1. Introduction
The Federal government in the United States has been investing in conservation programs for more than a century to improve the flow of ecosystem services for the overall well-being of the nation (Jones, Reference Jones2021). For instance, the 2018 Farm Bill allocated $29.5 billion over five years of mandatory funding for conservation on 13 different conservation programs (USDA, 2023b), including the Conservation Reserve Program (CRP). CRP is a voluntary land retirement program authorized by the Food Security Act of 1985 and reauthorized by the Agricultural Improvement Act of 2018. It remains the country’s largest private land retirement program (USDA, 2024c). CRP is administered by the United States Department of Agriculture (USDA) Farm Service Agency (FSA). It pays a yearly rental payment for a contract period of 10–15 years in exchange for agricultural producers (both landowners and operators) voluntarily removing their environmentally sensitive lands from agricultural production and planting perennial species that improve environmental quality (USDA, 2024c). USDA has continuously supported CRP program since its initiation in 1985. The FSA spent about $1.8 billion in 2023 (USDA, 2024c) and plans to spend about $2.4 billion per year between 2023 and 2032 (Coppess et al., Reference Coppess, Swanson, Paulson, Schnitkey and Zulauf2022).
Although CRP was initially designed to reduce soil erosion, later, it has broadened its focus on a wide range of environmental issues such as wildlife habitat, water quality, and air quality (Stubbs, Reference Stubbs2019), and carbon sequestration (USDA, 2021c). Previous studies have found that CRP increased wildlife populations and enhanced habitat restoration (Adkins et al., Reference Adkins, Roy, Andersen and Wright2019; Geaumont et al., Reference Geaumont, Sedivec and Schauer2017; Harryman et al., Reference Harryman, Grisham, Boal, Kahl, Martin and Hagen2019; Sullins et al., Reference Sullins, Kraft, Haukos, Robinson, Reitz, Plumb, Lautenbach, Lautenbach, Sandercock and Hagen2018), reduced soil erosion and improved soil quality (Dale et al., Reference Dale, Dale, Loren, Loren, David, David, Lacrecia, Lacrecia, Scott and Scott2014; Jobe et al., Reference Jobe, Kalra and Ibendahl2018), and increased soil carbon (Abraha et al., Reference Abraha, Hamilton, Chen and Robertson2018, Reference Abraha, Gelfand, Hamilton, Chen and Robertson2019; Johnson et al., Reference Johnson, Dalzell, Donahue, Gourevitch, Johnson, Karlovits, Keeler and Smith2016; Li et al., Reference Li, Fultz, Moore-Kucera, Acosta-Martínez, Horita, Strauss, Zak, Calderón and Weindorf2017). However, maintaining these environmental benefits depends on continued producers’ voluntary enrollment in CRP.
Agricultural producers can enroll their environmentally sensitive lands in CRP through three signups: General, Continuous, and Grassland (USDA, 2024c). As of 2024, Grassland CRP is the most prevalent, enrolling 9.7 million acres of environmentally sensitive land, followed by Continuous CRP (8.4 million acres) and General CRP (7.8 million acres). In 2024, approximately 199,214 acres of environmentally sensitive lands were enrolled through General signup. As a result, General CRP now accounts for 30% of the total land (25.9 million acres) enrolled under CRP nationwide (USDA, 2024d). This study only focuses on General CRP, where producers submit their offers to enroll their environmentally sensitive lands and get selected based on their land’s potential environmental benefits and costs on a competitive basis (Hellerstein, Reference Hellerstein2017; USDA, 2023a).
The FSA pays an annual rental payment as part of the CRP contract, covers up to 50% of the cost for establishing conservation practices, and provides a conservation plan for these practices (USDA, 2023a). CRP includes practices such as native and non-native perennial grass, softwood and hardwood tree, and wildlife habitat plantings under General CRP and field windbreaks, grassed waterways, filter strips, and riparian buffers under Continuous CRP (USDA, 2024b). Furthermore, General and Continuous signups allow non-emergency and emergency haying and grazing, while Grassland CRP allows haying and grazing as core parts of the program. However, General CRP and Continuous CRP imposes a 25% annual rental payment reduction for non-emergency grazing and haying unless used as a mid-term management technique (USDA, 2020b, 2021b).
Despite CRP’s benefits and widespread promotion, overall enrollment in General CRP has declined over the years due to relatively high crop prices and limited re-enrollment opportunities by lowering the cap from 32 million acres to 24 million acres between 2013 and 2016 (Atkinson et al., Reference Atkinson, Romsdahl and Hill2011; Bigelow et al., Reference Bigelow, Claassen, Hellerstein, Breneman, Williams and You2020; Morefield et al., Reference Morefield, LeDuc, Clark and Iovanna2016; USDA, 2020a). The area under General CRP has declined by more than half from 17.9 million acres in 2015 to 7.8 million acres in 2024, whilst the area under Continuous CRP increased from 6.2 million acres to 8.4 million acres during the same period (USDA, 2015, 2024a). However, the area under Grassland CRP increased from 89,052 acres to 9.7 million acres in the same period (USDA, 2017, 2024a ). The appeal may be attributed to the program’s allowance for largely unrestricted grazing and haying practices while receiving an annual payment.
