Impacts of Alternative Manure Application Rates on Texas Animal Feeding Operations: A Macro Level...

IMPACTS OF ALTERNATIVE MANURE APPLICATION RATES ON TEXASANIMAL FEEDING OPERATIONS: A MACRO LEVEL ANALYSIS1

E. Osei, B. Du, A. Bekele, L. Hauck, A. Saleh, and A. Tanter2

ABSTRACT: An integrated economic and environmental modeling system was developed for evaluating agro-environmental policies and practices implemented on large scales. The modeling system, the ComprehensiveEconomic and Environmental Optimization Tool-Macro Modeling System (CEEOT-MMS), integrates the Farm-level Economic Model (FEM) and the Agricultural Policy Environmental eXtender (APEX) model, as well asnational databases and clustering and aggregation algorithms. Using micro simulations of statistically derivedrepresentative farms and subsequent aggregation of farm-level results, a wide range of agricultural best man-agement practices can be investigated within CEEOT-MMS. In the present study, CEEOT-MMS was used toevaluate the economic and water quality impacts of nitrogen (N) and phosphorus (P) based manure applicationrates when implemented on all animal feeding operations in the State of Texas. Results of the study indicatethat edge-of-field total P losses can be reduced by about 0.8 kg ⁄ ha ⁄ year or 14% when manure applications arecalibrated to supply all of the recommended crop P requirements from manure total P sources only, when com-pared to manure applications at the recommended crop N agronomic rate. Corresponding economic impacts areprojected to average a US$4,800 annual cost increase per farm. Results are also presented by ecological subre-gion, farm type, and farm size categories.

(KEY TERMS: nonpoint source pollution; best management practices; animal feeding operations; environmentalimpacts; clustering; costs; simulation modeling.)

Osei, E., B. Du, A. Bekele, L. Hauck, A. Saleh, and A. Tanter, 2008. Impacts of Alternative Manure ApplicationRates on Texas Animal Feeding Operations: A Macro Level Analysis. Journal of the American Water ResourcesAssociation (JAWRA) 44(3):562-576. DOI: 10.1111 ⁄ j.1752-1688.2008.00182.x

INTRODUCTION

Efforts to control nutrient losses from agriculturalland uses have continued into the 21st Century withnew U.S. Environmental Protection Agency (USEPA)

and U.S. Department of Agriculture (USDA) effortsas well as local stakeholder initiatives (USDA andUSEPA, 1999; USEPA, 2003). The new concentratedanimal feeding operation (CAFO) rule released byUSEPA in February 2003 is an effort to reduce pollu-tion from animal feeding operations (AFOs) by

1Paper No. J06136 of the Journal of the American Water Resources Association (JAWRA). Received October 10, 2006; accepted September 5,2007. ª 2008 American Water Resources Association. Discussions are open until December 1, 2008.

2Respectively, Senior Research Economist, Texas Institute for Applied Environmental Research (TIAER), Box T-0410, Tarleton StateUniversity, Stephenville, Texas 76402; Assistant Research Scientist, TIAER, Box T-0410, Tarleton State University, Stephenville, Texas76402; Research Scientist, Imperial Oil Resources, 3535 Research Road N.W., Calgary, Alberta, Canada T2L 2K8; Deputy Director, TIAER,Box T-0410, Tarleton State University, Stephenville, Texas 76402; Economic and Environmental Modeling Coordinator, TIAER, Box T-0410,Tarleton State University, Stephenville, Texas 76402; and Research Associate, TIAER, Box T-0410, Tarleton State University, Stephenville,Texas 76402 (E-Mail ⁄ Osei: [email protected]).

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focusing regulatory action on the largest operations(USEPA, 2003). USDA analyses indicate that largeoperations are the most likely to have excess on-farmmanure nutrients. It is the expectation that thesmaller operations will implement comprehensivenutrient management plans (CNMPs) voluntarilywhile large operations would be required to imple-ment CNMPs.

Economic and environmental simulation modelscontinue to play a key role in the development ofcost-effective and equitable environmental policies foragriculture. Over the past 20 years, a number ofinterdisciplinary research efforts have provided datato inform decision-making on sustainable agricultureand water quality improvement initiatives (e.g.,Mozaffari and Sims, 1994; Goldstein and Ritter,1995; Osei et al., 2000b). Most of these research stud-ies have focused on local watershed assessments.Large scale assessments are much fewer, partlybecause of the massive data requirements and signifi-cant computer modeling efforts they often require.

Notwithstanding these obstacles, the need for largeregional and even national studies on economic andenvironmental impacts of conventional agriculturalpractices is still great. Watershed studies are notdirectly relevant for large-scale policy developmentbecause of the heterogeneity that exists betweenwatersheds. In recent years, a few regional studieshave been attempted (e.g., Babcock et al., 1997). Tohelp address the need for macro level studiesdesigned to provide timely and reliable informationon cost-effective environmental policies, the TexasInstitute for Applied Environmental Research(TIAER) has developed a macro-modeling system aspart of a USEPA-funded project. The macro modelingsystem, named the Comprehensive Economic andEnvironmental Optimization Tool-Macro ModelingSystem (CEEOT-MMS) (Osei et al., 2003a), is builtupon the CEEOT framework for policy assessmentthat has been applied for watershed studies (Oseiet al., 2000a; Gassman et al., 2002). CEEOT-MMSconsists of an environmental modeling system, afarm-level economic model, a data repository, and aprogramming interface that links all the model com-ponents together and performs automation functions.

The watershed version of the CEEOT suite of mod-els included three key simulation models: the Agricul-tural Policy Environmental eXtender (APEX) model,the Soil and Water Assessment Tool (SWAT) model,and the Farm-level Economic Model (FEM). Thesemodels were used initially to investigate alternativepolicy options for livestock enterprises in severalagricultural watersheds (Osei et al., 2000b, 2003b,c;Saleh et al., 2000; Gassman et al., 2002; Keplingeret al., 2005). Both APEX and SWAT are widely usedenvironmental models developed by scientists at the

Texas A&M Blackland Research and Extension Cen-ter and the USDA-ARS Grassland Soil and WaterResearch Laboratory in Temple, Texas (Arnold et al.,1998; Williams et al., 2000). FEM is an annual eco-nomic simulation model developed at TIAER in part-nership with the Center for Agricultural and RuralDevelopment (CARD) of Iowa State University (Oseiet al., 1995, 2000a; Gassman et al., 2002). CEEOT-MMS is based on farm-level simulations using APEXand FEM as the main simulation models.

CEEOT-MMS uses a micro simulation approach todetermine the impacts of various best managementpractices on agricultural operations. Using the repre-sentative farm as the basic modeling unit, individualfarm simulations are designed such that the aggre-gated land uses, management practices, and otherinput information are statistically similar to the mea-sured aggregates to be modeled. More informationabout the development of CEEOT-MMS and determi-nation of representative farms is given in Osei et al.(2003a).

This paper presents estimates of the average farm-level and aggregate economic and water qualityimpacts that result from implementation of alterna-tive manure application rate scenarios on TexasAFOs. The relevance of CEEOT-MMS applications isthat we are able to perform simulations over largeregions within a short period of time due to the largedata preparations accomplished during CEEOT-MMSdevelopment and the automation routines incorpo-rated within the modeling system.

METHODS

Macro level assessments are accomplished withCEEOT-MMS by performing numerous micro simula-tions and aggregating the results across the entirestudy area. In CEEOT-MMS micro simulations arecomputer runs performed on individual farms. Statis-tical clustering methods were used to determinegroups of farm types and representative farms withineach group. Representative farms were then simu-lated and the results were aggregated across eachgroup of farms using the number of farms repre-sented in each group as the weights. For environmen-tal model simulations, the land areas simulated forthe representative farms were also used as weights.Micro simulations permit us to analyze a wide rangeof scenarios, because some practices such as filterstrips can be properly assessed only at the farm levelrather than a more aggregate level.

