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SPECIAL SUBMISSIONS
Findings from the USDA-sponsored Lake Erie Agricultural Systems for Environmental Quality Project

Effects of Conservation Tillage on the Performance of Lake Erie Basin Farms

Department of Agricultural, Environmental, and Development Economics, The Ohio State University, 2120 Fyffe Rd., Columbus, Ohio 43210

* Corresponding author (forster.4{at}osu.edu)

Received for publication August 12, 2000.

	ABSTRACT

TOP ABSTRACT INTRODUCTION ECONOMIC EFFECTS OF CONSERVATION... ECONOMIC PERFORMANCE OF... CONCLUSIONS AND IMPLICATIONS REFERENCES

 
This paper summarizes research that investigates the effects of alternative farming practices on the performance of Lake Erie basin farms. First, data from a representative panel of about 100 farmers is analyzed to determine how conservation tillage, rotations, and other factors affected farms' economic returns during 1987–1992. Statistical analysis of these data is unable to demonstrate that there is any significant relationship between farming system (i.e., tillage and rotation) variables and farm profitability. Next, a farm-level bioeconomic simulation model is used to analyze the effects of conservation tillage adoption on farm profitability, farm size, and pollutant emissions. Findings are that tillage system, farm size, and crop selection are determined jointly and may substantially improve economic performance of farms. Conservation tillage enables farms to be larger and more specialized, and as a result, farm profitability improves. Statistical analysis of farm panel data is unable to show the effect of tillage on profitability because it neglects to account for endogeneity of variables (or joint effects of tillage, size, crop selection, and performance) in production decisions.

Abbreviations: ROA, return on assets

	INTRODUCTION

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THE MOST STRIKING change in Lake Erie basin farming practices has been the adoption of conservation tillage. In the Maumee and Sandusky River watersheds, which account for more than 50% of the cropland draining into Lake Erie from both the United States and Canada, conservation tillage practices were used on more than half of the crop acreage in 1995. A decade earlier, they were used on only about 10% (Richards et al., 2002). With such rapid voluntary adoption of conservation tillage, it seems that adoption of these practices must have offered farmers substantial economic benefits. This analysis addresses the economic impacts of conservation tillage practices on farms in these two watersheds. Two approaches are used to examine farms' economic performance using various rotation and tillage systems. First, a statistical approach is used to investigate the effects of rotation and tillage system on farm economic performance, using data from a sample of representative farms in the Lake Erie basin. Second, a bioeconomic simulation model is developed that links alternative farming practices with production, profits, pollutants, and farm size. Representative farms are modeled to simulate typical farming systems in the western Lake Erie region of Ohio. Performance of representative farms is assessed under alternative tillage technologies. Conclusions from these two approaches (statistical analyses and simulation modeling) differ, and an explanation for these differing conclusions is presented.

	ECONOMIC EFFECTS OF CONSERVATION PRACTICES USING FARMERS' ACCOUNTING DATA

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Objectives
The purpose of this analysis is to investigate factors affecting two measures of performance, relative overall efficiency and return on assets (ROA), for a representative sample of northwestern Ohio farms located in the Lake Erie basin. Both measures of performance are used in the literature, and both are used in this study to test for consistency in results. Factors hypothesized to be associated with differences in relative overall efficiency and ROA include farming practices, capital structure, and farm operator characteristics. Of particular interest are the effects of farming practices, such as conservation tillage and crop rotations, which are capable of reducing agricultural pollution in Lake Erie and its tributaries.

