2. Background on cover crops

Water quality issues caused by agricultural nonpoint sources are increasing (Sharpley and Smith, Reference Sharpley, Smith and Hargrove1991; Zhang et al., Reference Zhang, Tan, Zheng, Welacky and Wang2017) and can be mitigated by the use of cover crops. Nitrogen (N) losses occur primarily through leaching, denitrification, and volatilization potentially contributing to groundwater pollution, eutrophication, hypoxia, and climate change (Cameron, Di, and Moir, Reference Cameron, Di and Moir2013; Ribaudo et al., Reference Ribaudo, Delgado, Hansen, Livingston, Mosheim and Williamson2011). Nitrate (NO₃) is a toxic compound that can leach into groundwater. It is produced via mineralization and nitrification of existing organic N by microbes and microorganisms in soil (Johnson et al., Reference Johnson, Albrecht, Ketterings, Beckman and Stockin2005). Dinnes et al. (Reference Dinnes, Karlen, Jaynes, Kaspar, Hatfield, Colvin and Cambardella2002) and Castellano et al. (Reference Castellano, Helmers, Sawyer, Barker and Christianson2012) argue that there are temporal differences between nitrate use by crops and soil nitrate production under U.S. midwestern conditions. Most nitrate production from soil occurs in spring, but the peak of crop nitrogen requirements occurs in summer (Dinnes et al., Reference Dinnes, Karlen, Jaynes, Kaspar, Hatfield, Colvin and Cambardella2002). Thus, cover crops can mitigate nitrate leaching by taking up the available N during periods when crop utilization is low (Castellano et al., Reference Castellano, Helmers, Sawyer, Barker and Christianson2012; Strock, Porter, and Russelle, Reference Strock, Porter and Russelle2004; Tonitto, David, and Drinkwater, Reference Tonitto, David and Drinkwater2006). Reduced amounts of NO₃ reduce gaseous losses, including nitrous oxide (N₂O), a greenhouse gas (Davidson et al., Reference Davidson, Keller, Erickson, Verchot and Veldkamp2000). These reductions of available nitrogen also can reduce N runoff.

Cover crops can reduce soil erosion. Laloy and Bielders (Reference Laloy and Bielders2010) found that there was less soil loss and runoff in fields using cover crops than in those left untilled postharvest. Phosphorus (P) typically moves to surface water in runoff with the potential to cause eutrophication, especially in freshwater systems (Carpenter, Reference Carpenter2005). Runoff contains both soluble P and eroded soil particles, which have a high P content (Devlin et al., Reference Devlin, Losses, Pierzynski and Janssen2002). Therefore, using cover crops can reduce runoff of total P (Langdale, Leonard, and Thomas, Reference Langdale, Leonard and Thomas1985; Yoo, Touchton, and Walker, Reference Yoo, Touchton and Walker1988). Runoff is reduced because cover crops reduce rainfall energy on soils because of interception by their stalks and leaves, and they also increase infiltration capacity and water storage ability. For example, tap-rooted cover crops can penetrate soil and create pores that facilitate the infiltration of rainfall (Chen and Weil, Reference Chen and Weil2010; Reeves, Reference Reeves, Hatfield and Stewart1994). Cover crops can also increase infiltration by reducing crusting, or the surface sealing of soil. Therefore, cover crops can reduce nonpoint-source pollutants from agriculture.

A concern is that cover crops and weeds might compete with cash crops for nutrients, but Wittwer et al. (Reference Wittwer, Dorn, Jossi and Van Der Heijden2017) found that using legume cover crops not only reduced weed pressure but also increased yields because of the additional N source from atmospheric nitrogen fixation. Nutrient competition by cover crops may inhibit weeds, as can waterborne components (leachates) from the decomposition of some cover crops, such as winter rye and black oats. This can be useful in soybean production because the crop itself is insensitive to winter rye leachates (Forcella, Reference Forcella2014; Midwest Cover Crops Council, 2015).