The foundational data for macro simulations andassessments are in a database of hypothetical farms

IMPACTS OF ALTERNATIVE MANURE APPLICATION RATES ON TEXAS ANIMAL FEEDING OPERATIONS: A MACRO LEVEL ANALYSIS

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that represents the distribution of all agriculturaloperations within the United States (U.S.). The hypo-thetical farms database was obtained by applying astatistically valid disaggregation process to the 1997Census of Agriculture such that the resulting distri-bution of farms mimics the published AgriculturalCensus farm distributions for all major farm attri-butes (Osei et al., 2003a, 2004). Scores of farm attri-butes were included in the disaggregation process(e.g., land in farms, acreage of individual crops,inventories of individual livestock species, and salesof individual livestock products) (Osei et al., 2003a,2004). The farm attribute information was also usedto determine farm type and size designations for eachhypothetical farm. While the individual hypotheticalfarms do not represent specific real world farms, thedistribution of hypothetical farms mirrors that of realworld farms at the county and state levels, and at thelevels of various aggregations portrayed in the 1997Agricultural Census (Osei et al., 2004).

Hypothetical farm data obtained from the Agricul-tural Census were augmented with managementpractice and other farm and economic data fromother sources. Field operations performed on croplandand pasture were obtained from a Crop OperationsLibrary, which was developed from the USDA Crop-ping Practices Survey (USDA, 1995) and state-levelcrop enterprise budget information. The Crop Opera-tions Library contains field operations typical of themajor cropping systems for each state.

The Agricultural Census data do not contain infor-mation on crop rotation histories for farms. However,crop history information is needed for economic andenvironmental model simulations. To determine thecrop history for each farm, a distribution of croppingsystems was first determined for each county basedon the Agricultural Census county totals of crop landareas and approximations of crop histories from theNational Resources Inventory. Once a distribution ofcrop histories was obtained for each county, croppingsystems were assigned to each farm based on the dis-tribution of crop land uses on the farm and the esti-mated cropping system distribution for the county.Consequently, while the cropping system for eachfarm may not be an exact depiction of real world pat-terns, it would in virtually all cases reflect the pre-dominant cropping systems in use in the county.

Crop yields were obtained from USDA Crop Pro-duction databases. Recommended agronomic rates forcrops were obtained by multiplying crop yields bycrop agronomic ratios (recommended agronomic ratesper unit yield). Agronomic ratios for nitrogen (N),phosphorus (P), and potassium (K), were obtainedfrom the Potash and Phosphate Institute (PPI, 2002)and Kellogg et al. (2000). Prices farmers receive forfeed ⁄ grain sales as well as fertilizer and other input

prices were obtained from the annual USDA Agricul-tural Prices publications (USDA-NASS, 2006).

Several other assumptions pertaining to manureproduction and handling were established for eachfarm type and size category. For this study, therewere a total of six main farm types based on predomi-nant livestock species (dairy, beef, swine, broiler,layer, and sheep operations) and five farm size cate-gories based on total animal units (AU) on the farm(very small – less than 200 AU, small – 200-400 AU,medium – 400-1,000 AU, large – 1,000-2,000 AU, andvery large – over 2,000 AU). Farm types were furthersubdivided into subcategories based on productionsystems and housing styles, yielding a total of 125potential farm types. Turkey, goat, and other spe-cialty animal farms were not considered in thisCEEOT-MMS application.

Manure production data were obtained from ASAE(1995) per unit of liveweight for each livestock spe-cies. Total manure production for each farm was thencalculated as a summation across all livestock spe-cies, weighted by their respective liveweights, num-ber on the farm, and duration in the herd. Manurehandling systems in place were based on the manuremanagement systems distribution used in Kellogget al. (2000). Manure nutrient loss prior to land appli-cation was based on assumptions widely used in theState of Texas.

The present study applied to AFOs, a subset of the194,000 farms represented in the 1997 AgriculturalCensus for Texas. To determine which farms shouldbe categorized as AFOs, several criteria were used,similar to the criteria used in a recent USDA studyon the cost of implementing CNMPs on AFOs acrossthe U.S. (USDA-NRCS, 2003). In particular, most ofthe farms classified as AFOs in USDA-NRCS (2003)either had at least 35 AU or consisted predominantlyof confined livestock types. In this study, only farmswith at least 35 AU were categorized as AFOs. Fur-thermore, an operation with over 35 AU was not con-sidered an AFO if it was a sheep or beef cattleoperation that had enough pasture biomass to pro-vide adequate forage for the livestock on the farm. Itwas assumed for this study that if a sheep or beefcattle operation had at least 0.2 ha of pasture landper AU, it was not an AFO.

Using the above procedure, 13,760 AFOs wereidentified for Texas, which represents almost 8% ofall the farms in the state. Once the set of AFOs wasdetermined, a multi-stage clustering process wasused to define a set of representative farms for eco-nomic and environmental model simulations. In thefirst-stage of clustering, the entire study region (theState of Texas) was divided into subregions basedon relative ecological homogeneity. For this appli-cation, precipitation and minimum and maximum

OSEI, DU, BEKELE, HAUCK, SALEH, AND TANTER


temperature values were used as the cluster vari-ables for dividing the State of Texas into subregions.

The K-means partitional clustering method wasused to divide the State of Texas into the ecologicalsubregions defined for this study. K-means clusteringwas accomplished for this application using theFASTCLUS procedure of SAS (SAS Institute, 1999).To determine the optimal number of clusters, a proce-dure combining two empirical criteria was used. TheCubic Clustering Criterion (CCC) (SAS Institute,1983, 1999) and the Pseudo F-statistic were both con-sidered. The optimal number of clusters was chosenas the number of clusters that yielded high CCC andPseudo F-values and also represented distinctimprovements over other partitions with almost thesame number of clusters. Using this procedure, thefirst-stage clustering process yielded 11 subregions.

In the second-stage of clustering, all AFOs in eachsubregion were clustered to obtain farm clusterswithin each subregion. Representative farms werethen obtained as the farm(s) closest to the centroid ofeach farm cluster. The K-means partitional clusteringmethod was also used for second-stage clustering.Parameters used for the second-stage clustering areshown in Table 1. The second-stage clustering processwas performed for each combination of the six farmtypes and five farm sizes represented in each subre-gion. The result of this two-step clustering processwas a set of 780 representative farms, representingthe distribution of farms by size and type in each eco-logical region. Figure 1 shows the spatial distributionof representative farms by farm size and by farm typeacross the 11 subregions.

Farm-Level Simulation in CEEOT-MMS Using APEXand FEM

All representative farms were simulated in APEXand FEM. Environmental simulations were per-formed in APEX while economic simulations wereperformed in FEM. APEX simulates individual fieldsand provides routing functions that can be used toroute field-level values of environmental indicators tothe outlet of a small ‘‘watershed’’. APEX is a sophisti-cated process-based model that includes a number ofcomponents for field-scale simulation as well as func-tions for routing water, sediment, nutrients, and pes-ticides across complex landscapes and channelsystems (Williams et al., 2000). Water quality indica-tors simulated in APEX include sediment, N (ammo-nium, nitrate, nitrite, and organic), P (soluble,adsorbed ⁄ mineral, and particulate), and pesticides. InCEEOT-MMS applications, field-level indicators arerouted in APEX to obtain an aggregate value for eachfarm (Osei et al., 2003a).

FEM is a whole-farm economic simulation modelthat uses various optimization and simulation rou-tines to simulate the farm-level economic indicatorsof agricultural operations. Costs of field operationsand other economic indicators are estimated in FEMusing a number of dynamic routines. For instance,costs of manure applications on land are computeddynamically in the model based on machinery owner-ship rules, equipment characteristics, and fuel, labor,and other input prices (Osei et al., 2000a). Prior toFEM simulations in this CEEOT-MMS application,the model was calibrated by adjusting model

TABLE 1. Farm Attributes Used in Second-Stage Clustering.

