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Effect of Cover Crop on Farm Profitability and Risk in the Southern High Plains

Department of Agricultural Economics and Agricultural Business, New Mexico State University, Las Cruces, NM 88003, USA
Agricultural Science Center, New Mexico State University, 2346 NM 288, Clovis, NM 88101, USA
School of Economic Sciences, Washington State University, Pullman, WA 99164, USA
Author to whom correspondence should be addressed.
Sustainability 2019, 11(24), 7119;
Received: 27 November 2019 / Revised: 10 December 2019 / Accepted: 10 December 2019 / Published: 12 December 2019


Cover cropping has been promoted for improving soil health and environmental quality in the southern High Plains (SHP) region of the United States. The SHP is one of the more productive areas of the country and covers a large landmass, including parts of Oklahoma, New Mexico, and Texas. This region faces challenges in sustainable crop production due to declining water levels in the Ogallala Aquifer, the primary source of water for irrigated crop production. This study examines the impact of integrating cover crops in the winter wheat (Triticum aestivum L)-based rotations on farm profitability and risk in the SHP. The study combines experimental yield data with other secondary information, including market prices, to conduct simulation analysis and evaluate the risk involved in introducing cover crops in a wheat-fallow cropping system. The results show that, due to the additional monetary costs involved, none of the cover crop options is economically viable. However, when secondary benefits (erosion control and green nitrogen) or government subsidies are included in the analysis, one of the cover crop options (peas) dominates the fallow alternative. Moreover, when the secondary benefits and a government subsidy are combined, two cover crop alternatives (peas and oats) emerge as more profitable options than leaving land fallow. These results highlight the importance of agricultural research and extension programs that are making a concerted effort to develop more productive farming techniques and increase public awareness about the long-term benefits of adopting soil health management systems such as cover cropping in the SHP region.
Keywords: sustainability; subsidy; risk; profitability soil erosion; nitrogen fixation sustainability; subsidy; risk; profitability soil erosion; nitrogen fixation

1. Introduction

Sustainable production is one of the significant challenges faced by farming communities throughout the world. Climate change-associated increases in extreme weather events, including extended periods of drought and changing temperature and precipitation patterns, are severely affecting farm productivity, income, and sustainability [1,2,3]. While rising heat and aridity increase evapotranspiration and needs for supplemental irrigation, declining surface and groundwater supplies are forcing growers to convert from irrigated to dryland farming, particularly in the arid regions of the U.S. [4,5]. Moreover, conversions to dryland farming are likely to be much higher for farms that are highly reliant on the Ogallala Aquifer (OA), one of the largest groundwater reservoirs in the world that underlies a 450,660 km2 area in eight Great Plains states from South Dakota to Texas and New Mexico, where more than 90% of the water pumped from the Aquifer is used for irrigated agriculture [6,7].
Recent studies show that excessive withdrawal from the OA has substantially reduced its water level (about 50 m in some areas), significantly increased pumping costs, and reduced irrigated areas by nearly 50 percent [8,9,10,11]. If this trend continues, an additional 40 percent of the irrigated land in some areas of the southern High Plains (SHP) will likely be converted to dryland farming soon. The SHP region covers a vast area (126,470 km2), including parts of New Mexico, Texas, and Oklahoma, and is one of the most significant dryland cropping areas of the country [12]. Intensive farming and poor soil health management practices in this semiarid environment have depleted 30–60 percent of soil organic carbon (SOC), reduced biodiversity by up to 60 percent, degraded most soil quality attributes, and reduced crop productivity [13,14,15]. Additionally, recent rainfall trends show that more than 60 percent of the precipitation occurs during the fallow period of typical crop-fallow rotation systems, and precipitation storage efficiency during the fallow period is relatively low. Climate change-induced fluctuations in rainfall are expected to make the region much drier and substantially increase crop yield variability [16].
Cropping systems under dryland or very limited-irrigated condition often use cereal only rotations, a long fallow-period between crops, repeated tillage for weed control, and dust-mulch effects that may potentially result in higher soil moisture retention. For instance, in the dryland wheat-fallow system, the land is left bare for nine to thirteen months to conserve water and soil nutrients, enhance crop yield, and improve farm profitability [17,18]. Not much research has been done in cropping systems with minimal irrigation or transitioning to dryland. Recent studies in drylands have shown that an extended period of fallow can have a devastating ecological and economic impact in the long-run, including a substantial reduction in soil organic carbon (SOC) and overall soil fertility [18,19]. Soil erosion, rainwater run-off, yield volatility, and crop enterprise risk are also higher under the extended fallow approach.
A multitude of other factors, including the complex biological processes involved in growing crops, coping with extreme weather events, and the long lag between the crop planting decision and the harvest of the crop, can combine to make farming an inherently risky business. The level and nature of risk become more complicated when planting a new crop variety or using an experimental farming technique with less familiar management alternatives such as conservation tillage, crop rotation, and cover cropping. Some benefits of cover crop and other sustainable cultivation practices (e.g., reduction in wind erosion, improvement in soil organic matter, and environmental benefits) are not generally included in standard enterprise crop budgets or other cost-benefit analysis tools, making them appear economically less attractive options for producers. As a result, growers often perceive new production processes as riskier than traditional crops or farming practices. Such misperceptions tend to delay the adoption of improved methods and hinder the adoption of new technologies that may lead to more efficient use of productive resources.
The adoption of cover crops has also been observed to reduce soil erosion [20], leaching [21,22], greenhouse gas emissions [23,24], and chemical use [25,26]. The SHP region has a very high incidence of wind erosion that exerts various cost burdens on the local economy. In particular, recent estimates show that, on average, the cultivated areas in New Mexico lose more than 23 tons/acre of topsoil annually. Therefore, adopting conservation practices, including reduced-tillage, crop rotation, and cover crops, has a high potential for enhancing crop yield and supporting succeeding crop plantings through improved soil health and water conservation.
Because of the potential benefits previously noted, most state governments and the Natural Resources Conservation Service (NRCS) of the U.S. Department of Agriculture (USDA) promote the use of cover crops as a conservation measure by offering various incentive-programs [27,28]. In 2019, the State of New Mexico enacted the ‘Healthy Soil Act’ and is now promoting conservation farming practices, including cover cropping. In this light, this study examined the potential impact of introducing cover crops during the fallow period on crop yield, farming risk, and profitability, making use of experimental data from the New Mexico State University Agricultural Science Center at Clovis, NM. The study also examines the potential effects of incorporating secondary benefits (e.g., the addition of green fertilizer and adoption of erosion controls), and the role of possible government subsidies on farming risk and profitability.

