Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study investigates the application of unmanned aerial vehicle (UAV)-mounted multispectral sensors to estimate biomass in oats, Austrian winter peas (AWP), turnips, and a combination of all three crops across six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining the normalized difference red-edge (NDRE) index and CHM provided the best biomass model before termination (R² = 0.84). For bitemporal images, green reflectance, CHM, and the ratio of near-infrared (NIR) to red achieved the best performance (R² = 0.85). Cover crop species also influenced the model performance. Oats were best modeled using the enhanced vegetation index (EVI) (R² = 0.86), AWP with red-edge reflectance (R² = 0.71), turnips with NIR, GNDVI, and CHM (R² = 0.95), and mixed species with NIR and blue band reflectance (R² = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture.

Agro-morphological traits serve as an indirect selection criteria for developing new cultivars with superior performance ability. In order to study variability of agro-morphological traits of exotic barley genotypes under normal and late sown conditions, a field experiment was conducted at the research field of Institute of Agriculture and Animal Science, Chitwan, Nepal in 2015. Altogether, 13 genotypes of barley were sown under two factor factorial RCBD with three replications in two dates. Normal sowing was done on 29 Nov 2014 while late sowing was done on 1 Jan 2015. Agro-morphological traits were found to be varying significantly in late sowing condition as compared to normal one. The mean number of days to booting and heading reduced by 15.19% and 9.64%, respectively in late sown condition. Similarly, plant height reduced by 19.53%, peduncle length by 16.62%, flag leaf width by 42.87%, flag leaf area by 36.44% and FL-1 leaf area by 36.51% in late sown condition. Biomass, grain yield and harvest index were also found to be reduced by 39.66%, 69.77% and 46.48%, respectively for late sowing condition. Nepalese landrace Soluwa performed better in normal sown condition while exotic genotype SBYT 14-1 performed better in late sown condition. SBYT 14-27 and SBYT 14-38 genotypes exhibited stable yield in both sowing conditions. Stability attribute provides an avenue for further study of such promising genotypes from agronomic and breeding perspectives under varying edaphic and agronomic conditions, and also open up the possibility of developing best performing cultivar of barley for lower plain region of the country.

Accurate cover crop biomass estimation is critical for evaluating their ecological benefits. Traditional methods, like destructive sampling, are labor-intensive and time-consuming. This study assesses unmanned aerial vehicle-mounted multispectral sensors to estimate biomass across oats, Austrian winter peas (AWP), turnips, and a mix of all three replicated in six experimental plots. Five spectral images were collected at two growth stages, analyzing band reflectance, nine vegetation indices, and canopy height models (CHMs) for biomass estimation. Results indicated that most vegetation indices were effective during mid-growth stages but showed reduced accuracy later. Stepwise multiple linear regression revealed that combining normalized difference red-edge (NDRE), vegetation index (NDVI), and CHM provided the best biomass model before termination (R2 = 0.90). For bitemporal images, green reflectance, CHM, near-infrared (NIR)/red ratio, and green normalized difference vegetation index (GNDVI) achieved optimal performance (R2 = 0.86). Cover crop species influenced the model performance. Oats were best modeled with enhanced vegetation index (EVI) (R2 = 0.86), AWP with red-edge reflectance (R2 = 0.71), turnips with NIR, GNDVI, and CHM (R2 = 0.95), and mixed species with NIR and blue band reflectance (R2 = 0.93). These findings demonstrate the potential of high-resolution multispectral imaging for efficient biomass assessment in precision agriculture.