Farm Attribute Dairy Beef Swine Broilers Layers Sheep

Total cropland (acres) X X X X X XHarvested cropland (acres) X X X X X XCropland used for pasture or grazing only (acres) X X X X X XPastureland and rangeland other than cropland and woodland pastured (acres) X X X X X XCorn for grain or seed (acres) X X X X X XCorn for silage or green chop (acres) X X X X X XSorghum for grain or seed (acres) X X X X X XWheat for grain (acres) X X X X X XCotton (acres) X X X X X XSoybeans for beans (acres) X X X X X XAll hay (acres) X X X X X XBeef cows XMilk cows XHeifers and heifer calves XSteers, steer calves, bulls, and bull calves XCattle fattened on grain and concentrates XHogs and pigs inventory XSheep and lambs of all ages inventory XLayers and pullets: 13 weeks old and older inventory XBroilers and other meat-type chickens sold X



parameters to ensure that all cost and revenue indica-tors were consistent with the relevant published data.

Alternative Manure Application Rate Scenarios

The objective of this study was to evaluate the eco-nomic and environmental impacts of alternative man-ure application rates when implemented on AFOs inTexas. Manure application rates were investigatedbecause recent studies indicate that nutrient manage-ment on fields receiving manure remains one of themost relevant practices for water quality enhance-ment (Osei et al., 2000b), particularly in livestockimpacted areas. Three manure application rate sce-narios were investigated during this study: one repre-senting manure application at the recommended Nrate of receiving crops, and two alternative specifica-tions of manure application to meet P requirementsof receiving crops. Solid manure incorporation andliquid manure injection were not simulated in this

study. In general, manure incorporation does notapply to the perennial pasture lands that receivemost of the manure applications in Texas.

If manure applications had been based on cropremoval rates instead of recommended crop agro-nomic rates, costs and land area requirements wouldhave been higher, and nutrient losses would havebeen lower. For this study, manure applications werebased on recommended crop agronomic rates so as tobe consistent with the assumptions used in previousstudies. Furthermore, it was assumed that all soilshad very low levels of N, P, and K. Accounting fortypical soil nutrient levels would have resulted ineven higher land area requirements in areas wherethere is soil nutrient buildup.

N Rate Scenario. The N rate scenario was alsoassumed to be the baseline or status quo scenario forpurposes of comparison because it most closelyreflects current practices. Under this scenario, AFOsapply manure on crop fields and pastures at rates

FIGURE 1. Distribution of Representative Farms by Size and Type Across Subregions.



calculated to supply all of crop N needs, taking intoaccount all applicable losses and unavailable portionsof nutrients. Manure N losses before land applicationvaried depending on the manure handling optionsused. Manure N losses during land application werebased on widely used specifications in manure nutri-ent management plans in Texas. It was generallyassumed that about 20% of manure N is lost throughvolatilization during land application and that 50% ofsolid manure N in the soil profile is not readily in aplant available form. After manure application onland, any remaining cropland or pasture on the farmthat does not receive manure was assumed to receivecommercial fertilizer nutrients at recommended cropagronomic rates. It was assumed that no inorganicfertilizer was applied on fields receiving manure atthe N rate.

If the land farmed by the representative AFO wasnot adequate for manure applications, it wasassumed that liquid manure was given priority andwas applied on land owned or operated by the AFO,or on the closest possible land available. It wasassumed that only solid manure would be hauled asignificant distance from the AFO as it is less expen-sive to haul solid manure than liquid manure. In thebaseline scenario, any remaining solid manure washauled to a nearby landowner’s premise and appliedthere at the recommended N agronomic rate. InAPEX, the land area receiving manure or inorganicfertilizer was simulated in total, whether it wasowned by the AFO operator or not. The representa-tive AFO was assumed to bear all solid manure haul-ing and application expenses. This assumption wasmade because in Texas livestock farms in most areaswith surplus on-farm manure bear the brunt of man-ure hauling expenses. This is in contrast to the situa-tion in areas such as the Midwest where cropnutrient demand is high and livestock operations canoften sell manure or at least do not incur the cost ofhauling it to other landowners (Osei et al., 2000c;Gassman et al., 2006). For example, in a study of theupper Maquoketa river watershed in Iowa, theauthors found that manure nutrient managementpractices would actually save farmers money, withfarm profit increases of over US$30 ⁄ ha in many cases(Osei et al., 2000c; Gassman et al., 2006). This is notgenerally true, however, of farms in livestockimpacted watersheds in Texas (Osei et al., 2000b).Finally, for this study, AFO owners were assumed toperform manure applications themselves rather thanhiring a custom operator to do it.

The P Rate Scenarios. Due to the disparitybetween manure nutrient ratios and crop agronomicnutrient ratios, manure applications at the recom-mended N agronomic rate of receiving crops often

means an overapplication of P. Thus, while the Nrate scenario is preferred to higher manure applica-tion rates, it still results in substantial overapplica-tion of P in most cases. The P rate scenarios provideimproved manure P management while at the sametime conserving manure N. Two alternative defini-tions of the P rate were simulated.

The first alternative option to the baseline requiresmanure application at a rate referred to as the HighP rate. With the High P rate, manure applicationrates are calculated such that manure inorganic Psupplies all crop P requirements. The second alterna-tive scenario calls for manure application at what wewill call the Low P rate, a more stringent option inwhich manure applications are calculated such thatmanure total P supplies all crop P requirements. TheHigh P rate is a compromise to the Low P rate andmay help in situations where the land requirementsfor the Low P rate impose severe burdens on produc-ers, and soils are not overly saturated with P. TheHigh P rate is usually about twice the Low P rate.Generally, manure application at a P-based rate alsonecessitates commercial fertilizer N supplementationon these fields because of the disparity between themanure N ⁄ P ratio and the N ⁄ P ratio of nutrientsneeded for most crops.

Other Baseline and Scenario Specifications

The application rates specified for all three scenar-ios were simulated for both liquid and solid manurefields. All scenarios were simulated for a 30-year per-iod so as to account for different weather regimes andthe dynamic patterns of crop histories reflected in thecropping systems. As P application rates are lowerthan N rates for most cases, solid manure also needsto be applied over greater acreage under the P-ratescenarios. Furthermore, commercial fertilizer usewould change from one scenario to another. For eachscenario, manure was applied at the same frequencyand timing that commercial fertilizer applications aremade on receiving land areas, typically three timesannually, based on predominant nutrient applicationpractices in Texas (USDA, 1995; Pratt et al., 1997;McNitt et al., 1999).

As mentioned previously, some AFOs do not haveenough land on their farms to utilize manure for cropneeds. On these farms, solid manure remaining afterappropriate land application is hauled to the closestavailable landowner who is willing and has adequateland to receive the manure. Hauling costs accruing tothe AFO operator are in direct proportion to the dis-tance to the receiving land owner. For this study, itwas estimated that the average hauling distance is5 miles and that solid manure hauling expenses



would entail a cost of US$1.83 ⁄ ton for the first mile,and an additional cost of US$0.12 per ton-mile forhauling distances in excess of 1 mile. This haulingcost was based on substantial data collected during arecent (2000-2006) manure hauling and compostingproject in the north central Texas dairy productionregion, which was funded by several Federal andState agencies. The hauling cost used here is alsosimilar to data used in other studies that report haul-ing costs between US$0.10 and US$0.13 per ton-milefor solid manure (Pelletier et al., 2001; Aillery et al.,2005; Keplinger and Osei, 2005). It was assumed thatonly solid manure would be hauled off the farms.Liquid manure would be applied close to the confine-ment areas on land farmed by the livestock operator,although some of this land may be leased from anadjacent land owner.

On some AFOs, such as open lot and freestall dair-ies, some portion of manure is handled in liquid formand collected in lagoons or waste storage ponds. Theeffluent from these lagoons or ponds is then irrigatedon liquid manure application fields. In addition toassuming an N agronomic rate for manure applica-tions in the baseline, it was also assumed that irriga-tion facilities for liquid manure handling were sizedto apply lagoon effluent on just the amount of landarea required to receive the effluent at the recom-mended N agronomic rate. Thus, under the P ratescenarios, irrigation facilities on these farms wouldbe expanded. This expansion generally constitutes agreat portion of the cost of moving from an N to a Pagronomic rate on farms that irrigate wastewater onmanure application fields.

