ASSESSING FARMERS' KNOWLEDGE OF WEED SPECIES, CROP TYPE AND SOIL MANAGEMENT PRACTICES IN RELATION TO...

land degradation & developmentLand Degrad. Develop. 27: 120–133 (2016)

Published online 11 July 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ldr.2233

ASSESSING FARMERS' KNOWLEDGE OF WEED SPECIES, CROP TYPE ANDSOIL MANAGEMENT PRACTICES IN RELATION TO SOIL QUALITY STATUS IN

MAI-NEGUS CATCHMENT, NORTHERN ETHIOPIA

Gebreyesus Brhane Tesfahunegn1,2*, Lulseged Tamene3, Paul L. G. Vlek2, Kirubel Mekonnen1

1College of Agriculture-Shire Campus, Aksum University, PO Box 314, Shire, Ethiopia2Centre for Development Research (ZEF), University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany

3Chitedze Agricultural Research Station, International Center for Tropical Agriculture (CIAT), PO Box 158, Lilongwe, Malawi

Received 5 August 2012; Revised 6 June 2013; Accepted 9 June 2013

ABSTRACT

Soil quality (SQ) assessment from farmers' point of view can be used as a primary indicator for planning sustainable agriculture. Despite thisfact, limited information is documented with regard to SQ indicators, for example weed species, crop types and management practices fromfarmers' knowledge perspectives. The aims of this study are to analyse factors that determine farmers' knowledge of SQ, identify SQindicators of weed species and crop types across different SQ status and assess soil-and-crop management practices that maintain SQ in theMai-Negus catchment, northern Ethiopia. Fifty-two farmer household heads were chosen randomly for questionnaire interview. The resultsshowed significant (p≤ 0·05) differences in the proportion of respondents who used different crop-and-soil management practices. Thesuccess of overall prediction by the logistic regression model (model χ2 = 0·84, p< 0·01) and level of model correct predictions (86%)indicated that the explanatory variables have sufficiently explained farmers' knowledge of SQ indicators. Provided that other conditionsremained constant, the odds ratio of variables such as farmer experience, access to information, farm location, education, field slope and landtenure have significantly increased the likelihood of farmers for being knowledgeable of SQ indicators. This study also demonstrated thatmost farmers are knowledgeable in identifying weed species, crop types and management practices across various SQ status, which suggeststhat such SQ indicators should be used to assess SQ status (degradation severity) while locating fields to be intervened using appropriatemanagement strategies. © 2013 The Authors. Land Degradation & Development published by John Wiley & Sons, Ltd.

keywords: soil quality status; soil quality indicator; farmers' knowledge; weed species; crop types; management practices

INTRODUCTION

In the past century, about 2 billion of the 8·7 billion ha ofagricultural land, permanent pastures and forests and wood-lands in the world have been degraded, with the largest partbeing from developing countries (Chadha, 1996). However,human-induced degradation is most severe in Africa, where30% of the agricultural land, pastures, forests and wood-lands, which are the major sources of food, income andemployment, are degraded (Sheikh & Soomro, 2006). Otherstudies reported that about 65% of Africa's arable soils areseverely degraded as a result of human-induced degradation(e.g. Sheikh & Soomro, 2006; Mairura et al., 2007). This typeof degradation could be the main cause for expansion of globalgrain production dropped from 3% in the 1970s to 1·3% inthe period 1983–1993 (Steer, 1998; Arshad &Martin, 2002).Degradation in soil quality (SQ) can be associated with the

type of intensive land use, land cover changes and local knowl-edge involved in agricultural production and understanding of

* Correspondence to: G. B. Tesfahunegn, College of Agriculture-ShireCampus, Aksum University, PO Box 314, Shire, Ethiopia.E-mail: [email protected]

This is an open access article under the terms of the Creative CommonsAttribution-NonCommercial-NoDerivs License, which permits use and distri-bution in any medium, provided the original work is properly cited, the use isnon-commercial and no modifications or adaptations are made.

© 2013 The Authors. Land Degradation & Development published by John Wile

erosion influencing factors (Arshad & Coen, 1992; Ólafsdóttir& Júlíusson, 2000; Arshad & Martin, 2002; Mairura et al.,2007; Wei et al., 2009). In natural conditions, the soil tendsto maintain equilibrium as a result of pedogenetic processes(Parr & Papendick, 1997; Masto & Chhonkar, 2008).However, this equilibrium is easily upset by human activity(e.g. poor agricultural activities), and this effect is moreaggravated in arid and semi-arid regions of developing coun-tries with poor technical and financial resources. Human actionssuch as deforestation, soil, water and crop management prac-tices, often lead to soil unequilibrium as a result of effects onsoil organic matter, soil depth, erosion, soil compaction, salini-zation, acidification, alkalinization, nutrient depletion, chemicalor heavy metal contamination and/or diversity and activity ofsoil organisms (Masto et al., 2007; Wei et al., 2009). Suchforms of soil degradation problems can pose a serious threatto national household food security (Bekele & Holden, 1999).Substantial studies suggested that degradation of soil, air

and water quality have sparked interest in the concept ofSQ and its assessment (e.g. Doran & Parkin, 1994; Larson& Pierce, 1994; Karlen, 2004). Although it has a variety ofdefinitions in literature, SQ is often defined as ‘the capacityof the soil to function’ (Karlen et al., 1997). The SQ conceptintegrates soil biological, chemical and physical attributes toassess soil capacity to function (Doran & Parkin, 1994;Karlen et al., 1997). However, from farmers' point of view,

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121ASSESSING FARMERS' KNOWLEDGE OF SOIL QUALITY INDICATORS

SQ can be defined by a combination of factors such as cropyield, soil depth, erosion and nutrient status and agriculturalweed species indicators (Mairura et al., 2007). The sameauthors also stated that local farmers are capable in using croptypes as indicators of a specific SQ status, but such studies arelimited in depth and scope. Crop growth and productivity isthus a reflection of SQ, and any degradation of the soil canbe expected to adversely affect the stability of agriculturalsystem (Andrews et al., 2004; Mairura et al., 2007).According to Mairura et al. (2008), ‘farmers in Kenya used

several weed species as land quality indicators to differentiatesoil fertility status on their fields’. The same authors alsoreported that ‘farmers planted food crops with high stapleand economic value on fertile fields, while fodder crops orlow value crops were predominantly cultivated on poorfields’. Assessing the applicability of such SQ indicators fromthe northern Ethiopia catchment context is important in orderto use local farmers' knowledge in soil resources managementeven in the absence of experts. The use of plant indicators(weed species and crop types) to characterize SQ by farmersis an important visual criteria; however, farmers' knowledgeof plant species with respect to SQ categories has not yet beenwell documented (Nandwa & Bekunda, 1998; Mairura et al.,2007, 2008). Despite this fact, the use of plant species forSQ evaluation using farmers' knowledge acquired throughlong-term agricultural production could help in planningappropriate interventions for the sustainability of agriculturalproduction in developing countries such as Ethiopia, in whichagriculture is the main economic stay of most people. Also, incountries with scarce resources and having scattered data,generating scientific data that support decision making pro-cesses is a challenge. Thus, assessment of SQ degradation mustgo beyond estimating soil erosion and soil properties indicators,using the concept of SQ in the context of local farmers' knowl-edge (Brunner et al., 2008; Odendo et al., 2010).Local knowledge in general is complex, multifaceted and of-

