Dry matter estimation of standing corn with near-infrared reflectance spectroscopy

Abstract

Determining the appropriate time to harvest whole-plant corn is an essential factor driving the successful preservation via anaerobic fermentation (ensiling). The current options for timely on-farm monitoring of corn moisture in the field include selecting a set of representative plants, chopping and drying a subsample, or harvesting a portion of the field using a harvester equipped with an on-board moisture sensing system. Both methods are time-consuming and expensive, limiting their practicality for harvest decision-making. This work’s objective was to develop a practical solution that utilizes the moisture content of the ear to estimate whole-plant moisture. An improvement of this method was also considered that utilized a hand-held near-infrared reflectance spectroscopy (NIRS) device to predict ear moisture in situ. Based on the data collected during this work, a quadratic relationship was developed where ear moisture explained 90% of the variability in whole-plant corn moisture. However, based on our observations, the hand-held NIRS evaluated would have little utility in predicting whole-plant corn moisture with either the calibration developed here or provided by the manufacturer. The manufacturer’s prediction model yielded the best result with an R2 of 0.92, and a ratio of performance to deviation of 3.19. However, the 95% prediction band was +6.85% w.b. Finally, we determined that for a corn field uniform in appearance, sampling five to ten plants is likely to provide a reasonable estimate of field moisture.