Several studies have focused on economic aspects of alternative farming systems. Smolik and Dobbs (1991) investigated the economics of alternative farming systems in an experimental agronomic setting using enterprise budgets. They compared the use of an alternative system (no commercial fertilizer; no herbicides and moldboard plow; oat [Avena sativa L.]–alfalfa [Medicago sativa L.]–soybean [Glycine max (L.) Merr.]–corn [Zea mays L.] rotation), a conventional system (moldboard plow, corn–soybean–spring wheat [Triticum aestivum L.]), and a ridge-till system (corn–soybean–spring wheat). Five years of results indicated that the alternative system had the highest average net income over costs, excluding management costs. Foltz et al. (1993), using simulation and linear programming techniques, found that introducing alfalfa into an eastern Corn Belt rotation reduced net returns by 38%. They concluded that reducing corn acreage by incorporating an alfalfa-based rotation might be an environmentally sound policy; however, it would be costly to farmers.

Numerous studies (as exemplified by Williams, 1988 and Klemme, 1985) have shown alternative tillage practices (conservation tillage, no-till, and ridge-till) to be more profitable than conventional systems (moldboard plow and disk twice) under a wide range of operating environments. However, Featherstone et al. (1991) reported no statistically economic differences among tillage systems (conventional, ridge-till, and no-till) used on a sample of farms. One of the concerns of Featherstone et al. (1991) was that in spite of reported positive attributes of alternative tillage systems, there may be some short-term penalties associated with their adoption.

Model and Data
Return on assets (ROA) and relative overall efficiency are the two measures of performance used to assess the economic effects of alternative farming practices, capital structure, and farm operator characteristics. Return on assets is an accounting-based measure that reflects an economic return to assets deployed in the production process (Blue and Forster, 1997).

The measure of overall efficiency is derived from an expenditure-constrained profit optimization model proposed by Färe et al. (1990). Their technique, data envelopment analysis (DEA), uses observed gross revenues, variable costs, and fixed costs from a sample of firms, to construct a frontier technology that maximizes profit for each firm in the sample. Overall efficiency is defined as the ratio of actual profit to the short-run unconstrained profit derived from the profit optimization model. (For details on how overall efficiency is generated from the expenditure-constrained profit optimization mode, see Färe et al. [1990].) It measures how much a firm's actual profit falls short of a theoretically derived maximum profit because of production choices and expenditure constraints. After efficiency measures are obtained for each farm in the sample, regression analysis is performed to determine which factors are most associated with the relative efficiency measure. The set of Lake Erie basin farms participating in the Ohio Farm Household Longitudinal Study (Stout et al., 1992) for 1987, 1988, 1990, and 1992 are included in the sample. The sample is restricted to farms having gross farm income larger than $40000 in order to represent commercial farming and exclude rural residents with a peripheral interest in agriculture. Demographic, off-farm employment, financial, production, and marketing data were collected each year of the survey. Numbers of farms in the samples totaled 98, 112, 127, and 113 in each of the respective years.

Regression Analysis of Return on Assets and Efficiency Scores
Overall efficiency and return on assets are used as dependent variables in regression models, which included operator and farm characteristics as independent variables. Similar types of analyses have been performed to assess the factors associated with nursing home efficiency (Fizel and Nunnikhoven, 1993), educational efficiency (Lovell et al., 1989), New York dairy farm efficiency (Tauer, 1993), and West Bengal farm efficiency (Ray, 1985).

In this analysis, gross sales and its squared term are used as a measure of farm size. Overall efficiency and ROA are expected to increase as farm size gets larger because of technological and pecuniary economies of scale. The squared term allows results to capture the expected curvilinear relationship between performance and farm size that is caused by economies of scale.

Personal characteristics such as motivation and willingness to accept risk change over the operator's lifetime and may contribute to a life cycle of growth and decline of the farm business (Nalson, 1968). On the other hand, older farm operators may have acquired skills to more efficiently allocate resources to end uses. Thus, in this analysis, age of the farm operator is used as an independent variable in overall efficiency and ROA equations.

Number of years of education possessed by the farm operator may positively influence ROA and overall efficiency because more highly educated producers may be better at evaluating new information, quicker to adopt innovations, and more technically efficient (Asplund et al., 1989; Rogers et al., 1988; Tauer, 1993; Kalirajan and Shand, 1986). Years of education is identified by dummy variables indicating the highest level of education: less than 12 years, 12 years, 12 to 16 years, or more than 16 years.

