Prediction of wheat (Triticum aestivum) grain and biomass yield under different irrigation and nitrogen management practices using canopy reflectance spectra model - PRADHAN, S; BANDYOPADHYAY, K K; SAHOO, R N; SEHGAL, V K; SINGH, R; JOSHI, D K; GUPTA, V K (The Indian Journal of Agricultural Sciences, 2026)
A field experiment was carried out during rabi 2010-11 and 2011-12 to study the canopy reflectance and to predict the grain and biomass yield of wheat (Triticum aestivum L.) under different irrigation and nitrogen management practices using canopy reflectance spectra model. Wheat (cv. PBW 502) was grown with four levels of irrigation, i.e. 0.4, 0.6, 0.8 and 1.0 IW/CPE and three N sources, i e 120 kg N/ha as urea, 60 kg N/ha as urea + 60 kg N/ha as farmyard manure (FYM) and 120 kg N/ha as FYM. Three spectral reflectance indices, viz. Red Normalized Difference Vegetation Index (RNDVI), Green Normalized Difference Vegetation Index (GNDVI) and Simple Ratio (SR) were computed using the spectral reflectance data. It was observed that across the treatments, the RNDVI, GNDVI, and SR increased from crown root initiation (CRI) to booting stage and thereafter decreased progressively till harvest. The pooled yield data of both the years showed significantly higher yield in 0.8 and 1.0 IW/CPE irrigation levels than 0.4 and 0.6 IW/CPE irrigation levels. The pooled data of grain yield under different nitrogen practices showed significantly higher yield in urea treatment followed by urea+FYM treatment and FYM treatment. The biomass yield under different nitrogen management practices followed trend similar to grain yield. A significant and positive correlation coefficient was observed between grain and biomass yield and spectral reflectance indices (RNDVI, GNDVI, SR) for all the phenological stages except at CRI stage and maturity stage. Highest correlation coefficient (0.97 for grain yield and 0.93 for biomass yield) was observed for GNDVI measured at milking stage. The model could account for 79 % variation in the grain yield of wheat with root mean square error (RMSE) (%) of 17.1. Similarly the model could account for 86% variation in the biomass yield of wheat with RMSE(%) of 12.7. The models slightly underestimate the grain and biomass yield of wheat with coefficient of residual mass (CRM) value of 0.13 and 0.08, respectively.