Vegetation Indices to Quantify in-Field Cotton Yield Variability

Crop yield is an important benchmark that helps both researchers and agronomists make informed management decisions. While commercial yield monitor data is useful for postdictive analysis and planning, actionable information about in-season spatial variability is unavailable. Technology such as unmanned aerial vehicles (UAVs) could be leveraged to quantify yield variability while minimizing labor. Therefore, the objective of this study was to determine the optimal in-season timing for the strongest relationship between UAV-derived vegetation indices and cotton yield. This study over four growing seasons (2017-2020) comprised a 3x8 factorial design with four replications. The first factor was three levels of irrigation applied as a percentage of the estimated crop evapotranspiration (ETc) requirement: 0, 40, and 80 percent and the second factor was eight commercial cotton varieties. Multispectral UAV imagery was acquired at biweekly intervals from 30 meters above ground level and processed to produce high resolution orthomosaics. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge (NDRE) were derived from the orthomosaics and analyzed to determine the amount of final yield variability explained. NDVI had a positive linear relationship with yield, which was strongest at approximately 1200 heat units (R² = 0.61, 0.78, 0.49, and 0.78 in 2017, 2018, 2019 and 2020, respectively). Results suggest that vegetation indices could explain cotton yield variability within the growing season and therefore have potential to inform in-season spatial management decisions.