2. Research Methodology

2.1. Analysis Tools

Expected utility theory (EUT) is the cornerstone of economic analysis of individual choice [29]. The method was initially developed to explain observed inconsistencies in human behavior when faced with events that have uncertain outcomes, such as lotteries or capital investments [29,30]. The model postulates that when faced with risky prospects, an individual chooses the alternative that offers the maximum expected utility. In particular, a combination of the two existing concepts of ‘expected value’ and ‘profit maximization’ was used to derive consistent rules for determining the maximum expected utility attainable from different options available to assist decision-makers and help choose the optimal decision. The EUT and its derivatives are the basis for individual choice analyses in many fields, including psychology, management, finance, and economics, to evaluate risky decisions [30].
The EUT is also used in agricultural applications, particularly for analyzing crop farming risk and profitability, because both yield and market prices are uncertain when crop-planting decisions are made. Assuming that the utility function of a grower with respect to performance criteria x (net return) is U(x), the probability density functions (PDF) associated with different alternatives are f1(x), f2(x), …, fn(x), and their corresponding cumulative distribution functions (CDF) are F1(x), F2(x), …, Fn(x), respectively, the utility of these alternatives can be expressed as
U ( x ) = EU ( x ) = U ( x ) f ( x ) dx = U ( x ) dF ( x ) .
Thus, the worth of a risky alternative is its expected utility. A discrete approximation of the last term in the right-hand side of Equation (1) above (i.e., U ( x ) dF ( x ) i = 1 n U ( x i , r ( x ) ) P ( x i ) is used in most empirical applications.
Various analytical tools are proposed in the literature to incorporate and evaluate risk in decision making. Some of the widely used analytical tools that are consistent with the EUT include mean-variance (MV), stochastic dominance (SD), and stochastic efficiency (SE) analysis. The MV is a standard tool commonly used in economic and financial analysis. It is consistent with the EUT if: (i) the decision maker’s utility function (U(x)) is quadratic, has a positive first derivative (i.e., U1(x) > 0; with respect to return), and has a negative second derivative (i.e., U2(x) < 0), or (ii) the probability functions are distributed normally [31,32]. However, recent studies report that MV does not provide clear guidelines for selecting risk-efficient options and may not be applicable in most situations because it imposes theoretically unacceptable conditions on utility functions [32,33].
The SD analysis is more flexible than MV because it is non-parametric, and its assumptions about the utility function are less restrictive. In general, the SD approach involves comparing the CDF of different options to identify a risk-efficient alternative. The SD approach provides decision criteria based on various assumptions about the decision maker’s preference (utility) for wealth or expected returns from investments (x). For instance, the first-order stochastic dominance (FSD) assumes a utility function that is non-decreasing (monotonic) with respect to wealth or net returns (i.e., positive first derivative or δU(x)/δx = U1(x) > 0), and the second-order stochastic dominance (SSD) assumes a monotonic (U1(x) > 0) as well as a concave (i.e., negative second derivative or U2(x) < 0) utility function. Similarly, the third-order stochastic dominance (TSD) assumes a positive third derivative (i.e., U3(x) > 0) in addition to the monotonic and concave utility functions considered by SSD. The FSD and SSD criteria can be defined as follows.
Consider two risky cropping systems that involve leaving land fallow (f) between two cash crops, or planting cover crop (g). If the cumulative distribution functions (CDF) of net returns of ‘f’ and ‘g’ are given by F(x) and G(x), respectively, then F(x) dominates G(x) by FSD if and only if:
G ( x ) F ( x ) 0
and if at least one strict inequality is satisfied.
In other words, the FSD criteria are based on the assumption that all decision-makers face an increasing utility function concerning net return (i.e., U1(x) > 0). Satisfying the FSD implies that if F(x) dominates G(x), all farm producers with U1(x) > 0 would prefer F(x) to G(x). Although the FSD criteria are consistent with the utility theory, their real-world applications have been limited because most CDFs intersect each other, violating the condition in (1) above and making it challenging to identify optimal options.
The second-order stochastic dominance (SSD) is more discriminating than the FSD because it provides more structure to the utility function by requiring it to be concave (U2(x) < 0). Thus, f SSD dominates g if and only if:
x [ G ( x ) F ( x ) ] dx 0
and if at least one strict inequality is satisfied.
Graphically, the SSD criteria require that the area under F(x) be less than G(x). In other words, all growers who face increasing utility functions (U1(x) > 0) and are risk-averse (U2(x) < 0) prefer F(x) to G(x) if condition (2) above is satisfied. Although SSD has more discriminating power than the FSD, both approaches are better suited for making pairwise comparisons than for evaluating multiple alternatives.
Many studies have attempted to develop a framework for classifying and identifying stochastically efficient (SE) options [34,35,36,37,38,39,40,41]. For instance, Meyer (1977) proposed using stochastic dominance analysis concerning a function (SDRF) to simplify the process involved in ranking risky alternatives [40]. The SDRF approach uses lower rL(w) and upper rU(w) absolute risk aversion (ra(w)) bounds for ranking different alternatives (i.e., rL(w) ≤ ra(w) ≤ rU(w)). The SDRF analysis is valid for all individuals whose risk aversion level falls within the specified bounds. Hardaker et al. (2004) proposed stochastic efficiency concerning a function (SERF) as the most ‘transparent’ method for analyzing risky prospects and detecting ‘utility efficient’ options [36].
The SERF involves calculating certainty equivalent (CE) values within the pre-specified absolute risk bounds for each alternative and compares them to identify the most efficient set of alternatives. The CE is an assured value that provides the same level of utility to the decision-maker as the expected utility of a risky alternative. The CE value of a risky choice to a risk-averse decision-maker is less than the expected monetary value (EMV) of the prospect. In other words, the risk premium (RP) that makes a risk-averse person indifferent between a sure return (zero risks) amount and the return from the risky prospect is the gap between EMV and CE (i.e., RP = EMV-CE). The CE is generally measured as the inverse of the utility function, i.e., CE(x, r(x)) = U-1(x, r(x)). However, the actual calculation (or formula) of the CE depends on the empirical specification of the utility function [36]. We intend to use SERF in this study because it is more flexible (i.e., can be applied to any invertible utility function), identifies a smaller set of utility efficient alternatives, and is easier to understand and implement than the SDRF approach [36].