Given the declining trend in General CRP enrollment, a deeper understanding of the underlying drivers of enrollment becomes increasingly critical for successful design, implementation, and continuation. This is particularly pertinent, as only a few studies have explored the factors affecting agricultural producers’ decisions to enroll in CRP. Cramton et al. (Reference Cramton, Hellerstein, Higgins, Iovanna, López-Vargas and Wallander2021) found that excessively tight price bid caps negatively affect CRP enrollment and reduce the program’s efficiency and cost-effectiveness. Additionally, a qualitative study by Wachenheim (Reference Wachenheim2019) reported that positive attitudes and perceptions about conservation, participation in different conservation programs, and CRP specifically contribute to increased enrollment. Morrison and Hardy (Reference Morrison and Hardy2014) applied an institutional analysis and development framework. They discovered that the biophysical environment (less productive and environmentally sensitive land), behavioral norms (positive attitudes towards conservation and sustainability), and institutional rules (flexible regulations for choosing conservation practices and interagency cooperation) significantly contribute to the enrollment and implementation of CRP. Lambert and Sullivan (Reference Lambert and Sullivan2006) reported that the percentage of land enrolled in CRP was negatively related to the presence of high-value crops and positively related to farm ownership and participation in other conservation programs. Barnes et al. (Reference Barnes, Dayer, Gramza, Sketch, Dwyer and Iovanna2023) observed that intrinsic motivations to improve the beauty of the field and to increase the value of wildlife promoted landowners’ intention to enroll in CRP. Thapa et al. (Reference Thapa, Chapagain, McMurry, Smith and Joshi2024) noted that positive attitudes, subjective norms, and perceived behavior increase the likelihood of intention to enroll in CRP. Lim and Wachenheim (Reference Lim and Wachenheim2022) conducted a discrete choice experiment and analyzed data using a mixed-ordered logit model. They observed that older and male producers were more willing to enroll in CRP than their younger and female counterparts. They also reported that higher rental payments and cost share, a mid-contract adjusted payment, and more flexible land use increased interest in enrollment in CRP. Brimlow (Reference Brimlow2008) utilized a two-sided Tobit model and estimated that lower land productivity in a land parcel increased the likelihood of CRP enrollment.
Although several studies have investigated factors affecting General CRP at a single point in time (e.g., either enrolled or not enrolled at the time of the survey) using cross-sectional survey data (Konyar and Osborn, Reference Konyar and Osborn1990; Lim and Wachenheim, Reference Lim and Wachenheim2022; Thapa et al., Reference Thapa, Chapagain, McMurry, Smith and Joshi2024; Wachenheim et al., Reference Wachenheim, Roberts, Dhingra, Lesch and Devney2018), they assume that those producers who are currently not enrolled may remain non-enrollees forever. However, no study has considered factors that might influence the length of time the producer waits from first hearing about the program or the year in which a producer began managing/operating a farm until they enroll in General CRP. Because General CRP enrollment is typically a complex, multi-stage process (Hellerstein, Reference Hellerstein2017), it is critical to consider enrollment patterns over time (Dimara and Skuras, Reference Dimara and Skuras2003; Feder and Umali, Reference Feder and Umali1993; Konyar and Osborn, Reference Konyar and Osborn1990). Therefore, to bridge this gap, we employed a discrete-time duration analysis approach to examine the factors influencing the time that transpired before an agricultural producer enrolled in General CRP, starting at either the initiation of CRP (in 1985) or the year in which a producer began managing/operating a farm – whichever is the latest. This model assumes that producers who are not currently enrolled may enroll in the future if they have conducive conditions to enroll in CRP. Thus, investigating the determinants of the duration of General CRP enrollment decision could help to illuminate barriers to participation and inform evidence-based and context-specific policies for accelerating participation in General CRP. Furthermore, this model helps evaluate the impact of CRP processes, such as the Sign-up ranking process, enrollment process, rules, etc., on the length of time taken to enroll in General CRP.
2. Materials and methods
2.1. General CRP
Enrollment in General CRP involves several steps (Figure 1). First, the FSA announces a specific application submission date, which usually occurs annually. Once the application period opens, producers submit an offer/bid for a particular parcel of their environmentally sensitive agricultural lands with a proposed conservation practice. Annual payment rates are capped by a parcel’s soil rental rate (SRR). The SRR is calculated using parcel-specific soil productivity measures, county-level average estimates of non-irrigated cropland rental rates, and professional expertise of field office staff (Hellerstein, Reference Hellerstein2017). After submission, FSA ranks the submitted offers based on the Environmental Benefits Index (EBI), which considers wildlife habitat potential (10–100 points), water quality benefits (0–100 points), erosion reduction (0–100 points), post-CRP environmental benefits (0–50 points), air quality benefits (3–45 points) and annual payments (USDA, 2021a). The FSA then sets a threshold for EBI after the distribution of offers is known, to accept only those offers that meet or exceed the threshold. In addition, to be eligible for General CRP, the land must have a crop history (the land must have been planted to an agricultural commodity in four of the six crop years before the year of the most recent Farm Bill) or be expiring CRP acres. Furthermore, the land must have considerable erosion potential or be in a national or state CRP conservation priority area (USDA, 2024a). The number of acres enrolled is subject to both a National Cap mandated by the most recent Farm Bill and a county limit of 25% cropland (Hellerstein, Reference Hellerstein2017). Finally, producers enroll their environmentally sensitive lands in General CRP for 10–15 years and, upon the contract expiration, either seek re-enrollment or exit the program.