Larger farm size and improved profits are often accompanied by financial leverage (i.e., increased use of debt in financing farm assets). This may be due to technical change (Shepard and Collins, 1982) or personal characteristics such as motivation, ambition, and willingness to accept risk (Upton and Haworth, 1987). Bravo-Ureta and Pinheiro (1993) demonstrated that credit use has a positive impact on technical efficiency. The use of debt may ameliorate expenditure constraints faced by some firms and thus lead to increased overall efficiency and ROA. In this study, use of debt is measured as the debt to asset ratio.

Information collection and use are important managerial activities. As a farm becomes larger, more management expertise is required. Often this information comes from outside sources via consultants or through the use of computers. Farmers who seek greater amounts of information from numerous sources are more likely to adopt innovations (Feder and Slade, 1984; Asplund et al., 1989). Bravo-Ureta and Pinheiro's (1993) review of efficiency in developing country agriculture verified that information use positively impacts efficiency. Tauer (1993) showed that the use of a more elaborate accounting system improves dairy farm efficiency. In this study, a 0 or 1 dummy variable measures information use with 1 indicating that the farm operator used computers, consultants, or extension agents, and 0 indicating that these information sources were not used.

Rotations and tillage practices were used as explanatory variables to examine the effects of various farming systems on actual and financial efficiency. The array of rotations and tillage practices used on farms in the sample were categorized into four rotations and four tillage systems and represented by three rotation and three tillage system dummy variables. The four rotations are continuous row crop, row crop–small grain, row crop–small grain–pasture or hay, and other. The four tillage systems are conventional tillage (e.g., moldboard plow and disk before planting), chisel-plow tillage (e.g., chisel plow and field cultivate before planting), minimum tillage (e.g., disk before planting), and no-tillage before planting.

Regression Results
Return on Asset Regression
Gross sales and age are the only variables that consistently influenced ROA year to year (Table 1). In three out of the four years, larger farms have statistically higher returns on assets, as expected. In addition, the squared term of gross sales also is significant for all years. In 1987 and 1990 the squared term was negative, indicating that ROA increased at a decreasing rate; in 1990 the squared term was positive, implying that ROA increased at an increasing rate.

Overall Efficiency Regression
Gross sales and capital structure (debt to asset ratio) are the only variables that have consistent effects on overall efficiency. Other variables such as age, education, rotation, and tillage are statistically significant in one particular year, but not all four years.

Larger farms have a higher overall efficiency in 1987, 1990, and 1992. The 1990 regression results indicate that as farm size increased, overall efficiency decreased slightly, reached a minimum for medium-sized farms, and then increased for larger farms.

The strong positive relationship of debt use (debt to asset ratio) with overall efficiency implies that farms using more debt have higher overall efficiency. Again, as suggested earlier, this result suggests that the use of debt ameliorates the effect of expenditure constraints and enhanced overall efficiency.

	ECONOMIC PERFORMANCE OF CONSERVATION PRACTICES USING BIOECONOMIC MODELING (COMPUTER MODEL SIMULATING REPRESENTATIVE FARMS)

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Objectives
The above results imply that tillage systems have no statistical effect on ROA or overall efficiency. However, the conventional wisdom during the past decade seemed to be that conservation tillage was more profitable than conventional tillage. And the proportion of farmers using conservation tillage tended to increase in the past two decades.

To analyze farmers' decision to adopt conservation tillage, a bioeconomic model was developed that linked alternative farming practices with production, profits, pollutants, and farm size. Two geographic sites are modeled to simulate representative farms on flat (i.e., Hoytville [fine, illitic, mesic Mollic Epiaqualf] soils) and sloping (i.e., Blount [fine, illitic, mesic Aeric Epiaqualf]–Glynwood [fine, illitic, mesic Aquic Hapludalf]–Pewamo [fine, mixed, active, mesic Typic Argiaquoll] soil associations) farmland typical of the western Lake Erie region of Ohio. On each of these geographic sites, performance of representative farms is assessed under alternative tillage technologies. Then various scenarios of conservation tillage adoption in a region are compared by extrapolating results of the analysis of representative farms (E.C. Smith, Ohio State Univ., personal communication, 1997).