2.2. Data Sources

The data used in this study were obtained from various primary and secondary sources. The cover crop biomass and wheat yield data were generated from an on-going field experiment conducted at the New Mexico State University Agricultural Science Center at Clovis, NM (34°35′ N, 103°12′ W, elev. 1348 m). The study site has a semiarid climate with a long-term (110-yr) mean annual temperature of 15°C and a mean annual precipitation of 470 mm. The study site had a clay loam soil with a bulk density of 1.1 to 1.3 Mg m−3, pH 8.1, electrical conductivity ranging from 0.28 to 0.51 ds m−1, and soil organic matter ranging from 13 to 16 Mg ha−1 at the 0- to 15-cm depth at the beginning of the experiment in 2016.
Data on farm inputs, management practices, soil quality attributes, and subsequent crop yields after leaving the land fallow or planting cover crops were collected in 2016 and 2017 from the on-going study. The study has eight different treatments, including a fallow, three sole cover crops (pea (Pisum sativum L.), oat (Avena sativa L.), canola (Brassica napus L.)) and four mixtures (pea and canola mixture (PCM), pea and oat mixture (POM), pea, oat, and canola mixture (3XM), and pea, oat, canola, hairy vetch (Vicia villosa L.) + forage radish (Raphanus sativus L.) + barley (Hordeum vulgare L.), barley, forage radish mixture (6XM)), and three replications. Cover crops were planted in February and terminated in May, followed by winter wheat planting in October and harvesting in June of the following year. A no-till drill (Great Plains 3P600, Moline, IL) was used for planting cover crops as well as subsequent winter wheat. The seeding rates for pea, oat, and canola were 22.4, 44.8, and 4.5 kg ha−1, respectively. Cover crop species used in two-, three-, and six-species mixtures used only 50.0%, 33.3%, and 16.7% of the monoculture seeding rates. The monoculture seeding rates for hairy vetch, forage radish, and barley was 11.21, 4.48, and 44.84 kg ha−1, respectively. Cover crops were not irrigated, and no fertilizers were applied. Management practices for the cash crop were the same with and without cover cropping. Management detail and the effect of cover crops on soil quality, including soil organic carbon, was analyzed and reported elsewhere [42]. Winter wheat was harvested at physiological maturity by harvesting a 3.05 × 1.2 m area with a Sickle Bar mower (Santa Fe Equipment Sales Inc., Santa Fe, NM, USA) and thrashing by using a plot combine. The soil moisture content at harvest varied between plots, and it was adjusted to 14% moisture for comparing yields. Winter wheat yield and management data were utilized in this study. The production cost and return estimates were obtained from wheat enterprise budgets published online by the Department of Agricultural Economics and Agricultural Business, NMSU ( Additionally, the farm level wheat prices, soil erosion (water and wind), and subsidy information were acquired from the National Agricultural Statistics Service ( and the Economic Research Service (, both agencies of the USDA, and other literature.

