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© 2005 Plant Management Network. Managing Tillage, Crop Rotations, and Environmental Concerns in a Whole-Farm Environment Steven W. Martin, Assistant Professor/Extension Economist, Delta Research and Extension Center, Stoneville, MS 38776; John R. C. Robinson, Associate Professor/Extension Economist, Texas Agricultural Experiment Station, Weslaco, TX 78596; Fred T. Cooke, Jr., Professor, Delta Research and Extension Center, Stoneville, MS 38773; David Parvin, Professor, Department of Agricultural Economics, Mississippi State University, Mississippi State 39762 Corresponding author: Steven W. Martin. smartin@ext.msstate.edu Martin, S. W., Robinson, J. R. C., Cooke, F. T., Jr., Parvin, D. 2004. Managing tillage, crop rotations, and environmental concerns in a whole-farm environment. Online. Crop Management doi:10.1094/CM-2004-0426-01-RV. Introduction Tillage practices are a topic of much conversation among row crop producers due to environmental requirements and as a method to reduce production costs. Crop rotations are also an important topic to many row crop producers. Extension personnel as well as crop advisors are often asked to make recommendations on various tillage practices or row crop mixes (i.e., crop rotations). There are environmental and agronomic reasons for implementing certain tillage practices and crop rotations. Additionally, there are economic reasons. Often published university crop budgets are used to make economic recommendations. However, in a whole-farm scenario, per-acre enterprise budgets do not always tell the complete story. For example, a no-till budget may appear more expensive than a conventional tillage budget based on increased herbicide costs, even though future capital investment costs (i.e., disks, cultivators, etc) are reduced. On the other hand, a no-till budget may appear less expensive where capital investment expense (i.e., fixed costs) is substantially reduced for one enterprise unit, even though the capital investment is still needed on the farm to raise other crops in the farm’s rotation. These factors can vary in degree of magnitude across farm sizes. A whole-farm mixed-integer programming model can evaluate all these alternatives simultaneously, including environmental soil loss requirements. This article presents an example of the results obtainable with this type model. Additionally, this report analyzes and summarizes the major economic implications of adopting reduced tillage practices. Background In the mid-South, as elsewhere, cotton growers have sought technology that will lower costs and/or increase productivity. For example, alternative tillage systems continue to be the focus of economic analysis for potential improvements in economic efficiency (2,3,7). The Mississippi Delta is a region with both dryland and irrigated cotton production, although limited moisture is not a major impetus for adoption of reduced tillage systems. Rather, the main motivation for reduced tillage is cost savings and potentially greater profitability. Soil conservation is also not a major issue vis-a-vis productivity, although some soils/crops/tillage systems in the Delta may result in enough erosion to violate Natural Resources Conservation Service (NRCS) soil conservation compliance constraints. Since the 1990s, researchers at Mississippi State University and the Delta Research and Extension Center have collaborated with growers to compare alternative tillage systems. Parvin et al. (3) reported that no-till cotton systems may not differ significantly in yields or major variable cost categories. Their study showed differences in labor, fuel, lube, and repairs, based on per-acre budget comparisons, and suggested that additional research was needed to capture the benefits of equipment disinvestment. Method of Analysis Partial budgeting comparisons are adequate to assess the potential tradeoffs between input costs, e.g., higher herbicide cost and lower machinery cost for reduced tillage. However, budgeting approaches cannot easily show how the most profitable choice of crop/tillage system might vary with increasing size of operation. The profit maximizing crop mix/tillage system would also likely be influenced by the pattern of acquisition of "lumpy" resources like full-time labor and equipment. For example, Robinson and Falconer (5) showed that over a range of farm sizes, the profit maximizing crop mix, tillage system, and discrete equipment investment varied to produce a mix of greater/lesser levels of reduced tillage on the same representative farm. For this study, a whole-farm, mixed integer programming model was developed to answer growers’ questions such as: • What is the most profitable crop/tillage combination at different acreage sizes? • What are the actual economies of size (in dollars per acre) in row crop farming? • How many acres are required to be economically viable from farming alone (i.e., without decoupled payments)? • What is the best acreage size to minimize or optimize full-time labor? • What are the profitability trade-offs, including farm program eligibility, between growing conventional versus reduced tillage cotton? This study compared conventional, reduced tillage, and no-till systems for the Mississippi Delta. Most of the necessary parameters (e.g., yields, costs, equipment, field operations, by crop) were obtained from published budgets (1): • cotton, conventional, sandy soils, nonirrigated (MSU Delta Budget 4) – this was adapted for irrigation using MSU parameters; • cotton, reduced tillage, sandy soils, nonirrigated (MSU Delta Budget 5) – this was adapted for irrigation using MSU parameters, and