Work Description

We compiled the following datasets for lower Michigan at the county scale from 2008-2019: area under successful (high biomass) overwintering cover crops, EQIP payments for cover crops (and all other EQIP payments), area associated with EQIP contracts for cover crops, CRP payments, crop subsidy and i...  [more]

We compiled the following datasets for lower Michigan at the county scale from 2008-2019: area under successful (high biomass) overwintering cover crops, EQIP payments for cover crops (and all other EQIP payments), area associated with EQIP contracts for cover crops, CRP payments, crop subsidy and insurance payments, and two environmental factors that affect cover crop growth (growing degree days (GDD) and precipitation). To quantify the area under successful overwintering cover crops, we produced annual maps at 30 m resolution of the main classes of agricultural landcover that are present from September to April. To create these maps, we combined information from two data layers developed by the United States Department of Agriculture (USDA) National Agricultural Statistics Service Information (NASS) and a map of winter cover that we produced using Landsat satellite data. We mapped five classes of agricultural landcover: bare/fallow, winter wheat, alfalfa hay, low biomass cover that represented weedy fallow or unsuccessful cover crops, and high biomass cover that represented successful cover crops (e.g., cereal rye, ryegrass), which obtained appreciable biomass during the overwintering period. For our statistical analyses, we use the overwintering cover crop class as our dependent variable in all regression models. We aggregated these 30 m satellite data to the county scale for each year in two ways. The first quantified the area under overwintering cover crops for all agricultural parcels, and the second measure quantified the overwintering cover crop area only for parcels that were classified as row crops (corn, soybeans, or wheat) for any year from 2008-2019. We used the raster and exactextractr packages in R project software for all data layer creation, calculations, and extraction. Full details of the project and methods can be found in: Surdoval et al. 2024. Financial incentive programs and farm diversification with cover crops: Assessing opportunities and challenges. Environmental Research Letters.   [less]