3. Results and Discussion

3.1. Empirical Results

The economic impact of introducing different combinations of cover crops in the wheat-fallow cropping system, which is common in the SHP region, is evaluated in this study by using field experiment data. The summary statistics show that, on average, plots, where peas were used as a cover crop, have the highest yield (81.51 bushels/acre) followed by parcels left fallow (81.01 bushels/acre) (Table 1). The relative production risk, which is measured as the variation in yield (i.e., σ = standard deviation of the yield or coefficient of variation (CV), which accounts for the relative mean difference between two experiments), is also lowest for plots where peas were grown as cover crops (σ = 12.45 and CV = 0.15), followed by parcels left fallow (σ = 14.78 and CV = 0.18).
The operating cost reported in Table 1 includes the cost of all purchased inputs (e.g., seed fertilizer, pesticides, herbicides, and irrigation), pre-harvest operation costs (land preparation), and harvesting costs. The cover crop cost includes the estimated cost of planting and terminating cover crops. The plots that are left fallow require an application of herbicides for weed control. An average cost of $2.54/acre is included in the budget to account for the weed control expenses. A five-year average wheat price for the state was used as the grower price to calculate the total revenue and to derive the net return.
Although cover cropping with peas produces the highest wheat yield with the lowest variation, significantly higher costs of planting and terminating cover crops ($43.76 for peas) make it more profitable to leave the land fallow in the short-term (see Figure 1). Moreover, keeping the plot fallow is also the least risky option (σ = 75.70 and CV = 0.50) as compared to all other cover crop options, including the second most profitable option, peas (σ = 63.75 and CV = 0.57). These results are consistent with previous studies, particularly those that report limited direct benefits of using cover crops in the short-term [27,43]. These findings are also consistent with the observed behavior of the farm producers who prefer leaving land fallow rather than planting cover crops in the SHP region.

3.2. Baseline Scenario

The baseline scenario evaluates the impact of planting cover crops during the fallow period on farm profitability and risk without considering the secondary benefits of cover cropping. The results show that leaving land fallow between two cash crops generates the highest net return on average at the lowest risk (Table 1 and Figure 1). However, the first-degree stochastic dominance analysis shows that none of the alternatives analyzed in the study are stochastically dominant over others, implying that none of the options are superior under all possible scenarios (see Figure 2).
The SERF analysis, which is more discriminating than the stochastic dominance analysis, shows that two of the eight options analyzed in the study—fallow (leaving land fallow between two crops) and planting peas as a cover crop—are in the efficient set (Figure 3). The result shows that risk-neutral growers would prefer the fallow option, and those who are relatively risk-averse may prefer using peas as cover crops. These results are consistent with the fact that the yield variability in plots planted with peas as cover crops was the lowest.

3.3. Secondary Benefit Consideration

Recent studies show that planting cover crops during the fallow period can generate several secondary benefits that are usually not included in enterprise crop budget analysis. The USDA Sustainable Agriculture Research and Education [27] report identified eleven different benefits of cover crops, including reductions in soil erosion, weed density, soil compaction, and increases in soil organic matter, water retention, and nitrogen. However, it may take more than two years to see the benefits of cover cropping on soil health indicators such as soil organic matter, total nitrogen, soil aggregation, etc. This study incorporates two significant benefits associated with cover cropping that are very important for semiarid regions; (i) addition of green nitrogen and (ii) reductions in soil erosion. When cover crop biomass is added to the soil, it changes the composition of the land, including soil organic matter and nutrient addition. Longer-term study on multiple benefits of cover cropping will help farmers to identify suitable cover crop, and the policymakers to develop an incentive mechanism to maximize benefits from cover cropping in semiarid regions.
The value of nitrogen added to the ground by cover crops is estimated by combining the actual biomass production data from the field trial; nitrogen content on various cover crops biomass and local market price for liquid nitrogen (i.e., cover crop biomass*proportion of nitrogen in the biomass*price) showed that two or three species mixtures of cover crops could maximize the N addition (Table 1). Although biomass production was higher in the Oat cover crop [44], the 3XM cover crop (a mixture of Pea, Oat, and Canola) had the highest amount of biomass nitrogen, followed by two species mixtures (POM and PCM). Since the soil nitrogen added through root nodulation of legumes was not measured in the field study, nitrogen addition through atmospheric nitrogen fixation in root nodules is not included in calculating total nitrogen contribution.
A recent NRCS study shows that the average annual erosion rate for cultivated lands in New Mexico is 53 Mg ha−1 (23.64 tons/acre) [28], more than 97 percent of which is attributed to wind erosion. The wind erosion from the cultivated area is severe during the fallow period when there is no land cover, and the soil is loose. Although a wide range of environmental, topographical, land management, location, and soil-related factors affect erosion, empirical studies show that planting cover crops between two cash crops can substantially reduce erosion (by 47 percent to 96 percent) and improve soil fertility [20,27,44,45].
Hansen and Ribaudo (2008) measured the economic value of controlling both water and wind-based soil erosions [46]. Water erosion (i.e., sheet and reel erosion) primarily affects water quality as floodwaters move through different water bodies, including irrigation ditches, reservoirs, navigation systems, drinking water, and other recreational and fishery production systems. In total, the study evaluated the impact of erosion on twelve different water uses and treatments plus soil fertility. Wind erosion reduces soil fertility and increases dust cleaning costs. Assuming that the reported erosion rate remains the same and using the valuation measures developed by Hansen and Ribaudo (2008), the total social benefit of controlling soil loss from cultivated lands in New Mexico would be $31.55/acre for 2018 [28,46]. A round number of $30.00/acre was used in this study to account for soil conservation benefits of cover cropping, as shown in Table 1.
Thus, planting cover crops between cash crops not only increases cost, but it also generates several secondary benefits, including the reduction in soil erosion and the addition of green fertilizer (about $5.00/acre). Although the incorporation of these secondary benefits in the enterprise crop budgets makes cover crops more attractive options, both the fallow and the peas only remain in the efficient set (Figure 4). The optimal solution switches from fallow to cover crops with peas for growers who are “somewhat risk-averse” (i.e., when the absolute relative risk coefficient (ra(x)) moves beyond 0.005).