Soil organic matter or carbon is another important factor for productivity and, to farmers, one of the well-known benefits of using cover crops (CTIC, 2017; Kaspar and Singer, Reference Kaspar, Singer, Hatfield and Sauer2011; Myers and Watts, Reference Myers and Watts2015). Mbuthia et al. (Reference Mbuthia, Acosta-Martínez, DeBruyn, Schaeffer, Tyler, Odoi, Mpheshea, Walker and Eash2015) conducted a long-term (31 year) cotton production study in Tennessee and found there was more total organic carbon under cover crop (legume) and no-till, as well as higher yields, than no cover crop and tilled fields.

In relation to climate change, cover crops may contribute to both mitigation and adaptation. Important benefits may include carbon sequestration, as well as enhanced water infiltration and reduced evaporation (Kaye and Quemada, Reference Kaye and Quemada2017). Sequestration of carbon transfers carbon dioxide, CO₂, from the atmosphere into the soil and is one way to increase soil organic matter. Using cover crops can increase carbon sequestration and may thus help mitigate climate change (Kaye and Quemada, Reference Kaye and Quemada2017; Lal, Reference Lal2004). Heavy rainfall events and drought could increase because of climate change (Kaye and Quemada, Reference Kaye and Quemada2017; Lu et al., Reference Lu, Bryant, Buda, Collick, Folmar and Kleinman2015). During spring storms, the ability of cover crops to reduce erosion and runoff by covering soil and to reduce nutrient loss by uptake of residual nutrients (Steenwerth and Belina, Reference Steenwerth and Belina2008; Strock, Porter, and Russelle, Reference Strock, Porter and Russelle2004) would be particularly helpful. In a drought, cover crops can help retain soil moisture because of the mulch provided by cover crop residues (Kaye and Quemada, Reference Kaye and Quemada2017). Also, certain crops with taproots, like radish, can create soil pores, which increase infiltration, and also enable the roots of cash crops to penetrate deeper (Chen and Weil, Reference Chen and Weil2011). However, farmers need to be careful with cover crop termination because of water use competition with cash crops (Alonso-Ayuso, Gabriel, and Quemada, Reference Alonso-Ayuso, Gabriel and Quemada2014). Specifically, if termination of cover crops is delayed,Footnote ¹ they will deplete soil moisture, especially in some regions that have soils with limited water holding capacity, and thus negatively affect cash crop yields (Balkcom et al., 2016).

Even though there are many advantages to using cover crops, this practice is complex and farmers need to consider issues such as timing of planting and termination, as well as the additional time and labor costs (Ward et al., Reference Ward, Sharpley, Miller, Dick, Hoorman, Fulton and LaBarge2018). The first concern of farmers considering adoption of cover crops is successful establishment of cover crops to get the associated benefits. According to a cover crops survey (CTIC, 2017), 73% of respondents planted cover crops after harvest. Regions with a short growing season have a short window to plant cover crops because cover crops that are not winter hardy, such as legumes, need at least 4 to 6 weeks to obtain full benefits (Midwest Cover Crops Council, 2015). Also, farmers prefer to plant cover crops before soybeans rather than before corn (Midwest Cover Crops Council, 2015). This may be because there is a possibility of reduced yields of corn because of later planting if cover crop termination is delayed (Wallace et al., Reference Wallace, Williams, Liebert, Ackroyd, Vann, Curran, Keene, VanGessel, Ryan and Mirsky2017). Competition for water, as well as establishment and termination issues, shows that climate is important.

Additionally, trialing cover crops might be easier for farmers who have already adopted no-till because of their equipment. For example, farmers need planting equipment with the ability to handle residue, such as no-till drills or planters (Grisso, Holshouser, and Pitman, Reference Grisso, Holshouser and Pitman2009; Mirsky et al., Reference Mirsky, Ryan, Teasdale, Curran, Reberg-Horton, Spargo, Wells, Keene and Moyer2013).

