Evaluating YieldTracker Forecasts for Maize in Western Kansas

We seek to predict in‐season land productivity to guide irrigation management decisions designed to optimize water utilization in the Ogallala Aquifer region. YieldTracker is a mathematical model that simulates growth and yield of graminoid crops using weather and leaf area index (LAI) as inputs, where LAI can be derived by remote sensing. We tested this model using 3 yr of maize (Zea mays L.) yield data from Colby, KS. Four replications of three treatments—rainfed and subsurface drip irrigation (SDI) at 3.8 and 7.6 mm d−1—were compared with simulated yields (36 model runs). Results indicated that YieldTracker has potential as a decision aid for managing irrigated maize, but has insufficient mechanistic complexity to simulate yields of water‐stressed maize. YieldTracker projected canopy development well, but LAI does not necessarily correlate with canopy efficiency in capturing solar radiation and converting it to biomass and then partitioning biomass to grain under conditions of limiting soil water. Remotely sensed normalized difference vegetation index (NDVI), a surrogate for LAI, tends to saturate at LAI > 3. Using hyperspectral reflectance data, we found a total chlorophyll vegetation index (TCI) responded nearly linearly to LAI values as high as 6. Similarly, a simple ratio vegetation index, based on a narrow band of wavelengths in the red edge spectral region, responded linearly to increasing LAI. Water band indices (WBI) in the 900 to 970 nm waveband were sensitive to changes in TCI as available soil water decreased. Incorporating TCI and a WBI might improve YieldTracker performance across a range of soil water conditions.