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a Department of Agronomy, 210 Waters Hall, University of Missouri, Columbia, MO 65211
b Department of Agricultural Economics, 223 Mumford Hall, University of Missouri, Columbia, MO 65211
c Department of Biological Engineering, 207 Agricultural Engineering Building, University of Missouri, Columbia, MO 65211
d Department of Animal Science, 133 Animal Sciences Center, University of Missouri, Columbia, MO 65211
* Corresponding author (loryj{at}missouri.edu).
Received for publication July 16, 2003.
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ABSTRACT |
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 TOPNOTES ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Abbreviations: AU, animal unit
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INTRODUCTION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Increased concentration of animals on fewer farms has effects on manure management. Larger farms can obtain economies of scale reducing manure management costs (Boland et al., 1999; Forster, 1997; Roka et al., 1995). However, larger operations may have less control of land receiving manure. Gollehon et al. (2001) estimated that 50% of the nutrients generated on swine farms needed to be applied off farm but larger operations only had sufficient land for 30% of manure nutrients. They estimated that 27% of potential CAFOs controlled sufficient land for N-based manure management in 1997 compared with 78% of all animal-feeding operations. Lower costs improve the ability of farmers to pay for land application of manure but less control of the acres receiving the manure make it more difficult to recover the value of the nutrients in the manure.
Anaerobic lagoons and slurry structures are the dominant manure storage systems for U.S. swine operations. Lagoons are clay-lined earthen structures with the capacity for anaerobic treatment of manure solids. Typically, lagoons are not agitated and the effluent is pumped from the surface for application to crops. Slurry systems are under-building pits, external cement or glass-lined tanks, or earthen pits with no permanent treatment volume. Pit manure is agitated before land application and typically all manure (solids and liquids) entering the storage is applied at least annually.
Previous research indicated that the manure handling system affects the net value of manure management on the farm. Fleming et al. (1998) estimated that Iowa swine operations using lagoons had a net manure management cost while operations using slurry pits had a net manure management gain. Roka et al. (1995) predicted that the least-cost option for Iowa farmers was slurry operations whereas the least-cost alternative for North Carolina operations was anaerobic lagoons. Boland et al. (1999) predicted that deep pit slurry manure handling was the least-cost method for 150-sow operations but a high nutrient loss lagoon system was the least-cost method for larger operations. Factors determining the optimum handling system for particular locations include operation size, productivity of the cropland receiving manure, and climate (Roka et al., 1995).
Economic and management forces shaping farmer decisions on where to apply manure and the value of manure to the farm operation continue to be debated. Before the advent of commercial fertilizers, Stevenson et al. (1926) observed that manure "is regarded as a waste material to be disposed of in the easiest way possible. Frequently no care is taken to prevent losses before it is applied and on many farms fields at some distance from the barns and feed yards are sometimes not manured at all." Debate continues about whether manure is a nutrient resource with value or a waste (Fleming et al., 1998).
Several papers have estimated the breakeven hauling distance of manure used as a fertilizer (Janzen et al., 1999; Lazarus and Koehler, 2002; Araji et al., 2001). The breakeven hauling distance is greater than what is observed on most farms. The results raise the question of why livestock producers are not taking advantage of the opportunity to maximize returns from manure.
Most research on the economics and feasibility of manure management are based on county aggregate data and economic models of agricultural systems. Aggregate data from county summaries can misrepresent the true number of operations having sufficient land for manure application because of non-uniform distribution of crop and animal production in a county (Letson and Gollehon, 1998). Idealized economic models may not capture the full range of conditions in the field or consider important controlling factors.
Our objective was to collect farm-specific information about current manure management practices to (i) estimate current monetary fertilizer value and distribution cost and time and acreage requirements of manure management and (ii) assess the system implications of various changes in management practices to these resources. Diversity of region, size, manure storage system, manure application, and cropping system were sought to understand how different facilities respond to various incentives.
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MATERIALS AND METHODS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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In each state, we met with representatives of the respective Pork Producers Associations and state universities to create a list of the major phases of pork production and sizes of operations that typified pork production in that state. The same group identified farms willing and able to work with us that represented important phases of production, size, and manure handling systems that had been previously identified.
