**3. Methods**

Experimental data that contain all of the variables that need to be explored when examining tradeoffs between key variables are lacking. However, it is possible to develop a conceptual framework and provide an illustration using available data sources pertaining to crop budgets, soil health parameters, and net greenhouse gas emissions.

Crop budgets for 2015–2021 were developed using several sources of information. Crop yield data were obtained from Precision Conservation Management [7], Schnitkey and Swanson [18, 19], and Sellars et al. [12]. Most of the tillage scenarios reported in Precision Conservation Management [7] could be considered conservation tillage systems, so the crop system used to compare with the no-till and cover cropping systems will be referred to as a conservation tillage system. Crop yield data for the conservation tillage system were obtained from Precision Conservation Management [7] and Schnitkey and Swanson [18, 19]. Yields for corn and soybeans produced after a cover crop were obtained from Sellars et al. [12]. Annual yield data were not available for the no-till practice from the sources noted above so additional assumptions were needed to estimate crop yields. For corn, the average difference between corn yields for a no-till and a 1-pass light (i.e., one pass with low disturbance) was used to estimate annual yields. For soybeans, the relationship between soybean crop yields generated under a no-till system and under miscellaneous tillage systems reported by the Center for Farm Financial Management [20] was used to estimate annual yields. Crop price, government payments, and crop insurance indemnity payments were obtained from Schnitkey and Swanson [18, 19].

Cost information for direct costs (fertilizers, pesticides, seed, cover crop seed, drying, storage, and crop insurance), power costs (tillage, fertilizer application, spraying,

#### *Examining the Relationship between Crop Net Returns, Risk, and Conservation Practices DOI: http://dx.doi.org/10.5772/intechopen.112263*

planting, harvesting, and grain hauling), and overhead costs (hired labor, building repairs and depreciation, insurance, and interest) was obtained from Precision Conservation Management [7] and Schnitkey and Swanson [18, 19]. To account for the use of soybean yields from the Center for Farm Financial Management [20] for the no-till system, the relationship between soybean costs using a no-till system and miscellaneous tillage systems was utilized to estimate soybean costs.

Net return was computed by subtracting direct costs, power costs, and overhead costs from gross revenue, which consisted of crop revenue (crop price × crop yield), government payments, and crop insurance indemnity payments. Net return excludes operator and land costs so it can be interpreted as a net return to operator and land.

For risk averse decision makers, the choice between conservation practices depends on not only the expected net return and soil health and net greenhouse gas emissions, but also the variability in net returns. For example, if a particular conservation practice reduces or increases crop yield variability, this fact will be important to decision makers. One of the ways to incorporate net return variability into the analysis is to compute the certainty equivalent of net returns for each conservation practice. The certainty equivalent of net returns represents a risk-adjusted net return. In other words, it incorporates both expected net return and risk. Certainty equivalents are computed using an expected utility function and specific levels of risk aversion [21]. The power utility function was used to compute certainty equivalents in this study. This utility function has been used extensively to model risk aversion in production agriculture [22]. Relative risk aversion levels of 0, 1, 3, and 5 were used in this study. A relative risk aversion level of 0 is applicable to a risk-neutral decision maker. Someone with these preferences would choose the conservation practice with the highest average net return. Risk aversion levels of 1, 3, and 5 represent slightly, moderately, and strongly risk averse preferences [21].

In addition to comparing the expected net return and certainty equivalents among the conservation practices, a conceptual framework was developed to examine the tradeoffs between net return, risk, soil loss, and GHG emissions. This conceptual framework is consistent with the notion that farms use a multidimensional framework when making decisions [23, 24]. The specific model used represents a modified version of the Target MOTAD model. The Target MOTAD model maximizes expected net return subject to a constraint on downside risk, which is measured as the total negative deviations below a specified target [25, 26]. A target income or net return to land and operator labor of \$280 was used. This target income represents the average cash rent over the study period. Constraints related to soil loss and gas emissions were added to the Target MOTAD model so that tradeoffs could be examined. A similar approach was used by Stucky [27] and Stucky et al. [28] to examine the tradeoff between return to labor and management, risk, and water quality for crop rotations in Kansas.

Solutions to the modified Target MOTAD model are generated by relaxing the constraints on downside risk, soil loss, and net greenhouse gas emissions. Each solution contains information on the expected net return, downside risk or the total negative deviations below a specified target, soil loss, and net greenhouse gas emissions.

## **4. Results and discussion**

**Table 1** presents the annual net return, average net return, level of downside risk, soil loss, and greenhouse gas (GHG) emissions for each crop and conservation practice. The base case, as noted above, assumes the use of a conservation tillage system.


#### **Table 1.**

*Crop net return, soil loss, and GHG emissions for alternative conservation practices.*

Downside risk in **Table 1** is represented using average annual negative deviations rather than total negative deviations below the target net return of \$280. In general, soybeans were more profitable than corn during the study period.

Without considering soil health and net greenhouse gas emissions which will be addressed below, a risk neutral decision maker would choose the conservation practice with the highest net return. Assuming the adoption of a corn/soybean rotation, the net return for corn and soybeans is the highest for the base case. The average difference in net returns between the base case and the no-till system was \$20 per acre for corn and \$8 per acre for soybeans. The difference between net returns was larger for the comparison between the base case and the cover crop system. For corn, net return using cover crops was \$44 per acre lower than the base case. The net return difference for soybeans was even greater at \$49 per acre.

Soil loss and net greenhouse gas emissions can be reduced by adopting a no-till or cover crop system. The no-till system results in the largest drop in soil loss, while the cover crop system exhibits the largest reduction in net greenhouse gas emissions.