General Conservation Reserve Program enrollment procedure.
2.2. Study area
Our survey population included agricultural producers in six southeastern states (Alabama, Florida, Georgia, Mississippi, North Carolina, and South Carolina) either currently (as of 2023) or previously enrolled in the program (Figure 2). One reason for choosing this study area is the rapid pace of land-use change in the Southeast region as a result of economic and population growth and urban expansion (Napton et al., Reference Napton, Auch, Headley and Taylor2010; Zhao et al., Reference Zhao, Liu, Sohl, Young and Werner2013). Additionally, this region is significant from a conservation point of view. According the National Environment Education Foundation (2024), the Southeast includes one of the world’s oldest and best-preserved freshwater systems. The United States Fish and Wildlife Service (2019) National Wetlands Inventory reported that stormwater runoff significantly contributes to water quality problems in North Carolina, and wetlands are the most essential filters of agricultural runoff in the coastal region. Moreover, the region is also a part of the North American Biodiversity Hotspot (Walls, Reference Walls2014), and is, therefore, a critical target for conservation efforts. Finally, CRP enrollment fell roughly 50% between 2015 and 2023 to 752 thousand acres in the study area (2023c, USDA, 2015).
Land use map of the six southeastern states selected for the study (USDA, 2021d).
2.3. Sampling method and data collection
We used the USDA FSA administrative data as a sampling frame to select farmers enrolled in CRP (General and Continuous CRP). We followed a stratified random sampling strategy to select CRP participants from six southern states who were previously or currently enrolled in the CRP as of 2023. The survey was sent to a total of 5,000 randomly selected CRP participants; the percentage randomly selected from each state was chosen to be proportional to the percentage of CRP participants in each state from a total of six states (USDA, 2023c, 2023d). The Institutional Review Board (#00007239) approved the mail survey at the University of Georgia, and the mail packet included a survey questionnaire and a pre-addressed stamped return envelope to return the completed questionnaire. The survey included questions related to socio-demographics (gender, age, educational status, annual income, employment status, etc.), farm characteristics (farm tenure, total owned and operated land, and distance of home from land), information regarding producers’ awareness of, sentiments about, and participation in CRP.
2.4. Conceptual framework
Producers with eligible agricultural lands can choose to enroll in General CRP. According to the random utility theory, they anticipate the costs and direct and indirect benefits when deciding whether to enroll or not (Ochieng et al., Reference Ochieng, Afari-Sefa, Muthoni, Kansiime, Hoeschle-Zeledon, Bekunda and Thomas2022; Pham et al., Reference Pham, Chuah and Feeny2022). A rational producer compares the expected utility received during the contract period (between 10 and 15 years after enrollment) with the expected utility received during the same period of non-enrollment. In complex and uncertain situations involving risk, attitudes and social norms can guide decision-making more effectively than relying solely on calculated expected utility. Regarding the benefits of non-enrollment in CRP, producers presumably continue to earn income from crop production or other land uses. Accordingly, under the random utility framework, the expected utility for the i th producer in any enrollment year (t) from the enrollment in CRP can be represented as (π eit ), which is compared to the expected utility with non-enrollment (π neit ). The i th producer will choose to participate in CRP in any given year (t) if the expected utility (benefit) from enrolling in CRP is greater than the utility (benefit) of not enrolling in CRP (Plantinga et al., Reference Plantinga, Alig and Cheng2001). The expected net utility (U i *) by the producer in any given enrollment year (r t ) can be represented as:
$$U_{i}^{*}=\left[\pi _{eit}-\pi _{neit}\right]\gt 0$$
Enrollment in General CRP is a stepwise (i.e., multi-stage) and dynamic process (i.e., the enrollment process can change based on eligibility criteria, changes in program rules, changes in environmental priorities, etc.) affected by policy changes in which producers, based on their knowledge and experiences, form an attitude towards the program (i.e., expected net benefits, suitability with existing agricultural conditions, and CRP attributes) over time and then decide to enroll or not enroll at a particular time. However, the decision to enroll in General CRP also varies with individual producers, which might be related to their household socio-demographics, farm characteristics, perspectives on CRP enrollment process, and perceptions and attitudes. Furthermore, external factors such as agriculture and energy policies could also influence CRP enrollment. We used a discrete-time duration analysis model to examine the key determinants influencing how soon enrollment in General CRP occurs. This model assumes that producers who have higher expected returns from enrolling in General CRP enroll sooner. The early enrollees are less likely to be influenced by time-varying factors, as they have already committed to the program and are receiving its benefits.
