, the average price for one hectare in the study area was around U$S 2,000.


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7. Conclusions and recommendations This final chapter presents the conclusions derived from the analysis of the applied methods and the achieved results in this study. After the conclusions, some research recommendations and further lines of investigation are suggested.

7.1. Conclusions

The methods and techniques applied in this research allowed unveiling the particular relief-soil-plant relationships existing in the study area. By performing an extensive but focused field campaign; and making use of simple soil survey techniques and basic field equipment (as described in sub-section 4.4.3), the initial research ideas about the relief-soil-plant interactions and their relationships with the ponding problem in the study area were corroborated. Moreover, as an outcome of the research development the most relevant terrain and hydrologic variables that need to be surveyed, measured and analyzed in order to define and map ponding susceptibility areas were determined. The upscalling/downscaling of soil information, supported by fieldwork activities and professional expertise, confirmed to be an adequate technique for the ponding mapping process in situations where the lack of detailed ancillary data is a drawback. Qualitative interpretation of multitemporal satellite imagery proved also to be an appropriate approach, in this case, for defining terrain units and inferring soil and vegetation conditions. Remotely-sensed vegetation information is readily linked to soil characteristics through this method, and the amount of information that can be generated in this way is enlarged if additional field knowledge is incorporated, as this study has demonstrated. This technique resulted suitable for facing the problem of ancillary data scarcity at the selected scale of analysis. This work demonstrated how a specific mapping methodology, which combined the previously mentioned techniques in a GIS-assisted environment, allowed obtaining an accurate and realistic identification of the distinctive terrain units (with specific relief-soil-plant characteristics) which compose the study site. The lack of basic and detailed terrain, hydrological and land cover information in the NWR of the Salado basin constitutes a severe constraint for scientific research. However, the applied methods can be considered as proper ones for handling this difficulty when working at farm level of analysis. The developed research approach showed strengths and limitations. Nevertheless, its utility for assessing land diagnosis and deriving ponding susceptibility and potential land reclamation maps at farm-scale was demonstrated. After the new Water Law is fully implemented, any future water-related project at farm level in the Province of Buenos Aires will have to face new regulated procedures and approvals. As such, the project must have to strictly comply with a set of operational “boundary conditions”, which can be imagined as a triangle formed by the landowner interests and traditions; the legal frame provided by the new Water Law; and the political


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and technical directives presented in the Halcrow’s project. The INTA’s Agro-hydrologic methodology, as exposed in this research, has proven to offer an adequate approach to contemplate all these three constraints. A multidisciplinary approach that include also socioeconomic dimensions, as applied in this study, is ethically and technically the proper way for dealing with this kind of problems, where natural aspects (such as relief, soil, vegetation and water) tightly interact with social, cultural and economic aspects (land-management traditions, labour methodologies, etc.) in a farming-production environment where profit is the decisive factor for any initiative. Regarding specifically to the study site, the surface proximity of the water table, and the measured salinity values are clear indicators of the current dynamic state of the groundwater system. Salinization processes are visible and remain active in many sectors of the ranch. Consequently, it is of key importance to monitor both the phreatic level position and the antecedent soil moisture conditions in order to establish the actual ponding risk (locally known as “alert”) in different sectors of the ranch. The installation of phreatimeters and soil moisture gages would be an economically-feasible and useful tool for the correct assessing of such risk. Local extension programmes (belonging to INTA and other governmental institutions) offer alert network services to the farmers wishing to contribute providing information. Based on the present land situation of “Los Recuerdos” ranch, the full implementation of the current agro-hydrologic project is expected to bring the following benefits and improvements: − To control and manage the incoming water more efficiently, draining the excess and storing it in the less

productive areas. − To avoid the recurrent ponding conditions affecting the “Ponding lows” and lowest sectors of the “Lying

planes” units. − To restrain salinization processes derived from those recurrent ponding conditions. A detailed cost-benefit analysis to establish the actual agro-economic feasibility of a reclamation process in the ranch is required. However, a preliminary analysis derived from the results obtained in this research and previous experiences in the region reveal that the execution of reclamation procedures in the currently hydro-halomorphic affected sectors would allow incorporating them to the productive chain of the establishment, expanding the present livestock area up to a 25% of the total ranch area. Although this research may be considered as a starting point, the obtained results are expected to be of useful application in scientific research, agricultural development, and integrated land-water management policy-making in flatlands, especially for the Salado River basin area. In this sense, this document constitutes an appropriate and flexible template that can be adapted in every case for presenting to the water authorities the necessary information (related to “Land diagnosis”, as the first module of the INTA’s methodology is called) to get their approval for the realization of agro-hydrologic projects at farm or consortium level.

7.2. Recommendations

This research experience was built up through gathering and relating several scientific literature and local research studies; interviewing senior top professionals, technicians and farmers working in the problem for many years; executing a thorough field campaign; and using modern RS & GIS techniques to compile all the information and to generate specific results for a particular study case. As an outcome of that process, this


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research has provided a considerable amount of useful information about the study area and the applied methods. However, certain aspects that could not be treated in depth may be of significance for further investigations. Additionally, during the research development we became aware that the existence of some specific information would have simplified the mapping process quite drastically; and allowed obtaining enhanced and better supported results. Therefore, the following paragraphs intend to give some research recommendations, mainly related to focus future investigation efforts towards making basic and detailed information readily available for similar studies in the Salado River region, or in other flatland environments elsewhere: − The building up of (simple) phreatimetric information networks would be a significant asset, in order to

obtain regular and reliable values of water table depths at local and regional scales. In a similar way, the implementation of a sampling scheme of pH and EC values in permanent ponds (directly connected to water table) and windmill pumps would permit to have a more adjusted vision of the groundwater flow behaviour and its quality.

− The use of recent aerial photographs (if available) or fine spatial-resolution satellite imagery (SPOT,

Ikonos or Quickbird) will be an important input for a better delineation of terrain units, and for an enhanced interpretation of the vegetation response in different hydrologic conditions. Related to this aspect, further study should be done in order to evaluate whether replacing (as far as possible) subjective, expert-based visual interpretation with objective procedures allow achieving better results.

− Specific research about the using of Lidar or Interferometry in this kind of studies is of key importance.

A detailed representation of flatland’s topography (which is not possible with the currently available maps) will constitute an invaluable tool for improving the developed ponding susceptibility mapping method. Moreover, the availability of such kind of topographic information will add to the mentioned approach the significant benefits provided by the hydrologic and hydraulic modelling.

