Publications

Butler, E; Howell, N; Guerrero, B; Mulamba, O (2018). Enhancing Crop Acreage Estimation within a Semiarid Watershed via Statistical Assessments and Techniques. AGRONOMY JOURNAL, 110(6), 2400-2407.

Abstract
It is necessary to examine if the statistical methods designed to improve Cropland Data Layer (CDL) acreage estimates at a national level improve estimates at the county level. This study determined whether the calibration of CDL by statistical techniques increased the accuracy of crop estimates on a semiarid West Texas watershed. Since there is a substantial amount of agriculture and farming in areas like West Texas, accurate crop estimates of datasets at smaller scales become important. We compared three methods: the pixel counting method of the CDL, linear regression of the CDL against the MODIS Land Cover Type Product (MLCT), and the Bayesian method (posterior probability) on the National Land Cover Dataset (NLCD) against the USDA National Agricultural Statistical Service (NASS) Quick Stats. Four crops [corn (Zea mays L.), cotton (Gossypium hirsutum L.), sorghum (Sorghum bicolor [L.] Moench), and winter wheat (Triticum aestivum L.)] were analyzed at state and county levels during two growing seasons (2009 and 2011). Prediction errors were estimated by comparing computed acreage with the appropriate NASS Quick Stats values. The total error for each assessment method was estimated for all acreage values in that particular assessment category. We found that the Bayesian method improved the estimates of sorghum and winter wheat and linear regression improved acreage estimates for cotton and corn in 2011 and 2009, respectively. Our results indicate that county-level adjustments are very sensitive. Therefore, one must carefully select the method that best fits the type of crop, geographical location, and growing season being considered.

DOI:
10.2134/agronj2018.01.0065

ISSN:
Feb-62