In this section, we have focused on the scientific background related to cover crops, and in the next section we examine the literature on adoption in order to develop our conceptual model and testable hypotheses.

3. Conceptual framework

Farmers will adopt cover crops if their expected utility, subject to their preferences and constraints (e.g., time and climate), is maximized by doing so. Utility is a function of various factors including expected benefits and costs of adopting a practice versus not adopting. The factors affecting adoption can be divided into five categories: (1) demographic factors, (2) farm characteristics, (3) adoption of related management practices or technologies, (4) environmental attitudes, and (5) climate. These categories and explanatory variables are based on the extensive literature related to adoption studies of technologies and best management practices.

Among demographic factors, age is considered to negatively affect adoption of new practices. Older farmers are less likely to change their practices because of a shorter planning horizon (McBride and Daberkow, Reference McBride and Daberkow2003; Prokopy et al., Reference Prokopy, Floress, Klotthor-Weinkauf and Baumgart-Getz2008; Soule, Tegene, and Wiebe, Reference Soule, Tegene and Wiebe2000). In their review, Bergtold et al. (Reference Bergtold, Ramsey, Maddy and Williams2017) highlight the fact that short-term versus long-term considerations are important for cover crop adoption decisions. For cover crops, we hypothesize that younger farmers will have a higher likelihood of adoption, because benefits are realized over a period of years. Education assumes a linkage between knowledge and adopting a practice and has had a positive effect on the likelihood of adoption (McBride and Greene, Reference McBride and Greene2013; Rogers, Reference Rogers2003; Schimmelpfennig and Ebel, Reference Schimmelpfennig and Ebel2016; Weber and McCann, Reference Weber and McCann2015). Bergtold et al. (Reference Bergtold, Duffy, Hite and Raper2012) used an education variable in their study of cover crops in Alabama, but it was not significant. Nevertheless, we expect that farmers with a higher education level are more likely to adopt cover crops because of the complexity discussed in the previous section.

Off-farm employment could be a factor affecting the adoption decision. In the literature, off-farm employment has shown mixed results because it can increase financial capability (Schimmelpfennig and Ebel, Reference Schimmelpfennig and Ebel2016) and reduce time available to consider and adopt a new technology (McBride and Greene, Reference McBride and Greene2013). Gedikoglu, McCann, and Artz (Reference Gedikoglu, McCann and Artz2011) explained that off-farm employment may have a positive impact on a capital-intensive practice but a negative effect on a labor-intensive one. In a cover crop study, Bergtold et al. (Reference Bergtold, Duffy, Hite and Raper2012) used a dummy variable for off-farm income, but it was not significant. Concerns about complexity and time may outweigh concerns regarding out-of-pocket expenses (Midwest Cover Crops Council, 2015; Ward et al., Reference Ward, Sharpley, Miller, Dick, Hoorman, Fulton and LaBarge2018). Thus, we included operator’s off-farm work days to capture the time constraint and expect that farmers who have more off-farm works days will be less likely to adopt this practice.

Farm characteristics also have been shown to affect adoption. Larger farm size is often associated with adoption. Larger farms may benefit from economies of scale if there are fixed costs associated with a practice. Also, farm size could be a proxy for profitability of the farms. In a cover crop study, Dunn et al. (Reference Dunn, Ulrich-Schad, Prokopy, Myers, Watts and Scanlon2016) found farmers who have larger farms are more likely to plant more cover crops. On the other hand, according to CTIC (2017), one of the concerns about using cover crops for nonusers was time/labor costs that could become an issue for larger farms. So, we suspect that given the nature of the practice, while farmers operating larger farms will be more likely to adopt cover crops, the size effect will not be linear. Economies of scale with respect to equipment and information may benefit larger farms, but for very large farms, this effect is expected to level off. Additionally, the time constraint varies over the year; farmers plant and terminate cover crops when they are also planting and harvesting cash crops. Therefore, if farmers have more labor availability, it may enable them to more easily manage cover crops. However, labor costs are also one of the concerns farmers have about using cover crops (CTIC, 2017; Midwest Cover Crops Council, 2015). So, farmers with higher labor costs may be reluctant to adopt cover crops because of additional costs and labor management requirements. We include the number of hired laborers in the analysis and expect farmers who hire more workers to be more likely to use cover crops but that the relationship may be nonlinear. In addition, we included an interaction term between farm size and labor to capture potential interdependencies that may exist in farming systems, such as the higher labor requirements of systems that include animal production.