− The National Institute for Agricultural Technology (INTA) as well as local governments (municipalities)

should take the advantages of this research. Its results could be used in conjunction with some new production technologies that are now under experiment in the study region, in order to increment the farming efficiency and reduce the agro-business risks in one of the most productive areas of Argentina.


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Appendices

Appendix A: Geo-reference information

The detailed information concerning the coordinate system used throughout the study is given below: Name: Gauss-Kruger Projection: Transverse Mercator Datum: Campo Inchauspe Ellipsoid: International 1924 Ellipsoid parameters: a= 6378388.000, 1/f= 297.000 Reference zone: 4 Central meridian: 63° 00’ 00’’ W Latitude origin: 90° 00’ 00’’ S False easting: 4500000.000 False northing: 0.000 Scale factor: 1.000


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Appendix B: Location details of ground control points (GCPs)

During the field campaign 156 control points were measured for land cover/land use identification, terrain features positioning, geo-referencing of topographic maps, and ortho-mosaicking of aerial photographs. The coordinates and height of each GCP is presented in the table below.

ID_ X coordinate

Y coordinate

Elevation from DEM (m.a.s.l.)

S1 4531401.000 6139274.000 108.93S2 4530659.000 6139069.000 109.43S3 4530724.000 6137258.000 109.43S4 4530744.000 6136786.000 109.10S5 4529962.000 6137069.000 110.00S6 4529968.000 6137614.000 110.00S7 4528859.000 6137495.000 109.51S8 4529632.000 6136980.000 109.88S9 4531284.000 6137370.000 108.75S10 4532431.000 6137689.000 108.90S11 4532206.000 6137030.000 108.59S12 4529978.000 6138798.000 109.61S13 4532388.000 6139552.000 108.91S14 4532804.000 6139593.000 109.10S15 4531782.000 6139312.000 109.03S16 4530703.000 6138307.000 109.44S17 4531259.000 6138335.000 108.89S18 4531836.000 6137594.000 108.75S19 4531813.000 6138348.000 108.83S20 4529376.000 6138736.000 110.01S21 4528371.000 6138362.000 110.05S22 4527946.000 6137390.000 109.51S23 4527004.000 6137981.000 110.08S24 4526265.000 6137776.000 110.17S25 4526295.000 6136921.000 109.84S26 4525688.000 6137615.000 110.06S27 4524845.000 6137405.000 111.25S28 4524889.000 6136595.000 112.03S29 4524389.000 6137351.000 111.58S30 4527909.000 6138233.000 110.11S31 4531369.000 6140226.000 108.27S32 4531337.000 6141323.000 109.06S33 4531332.000 6141878.000 109.14S34 4532320.000 6141905.000 109.31S35 4532302.000 6142527.000 109.89S36 4532290.000 6142890.000 109.95S37 4531298.000 6142569.000 109.94S38 4531312.000 6142146.000 109.82S39 4531269.000 6143506.000 110.71S40 4531262.000 6143816.000 110.82S41 4532005.000 6143840.000 110.38S42 4532407.000 6143852.000 109.63S43 4531245.000 6144445.000 111.23S44 4531224.000 6145095.000 110.59S45 4532115.000 6145123.000 109.49S46 4531200.000 6146361.000 112.49S47 4532078.000 6146385.000 110.18S48 4531182.000 6146966.000 112.11S49 4532060.000 6146997.000 111.66S50 4531168.000 6147580.000 112.45S51 4532672.000 6147636.000 112.45S52 4531133.000 6148229.000 112.39S53 4532022.000 6148257.000 112.39S54 4526609.000 6137962.000 110.10S55 4525656.000 6138594.000 110.75S56 4525668.000 6138214.000 110.10S57 4525016.000 6138188.000 111.03S58 4525636.000 6139161.000 111.04S59 4525068.000 6139145.000 110.91S60 4525616.000 6139896.000 111.46S60B 4525620.000 6139733.000 111.52S61 4529226.000 6143630.000 111.17S62 4529103.000 6142500.000 110.36S63 4528337.000 6141850.000 110.35S64 4528356.000 6141226.000 110.14S65 4528373.000 6140624.000 110.29

ID_ X coordinate

Y coordinate


S66 4528391.000 6139980.000 110.37S67 4527792.000 6140002.000 110.01S68 4527470.000 6140252.000 110.10S69 4527493.000 6139408.000 110.00S70 4528857.000 6137500.000 109.51S70A 4527511.000 6138658.000 110.24S71A 4526844.000 6138636.000 110.34S72 4526667.000 6138630.000 110.29S73 4526678.000 6139928.000 110.44S74 4525049.000 6139712.000 110.43S75 4525604.000 6140231.000 112.49S76 4525028.000 6140214.000 110.19S77 4524954.000 6140797.000 112.43S78 4526550.000 6141165.000 110.22S79 4525558.000 6141565.000 112.01S80 4524928.000 6141557.000 112.43S81 4524974.000 6142256.000 112.16S82 4525535.000 6142385.000 112.44S83 4526513.000 6142418.000 112.42S84 4525523.000 6143184.000 111.68S85 4524949.000 6143162.000 112.15S86 4524932.000 6143618.000 111.97S87 4525498.000 6143637.000 112.01S88 4526477.000 6143666.000 112.41S89 4527408.000 6142486.000 111.90S90 4527389.000 6143270.000 112.39S91 4530424.000 6142542.000 110.06S92 4530235.000 6142042.000 109.93S93 4529873.000 6142072.000 110.08S94 4529574.000 6142113.000 110.14S95 4529859.000 6142524.000 110.35S96 4530569.000 6141919.000 109.13S97 4530586.000 6141331.000 108.75S98 4530598.000 6140122.000 108.99S99 4529996.000 6140103.000 109.98S100 4529336.000 6140079.000 110.19S101 4529304.000 6141030.000 110.11S102 4529248.000 6141254.000 110.10S103 4529956.000 6141276.000 109.35S104 4528364.000 6142475.000 110.59S105 4528285.000 6143722.000 111.80S106 4527376.000 6143657.000 112.39S107 4525482.000 6144078.000 112.24S108 4525465.000 6144592.000 112.44S109 4524578.000 6144559.000 112.14S110 4525452.000 6145087.000 111.25S111 4525436.000 6145572.000 112.40S112 4524776.000 6145557.000 112.48S113 4528296.000 6144978.000 112.26S114 4527342.000 6145018.000 112.30S115 4527268.000 6146196.000 111.86S116 4526332.000 6146166.000 112.46S117 4526288.000 6147376.000 112.57S118 4525414.000 6146139.000 112.58S119 4524792.000 6146783.000 111.35S120 4523812.000 6146804.000 112.48S121 4525376.000 6147399.000 112.64S122 4524892.000 6147805.000 112.37S123 4525336.000 6148673.000 112.69S124 4524411.000 6148643.000 113.29S125 4525319.000 6149234.000 113.13S126 4524679.000 6149894.000 113.75S127 4526245.000 6148700.000 113.52S128 4526570.000 6149124.000 113.81S129 4527241.000 6148731.000 113.96S130 4527259.000 6149989.000 113.77