assuming two irrigations; • cotton, no-till, sandy soils, nonirrigated (MSU Delta Budget 7); • UNR cotton, no-till, heavy soils, nonirrigated (MSU Delta Budget 12 ); • corn, conventional and reduced, irrigated and nonirrigated (MSU Delta Budgets 20, 21, 22 and 23); • soybeans, conventional and reduced, irrigated and nonirrigated, April & May planted, heavy soils (MSU Delta Budgets 13 14, 15, 16, 17 and 18); • sorghum, reduced tillage, light and heavy soils, nonirrigated (MSU Delta Budget 25). In terms of field operations, the conventional systems typically involved subsoiling, disking, field cultivating, hipping, and in-season cultivation (1). The reduced tillage systems substituted herbicides for heavy pre-plant soil preparation operations and perhaps also for in-season cultivation, while no-till systems substituted herbicides for all tillage operations (1). Case Study Farm In addition to the published budget parameters, information from on-farm studies by Parvin et al. (3,4) and Cooke et al. (2) was used to refine per-acre budget parameters for cotton. Lastly, to endow this study with a realistic whole-farm setting, a representative large mid-Delta farming operation was modeled to reflect its actual farm program acreage, farm program payment yields, baseline acreage and crop mix, soils, and baseline equipment investment. This farm has 3,000 acres of lighter soil "cotton land" and 1,500 acres of heavier "soybean ground." About half of the lighter soil land is irrigated, along with 1,000 acres of the heavier ground. The farm currently produces cotton on the lighter soils, in rotation with either corn (for irrigated land) or grain sorghum (for dryland), in a 4Ycotton:1Ygrass rotation. Model Formulation A mathematical programming model was developed to reflect the general set of choices and trade-offs faced by a farm manager. These include choices about tillage systems that potentially influence NRCS and thus eligibility for farm program payments. Other more basic choices include how to allocate land and lumpy capital resources (e.g., equipment, full-time labor) to the most profitable activity. Complete model specification and GAMS codes are available from the authors (GAMS Development Corporation, Washington, DC). A conservation compliance (soil loss) parameter was formulated using revised universal soil loss (RUSL) equation estimates of soil erosion under each crop/tillage combination (Table 1). If soil loss was more than the allowable amount using NRCS t-factors and t-values, then government payments were eliminated. According to NRCS (J. S. Parkman, personal communication, 2003), the typical t-value in the study area is 5 tons of annual soil loss per acre. That is, a typical soil can experience a loss of 5 tons/acre annually and not have productivity affected. Table 1. Estimated annual soil loss for alternative tillage:crop:rotation combinations, in tons/acre.ab
a Estimates are regional NRCS estimates using the revised universal soil loss equations (NRCS 2003). b Abbreviations: contin. = continuous; rtn. = rotation; soy = soybean; sorg. = sorghum. Economies of Size Several points can be made regarding the results. The most obvious is the clear economies of size in row crop farming. Figure 1 and Table 2 show that the model selected the default land rental option (i.e., $35/acre) from 100 through 1,100 acres. This result is similar to large cotton operations in Texas (5). Since this model reflects the true pay-as-you-go approach, these results should not be that different from any financially feasible machinery acquisition and farm size. Beyond 1,200 acres the size is large enough to sufficiently spread the fixed cost of one full-time operator and an equipment complement. As farm size increases from this point, the fixed costs are spread over more acres, reducing total per-acre costs and increasing net returns per acre (Table 2).
Table 2. Selected results for acreages between 100 and 4,500 acres.a,b
a Net Returns per acre, including government payments. b Abbreviations: dry = dryland; irr = irrigated; rt = reduced tillage; - = take out the operation that was previously selected. Reduced Tillage The crop mix is a rotation of reduced tillage cotton, both irrigated and dryland, on the light soils, with the required proportion (4 acres cotton, 1 acre grass crop) of irrigated reduced tillage corn and dryland reduced tillage sorghum. In no case were conventional tillage systems selected, even though the per-acre budget returns to land and machinery were higher for some conventional crops. The major reason for no conventional tillage systems in the solution was lack of compliance with an NRCS soil loss constraint, which was linked to inclusion of government payments. As different crop rotations were selected as acres increased, fixed costs and the labor associated with additional equipment also contributed to the non-selection of crops requiring more tillage. Note that the Total Return values in Table 2 and Figure 1 do not reflect government payments. The reduced tillage crop mix was in compliance with the NRCS soil loss constraint, therefore government payments of about $45/acre were included in overall profits. The net returns (NR) per acre shown in Table 2 reflect these decoupled payments. Given the current high prices used in this model, there were no counter-cyclical payments (CCP) nor loan deficiency payments (LDP) included in the government payments. Lower output prices would certainly mean increased CCP. However, since these payments are decoupled, they would not affect the analysis in the sense of one tillage system being preferred to another. The Commodity Credit Corporation (CCC) loan program effectively