We extensively surveyed individuals representing each selected farm regarding farm characteristics and their management of crops, animals, and manure. The survey collected information about the location of the farm; the number, production phase, and size of swine on the farm; water use in the buildings; N, P, and K concentration in the ration; description of the manure handling and storage system including details of the type, size, cost, and age of all manure storages and manure handling equipment; estimates of annual manure volume; manure test results; location of fields receiving manure; streams, wells, and other sensitive areas near the land application areas; description of crop rotations including yield goals; equipment used for manure application; method(s) used for manure application; and estimates of the time required for manure application. Farmers were also asked for soil test P levels for each field. All information was not available on all farms.
The collected data were used to develop the input and validate the results of a simulation model used to estimate time requirements, land requirements, and economic indicators associated with manure management for each farm.
Mechanistic Model
The mechanistic simulation model contained the following three modules: (i) a manure storage design module and nutrient generation module, (ii) a manure land-application module, and (iii) an economic simulation of swine production module (Massey et al., 2000).
The storage design model estimated volume of manure or effluent pumped annually from the manure storage facility based on county weather data, animal numbers and sizes reported by the farmer, and the geometry and type of the manure storage facility present. Nutrients excreted by the animals were estimated in the nutrient generation model based on the quantity of nutrients fed to the animals, efficiency of nutrient retention (Table 1), and losses during storage. Lagoon effluent was assumed to contain 5 to 10% of N excreted and 4 to 5% of excreted P; pit slurry was assumed to contain 70% of excreted N and 100% of excreted P. Organic N was assumed to be 35% of total N in slurry pits and 20% of total N in lagoons. These values were based on typical values for these systems (Lorimor et al., 2000) but were adjusted when farm-specific data for the majority of farms indicated that book values were not appropriate. Results of the predicted manure volume and nutrient concentration were compared with manure test results and farmer estimates of manure volume as a check on accuracy of volume and manure nutrient concentration estimates used in the analysis. Typically, we used model estimates of mean volume of manure pumped annually and the farmer manure test result to estimate nutrient generation. In some cases farmer manure test results were rejected when low manure test nutrient concentrations implied improbably high animal nutrient efficiency based on model results. Feed-based estimates of nutrient content of the manure were used when no manure test data were available.
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Fertilizer need for each field for each year of a 4-yr crop rotation was determined based on farmer-reported yield goals. Nitrogen need of non-legume crops was calculated based on the state-specific fertilizer recommendations. Phosphorus and K fertilizer need of all crops and N fertilizer rate for legumes was calculated based on removal capacity of the crops (Table 2). This approach underestimates the value of manure on fields that are deficient in P and K and overestimates the value of manure on fields with very high or excessive soil test P and K levels.
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Time required to distribute manure was calculated using a mechanistic budgeting approach. Manure distribution time is composed of setup time, transport time, and land application time. Farmer-supplied data were used, where available, to estimate time parameters such as travel speed and pipe layout time. Where no farmer-supplied data were available, a time motion study performed at the University of Missouri in 1999 (unpublished data) was used to estimate time parameters. Storage setup activities included positioning and setup of manure pumps and agitators used to unload the manure. It was assumed that each storage required a 2-h setup time. If the storage was agitated before pumping, agitation time was added to setup time based on the farmer estimate. Transportation time for tanker technology is a function of the distance from storage to field. Our study assumed a road travel speed of 4.5 m s–1 when the tank is pulled by a tractor and 13.4 m s–1 when mounted on a truck. Within-field travel speed (travel from the road to the point within the field where manure is applied) was assumed to be 2.2 m s–1 for tractor-pulled spreaders and 2.7 m s–1 for truck-mounted tanks.
The time required for setup of distribution pipes for technologies such as irrigation and dragline was viewed as transportation time. Lay down and pickup time for aluminum pipe was assumed to require three persons and was estimated to take 18.7 h km–1 of pipe. Lay down and pickup time for flexible hose was assumed to require two persons and was estimated to take 3.2 h km–1. In traveling gun systems, an additional setup time of 1 h per pull was included in transportation time to move the irrigation equipment to the next pull lane and to extend the traveling gun to the end of the pull lane. In dragline systems, an additional setup time of 30 min was added for each additional pull from a pivot point for moving the tractor and hose from the end of the first pull to the beginning of the second.
The producer's choice of discharge rate, application swath width, and application rate established field travel speed. When discharge rate was not reported we used the highest mechanically attainable discharge rate for the equipment for the reported field speeds.