128

ID_ X coordinate

Y coordinate


S131 4528144.000 6148760.000 112.89S132 4529069.000 6148788.000 113.16S133 4529113.000 6149422.000 113.51S134 4531125.000 6148853.000 112.46S135 4530335.000 6148831.000 112.86S136 4530326.000 6149459.000 113.74S137 4530294.000 6150081.000 113.71S138 4530281.000 6150912.000 113.75S139 4528233.000 6146230.000 111.46S140 4527290.000 6146826.000 111.04S141 4527201.000 6147463.000 111.67S142 4528162.000 6147495.000 111.40S143 4529103.000 6147598.000 112.33S144 4530124.000 6147558.000 112.42S145 4530146.000 6146868.000 112.44S146 4530433.000 6146303.000 112.46S147 4529145.000 6146261.000 112.05S148 4529181.000 6145005.000 111.77S149 4530470.000 6145021.000 111.22S150 4529059.000 6150254.000 113.70S151 4529024.000 6150753.000 113.73IGM 4527374.000 6145456.000 111.24PILETA 4530422.000 6143795.000 110.74S160 4530716.000 6144428.000 110.95


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Appendix C: Soil series analytical information (from INTA’s 1:50,000 soil maps)

Series: Ameghino Location: 6300 m ESE from Ameghino, District of General Pinto, Province of Buenos Aires. Lat: 34° 51’ 20’’ S Long: 62° 24’ 20’’ S Height: 118 m.a.s.l. (IGM) This series has been classified as Entic Hapludoll, coarse-loamy, mixed and thermic; some-excessively drained; intermediate surface runoff and moderately-fast permeability; vegetation cover: crops; low water-retention capability as use limitation; land use capability IIIs; productivity index (PI) 48 to 51. Description of typical profile: A1: 0-36 cm; brownish (7.5 YR 5/3) when dry; dark brown (7.5 YR 5/3) when wet; sandy loam; fine, weak sub-angular structure that breaks into granular; very friable when humid; not plastic; not adhesive; gradual, smooth bottom limit. AC: 36-74 cm, dark brown (7.5 YR 3/4) when wet; sandy loam; fine, weak sub-angular structure that breaks into massive; very friable when wet; no plastic; no adhesive; gradual, smooth bottom limit. C: 74-140 cm; clear brown (7.5 YR 6/4) when dry; brown (7.5 YR 4/4) when wet; sandy loam; without structure; very friable when humid; not plastic; not adhesive.

Horizon A1 AC C Depth (cm) 10-26 41- 79-135Org. matter (%) 1.33 0.52 0.1Total carbon (%) 0.77 0.3Nitrogen (%) 0.09 0.043Relation C/N 8.5 7 Phosphorous (PPM) 13.6 2.6 2.3Clay (%) 13.1 13.5 11.7Silt 2-20 (%) 4.7 1.5 6.5Silt 2-50 (%) 9.3 8.5 8.2Very fine sand 50-75 (%) 4.5 6 3.9Very fine sand 75-100 (%) 20 23.7 24.8Very fine sand 50-100 (%) Fine sand 100-250 (%) 47 45.1 47.3Medium sand 250-500 (%) 5.8 3 3.8Coarse sand 500-1000 (%) 0.3 0.2 0.3Very coarse sand 1-2 mm (%) Calcareous (%) Equivalent humidity (%) 19.3 14.8 13.8Resistance (Ohms) Conductivity (mmhos/cm) pH (paste) 6 6.1 6.5pH H2O 1:2.5 6.3 6.5 7.1pH CLK 1:2.5 5.2 5.2 5.5Ca (meq/100 g) 5.2 4.5 3.8Mg (meq/100 g) 2.4 2 2.5Na (meq/100 g) 0.3 0.3 0.4K (meq/100 g) 1.4 0.9 0.9H (meq/100 g) 3.8 2.6 2.2Na (% of total) Sum of bases 9.3 7.7 7.6CEC (meq/100 g) 8 7.2 6.4Bases sat. % 100 100 100Gypsum Ca++ soluble Mg++ soluble Na+ soluble K+ soluble Nitrates Bicarbonate Sulphates Chloride SAR pH extract


130

Series: Balbín Location: 15 km ESE from Cañada Seca railway station, District of General Villegas, Province of Buenos Aires. Lat: 34° 27’ 26’’ S Long: 62° 48’ 28’’ S Height: 118 m.a.s.l. (IGM) This series has been classified as Duric Natraquoll, fine-silty, mixed and thermic; imperfectly drained; slow permeability; strongly alkaline; non-saline; vegetation cover: natural pastures; land use capability VIws; limitations: alkalinity and hydromorphism; PI 22 to 23. Description of typical profile: A1: 0-24 cm; dark red-brown (5 YR 2/2) when wet; dark brown (7.5 YR 5/3) when wet; loam; intermediate, moderate sub-angular structure to granular structure; friable when humid; slightly plastic; slightly adhesive; abrupt, smooth bottom limit. B21: 24-39 cm, brown-redish (5 YR 4/3) when wet; clay loam; simple, regular prismatic structure that breaks into sub-angular blocks; tough when dry; firm when wet; plastic; adhesive; abundant fine, weak mottles; abundant roots; abundant clay-skins; abrupt, undulate bottom limit. B22M: 39-76 cm, brown-redish (5 YR 4/4) when wet; loam; laminar structure that breaks into sub-angular blocks; very firm when wet; plastic; adhesive; abundant calcium concretions; abundant coarse, precise mottles; scarce roots; scarce clay-skins; partially harden; gradual, smooth bottom limit. B31M: 76-110 cm, redish to yellow (5 YR 4/6) when wet; sandy loam to loam; sub-angular blocks structure; firm when wet; slightly plastic; slightly adhesive; abundant coarse, weak mottles; scarce roots; moderate carbonatic reaction; abrupt, irregular bottom limit. B32CA: 110-124 cm; sandy loam; weak angular blocks structure; abundant coarse, precise mottles; strong carbonatic reaction.