Model
The bioeconomic model consisted of two components. First, the Erosion Productivity Impact Calculator (EPIC model) is used to simulate crop yields and pollution parameters (Sharpley and Williams, 1990). Second, using production results from the EPIC model, a farm-level integer programming model is used to select the set of profit-maximizing farming practices (i.e., crop rotations, level of fertilizer and pesticide inputs, and farm size) on representative farms. Decision variables in the model are type of farming practice and amount of each practice (e.g., acres of corn, soybean, and wheat using conventional tillage, mulch tillage, or no-till). Constraints to the decision are time availability for field work throughout the year. Each farming practice has labor requirements in particular weeks during the year, and the sum of all labor required in a particular week by all farming practices has to be less than the time available (E.C. Smith, Ohio State Univ., personal communication, 1997; Forster et al., 2000).

Once optimal rotations, crop acreages, and input levels for each representative farm are determined by the integer programming model, the results are used in a regional aggregation of representative farms. These analyses allow comparison of the structural, economic, and environmental impacts brought on by alternative levels of conservation tillage adoption. Structural impacts are measured by changes in the number and average size of farms making up the region as well as by changes in crop production. Economic impacts are measured by changes in the region's revenues and costs. Finally, environmental impacts are measured by changes in individual pollution parameters (i.e., sediment, organic nitrogen, phosphorus, nitrates, and pesticides).

Four regional tillage scenarios are on hypothetical units of 100000 cropland acres. The four scenarios, shown in Table 2, depict alternative rates of conservation tillage adoption. In each of these four scenarios, the three tillage systems were used in varying proportions ranging from predominantly conventional tillage (Scenario 1) to predominantly no-till (Scenario 4).

View this table: [in this window] [in a new window]   Table 4. Environmental comparisons of scenarios, Hoytville region.

Second, in the mulch till– and no till–dominated scenarios, labor and machinery operating costs decreased. In part, this was because labor and machinery costs were linked essentially to the management system's efficiency in field time utilization. Decreases of 2 to 5% in labor costs were realized on a regional scale. The same was true for machinery operating costs, which were 6 to 12% lower for the region in the alternative scenarios.

A large decrease in aggregate machinery fixed costs occurred for the region as tillage shifted from conventional to the three alternative scenarios, especially no-till. First, at the individual farm level, no-till fixed costs were less than conventional and conservation fixed costs. (Machinery fixed costs includes depreciation, interest, insurance, and housing. Machinery is assumed to be 100% owned and recently purchased. All machinery is assumed to be used only on operator's farm, and no custom work is included.) Second, the number of farms in a region decreased by about 10% in the no till–dominated scenario. In short, fixed costs decreased in the three alternative scenarios because of less-expensive implements and the ability of those implements to farm more acres.

Several input costs remained the same or increased slightly from the base scenario to the alternative scenarios. Nitrogen fertilizer use increased in the three alternative scenarios because of increased corn acreage. Transportation, seed, insurance, and interest ("other costs") also increased slightly in the alternative scenarios because of increased corn acreage; however, the magnitude of these changes was not large.

The only cost to substantially increase when moving toward the conservation and no-till systems was herbicide. In the fourth scenario, where no-till acreage comprised 50% of the total, herbicide costs increased by 18% compared with the base scenario. For the sake of brevity, results for simulations on Blount–Glynwood–Pewamo soils are not discussed here, but are presented in Forster et al. (2000). Hoytville soils are more representative of soils in the study region. Results generally were similar in the two soil regions.