Land tenure determines whether farmers can make sustainable management plans and whether they would receive the future benefits from planting cover crops (McBride and Daberkow, Reference McBride and Daberkow2003; Soule, Tegene, and Wiebe, Reference Soule, Tegene and Wiebe2000). In a cover crop adoption study, Bergtold et al. (Reference Bergtold, Duffy, Hite and Raper2012) found farmers renting more area were less likely to use cover crops. In a survey, a farmer said that “farmers are not farming rented land like the ground that they own” and “renters are afraid to make long-term investments” (Midwest Cover Crops Council, 2015, p. 23). We therefore include the percentage of acres owned of the total operated acres (owned plus rented) and expect that farmers with more owned acres are more likely to adopt cover crops.

Production diversity on a farm may create synergies and thus be an indicator of adoption of cover crops. Farmers planting a variety of crops may have a lower barrier to trying cover crops because of their equipment infrastructure and experiences with multiple crops (Arbuckle and Roesch-McNally, Reference Arbuckle and Roesch-McNally2015; Singer, Nusser, and Alf, Reference Singer, Nusser and Alf2007). For example, a particular planter or drill can handle different sizes or types of seeds. Thus, we included the number of field cropsFootnote ² and expect farmers who planted more crops will be more likely to use the practice. Farmers with livestock could get additional benefits from cover crops (Arbuckle and Roesch-McNally, Reference Arbuckle and Roesch-McNally2015; Singer, Nusser, and Alf, Reference Singer, Nusser and Alf2007). Farmers having livestock can receive short-term economic returns from planting cover crops because they could provide grazing or forage for livestock (Lazarus and Keller, Reference Lazarus and Keller2018; Midwest Cover Crops Council, 2015). Specifically, we included a variable for having cattle and calves on their farm and expect those farmers to be more likely to adopt cover crops.

Controlling weeds is one of the other potential benefits of using cover crops. Mirsky et al. (Reference Mirsky, Ryan, Teasdale, Curran, Reberg-Horton, Spargo, Wells, Keene and Moyer2013) found cover crop mulches can reduce weed germination by reducing sunlight and physical contact between weed seeds and soil. Also, Myers and Watts (Reference Myers and Watts2015) found 28% of respondents used cover crops for weed control. Thus, we included percentage of cropped land (including pasture) treated for weeds and expect that farmers who treated land for weeds will be more likely to plant cover crops.

Crop insurance might have either a positive or negative effect on adoption of cover crops. Kaye and Quemada (Reference Kaye and Quemada2017) argue that using cover crops can reduce risk from extreme weather conditions, such as severe storms and drought, and other studies show cover crops are beneficial for the risk-averse farmer (Lu et al., Reference Lu, Watkins, Teasdale and Abdul-Baki2000; Sarrantonio and Gallandt, Reference Sarrantonio and Gallandt2003). However, farmers who used cover crops had an issue with possibly losing eligibility for crop insurance because of a tight deadline for cover crop termination (Marzen and Ballard, Reference Marzen and Ballard2016). So, risk-averse farmers in some areas may be reluctant to use cover crops. Thus, the effect of federal crop insurance is unclear based on the literature. In our model, we use a dummy variable for whether they enrolled in federal crop insurance to capture these potential effects.