Finally, it was assumed that AFO operators wouldnot purchase or lease additional land for solid man-ure application. However, in situations where addi-tional acreage is needed for liquid manureapplication, it was assumed that AFO owners wouldpurchase additional land for that purpose and thatthey would harvest the grain or forage grown on theleased area. In the latter case, additional grain or for-age harvested from the leased land area would haveimpact on livestock feed costs and grain or foragesales. These impacts were determined within the live-stock nutrition module of FEM. The livestock nutri-tion module incorporates an optimization processsimilar to the procedure used by nutritionists todetermine livestock rations based on nutrientrequirements, livestock inventories, feed prices, andother feed characteristics.

Limitations

Besides the usual limitations encountered whenusing computer simulation models, a few limitations

are associated with this study. First, the distributionof AFOs used for this study was based on a statisticalclustering of hypothetical farms. Thus, while theentire distribution of representative farms is similarto the distribution of actual farms (Osei et al., 2004),distributions of specific farm types or sizes do notnecessarily mimic the actual distributions of specificfarm types or sizes in all cases. Second, it wasbroadly assumed that manure cannot be sold to gen-erate additional income for farmers. In reality, whilemost livestock farms in Texas cannot generateincome from manure sales, there are a few areaswithin the State, particularly in low-density CAFOareas, where some income can be generated frommanure sales. Regional differences in the selling priceof manure were not incorporated in this study due tolack of readily available information. Finally, otherminor differences among farms, such as variations inlivestock stocking rates and differences in liquid man-ure application equipment were not accounted for inthis study due to resource constraints and lack ofdata, and also because it was the opinion of theauthors that their impacts on the results of the pres-ent study were relatively negligible. Future analyseswill also use more refined data on the differences incrop yields and commodity prices across the countiesin Texas.

RESULTS AND DISCUSSIONS

The results presented here are aggregate summa-ries across all AFOs within the state, and were calcu-lated as weighted averages across all representativefarms in the respective categories, as explained ear-lier. For instance, aggregate results by subregionwere obtained as weighted averages across all repre-sentative farms within each subregion. The weightsrefer to the number of farms each simulated (repre-sentative) farm represents, as determined from thestatistical clustering process. For APEX results theland area involved in simulating each representativefarm was also used as a weighting factor for aggre-gating the results. APEX results represent edge-of-field values of sediment and nutrient forms.

Impacts on Environmental Indicators

APEX simulations predict that aggregate sedimentlosses from AFOs in Texas for the N baseline scenariowould average about 3.3 tons ⁄ ha per year, whileaverage total N and total P losses are estimated to beabout 16 and 6 kg ⁄ ha per year, respectively (Table 2).



The APEX results are based on the simulation of all780 representative farms. The average sediment andnutrient loss values vary substantially from one sub-region to another. Subregions 4, 5, and 7 are esti-mated to have much higher average levels ofsediment and nutrient losses than most of the othersubregions. The relatively high per hectare sedimentand nutrient losses associated with these three subre-gions are largely corroborated by the average precipi-tation values shown in Figure 1. Subregions 5, 7, 1,and 4 have the highest precipitation levels in theState in that order. The impacts of the two P-basedscenarios on total N and total P are also shown inTable 2 for each subregion and for the entire state.

The High P scenario results in reductions in sedi-ment, nitrate, ortho-P and total P losses for the entirestate, although these reductions are small in magni-tude (Table 2). Sediment losses are projected todecline by about 0.1 tons per hectare or 3% from thebaseline value, nitrate losses by 0.08 kg ⁄ ha or lessthan 1% from the baseline, and ortho-P losses by0.11 kg ⁄ ha or about 5% on average across the entirestate. However, no reductions are indicated fororganic N, organic P, and total N, and the simulationresults suggest that the levels of these three indica-tors may actually rise on average across the entirestate. The impacts of the High P scenario also differfrom one subregion to another.

TABLE 2. Simulated 30-Year Average Values of Environmental Indicators and Associated P Scenario Impacts by Subregion.

SubregionSediment

(t ⁄ ha)Organic N

(kg ⁄ ha)Organic P

(kg ⁄ ha)Nitrate(kg ⁄ ha)

Ortho-P(kg ⁄ ha)

Total N(kg ⁄ ha)

Total P(kg ⁄ ha)

BaselineEntire State 3.3 9.2 3.8 6.4 2.1 15.7 5.91 2.8 5.4 2.3 5.5 1.5 10.9 3.82 0.3 1.0 0.5 5.1 0.4 6.1 0.93 2.7 7.0 2.5 3.1 1.3 10.1 3.94 8.6 22.5 10.5 29.0 5.6 51.5 16.15 12.6 44.2 17.2 13.0 10.0 57.1 27.26 1.9 4.5 2.4 5.4 1.1 10.0 3.57 11.2 16.7 7.2 11.8 2.8 28.5 10.08 0.5 2.2 0.8 7.6 1.1 9.7 1.99 1.5 6.7 3.1 5.6 1.5 12.3 4.6

10 1.5 5.5 2.4 16.1 1.5 21.7 3.911 0.0 0.0 0.0 1.7 0.0 1.8 0.0

Impacts of High P and Low P Scenarios: Changes From Baseline Scenario

High P ImpactEntire State )0.12 0.64 0.05 )0.08 )0.11 0.56 )0.061 )0.14 0.95 0.12 )0.01 )0.09 0.95 0.042 )0.01 0.33 0.05 )0.07 )0.03 0.26 0.023 )0.11 0.75 0.09 )0.07 )0.06 0.68 0.034 )0.47 3.19 0.64 )0.70 )0.12 2.49 0.525 )0.35 0.28 )0.25 )0.37 )0.53 )0.08 )0.786 )0.01 0.40 0.04 )0.09 )0.06 0.31 )0.017 )1.28 2.40 )0.09 0.19 )0.29 2.59 )0.388 )0.03 0.11 0.00 0.35 )0.05 0.46 )0.059 )0.03 1.14 0.29 0.02 )0.09 1.16 0.19

10 )0.05 0.26 0.02 )0.03 )0.10 0.23 )0.0811 0.00 0.01 0.00 0.23 0.00 0.25 0.00

Low P ImpactEntire State )0.60 1.50 )0.32 0.24 )0.50 1.74 )0.821 )0.67 2.48 0.04 0.45 )0.33 2.93 )0.292 )0.05 0.68 0.06 0.26 )0.12 0.94 )0.063 )0.60 1.42 )0.04 )0.04 )0.30 1.37 )0.344 )1.81 7.15 0.19 )1.14 )1.52 6.01 )1.335 )1.77 1.68 )2.13 )0.25 )2.13 1.43 )4.266 )0.13 0.84 )0.40 0.09 )0.37 0.93 )0.767 )4.12 4.12 )1.18 1.85 )0.64 5.96 )1.828 )0.16 0.32 )0.10 2.41 )0.23 2.73 )0.349 0.24 3.06 0.45 0.52 )0.41 3.58 0.04

10 )0.30 0.95 )0.14 1.19 )0.39 2.13 )0.5311 0.00 0.04 0.01 1.36 0.00 1.41 0.01



Of the two alternative scenarios, the Low P sce-nario is expected to show greater benefit for total Ploss reduction. This expectation is supported by thesimulation results. Sediment and all P indicators areprojected to decline from baseline values when theLow P scenario is implemented and the magnitudesof the reductions are greater than indicated for theHigh P scenario (Table 2). Simulation results fromAPEX suggest a decline in average total P losses of0.8 kg ⁄ ha or about 14% from the baseline value of5.9 kg ⁄ ha. The greatest absolute reductions in total Plosses are in Subregions 4, 5, and 7, the same subre-gions with the highest total P losses. The greatestpercentage reductions (not shown in the table) areprojected for Subregions 5, 6, 7, and 8.

