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Daloğlu, I., J. I. Nassauer, R. Riolo, and D. Scavia. 2014. An integrated social and ecological modeling framework—impacts of agricultural conservation practices on water quality. Ecology and Society 19(3): 12.
An integrated social and ecological modeling framework—impacts of agricultural conservation practices on water quality
1School of Natural Resources and Environment, University of Michigan, 2Center for the Studies of Complex Systems, University of Michigan, 3Graham Environmental Sustainability Institute, University of Michigan
We present a modeling framework that synthesizes social, economic, and ecological aspects of landscape change to evaluate how different agricultural policy and land tenure scenarios and land management preferences affect landscape pattern and downstream water quality. We linked a stylized agent-based model (ABM) of farmers’ conservation practice adoption decisions with a water quality model, the Soil and Water Assessment Tool (SWAT), to simulate the water quality effects of changing land tenure dynamics and different policies for crop revenue insurance in lieu of commodity payments over 41 years (1970–2010) for a predominantly agricultural watershed of Lake Erie. Results show that non-operator owner involvement in land management decisions yields the highest reduction in sediment and nutrient loads, and crop revenue insurance leads to more homogeneous farmer decisions and a slight increase in sediment and nutrient loads unless cross compliance with expanded conservation requirements is implemented.
Key words: ABM; agricultural policy; agriculture; conservation practice; integrated modeling; SWAT; water quality
Agricultural policy in the USA strongly impacts the land use and land management decisions of farmers and indirectly, but profoundly, impacts water quality (Broussard et al. 2012). An overall goal of conservation policies with regard to water quality is to reduce sediment and nutrient load from agricultural landscapes by promoting nutrient efficiency and managing nutrient and sediment runoff through conservation best management practices (Sharpley et al. 1994). Thus, detailed studies of the connections between agricultural policies and water quality can help identify more effective strategies to reduce agricultural pollution.
High surface water concentrations of nitrogen and phosphorus are correlated with inputs from fertilizers used for crops (Ribaudo and Smith 2000, Boyer et al. 2002, Galloway et al. 2004). For example, the current resurgence of eutrophication symptoms in the Great Lakes is attributed to the intensification in agricultural production and concomitant soil erosion and nutrient runoff from nonpoint sources (Dolan and Chapra 2012). To address these issues, conservation practices, such as conservation tillage, filter strips, land retirement, and nutrient management—the focus of this research—are employed to mitigate sediment and nonpoint source nutrient delivery, enhance water quality, and improve sustainability in agricultural production by increasing resilience (National Research Council 2010).
The focus of this study is the impact of plausible future policy and land tenure scenarios on the delivery of bioavailable dissolved reactive phosphorus (DRP) and total phosphorus (TP) to Lake Erie by exploring links between human and environmental systems. We describe a social–ecological system (SES) that links farmer decisions and actions to ecological responses in reciprocal feedbacks. Social–ecological systems are affected by complex relationships arising from the biophysical, institutional, infrastructural, demographic, economic, and sociopolitical contexts. Hence, SESs exhibit emergent properties—unique properties not belonging to human or natural systems separately but emerging from their interactions (Janssen 1998, Monticino et al. 2007, Rammel et al. 2007, Levin et al. 2012). Understanding the complexities of SESs may affect the success or failure of their management (Liu et al. 2007). Unforeseen and undesirable consequences can result if biophysical and human systems are not examined together (Veldkamp and Verburg 2004, Levin et al. 2012).
We describe an SES model to investigate the impact of different plausible future policy and land tenure scenarios on farmer adoption of conservation practices intended to enhance water quality. We link a social agent-based model (ABM) of farmer adoption of conservation practices with a biophysical water quality model, the Soil and Water Assessment Tool (SWAT), which integrates land management decisions with soil properties, climate information, and land topography to estimate water quality metrics (Arnold et al. 1998). We define farmers as owners or renters of land on which cash crops are grown, who make decisions about land management based on policy scenarios involving economic, institutional, and environmental information. This ABM incorporates the heterogeneity and complexity of Corn Belt farmers through a typology (Daloğlu et al. in review) that simulates farmer decisions in terms of their tendency toward adoption of conservation practices.