Application rates based on N need were based on the plant-available N content of the manure. Manure plant-available N (also defined as the fertilizer N equivalent of the manure) was estimated by assuming 62% of organic N was available to the crop; availability of ammonium N was assumed to be 60% for surface-applied manure and 100% for injected manure. Manure P and K were assumed to be 100% equivalent to other P and K fertilizer sources. A spreadsheet simulation was used to calculate the manure application rate and distribute manure to prioritized fields until all manure was distributed.
Manure nutrients were given value if they were needed for crop production (Lazarus and Koehler, 2002; Roka et al., 1995). Nitrogen had value of $0.44 kg–1 when applied to nonlegume crops such as corn and wheat but not to legume crops that can fix their own N such as soybean. Phosphorus and K were valued at $1.40 and $0.35 kg–1, respectively, based on the crop removal capacity of the crop(s) between manure applications. A $12.30 ha–1 custom application credit was given any year when manure provided either all of the N or all of the P and/or K needs of a crop because the manure application replaced a commercial fertilizer application expense. No application credits or fertilizer value were given for P or K if the farmer-provided soil tests indicated that soil test levels of P or K were "very high" and no P or K fertilizer was recommended.
The remaining value coefficients estimated by Cross and Perry (1995) were used for estimating ownership costs of depreciation, interest (7% yr–1), tax and insurance (2% yr–1), and repairs. Fuel cost was set at $0.26 L–1. A labor rate of $10 h–1 was charged regardless of the season when manure is distributed or the total number of hours needed for manure distribution. Manure management costs also included the costs of soil and manure sampling and of managing a nutrient management plan. The investment costs for the manure storage facility were not considered a manure management cost.
Ten producers used a custom manure applicator rather than personally owned and operated equipment. An hourly custom rate was charged to the number of manure setup and application hours estimated by the model. The custom rate, as reported by producers, ranged from $45 to $94 h–1 (mean $65 h–1). Higher hourly rates were associated with tractor-pulled injection systems and lower hourly rates with truck-mounted surface applicators.
The economic simulation module estimated revenues and costs associated with pork production (Boessen and Zulovich, 2003). Manure fertilizer value was computed as an income to pork production. No crop revenue was considered in the module. The economic indicators of net manure value and manure cost to sales ratio were estimated for each farm. The net manure value is the fertilizer value of the manure applied less the cost of land application. This indicator was used to evaluate different manure storages and business tenures on farm profitability. The cost to sales ratio was used as a benchmark to measure the importance of manure management on cost control. The USEPA (2003) used cost to sales ratio as an indicator of feasibility of various policy changes in its analysis of revised regulations for concentrated animal feeding operations.
Individual operations were designated as either "lagoon operations" or "slurry operations" based on the type of storage system they used. In one case a slurry operation also had a significant amount of manure in solid form.
The number of animals on U.S. swine operations was based on USDA National Agricultural Statistics Service (2000) data with 1 AU equal to 2.5 animals. On farms in this study AU was calculated as 1 AU equal to 2.5 pigs greater than 25 kg or 10 pigs less than or equal to 25 kg.
General linear models and linear and nonlinear regression procedures (SAS Institute, 1987) were used to evaluate the effect of farm attributes on selected indicators of the feasibility of manure management. In regression model building the model with the highest coefficient of determination (r2) was selected. Mean separation was evaluated with the Waller option at the 0.05 probability level (SAS Institute, 1987).
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RESULTS AND DISCUSSION |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Nitrogen-Based Management
Slurry operations managed more than four times more N in their manure (19494 vs. 4060 kg N) although they had fewer animal units among the surveyed operations (Table 5). The lower quantity of N per animal unit in the manure on lagoon operations is the result of higher losses of N during storage in lagoon systems.
The 39 operations needed an average of 52 ha (127 acres) of land for N-based manure application (median = 39 ha) or 57% of the controlled land suitable for manure application (median = 34%). Seven of the 39 operations (18%) required more land for N-based manure application than was owned or rented by the operation. This ratio is similar to national county aggregate statistics for confined animal feeding indicating that 22% of operations have insufficient land for N-based manure management (Gollehon et al., 2001). The 39 operations generated an average of 138 kg ha–1 manure N (median = 77 kg ha–1). Slurry operations required more land for manure application (Table 5), a result of the higher manure N amounts on these farms.
