Academic journal article Journal of Agricultural and Resource Economics

Modeling Field-Level Conservation Tillage Adoption with Aggregate Choice Data

Academic journal article Journal of Agricultural and Resource Economics

Modeling Field-Level Conservation Tillage Adoption with Aggregate Choice Data

Article excerpt

(ProQuest: ... denotes formulae omitted.)

Introduction

Conservation tillage (CT) is defined as any tillage system that leaves at least 30% of crop residue on the soil surface at the time of planting. CT improves soil structure, reduces soil temperature and evaporation, increases infiltration, and reduces soil erosion and nutrient runoff(Karlen et al., 2009). CT also contributes to soil organic matter and nutrient availability, water retention, macro-invertebrate activity, and carbon sequestration (Horowitz, Ebel, and Ueda, 2010; Center for Agricultural Science and Technology, 2011). Since the benefits of CT accrue to the farmer as well as to society, the practice is recognized as a potent soil and water conservation tool in conservation policy. Climate change mitigation and bioenergy policies are creating a renewed demand for CT use data and models to help understand the environmental footprint of land reverting to cropping from the Conservation Reserve Program and as crop rotations change (Secchi et al., 2011; Center for Agricultural Science and Technology, 2012).

CT also has potential as a climate change mitigation strategy by awarding farmers offset credits that may be sold to point-source emitters of greenhouse gasses (Horowitz, Ebel, and Ueda, 2010). The majority of current conservation programs, such as the Environmental Quality Incentives Program (EQIP), follow an alternative route by providing financial assistance payments to farmers who voluntarily adopt conservation practices (U.S. Department of Agriculture, National Resources Conservation Service, 2012). In either case, to successfully design and implement realistic, costminimizing conservation programs, there is a need for both location-specific data on the use of CT and models of farmer- and location-specific tillage adoption (Claassen, Cattaneo, and Johansson, 2008).

Despite significant data collection efforts and research, the two interrelated tasks-attaining spatially explicit CT data and developing models of CT costs-remain challenging. Data unavailability remains a significant obstacle for empirical CT costs estimation. Though spatially detailed Geographic Information Systems (GIS) data on cropping patterns and soil properties are becoming increasingly accessible (Stern, Doraiswamy, and Akhmedov, 2008; Secchi et al., 2009, 2011; Khanna et al., 2011; Archer and Johnson, 2012), CT use data are rarely available at the same spatial scale. Aerial photography-based CT data methodologies are developing, but their accuracy is still low over large geographic areas (Thoma, Gupta, and Bauer, 2004; Bricklemyer et al., 2006; Zheng et al., 2013). The commonly used Conservation Technology Information Center (CTIC) National Crop Residue Management Survey (NCRMS) tillage data (Conservation Technology Information Center, 2012a,b) are only available as aggregated, county-total estimates. A noteworthy recent data collection effort is Conservation Effects Assessment Project (CEAP) within the National Resources Inventory (NRI) (U.S. Department of Agriculture, National Resources Conservation Service, 2012). However, because of confidentiality restrictions, the results of the CEAP-NRI studies are only available as aggregated totals over large watersheds that span across multiple states (U.S. Department of Agriculture, National Resources Conservation Service, 2012). The other notable source of U.S. CT use estimates is the U.S. Department of Agriculture's Agricultural and Resource Management Survey (ARMS) (Horowitz, Ebel, and Ueda, 2010). However, data collection budgets limit the periodicity and sample size of ARMS, and confidentiality concerns frequently result in the availability of the survey results in county- or state-total form only (Banerjee et al., 2009; Smith, 2013). In effect, only spatially aggregated data are readily available for public use.

While spatially aggregated CT use data are useful for assessing farmers' choices at the macro scale, to capture marginal rather than average effects, microeconomic analysis requires spatially disaggregated data to account for the heterogeneity in land resources, climate, farm organization, and farmers' characteristics (Just, 2000; Lambert et al. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.