THE MODELING FRAMEWORK
The study area is the Sandusky watershed of Lake Erie, which represents a typical watershed of the Corn Belt region (Fig. 1). Lake Erie has experienced significant eutrophication because of excessive phosphorus loading, primarily from agricultural runoff and point-source discharges (Dolan and Chapra 2012); however, non-point sources, particularly from agriculture, are currently the major causes of nutrient pollution (Forster 2000). Agricultural runoff has resulted in algal blooms (Michalak et al. 2013), poor water clarity, and summer hypoxia (low oxygen) (Hawley et al. 2006, Zhou et al. 2013, Scavia et al. 2014) that impact fisheries, recreation, and drinking water throughout many aquatic and coastal systems (Carpenter 2008). To address these issues, effective adoption of conservation practices is essential.
With our linked ABM–SWAT framework, we investigate how policy and farmer characteristics influence conservation practice selection and, in turn, their effects on water quality. The framework—the ABM is implemented in Java using Repast J agent-based libraries within the Eclipse integrated environment and linked to SWAT using MatLab—includes the landscape, agents (farmers, in a typology that represents their heterogeneity), conservation practice adoption, and ecosystem responses (sediment, DRP, and TP loading) to changes in land management (Fig. 2). The farmer typology (Daloğlu et al. 2014) represents the heterogeneity among Corn Belt farmers and provides the necessary pillar for the ABM. An existing fully calibrated and verified SWAT model of the Sandusky watershed (Daloğlu et al. 2012) is used to simulate nutrient load responses.
The ABM is constructed at the individual decision-making level, and so we use our farmer typology to represent farmer behavior and decisions. In the model, farmer agents of different types make adoption decisions every year based on decision algorithms. If farmer decisions include the adoption of conservation practices, the landscape is altered, which eventually changes the land management strategy. Through this model, we explore possible changes in the structure of U.S. agriculture through land tenure dynamics and the influence of crop revenue insurance on farmers’ conservation practice adoption decisions.
The output from the ABM, in the form of updated land management maps (Fig. 2), is used to examine the impacts of these changes as presented in plausible future scenarios. To understand the impacts of these scenarios, SWAT is used to simulate sediment and phosphorus loss from the landscape over a 41-year period (1970–2010).
The Model Landscape
The model landscape consists of a two-dimensional grid, built within the ABM, abstractly representing the agricultural landscape of the Sandusky watershed. Because the ABM is linked with the SWAT, the specifics of the water quality model are taken into consideration during ABM setup. The SWAT uses hydrologic response units (HRU) as its fundamental computational unit. Runoff flow, sediment, and nutrient loads are calculated separately for each HRU and then summed to determine the total load contribution from each subwatershed (Neitsch et al. 2011). Land management decisions are represented at the HRU scale; we used an HRU size corresponding to the average farm size in the Sandusky basin (258 acres; United States Department of Agriculture (USDA) 2009, Daloğlu et al. 2012). This strategy resulted in 147 subbasins and 351 agricultural HRUs. Therefore, 351 farmers are represented as agents in the ABM.
Agents as Farmers
Farmers are exceptionally diverse, particularly with regard to farm size, land tenure, education, age, sources of income, and socioeconomic attributes. To represent this heterogeneity in the ABM, we used a typology (Table 1; Daloğlu et al. 2014) derived from an extensive literature review and previous surveys conducted in the Corn Belt region. Because ABMs require simplicity (Axelrod 1997), like others (Valbuena et al. 2008, Robinson et al. 2012), our typology represents diversity and heterogeneity in simple terms based on four farmer types: traditional, supplementary, business-oriented, and nonoperator owners (Table 1) (Daloğlu et al. 2014).
Because of limited data, it is not possible to identify exact locations of farms and management decisions. Therefore, we chose to represent the study area in a more stylized model, as described in Appendix 1, using the “overview, design concepts, and details” (ODD) protocol (Grimm et al. 2010).