ten quite subtle in its expression and involves much experientialtrial and error that includes scientific processes (e.g. Sillitoe,1998; Prain et al., 1999). Other studies noted that local commu-nities are able to fully realize their capacity to monitor andrespond to challenges related to SQ degradation by involvingin technology selection processes, introducing and dissemina-tion of practices and technologies that well suited to theconditions local farmers face and in the evaluation of their im-pacts achieved (e.g. Kebrom, 1999; Badege, 2001;WinklerPrins& Sandor, 2003). Assessment of SQ from farmers' point of viewcan be used as a primary indicator of sustainable agriculture(Roming et al., 1996; Karlen et al., 1997; Andrews et al.,2004) and thus could lead to further improvement of policiesand strategies implemented in the future.As SQ depends on the intended functions of soils, the direct

users of soils should participate in defining the important func-tions, criteria for indicators selection as well as in the evaluationprocess of implemented technologies related to soil improve-ment and maintenance. Participatory approach does not onlylead to the development of appropriate management systemsbut also to the enhancement of their adoption (Carvalho et al.,

© 2013 The Authors. Land Degradation & Developmentpublished by John Wiley & Sons, Ltd.

2002; WinklerPrins & Sandor, 2003; Andrews et al., 2004;Nabahungu & Visser, 2011). In line to this, Denboba (2005)stated that ‘identifying and analyzing the determinant factorsthat influence farmers' knowledge is the key step to facilitatethe development and transfer of appropriate technologies’.Despite the aforementioned facts, limited information is

documented with regard to farmers' knowledge of weedspecies, crop types and soil-and-crop management practicesacross different SQ categories (status) and in identifying thekey influencing factors for farmers' knowledge. The aims ofthis study are to (i) analyse socio-economic and biophysicalfactors that determine farmer understanding (knowledge) ofSQ, (ii) identify and assess SQ indicators of weed speciesand crop types used by farmers across different SQ statusand (iii) assess the soil-and-crop management practices usedby farmers for maintaining SQ in the Mai-Negus catchment,northern Ethiopia. Such study would enhance our understand-ing of both the local degradative and beneficial process of SQindicators, promote local participation and help in the redesignof strategies, investment programs and projects that enhanceSQ and thereby food security of smallholder farmers.

MATERIALS AND METHODS

Study Area

This study was conducted in the Mai-Negus catchment ofTigray region, northern Ethiopia (Figure 1). The catchmentarea is about 1,240 ha and altitude ranging from 2,060 to2,650m above sea level. The study catchment has a mean an-nual temperature of 22 °C and precipitation of 700mm. Mostof the rainfall (>70%) occurs within July and August. Landuse is predominantly arable, with teff (Eragrostis tef) beingthe major crop along with different proportions of pasture landand scattered patches of trees, bushes and shrubs. The majorrock types are lava pyroclastic and metavolcanic. Accordingto Food and Agriculture Organization of the United Nations-United Nations Educational, Scientific and Cultural Organiza-tion (FAO-UNESCO) Soil Classification System, soils aremainly Leptosols on the very steep positions, Cambisols onmid-dle to steep slopes and Vertisols on the flat areas (FAO, 1998).

Research Approach and Sampling Strategy

This study was conducted from June 2009 to December 2009.In this study, farmers in the catchment were first categorizedinto different socio-economic groups using the local criteriaembedded in the catchment. Informal discussions were madeon the suitability of the suggested criteria with village leaders,development agents and farmers. However, categorizingfarmers into different socio-economic groups was doneentirely by farmers themselves using local criteria such as (i)food security status and (ii) draught oxen ownership and thenumber of other livestock held by a household. The goal fordifferentiating farmers into different socio-economic groupswas to include farmers' knowledge associated with differentlevels of resources, as they may have different views on SQ in-dicators. Accordingly, three types of farmer wealth groupswere identified in the study catchment: poor, medium and rich.

LAND DEGRADATION & DEVELOPMENT, 27: 120–133 (2016)

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Figure 1. The study area Ethiopia (A), Tigray (B) andMai-Negus catchment (study site) (C). Thisfigure is available in colour online at wileyonlinelibrary.com/journal/ldr.

122 G. B. TESFAHUNEGN ET AL.

Rich farmers were defined as those who are able to feedhousehold members throughout the year; medium farmerswere those who sometimes have problems with their dailyfood supply; and poor farmers were those with no meansof getting daily food and are thus dependent on the sale offuel-wood, grass and wage labour for most months of theyear. Physical assets such as the condition of the house, farmsize and ownership of permanent trees and other crops werealso considered as additional criteria for categorizingfarmers into different wealth groups. Resource-rich farmersaccount only for 13% of the total households in the studyarea. Medium farmers constituted about 47% of the totalhousehold heads, and the rest are poor. On the basis of thelist of farmers assigned to each wealth category, their nameswere randomly drawn to participate in the catchment's soilquality assessment survey. Finally, a participatory fieldsurvey and household head interview were carried out tocollect relevant information related to SQ indicators, forexample socio-economic and physical factors, weed species,crop types and soil-and-crop management practices in thecatchment. For the participatory transect-walk and groupdiscussion, a specific checklist of issues that guided thediscussions during the transect-walk field observation wasdeveloped. Soil quality was categorized into high, mediumand low, by the farmers based on biological (e.g. crop yield,crop appearance and vigor and biomass) and physical indi-cators (e.g. erosion, soil depth, slope and colour). Generally,the SQ indicators used by local farmers varied from cropyield, vigor and appearance, weed infestation, soil responseto fertilizers, to soil thickness, soil fertility, colour, erosion,


texture and drainage (slope). Detail procedure for the imple-mentation of transect-walk and group discussion whileidentifying different SQ categories using the differentindicators can be found in Tesfahunegn et al. (2011a). Inthis study, the procedure used for household interview isdepicted in the succeeding text.

Household Interview

For the questionnaire interview, 52 household heads werechosen at random from the farmers with different wealthgroup in the study catchment. The questionnaire developed(Appendix) was pre-tested using randomly selected 15farmers in order to check if it is properly developed to addressthe objective of the study. The interview was carried out togather data related to socio-economic and physical factors thatinfluenced farmers for being knowledgeable or not on SQ.Besides, the interview was conducted to assess farmers' expe-riences on a range of issues of SQ indicators such as weedspecies and crop types across different SQ categories. In thisstudy, respondents were asked to list weed species and croptypes that were found in the SQ categories from the most tothe least important. The interview with local farmers was alsofocused on assessing soil-and-crop management practicesundertaken in relation to the SQ categories owned by thefarmers in the catchment. Specific data related to soil-and-cropmanagement issues, for example fertilizer, cropping system,tillage, agro-forestry, soil and water conservation (SWC) prac-tices with regard to the SQ categories (high, medium and low)were also collected. Note that in this paper, the phrases ‘SQcategories’ and ‘SQ status’ are used synonymously.



