Agro-Climatic Data by County: A Spatially and Temporally Consistent U.S. Dataset for Agricultural Yields, Weather and Soils

Abstract

Agro-climatic data by county (ACDC) is designed to provide the major agro-climatic variables from publicly available spatial data sources to diverse end-users. ACDC provides USDA NASS annual (1981–2015) crop yields for corn, soybeans, upland cotton and winter wheat by county. Customizable growing degree days for 1 °C intervals between −60 °C and +60 °C, and total precipitation for two different crop growing seasons from the PRISM weather data are included. Soil characteristic data from USDA-NRCS gSSURGO are also provided for each county in the 48 contiguous US states. All weather and soil data are processed to include only data for land being used for non-forestry agricultural uses based on the USGS NLCD land cover/land use data. This paper explains the numerical and geo-computational methods and data generating processes employed to create ACDC from the original data sources. Essential considerations for data management and use are discussed, including the use of the agricultural mask, spatial aggregation and disaggregation, and the computational requirements for working with the raw data sources.

Corn and soybean prevented planting acres response to weather

“…To build weather and soil data, we adopted the approaches used in the Agro-Climatic Data by County (ACDC) (Yun & Gramig, 2019) for county-level current contemporary soil and weather data. Using the most recent data sources in the ACDC, we replicated weather data for 1996-2019, which covers the study periods in the county-level prevented planting data.…”

“…We tested extensive random effects models as a counterpart to fixed effects in the same specifications, but we could not find any statistically superior random effects models compared with those of fixed effects models. Lastly, we take the approaches used in Yun and Gramig (2019) to build the soil variables allowable to vary over the land use. Using 1992Using , 2001Using , 2006Using , and 2011 National Land Cover Database, we build the average soil characteristics over counties only for working agricultural land.…”

Recycling nutrients in the beef supply chain through circular manuresheds: Data to assess tradeoffs

“…We used data from secondary sources and select equations and coefficients to develop the four datasets. Figure 4 is a summary of the data processing and analytical workflow (sensu Yun & Graming, 2019). Coefficients were selected to depict reality, including the variability inherent in agricultural systems to the extent possible, but in some cases only a central tendency is reflected (discussed in Data Issues).…”

Spatial Panel Models of Crop Yield Response to Weather: Econometric Specification Strategies and Prediction Performance

“…In the regression, we take the log of crop yields as y it = log (corn yield it 1). We use Yun and Gramig's (2019) agricultural land use and land cover adjusted soil characteristics based on the 1992, 2001, 2011, and 2016 US Geological Survey's National Land Cover Database (NLCD), and we match them to the years in the balanced panel data (1992 to 1981-1995; 2001 to 1996-2003; 2011 to 2004-2013; and 2016 to 2014-2018). The soil variable summary statistics, therefore, are the descriptive statistics for soil underlying agricultural land uses in the 4 years of the NLCD.…”

“…Summary statisticsNotes: Weather variables calculate for March to August; Summary statistics of soil variables are calculated only for agricultural land in 1,042 counties based on four separate years of the National Land Cover Database spanning the study period fromYun and Gramig (2019).…”