Agricultural programs in the USA generally allow farmers to choose which programs to participate in, with flexibility to select practices that fit their climate, soils, and, most importantly, management skills (Bernstein et al. 2004). So, in our model, adoption of structural practices, nonstructural practices, nutrient management plans, and enrollment in land retirement are voluntary (Table 2). Each farmer determines whether to participate in land retirement or adopt certain practices depending on policy drivers and the farmer’s overall objectives. Land retirement programs such as the CRP generally remove land from agricultural production for a long period (at least 10 years) or, in some cases, permanently, for an annual rental fee. Structural practices are eligible for cost-share where farmers receive 50% of the implementation cost from the federal government as an economic incentive in return for multi-year commitments. When farmers receive economic incentives for structural practice adoption and land retirement enrollment, penalties are levied for noncompliance (Claassen 2012). Because economic incentives are not provided for nonstructural practices and enrollment in nutrient management plans, noncompliance does not incur penalties.
Conservation Practice Adoption Decisions in the Model
This model focuses on helping understand why some farmers adopt conservation practices and others do not, and how spatial relationships among farmers impact those decisions, with special emphasis on their heterogeneity. At each annual time-step in the model, every farmer agent decides on a land management strategy for conservation practice adoption (Table 2). The decision-making algorithm includes net income generated from government programs and agricultural production, the farmers’ preferences and land tenure, and influences of their neighbors (summarized below and detailed in Appendix 1).
Every farmer agent in the model uses the same decision algorithm but with different parameters based on the preferences associated with their type. Given these differences, individual agents react differently to the same agricultural policies, including their decision to adopt none or combinations of available practices. A critical variable in the model is land tenure: whether farmers are owners or operators, and whether owners or operators make adoption decisions. Most empirical research concludes that operators control decisions regarding production and adoption of conservation practices on farmland owned by nonoperators (Constance et al. 1996, Soule et al. 2000, Arbuckle 2010); however, we also investigate land tenure dynamics, the possible impact of the growing proportion of farmland owned by nonoperators, and their influence on adoption decisions.
In the ABM, farmers calculate their agricultural income generated from production and collect financial incentives by enrolling in government programs. For agricultural income, farmer agents use Bayesian inference for the expected price and yield from a probability distribution. We represent farmer heterogeneity by setting different parameters for Bayesian updating for different farmer types (Table 1). For example, traditional farmers have more stable price and yield expectations, and business-oriented farmers more likely follow fluctuations in the market because we assume they are more connected to information networks. Farmers’ perceptions of crop prices and yields change from year to year. Thus, at the beginning of each year, farmer agents use publicly available price and yield information, their experiences, and their type characteristics to form future price and yield expectations.
Farmer agents also use their social and spatial information networks to evaluate which practices their neighbors adopt. In the model, nonoperators are not initially connected to the information networks, whereas operators (traditional, supplementary, and business-oriented farmers) are connected to both spatial and social networks, and business-oriented farmers have higher network connectedness compared with traditional and supplementary farmers (Daloğlu et al. 2014). As described below, we also test a case in which nonoperator owners increase involvement. Farmer agents’ intrinsic environmental attitudes toward each available conservation practice, as reflected by their type (Table 1), also influence their adoption decisions. Based on these variables, the model uses the farmers’ decision algorithm to decide which conservation practice to adopt (see Appendix 1).
Land Tenure Changes
Agricultural land tenure in the USA has undergone critical changes; especially through increases in nonoperator ownership followed by increases in part ownership or full renting (Wunderlich 1993, Duffy 2008). Our study site, the Sandusky watershed, followed these national trends, especially in relation to increased nonoperator ownership (Daloğlu et al. 2014). Studies of the impact of these increases in the Corn Belt region have shown that structural practices are appealing to nonoperators (Petrzelka et al. 2009, Nassauer et al. 2011). However, nonoperator ownership is not defined consistently across studies. Nearly half of Corn Belt farmers are absentee landowners, defined as owners living more than 50 miles from their land (Petrzelka et al. 2009), and land retirement enrollment is lower among absentee landowners compared with other farmers in the Great Lakes Basin (Petrzelka et al. 2009). However, Nassauer et al. (2011) found that one type of nonoperator, investors (defined as landowners who have never farmed), has higher land retirement enrollment rates than other Iowa farmers.
