Using Spatio-Temporal Analyses to Investigate Crop Migration and Change in the North Central US

Authors: Lee Ebinger*, USDA/NASS, Avery Sandborn, USDA/NASS
Topics: Agricultural Geography, United States, Land Use and Land Cover Change
Keywords: Keywords: spatio-temporal analysis, multi-year imagery, crop change, Cropland Data Layer
Session Type: Poster
Day: 4/5/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download

Spatio-temporal analyses are utilized for investigating migration and change of three leading crops (corn, soybeans and spring wheat) grown in the North Central region of the United States over ten consecutive years. Input for the analyses are multi-year raster crop masks derived from the Cropland Data Layer (CDL). The CDL is a crop-specific categorized 30m imagery produced by USDA’s National Agricultural Statistics Service. The crop masks are layered to produce 10-year time stacks for each of the three crops. Multi-temporal tabular and raster analyses are performed on each crop to examine: 1) crop acreage and directional migration trends of crop areas; 2) location and year a crop was first grown and last grown; 3) crop planting frequency; 4) land cover replaced by the crop; and 5) crop rotation patterns. Outputs from the analyses are in the form of maps, graphs and numeric tables. The analytical results identified a definite north-western expansion trend for corn and soybeans, and a western migration trend for spring wheat. Results also indicated that these crops have supplanted areas of grassland, pasture, non-cropland and other crops, in addition to rotating with each other to maintain soil nutrients and productivity. Possible factors contributing to the crop migration and change patterns include profitability of row crops over small grains, modified plant genetics for growing crops in sandy soil and farm management program changes. Identifying and visualizing crop migration and change can assist in formulating agricultural best practices, monitoring crop migration patterns and quantitatively forecasting future agricultural trends.

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