Data Analysis

Data were subjected to analyses using SPSS 18.0 software(IBM Company, Chicago, USA) (SPSS, 2011). Descriptive,nonparametric (chi-squared/χ2/) test and logistic regressionanalysis were used. Nonparametric tests do not require as-sumptions to data distribution of normal distribution becausedata are in the form of dichotomous variable.For chi-squared test analysis, data were described in the

form of dichotomous variable, which is taking only two pos-sible values of 0 or 1. The value 1 was assigned to indicatepresence, otherwise 0. The chi-squared test at probabilitylevel (p≤ 0·05) was carried out to test the null hypothesis thatthe number of farmers identified the presence of a certain pa-rameter, for example weed species and crop type, by indicat-ing 1 is equal to those not identified (0). Scientific name oflocal weed species identified in the SQ categories was trans-lated on the basis of the documented information in HoneyBee Flora of Ethiopia (Fichtl & Adi, 1994).In addition, a logistic regression analysis method was

employed to identify the socio-economic and biophysicaldeterminants of farmers for being knowledgeable or noton SQ indicators such as weed species, crop types andmanagement practices. Logistic regression is useful inpredicting a discrete outcome of a dichotomous dependentvariable from a set of independent variables that may becontinuous, discrete, dichotomous or a combination ofthese (Retherford & Choe, 1993; Southavilay et al.,2012). In this study, the dependent variable is a dichoto-mous variable with a value of 1 and 0; that is, farmers areknowledgeable (1) or not knowledgeable (0) of SQ indica-tors such as weed species, crop types and soil-and-cropmismanagement practices across the SQ categories. Suchdata were generated from the questionnaire survey as a bi-nary response, besides to a mixture of discrete and contin-uous independent socio-economic and biophysicalvariables collected during the survey (Table I). Selectionof the explanatory variables was based on characteristicsof the surveyed households and theoretical backgroundin literature (e.g. Daba, 2003; Denboba, 2005). Accordingto these authors, household heads age, education, access toinformation, perception on land tenure, land size, familysize, experience or current practices of soil and water man-agement and access to training are among the variablesthat hypothesized to positively influence farmers for beingknowledgeable on SQ indicators. For example, the highernumber of farm labour in the household indicates thatmore involvement in farm activities (not depended on ex-ternal labour), and it is hypothesized to positively affectfarmers knowledge on SQ, and the reverse is true for moreoff-farm oriented farmers.In this study, a logistic regression model was chosen

because of its advantages over other models, for exampleprobit model, in mathematical simplicity that givesmeaningful results (Southavilay et al., 2012). The multiplelogistic regression function, which estimates the likelihoodof the effects of the independent (explanatory) variables on


the dependent (response) variable, has the form (Retherford& Choe, 1993; Hosmer & Lemeshow, 2000) as:

ln P= 1� Pð Þ½ � ¼ β0 þ β1x1 þ β2x2 þ…þ βkxk

The quantity P/(1�P) is called the odds (likelihoods),which is the ratio of the probability that a household willbe knowledgeable of SQ indicators to the probability thatwill not be knowledgeable, if P is the probability of beingfarmers' knowledgeable, then 1-P represents the probabilityof not being knowledgeable of the SQ indicators; β0 is theintercept, β1, β2 … and βk are regression coefficients of theassociated independent variables of x1, x2 … and xk. Theeffect of the independent variables (e.g. β1) is interpreted asthe odds (likelihoods) of the outcome increases or decreasesby a factor of eβk. The odds ratio is a measure of associationbetween the independent and the dependent variables. If thevalue of β is greater than 1, the likelihood of the effect ofthe independent variable on dependent variable is increased;and vice versa, if β is less than 1 (Field, 2009).

RESULTS AND DISCUSSION

Soil Quality Categorization Based on LocalFarmers Knowledge

In the study catchment, farmers used a holistic view of indica-tors to categorize soils into three SQ categories namely, high,medium and poor. The major SQ indicators cited by thefarmers included crop yield, topsoil thickness, rill and gullyerosion, runoff amount, roots and sub-soil exposure, colourof crop leaves, stunted crop, palatable and unpalatable weedspecies for livestock and appearance and disappearance ofgrass species. Farmers commonly used soil colour and topsoilthickness to describe soils easily into SQ categories, besides tothe use of crop yield and yield component, weeds and fertilizerresponses of soil. Generally, SQ indicators such as crop yieldand soil depth are the most frequently used indicators byfarmers while categorizing soil into different SQ categories.Weed infestation is also considered among the best suitable in-dicator, but the problem is that some weeds may grow in oneseason and disappeared in the other. Details of SQ categoriza-tion based on farmers' knowledge of indicators can be found inTesfahunegn et al. (2011a). However, the study reported bythese authors have limitation in addressing specific issues re-lated to type of weed species, crop type and soil-and-cropmanagement practices across different SQ categories, whichis the focus of this study.In addition, for using farmers' profound knowledge of SQ

in similar situation while decision making, possible compari-sons between farmers' classification and scientific soil criteriasuch as laboratory measurements across the SQ categories isreported for the study catchment by Tesfahunegn et al.(2011b). The scientific analysis supported for the farmers'SQ categorization in the point of view that soils described ashaving high SQ by the farmers using their own descriptorswas confirmed by higher pH, soil aggregate stability, total ni-trogen, available phosphorus, organic carbon, cation exchange


Table I. Definition of variables used in logistic regression analysis and their hypothesized effects

Variable Definition and description Effecta, b

DependentFarmers' knowledge of soilquality indicators

Dummyc: 1 If a farmer is knowledgeable of soil quality indicator, 0 otherwise

Independent: household characteristicsAged Age of household head in years +/�Gendere Dummy: 1 if the respondent is male, 0 otherwise +/�Wealthf Dummy: 1 if the economic category of the household head is rich, 0 otherwise +/�Educationg Dummy: 1 if the respondent is literate (primary, secondary education

or read and write), 0 otherwise+/�

Off-farm activitiesh Dummy: 1 if the respondent frequently involves in off-farm activities, 0 otherwise -Family sizei Total number of household members +/�Land sizej Dummy: 1 if total land size is larger than 1 ha, 0 otherwise +Land tenurek Dummy: 1 if a respondent feels secured in tenure (land remains

for life time and then pass it to children), 0 otherwise+

Experience of soil managementl Dummy: 1 if a farmers has experiences in soil managementpractices as soil quality indicator for the soil quality status, 0 otherwise

+

Livestock sizem The total number of livestock by a household head +Access to informationn Dummy: 1 if a farmer has accessed to regular information related

to soil quality indicators through media (e.g. radio) and extension(e.g. training), 0 otherwise

+

Local association membershipo Dummy: 1 if a farmer is member of a local association suchas catchment development group and credit, 0 otherwise