2.5. Econometric model specification
Duration analysis deals with the simultaneous adoption and diffusion of practice or technology within a dynamic process that helps in identifying barriers to adoption and strategies to promote faster and more widespread diffusion (Alcon et al., Reference Alcon, de Miguel and Burton2011; Genius et al., Reference Genius, Koundouri, Nauges and Tzouvelekas2013). More recently, duration analysis has been widely used in the adoption of happy seeder technology (Kaur et al., Reference Kaur, Singh, Anand and Singh2023), laser land leveler technology (Sheikh et al., Reference Sheikh, Mugera, Pandit, Burton and Davies2022), conservation tillage (D’Emden et al., Reference D’Emden, Llewellyn and Burton2006; Gao et al., Reference Gao, Ren, Lu and Feng2023), sustainable agricultural practices (Dadi et al., Reference Dadi, Burton and Ozanne2004; Yigezu et al., Reference Yigezu, Mugera, El-Shater, Aw-Hassan, Piggin, Haddad, Khalil and Loss2018), conservation agriculture (Khataza et al., Reference Khataza, Doole, Kragt and Hailu2018), and organic farming (Burton et al., Reference Burton, Rigby and Young2003; Kallas et al., Reference Kallas, Serra and Gil2010). Our study used duration analysis to examine the factors influencing the length of a spell, i.e., the length of time taken to enroll in General CRP. The spell starts when CRP enrollment is initiated for the first time (in 1985) or the year in which the producer began managing/operating the farm, if that is later; the spell ends when a producer enrolls the farm in the General CRP. Producers who enrolled before the end of the survey are called uncensored producers. We only included uncensored producers because, due to our sample frame, our sample does not include those who have never participated in the program. In cross-sectional surveys, the researchers are dependent on recall data on the first enrollment date of General CRP or the year in which a producer began managing/operating the farm, if that is later; these dates are usually reported on grouped or interval-censored scales, such as months or years (An and Butler, Reference An and Butler2012; Burton et al., Reference Burton, Rigby and Young2003; Khataza et al., Reference Khataza, Doole, Kragt and Hailu2018). It is reasonable to apply a discrete-time duration analysis model over the continuous-time duration analysis approach for interval-censored (discrete time) data (Jenkins, Reference Jenkins1995; Khataza et al., Reference Khataza, Doole, Kragt and Hailu2018; Tiller et al., Reference Tiller, Feleke and Starnes2010). Thus, we applied the discrete-time duration model, and the model specification for it is as follows (Jenkins, Reference Jenkins1995; Khataza et al., Reference Khataza, Doole, Kragt and Hailu2018):
Let ′T′ be a random variable, which represents the discrete-time (T ≥ 0) before enrollment in General CRP (in years); f(t) represents a continuous probability density function of a random variable (T), where ′t′ is the time duration from initiation of General CRP or the year in which a producer began managing/operating the farm, if that is later, until enrollment in General CRP; F(t) refers to the corresponding cumulative density function of a random variable (T) (describing the probability that a producer will have enrolled in General CRP before the time ′t′), which is given by:
$$ F\left(t\right)=\int _{0}^{t}f\left(s\right)\,{\rm d}s= {\rm Pr} \; \left(T\leq t\right),\quad t\geq 0 $$
The survival function (S(t)) refers to the probability of surviving (i.e., not enrolling in General CRP) until time ′t′, which can be expressed as:
$$ S\left(t\right)=1-F\left(t\right)={\rm Pr}\; \left(T\gt t\right) $$
The hazard function (h(t)) estimates the probability of enrolling in General CRP at the time ′t′, given that the producer has not enrolled before the time ′t′, which can be specified as follows.
$$ h\left(t\right)=\lim _{{\rm d}t\rightarrow 0} \left[{\Pr \left(t\leq T\lt t+{\rm d}t|T\geq t\right) \over {\rm d}t}\right]=\lim _{{\rm d}t\rightarrow 0} \left[{{\rm F}\left({\rm t}+{\rm d}t\right)-{\rm F}\left({\rm t}\right) \over {\rm d}t\left(1-F\left(t\right)\right)}\right]={f\left(t\right) \over S\left(t\right)} $$
The hazard function depends on the elapsed time length and explanatory variables/covariates (e.g., household socio-demographics, farm characteristics, CRP attributes, institutional factors) on the time duration until enrollment. A proportional hazard specification examines the effects of both the baseline hazard and explanatory variables on a hazard function (Jenkins, Reference Jenkins1995; Khataza et al., Reference Khataza, Doole, Kragt and Hailu2018). The baseline hazard function is only a function of time but not any covariates, and it is common for all producers, i.e., enrollment is associated with time only. In contrast, the relative hazard is contingent on explanatory variables or covariates, which vary with each producer (Yigezu et al., Reference Yigezu, Mugera, El-Shater, Aw-Hassan, Piggin, Haddad, Khalil and Loss2018). For the estimation of the proportional hazard model, parametric duration models (Weibull, exponential, logistic, log-normal, log-logistic, and Gompertz) were used for continuous-time duration data (Kiefer, Reference Kiefer1988), while logistic and complementary log–log specifications were used for discrete-time duration data (Khataza et al., Reference Khataza, Doole, Kragt and Hailu2018). The generalized representation of the proportional hazard model is specified as follows:
$$ h\left(t,X\right)= h_{0}\left(t\right)\exp \left(\beta^\prime X\right) $$
where h(t, X) is the hazard rate at the time ′t′; h 0(t) is the baseline hazard function, which depends on ′t′ (but not on any covariates ′X′); exp (β′X) is a producer-specific non-negative function of covariates ′X′, which scales the baseline hazard function.