The environmental impacts of the two alternativescenarios are also shown in Table 3 by farm type,along with baseline values. Most AFOs simulated forthis study were beef operations and they are alsoindicated to have the highest levels of most indica-tors, as well as the highest absolute reductions underthe P scenarios. Dairy farms are also projected toexperience sizable reductions in per hectare total Plosses, a reduction of about 1.2 kg ⁄ ha from the base-line value or about 30% on average when the Low Prate is in effect.

Table 4 summarizes the environmental impacts byfarm size. The highest per hectare total N and total Plosses under the baseline are associated with thelarge AFO size group, the lowest per hectare total P

losses are indicated for the very small size group, andthe lowest per hectare total N losses are associatedwith the small size group. The small AFO size groupis also projected to show the greatest average perhectare reductions in total N and total P losses underthe High P scenario, as well as the greatest per hect-are total P reduction under the Low P scenario. Thevery large AFOs are associated with the highest perhectare total N reduction under the low P scenario.The differences in impacts across the AFO sizegroups are confounded by other factors such as cropland uses, climate, and soil type.

Impacts on Economic Indicators

Table 5 shows the economic impacts of the threealternative application rates by subregion and for theentire state. The table shows the average values ofnet farm returns, net farm returns per AU, deprecia-tion and interest, fertilizer and other crop inputs,manure spreading and other crop operating expenses,solid manure hauling cost, and percent of solid man-ure hauled off the farm. The first set of values shownin the table are baseline values obtained as weightedaverages across all farms within each subregion. Netfarm returns values reported here are the result ofdeducting all explicit and implicit costs from totalrevenues. Negative net returns values are shown inTable 5 for some subregions because the economic

TABLE 3. Simulated 30-Year Average Values of Environmental Indicators and P Scenario Impacts by Farm Type Categories.

Farm TypeSediment

(t ⁄ ha)Organic N



Ortho-P(kg ⁄ ha)

Total N(kg ⁄ ha)

Total P(kg ⁄ ha)

BaselineBeef 2.9 14.6 5.0 9.2 2.9 23.8 7.9Broiler 2.9 3.4 2.7 8.8 2.0 12.1 4.7Dairy 1.5 7.9 2.2 10.8 1.7 18.7 3.9Layers 3.9 3.6 2.8 8.2 2.3 11.8 5.1Sheep 2.3 5.9 2.5 5.5 2.1 11.5 4.6Swine 3.1 5.1 2.7 7.4 2.2 12.5 4.9


High P ImpactsBeef 0.00 )1.66 )0.62 )0.45 )0.38 )2.11 )0.99Broiler 0.00 )0.03 )0.02 )0.02 )0.02 )0.04 )0.04Dairy )0.17 0.22 )0.18 )0.49 )0.36 )0.28 )0.53Layers 0.00 0.01 )0.01 )0.01 )0.02 0.00 )0.02Sheep )0.04 0.03 )0.04 )0.17 )0.12 )0.14 )0.16Swine )0.29 1.70 0.03 0.12 )0.14 1.81 )0.11

Low P ImpactsBeef )0.07 )4.09 )1.60 )0.16 )0.87 )4.25 )2.48Broiler )0.02 0.20 )0.04 0.00 )0.04 0.20 )0.09Dairy )0.17 )1.36 )0.62 )0.62 )0.62 )1.98 )1.24Layers )0.04 0.16 )0.02 0.00 )0.05 0.16 )0.07Sheep )0.20 0.61 )0.22 )0.15 )0.30 0.46 )0.52Swine )0.31 1.39 )0.12 0.22 )0.27 1.61 )0.39



simulations were based on input and output pricesapplicable to only one year, the most recent year ofdata available, rather than a multi-year average.

The results from FEM simulations indicate thatabout 8% of solid manure is hauled off the averageAFO in Texas when manure is applied at the rec-ommended N agronomic rate of receiving crops.Under the baseline, net farm returns is almostUS$41,600 per farm per year, fixed costs of farmfacilities and equipment (depreciation and interest)average almost US$98,000, fertilizer and other cropinput expenses are about US$40,000, and other cropoperating expenses, including manure spreading,averaged about US$36,000 per farm per year. Theeconomic indicators vary markedly from one subre-gion to another. Most prominent among the varia-tions is the percent of solid manure hauled off thefarms, which is about 39% in Subregion 3, but lessthan 10% for each of the other subregions. Subre-gion 3 is home to some of the most concentrateddairy and beef cattle operations in the State. Subre-gion 6, which is projected to have the second high-est solid manure haul off percentage is home to anumber of large beef feedlots. No off-farm manurehauling is indicated for Subregion 11. Percent ofsolid manure hauled off is primarily a function ofmanure nutrient production on the farms and theamount of crop nutrient demand, which in turndepends on the amount of land available for manureapplication.

The next set of values in Table 5 show the impactsof the High P scenario on the selected economic indi-cators. The impacts are shown as the change in costs

or returns, expressed as US$ ⁄ farm, for each indica-tor, except for percent of solid manure hauled off,which is expressed as the simple difference in percentof solid manure hauled off between the baseline andthe alternative scenario.

On average, the High P rate scenario is projectedto cost over US$1,800 per farm, though the averagefor each subregion is projected to range from a costincrease of over US$6,200 per farm in Subregion 2 toa net profit increase or cost decrease of US$33 perfarm in Subregion 9. Three primary factors areresponsible for the economic impacts. The P rate sce-nario conserves nutrients, particularly phosphate andpotash fertilizer nutrients, resulting in lower fertil-izer costs. On the other hand, manure applicationover greater land areas under the P scenario resultsin higher manure spreading costs, reflected in higherdepreciation and interest, and manure spreading andother crop operating expenses. Finally, solid manurehauling costs increase because many farms do nothave enough land to apply manure at the recom-mended P agronomic rate. Under the High P sce-nario, the percent of solid manure hauled off isindicated to increase by almost 4%, from 8 to 12%,for the average AFO in Texas.

The last set of numbers in the table show theimpacts of the Low P application rate scenario on theselected economic indicators. The Low P applicationrate is projected to cost the average farm aboutUS$4,200 per year relative to manure application atthe recommended N agronomic rate. Depreciation andinterest expenses are projected to increase by overUS$2,800 per farm, manure spreading and other crop

TABLE 4. Simulated 30-Year Average Values of Environmental Indicators and P Scenario Impacts by Farm Size Categories.

Farm SizeSediment

(t ⁄ ha)Organic N



Ortho-P(kg ⁄ ha)

Total N(kg ⁄ ha)

Total P(kg ⁄ ha)

BaselineVery small 3.2 12.4 4.3 9.9 2.5 22.4 6.8Small 2.0 12.6 4.4 8.2 2.7 20.8 7.1Medium 3.2 14.6 5.5 10.5 3.3 25.1 8.8Large 3.6 17.3 6.4 10.3 3.6 27.7 10.0Very large 4.3 14.6 4.8 6.4 2.9 21.0 7.6


High P ImpactsVery small )0.09 )0.06 )0.13 )0.20 )0.19 )0.25 )0.32Small 0.09 )2.85 )0.97 )0.65 )0.51 )3.50 )1.47Medium )0.17 )0.50 )0.48 )0.49 )0.43 )0.99 )0.90Large )0.06 )0.65 )0.37 )0.42 )0.35 )1.07 )0.72Very Large )0.12 )2.51 )0.72 )0.65 )0.52 )3.16 )1.25

Low P ImpactsVery small )0.15 )1.62 )0.81 0.26 )0.56 )1.36 )1.37Small 0.03 )5.38 )1.90 )0.63 )0.95 )6.01 )2.84Medium )0.21 )3.23 )1.68 )0.56 )1.09 )3.78 )2.77Large )0.06 )3.21 )1.67 )0.56 )1.12 )3.77 )2.79Very Large )0.39 )5.40 )1.73 )0.91 )0.98 )6.31 )2.72



operating expenses are projected to increase by almostUS$3,600 per farm per year, and fertilizer expensesare projected to decline by over US$2,700 per farm peryear. The percent of solid manure hauled off the typi-cal AFO is projected to increase by almost 9%, thoughthis would range from less than 3% in Subregion 2 toover 34% in Subregion 8. In terms of overall costs, Sub-regions 3, 6, and 7 account for the greatest share of thecost incurred under the P-based manure applicationrates, with each of these regions incurring a costincrease of between roughly US$14 million andUS$16 million annually.