We exploit the difference between these two different studies of different, overlapping subsets of nonoperators as we investigate the impact of changing land tenure dynamics on conservation practice adoption and water quality. In our model, we define absentee landowners and investors as mutually exclusive subtypes of nonoperator owners. Over time, our simulations assume increased involvement of nonoperator owners in conservation decisions (from 0% to 50% by the end of the simulation) as they become increasingly connected to information networks over time.
Changes in Agricultural Policy: Crop Revenue Insurance
Current agricultural policies have numerous drivers for farmers to adopt conservation practices; however, policy incentives frequently outweigh these, resulting in a patchwork of adoption that is not sufficiently effective in improving water quality (Doering et al. 2007). Current Farm Bill discussions include replacement of commodity payments with subsidized crop insurance. Starting in 1985, participation in subsidized crop insurance programs required conservation compliance, i.e., refraining from draining wetlands and implementing a whole farm conservation plan to reduce erosion to acceptable levels. However, in the 1996 Farm Bill, the conservation compliance requirement was removed from the insurance program (Smith and Glauber 1997).
Currently, farmers can choose between two insurance policies, crop yield or revenue insurance, where the USDA provides subsidies for two-thirds of the cost of farmers’ premiums (Coble and Barnett 2013). Crop yield insurance protects farmers from the income effects of reduced yield due to weather and other factors, whereas revenue insurance protects farmers’ income from both yield changes and market fluctuations and indirectly encourages farmers to increase their production area. To evaluate potential impacts of crop insurance replacing commodity payments, we concentrate on revenue insurance (Coble and Barnett 2013).
Numerous studies have investigated the role of risk aversion in adoption of nonstructural practices (conservation tillage and no till) and have consistently found a negative relationship between risk aversion and practice adoption (Bultena and Hoiberg 1983, Belknap and Saupe 1988). Similarly, when farmers consider implementing a nutrient management plan (fertilizer reduction), they generally assume increased yield uncertainty. Providing revenue insurance for farmers reduces the risks involved with nutrient management plan implementation and nonstructural practice adoption (Bosch and Pease 2000).
Plausible Future Scenarios
The primary goal of this analysis is to understand the drivers of conservation practice adoption and subsequent water quality impacts under plausible futures. For this purpose, we constructed four scenarios by crossing two policy futures with two assumptions about nonoperator owners (Table 3). These scenarios are intended to be prospective and informative rather than projective or prescriptive of the future (Nassauer and Corry 2004).
The “Baseline scenario” (1) represents existing land tenure where operators (traditional, supplementary, and business-oriented farmers) are responsible for conservation practice adoption decisions, and nonoperator owners have no involvement in production and conservation decisions. In this scenario, existing crop insurance programs are not included. The “Nonoperator owner involvement scenario” (2) simulates the potential impact of increased involvement of nonoperator owners on the baseline scenario. In this scenario, we assume natural resource agencies and nongovernmental organizations (NGOs) reach out to nonoperator owners and effectively inform them about existing and available conservation practices (Table 4). The “Crop revenue insurance scenario” (3) follows recent U.S. Farm Bill discussions about providing federally subsidized crop revenue insurance rather than commodity production subsidies. This scenario does not assume conservation compliance is required; however, we do explore the alternative below. Here, we assume only operators are decision makers, that they purchase crop revenue insurance at 75% coverage level for all the land that they manage including the rented land, and that the reduced risk encourages them to increase production area (Table 4). The “Crop revenue insurance with nonoperator owner involvement scenario” (4) explores the impact of increased nonowner involvement on the crop insurance scenario (3). Crop revenue insurance provides a safety net and indirectly motivates both operators and nonoperator owners to increase their production area (Table 4). Cross compliance with conservation programs is also not included in this scenario.