puts a floor under output prices at their respective loan rates. If all output prices are set at their respective loan values, the model simply "shifts to the right." In other words, Table 2 and Figure 1 would leave land in the rental default until acreage reached approximately 1,400 acres. Results/returns beyond 1,400 acres are similar to those shown for the higher output prices in Table 2 and Figure 1. Interestingly, CCP payments are required to be included in the model in order to allow "farming" to be "profitable" and thus keep the model from selecting the default rental option for all acreages. Marginal Effects The realistic interactions of lumpy resource acquisition, rotation constraints, and increasing farm size produce some interesting results for the profit-maximizing mix of crops, tillage systems, full-time hires, and machinery investment. The profit-maximizing crop mix contained irrigated and dryland reduced tillage cotton, rotated with either reduced tillage corn or reduced tillage sorghum, on light soils and also April-planted, reduced tillage soybeans on heavy ground rotated with sorghum. The apparent pattern is that additional acres are allocated to cotton until available labor and machinery constraints become binding. At 1,500 acres, additional land is allocated to dryland corn, and some dryland cotton land is also substituted by dryland corn. At 2,400 acres, another picker/module/boll buggy is purchased, and the solution reverts to the original reduced tillage crop mix. The discrete periods of machinery and/or labor acquisition are clearly visible as upward shifts in costs and returns in Figure 1. At higher acreages, the same effects are observed although no-till cotton (which also uses less labor and machinery capacity) and land rental are also in the mix. Thus the impact of increasing economies of size is evident. In addition, the model suggests an unconventional but useful and intuitive prescription for growers that do not have enough time for additional acres of their current crop/tillage systems. Provided there is no additional investment requirements, those growers might be able to reduce fixed costs and increase returns/acre by adding residual amounts of more reduced tillage crops. A final point is to emphasize that these results cannot be obtained using partial budgeting. Since tillage innovations are fundamentally about machinery and labor optimization, it is important to consider the scale of operation before making conclusions about what crop mix and tillage system is the most profitable. Case Study Farm Implications The profit maximizing resource allocation for the 4,500 acre case study farm is shown in the last row of Table 2. The reduced tillage crop mix is validated by the actual crop mix practiced on this farming operation. However, the model prescribes a much smaller equipment complement than that actually owned in this farming operation. The reason for the discrepancy is probably due to risk. While the model calculates the profit maximizing equipment investment based on average performance rates, average available field days, etc., farm managers are also influenced by the risk of equipment breakdowns and reduced field days due to bad weather. This risk is managed, quite rationally by risk averse managers, by owning more machinery capacity than is needed on average. However, it should be remembered that additional equipment can also increase risk through increased capital requirements. To further customize this analysis for the case study farm, the machinery performance rates would need to be inflated to calibrate the model outcomes to those reflecting the farm manager’s level of risk. However, all things being equal, these results clearly demonstrate that reduced tillage cropping systems are more profitable than conventional tillage. Acknowledgments This project was supported through Cotton Incorporated, and the Mississippi State University Land Grant System. Literature Cited 2. Cooke, F. T., Jr., Andrews, G., and Martin, S. W. 2003. First year no-till "three regimes." Pages 443-449 in: Proceedings Beltwide Cotton Conferences. Nashville, TN. January 6-10, 2003. Nat. Cotton Counc., Memphis, TN. 3. Parvin, D. W., Cummings, S., Cooke, F. T., Jr., and Martin, S. W. 2002. Three years experience with limited seedbed/chemical tillage production in Mississippi, 1999-2001. In Proceedings Beltwide Cotton Conferences. Atlanta, GA. January 8-12, 2002. Nat. Cotton Counc., Memphis, TN. 4. Parvin, D. W., Cummings, S., Cooke, F. T., Jr., and Martin, S. W. 2002. Three years experience with no-till cotton production in Mississippi, 1999-2001. In Proceedings Beltwide Cotton Conferences. Atlanta, GA. January 8-12, 2002. Nat. Cotton Counc., Memphis, TN. 5. Robinson, J. R. C., and Falconer, L. L. 2003. Optimal land, equipment and labor allocation under alternative tillage systems in south Texas. Pages 438-442 in: Proceedings Beltwide Cotton Conferences. Nashville, TN. January 6-10, 2003. Nat. Cotton Counc., Memphis, TN. 6. Spurlock, S. R., Buehring, N. W., and Caillavet, D. F. 1995. Pages 1-10 in: Days suitable for fieldwork in Mississippi. Miss. Agric. For. Exp. Sta. Bull. No. 1026. May 1995. Mississippi State University, Mississippi State, MS. 7. Triplett, G. B., Robinson, J. R. C., and Dabney, S. M. 2002. A whole-farm economic analysis of no-tillage and tilled cropping systems. Pages 48-52 in: Making Conservation Tillage Conventional: Building a Future on 25 Years of Research. Proceedings 25 Annual Southern Conservation Tillage Conference for Sustainable Agriculture. Auburn, AL. June 24-25, 2002. E. van Santen, ed. Special Report No. 1, Alabama Agricultural Experiment Station, Auburn, AL. |