+

Independent: biophysical variablesField slopep Dummy:1 if a farmer has a wide range of slope in his/her farm field, 0 otherwise +Farm locationq Dummy:1 if a farmer has farm land, which is located in both distance

and homestead, 0 otherwise+

aThis indicated the positive (+) effect of variables for a farmer in becoming knowledgeable of the soil quality indicators such as weed species, crop types andsoil-and-crop management practices in different soil quality categories.bThis indicated the negative (�) effect of variables for a farmer in becoming not knowledgeable of the soil quality indicators such as weed species, crop typesand soil-and-crop management practices across the soil quality categories.cThis is a way of coding categorical, nominal or ordinal variables as a dichotomous variable, which is 1 (presence) and 0 (absence).dWith increasing age, a farmer is expected to get better knowledge about his/her farm soil quality indicator. Thus, it is expected that ‘age’ in some intervals tohave a positive effect on farmers to become knowledgeable of the soil quality indicators and vice versa.eMale headed household is expected to become more knowledgeable of the soil quality indicators because social factors are allowed for male family member to beresponsible most often in handling agricultural related issues, which can increase knowledge on soil quality indicators. A household is headed by a female only inthe absence of male because of different reasons, and this may reduce their fundamental knowledge of soil quality indicators as acquiring soil knowledge is de-manding much experiential trial and error for a long time. Thus, this variable can have both positive effect if the household is headed by male and vice versa.fRich farmers are often engaged on their on-farm activities as this is the sole source of their income. Such situation can be given an ideal condition to learn andunderstand their soils quality indicators and is hypothesized to be positively associated with knowledge. Conversely, economically poor farmers less often usedtheir farm fields as they rent to rich farmers in a form of lease. In such condition, poor farmers are not becoming knowledgeable of soil quality indicators and arethus hypothesized to be negatively associated with knowledge. For logistic analysis, economically medium farmers are viewed as rich.gExposure to education can increase farmers' management capacity and better understanding of the benefits and constraints of soil quality indicators. Education isthus hypothesized to positively increase the probability that a farmer to become knowledgeable of soil quality indicators, and the opposite is true for illiterate farmers.hFarmers that are involve commonly to off-farm activities cannot have sufficient time to understand their soils behavior because they are less likely to engage inactivities that increase their knowledge of soil quality indicators. Thus, this variable can be negatively associated with farmers' knowledge of soil quality indicators.iLarge family size households could have the opportunity to discuss and share experiences that can harmonize their knowledge on soil quality indicators.Conversely, large families may need more land to fulfil their subsistence, otherwise this can lead family members to engage on off-farm activities. Besides, iffamily size is small and is mainly dependent on daily laborer, soil quality related knowledge cannot be acquired by household members. Thus, this variable canhave both positive and negative effects.jThis is often related to the economic status of a farmer, and as land size increase, the condition a farmer to see and test different soil quality indicators isbecoming high. In this study, if land size is >1 ha, it is thus expected to positively affect farmers' knowledge of soil indicators.kA tenured land is hypothesized to be positively associated with a farmer to be knowledgeable of soil indicators because land tenure is a strong incentive forfarmers to understand well soil quality indicators as they invest more time and other resources on their farm.lThis indicates that farmers with adequate experiences in using soil management practices, such as fertilizer, soil conservation measures and soil fertilityenriching cropping system, are becoming knowledgeable of the soil quality indicators. This is the rational for the variable to be hypothesized as positive effecton the dependent variable.mFarmers with higher number of livestock are easily accessed to large mass of manure that can be applied to farm fields based on the knowledge of soil qualityindicators differences. Thus, this variable can affect positively to the dependent variable.nFarmers who accessed to information sources, such as media and training, are knowledgeable of the soil quality indicators and vice versa. Thus, the variable ishypothesized to have positive effect on the dependent variable.oMembership in local organizations is hypothesized to positively influence likelihood of farmers' knowledge on soil quality indicators.pIf a farmer is possessing farm lands that include flat to steep slope ranges; this can create a chance to observe different vegetation cover, crop types, erosion–depositionand soils conditions, which are used as soil quality indicators. Thus, this variable is hypothesized to affect positively for farmers' knowledge of soil quality indicators.qIf a farmer has farmland, which is located in both distance and homestead, discrepancy in crop type, weed species and soil-and-crop management practices caneasily be observed. This variable can thus demonstrate diversity of information to improve farmers' knowledge of soil quality indicators and is hypothesized tobe positively associated with farmers to be knowledgeable.


© 2013 The Authors. Land Degradation & Developmentpublished by John Wiley & Sons, Ltd. LAND DEGRADATION & DEVELOPMENT, 27: 120–133 (2016)


capacity, base cations and clay content, and lower sandcontent and bulk density than in the medium and low SQcategories (Mairura et al., 2008; Tesfahunegn et al., 2011b).This suggests that results of scientific soil analysis across theSQ categories in the study catchment agreed well withfarmers' knowledge of SQ. Thus, local farmers' SQ knowledgecan be considered as comparable and reliable approach withthat of the scientific measurement, but it is more rapid, lesscostly and efficient in appraising SQ at large scale than thescientific soil analysis (Tesfahunegn et al., 2011b).

Household Socio-economic and Farm Characteristics

The general household socio-economic and farm character-istics in the Mai-Negus catchment are summarized inTable II. The economic distribution of the respondents aspoor, medium and high, wealth group was 34·6%, 34·6%and 30·8%, respectively. The chi-squared test thus indicatedthat there was no significant difference in the proportion ofrespondents in each wealth group. With regard to gender,however, the proportion of male respondents was signifi-cantly higher than the female respondents (Table II). Thisis because the numbers of male headed households werehigher than that of female headed households in thecatchment. The percentage of respondents in the differentwealth and educational group with respect to gender andage is shown in Figure 2A–D. The percentage of femalerespondents was high in poor wealth category and men inrich category (Figure 2A). Among all the respondents, menwere the most illiterate. However, in primary educationlevel, the number of educated men was higher than women.Only men had secondary education level (Figure 2B).

Table II. Socio-economic and farm characteristics of farmer households

Parameters Value, (%) χ2 testa

Socio-economic category ns Farmer anPoor 34·6 Average hMedium 34·6 Average faRich 30·8 Average y

the househGender * PercentageMale 71·2 PercentageFemale 28·8 Mean num

Education ** Farmers kIlliterate 53·8 CroppingPrimary school 38·5 Farmers pSecondary school 7·7 Farmers u

Land tenure: owner (47);owner and inherited (19);owner and rent (34)

ns Farmers pand monoc

Farmers used soilmanagement practices

** Farmers pand mixe

Farmers used manure only 25·0 Practiced mFarmers used DAP and manure 6·3 Practiced mFarmers used urea, DAPand manure

68·7 Farmers u

DAP, di ammonium phosphate.*Significant at 0·05 probability level (p). **Significant at p≤ 0·01 l; ns, probabiliaChi-squared test (two-sided test).bValues are in number.


The majority of the respondents (53%) had between31–50 years of age. The other 47% had more than 51 yearsof age. This indicated that the proportion of young farmersthat can be involved in agriculture and SQ managementwas slightly higher. The graph of wealth versus age of therespondents also showed that the percentage of youngerfarmers (31–40 years) was higher in the rich wealth categoryas compared with the other age groups (Figure 2C). It isimportant to note that the medium wealth category versusage is so heterogeneous because this wealth category is thetransition between the rich and poor wealth categories. It isvisible in the Figure 2D that the older farmers are the mostilliterate and the younger with high-level education. Suchhigher numbers of illiterate farmers suggest that analysingof SQ related issues and adopting the best fit technology thatimproves SQ may not be easy. The chi-squared test alsoshowed significant difference in the proportion of respon-dents in each education level. The family size, land sizeand cattle kept by the respondents also differed significantlyin the catchment (Table II). Even though there was no sig-nificant differences in the conditions of land tenure systems,this study showed that farmers feeling of tenure is strong forhomestead land as compared with distant farm land.This study reported that age, wealthy category and

educational level have implications on the type of soil man-agement practices. For example, older farmers used lessamount of manure as they are less accessed to livestockkeeping. Even if they have more manure, the high-labourdemand for transportation is a problem. The first author alsoobserved that older farmers were less involved on SWCpractices and other short-term trainings. With increasing

in the Mai-Negus catchment, northern Ethiopia (n= 52)

Parameters Value, (%) χ2 testa

d farm characteristicsousehold members 6·5b **rm size (hectares) 1·2b *ears since the farm cultivated byold head

18b ns

of farm that has flat land (homestead) 40 **of farm that has flat land (distance farm) 53 nsber of cattle kept 3·8b **eeping livestock 100 nssystem used as soil management *racticed rotation only 18·8sed monocropping only 32·3racticed both rotationropping

18·8

racticed rotation, monocroppingd cropping

14·5

ixed cropping only 9·4onocropping and mixed cropping 3·1

sed rotation and then fallowing 3·1

ty values that are not significant at p> 0·05 levels.