The certainty equivalent of net return (CE) for each crop and conservation practice is depicted in **Table 2**. The crop enterprise with the #1 label refers to the base case. The crop enterprise labels designated as #2 and #3 refer to the no-till system and the cover crop system, respectively. The differences in CEs among risk aversion levels for corn are similar to the difference in average net returns illustrated in **Table 1**. In contrast, the difference in CEs between soybean enterprises depends on the level of risk aversion. What explains these results? If there is not much difference in annual net returns between conservation practices, which is the case for corn, the CE results will not differ very much from the average net return results. For soybeans, the annual net returns across conservation practices differ more than it does for corn; thus, comparisons between soybean enterprises will depend more on the level of risk aversion. Too see this, let us focus on the comparison between soybean net returns for the base case and soybean net returns for the no-till system. The largest divergence in net returns was for 2015 and 2021. In 2015, soybean net returns were substantially lower for the no-till system. In contrast, for 2021, net returns were relatively lower for the base case.

*Examining the Relationship between Crop Net Returns, Risk, and Conservation Practices DOI: http://dx.doi.org/10.5772/intechopen.112263*


#### **Table 2.**

*Certainty equivalent of net returns for corn and soybean enterprises (\$/acre).*

In summary, results in **Tables 1** and **2** indicate that net returns and risk-adjusted net returns were higher for the base case than they were for the no-till and cover crop systems. Assuming a corn/soybean rotation, the average difference in CEs between the base case and the no-till system was \$26, and the average difference in CEs between the base case and the cover crop system was \$43. These results did not account for differences in soil loss and GHG emissions. The modified Target MOTAD results discussed below will incorporate differences in these items.

Three sets of results for the modified Target MOTAD model are illustrated in **Table 3**. Scenario #1 represents the maximum expected net return solution. Because soil loss and GHG emissions were not constrained for the base case, the values for these two conservation dimensions are the highest for the base case scenario. Also, note that corn and soybeans produced under the base case are part of a conservation tillage system. The other two scenarios illustrate solutions for a


#### **Table 3.**

*Expected net return, risk, soil loss, and GHG emissions for alternative scenarios.*

GHG emission constraint of −0.50 and −1.00, respectively. As in **Table 2**, the crop enterprise with the #1 label refers to the base case, the crop enterprises with the #2 label refers to the no-till system, and the crop enterprise with the #3 label refers to cover cropping system.

As expected, the maximum net return solution produces corn and soybeans using the conservation tillage system, or the base case. Expected net return for the solution is \$365 and average annual negative deviations below the \$280 target net return is \$4. Soil loss for this solution is 1.11 tons per acre and GHG emissions are 0.01 mt C per acre. With scenario #2, GHG emissions are reduced to −0.50 mt C per acre and soil loss is reduced approximately 24 percent. To achieve these reductions, net return per acre was reduced \$10 per acre and downside risk increased from \$4 per acre to \$12 per acre. Under this scenario, approximately 30 percent of crops were produced using the conservation tillage rotation. The other 70 percent of crops were produced using the no-till system.

With scenario #3, GHG emissions are reduced to −1.00 mt C per acre and soil loss is reduced approximately 31 percent. These reductions were achieved by producing 46 percent of the crops under the no-till system and 54 percent of the crops under the cover cropping system. Not surprisingly, given the difficulty of reducing GHG emissions from 0.01 to −1.00, the reduction in expected net returns and increase in downside risk when comparing the shift from scenario #1 to #2 with the shift from scenario #1 to #3 were larger in this instance. Specifically, net return was reduced by \$32 per acre and downside risk increased from \$4 per acre to \$17 per acre under scenario #3.

There are several extensions that could be made to the conceptual framework presented in this study. First, it would be helpful to include more years in the comparisons among crop systems. This would ensure that we are properly capturing the difference in crop yields and costs between the systems. Given the learning curves associated with adopting a no-till or cover crop rotation, this point is particularly salient [6, 10]. Second, it would be helpful if annual data pertaining to soil loss and GHG emissions were available. With these data, it would be possible to generate another set of constraints that would capture the risk associated with not achieving a target level of soil loss or GHG emissions. With annual data, years with higher soil loss or GHG emissions, due to whatever cause (e.g., weather fluctuations), than a target, which could be represented by the averages for a sample period, would be penalized. For example, using a similar conceptual framework, Stucky et al. [28] examined improvements in water quality one variable at a time. Solutions examining each water quality variable were constrained so that deviations below the target could not increase above those from the net return maximizing solution and by not allowing any of the water quality variables except for the variable of interest to exceed average levels.

## **5. Conclusions**

This chapter examined the relationship between crop net returns and conservation practices. The conceptual framework developed can be used to compare crop net returns, risk, soil loss, and GHG emissions for different conservation practices. Using an example that compared net returns, downside risk, soil loss, and GHG emissions, it was possible to reduce both soil loss and GHG emissions. However, these reductions came at a cost. To achieve a reduction in GHG emissions of approximately −0.50 mt C per acre, it was necessary to produce part of the crops under a no-till

*Examining the Relationship between Crop Net Returns, Risk, and Conservation Practices DOI: http://dx.doi.org/10.5772/intechopen.112263*

system. For this case, expected net return was reduced approximately \$10 per acre and downside risk increased approximately \$8 per acre compared to the maximum expected net return solution, which involved producing corn and soybeans utilizing a conservation tillage rotation. A reduction in GHG emissions of −1.00 mt C per acre was achieved by utilizing a combination of the no-till and cover crop systems. The reduction in expected net return was approximately \$32 per acre, and the increase in downside risk was approximately \$13 per acre in this case.