Water Quality Model—SWAT
The SWAT is a distributed and spatially explicit continuous-time water quality model at the scale of river basins or watersheds. This model divides watersheds into subbasins with HRUs that represent areas with common land cover and slope and soil properties (Arnold et al. 1998). It is a process-based model of surface hydrology, weather, sedimentation, soil temperature, crop growth, nutrients, pesticides, and groundwater that can simulate the effects of climate and land-use changes on nutrient and sediment delivery from watersheds and is used widely for evaluating and predicting impacts of conservation practices (Arabi et al. 2008). Models created using SWAT have been developed and applied for Lake Erie watersheds to predict potential impacts of conservation practice adoption on water quality (Bosch et al. 2011). More recent SWAT applications indicate that more aggressive strategies than currently employed are needed to substantially reduce nutrient and sediment delivery (Bosch et al. 2013), especially under anticipated future climates (Bosch et al. 2014). .
For this study, we used an existing, higher spatial resolution SWAT model developed for the Sandusky watershed (Daloğlu et al. 2012). The model is employed at a spatial scale in which the smallest computational unit of SWAT, average HRU size, corresponds to the average farm size in the Sandusky basin (258 acres; USDA 2009). The model was calibrated and validated with extensive daily observed flow and water quality data for the simulation period (1970–2010) and can be used for future scenario testing. Our previous modeling efforts indicated the importance of weather and farmer management decisions on nutrient delivery especially on DRP runoff (Daloğlu et al. 2012).
Water Quality Impacts of Land Management Strategies
This modeling framework evaluates impacts of farm-scale decisions at the watershed scale. Farmers update their adoption decisions annually during each 41-year simulation; however, because decision-relevant model parameters are sampled from distributions in the decision-making algorithms, each scenario consists of 25 runs, each representing a sampling from the distribution.
We ran the ABM for the 41-year simulation period (1970–2010) to get the landscape management data for the simulation period. We then used that output from the ABM as input to the SWAT and report water quality model output as the average of the 25 SWAT runs over the simulation period (1970–2010) (Figs. 3, 4). Regarding the linkage of ABM and SWAT, for each year, farmers’ decisions on conservation practice adoption are used to modify several SWAT input files. Once information on all farmer adoption statuses is updated, the ABM output in the abstract grid file provides the adoption status for every farmer in every period and is used to make the necessary updates in relevant input files of the SWAT in the Sandusky watershed file. Abstract grid cell characteristics are assigned to Sandusky watershed locations by the smallest computational unit of the SWAT, HRUs. The SWAT is then run for the whole simulation period (1970–2010) to provide water quality metrics such as sediment and phosphorus loads. The input files for the SWAT are all in ASCII text format, making it easy to interface with the ABM, and this link is supported with the MatLab programming language. Because ABM results represent adoption statuses for each individual farmer in every period, it is not appropriate to represent the result as averages across multiple simulations. For that reason, results (Figs. 3, 4) are reported as simulation envelopes that contain the results from all 25 simulations. Appendix 2 has further details on how we link ABM with the SWAT.
Impacts of Land Tenure Change
Model results are consistent with observed trends in land tenure (Appendix 1). For example, U.S. agriculture has undergone a structural change of land tenure with a decline in full ownership and an increase in nonoperator ownership and large-scale operations (Wunderlich 1993, Duffy 2008). To represent this, we assume that, after age 65, traditional farmers switch to be nonoperator owners or sell their land to business-oriented or supplementary farmers. This leads to an increase in the percentage of large business-oriented farms and smaller supplementary farms, at the expense of traditional farms (Fig. 3A). We assume supplementary and business-oriented farmers to not change their types as they age. This obviously also leads to an increase in the percentage of nonoperator owners among the farmer population (Fig. 3B), as well as production area under their control (Appendix 1 for details).
