Using Canopy Measurements to Predict Soybean Seed Yield

• Main Authors: Peder K. Schmitz, Hans J. Kandel
• Format: Article
• Language: English
• Published: MDPI AG 2021-08-01
• Series: Remote Sensing
• Subjects: soybean yield prediction; fractional green canopy cover; NDVI; PAR; stepwise multiple regression; lasso multiple regression
• Online Access: https://www.mdpi.com/2072-4292/13/16/3260

Predicting soybean [<i>Glycine max</i> (L.) Merr.] seed yield is of interest for crop producers to make important agronomic and economic decisions. Evaluating the soybean canopy across a range of common agronomic practices, using canopy measurements, provides a large inference for soybean producers. The individual and synergistic relationships between fractional green canopy cover (FGCC), photosynthetically active radiation (PAR) interception, and a normalized difference vegetative index (NDVI) measurements taken throughout the growing season to predict soybean seed yield in North Dakota, USA, were investigated in 12 environments. Canopy measurements were evaluated across early and late planting dates, 407,000 and 457,000 seeds ha⁻¹ seeding rates, 0.5 and 0.8 relative maturities, and 30.5 and 61 cm row spacings. The single best yield predictor was an NDVI measurement at R5 (beginning of seed development) with a coefficient of determination of 0.65 followed by an FGCC measurement at R5 (R² = 0.52). Stepwise and Lasso multiple regression methods were used to select the best prediction models using the canopy measurements explaining 69% and 67% of the variation in yield, respectively. Including plant density, which can be easily measured by a producer, with an individual canopy measurement did not improve the explanation in yield. Using FGCC to estimate yield across the growing season explained a range of 49% to 56% of yield variation, and a single FGCC measurement at R5 (R² = 0.52) being the most efficient and practical method for a soybean producer to estimate yield.