We used a complementary log–log specification for the proportional hazard model because it is the discrete-time equivalent of the continuous-time proportional hazard model (Bontemps et al., Reference Bontemps, Bouamra-Mechemache and Simioni2013; Jenkins, Reference Jenkins1995; Khataza et al., Reference Khataza, Doole, Kragt and Hailu2018), which can be represented as follows:
$$\ln \left(-\ln \left[1-h\left(t,X\right)\right]\right)=h_{0}\left(t\right)\exp \left[X\left(t\right)\beta +e_{i}\right]=\alpha \gamma _{t}+\beta^\prime X+e_{i} $$
where h(t, X) is the hazard rate for the t th time interval; h 0(t) is the baseline hazard function; γ t is a time variable capturing the pattern of duration dependence (baseline hazard); α and β are the unknown parameters to be estimated; X is a vector of covariates; e i is an error term representing producer-specific characteristics, unobserved variables (e.g., market conditions, policy changes, motivation, risk tolerance, life-events, etc.). All coefficients for the discrete-time duration analysis model are reported in terms of hazard ratios (HRs), which are reported in the exponential form (e β′). The coefficient of HR (the null hypothesis of HR is not different from one) is interpreted as a proportional shift in the hazard function to a per unit change in the covariates (Beyene and Kassie, Reference Beyene and Kassie2015). For instance, an HR of two for a particular covariate implies a doubling of the hazard rate with a one-unit increase in the covariate (Khataza et al., Reference Khataza, Doole, Kragt and Hailu2018). This model also assumes a higher hazard rate for early enrollees than for late enrollees in General CRP. In this complementary log–log model, explanatory variables or covariates can enter the model in two ways: time-invariant covariates (e.g., gender) enter as fixed predictors across all time intervals, while time-varying covariates (e.g., policy changes) are allowed to change at each discrete time point. These covariates influence the hazard or probability of the event (i.e., enrolling in General CRP) occurring at each time point, conditional on survival up to that point.
2.6. Data and variable description
The dependent variable in our model is the time duration between the year 1985, when CRP was first initiated or the year a producer begins to manage/operate the farm, if that is later, until the moment the producer enrolls in General CRP. The explanatory variables in the model include socio-demographic factors (gender, age, employment status, annual income, educational status, and race), farm characteristics (farm tenure type and total area of owned and operated land), and awareness about CRP incentives and perspective with CRP attributes (sign-up ranking process, enrollment process (office and site visit), rules and regulations, grass establishment, and tree establishment). A description of all model variables appears in Table 1.
Description of dependent and explanatory variables used in the discrete-time duration analysis model
Note: *Total land area: We rescaled the total land area by dividing by 100. As the original variable’s value decreases by 100, its original regression coefficient (0.00016) increases by 100 to balance this change in the original variable’s value. However, we reported the hazard ratio in our study; it changes the original hazard ratio (1.00016) to a new hazard ratio (1.016) after rescaling.
** CRP awareness: CRP awareness means knowledge about CRP objectives, eligibility criteria for enrollment, knowledge of the application process for enrollment, knowledge of enrollment types, knowledge about program benefits.
For the estimation of a discrete-time duration analysis model, the original data structure was reorganized from one producer with one observation format to one producer with multiple observation formats (Jenkins, Reference Jenkins1995). In other words, a unique producer can have as many data rows of observations as the total number of time intervals (i.e., years) with a risk of the event (i.e., enrolling in CRP) occurring. A unique producer consists of data rows of zeros (since the inception of General CRP Sign-up in 1985 or the year in which the producer began managing/operating the farm, if that is later) and is replaced with a value of 1 when the producer had enrolled in General CRP in time t ≤ T. Reorganizing our data in this way also helps to incorporate time-varying variables in the model and easy estimation of log-likelihood for the model (Jenkins, Reference Jenkins1995).
3. Results and discussion
3.1. Descriptive statistics
A total of 761 surveys were returned with a 15.2% response rate. Out of 761 producers, we used only the 416 observations reporting about the time duration (dependent variable) between the year 1985, when CRP was first initiated or the year a producer begins to manage/operate the farm, if that is later, and the moment the producer enrolls in General CRP. Therefore, we used these 416 observations of our dependent variable as the final dataset for our regression analysis because regression analysis only considers an equal number of observations of the dependent and independent variable samples. Descriptive statistics show that most producers (83.0%) were currently enrolled in General CRP, and the average duration from initiation until enrollment in the General CRP for all producers in our sample was seven years (Table 2). The average total land area owned by the sampled producers was about 475 acres, ranging from 4 to 9600 acres (Table 2).
Summary of producers currently and previously enrolled in CRP, time length until enrollment, and owned agricultural land
Note: 1 ha = 2.47 acres; Total number of observations varies with each question answered by the producers. * In our sample, the number of observations for the duration is much smaller than the total number of observations because many producers did not indicate how long they owned the farm or when they first enrolled in CRP. As it is a recall method, they might be unable to recall the exact year when they first enrolled in CRP or began operating the farm. These observations represent the final dataset used for the discrete-time duration analysis model.