Gender

02468

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Illiterate Primary Secondary

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Figure 2. Distribution of respondents by (A) wealth versus gender, (B) education versus gender, (C) wealth versus age and (D) education versus age. Thisfigure is available in colour online at wileyonlinelibrary.com/journal/ldr.


age (which is directly related to experience), it is a logic thata farmer is expected to get better knowledge about his/herfarm land soil quality indicators. But, it is observed that ifa farmer is getting older (>60 years) involvement on farmactivities and trainings decreases. Such condition leads toknowledge depreciation of farmers on the existing SQindicators in relation to different SQ categories. This study,however, generalized that experience of farmers positivelyinfluenced knowledge on SQ indicators.The results showed that there were significant differences in

the proportion of the respondents who used the different soil-and-cropmanagement practices (Table II). For example, manyfarmers in the study catchment commonly practicedmonocropping of tef (Eragrostis tef) and maize (Zea mays)compared with the other cropping systems (Table II). The tefcrop covered 65–80% of the cultivated land every year be-cause of its market demand used as staple food and suitableon a range of climate and SQ status (Figure 3). The typicalexamples of mixed cropping identified by the farmers as

(A) (B)

Figure 3. Tef crop grown in (A) high soil quality, (B) medium soil quality and (Cand management practices. This figure is available in co


soil-and-crop management practice in the catchment were tefwith nug (Guizotia abyssinica), sunflower (Carthamustinctorius) or tomato (Lycopersicon esculentum).This study demonstrated that the application of fertilizer

as a soil management practice was significantly differentamong the farmers in the catchment. The majority (69%)of the farmers used a combination of urea, di ammoniumphosphate (DAP) and manure fertilizers. The percentage offarmers who used manure as the sole fertilizer was about25%. As the distance of farm land increased from home-stead, farmers reduce the amount of manure to be appliedbecause of transportation problem. As a result, they tend toapply no/low amount of manure. As an option, farmersapplied manure (compost) to the fields near to their homeand mineral fertilizers to distant fields. This study indicatedthat manure and mineral fertilizer and soil erosion control-ling structures were applied more intensively on homesteadfarm plots when compared with the distant farms. Previousstudies reported that soil management practices such as

(C)

) low soil quality categories located in similar slope position, planting timelour online at wileyonlinelibrary.com/journal/ldr.



manure application has the potential to reduce erosion as thisimproves soil structure, infiltration and water holding capacityof the soil (e.g. Ervin & Ervin, 1982; Denboba, 2005; Mastoet al., 2007). In general, this study implies that options forsoil-and-crop management strategies should take into consider-ation the socio-economic issues, besides the biophysical factorsand soil quality status of the farmlands.

Determinants of Farmers' Knowledge of SoilQuality Indicators

The logistic regression analysis revealed that the disparity offarmers' knowledge of SQ indicators was attributed tosocio-economic and biophysical variables, even though thedifferences were observed especially in biophysical variables(Table III). The success of the overall prediction by the logisticregression model (model χ2= 0·84, p< 0·01) and the level ofmodel correct predictions (86%) indicated that the explanatoryvariables have sufficiently explained farmers' knowledge ofSQ indicators. A strong association between farmers' knowl-edge of SQ indicators and the group of explanatory variables(Nagelkerke R2 = 0·78) was also observed. Such model valuesindicated the acceptability (fitness) of the logistic model inpredicting the dichotomous dependent variable using theindependent variables.If the other conditions remain constant, the odds ratio of

the variables such as farmer experience, access to informa-tion, farm location, education, field slope and land tenuresignificantly increased the likelihood of farmers in becomingknowledgeable of the SQ indicators by a factor of 11·58,6·89, 5·05, 4·27, 3·18 and 2·69, respectively. Of these

Table III. Binary logistic regression results on the dependent variablMai-Negus catchment, northern Ethiopia

Dependent variable: knowledge of SQ indicators β

Independent: socio-economic variablesAge (years) 1·143Gender 0·064Wealth 0·783Education 2·751Off-farm activities �1·125Family size 0·324Land size 1·352Land tenure 2·053Experience 5·414Livestock size 0·626Access to information 3·240Local association membership 0·065

Biophysical variablesField slope 2·485Farm location 2·952Model constant 7·628Model chi-squared 83·973Model Nagelkerke R2 0·785Model correct prediction 86%

β, estimated coefficient; SE, standard error; CI, confidence interval; SQ, soil qual**Is significant at probability level, p< 0·01. *Is significant at p< 0·05; values waThe Wald statistic is the square of the ratio of the estimated coefficient and its stWilkinson, 1997).


variables, farmers' experience and access to informationwere significantly influenced at 99% confidence level forfarmers' SQ knowledge. In addition, factors such as farmlocation, education, field slope and land tenure were thesignificant determinants for farmers in becoming knowl-edgeable of the SQ indicators at 95% confidence level.The result of this study showed consistency with the find-

ings in Gould et al. (1989) and Negatu & Parikh (1999),who reported that farmers' prior experience can contribute tothe positive perception of technologies such as soil-and-cropmanagement, which ultimately reduces soil degradation.Similarly, Ervin & Ervin (1982) stated that farmers withadequate experience of conservation measures and soil man-agement practices can understand SQ indicators better thanthe inexperienced counterparts. Other studies (e.g. Conacher,1995; Daba, 2003; Moges & Holden, 2007) reported thatfarmers' low education level and ignorance causes for theirlower awareness on SQ indicators such as soil erosionprocesses and soil management practices. Thus, illiteracy(non-educated) can be among the major impediments for suc-cessful implementation of suitable soil conservation measures(Conacher, 1995), as illiterate farmers are less dynamic forchanges and are tolerable in adopting new techniques. Thisstudy also highlighted that farmers having farmlands on steepslopes were more likely to be knowledgeable of SQ indicatorsthan those who did not have because they often observechanges in soils in shorter time.The remaining independent variables presented in

Table III have not significantly affected the dependent vari-able. For example, farmers frequently involved on off-farmactivities were not significantly influenced by the dependent

e, which is ‘farmer knowledge of soil quality indicators’ in the

SE aWald (χ2) Probability Odds ratio (95% CI)