Because different farmer types have different tendencies toward adoption decisions, as the composition of types changes, the emergent adoption pattern evolves. For example, when nonoperator owners are not involved in decision making and crop revenue insurance is not available (Scenario 1), the percentage of farmers who adopt nutrient management and structural practices shows a significant increase over time, with a more pronounced increase in nutrient management adoption (Fig. 4). Because nonstructural practices, such as no-till technologies were not available until the mid-1980s, these practices were not present in the model until then. Coupled with evolved composition of farmer types, this resulted in a significant increase in adoption over the next 10 years. Land retirement, on the other hand, was minimally adopted, due mostly to the requirement for enrolled land to be retired for 10 years, with penalties for noncompliance (Fig. 4). In Scenario 2, when nonoperator owners take a more active role, they tend to have higher adoption rates for structural practices and land retirement. Average adoption rate for structural practices increased from 17% to 23% when nonoperators are active decision makers (Fig. 4).
Impacts on Water Quality
In Scenario 2, by the end of the simulation period, 50% of the nonoperator owners are the decision makers for conservation practice adoption (Table 4). The positive attitudes of non-operator owners toward conservation practices results in higher adoption rates for structural practices and enrollment in land retirement programs reducing TP loads (Figs. 5, 6). In Scenario 2, results for sediment, organic P (OrgP), and DRP load are similar, with the improvement more pronounced for sediment because nonoperator owners favor structural practices, which are more effective in reducing the sediment load.
Impacts of Agricultural Policy Change
Options under consideration for the U.S. Farm Bill include replacing commodity payments with subsidized crop revenue insurance premiums to create stronger incentives for farmers to enroll in crop revenue insurance. In these simulations, conservation compliance is not required for crop revenue insurance enrollment. The effects of the insurance program can be seen by comparing Scenarios 1 and 3 (Table 3). The insurance protects farmers from both market and crop yield fluctuations, and because the payments are based on production area, farmers appear encouraged to increase their production area. Under this scenario, nutrient management plans increase, followed by a steep increase in nonstructural practices when these practices become available (Fig. 4). When crop revenue insurance premiums are subsidized, structural practice adoption and land retirement enrollment rates decrease both for operators and nonoperator owners, whereas enrollment in nutrient management plans increase. The ABM results also indicate a decrease in land retirement and structural practices regardless of the nonoperator owner involvement (Scenarios 3 and 4), which leads to a more homogenous conservation landscape (Fig. 4). With subsidized crop revenue insurance, average TP, OrgP, DRP, and sediment loads are higher (Figs. 5, 6), primarily due to the reduction in structural practices and land retirement.
However, a moral hazard can result if farmers use crop insurance as an incentive to underfertilize their crops to receive indemnities (Sheriff 2005) or to introduce practices or enterprises that they might avoid without crop insurance, i.e., planting corn or soybeans where adequate seasonal rainfall is uncertain. Goodwin and Smith (2003) also raised concerns about crop insurance and other disaster relief programs discouraging land retirement. Another criticism is the potential of supporting an increase in production on erodible land (Keeton et al. 2000).
Modifications in Agricultural Policy: Closing the SES Model Loop
In our modeling framework, we built plausible scenarios that follow the latest U.S. Farm Bill discussions of providing federally subsidized crop revenue insurance rather than commodity production subsidies (Scenario 3) and assumed conservation compliance is not tied to crop revenue insurance (Stubbs 2012). The model results suggest slightly higher TP, OrgP, DRP, and sediment yields (Fig. 6) under this scenario compared with the baseline (Scenario 1), attributed to the reduction in structural practice and land retirement enrollment (Fig. 4).
To close the loop in the SES model where social and environmental systems have reciprocal feedbacks, we add a policy modification step and allow farmer agents to respond to the new set of incentives, sanctions, and regulations. For this purpose, we linked conservation compliance to crop revenue insurance and evaluated different conservation compliance definitions. There have been discussions of strengthening and expanding conservation compliance requirements (Perez 2007, American Farmland Trust 2011, Cox et al. 2011). So, in our framework, we tested three conservation compliance definitions where farmers can; (a) adopt nonstructural practices; (b) implement structural practices; or (c) choose either nonstructural or structural practices (Table 5). Although the U.S. General Accounting Office (GAO 2003) has emphasized that compliance enforcement needs updating and upgrading, we assume 100% adherence to conservation compliance.
