Estimating hard winter wheat yield with historical and novel methods

Estimating potential crop yield during the growing season allows growers to adjust inputs, set reasonable harvest expectations, and guide marketing decisions. Therefore, the ability to estimate yield is a valuable yet difficult goal for growers. To evaluate the potential of several methods of wheat (Triticum aestivum L.) grain yield prediction, this experiment used published methods based on phenological characteristics: stand and tiller/spike counts, and newer methods that employ image- and reflectance-based approaches, such as fractional green canopy cover (FGCC) and normalized difference vegetation index (NDVI) readings. This experiment was conducted across six locations (Banner, Box Butte, Cheyenne, Intensively Managed at Cheyenne, Deuel, and Kimball counties) in western Nebraska during 2019–2020, 2020–2021, and 2021–2022 for a total of 11 site-years. Treatments consisted of seven winter wheat varieties that were evaluated in the Winter Wheat State Variety Trials. Stand count did not show a significant correlation with wheat yield (0.09) nor model fit for yield estimation. Spike count was significantly correlated with yield (0.54), but efforts to use it to estimate final yield were not significant. Due to the inconsistency of yield prediction with historical methods, analyses of novel methods of yield estimation were warranted. NDVI and FGCC readings correlate with wheat yield and model fit efforts were successful. NDVI at Feekes 10 correlated significantly at 0.39, while FGCC had correlations of 0.56, 0.50, and 0.68 at Feekes 2, 4, and 10 (respectively). This experiment suggests that NDVI and FGCC are methods that could be used to replace outdated and laborious approaches.