Horizon A1 B21 B22M B31M B32CA Depth (cm) 5-20 27-36 45-70 80-105 115-133 Org. matter (%) 2.72 0.76 0.22 0.12 0.08 Total carbon (%) 1.58 0.44 0.13 0.07 0.05 Nitrogen (%) 0.141 0.072 Relation C/N 11.2 6.1 Phosphorous (PPM) Clay (%) 19.3 27.4 18.0 17.8 9.8 Silt 2-20 (%) 16.7 13.6 21.8 10.8 18.0 Silt 2-50 (%) 34.5 40.3 45.6 30.5 26.2 Very fine sand 50-75 (%) 10.4 8.1 15.4 18.4 10.0 Very fine sand 75-100 (%) 14.4 11.6 8.6 18.5 22.4 Very fine sand 50-100 (%) Fine sand 100-250 (%) 20.7 12.0 12.0 20.3 16.3 Medium sand 250-500 (%) 0.6 0.4 0.3 0.7 3.7 Coarse sand 500-1000 (%) 0.1 0.2 0.1 0.4 Very coarse sand 1-2 mm (%) Calcareous (%) 11.5 Equivalent humidity (%) 18.9 29.0 25.8 23.3 15.0 Resistance (Ohms) Conductivity (mmhos/cm) pH (paste) 5.7 7.7 8.5 8.9 8.7 pH H2O 1:2.5 6.5 8.8 9.4 9.6 9.4 pH CLK 1:2.5 4.6 6.3 7.0 7.0 7.2 Ca (meq/100 g) 4.4 6.0 6.0 3.1 Mg (meq/100 g) 3.9 6.7 3.2 4.2 Na (meq/100 g) 0.6 5.7 9.1 6.1 8.3 K (meq/100 g) 1.4 2.4 3.1 2.3 2.9 H (meq/100 g) 6.8 Na (% of total) 4.0 28 50 42 62 Sum of bases 10.3 20.8 21.4 15.7 CEC (meq/100 g) 14.9 20.2 18.1 14.6 13.4 Bases sat. % 69 100 100 100


131

Series: Cañada Seca Location: 11.5 km SW from Saboya, District of General Villegas, Province of Buenos Aires. Lat: 34° 44’ 20’’ S Long: 62° 34’ 12’’ S Height: 106 m.a.s.l. (IGM) This series has been classified as Thapto-Argic Hapludoll, coarse-loamy, mixed and thermic; moderately-well drained; intermediate surface runoff; moderate permeability; alkaline in depth but non-saline; land use capability IVws; drainage and low cationic-interchange capability as limiting factors; PI 58 to 62. Description of typical profile: A1: 0-25 cm; dark redish-brown (5 YR 2/2) when wet; sandy loam; intermediate, moderate sub-angular structure that breaks to granular; friable when humid; abundant roots; gradual, smooth bottom limit. AC: 25-39 cm; grey dark-redish (5 YR 4/2) when wet; sandy loam; angular and sub-angular blocks that break into minor blocks; very friable when humid; abrupt, smooth bottom limit. IIB2T: 39-60 cm; brown-redish (5 YR 4/3) when wet; loam sandy loam; coarse prisms that break into intermediate prisms; firm when wet; slightly plastic; common fine, precise mottles; scarce clay-skins; clear, smooth bottom limit. B31: 60-95 cm; redish-brown (5 YR 4/6) when wet; sandy loam; coarse angular blocks that break into intermediate angular blocks; firm when wet; scarce carbonatic concretions; abundant medium, precise mottles; slightly harden; clear, smooth bottom limit. B32M: 95-138 cm; sandy loam; extremely firm when wet; weak angular blocks structure; abundant carbonatic concretions; scarce iron concretions; abundant mottles; harden.

Horizon Al AC II B2T B31 B32M Depth (cm) 5-20 27-36 43-57 65-90 100-130 Org. matter (%) 1.93 0.38 0.45 0.17 0.27Total carbon (%) 1.12 0.22 0.26 0.1 0.16Nitrogen (%) 0.102 Relation C/N 10.9 Phosphorous (PPM) 1.7 3 5.7 3.6Clay (%) 14.5 9.2 17.5 15.1. 20.9Silt 2-20 (%) 8.3 8.5 6.3 7.8 13.9Silt 2-50 (%) 20.7 18.4 14.2 15.5 31.1Very fine sand 50-75 (%) 6.7 7.3 7.4 7.1 6.8Very fine sand 75-100 (%) 23.5 21.7 21.2 20.7 18.1Very fine sand 50-100 (%) Fine sand 100-250 (%) 32.2 40.8 37.5 39.7 20.6Medium sand 250-500 (%) 1.8 2.1 1.7 1.6 9.7Coarse sand 500-1000 (%) 0.6 0.5 0.5 0.3Very coarse sand 1-2 mm (%) Calcareous (%) 0.8Equivalent humidity (%) 14.9 10.1 16.4 1.5.2 23.9Resistance (Ohms) Conductivity (mmhos/cm) pH (paste) 3.1 7 7.2 7.7 8.3pH H2O 1:2.5 6.7 7.6 8 8.4 9.2pH CLK 1:2.5 5.1 5.5 5.8 6.3 6.6Ca (meq/100 g) 5 2.4 4 5.4Mg (meq/100 g) 1.5 2.5 5.4 5.4Na (meq/100 g) 0.3 0.3 0.7 0.9 8.8K (meq/100 g) 1.3 0.9 1.4 1.9 3.8H (meq/100 g) 4.6 1.5 2Na (% of total) 30.0Sum of bases 8.1 6.1 11.5 13.6CEC (meq/100 g) 10.6 6.4 12.2 11.8 22.2Bases sat. % 76 95 94 100