3.4. Subsidy and Incentive Payments

Increasing realization that multiple benefits of maintaining land cover during the off-season, including management of soil fertility, water, weeds, disease, pests, erosion, and other ecological services, has led to several states, and the federal government, offering various incentives for growers to replace fallow periods with cover crops. The Sustainable Agriculture Research and Education (SARE) (2019) study identifies eleven different benefits of cover crops and reports that cover crops are an investment that would start enhancing farm profitability by the second or third year of their use [27].
Most states provide a $50.00/acre payment as an incentive to utilize a single-species cover crop and a higher amount for a mixture of multiple cover crop species and other exceptional cases. The State of New Mexico has not yet set its subsidy rate for cover cropping. However, a majority of states offer incentives that range from $16.89/acre (North Dakota) to $79.23/acre (Pennsylvania) to promote the practice under different scenarios. The State of New Mexico enacted the Healthy Soil Act in 2019 and is soliciting proposals from producers for improving the integration of cover crops in different cropping systems. The level of subsidy has not been decided yet. Therefore, the potential impact of the State of New Mexico, providing monetary support to growers on farm-profitability and risk, is evaluated in this study. The results of this analysis can help in determining an effective subsidy to attain optimal adoption rates.
Since most other states with similar soil health initiatives provide, on average, a $50.00/acre subsidy for participating in the program, this study evaluates the potential impact of offering the same level of support in New Mexico. The results show that providing such a subsidy would make a cover cropping with peas the most efficient options among eight different alternatives analyzed in the study (Figure 5). Fallow dominates all other options without any incentive program and may not provide benefits to the growers.

3.5. Secondary Benefits, Subsidy, and the Role of Agricultural Research and Extension Programs

While sustainable production practices such as crop rotation, crop residue management, and cover cropping help to sustain and enhance soil fertility, heavy rainfall, and intense wind tend to displace topsoil and reduce farm productivity. Long-term research on the benefits of sustainable production practices, including cover crops and development of education and extension programs to transfer knowledge on such practices, can improve sustainability. Since cover cropping is not a common practice currently in the SHP region, analysis of the potential impact of providing a subsidy and conducting other promotional programs could significantly increase the returns from cover crops.
Studies at the same experimental location revealed that cover crops have increased soil biological activity, improved soil organic matter accumulation, and suppressed weeds [44,47]. We did not analyze the potential long-term economic benefits of these aspects. However, some cover crop options become more profitable than leaving land fallow when the potential benefits of cover crops, such as an addition of green nitrogen, reduction in soil erosion, and a subsidy, are included in the analysis. The results clearly show that peas and oats are the two most beneficial cover crop options in the SHP region for growers to consider (Figure 6).

4. Conclusions

The baseline analysis shows that the current practice of leaving land fallow between cash crops is the optimal option in the short-term among eight different cropping systems analyzed in the study. However, encouraging growers to incorporate the potential long-term benefits of cover crops, such as erosion control and green nitrogen addition, in the analysis makes cover cropping profitable. It may take more than two years to see significant soil health benefits of cover cropping. Based on this two-year study, cover crop options of peas and oats were the most profitable with a $50.00/acre subsidy and potential secondary benefits. Increased awareness of the secondary benefits of replacing the fallow period with cover crops and their long-term economic and environmental benefits will increase the adoption of cover cropping in the SHP region. Until now, cover cropping is not a common practice in the SHP region because of the potential risk associated with it.
The recent efforts by governmental agencies to promote the soil health management practices through subsidy may encourage some growers; however, it may not draw much attention if growers are not sufficiently motivated and do not clearly understand the long-term benefit of replacing the fallow period with ecologically sustainable agricultural practices such as crop rotation and cover cropping. However, agricultural researchers and extension agents who have been working with the local farming communities for a long time can serve as change instigators by educating and demonstrating how conservation practices such as cover cropping can increase soil organic matter, rainwater infiltration and storage, soil fertility, crop productivity, and farm profitability in the long run.
As in most empirical studies, the analysis conducted in this study relies on several underlying assumptions about the experimental setting, grower perception and response to production risk, and market prices. Therefore, we caution the reader to consider the following limiting factors in generalizing the results of this study. First, the study evaluates the short-term impact of cover cropping (three replications of each experiment for two years) and does not capture the long-term benefits (or costs) of cover crops. In our trial, cover crop growth was variable partly because of the planting depth issue. We had to compromise the planting depth of both large and small seeds in cover crop mixtures, which may have affected seed emergence and biomass production. Second, the fallow management cost calculated in this study may be low for other applications. Typical fallow plots require 3–4 tillage under conventional tillage and 3–4 sprays of herbicides in no-tillage condition. We included the cost of only one application of the herbicide in the fallow plot during the cover crop growing period. Mesbah et al. (2019) demonstrate weed suppression with cover cropping, which can reduce 1–2 more sprays during summer and change the relative effects of cover crops. Finally, the analytical framework used in this study has its own set of limitations, including the concavity of utility function and risk bounds that requires caution in generalizing the results.