Farmers may adopt soil conservation and nutrient management practices because of on- and off-farm benefits of the practices. One popular perceived benefit is reducing soil erosion, which can improve productivity (CTIC, 2016; Midwest Cover Crops Council, 2015) in addition to improving water quality. Thus, we included whether the soybean field on the operation was classified as highly erodible by the NRCS and expect this to be positively correlated with adoption.

Previous adoption of other management practices or technologies that improve water quality could be an indicator for adoption of cover crops (Schimmelpfennig and Ebel, Reference Schimmelpfennig and Ebel2016; Weber and McCann, Reference Weber and McCann2015). Cover crops may improve water quality by reducing nutrient loss (Sharpley and Smith, Reference Sharpley, Smith and Hargrove1991; Zhang et al., Reference Zhang, Tan, Zheng, Welacky and Wang2017). A study found farmers adopted practices for reducing fertilizer use if they believed the practices would improve water quality (Feather and Amacher, Reference Feather and Amacher1994). Farmers using soil tests can get detailed information about nutrients in their soil, which could help farmers use less fertilizer (Williamson, Reference Williamson2011) and reduce nutrient runoff. Soil tests also provide information on organic matter in the soil, so these farmers would have information on this soil health benefit of cover crops. Traditional tillage tends to increase soil erosion and reduce water infiltration, thus increasing water and nutrient runoff (Lal, Reicosky, and Hanson, Reference Lal, Reicosky and Hanson2007). Using conservation/no-tillage can reduce these problems and thus reduce water pollution (Gebhardt et al., Reference Gebhardt, Daniel, Schweizer and Allmaras1985). We include adoption of reduced fertilizer use practices, soil testing, and conservation tillage/no-till in our model and hypothesize that farmers using these practices and technologies will be more likely to adopt cover crops.

The federal government has programs that encourage cover crop adoption. Conservation programs subsidize farmers to help them use environmentally friendly agricultural practices including cover crops. These programs include the Environmental Quality Incentives Program (EQIP) and the Conservation Stewardship Program (CSP). We expect farmers who have received funding from these programs will be more likely to adopt cover crops.

General pro-environmental attitudes could be a factor influencing adoption of cover crops and other environmental practices such as no-till. ARMS data do not include explicit questions on environmental attitudes that could be used to test this directly. One of the motivations to invest in solar energy use among early Austrian adopters was environmental awareness (Haas et al., Reference Haas, Ornetzeder, Hametner, Wroblewski and Hubner1999), and this has been confirmed more generally (e.g., Chen, Reference Chen2014). Beckman and Xiarchos (Reference Beckman and Xiarchos2013) found farmers using conservation practices were more likely to be using renewable energy than the average farmer in California (USDA-NASS, 2011). Renewable energy systems, such as solar, are not associated with on-farm or off-farm benefits of cover crops or impacts on water quality. Thus, we included farmers’ use of renewable energy systems (i.e., solar panels, wind turbines, geothermal, or small hydroFootnote ³ ) as a proxy for general pro-environmental attitudes and hypothesize that farmers using them will be more likely to adopt cover crops. These systems are not related to crop production and, because they require a substantial investment, may be an indicator of relatively strong environmental preferences.

As mentioned previously, timing is a very important factor for successful establishment and termination of cover crops. The window for establishment or termination is largely dependent on climate variables, such as growing season and precipitation. As mentioned before, cover crops that are not winter hardy, like legumes, need at least 4 weeks for establishment (Midwest Cover Crops Council, 2015). If regions have longer growing seasons, farmers can have a longer window for seeding cover crops and higher probability of successful establishment. Also, precipitation is another important climate factor for termination. Farmers in dry regions without irrigation need to be careful about termination of cover crops because they can deplete the water available for cash crops. So, farmers in dry regions need to terminate cover crops earlier than in average or wet regions. Thus, we created variables for the 5-year state average of growing season and of county-level precipitation, as detailed in the next section. We expect longer growing season and more precipitation to be associated with a higher likelihood of adoption of cover crops. We used the previous 5 years because the climate is changing (EPA, 2016) and recent events/weather are more likely to affect behavior than weather in the more distant past (Hertwig et al., Reference Hertwig, Barron, Weber and Erev2004; Kaufmanna et al., Reference Kaufmanna, Mannb, Gopala, Liedermanc, Howed, Pretise, Tanga and Gilmore2017; Li, Johnson, and Zaval, Reference Li, Johnson and Zaval2011). In addition, we included an interaction term between growing season and precipitation to capture the fact that either one may be limiting for crop growth. In one model, we also included state dummy variables as controls instead of growing season to capture not only state specific climatic conditions but also policy and farming system differences.

