Using Canopy Measurements to Predict Soybean Seed Yield

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