In our sample, we noticed a cyclic rise and fall in General CRP enrollment due to the availability of signups, the acreage cap in each farm bill, the availability of funds, market prices of agricultural crops, and other related reasons (Figure 3).
Number of agricultural producers enrolled in the Conservation Reserve Program General signup by year.
Around 75.1% of producers were male, and 24.1% were female; among those producers, more than three-fourths (77.4%) were 65 years of age and above (Table 3). In our sample, nearly two-thirds (64.9%) of the producers retired, and roughly one-quarter (23.4%) of producers worked full-time (off-farm). More than 13.7% of the producers had an annual income between $25,000–$49,999, 20.1% had an annual income between $100,000–$199,999, and 21.3% of producers did not disclose their annual income. Around 33.7% of producers had bachelor’s degrees, whilst 26.9% had graduate degrees. Around 71.7% of producers owned and operated agricultural land, while the rest only owned land. More than 90.0% of producers were white, while 2.5% were Black (as compared to 1.4% of the Black farmers in the region). Nearly two-thirds (63.5%) of producers had either a positive or very positive perspective on CRP signup ranking process (Table 4). Furthermore, more than three-fourths (77.2%) of producers had either positive or very positive perspectives on the General CRP enrollment process in terms of office and site visits for enrollment. Forty percent of producers had a positive perception about CRP rules and regulations on conservation practices and maintenance, while nearly one-third (32.4%) reported a neutral perception. Around 40.0% of producers had a neutral perspective on grass establishment (i.e., the importance of grass establishment in land management and conservation without emphasizing its benefits or shortcomings), followed by 21.0% of producers who had a positive perspective (i.e., more awareness about the environmental and economic benefits from grass establishment). In comparison, more than two-thirds (70.9%) of producers had a positive or very positive perspective on tree establishment (i.e., more awareness about the environmental and economic benefits of tree establishment) (Table 4). Around 7.0% of producers reported that CRP awareness is a major challenge, while 55.0% considered it not a challenge for enrollment in General CRP.
Descriptive statistics of southeastern US agricultural producers’ socio-demographic attributes in CRP General Sign-up
Note: The total number of observations varies with each question answered by the producers.
Descriptive summary of southeastern US agricultural producers’ perspective on various aspects of CRP
Note: Numbers in parentheses represent the percentage; the total number of observations varies with each question answered by the producers.
3.2. Regression results of discrete-time duration analysis
We have reorganized our data from one producer with one observation format to one producer with multiple observation formats. Specifically, each of the 416 producers can have up to 38 yearly observations from 1986 to 2023, totalling 15,808 observations. For example, if a producer enrolled in 1995, they receive a value of 0 for each year from 1986 to 1994, and a value of 1 in 1995. All subsequent years (1996–2023) are removed, resulting in 28 dropped observations. Similarly, a producer who enrolled in 2000 is assigned 0 from 1986 to 1999, and 1 in 2000. Observations from 2001 onward are removed, eliminating 23 observations for that producer. Following this approach across all the producers, we removed post-enrollment years, ultimately retaining 1,230 observations relevant for the analysis. Our complementary log–log model was estimated using maximum likelihood estimation using STATA 18. The discrete-time duration analysis showed that the baseline hazard (HR = 1.72) function rises with elapsed survival time. In other words, the likelihood that producers enroll their environmentally sensitive agricultural lands in CRP General signup increases over time, irrespective of their characteristics. This baseline hazard ratio is also influenced by the time producers owned or started operating a farm. Sheikh et al. (Reference Sheikh, Mugera, Pandit, Burton and Davies2022) have also observed that the baseline hazard function rises with elapsed survival time; that is, the adoption of the innovation (in their case, a laser land leveler) increased as time went by (i.e., years) at risk from the first introduction of the innovation. Figure 4 shows a cumulative enrollment over time. We found that more than half of the producers enrolled in General CRP Sign-up by 2005, and there was a sharp increase in General CRP enrollment in 2008 and 2013 (Figures 3 and 4).
Cumulative enrollment in General CRP.
Younger producers enrolled their environmentally sensitive lands faster in General CRP than their older counterparts (Table 5). This may be because younger producers, particularly those recently attracted to the Sunbelt (Glaeser and Tobio, Reference Glaeser and Tobio2008), are more likely to adopt conservation programs as they see them as an opportunity for long-term benefits than older producers. Additionally, younger producers might be more financially constrained and risk-averse than older producers, and enrolling in CRP could provide a stable income for them, hence allowing them to enroll faster than older producers. Our finding corroborates those of Konyar and Osborn (Reference Konyar and Osborn1990), who reported that the older the producer, the lower the probability of participation in CRP. In contrast, Parks and Kramer (Reference Parks and Kramer1995) and Wachenheim et al. (Reference Wachenheim, Roberts, Dhingra, Lesch and Devney2018) noted that the likelihood of enrollment in CRP increased with age in the Prairie Pothole region of the country and across the nation. Similarly, Adhikari et al. (Reference Adhikari, Grala, Grado, Grebner and Petrolia2022) and McLean-Meyinsse et al. (Reference McLean-Meyinsse, Hui and Joseph1994) also noted that older landowners are more willing to participate in CRP than their younger counterparts in Louisiana and across the United States.