The economic impacts are also shown by farm typecategory in Table 6. Simulated impacts of the twoalternative scenarios relative to the N rate status quoare shown for beef, dairy, swine, broiler, layer, andsheep AFOs in Texas. Percent of solid manure hauledoff is projected to increase under the P rate scenariosprimarily for dairy, boiler, swine, and sheep AFOs.Most beef and layer operations appear to have ade-quate land by and large for manure application atthe recommended P agronomic rate of receivingcrops. The increase in percent of solid manure hauledfrom the AFOs is only 1% on average for beef and

TABLE 5. Simulated Average Annual Values of Economic Indicators and Impacts of P Rate Scenarios by Subregion.

Subregion

Net FarmReturns

Net FarmReturnsPer AU

Depreciationand

Interest

Fertilizerand Other

Crop Inputs

Manure Spreadingand Other Crop

Operating ExpensesHauling

CostHauled

Off

AggregateNet

Returns

US$ Per Farm % Million US$

BaselineEntire State 41,583 176 97,682 40,505 35,892 770 8.1 572.21 3,976 24 74,821 34,040 17,330 723 1.6 11.22 167,361 525 129,949 56,976 52,663 1,407 1.1 36.33 16,336 63 109,561 38,986 29,623 2,111 38.8 36.64 95,481 323 137,352 58,712 79,338 32 1.6 11.95 92,866 441 108,487 47,612 64,089 81 0.7 193.66 )794 )2 143,213 51,930 34,756 1,263 7.3 )1.17 89,754 360 74,035 19,373 7,320 539 1.6 179.38 29,619 183 61,054 22,668 13,543 61 2.5 10.19 13,197 71 174,617 96,918 165,626 297 2.0 7.6

10 45,524 255 73,455 40,885 31,042 140 1.5 86.811 )9,784 )50 64,363 19,888 10,183 0 0.0 )0.1


High P ImpactEntire State )1,813 )8 1,071 )1,163 1,318 472 3.8 )24.951 )630 )4 602 )1,457 866 517 5.0 )1.782 )6,284 )20 1,386 )891 4,784 812 1.4 )1.363 )2,994 )12 1,728 )1,352 1,677 734 5.4 )6.704 )789 )3 435 )419 648 124 0.4 )0.105 )1,178 )6 423 )220 776 186 1.8 )2.466 )3,320 )7 2,494 )2,190 2,651 337 2.3 )4.787 )2,866 )11 1,367 )1,253 1,546 873 5.2 )5.738 )3,006 )19 2,108 )2,446 2,959 368 9.1 )1.039 33 0 606 )1,244 369 229 1.6 0.02

10 )529 )3 268 )481 543 175 2.5 )1.0111 )1,627 )8 1,840 )2,709 2,495 0 0.0 )0.02

Low P ImpactEntire State )4,850 )21 2,875 )2,712 3,584 872 8.7 )66.741 )1,979 )12 1,806 )3,685 2,901 759 9.3 )5.582 )8,812 )28 3,847 )2,723 6,145 1,192 3.8 )1.913 )6,569 )25 3,932 )3,497 4,552 1,198 10.7 )14.714 )893 )3 1,040 )1,624 1,189 288 2.8 )0.115 )3,743 )18 1,652 84 1,603 384 4.8 )7.806 )10,419 )24 7,500 )7,620 9,450 1,042 5.6 )15.017 )8,063 )32 3,155 )999 3,467 1,701 13.6 )16.118 )7,943 )49 4,840 )4,546 6,480 1,145 34.1 )2.719 )672 )4 1,775 )2,685 1,084 486 7.3 )0.39

10 )1,222 )7 740 )1,215 1,387 265 4.1 )2.3311 )5,117 )26 4,709 )6,752 6,638 522 15.4 )0.08



layer operations. In general, the overall impacts onnet farm returns mirror the impacts on solid manurehaul off to a great extent. The greatest per farm costimpacts are associated with broiler, dairy, sheep, andswine operations. Beef and layer AFOs are expectedto see very little economic impact on average perfarm.

In terms of aggregate cost increases, beef and dairyfarms are projected to incur the vast majority of thecost increases associated with the P-based manuremanagement practices. In spite of the low averagecost per farm associated with beef farms, altogetherthey account for over US$33 million of theUS$66 million total annual cost of the Low P ratescenario when implemented on Texas AFOs, largelybecause beef operations represent a great proportionof the farms classified as AFOs in this study. Dairyfarms are projected to incur over US$17 millionannually primarily because of high per farm costs.Broiler farms are projected to incur about US$10 mil-lion annually for the Low P scenario, relative to thebaseline. A key difference between the different farmtypes simulated relates to differences in manurenutrient characteristics. However, regardless of thefarm type, the main factors underlying the economicimpacts are farm size and availability of on-farm cropnutrient demand for the manure nutrients produced,and the economic impacts are expected to vary from

one farm to another within these farm type groupslargely in response to these parameters.

Table 7 shows the economic impacts of the scenar-ios by farm size category. Simulated baseline valuesfor net farm returns indicate higher returns for lar-ger sized operations, as expected. Similarly, percentof solid manure hauled off is higher for the largersized operations, which is also commonly expected.Very large farms are projected to haul almost 30% oftheir solid manure off the farm when the manure isapplied at the recommended N agronomic rate ofreceiving crops. However, very small farms are indi-cated to haul off a greater percent of solid manure(12%) than small farms (1%). This suggests thatsmall farms have greater on-farm crop nutrientdemand, because of larger available land areasfarmed and ⁄ or higher crop nutrient uptake per hect-are, than very small farms.

The economic impacts of the High P and Low Pscenarios are greater for larger AFOs than smallerones (Table 7). However, the impacts in dollars perAU are not correspondingly greater, particularly forthe Low P scenario. For both scenarios, the averagecost per farm more than doubles from one size groupto the next larger size group, but average AU perfarm also increases. For very small farms, net farmreturns are projected to decline by over US$200 perfarm under the High P rate and by close to US$1,000

TABLE 6. Simulated Average Annual Values of Economic Indicators and Impacts of P Rate Scenarios by Farm Type Categories.

Farm Type

Net FarmReturns


Depreciationand

Interest

Fertilizerand Other

Crop Inputs



CostHauled

Off

AggregateNet

Returns


BaselineBeef 24,439 121 91,236 44,245 40,664 256 8 264.51Broiler 53,613 94 61,632 14,566 8,314 1,318 11 26.22Dairy 153,161 422 144,997 25,268 16,777 4,534 9 236.17Layers 52,616 171 60,100 29,281 16,489 10 2 18.68Sheep 40,932 306 68,665 26,681 24,120 142 3 12.24Swine 57,036 198 242,310 55,673 42,712 566 5 14.37


High P ImpactBeef )953 )5 773 )864 983 62 1 )10.32Broiler )5,332 )9 1,764 )238 2,763 1,043 8 )2.61Dairy )5,960 )16 2,481 )3,150 2,483 3,120 21 )9.19Layers )230 )1 143 )268 351 5 1 )0.08Sheep )4,586 )34 3,983 )4,488 4,811 280 3 )1.37Swine )5,473 )19 1,743 )945 2,982 1,694 23 )1.38

Low P ImpactBeef )3,088 )15 2,446 )2,781 3,172 251 4 )33.42Broiler )20,495 )36 7,781 )43 10,523 2,234 25 )10.02Dairy )11,477 )32 3,917 )2,768 3,543 4,722 34 )17.70Layers )1,099 )4 722 )907 1,255 29 5 )0.39Sheep )11,284 )84 7,909 )7,739 9,759 1,355 21 )3.37Swine )7,262 )25 2,476 )1,165 4,004 1,946 32 )1.83



under the Low P rate. The impacts are more thanquadruple for the small size AFOs. The High Pimpact on net farm returns is a decline (cost) of overUS$1,400 per farm for small AFOs while the Low Pimpact for the same size group is a cost increase ofalmost US$4,000 per farm. The highest cost increaseper farm is associated with very large AFOs: adecline in net farm returns of over US$38,000 underthe High P rate and about US$90,000 under the LowP rate scenario.