132

Series: Drabble Location: 12.5 km ESE from Santa Eleodora railway station, District of General Pinto, Province of Buenos Aires. Lat: 34° 44’ 20’’ S Long: 62° 34’ 12’’ S Height: 106 m.a.s.l. (IGM) This series has been classified as Typic Natraquoll, fine, illitic and thermic; poorly drained; very slow surface runoff; very slow permeability; strongly alkaline; slightly saline from surface; land use capability VIws; poor drainage, strong alkalinity and salinity as limiting factors; PI 10. Description of typical profile: A1: 0-22 cm; grey (5 YR 2/2) when wet; sandy loam; fine, weak sub-angular structure; abrupt, smooth bottom limit. IIB2T: 22-44 cm; brown (5 YR 3/2) when wet; clay loam; columnar structure that breaks into strong angular blocks; plastic; adhesive; scarce concretions of iron and manganese; common clay-skins; abrupt, smooth bottom limit. B3X: 44-72 cm; redish-brown (5 YR 4/4) when wet; loam; intermediate angular blocks that break into minor angular blocks; slightly plastic; slightly adhesive; abundant iron, manganese and carbonatic concretions; abundant clay-skins; moderate carbonatic reaction; hardened calcium carbonate nodules; clear, smooth bottom limit. CX: 72-105 cm; redish-brown (5 YR 4/4) when wet; loam; weak sub-angular blocks structure; slightly plastic; slightly adhesive; abundant carbonatic concretions; scarce iron concretions; abundant mottles; abundant calcium carbonate concretions and hardened nodules.

Horizon Al B2T B3X CXDepth (cm) 8-14 30-38 49-60 78-100Org. matter (%) 1.93 0.86 0.27 0.12Total carbon (%) 1.12 0.50 0.16 0.07Nitrogen (%) 0.094 0.069Relation C/N 11.9 7.2 Phosphorous (PPM) 13.0 6.1 Clay (%) 10.0 36.6 20.6 17.6Silt 2-20 (%) 17.6 15.2 22.5 21.9Silt 2-50 (%) 35.2 26.6 42.4 42.2Very fine sand 50-75 (%) 8.4 6.3 7.7 9.2Very fine sand 75-100 (%) 16.9 10.2 10.4 8.9Very fine sand 50-100 (%) Fine sand 100-250 (%) 27.9 19.3 15.2 20.5Medium sand 250-500 (%) 1.4 0.9 0.6 0.8Coarse sand 500-1000 (%) 0.2 0.1 0.1 0.1Very coarse sand 1-2 mm (%) Calcareous (%) 3.0 0.7Equivalent humidity (%) 14.5 41.4 26.1 23.7Resistance (Ohms) 493 329 398 388Conductivity (mmhos/cm) 5. 1 4 .5 4. 1 4.5pH (paste) 7.3 8.4 8.4 8.0pH H2O 1:2.5 8.0 8.9 9.0 8.9pH CLK 1:2.5 5.5 7.4 7.2 7.1Ca (meq/100 g) 4.5 10.4Mg (meq/100 g) 2.8 6.4 Na (meq/100 g) 3.3 10.7 12.2 8.8K (meq/100 g) 1.2 2.9 2.9 2.7H (meq/100 g) 1.4 1.9 Na (% of total) 29 35 51 44Sum of bases 11.8 30.9CEC (meq/100 g) 11.2 30.9 23.7 19.8Bases sat. % 100 100Gypsum Ca++ soluble 4.2 0.8 1.3 3.0Mg++ soluble 1.9 0.5 0.7 1.2Na+ soluble 49.6 52.8 52.8 54.4K+ soluble 0.7 0.6 0.6 0.6Nitrates Bicarbonate 7.4 6.2 6.4 9.6Sulphates 9.2 5.8 9.5 14.3Chloride 44.8 36.4 37.6 39.2SAR 28 65 75 37pH extract 6.7 7.3 7.9 7.5Cond. extract


133

Series: Lincoln Location: 3 km SW from L.N. Alem, District of L. N. Alem, Province of Buenos Aires. Lat: 34° 32’ 28’’ S Long: 61° 25’ 16’’ S Height: 84 m.a.s.l. (IGM) This series has been classified as Typic Hapludoll, coarse-loamy, mixed and thermic; well to some excessively drained; intermediate surface runoff; moderate to moderately-fast permeability; alkaline in depth; non-saline; land use capability Iis; moderate water-retention capability as limiting factor; PI 73. Description of typical profile: A1/AP: 0-30 cm; black (10 YR 2/1) when wet; sandy loam; fine, intermediate sub-angular blocks structure that break into fine sub-angular blocks to granular; friable when wet; slightly plastic; slighty adhesive; abundant roots. B2: 30-70 cm; dark brown (7.5 YR 3/2) when wet; sandy loam; coarse sub-angular blocks structure that breaks into fine sub-angular blocks; friable when wet; plastic; not adhesive; scarce humic clay-skins; abundant roots. B3: 70-109 cm; brown (7.5 YR 4/4) when wet; sandy loam; intermediate sub-angular blocks structure that breaks into fine sub-angular blocks and grainy; very friable when wet; slightly plastic; not adhesive; coarse iron mottles; moderate roots. C: 109-130 cm; brown (7.5 YR 4/4) when wet; sandy loam; intermediate sub-angular blocks structure that breaks into fine sub-angular blocks and grainy; very friable when wet; slightly plastic; not adhesive; abundant coarse, precise iron mottles; moderate roots.

Horizon Al/AP B2 B3 C Depth (cm) 5-25 35-65 75-105 110-130 Org. matter (%) 2.22 0.63 0.31 0.15 Total carbon (%) 1.29 0.37 0.18 0.09 Nitrogen (%) 0.115 0.054 Relation C/N 11.2 68 Phosphorous (PPM) Clay (%) 12.1 14.1 3.4 Silt 2-20 (%) 10 9.7 11.8 7.4 Silt 2-50 (%) 19 15.2 19. 15.2Very fine sand 50-75 (%) 10.8 13.2 10.4 11.7 Very fine sand 75-100 (%) 29.1 22.3 36.3 25.2 Very fine sand 50-100 (%) Fine sand 100-250 (%) 25.9 32.5 22.7 34.7 Medium sand 250-500 (%) 2.4 2.7 2.2 2.8 Coarse sand 500-1000 (%) Very coarse sand 1-2 mm (%) Calcareous (%) Equivalent humidity (%) 13.7 13.4 12.7 11.3 Resistance (Ohms) Conductivity (mmhos/cm) pH (paste) 5.9 6.5 6.7 7.3 pH H2O 1:2.5 6.3 6.8 7.0 7.6 pH CLK 1:2.5 5.5 5.3 5.4 6.0Ca (meq/100 g) 7.2 6.9 5.9 6.2 Mg (meq/100 g) 2.5 2.8 2.9 3.1 Na (meq/100 g) 0.1 3.1 0.2 0.2 K (meq/100 g) 1.0 1.0 1.2 0.9 H (meq/100 g) 4.9 2.9 2.4 2.9 Na (% of total) Sum of bases 10.8 10.8 10.2 10.4 CEC (meq/100 g) 12.2 11.7 10.7 10.5Bases sat. % 88 93 95 99