Complementary log–log model results predicting the length of time until enrollment in General CRP
Note: The default categories for the independent variables in the model have been taken from the explanatory variables in Table 1. The default categories are the lowest values for the corresponding explanatory variables.
*** p < 0.01, ** p < 0.05, * p < 0.1. The null hypothesis is that the parameter = 1.
The total land area owned by the producer (HR = 1.02) significantly reduced the length of time until enrollment in General CRP (Table 5). This may be because producers with more land can enroll some portion of the total land while still retaining enough in production to withstand yield risk. Similarly, Lambert et al. (Reference Lambert, Sullivan and Claassen2007) reported that operators of larger farms are more likely to enroll in CRP. In addition, Adhikari et al. (Reference Adhikari, Grala, Grado, Grebner and Petrolia2022) indicated that landowners with relatively large cropland parcels were more satisfied with CRP than those who owned relatively smaller parcels across the United States and had a greater chance of enrolling in CRP. In contrast, Konyar and Osborn (Reference Konyar and Osborn1990) observed that the rate of participation in CRP by large farms was lower than that among smaller farms in the United States, while the effect is constrained by an annual adjusted gross income eligibility limitation (must be less than $900,000), the trend toward consolidation is increasing the average size of farms (Graddy, Reference Graddy2024).
Our findings reported that producers who were more familiar with CRP and faced fewer challenges (knowledge about the application process, enrollment types, and program benefits) enrolled faster (HR = 1.18) than producers who were unaware of CRP and had faced major challenges (Table 5). This finding aligns with Wachenheim (Reference Wachenheim2019), who also indicated that awareness about CRP increases enrollment. Similarly, Esseks and Kraft (Reference Esseks and Kraft1988) found that producers did not participate in the first four CRP signup periods due to being unaware of their eligibility, CRP payments, or the program’s basic incentive structure. Collectively, these results suggest that raising awareness about CRP could lead to faster enrollment in General CRP. Furthermore, Adhikari et al. (Reference Adhikari, Grala, Grado, Grebner and Petrolia2022) observed that an increase in the frequency of contact with federal agencies (i.e., more awareness) was associated with higher satisfaction with CRP across the United States and enrollment rates.
Positive perspectives regarding various aspects of CRP enrollment process, especially how offers are ranked and selected (HR = 1.38), accelerated enrollment in General CRP (Table 5). General CRP is a competitive program, and offers are ranked based on EBI scores. Positive perspectives may indicate a willingness to adjust scores to increase the likelihood of offer acceptance. Thus, providing more information on the ranking process and how producers can affect their EBI scores may accelerate enrollment. Vukina et al. (Reference Vukina, Zheng, Marra and Levy2008) also emphasized that combination of low bids and environmental scores of individual plots could increase EBI score and ultimately increased the chance of being selected in the submitted bidding for enrollment in CRP.
Our results showed that producers with a positive perception of rules and regulations in the CRP process (HR = 0.934) took slightly more time to enroll in General CRP compared to producers with a negative perception of rules and regulations (Table 5), although the estimate is not statistically significant. This finding may be because even with a positive perception of rules and regulations, producers might be more cautious due to uncertainties and risks concerning market conditions and policy changes and delay enrollment in General CRP.
Positive perspectives on tree establishment (HR = 1.55) increase the speed of enrollment in General CRP (Table 5). This result may be due to more familiarity with afforestation and the economic significance of tree production in this region than grass establishment. Tree establishment practices might also motivate producers to enroll in General CRP because conservation-oriented producers are more likely to be concerned about the various ecosystem services, such as fresh air and carbon sequestration from trees. However, various farmer groups have varying perspectives on ecosystem services (Teixeira et al., Reference Teixeira, Vermue, Cardoso, Claros and Bianchi2018). In addition, producers may plant trees because trees could symbolize their status and public recognition in societies (Barona et al., Reference Barona, Wolf, Kowalski, Kendal, Byrne and Conway2022). Producers could receive additional benefits from the tree harvest upon exiting General CRP or Continuous CRP at the end of the contract period. Moreover, Dale et al. (Reference Dale, Dale, Loren, Loren, David, David, Lacrecia, Lacrecia, Scott and Scott2014), Jobe et al. (Reference Jobe, Kalra and Ibendahl2018), and Onianwa et al. (Reference Onianwa, Wheelock, Dubois and Warren1999) indicated that tree establishment under CRP reduced soil erosion in Iowa and Missouri, the High Plains, and Alabama, which increased favorability and accelerated enrollment. Hence, these results suggest that positive perspectives of tree establishment could accelerate enrollment in General CRP. Increased support for pruning, thinning, and disease management could similarly speed up enrollment in General CRP. In contrast, the factors associated with CRP enrollment process and perspectives on grass establishment had minimal effects on the time until enrollment in General CRP.