Author Contributions

Conceptualization, R.N.A. and R.G.; field experiment and data, R.G.; software, secondary data collection, and analysis, R.N.A. and A.G.C.; writing—original draft preparation, R.N.A.; review and editing, R.N.A., R.G., A.G.C., and D.B.; project administration, R.N.A. and R.G.; funding acquisition, R.N.A. and R.G.


This research was funded partly by Project no. 2016-6800725066 of the USDA National Institute for Food and Agriculture’s Agriculture and Food Research Initiative and partly by the USDA Natural Resources Conservation Services, the New Mexico Agricultural Experiment Station, and USDA Hatch funding.


Thanks go to the Agricultural Science Center, Clovis staff, for their help in the field.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


  1. Nyuor, A.B.; Donkor, E.; Aidoo, R.; Saaka Buah, S.; Naab, J.; Nutsugah, S.; Bayala, J.; Zougmoré, R. Economic impacts of climate change on cereal production: Implications for sustainable agriculture in northern ghana. Sustainability 2016, 8, 724. [Google Scholar] [CrossRef]
  2. Kaiser, H.M.; Riha, S.J.; Wilks, D.S.; Rossiter, D.G.; Sampath, R. A farm-level analysis of economic and agronomic impacts of gradual climate warming. Am. J. Agric. Econ. 1993, 75, 387–398. [Google Scholar] [CrossRef]
  3. Baldos, U.L.; Hertel, T.W.; Moore, F.C. Understanding the spatial distribution of welfare impacts of global warming on agriculture and its drivers. Am. J. Agric. Econ. 2019, 101, 1455–1472. [Google Scholar] [CrossRef]
  4. Haacker, E.M.; Kendall, A.D.; Hyndman, D.W. Water level declines in the high plains aquifer: Predevelopment to resource senescence. Groundwater 2016, 54, 231–242. [Google Scholar] [CrossRef]
  5. Baumhardt, R.; Mauget, S.; Schwartz, R.; Jones, O. El niño southern oscillation effects on dryland crop production in the texas high plains. Agron. J. 2016, 108, 736–744. [Google Scholar] [CrossRef]
  6. Colaizzi, P.D.; Gowda, P.H.; Marek, T.; Porter, D.O. Irrigation in the texas high plains: A brief history and potential reductions in demand. Irrig. Drain. J. Int. Comm. Irrig. Drain. 2009, 58, 257–274. [Google Scholar] [CrossRef]
  7. Maupin, M.A.; Barber, N.L. Estimated Withdrawals from Principal Aquifers in the United States, 2000; Geological Survey (USGS): Reston, VA, USA, 2005; Volume 1279.
  8. McGuire, V.L. Water-Level and Storage Changes in the High Plains Aquifer, Predevelopment to 2011 and 2009–11; US Geological Survey: Reston, VA, USA, 2012.
  9. Schaible, G.D.; Kim, C.; Aillery, M.P. Dynamic adjustment of irrigation technology/water management in western us agriculture: Toward a sustainable future. Can. J. Agric. Econ./Revue Canadienne D’Agroeconomie 2010, 58, 433–461. [Google Scholar] [CrossRef]
  10. Scanlon, B.R.; Faunt, C.C.; Longuevergne, L.; Reedy, R.C.; Alley, W.M.; McGuire, V.L.; McMahon, P.B. Groundwater depletion and sustainability of irrigation in the us high plains and central valley. Proc. Natl. Acad. Sci. USA 2012, 109, 9320–9325. [Google Scholar] [CrossRef]
  11. Musick, J.; Pringle, F.; Harman, W.L.; Stewart, B. Long-term irrigation trends–texas high plains. Appl. Eng. Agric. 1990, 6, 717–724. [Google Scholar] [CrossRef]
  12. Brejda, J.J.; Moorman, T.B.; Karlen, D.L.; Dao, T.H. Identification of regional soil quality factors and indicators i. Central and southern high plains. Soil Sci. Soc. Am. J. 2000, 64, 2115–2124. [Google Scholar] [CrossRef]
  13. Ogle, S.M.; Breidt, F.J.; Paustian, K. Agricultural management impacts on soil organic carbon storage under moist and dry climatic conditions of temperate and tropical regions. Biogeochemistry 2005, 72, 87–121. [Google Scholar] [CrossRef]
  14. Newbold, T.; Hudson, L.N.; Arnell, A.P.; Contu, S.; De Palma, A.; Ferrier, S.; Hill, S.L.; Hoskins, A.J.; Lysenko, I.; Phillips, H.R. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 2016, 353, 288–291. [Google Scholar] [CrossRef]
  15. Paustian, K.; Lehmann, J.; Ogle, S.; Reay, D.; Robertson, G.P.; Smith, P. Climate-smart soils. Nature 2016, 532, 49–57. [Google Scholar] [CrossRef] [PubMed]
  16. Karl, T.R.; Melillo, J.M.; Peterson, T.C. Global Climate Change Impacts in the United States; Cambridge University Press: Cambridge, MA, USA, 2009. [Google Scholar]
  17. Baumhardt, R.; Stewart, B.; Sainju, U. North american soil degradation: Processes, practices, and mitigating strategies. Sustainability 2015, 7, 2936–2960. [Google Scholar] [CrossRef]