Pennsylvania was the only state for which mean land requirements for manure application exceeded the acres controlled by the operation (Table 5). All seven operations that relied on uncontrolled land for manure application were in Pennsylvania. In the other states farms annually used 35% of their controlled land for manure application. Among the factors that permitted Pennsylvania to export manure were a form of manure (slurry) with sufficient value to be transported, a demand for crop nutrients without a bias against manure supplied nutrients, and custom haulers that could facilitate the trade between animal feeding operation and crop producer.
North Carolina operations had the highest mean N application rate (Table 5). Six of eight North Carolina farms applied some or all their manure to bermudagrass (Table 3), a crop with high N utilization potential. All North Carolina farms surface-applied lagoon effluent maximizing losses of N during land application. These factors, combined with high N losses during manure storage in lagoon systems, explained the ability of North Carolina operations to concentrate a significantly larger number of animals on fewer acres than other states (Table 5).
The 39 operations annually spent an average of 129 h on manure application activities (median = 109). Operations required an average of 10.5 min AU–1 in land application activities annually (median = 8.5). There was no significant difference in manure application time per animal unit between lagoon and slurry operations (P = 0.96) although time per animal unit was more variable for lagoon than slurry operations (range for lagoons = 2–62 min AU–1; slurry = 5–21 min AU–1). Larger operations were more efficient at applying manure, requiring less time per animal unit for manure application activities (Fig. 3) .
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Mean annual cost of manure application was $7908 per operation (median = $7219), $10.49 AU–1 (median = $9.21), or $2.32 m–3 ($8.80 per 1000 gallons; Table 7). Costs for manure application averaged 2.7% of gross revenue on the 39 operations. Manure applied to controlled land had an annual mean fertilizer value of $5259 per operation (median $3330) or $6.56 AU–1 (median = $4.17) if farmers properly credited the N value of the manure and needed to replace P and K removed by the crop on acres they controlled. Potential manure value, when applied based on crop N need, exceeded all costs associated with land application on 14 of 39 farms if (i) farmers were paid for the fertilizer value of their manure when it was applied on other people's land and (ii) crop nutrient removal capacity was used to estimate P and K fertilizer value of crops.
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To capture manure fertilizer value, farmers need to reduce rates of N, P, and K from other purchased sources on land receiving manure and then harvest a crop with value from the land. Value can be realized as grain or hay from crops and meat and milk from pastures. On 100% of the lagoon and 62% of the pit-slurry operations all manure was being applied to owned or rented ground. A high proportion of the manure being applied to land controlled by the farmer makes it more likely that farmers are capturing at least some of the manure value under the current system.
Manure management costs are a greater financial burden on contract operations than independent operations. Independent and contract operations were evenly distributed among lagoon and slurry operations; half of slurry operations and 12 of 20 lagoon operations were independently operated. Independent operations had a lower manure management cost to sales ratio than contract operations (1.3 vs. 5.2%; P = 0.01). The cost to sales ratio is an indicator of the ability of farmers to pay for a specific activity. No independent operation had a cost to sales ratio greater then 3% for total manure costs whereas cost to sales ratio exceeded 5% on 44% of contract operations.
The higher cost to sales ratio for contract operations reflects that manure management is a larger part of contract growers' total responsibility. Independent growers have responsibilities for all activities associated with pork production whereas contract growers have a more limited set of activities for which they receive compensation. Any mandatory changes to manure management costs will have a larger effect on contract producers than independent producers.
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CONCLUSIONS |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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While there is evidence that larger operations were associated with higher animal densities on controlled land, operation size was a poor predictor of animal density. Larger operations experience economies of scale in manure management costs. They are able to spread the cost of owning and operating manure equipment over more animals. Independent producers have greater flexibility in manure management decisions than do contract growers because manure management expenses represent a smaller portion of their gross revenue. Slurry operations had greater potential value and greater challenges from land application of manure compared with lagoon operations. Many slurry operations potentially can recover manure application costs through fertilizer value of the manure but slurry operations needed more land per AU and were more dependent on applying manure to land not owned by the operation.
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ACKNOWLEDGMENTS |
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NOTES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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REFERENCES |
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TOPNOTESABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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