Irrigation can ease the water constraint in drier areas. Farmers using irrigation may use cover crops to increase soil water availability (Bergtold et al., Reference Bergtold, Duffy, Hite and Raper2012) and reduce irrigation costs (Curell, Reference Curell2012). In a cover crop adoption study, Bergtold et al. (Reference Bergtold, Duffy, Hite and Raper2012) used conservation tillage, irrigation, and the total number of conservation practices, but only irrigation was significant, and more irrigation increased adoption.

To summarize, based on the literature, we hypothesize that younger, more educated farmers with less off-farm work will be more likely to adopt cover crops. Larger farms, and those with more labor, a greater percentage of owned land, more cropping diversity, cattle, and more area treated for weeds will be more likely to adopt. Crop insurance may increase or decrease adoption. Renewable energy production, as a proxy for pro-environmental attitudes, as well as adoption of other conservation practices and participation in federal programs, is expected to increase adoption. Adoption will be more likely in areas with longer growing seasons and more precipitation, as well as on farms that irrigate.

4. Methods and data

4.1. Random utility theory and probit model

Decision making for adoption of cover crops can be explained by random utility theory (Train, Reference Train2003). Farmers want to maximize their expected utility when they make a decision regarding adoption. However, there will be some factors that cannot be observed in the data. Thus, a farmer’s expected utility as a function of cover crop adoption can be decomposed into U_ic = V _ic + ϵ_ic , where V_ic is based on observable factors, X_i and ϵ_ic which are unobserved or unobservable factors that affect can affect his or her utility and which are treated as random and assumed to have a normal distribution. The subscript c denotes whether a farmer adopted cover crops or not (1: adopted; 0 otherwise). The probability of individual i adopting cover crops can be written as follows:

$$\matrix{{{\rm {Prob}}(y = 1|X)} { = {\rm {Prob}}({V_{i1}} + {\varepsilon _{i1}} \gt {V_{i0}} + {\varepsilon _{i0}})} \cr {} { \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, = {\rm {Prob}}({\varepsilon _{i0}} + {\varepsilon _{i1}} \lt {V_{i0}} - {V_{i1}}).} \cr } $$ (1)

We use a probit model to estimate the likelihood of adoption because of the dichotomous nature of the dependent variable. If a farmer adopts cover crops, then the independent variable is 1; otherwise (does not adopt) it is 0. The probit regression is

$${\rm{P}}\left( {{y_i} = 1{\rm{|}}{X_i}} \right) = {\rm{\Phi }}\left( {{X_i}\beta } \right),$$ (2)

where ${\rm{\Phi }}\left( \cdot \right)$ is the cumulative normal distribution, X_i = (x ₁, …, x_k ) is a transposed vector including independent variables, and β = (β ₁, …, β_k )′ is a vector of coefficients.

Additionally, we used clustered error terms to correct for spatial heterogeneity at the state level. Observations in the same state could be correlated because of similar policies and climatic conditions, but errors of individuals in other states are less likely to be correlated (Cameron and Miller, Reference Cameron and Miller2015). We can thus increase the efficiency of the estimates by using a clustered standard error term (Rogers, Reference Rogers1993; Wooldridge, Reference Wooldridge2010).