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

Seong Do Yun,Benjamin M Gramig

doi:10.3390/data4020066

Seong Do Yun, Benjamin M Gramig

Open Access

https://doi.org/10.3390/data4020066

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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.

Highlights

Agriculture is one of the most impacted economic sectors studied in the context of climate variability and change [1,2]
The variables in Agro-climatic data by county (ACDC) are constructed from the widely-used high resolution gridded data sources PRISM, Gridded Soil Survey Geographic (gSSURGO), and National Land Cover Database (NLCD)
Data generating processes are discussed in detail along with the ACDC data structure, the role of agricultural land use mask, and potential data management issues

Summary

Introduction

Agriculture is one of the most impacted economic sectors studied in the context of climate variability and change [1,2]. Rapid recent technological improvements in high-resolution satellite imagery data (for simplicity, raster data1 ) have contributed to an increase in such agro-climatic research [8]. And freely available raster data have been widely employed to study the agro-climatic functional relationship described through two popular research frameworks: regression-based analysis [9,10], and process-based models [11,12]. The current research aims to help a broader base of research and decision-support communities access these geo-spatial data products. Prospective users of these raster data often encounter several difficulties. Widely-used raster data sources are built on a fine grid-size (for example, 10 or 30 meter-resolution), and their data size generally requires high-performance computing capability and time-consuming operations to extract the desired variables

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