We observe higher nutrient runoff; especially the bioavailable DRP when farmers choose nonstructural practices as conservation compliance requirements (Fig. 7), a common practice for economic reasons. However, if conservation compliance requirements are expanded to include nutrient management with a focus on promoting structural practices, linked model results indicate the effectiveness of structural practices in reducing nutrient delivery from agricultural landscapes (Fig. 7).
This framework provides a powerful tool to explore the impacts of plausible futures such as changes in the agricultural land tenure and policy on adoption of conservation practices. Our linked model distinguishes among nutrient management plans that reduce fertilizer application, nonstructural practices such as conservation and no-till, structural practices such as filter strips to trap soil particles and nutrients, and land retirement programs. Importantly, this model shows that changes in land tenure and crop insurance policy affect adoption of these practices, altering the agricultural landscape and affecting water quality.
Sandusky Watershed represents a typical watershed of the Corn Belt region. Therefore, the conclusions of the study are relevant for other Corn Belt watersheds. However, differences in location-specific environmental drivers and processes such as land cover, soil type, and climatic conditions, and their effects on the transformation of N and P need consideration.
By investigating the water quality impacts of four plausible scenarios, we demonstrate the importance of the understudied nonoperator owners and the possible effects of new policies related to crop revenue insurance. Our results indicate maximum load reductions, especially sediment load reductions, occur when nonoperator owners are involved in the decision-making process and when crop revenue insurance is not offered in lieu of commodity payments tied to compliance (Scenario 2). This improvement is mainly attributed to the increase in the percentage of farmers who favor structural practices, which are more effective in reducing sediment and nutrient load. Our results also point to the positive influence of nonoperator owner involvement and highlight the importance of devising innovative policies to reach out and inform nonoperator owners about the existing water quality problems, possible solutions, and their role in implementing them.
When subsidized crop revenue insurance is promoted as a risk management program, in the absence of conservation compliance, it incentivizes farmers, regardless of type, to increase production area, even including areas that are highly erodible or wetlands. This then results in a more homogenous conservation landscape that yields slightly higher loads (Scenarios 3 and 4) due to a decrease in structural practices and land retirement enrollment. In contrast, our results show that if crop revenue insurance is tied to conservation, particularly structural practices, sediment and nutrient loads decrease. A recent survey of Iowa farmers reveals support for expanding conservation compliance requirements to include nutrient management as well as erosion control (Arbuckle 2010). Moreover, because structural practices are visible by remote sensing, compliance enforcement would require fewer NRCS personnel and less federal budget.
Our analyses show only modest load reduction (1–6%) under the plausible future scenarios, which is comparable to other relevant applications of the SWAT that assume feasible levels of implementation (Arabi et al. 2008, Bosch et al. 2013). The adoption rates of conservation practices are also consistent with the observations (Smith and Goodwin 1996, Bosch and Pease 2000, Goodwin and Smith 2003, Duffy 2008, Petrzelka et al. 2009, Nassauer et al. 2011) and feasible levels of implementation used by other SWAT models (Arabi et al. 2008, Bosch et al. 2013). Collectively, these results point to the need for innovative policies that promote greater adoption of conservation practices and for attaching conservation compliance to crop revenue insurance with perhaps new definitions of conservation compliance. Indeed, previous SWAT models implemented in the Lake Erie Basin indicate up to 30–40% yield reduction effectiveness with significantly increased adoption rates (Bosch et al. 2013).
We thank Kyung Hwa Cho for his consultation regarding model building. This work was supported in part by Graham Environmental Sustainability Institute Doctoral Fellowship program, NSF (# DBI-1052875 to the National Socio-Environmental Synthesis Center) grant to J. Nassauer, and NOAA Center for Sponsored Coastal Ocean Research grant NA07OAR432000 to D. Scavia. Ecofore Lake Erie publication 13-006.
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