We found farm tenure to have little effect on time until enrollment in General signup. Konyar and Osborn (Reference Konyar and Osborn1990) observed that non-operating owners are more likely to enroll their eligible acres in CRP than owner-operators because owner-operators are more concerned about short-term profit and production. Similarly, gender, education, income, employment status, and race of the producers were not statistically significant predictors of time to enrollment in General CRP. Hence, we did not include these variables in the final model. Lute et al. (Reference Lute, Gillespie, Martin and Fontaine2018) did not find significant demographic differences among the landowners on CRP enrollment. Our results showed that male and female producers displayed no significant difference in the length of time until enrollment in General CRP. However, Hitchner et al. (Reference Hitchner, Kadam, Bolques, Harvey, Perry, Best, Atkins, Burke, Larson and Stukes2024) and Lim and Wachenheim (Reference Lim and Wachenheim2022) indicated that female producers enrolled less land overall than male producers across the southeastern United States and in the Prairie Pothole Region, respectively. For income, Plantinga et al. (Reference Plantinga, Alig and Cheng2001) found that an increase in median household income decreased participation in CRP in nine states. Moreover, the effect of Black producers on the length of time until enrollment in General CRP was insignificant, possibly due to the limited number of Black producers in our sample.
4. Conclusion
CRP enables agricultural producers to voluntarily remove their environmentally sensitive land temporarily from production for a contract period of 10–15 years in exchange for a yearly rental payment and cost-share for conservation-focused management practices. Previous studies have demonstrated that financial incentives, such as an annual payment rental rate, are not the only motivator for enrollment decisions in CRP (Barnes et al., Reference Barnes, Dayer, Gramza, Sketch, Dwyer and Iovanna2023; Lambert and Sullivan, Reference Lambert and Sullivan2006; Lim and Wachenheim, Reference Lim and Wachenheim2022). Instead, they emphasized that non-financial, producer-level factors, such as socio-demographic characteristics, farm attributes, awareness about CRP, attitudes, and perceptions of producers, are crucial to increasing enrollment in General CRP (Brimlow, Reference Brimlow2008; Morrison and Hardy, Reference Morrison and Hardy2014; Thapa et al., Reference Thapa, Chapagain, McMurry, Smith and Joshi2024). In our econometric investigation of the context-specific factors influencing the time length to enrollment in General CRP among producers in the Southern United States, we found that younger producers and producers with larger land area, awareness about CRP, positive perspective of the CRP signup ranking process, and positive perspectives on tree establishment accelerated the speed of enrollment. On the other hand, producers with positive perceptions of CRP rules and regulations were slower to enroll than those with negative perceptions of rules and regulations.
Considering these patterns, additional outreach and education could favorably impact enrollment in several ways. First, they could enhance conservation benefits by increasing producers’ awareness of CRP and encouraging smaller landholding producers to enroll faster in General CRP. This might be done by providing more information about ways to increase EBI scores during the ranking process. To reduce the length of time until CRP enrollment, outreach and education efforts could also focus on easy and timely access to information on tree establishments, which were positively viewed by most producers. In addition, FSA could provide detailed instructions and guidelines to ensure producers can prepare their applications efficiently to enroll faster in General CRP. Overall, our findings suggest that helping producers better navigate the CRP enrollment process (signup ranking process and perception of tree establishment) can accelerate enrollment in General CRP, expediting private land conservation efforts to deliver significant environmental benefits.
Although our model offers new insights regarding barriers to CRP General enrollment, results require cautious interpretation due to several limitations. First, the response rate to this survey was relatively low, so the possibility of non-response bias cannot be entirely ruled out. Perhaps producers who did not respond to the survey have different feelings about CRP and the enrollment process. Second, we did not have enough responses from some demographic groups (e.g., Black producers) to draw significant conclusions about them. Third, we assumed that producers in our sample had access to information about General CRP when they started managing or operating the farm, but this may not be the case. Fourth, we have only considered the factors influencing the time taken from first initiation to enroll in General CRP, but not in Continuous CRP. Fifth, our sample data is based on a recall method and there is a presence of potential recall bias for the enrollment year in General CRP when no official General signups were available for certain years. Finally, our study only accounted for current and past participants in General CRP and did not include the perspectives of producers who had never enrolled in CRP (i.e., most producers in the Southern United States). Producers who did not enroll (and were therefore not included in our sample) may experience different perceived barriers and constraints than those who enrolled. If we include never-enrolled individuals in this model, then we could have different HRs and different empirical outcomes compared to what we have reported in this study. Understanding the factors that obstruct enrollment, not just those that delay it, would be a worthwhile direction for future research. Future studies on General CRP enrollment must include both censored (never enrolled) and uncensored (enrolled) producers to avoid sample selection bias.
Data availability
Data was made available upon request.
Acknowledgments
The authors are thankful to all the survey participants.
Author contribution
Conceptualization, A.K.C., P.K., P.D., L.L., W.M., B.G. and R.I.; Data Curation, P.D.; Methodology, A.K.C., P.K., P.D. and R.I.; Formal Analysis, A.K.C.; Writing Original Draft, A.K.C.; Funding Acquisition, P.D.; Supervision, P.D., L.L. and W.M.; Writing Review and Editing, P.K., P.D., L.L., W.M., B.G. and R.I.
Financial support
This work was supported by the United States Department of Agriculture Farm Service Agency through a competitive grant (grant number FBC22CPT0012773).
Competing interests
The authors declare no competing interests.
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