Percent of solid manure hauled off AFOs alsoshows significant variations across size groups underthe High P and Low P scenarios. Very little change isindicated for the smallest AFOs: only a 2% increasein solid manure haul off is expected for very smallAFOs when manure application rates are changedfrom the N rate to the Low P rate. On the otherhand, very large AFOs are projected to see a 37%increase in the percentage of solid manure hauled offtheir farms when the Low P rate scenario is in placewhen compared to the N rate status quo. Under theLow P scenario the largest AFOs are expected to hauloff roughly two-thirds of all the solid manure gener-ated on their farms. Large AFOs are also projected tosee similar increases in solid manure haul off. Interms of aggregate costs across all AFOs within eachfarm size category, the small and very large AFO cat-egories account for much greater shares of the overallcost of the High P and Low P scenarios than anyother farm size category. The large share of the small

farms category is due to the number of farms in thatcategory (5,472 of the 13,760 total). The share of thevery large farm size category is because of the highper farm cost as the number of farms in the verylarge AFO category is relatively small (only an esti-mated 210 farms in this study).

SUMMARY AND CONCLUSIONS

A macro modeling system was developed for thepurpose of evaluating large-scale policies and prac-tices related to agriculture and the environment.The modeling system, named CEEOT-MMS, enablesresearchers to assess the environmental impacts ofproposed policies for agriculture more effectively.CEEOT-MMS is used to perform macro-level assess-ments through micro simulations involving represen-tative farms and summations of farm-level results tothe desired levels of aggregation. APEX and FEM arerespectively the environmental and economic modelsused in CEEOT-MMS.

In the present study, CEEOT-MMS was applied toevaluate the impacts of alternative livestock manureapplication scenarios when implemented on all AFOsin the State of Texas. A baseline scenario was evalu-ated, which was defined to be manure application atthe recommended N agronomic rate on AFOs. Two

TABLE 7. Simulated Average Annual Values of Economic Indicators and Impacts of P Rate Scenarios by Farm Size Categories.

Farm Size

NetFarm

Returns


Depreciationand Interest

Fertilizerand Other

Crop Inputs



CostHauled

OffAggregate

Net Returns


BaselineVery small 10,113 137 63,028 48,400 30,874 92 12 68.5Small 46,283 254 90,521 31,452 37,408 53 1 253.3Medium 117,863 210 163,818 39,877 47,652 2,485 12 128.5Large 173,173 113 317,704 30,427 45,630 10,189 25 37.1Very large 404,219 105 834,628 35,267 87,330 22,828 30 84.9


High P ImpactVery Small )238 )3 300 )577 503 12 1 )1.6Small )1,432 )8 1,200 )1,555 1,227 404 5 )7.8Medium )3,947 )7 1,990 )2,431 2,859 1,258 8 )4.3Large )14,918 )10 6,866 )4,175 7,492 4,230 15 )3.2Very Large )38,122 )10 11,898 )189 15,702 9,182 16 )8.0

Low P ImpactVery Small )953 )13 1,226 )2,094 1,796 25 2 )6.5Small )3,961 )22 2,914 )3,318 3,227 787 11 )21.7Medium )10,655 )19 5,256 )4,207 6,801 2,353 25 )11.6Large )37,485 )24 14,482 )4,148 18,459 7,802 39 )8.0Very Large )90,341 )24 30,872 2,355 38,691 15,668 37 )19.0



alternative scenarios were then simulated andcompared to the baseline: manure application to sup-ply receiving crop P needs through inorganic manureP (the High P rate), and manure application to sup-ply crop P needs through total manure P (the LowP rate).

Previous watershed studies indicated that theHigh P and Low P rate scenarios would lead to suc-cessively better environmental outcomes and succes-sively more expensive cost outcomes to AFO owners.The present CEEOT-MMS application supports thisassertion. The High P and Low P rate scenarios areprojected to lead to 0.1 and 0.8 kg ⁄ ha reductions intotal P losses from land areas farmed by AFOs. Theseimpacts differ by farm type and by ecological region,but the average impacts reflect a weighted sumacross all farms designated as AFOs using criteriathat are in line with USDA and USEPA definitionsfor AFOs. Though total N losses are projected toincrease under the P-based application rates, this isnot a major concern here as P losses from farmland isthe primary water quality issue in relation to TexasAFOs.

Economic impacts reveal that the Low P ratewould cost producers on average about US$4,800per farm per year or about US$21 per AU, whilethe High P rate would cost them about US$1,800per farm per year, about US$8 per AU. The overallcost of the High P and Low P rate scenarios acrossall Texas AFOs is estimated to be about US$25 mil-lion and US$66 million annually. Most of the costwould be borne by the small AFO category of farms,because of their sheer numbers, and the very largeAFO category because of high per farm costs forthese farms. In terms of farm types, beef operations,because there are so many of them, and dairyfarms, because of high per farm cost increases,account for the vast majority of the costs associatedwith the P-based application rates.

The cost increase related to the P-based manureapplication rate scenarios is due to the usual reasons:increased solid manure application costs, increasedliquid manure irrigation expenses, increased solidmanure hauling costs, and increased crop operatingexpenses that were not offset by proportionatedecreases in livestock feed costs and commercial fer-tilizer expenses. The economic impacts would changesomewhat if the scenarios were applied under differ-ent conditions such as higher or lower fuel and ferti-lizer costs, different land values and rental rates, andhigher or lower animal unit densities in livestockimpacted areas. However, the costs and environmen-tal impacts represented in this paper reflect what canbe expected in general when AFO operators arerequired to apply manure under more strict guide-lines based on crop P uptake.

ACKNOWLEDGMENTS

Support for this study was provided by the U.S. EnvironmentalProtection Agency, Office of Policy, Economics and Innovation andOffice of Wastewater Management under cooperating agreementnumber CR X7-83121301-1. The opinions expressed in this docu-ment remain the sole responsibility of the authors and do not nec-essarily express the position of the Texas Institute for AppliedEnvironmental Research or the U.S. Environmental ProtectionAgency.

LITERATURE CITED

Aillery, Marcel, Noel Gollehon, and Vince Breneman, 2005. Techni-cal Documentation of the Regional Manure Management Modelfor the Chesapeake Bay Watershed. Technical Bulletin Number1913. Economic Research Service, USDA, Washington, D.C.

Arnold, J.G., R. Srinivasan, R.S. Muttiah, and J.R. Williams, 1998.Large Area Hydrologic Modeling and Assessment; Part I: ModelDevelopment. Journal of the American Water Resources Associ-ation 34(1):73-89.

American Society of Agricultural Engineers (ASAE), 1995. ManureProduction and Characteristics. ASAE Data: ASAE D384.1,ASAE Standards. American Society of Agricultural Engineers,St. Joseph, Michigan.

Babcock, B.A., J. Wu, T. Campbell, P.W. Gassman, P.D. Mitchell,T. Otake, M. Siemers, and T.M. Hurley, 1997. RAPS 1997:Agriculture and Environmental Outlook. Center for Agriculturaland Rural Development, Iowa State University, Ames, Iowa.

Gassman, P.W., E. Osei, A. Saleh, and L.M. Hauck, 2002. Applica-tion of an Environmental and Economic Modeling System forWatershed Assessments. Journal of the American WaterResources Association 38(2):423-438.