134

Series: Ortiz de Rosas Location: 1.5 km W from Ortiz de Rosas railway station, District of 25 de Mayo, Province of Buenos Aires. Lat: 35° 24’ 47’’ S Long: 60° 23’ 10’’ S Height: 55 m.a.s.l. (IGM) This series has been classified as Thapto-Argic Hapludoll, fine-silty, mixed and thermic; moderately well-drained; non saline; non alkaline; intermediate surface runoff; moderately slow permeability; vegetation cover: crops; slight water excess as use limitation; land use capability IIw; PI from 77 to 85. Description of typical profile: AP: 0-23 cm; dark grey-brown (10 YR 4/2) when dry; loam; intermediate sub-angular blocks structure; friable when wet; not plastic; not adhesive; abundant roots; abrupt, smooth bottom limit. A12: 23-35 cm; very dark brown (10 YR 2/2) when wet; loam; intermediate sub-angular blocks structure; friable when wet; not plastic; not adhesive; abundant roots; clear, smooth bottom limit. A/C: 35-55 cm; dark brown (7.5 YR 3/2) when wet; sandy loam; granular structure; very friable when wet; plastic; adhesive; not plastic; not adhesive; common, fine and precise mottles; common roots; clear, smooth bottom limit. IIB2T: 55-73 cm; dark brown (7.5 YR 4/2) when wet; sandy clay loam; prismatic structure; firm when wet; abundant iron and manganese concretions; abundant illuvial clay-skins; common, intermediate and precise mottles; common roots; clear, smooth bottom limit. IIB3: 73-110 cm; brown (7.5 YR 4/4) when wet; sandy clay loam; sub-angular blocks structure; slightly firm when wet; slightly plastic; adhesive; common, fine and precise mottles; common roots; abrupt, smooth bottom limit. IICCA: 110-120 cm; brown (7.5 YR 5/4) when wet; loam; massive structure; abundant calcareous concretions; common precise, fine mottles; scarce roots; weak carbonatic reaction.

Horizon AP A12 A/C IIB2T IIB3 IICCA Depth (cm) 0-23 23-35 35-55 55-73 73-110 110-120 Org. matter (%) 2.48 2.2 0.49 0.38 0.26 0.12 Total carbon (%) 1.44 1.28 0.26 0.22 0.15 0.07 Nitrogen (%) 0.135 0.119 Relation C/N 10.7 10.7 Phosphorous (PPM) 4.3 3.5 1.9 0.9 1.3 1.3 Clay (%) 14.9 17.3 13.3 29.5 25.1 21.0 Silt 2-20 (%) 20.6 18.4 14.7 12.2 8.8 19.2 Silt 2-50 (%) 35.9 36.8 29.4 23.3 21.2 31.8 Very fine sand 50-75 (%) 15.2 14.9 18.8 20.9 17.5 18.2 Very fine sand 75-100 (%) 16.6 15.1 18.8 11.2 19.1 12.2 Very fine sand 50-100 (%) Fine sand 100-250 (%) 15.5 13.8 17.6 13.3 15.3 14.0 Medium sand 250-500 (%) 1.8 2.1 2.1 1.8 1.8 1.8 Coarse sand 500-1000 (%) Very coarse sand 1-2 mm (%) Equivalent humidity (%) 15.2 20.5 14.9 24.7 22.9 24.7 pH (paste) 5.8 5.9 6.3 6.3 7.2 7.8 pH H2O 1:2.5 6.0 6.2 6.6 6.8 7.6 8.5 pH CLK 1:2.5 5.3 5.4 5.5 5.4 6.2 7.0 Ca (meq/100 g) 8.9 8.9 5.1 8.1 10.1 Mg (meq/100 g) 4.2 3.2 2.1 6.8 4.3 Na (meq/100 g) 0.4 0.6 0.5 0.4 0.7 1.5 K (meq/100 g) 1.3 1.4 1.3 2.7 2.8 3.9 H (meq/100 g) 6.1 5.9 3.0 4.2 3.7 Sum of bases 14.8 14.2 9.0 18 17.9 CEC (meq/100 g) 15.7 15.7 9.5 19.5 17.7 19.3 Bases sat. % 94 90 95 92 100


135

Series: Pichincha Location: 5 km S from Pichincha railway station, District of General Villegas, Province of Buenos Aires. Lat: 35° 28’ 00’’ S Long: 62° 21’ 00’’ S Height: 106 m.a.s.l. (IGM) This series has been classified as Thapto-Natric Hapludoll, loamy-fine, mixed and thermic; moderately well-drained; intermediate surface runoff; moderate permeability; non-saline; alkaline below 50 cm depth; vegetation cover: natural pasture; presents sodicity and drainage as use limitation factors; land use capability IVws; PI 48 to 51. Description of typical profile: AP/A1: 0-24 cm; dark grey-brown (10 YR 5/2) when dry; very dark grey-brown (10 YR 3/2) when wet; sandy loam; intermediate sub-angular blocks structure that breaks into fine sub-angular blocks and to granular; slightly tough when dry; friable when wet; slightly plastic; abundant roots; presence of animal galleries; clear, smooth bottom limit. AC: 24-35 cm; brown (10 YR 5/3) when dry; dark yellowish-brown (10 YR 3/4) when wet; sandy loam; intermediate sub-angular blocks structure that breaks into simple granular; soft when dry; very friable when wet; slightly plastic; abundant roots; presence of animal galleries; abrupt, smooth bottom limit. B21T: 35-57 cm; dark brown (7.5 YR 4/4) when dry; dark brown (7.5 YR 3/2) when wet; sandy clay loam; coarse prismatic structure that breaks into strong sub-granular blocks; tough when dry; extremely firm when wet; very plastic; very adhesive; scarce carbonate concretions; common precise mottles; common roots; gradual, smooth bottom limit. B22T: 57-81 cm; brown (7.5 YR 5/4) when dry; dark brown (7.5 YR 4/4) when wet; sandy loam; intermediate prismatic structure that breaks into intermediate sub-granular blocks; tough when dry; very firm when wet; plastic; adhesive; small carbonate concretions; scarce iron and manganese concretions; common precise mottles; scrace roots; gradual, smooth bottom limit. B3: 81-105 cm; brown (7.5 YR 5/4) when dry; dark brown (7.5 YR 4/4) when wet; sandy loam; intermediate angular blocks structure that breaks into fine angular blocks; friable when wet; slightly plastic; scarce carbonate and iron-manganese concretions; abundant coarse mottles; scarce roots; abrupt bottom limit. C1: 105-132 cm; brown (7.5 YR 5/4) when dry; dark brown (7.5 YR 4/4) when wet; sandy loam; fine sub-angular blocks structure; friable when wet; scarce carbonate concretions; common precise mottles; scrace roots; gradual, smooth bottom limit. C2CAM: 132-140 cm; very harden; abundant calcium carbonate.

