Canopy Reflectance-Based Algorithm for in-Season Estimation of Rice Grain Yield.

The use of canopy reflectance in evaluating crop nitrogen (N) status has resulted in the development of remote sensor-based N decision tools for wheat and corn production. This study was initiated in 2008 to establish the major component required for the development of a remote sensor-based N decision tool for rice production in the mid-southern United States. It is essential to determine the optimal sensing dates where a predictive equation for rice yield potential (YP0) can be established thus collection of normalized difference vegetation index (NDVI) readings using the GreenSeeker^TM handheld sensor from multiple variety x nitrogen trials located in Crowley and Rayville, LA, and Stoneville, MS was conducted at different growth stages. Sensor readings were collected on a weekly basis for five consecutive weeks starting at panicle initiation stage from rice plots planted with Catahoula, Neptune and Clearfield 131 varieties. For different growth stages, the association of in-season estimated yield (INSEY, NDVI/number of days from seeding to sensing- DAS) and actual grain yield was evaluated. Between 70-90 DAS, strong associations between INSEY and grain yield were obtained with r² values that ranged from 0.64 to 0.70, the highest of which was obtained from the sensing dates between 70-75 DAS. From these findings, the initial predictive equation, YP0 = 1668^164.14*INSEY, was established from NDVI readings collected between 70-75 DAS.  With the inclusion of more data points and introduction of correction procedures using thermal time (growing degree days), opportunities exist for further refinement of the canopy reflectance-based YP0 algorithm for rice.