  18. Hansen, N.C.; Allen, B.L.; Baumhardt, R.L.; Lyon, D.J. Research achievements and adoption of no-till, dryland cropping in the semi-arid us great plains. Field Crops Res. 2012, 132, 196–203. [Google Scholar] [CrossRef]
  19. Ghimire, R.; Wen-chi, H.; Shrestha, R.B. Factors affecting adoption of improved rice varieties among rural farm households in central nepal. Rice Sci. 2015, 22, 35–43. [Google Scholar] [CrossRef]
  20. Langdale, G.; Blevins, R.; Karlen, D.; McCool, D.; Nearing, M.; Skidmore, E.; Thomas, A.; Tyler, D.; Williams, J. Cover crop effects on soil erosion by wind and water. Cover Crops Clean Water 1991, 15–22. [Google Scholar]
  21. Quemada, M.; Baranski, M.; Nobel-de Lange, M.; Vallejo, A.; Cooper, J. Meta-analysis of strategies to control nitrate leaching in irrigated agricultural systems and their effects on crop yield. Agric. Ecosyst. Environ. 2013, 174, 1–10. [Google Scholar] [CrossRef]
  22. Gabriel, J.; Muñoz-Carpena, R.; Quemada, M. The role of cover crops in irrigated systems: Water balance, nitrate leaching and soil mineral nitrogen accumulation. Agric. Ecosyst. Environ. 2012, 155, 50–61. [Google Scholar] [CrossRef]
  23. Abdalla, M.; Hastings, A.; Cheng, K.; Yue, Q.; Chadwick, D.; Espenberg, M.; Truu, J.; Rees, R.M.; Smith, P. A critical review of the impacts of cover crops on nitrogen leaching, net greenhouse gas balance and crop productivity. Glob. Chang. Biol. 2019, 25, 2530–2543. [Google Scholar] [CrossRef]
  24. Kaye, J.P.; Quemada, M. Using cover crops to mitigate and adapt to climate change. A review. Agron. Sustain. Dev. 2017, 37, 4. [Google Scholar] [CrossRef]
  25. Snyder, E.M.; Curran, W.S.; Karsten, H.D.; Malcolm, G.M.; Duiker, S.W.; Hyde, J.A. Assessment of an integrated weed management system in no-till soybean and corn. Weed Sci. 2016, 64, 712–726. [Google Scholar] [CrossRef]
  26. Price, A.; Monks, C.; Culpepper, A.; Duzy, L.; Kelton, J.; Marshall, M.; Steckel, L.; Sosnoskie, L.; Nichols, R. High-residue cover crops alone or with strategic tillage to manage glyphosate-resistant palmer amaranth (Amaranthus palmeri) in southeastern cotton (Gossypium hirsutum). J. Soil Water Conserv. 2016, 71, 1–11. [Google Scholar] [CrossRef]
  27. SARE. Cover Crop Economics: Opportunities to Improve Your Bottom Line in Row Crops; Sustainable Agriculture Research and Education (SARE): College Park, MD, USA, 2019; Available online: (accessed on 11 December 2019).
  28. NRCS/USDA. Summary report: 2015 national resources inventory. In Natural Resources Conservation Service, Washington, DC and Center for Survey Statistics and Methodology; Lowa State University: Ames, IA, USA, 2018. [Google Scholar]
  29. Starmer, C. Developments in non-expected utility theory: The hunt for a descriptive theory of choice under risk. J. Econ. Lit. 2000, 38, 332–382. [Google Scholar] [CrossRef]
  30. Schoemaker, P.J. The expected utility model: Its variants, purposes, evidence and limitations. J. Econ. Lit. 1982, 20, 529–563. [Google Scholar]
  31. Porter, R.B.; Gaumnitz, J.E. Stochastic dominance vs. Mean-variance portfolio analysis: An empirical evaluation. Am. Econ. Rev. 1972, 62, 438–446. [Google Scholar]
  32. Benítez, P.C.; Kuosmanen, T.; Olschewski, R.; van Kooten, G.C. Conservation payments under risk: A stochastic dominance approach. Am. J. Agric. Econ. 2006, 88, 1–15. [Google Scholar] [CrossRef]
  33. Meyer, J. Two-moment decision models and expected utility maximization. Am. Econ. Rev. 1987, 77, 421–430. [Google Scholar]
  34. Post, T. Empirical tests for stochastic dominance efficiency. J. Financ. 2003, 58, 1905–1931. [Google Scholar] [CrossRef]
  35. Barrett, G.F.; Donald, S.G. Consistent tests for stochastic dominance. Econometrica 2003, 71, 71–104. [Google Scholar] [CrossRef]
  36. Hardaker, J.B.; Richardson, J.W.; Lien, G.; Schumann, K.D. Stochastic efficiency analysis with risk aversion bounds: A simplified approach. Aust. J. Agric. Resour. Econ. 2004, 48, 253–270. [Google Scholar] [CrossRef]
  37. Richardson, J.W.; Outlaw, J.L. Ranking risky alternatives: Innovations in subjective utility analysis. WIT Trans. Inf. Commun. 2008, 39, 213–224. [Google Scholar]
  38. Anderson, G. Nonparametric tests of stochastic dominance in income distributions. Econom. J. Econ. Soc. 1996, 1183–1193. [Google Scholar] [CrossRef]
  39. Linton, O.; Song, K.; Whang, Y.-J. An improved bootstrap test of stochastic dominance. J. Econom. 2010, 154, 186–202. [Google Scholar] [CrossRef]
