Value of composite Normalized Difference Vegetative Index and growing degree days data in Oklahoma, 1999 to 2018

Abstract

For over 25 yr, sensor-based Normalized Difference Vegetative Index (NDVI) data has been collected from both satellite imagery and near-plant (3-m) readings. Because calibrated NDVI data coming from active sensors is still relatively new, limited research has returned to evaluate databases including multiple years and environments. Composite NDVI readings and final grain yield were collected from 1999 to 2018. This included growing degree day (GDD) records for each mid-season sensor measurement. This was attempted to potentially improve the use of a historical and subjective morphological scale. Using location-specific-archived-data from the Oklahoma Mesonet, the exact number of days from planting to sensing where GDD > 0 for each date and location were compiled. The ensuing relationship between NDVI (for a predetermined GDD > 0 range) and yield was determined. Grain yield prediction was improved between 80 and 115 GDDs. These ranges further targeted a climatologically identifiable metric that precisely determined when to collect sensor readings in future years. Compared with the current composite yield prediction equation for Oklahoma, the new exponential function created from this study was higher in the lower-yielding environments. Underestimation of fertilizer N rates has been voiced by producers in recent years. This has likely been the product of more current varieties, more efficient farming practices, and increased optimum N rates needed for higher yields. This validates the adoption of a new YP0 equation for OSU's on-line Sensor Based Nitrogen Rate Calculator, allowing accurate yield prediction between 80 and 115 GDDs.

Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia

“…For each Period, mean maximum and minimum, absolute maximum and minimum temperatures were prepared as predictor variables (features) that would help indicate heat or frost occurrence that could impact yield negatively; particularly pertinent at critical growth stages such as anthesis [ 46 ]. Growing degree days (GDD) corresponding to the imagery dates were also calculated and included as predictor variable [ 47 ]. Two rainfall datasets were prepared: rainfall depth (mm) in the preceding Period and cumulative rainfall depth (mm) since sowing date.…”

Effect of topdress nitrogen rates applied based on growing degree days on winter wheat grain yield

“…Recently, Figueiredo et al (2020) and Dhillon et al (2020a) deduced that a GDD-based numerical scale could be used for predicting topdress N rate in winter wheat, instead of subjective morphological scales. They further mentioned the ideal window for topdress N prediction was between 80−115 GDDs.…”

“…Additional uses of GDD include hybrid maturity descriptor by the seed industry; quantifying crop yields as affected by planting dates (Bollero, Bullock, & Hollinger, 1996); predicting N availability and losses from manure (Griffin & Honeycutt, 2000); and grain yield prediction. (Dhillon et al, 2020a;Figueiredo et al, 2020).…”