|NOLAND, REAGAN - University Of Georgia
|WELLS, M - University Of Minnesota
|COULTER, J - University Of Minnesota
|TIEDE, T - University Of Minnesota
|MARTINSON, K - University Of Minnesota
|SHEAFFER, C - University Of Minnesota
Submitted to: Field Crops Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/30/2018
Publication Date: 6/1/2018
Citation: Noland, R., Wells, M.S., Coulter, J.A., Tiede, T., Baker, J.M., Martinson, K., Sheaffer, C.C. 2018. Estimating alfalfa yield and nutritive value with remote sensing and environmental factors. Field Crops Research. 222:189-196. doi: 10.1016/j.fcr.2018.01.017.
Interpretive Summary: Forage producers need to make informed decisions about the best time to harvest alfalfa in order to maximize forage quality, productivity, and hence profitability. Typically, they have relied on either visual assessment or destructive sampling followed by laboratory analysis. The first approach is arbitrary and subject to error, and the second is time consuming. Recently developed remote sensing tools offer the possibility of rapid measurements at the field scale, based on canopy reflectance of radiation in certain wavelengths. We measured canopy reflectance in the 300-2500 nm waveband, and estimated crop height with LIDAR, which uses a handheld laser to measure the distance to the top of the canopy as a user walks across a field. These data were compared to alfalfa yield and nutritive value determined from destructive sampling over a two year period at Rosemount MN. The results were used along with growing degree-day data to build and test a series of models for predicting yield, crude protein, neutral detergent fiber, and fiber digestibility. For each variable of interest, models were developed with acceptable ability to predict forage quality with rapidly obtained remote sensing data and widely available air temperature data. This approach should ultimately be useful for livestock producers in planning forage harvests.
Technical Abstract: In-field estimations of alfalfa yield and nutritive value can inform management decisions to optimize forage quality and production. However, acquisition of timely information at the field scale is limited using traditional measurements such as destructive sampling and assessment of plant maturity. Remote sensing technologies (e.g. measurement of canopy reflectance) have the potential to enable rapid measurements at the field scale. Canopy reflectance (350-2500 nm) and LiDAR-estimated canopy height were measured in conjunction with destructive sampling of alfalfa across a range of maturity at Rosemount, MN in 2014 and 2015. Sets of specific wavebands were determined via stepwise regression to predict alfalfa yield and nutritive value. 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 (GDUALT), using a temporally graduating base temperature was also tested against the traditional static base temperature. GDUALT increased prediction accuracy for all response variables by 9 to 17%. Models using a common set of utility wavebands, combined with GDUALT, resulted in R2 of 0.89, 0.90, 0.86, and 0.83 for yield, crude protein, neutral detergent fiber, and neutral detergent fiber digestibility (48-hr in-vitro), respectively. This research establishes potential for remote sensing measurements to be integrated with environmental information to achieve rapid and accurate predictions of alfalfa yield and nutritive value at the field scale for optimized harvest management.