0·623 3·366 0·062 0·691(0·524–1·990)0·142 0·203 0·102 1·25 (1·199–2·798)0·380 4·246 0·087 0·823 (0·740–1·855)1·030 7·134 0·012* 4·269 (1·670–6·623)0·102 2·568 0·435 1·41 (1·37–2·577)0·421 0·592 0·652 0·167(0·127–3·096)0·815 2·752 0·353 1·050 (0·96–2·037)1·272 2·605 0·042* 2·694(1·012–3·759)1·307 3·469 0·002** 11·582 (9·354–12·974)0·183 4·893 0·125 1·010 (0·607–1·626)1·113 8·474 0·025* 6·895 (4·365–8·254)0·105 0·383 0·658 0·687 (0·423–1·097)

1·100 5·103 0·036* 3·180 (2·040–4·879)0·563 8·492 0·003** 5·050 (4·620–7·738)

0·021*0·001**

ity.ithout asterisks are non-significant at p> 0·05.andard error, closely approximates a chi-squared distribution (Cary &



variable even though there was an inverse relationship. Theresult showed that farmers involved on off-farm activitiesincreased their likelihood for not being knowledgeable of SQindicators by a factor of 1·13. Previous studies (Tenge et al.,2004; Denboba, 2005) showed a similar observation, that is,more off-farm oriented farmers have fewer tendencies toinvolve on farm activities and are hypothesized to negativelyaffect farmers' knowledge of SQ. Conversely, factors such asland tenure, family, livestock and land sizes showed positiveeffects on farmers' knowledge of SQ indicators. This isconsistent with the report in Denboba (2005) who stated thatthe factors listed previously positively affect farmers'perception of erosion and soil fertility indicators. In general,identification of socio-economic and biophysical determinantsto assess whether farmers are knowledgeable or not on SQ in-dicators, such as weed species, crop types and managementpractices, is useful not only for better understanding but alsohelps to formulate better strategies and appropriate policiestargeting on the influential variables.

Soil Quality Indicator Weed Species

When farmers asked to identify weed species across the SQcategories, most respondents (94%) reported that species suchas Datura Stramonium followed by Anowdonia polystachya

Table IV. Weed species identified by farmers to indicate high, medium aEthiopia (n= 52)

Scientific name Local name

High soil quality indicator speciesDatura Stramonium L. Mezerba´Anowdonia polystachya L. MugiyaCarduus nyassanus R.E.Fries Eshok DanderCarthamus lanatus L. Dander BeitaNicandra physalodes L. GheracciaHyperrehnia spps WazwazoCyanotis barbata D.Don. BarikoAnagallis arvensis L. Chigaqwa´hitGalinsoga parviflora L. Tsahyay Shewa

Medium soil quality indicator speciesCynodon dactylon (L.) pers TihagTrifollium rueppellianum Fresen. MesiLuecas martinicensis Jacq. KatatrGalinsoga parviflora Dka-NequelKalanchoe quarfiniana A.Rich. Dekwa`taGynandropsis gynandra L. BoekbehaPlantago lanceolata L. MandeldoMedicago polymorpha L. Kwa´kito

Low soil quality indicator speciesHygrophila auricumlata Schum. Eshok-MergemBidens pilosa L. QakutoBecium grandiflorum Lam. TebebRumex nervosus Vahl HohotSida schimperiana Hochst. TifreryaCyperus esculentus L. Michiqua´Asystasia gangatica L. GrbiyaStriga hermonthica L. MetselemCrepis rueppellii Sch.-Bip. Tsaba-TiealArgemone mexicaca L. Medafe-TilianAnethum graveolens L. Shilan

χ2, chi-squared (two-tailed test), significant at probability level, p≤ 0·05; ns, not


(88%) were frequently used as high SQ indicators. About88% of the respondents mentioned that Cynodon dactylon isused as medium SQ indicator. On the other hand, accordingto 84% of the respondents, poor SQ can be described by thepresence of weed species such as Hygrophila auricumlataSchum. Other examples of weed species that can be used to in-dicate high, medium and poor SQ categories are presented inTable IV. In this table, the respondents ordered the importanceof the weed species (based on the frequently noted) from themost (top) to the least (bottom line) in each SQ category.Farmers generalized that weed species found in the low SQcategory are less palatable to livestock compared with thosethat are growing in the medium and high SQ categories. Thereason for this could be related to increasing weed toxicity inlow SQ, but the explanation of the farmers' demands needsfurther research.The results showed that the probability values observed

with the chi-squared test were not significant for the major-ity of the weed species (Table IV). With such standard, werejected the null hypothesis that hypothesized the numberof farmers who identified weed species, for example DaturaStramonium, Anowdonia polystachya, Carduus nyassanus,Carthamus lanatus, Anagallis arvensis or Galinsogaparviflora, as high SQ indicators were equal to those who

nd low soil quality categories in the Mai-Negus catchment, northern

Botanical family Percentage χ2 test

Solanaceae 94 0·000Polyganaceae 88 0·000Asteraceae 78 0·002Asteraceae 72 0·020Jolanaceae 50 1·000ns

Gramineae 50 1·000ns

Commelinacea 44 0·597ns

Primulaceae 31 0·050Kompasiteae 19 0·001

Poaceae 88 0·000Leguminosae 75 0·007Lamiaceae 50 1·000ns

Asteraceae 44 0·597ns

Crassulaceae 38 0·215ns

Capparidaceae 34 0·110ns

Plantaginaceae 28 0·020Fabaceae 25 0·007

Acanthaceae 84 0·000Asteraceae 81 0·001Lamiaceae 72 0·020Polygonaceae 72 0·020Malvaceae 69 0·050Cyperaceae 59 0·377ns

Acanthaceae 53 0·860ns

Scrophulariceae 50 1·000ns

Asteraceae 34 0·110ns

Papaveraceae 28 0·020Apiaceae 25 0·007

significant at p> 0·05.



did not select. In a similar fashion, the null hypothesis wasrejected for some weed species identified by the farmers asmedium and low SQ indicators. This indicated that the alter-native hypothesis, which stated as the number of farmerswho identified some of the weed species in the SQ catego-ries were not equal to those who did it. However, there wereno significant differences in the proportion of farmers thatidentified certain weed species as SQ indicator and thosewho did not identify, which indicated that the null hypothe-sis is accepted (Table IV).According to farmers' view, Anowdonia polystachya is

more palatable by livestock among the weed species listedto indicate high SQ. This could be the reason for farmers be-ing interested to grow such weed specie. On the contrary,Datura Stramonium L. is less palatable weed specie, andfarmers removed it from the field on time before its seed isdisseminated by agents such as wind and water. Farmersnoted that Datura spp is easy to eradicate manually becauseof its shallow root system. Field observation showed thatthis specie is commonly found under the shade of Acaciaalbida and in soils with high organic matter. Generally,farmers noted that less palatable weeds are commonlyobserved on poor SQ soils, which used as an indicator ofSQ degradation (e.g. Bidens pilosa and Striga hermonthica).The farmers noted that weed species such as Cynodon

dactylon in the medium SQ category are highly palatableby livestock, but once it invades, it is hard to plow as itsroots are attached with the soil firmly. According tofarmers, this weed reduces the impact of run-off and rain-drop soil detachment. This could be attributed to improvingsoil cover and infiltration rate (Taddese et al., 2002). Thegrass Cynodon dactylon is also considered by farmers asthe most tolerant and persistent specie for high-livestockgrazing pressure. The farmers in the study catchment alsounderstood that as SQ started to decline, palatable weedspecies to livestock such as Trifollium rueppellianumFresen are growing. When crop vigor decreases as a resultof SQ declining, the population of Trifollium spp. increasesbecause of low competition for resources with a crop. Insuch condition, more seeds of this weed can grow rapidly.As this weed specie is leguminous and so fixes nitrogen,farmers experienced that plowing during vegetative andflowering stages can increase soil fertility (soil organicmatter and nitrogen). This finding is consistent with thestudy in Black et al. (2012) who stated that Trifolliumspp. is involved in nitrogen fixation. This indicated that na-ture is balancing the problem by introducing such suitablespecies that can improve SQ.According to farmers' understanding, weed species such

as Hygrophila auricumlata Schum are growing onexhausted soils. Farmers reported that species such asBecium grandiflorum and Rumex nervosus Vahl can be com-monly found on heavily eroded surfaces and heavily de-graded gully. Such species are associated with presence ofextreme soil erosion and are growing on exposed subsoilsmainly along gully sides and margins with pediment androcky sites, both of which are indicators of poor SQ.