Horizon AP/A1 AC B21T B22T B3 C1 C2CAM Depth (cm) 7-19 27-33 40-55 65-75 85-100 110-125 135-145 Org. matter (%) 2.15 0.65Total carbon (%) 1.25 0.38 0.32 0.06 0.04 0.04Nitrogen (%) 0.114 0.042 0.032Relation C/N 10.9 9.0 10.0Phosphorous (PPM) 6.4 5.3 2.1 6.1 9.6 7.8Clay (%) 13.3 10.5 21.8 17.6 16.5 15.4Silt 2-20 (%) 14.7 12.8 11.5 11.2 9.9 10.7Silt 2-50 (%) 29.8 27.9 23.2 22.5 24.0 24.4Very fine sand 50-75 (%) 10.7 10.3 9.7 8.7 10.6 11.9Very fine sand 75-100 (%) 21.3 21.9 22.1 22.8 24.1 20.4Very fine sand 50-100 (%) Fine sand 100-250 (%) 24.2 28.4 23.2 27.5 24.3 27.1Medium sand 250-500 (%) 0.7 1.0 0.9 0.5 0.8Equivalent humidity (%) 15.2 11.0 20.1 18.3 17.7 17.1Resistance (Ohms) 6735 12876 2179 1783 1486 1387pH (paste) 6.0 6.3 7.5 8.5 9.0 9.0pH H2O 1:2.5 6.5 6.9 8.2 9.3 9.5 9.5pH CLK 1:2.5 5.2 5.0 6.0 6.9 6.9 7.5Ca (meq/100 g) 6.4 3.9 8.5 5.4Mg (meq/100 g) 2.4 3.2 6.6 2.3Na (meq/100 g) 0.5 0.8 2.4 4.1 7.1 8.8K (meq/100 g) 1.2 0.8 1.9 1.9 1.8 1.8H (meq/100 g) 6.5 4.0 Na (% of total) 12.0 37.0 64.0 72.0Sum of bases 10.5 8.7 19.4 13.7CEC (meq/100 g) 11.9 8.2 19.8 11.0 11.0 12.0Bases sat. % 95 100 98 100Gypsum


136

Series: Saboya Location: 2.5 km N from Saboya town, District of General Villegas, Province of Buenos Aires. Lat: 34° 26’ 10’’ S Long: 62° 39’ 12’’ S Height: 115 m.a.s.l. (IGM) This series has been classified as Typic Argiudoll, loamy-fine, mixed and thermic; well-drained; intermediate surface runoff; moderate to moderately-fast permeability; neither saline nor alkaline; vegetation cover: crops (soya); land use capability I; no significant limiting factors; PI 85 to 90. Description of typical profile: AP/A1: 0-21 cm; very dark grey (10 YR 3/1) when wet; loam; intermediate sub-angular blocks structure that breaks into granular; friable when wet; slightly plastic; slightly adhesive; abundant roots; clear, smooth bottom limit. B1: 21-28 cm; dark brown (10 YR 3/3) when wet; loam; intermediate sub-angular blocks structure that breaks into minor blocks; friable when wet; plastic; adhesive; abundant roots; scarce skins; clear, smooth bottom limit. B2T: 28-62 cm; dark brown (7.5 YR 3/2) when wet; sandy clay loam; regular prismatic structure that breaks into angular blocks; firm when wet; very plastic; very adhesive; abundant roots; abundant skins; organic matter leaks; gradual, smooth bottom limit. B3: 62-98 cm; redish-brown (5 YR 4/4) when wet; sandy loam; regular prismatic structure that breaks into angular and sub-angular blocks; firm when wet; plastic; adhesive; common roots; scarce skins; organic matter leaks; slight carbonatic reaction; harden carbonatic nodules; gradual, smooth bottom limit. C: 98-145+ cm; pink-greyish (7.5 YR 7/2) when dry; brown (7.5 YR 5/4) when wet; regular sub-angular blocks structure; firm when wet; plastic; adhesive; scarce roots; scarce mottles; harden carbonatic concretions of 1cm diameter; strong carbonatic reaction.

Horizon AP/A1 B1 B2T B3 C Depth (cm) 0-21 21—28 28-62 62-98 98-145Org. matter (%) 3.39 1.84 0.96 0.38 0.14Total carbon (%) 1.97 1.07 0.56 0.22 0.08Nitrogen (%) 0.178 0.098 0.071Relation C/N 11.0 10.9 7.9Phosphorous (PPM) 11.9 3.2 1.9 8.4 0.5Clay (%) 18.9 18.5 23.8 18.7 13.4Silt 2-20 (%) 14.5 16.6 8.9 12.7 8.3Silt 2-50 (%) 29.9 31.8 24.7 24.8 23.5Very fine sand 50-75 (%) 10.7 10.4 10.4 11.6 13.5Very fine sand 75-100 (%) 17.5 20.9 16.7 23.0 17.9Very fine sand 50-100 (%) Fine sand 100-250 (%) 22.5 17.8 23.8 21.3 23.6Medium sand 250-500 (%) 0.5 0.5 0.5 0.5 0.9Coarse sand 500-1000 (%) 0.1 0.1 0.1Very coarse sand 1-2 mm (%) Equivalent humidity (%) 19.9 19.1 20.0 18.1 14.1Resistance (Ohms) 4964 2836 2925 4875 4609pH (paste) 6.0 6.1 6.1 6.4 7.4pH H2O 1:2.5 7.0 6.8 6.7 6.9 7.9pH CLK 1:2.5 5.5 5.3 5.3 5.5 6.6Ca (meq/100 g) 8.2 7.3 8.3 9.4Mg (meq/100 g) 1.1 2.2 2.7 3.1Na (meq/100 g) 0.3 0.3 0.4 0.3 0.4K (meq/100 g) 1.6 1.7 1.6 1.1 1.4H (meq/100 g) 6.1 4.6 4.5 3.4Sum of bases 11.2 11.5 13.0 13.9CEC (meq/100 g) 14.0 12.6 15.0 14.8Bases sat. % 80 91 87 94