In summary, conservation tillage systems improved regional net farm income because of three factors: reduced labor and machinery inputs, crop enterprise mix shifts, and improved economies of scale. On representative farms, time available for field work played a major role in the selection of no-till and mulch tillage as preferred systems. For example, under no-till, field time in spring was used almost exclusively to plant corn and soybean, without having to perform any of the seedbed preparation that conventional tillage requires. This allowed more acreage to be planted under the no-till system. Because more acreage was planted with no-till, fixed machinery costs were spread over a larger volume of production and per unit costs of production declined. Also, labor costs per acre were less with conservation tillage. Higher herbicide costs offset these lower machinery and labor costs to some degree. Total costs per acre are less (0–2%), total revenues per acre are higher (0–5%), and net farm income per acre improves (16–43%) with conservation tillage scenarios.

The second feature of farms using more conservation tillage was that crop enterprise mix changes with the adoption of mulch tillage and no-till systems. Using conventional tillage, the labor requirements of corn and soybean were intensive in the spring and fall. This allowed wheat, which was not as labor intensive during critical spring and fall months, to be grown profitably on some cropland. Wheat acreage sharply declined with the use of no-till and mulch-till systems. Thus, the technological transformation toward conservation tillage translated into larger and fewer farms that were more specialized.

The bioeconomic model results indicated that part of the consequences of adopting conservation tillage practices were that erosion lessened and future soil productivity was enhanced. Potential pollutants that are directly related to soil erosion, such as sediment, organic nitrogen, and total phosphorus loadings also decreased (Table 4). However, some pollution parameters increased (e.g., nitrates and herbicides).

	CONCLUSIONS AND IMPLICATIONS

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The following conclusions can be drawn from this work:

• On average, Ohio farms in the Lake Erie basin exhibited a 54% overall efficiency and 5.25% return on assets for these four years. Their relatively low overall efficiency implies that given their resource base, most northwestern Ohio farms were capable of improving profits.
  
• Farm size generally improved ROA and overall efficiency. Of course, in years when low output prices or drought resulted in financial losses for many farmers, large farms had little, if any, competitive advantage.
  
• Farms having higher debt to asset ratios had higher overall efficiency, which implies that farmers successfully used debt to alleviate financial constraints. However, the use of debt had no effect on return on assets.
  
• Statistical analysis of farm level data was unable to detect significant differences in profitability among tillage systems or crop rotations. These findings imply that those Lake Erie basin farmers adopting crop rotations and conservation tillage practices did so without sacrificing profits or efficiency. However, there were substantial differences in chemical, labor, and machinery expenses among tillage systems. Farmers in the sample using conservation tillage systems reduced labor, fuel, and machinery costs per acre and reduced machinery investment per acre, but increased herbicide costs per acre.
  

If these results are valid and conservation tillage systems offer little if any economic advantages to farmers, what accounts for the increased adoption of these tillage systems? Results from modeling farm-level decision making using the bioeconomic simulation model are that tillage system, farm size, and crop selection are determined jointly. Conservation tillage enables farms to be larger and more specialized, and as a result, farm profitability improves. The multivariate statistical analysis in the previous section was unable to show the effect of tillage on profitability because it was unable to account for endogeneity of variables (or joint effects of tillage, size, and crop selection) in production decisions.

Results from the bioeconomic simulation model indicate that adoption of conservation tillage technologies can be expected to have far-reaching consequences for a region's agricultural economy and environment (Table 5). First, soil erosion lessens and future soil productivity is enhanced. Second, potential pollutants that are directly related to soil erosion, such as sediment, organic nitrogen, and total phosphorus loadings, also decrease. Third, farm profitability improves. These effects have positive implications for a region's farmers, its environment, and the long-term sustainability of its agriculture.