  40. Meyer, J. Choice among distributions. J. Econ. Theory 1977, 14, 326–336. [Google Scholar] [CrossRef]
  41. McCarl, B.A. Preference among risky prospects under constant risk aversion. J. Agric. Appl. Econ. 1988, 20, 25–34. [Google Scholar] [CrossRef]
  42. Ghimire, R.; Ghimire, B.; Mesbah, A.O.; Sainju, U.; Idowu, O.J. Soil health response of cover crops in winter wheat-fallow system. Agron. J. 2019, 111, 2108–2115. [Google Scholar] [CrossRef]
  43. Nielsen, D.C.; Lyon, D.J.; Higgins, R.K.; Hergert, G.W.; Holman, J.D.; Vigil, M.F. Cover crop effect on subsequent wheat yield in the central great plains. Agron. J. 2016, 108, 243–256. [Google Scholar] [CrossRef]
  44. Kaspar, T.; Radke, J.; Laflen, J. Small grain cover crops and wheel traffic effects on infiltration, runoff, and erosion. J. Soil Water Conserv. 2001, 56, 160–164. [Google Scholar]
  45. De Baets, S.; Poesen, J.; Meersmans, J.; Serlet, L. Cover crops and their erosion-reducing effects during concentrated flow erosion. Catena 2011, 85, 237–244. [Google Scholar] [CrossRef]
  46. Hansen, L.; Ribaudo, M. Economic Measures of Soil Conservation Benefits: Regional Values for Policy Assessment. USDA Technical Bulletins No. 1922; 2008. Available online: (accessed on 11 December 2019).
  47. Mesbah, A.; Nilahyane, A.; Ghimire, B.; Beck, L.; Ghimire, R. Efficacy of cover crops on weed suppression, wheat yield, and water conservation in winter wheat–sorghum–fallow. Crop Sci. 2019, 59, 1745–1752. [Google Scholar] [CrossRef]
Figure 1. Coefficient of variation and returns from different cover crop options.
Figure 1. Coefficient of variation and returns from different cover crop options.
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Figure 2. Simulated cumulative density functions of net return.
Figure 2. Simulated cumulative density functions of net return.
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Figure 3. Baseline certainty equivalent (CE) values for fallow and seven different cover crop treatments.
Figure 3. Baseline certainty equivalent (CE) values for fallow and seven different cover crop treatments.
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Figure 4. The CE values for fallow and cover crop treatments with secondary benefit.
Figure 4. The CE values for fallow and cover crop treatments with secondary benefit.
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Figure 5. CE values for fallow and cover crop treatments with a subsidy.
Figure 5. CE values for fallow and cover crop treatments with a subsidy.
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Figure 6. CE values for fallow and cover crop treatments with subsidy and secondary benefit.
Figure 6. CE values for fallow and cover crop treatments with subsidy and secondary benefit.
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Table 1. Summary statistics for wheat yield, production costs, and net return by treatment.
Table 1. Summary statistics for wheat yield, production costs, and net return by treatment.
DescriptionFallowPeasOatsCanolaPOM PCM3XM6XM
Yield (Bushel/Acre)81.0181.5179.4771.8377.0278.2376.7279.68
Standard Deviation (σ)14.7812.4515.6817.0816.9420.8720.6124.89
Coeff. of Variation (CV)
Operating Cost ($/Acre)261.81261.81261.81261.81261.81261.81261.81261.81
Cover Crop Cost (($/Acre)2.5443.7636.7627.7640.2635.7636.0941.06
Price ($/Bushel)
Net Return ($/Acre)150.47111.82108.3678.2492.35103.0194.96105.14
Standard Deviation (σ)75.7063.7580.3187.4786.76106.85105.52127.46
Coeff. of Variation (CV)0.500.570.741.120.941.041.111.21
Other Benefits/Subsidy
Biomass Nitrogen ($/Acre)0.005.746.438.619.549.0010.208.99
Erosion Control ($/Acre)0.0030.0030.0030.0030.0030.0030.0030.00
Subsidy ($/Acre)0.0050.0050.0050.0050.0050.0050.0050.00
Note: The amount of nitrogen added through the cover crop was estimated as the total biomass produced * average nitrogen in the cover crop biomass * price of nitrogen. The expected annual benefits of controlling soil erosion from cultivated lands in New Mexico is $31.55/acre. A round number of $30/acre (about 95 percent of total expected gain) is used in this analysis. POM = peas + oats mixture; PCM = peas + canola mixture; 3XM = three species (peas, oats, canola) mixture; 6XM = six species (peas + oats + canola + hairy vetch + barley + forage radish) mixture.
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