Integrating remote measurements and environmental covariates to estimate forage yield and nutritive value

Abstract:

Estimating forage yield and nutritive value in-situ can inform management decisions to optimize forage quality, production, and use. Remote sensing technologies (e.g., measurement of canopy reflectance) can enable rapid measurements at the field scale. Canopy reflectance (350–2500 nm) and Light Detection and Ranging (LiDAR)-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturities at Rosemount, MN in 2014 and 2015. Sets of specific spectral wavebands were determined via stepwise regression to predict alfalfa yield and nutritive value and models were reduced by spectral range to improve utility. Cumulative growing degree units (GDUs) and canopy height were tested as model covariates. An alternative GDU calculation (GDU_ALT) using a temporally graduating base temperature was also tested against the traditional static base temperature. The inclusion of GDU_ALT increased prediction accuracy for all response variables by 9 to 17%. Models using a common set of seven wavebands, combined with GDU_ALT, explained 81 to 90% of the variability in yield, crude protein (CP), neutral detergent fiber (NDF), and NDF digestibility (NDFd; 48-hr in-vitro), respectively. Similar work is currently underway at San Angelo, TX to test these methods of estimation across a range of forage species in a contrasting environment. This research establishes potential for remote measurements integrated with environmental covariates to inform rapid and accurate predictions of alfalfa yield and nutritive value at the field scale, and ongoing work will assess the transferability of this remote sensing application across forage species and environments.