Grassland Productivity Estimates Informed By Soil Moisture Measurements: Statistical And Mechanistic Approaches

Soil moisture is a fundamental determinant of plant growth, but soil moisture measurements are rarely assimilated into grassland productivity models, in part because methods of incorporating such data into statistical and mechanistic yield models have not been adequately investigated. Therefore, our objectives were to (a) quantify statistical relationships between in situ soil moisture measurements and biomass yield on grasslands in Oklahoma and (b) develop a simple, mechanistic biomass-yield model for grasslands capable of assimilating in situ soil moisture data. Soil moisture measurements (as fraction of available water capacity, FAW) explained 60% of the variability in county-level wild hay yield reported by the National Agricultural Statistics Service (NASS). We next evaluated the performance of mechanistic, evapotranspiration (ET)-driven grassland productivity models with and without assimilation of measured FAW into the models' water balance routines. Models were calibrated by comparing estimated ET with ET measured using eddy covariance, and calibration proved essential for accurate ET estimates. Models were validated by comparing NASS county-level hay yields to the modeled yields, which were the product of normalized transpiration estimates (the ratio of transpiration to reference ET) and an empirically derived grassland water productivity (the ratio of accumulated biomass to normalized transpiration) estimate. The mechanistic model produced more accurate estimates of wild-hay yields with soil moisture data assimilation (Nash-Sutcliffe efficiency [NSE] = 0.55) than without (NSE = 0.10). These results suggest that improved estimates of grassland productivity could be achieved using in situ soil moisture, which could benefit grazing management decisions, wildfire preparedness, and disaster assistance programs.