Farmers also noted that weed species, such as Strigahermonthica, are commonly found in low SQ category,spread quickly and deplete soil fertility rapidly. This weedspecie is not easy to eradicate if it is once established. Inthe study catchment, Striga hermonthica is commonlyfound in sorghum (Sorghum bicolor (L.) Moench), maize(Zea mays L.), tef (Eragrosts tef (Zucc) Trot) and fingermillet (Eleusine coracana L.) fields; this is also occasion-ally observed in wheat (Triticum aestivum L.) and barley(Hordeum vulgare L.) fields. According to the farmers'opinion, the presence of high population of this weedspecie is an indicator of extreme problem of soil fertilityand moisture. Consistent with the previous results of weedspecies associated with SQ and land degradation, somestudies reported that farmers perceived indicators oferosion and soil fertility loss using the presence/ absenceof weed (Moges & Holden, 2007; Mairura et al., 2008).This means that the use of farmers' knowledge of SQindicators such as weed species can be used in documen-ting the status and trend of soil degradation and, thereby,in developing suitable soil management strategies.

Soil Quality Variability and Field Crops

Farmers' choices of crops to be grown are influenced by SQcategories and factors such as seasonal rainfall and availableresources (e.g. inputs). According to farmers, SQ could beinfluenced for planting time (early and late planting).Farmers reported that there are crop types, which can be bestgrown in high SQ, whereas some crops can be grown re-gardless of the SQ categories (Table V).Farmers described that the dominant crop, which is culti-

vated regardless of the SQ category is tef even though betteryield is expected from high SQ. Because of its extent andexpected high productivity for market, tef is more commonlycultivated in the high SQ category. The tef crop is Ethiopia'sstaple cereal and is among the few crops that tolerate mildwater logging and dry conditions. In support to the present re-sults, Ketema (1993) and Habtegebrial et al. (2007) reportedthat tef is adapted to diversified environmental conditions andwidely grown from low to high altitudes.Farmers of the study area noted that maize (Zea mays) is

growing well on soils of high SQ category. They alsoreported that pulses such as faba bean (Vicia faba) need welldrained and deep topsoil, which are commonly found in thehigh to medium SQ categories. Generally, all farmers usedyield of tef and maize crops as indicator of SQ differencesin the study catchment. For example, if the yield of tef is lessthan 1 t ha-1, this is a notice for a farmer that the SQ is poor.Farmers observed that oil crops, for example linseed

(Linum usitatissimum), and pulses such as lentil (Lensculinaris) and field pea (Pisum sativum) showed better yieldon poor SQ even with lower soil-and-crop managementpractices (Table V). This indicated that farmers understoodthat such oil and pulse crops do have the capacity to im-prove soil fertility, which is consistent with the findingreported in Jordan & Leake (2004), Black et al. (2012)and Mansa (2013).


Table V. Crop types used by farmers as high, medium and low soil quality indicators in the Mai-Negus catchment, northern Ethiopia (n= 52)

Common name Scientific name Percentage frequency

High soil quality indicator speciesTef Eragrostic tef (L.) 100Maize Zea mays (L.) 100Faba beana Vicia faba (L.) 88Nugb Guizotia abyssinica L. 66Lentilb Lens culinaris Medik. 38Wheata Triticum vulgare 28

Medium soil quality indicator speciesWheata Triticum vulgare L. 94Barleyc Hordeum vulgare L. 88Finger millet Eleusine coracana L. 59Faba bean Vicia faba L. 72

Low soil quality indicator speciesNugb Guizotia abyssinica L. 84Flax/linseed Linum usitatissimum L. 75Field pea Pisum sativum L. 72Lentilb Lens culinaris Medik. 66Barleyc Hordeum vulgare L. 56Sorghum Sorghum bicolor (L.) Moench 34

aCrops observed in both high and medium soil quality but with higher frequency in the high than medium soil quality category for faba bean and vice versa forwheat. This indicated that there was more likely to show high soil quality than medium soil quality using faba bean and vice versa for wheat.bCrops observed in both high and low soil quality but with higher frequency in the low soil quality category, indicatingmore likely to show the low soil quality category.cCrops observed in both medium and low soil quality but with higher frequency in the medium soil quality category, indicating more likely to show mediumsoil quality.


About 84% of the farmers reported that nug (Guizotiaabyssinica), the most important oil crop and crop native toEthiopia highlands, is commonly growing in the low SQ cat-egory compared with the other crops. This was followed byfield pea (Table V). The present result showed that farmersare capable of differentiating crops that can be grown betterin the poor SQ category. According to farmers' experiences,tef crop can be given better yield in poor SQ in the presenceof appropriate inputs and management practices such as fertil-izer, tillage and weed control. Farmers in the study catchmentunderstood that crops such as wheat (Triticum vulgare) (94%)followed by barley (Hordeum vulgare) (88%) were com-monly grown better in the medium SQ than the other crops.Poor SQ is also allocated to some extent for grazing land.The present finding indicated that knowledge of crop distribu-tion across different SQ categories as adapted by farmers can

Table VI. Soil erosion control measures and soil fertility enriching practEthiopia (n= 52)

Soil erosion control measures Farmersa (%) χ2 test

Cultivation along a contour 96 (50) 0·000Terracing 78 (41) 0·002Contour strip cropping 6 (3) 0·000Soil/stone bunding 84 (44) 0·000Grassed waterways 6 (3) 0·000Tree planting 81 (42) 0·001Construction of check dams 88 (46) 0·000Planting hedge on field boundaries 94 (49) 0·000

aSum >100% because many respondents practiced more than one practices, and soof respondents, and without parentheses are percentages of respondents.


contribute to the development of appropriate soil-and-cropmanagement strategies that maintain and improve SQ.