137

Appendix D: Morphologic information of analyzed soil profiles

Pit #1: Date: 14/09/2005

Sampling site: 1

Soil series: Saboya Classification: Typic Argiudoll

Location: Parcel 25 W 34º 48' 57'' S 62º 40' 37'' W

Landscape/unit: High plane

Natural vegetation/crops: Soya stubble

Parental material: Loess

Relief: Normal

Position: Hill

Slope (%): 0-1

Runoff: Slow

Permeability: Moderately slow

Drainage: Moderately well darined

Water table depth (cm): 180

Moisture distribution: uniform

Soil coverage (%): 100

Land use: Agriculture

Pit #2: Date: 15/09/2005 Sampling site: 2 Soil series: Balbín Classification: Duric Natraquoll Location: Parcel 13 34º 50' 16'' S 62º 40' 34'' W Landscape/unit: Lying plane Natural vegetation/crops: Old, degraded pasture Parental material: Loess Relief: Sub-normal Position: Foothill Slope (%): <1 Runoff: Very slow Permeability: Slow Drainage: Poorly drained Water table depth (cm): 97 Moisture distribution: Not uniform Soil coverage (%): 100 Land use: Livestock


138

Pit #3: Date: 17/09/2005 Sampling site: 3 Soil series: Drabble Classification: Typic Natraquoll Location: Parcel 26 W 34º 52' 51'' S 62º 42' 56'' W Landscape/unit: Ponding low Natural vegetation/crops: Halomorphic Parental material: Sand, Loess Relief: Sub-normal Position: Foothill Slope (%): <1 Runoff: Very slow Permeability: Very slow Drainage: Poorly drained Water table depth (cm): 67 Moisture distribution: Not uniform Soil coverage (%): 75 Land use: Rangeland

Pit #4: Date: 18/09/2005 Sampling site: 4 Soil series: Ortiz de Rosas Classification: Thapto-Argic Hapludoll Location: Parcel 8 W 34º 51' 36'' S 62º 41' 32'' W Landscape/unit: High plane Natural vegetation/crops: Soya stubble Parental material: Sand, Loess Relief: Normal Position: Hill Slope (%): 0-1 Runoff: Slow Permeability: Moderately slow Drainage: Moderately well drained Water table depth (cm): 137 Moisture distribution: Not uniform Soil coverage (%): 100 Land use: Agriculture


139

Pit #5: Date: 20/09/2005 Sampling site: 5 Soil series: Cañada Seca Classification: Thapto-Argic Hapludoll Location: Parcel 34 W 34º 53' 32'' S 62º 42' 46'' W Landscape/unit: High plane Natural vegetation/crops: Wheat crop Parental material: Sand, Loess Relief: Normal Position: Half-hill Slope (%): 0-1 Runoff: Intermediate Permeability: Moderately slow Drainage: Moderately drained Water table depth (cm): 120 Moisture distribution: Not uniform Soil coverage (%): 100 Land use: Agriculture


140

Appendix E: Hydraulic conductivity measurements


141


142


143


144


145


146


147


148


149


150

Appendix F: Bulk density and soil water-storage measurements


151


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153


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osas

Seri

es)

Typ

ic N

atr

aq

uo

ll(D

rab

ble

Seri

es)

Th

ap

to-A

rgic

Hap

lud

oll

(Cañ

ad

a S

eca

Seri

es)

A

25

cm

45

cm

80

cm

11

0 c

m

Th

ap

to-N

atr

ic H

ap

lud

oll

(Pic

hin

ch

a S

eri

es)

BC

DE

F

So

ya

stu

bb

le

1 1 1 c m1 1 1 c m

Ap

/A:

0-2

9 c

mA

p/A

: 0

-29

cm

Bt:

29

-50

cm

Bt:

29

-50

cm

BC

: 5

0-8

2 c

mB

C:

50

-82

cm

C:

82

-111

cm

C:

82

-111

cm

De

gra

de

d r

an

ge

lan

d 6 7 c m6 7 c m

A:

3-2

3 c

mA

: 3

-23

cm

Bt:

23

-45

cm

Bt:

23

-45

cm

BC

k:

45

-67

cm

BC

k:

45

-67

cm

Sa

lt c

rus

t: 0

-3 c

mS

alt

cru

st:

0-3

cm

Ph

rea

tic

ta

ble

-1(1

3.0

6 d

Sm

)P

hre

ati

c t

ab

le(1

3.0

6 d

Sm

)-1

Deg

rad

ed

pastu

re

(“tr

éb

ol”

+ “

card

o”)

Ag

ricu

ltu

re

(So

ya)

Aq

uati

c v

eg

eta

tio

n

(“ju

nco

”)

Halo

-hyd

rom

orp

hic

ran

gela

nd

(“p

elo

de c

han

ch

o”)

Hyd

rom

orp

hic

pastu

re

(“co

la d

e z

orr

o”)

Ag

ricu

ltu

re

(Wh

eat)

N

Farm

-scale

po

nd

ing

su

scep

tib

ilit

y m

ap

pin

g f

or

po

ten

tial la

nd

recla

mati

on

assessm

en

t in

larg

e f

latl

an

ds.

A c

ase

stud

y in

the

Sal

ado

Riv

er B

asin

, Pro

vinc

e of

Bue

nos

Air

es, A

rgen

tina

.

by S

ebas

tian

Lud

ueña

- M

arch

, 200

6

Farm-scale ponding susceptibility mapping for potential land ...

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