Gassman, P.W., E. Osei, A. Saleh, J. Rodecap, S. Norvell, andJ.R. Williams, 2006. Alternative Practices for Sediment andNutrient Loss Control on Livestock Farms in Northeast Iowa.Agriculture, Ecosystems and Environment 117:135-144.

Goldstein, A.L. and G.J. Ritter, 1995. A Performance-Based Non-Point Source Regulatory Program for Phosphorus Control inFlorida. In: Animal Waste and the Land-Water Interface,K. Steele (Editor). Lewis Publishers ⁄ CRC Press, LLC, BocaRaton, Florida, pp. 429-440.

Kellogg, Robert L., Charles H. Lander, David C. Moffitt, and NoelGollehon, 2000. Manure Nutrients Relative to the Capacityof Cropland and Pastureland to Assimilate Nutrients: Spatial andTemporal Trends for the United States. Publication No. nps00-0579. U.S. Department of Agriculture, Natural Resources Conser-vation Service, Economic Research Service, Washington, D.C.

Keplinger, K., J. Abraham, L. Hauck, and A. Saleh, 2005. Economicand Environmental Assessment of Phosphorus Control Practicesfor Broiler Operations. Environmental Practice 7(2):1-13.

Keplinger, K. and E. Osei, 2005. Using Metamodeling to SummarizeEnvironmental and Economic Simulations: Application to TexasAnimal Feeding Operations Using CEEOT-MMS Output. Tech-nical Report TR0511. Texas Institute for Applied EnvironmentalResearch, Tarleton State University, Stephenville, Texas.

McNitt, J., R. Jones, E. Osei, L. Hauck, and H. Jones, 1999.Livestock and the Environment: Precedents for Runoff Policy.Report No. PR9909. Texas Institute for Applied EnvironmentalResearch, Tarleton State University, Stephenville, Texas.

Mozaffari, M. and J.T. Sims, 1994. Phosphorus Availability andSorption in an Atlantic Coastal Plain Watershed Dominated byAnimal-Based Agriculture. Soil Science 157(2):97-107.

Osei, E., A. Bekele, B. Du, R. Rose, L. Hauck, A. Saleh, J. Houser,K. Keplinger, and L. Beran, 2003a. CEEOT-MMS: A Macro



Modeling System for Environmental Assessment. Report No.TR0303. Texas Institute for Applied Environmental Research,Tarleton State University, Stephenville, Texas.

Osei, E., B. Du, A. Bekele, L. Hauck, A. Saleh, and A. Tanter,2004. CEEOT-MMS: A Macro Modeling System for Environmen-tal Assessment, Application to Texas Animal Feeding Opera-tions. Technical Report TR0409. Texas Institute for AppliedEnvironmental Research, Tarleton State University, Stephen-ville, Texas.

Osei, E., P.W. Gassman, L.M. Hauck, R.D. Jones, L.J. Beran,P.T. Dyke, D.W. Goss, J.D. Flowers, and A.M.S. McFarland,2003b. Economic Costs and Environmental Benefits of ManureIncorporation on Dairy Waste Application Fields. Journal ofEnvironmental Management 68(1):1-11.

Osei, E., P.W. Gassman, L.M. Hauck, S. Neitsch, R.D. Jones,J. McNitt, and H. Jones, 2003c. Economic and EnvironmentalImpacts of Pasture Nutrient Management. Journal of RangeManagement 56:218-226.

Osei, E., P.W. Gassman, R.D. Jones, S.J. Pratt, L.M. Hauck,L.J. Beran, W.D. Rosenthal, and J.R. Williams, 2000b. Economicand Environmental Impacts of Alternative Practices on DairyFarms in an Agricultural Watershed. Journal of Soil and WaterConservation 55(4):466-472.

Osei, E., P. Gassman, and A. Saleh, 2000a. Livestock and the Envi-ronment: A National Pilot Project; Economic and EnvironmentalModeling Using CEEOT. Report No. PR0002. Texas Institutefor Applied Environmental Research, Tarleton State University,Stephenville, Texas.

Osei, Edward, Philip Gassman, and Ali Saleh, 2000c. Livestock andThe Environment: A National Pilot Project: CEEOT-LP Model-ing for the Upper Maquoketa River Watershed, Iowa: TechnicalReport. Report No. RR0001. Texas Institute for Applied Envi-ronmental Research, Tarleton State University, Stephenville,Texas.

Osei, E., P.G. Lashminarayan, S. Neibergs, A. Bouzaher, and S.R.Johnson, 1995. Livestock and the Environment: A NationalPilot Project; The Policy Space, Economic Model, and Environ-mental Model Linkages (Livestock Series Report 4). Staff Report95-SR 78, Center for Agricultural and Rural Development, IowaState University, Ames, Iowa.

Pelletier, Beth Ann, James Pease, and David Kenyon, 2001. Eco-nomic Analysis of Virginia Poultry Litter Transportation. Bulle-tin 01-1. February 2001. Virginia Agricultural ExperimentStation. College of Agriculture and Life Sciences, Virginia Poly-technic Institute and State University, Blacksburg, Virginia.

Potash and Phosphate Institute (PPI), 2002. Plant Nutrient Use inNorth American Agriculture. PPI ⁄ PPIC ⁄ FAR Technical Bulletin2002-1. Potash and Phosphate Institute, Norcross, Georgia.

Pratt, S., R. Jones, and C.A. Jones, 1997. Livestock and the Envi-ronment: A National Pilot Project; Expanding the Focus; PolicyOptions; CEEOT-LP. Report No. PR9603. Texas Institute forApplied Environmental Research, Tarleton State University,Stephenville, Texas.

Saleh, A., J.G. Arnold, P.W. Gassman, L.M. Hauck, W.D.Rosenthal, J.R. Williams, and A.M.S. McFarland, 2000. Applica-tion of SWAT for the Upper North Bosque River Watershed.Transactions of the American Society of Agricultural Engineer-ing 43(5):1077-1087.

SAS Institute Inc., 1983. SAS Technical Report A-108. SAS Insti-tute Inc., Cubic Clustering Criterion, Cary, North Carolina. 56pp.

SAS Institute, 1999. The SAS System for Windows. Version 8.SAS Inst., Cary, North Carolina.

U.S. Department of Agriculture (USDA), 1995. 1995 Cropping Prac-tices Survey, Unofficial USDA Data Files. http://usda.mannlib.cornell.edu/usda/nass/AgriPricSu//2000s/2006/AgriPricSu-07-21-2006_revision.pdf, accessed September 26, 2006.

U.S. Department of Agriculture and U.S. Environmental ProtectionAgency (USDA and USEPA), 1999. Unified National Strategyfor Animal Feeding Operations. Washington, D.C. March 9,1999. http://www.epa.gov/npdes/pubs/finafost.pdf, accessed May9, 2003.

U.S. Department of Agriculture-National Agricultural StatisticsService (USDA-NASS), 2006. Agricultural Prices: 2005 Sum-mary. USDA National Agricultural Statistics Service, July2006. http://usda.mannlib.cornell.edu/usda/nass/AgriPricSu//2000s/2006/AgriPricSu-09-15-2006.pdf, accessed September 25,2006.

U.S. Department of Agriculture-National Resources ConservationService (USDA-NRCS), 2003. Costs Associated With Develop-ment and Implementation of Comprehensive Nutrient Manage-ment Plans; Part I – Nutrient Management, Land Treatment,Manure and Wastewater Handling and Storage, and Record-keeping. Washington, D.C. http://www.nrcs.usda.gov/Technical/NRI/pubs/cnmp1.html, accessed September 25, 2006.

U.S. Environmental Protection Agency (USEPA), 2003. FederalRegister, February 12, 2003, pp. 7176-7274: 40 CFR 9,122,13, &412.

Williams, J.R., J.G. Arnold, and R. Srinivasan, 2000. The APEXModel. BRC Report No. 00-06, Texas A&M, Blackland Researchand Extension Center, Temple, Texas.



Impacts of Alternative Manure Application Rates on Texas Animal Feeding Operations: A Macro Level...

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