Soil-and-Crop Management Practices in MaintainingSoil Quality

In the study catchment, erosion control measures in terms offencing, conservation structures integrated with annual andperennial plant species and soil fertility enhancing practiceshave implemented mainly around homesteads, wherebymost medium to high SQ fields are located. Soils aroundhomesteads are the most intensively cultivated for a longtime, which drastically deteriorate SQ unless appropriatesoil-and-crop management practices are implemented(Table VI). Farmers reported that it is easier to implementsoil-and-crop management practices intensively aroundhomesteads because they could engage in management

ices implemented by farmers in the Mai-Negus catchment, northern

Soil fertility practices Farmersa (%) χ2 test

Use of inorganic fertilizer 91 (47) 0·000Use of manure 96 (50) 0·000Mixed cropping 9 (5) 0·000Compost 22 (11) 0·002Agroforestry 69 (36) 0·050Rotation 88 (46) 0·000Fallowing 3 (2) 0·000Combination of organicand inorganic fertilizer

96 (50) 0·000

, each practice was weighted from 100%. Values in parentheses are number


Figure 4. Terracing remained without maintenance on shallow soil depth marginal area in the Mai-Negus catchment, northern Ethiopia (photo by first author inJuly 2009). This figure is available in colour online at wileyonlinelibrary.com/journal/ldr.


practices in the evenings while relaxing or when oversee-ing other tasks such as housework and attending live-stock. Farmers witnessed that less attention was givenfor timely maintenance of terraces and introduction ofsoil fertility enriching practices for the marginalized andpoor SQ soils located at a distant from their homes(Figure 4).Because soil erosion is one of the key SQ indicators for

different erosion control measures, and soil fertility enrichingpractices have been implemented in the study catchment(Table VI). The results of chi-squared test analysis did detectsignificant differences among farmers' practices. From thispoint of view, we rejected the null hypothesis that the num-ber of farmers who practiced specific type of soil erosioncontrolling measure or soil fertility practices is equal to thosewho did not use. Introduction of alternative land use such asforage legume production, multipurpose trees and controlgrazing targeting to improve the poor SQ fields should be in-tegrated with appropriate SWC measures for sustainable useof soil resources for production.

47

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Figure 5. Soil quality characteristics (indicators) identified by farmers: the highertion of soil-and-crop management practices. Others includes such as soil erosion,

online at wileyonlinelibra


According to farmers' view and field observation by thefirst author, different factors, for example farm distance,slope and soil fertility (Figure 5) were identified that couldinfluence for the intensive implementation of soil conserva-tion measures and soil fertility practices. This figure showsthat farmers identified soil fertility (84%), soil depth (75%)and soil colour (66%) as the main soil characteristics thatinfluenced whether a given soil management practices isbeing implemented or not. The understanding of farmerson soil fertility could be reflected by a separate or combina-tion of indicators such as presence of clay dominated soils(farmers described it as dark black soil), deep soil depthand well-drainage soils. The other SQ characteristics (soiltexture, ability to hold water, slope, farm distance fromhome and drainage) also influenced the intensity of soilconservation and management practices for beingimplemented. Therefore, agricultural efforts for developingsuitable and alternative technologies should be consideredSQ characteristics in order to be adopted effectively byfarmers for sustainable soil management.

38

66

34

ristics

soil texture

soil depth

land slope

soil fertility

land drainage

soil water hold capacity

farm distance

soil color

others

frequency for a soil quality indicator the more that it influences the applica-weed infestation and crop type indicators. This figure is available in colourry.com/journal/ldr.



CONCLUSION

This study demonstrated that farmers are knowledgeable of SQindicators such as weed species, crop type and soil managementpractices across different SQ categories. Farmer's knowledge ofSQ indicators in general is influenced by socio-economic andbiophysical factors. Among such factors, farmer's experiences,access to information, farm location (distance from home),education, field slope gradient and land tenure are the factorsthat influenced their knowledge of SQ indicators. Of thesevariables, farmer's experience and access to information weresignificantly influenced at 99% confidence level and alsoshowed the highest odd ratio (the likelihood of farmers tobecome knowledgeable of SQ indicators). This study also con-firms that farmers are knowledgeable of weed species growingacross the SQ categories (high, medium and low) in thecatchment. Besides, farmers' view on crop types grown andsoil-and-crop management practices implemented across theSQ categories are crucial for sustainable use of soil resources.Farmers' knowledge of SQ indicators across the SQ categorieshas important role in decision making as it is more rapid,simple, less costly and has relatively acceptable efficiency.The results of this study thus suggest that farmers' knowledgeof SQ indicators such as weed species, crop types and manage-ment practices should be used to assess the SQ status (severityof degradation) in order to prioritize fields to be intervenedusing appropriate management strategies.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the financial support byDAAD/ GIZ (Germany) through the Centre for Develop-ment Research (ZEF), University of Bonn (Germany) andthe field work supported by Aksum University (Ethiopia).The authors also highly appreciate the assistance offeredby the local farmers, administration and extension agentsduring the field study. We are also grateful to the anony-mous reviewers and Professor Artemi Cerdà for theircomments, suggestions and corrections of this paper.

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APPENDSemi-structured questionnaire to evaluate farmers for being knowledge

Ethiopi

Ser. No. Question

1Household head: age___, sex___, wealth_______, educationinherited, rented), farmland slope-homestead (flat, steep) and

2Which inputs do you use on your farm lands? 1 =manure (caDAP 3 = urea only 4 =DAP only 5 = no fertilizer 6 = specify

3 Which criteria do you use to determine the rates of inputs at1 = soil texture 2 = fertility status (e.g. soil depth) 3 = drainagecondition 8 =weed infestation 9 = others

4 Which soil fertility enhancing practiced do you practice in y1 = inorganic fertilizer 2 =manure 3 =mixed cropping 4 = com8= organic + inorganic fertilizer 9 = specify, if any

5Are you knowledgeable of soil quality indicators such as weedifferentiate soil quality categories? 1 = yes 2 = no

6 What weeds species do you use to show high, medium or lo7 Do you observe appearances of plant species that signify dec

If yes, what are the names of these species (use local name)?

8Do you observe the emergence of unpalatable pasture specie2 = no 3 = don't knowIf yes, what are the names of these species (use local name)?

9 Which crop species grown in your area do you use as high,10 Do you use some kinds of practices to maintain, improve or en

If yes, which of the following practices do you use common1 = use of inorganic fertilizer 2 = use of manure 3 = intercropp


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Tesfahunegn GB, Tamene L, Vlek PLG. 2011a. A participatory soil quality as-sessment in northern Ethiopia's Mai-Negus catchment. Catena 86: 1–13.

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WinklerPrins AMGA, Sandor JA. 2003. Local soil knowledge: insight,application, and challenges. Geoderma 111: 165–170.

IXable of soil quality indicators in Mai-Negus catchment, northerna

_____, family size____, farm size____(ha), land tenure (owner,distance farms (flat, steep)ttle, sheep, goat, chicken manure, please underline) 2 = urea andif any otheryour field?4 = ability to hold water 5 = slope 6 = color at vegetative 7 = rainfall

our area?post 5 = agroforestry 6 = rotation 7 = fallowing

d species, crop types and soil-and-crop mismanagement practices to

w soil quality indicator? (best to least)line in soil quality? 1 = yes 2 = no 3 = don't know

s in the grazing areas as a result of soil quality decreasing? 1 = yes

medium or low soil quality indicator?rich soil quality/fertility of your cultivated soils/land? 1 = yes 2 = noly on different soil quality categories?ing 4 = compost 5 = agroforestry 6 = rotation 7 = specify if any other


ASSESSING FARMERS' KNOWLEDGE OF WEED SPECIES, CROP TYPE AND SOIL MANAGEMENT PRACTICES IN RELATION TO...

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