	REFERENCES

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• Asplund, N.M., D.L. Forster, and T.T. Stout. 1989. Farmers' use of forward contracting and hedging. Rev. Futures Markets 8:24–37.
• Blue, E.N., and D.L. Forster. 1997. Factors affecting performance measures of northwest Ohio farms. ESO 2394. Dep. of Agric. Econ., The Ohio State Univ., Columbus.
• Bravo-Ureta, B.E., and A.E. Pinheiro. 1993. Efficiency analysis of developing country agriculture: A review of the frontier function literature. Agric. Res. Econ. Rev. 22:88–101.
• Färe, R., S. Grosskopf, and H. Lee. 1990. A nonparametric approach to expenditure constrained profit maximization. Am. J. Agric. Econ. 72:574–581.
• Featherstone, A.M., J.J. Fletcher, R.F. Dale, and H.R. Sinclair. 1991. Comparison of net returns under alternative tillage systems considering spatial weather variability. J. Prod. Agric. 4:166–173.
• Feder, G., and R. Slade. 1984. The acquisition of information and the adoption of new technology. Am. J. Agric. Econ. 66:312–320.
• Fizel, J.L., and T.S. Nunnikhoven. 1993. The efficiency of nursing home chains. Appl. Econ. 25:49–55.
• Foltz, J.C., J.G. Lee, and M. Martin. 1993. Farm level economic and environmental impacts of eastern corn belt cropping systems. J. Prod. Agric. 6:290–296.
• Forster, D.L., E.C. Smith, and D. Hite. 2000. A bioeconomic model of farm management practices and environmental effluents in the western Lake Erie basin. J. Soil Water Conserv. 55:177–182.
• Kalirajan, K., and R.T. Shand. 1986. Estimating location-specific and firm-specific technical efficiency: An analysis of Malaysian agriculture. J. Econ. Develop. 11:147–160.
• Klemme, R.M. 1985. A stochastic dominance comparison of reduced tillage systems in corn and soybean production under risk. Am. J. Agric. Econ. 67:550–557.
• Lovell, C.A.K., L.C. Walters, and L.L. Wood. 1989. Exploring the distribution of DEA scores. IC² conference on new uses of DEA in management. IC² Inst., Univ. of Texas, Austin.
• Nalson, J.S. 1968. The mobility of farm families. Manchester Univ. Press, Manchester, UK.
• Ray, S.C. 1985. Measurement and test of efficiency of farms in linear programming models: A study of West Bengal farms. Oxford Bull. Econ. Stat. 47:371–386.
• Richards, R.P., D.B. Baker, and D.J. Eckert. 2002. Trends in agriculture in the LEASEQ watersheds, 1975–1995. J. Environ. Qual. 31:17–24 (this issue).[Abstract/Free Full Text]
• Rogers, E.M., R.J. Burdge, P.F. Korsching, and J.F. Donnermeyer. 1988. Social change in rural societies: An introduction to rural sociology. 3rd ed. Prentice Hall, Englewood Cliffs, NJ.
• Sharpley, A.N., and J.R. Williams (ed.) 1990. EPIC—Erosion/productivity impact calculator 1. Model documentation. Tech. Bull. 1768. USDA, Washington, DC.
• Shepard, L.E., and R. Collins. 1982. Why do farmers fail? Farm bankruptcies 1910–1978. Am. J. Agric. Econ. 64:610–615.
• Smolik, J.D., and T.L. Dobbs. 1991. Crop yields and economic returns accompanying the transition to alternative farming systems. J. Prod. Agric. 4:153–161.
• Stout, T.T., D.L. Forster, and G. Edgington. 1992. Organization and performance of Ohio farm operations in 1990. Res. Bull. 1189. Agric. Res. Development Center, Wooster, OH.
• Tauer, L.W. 1993. Short-run and long-run efficiencies of New York dairy farms. Agric. Res. Econ. Rev. 22:1–9.
• Upton, M., and S. Haworth. 1987. The growth of farms. Eur. Rev. Agric. Econ. 14:351–366.[Abstract/Free Full Text]
• Williams, J.R. 1988. A stochastic dominance analysis of tillage and crop insurance practices in a semiarid region. Am. J. Agric. Econ. 70:112–120.

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