Spectral indices for characterizing lentil accessions in the dryland of Pacific Northwest

Authors: AMIN, M - Bangladesh Agricultural Research Institute; ISLAM, M - Bangladesh Agricultural Research Institute; Coyne, Clarice - Clare; CARPENTER-BOGGS, LYNNE - Washington State University; McGee, Rebecca

Submitted to: Genetic Resources and Crop Evolution
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/19/2023
Publication Date: 5/29/2023
Citation: Amin, M.N., Islam, M.M., Coyne, C.J., Carpenter-Boggs, L., McGee, R.J. 2023. Spectral indices for characterizing lentil accessions in the dryland of Pacific Northwest. Genetic Resources and Crop Evolution. https://doi.org/10.1007/s10722-023-01614-8.
DOI: https://doi.org/10.1007/s10722-023-01614-8

Interpretive Summary: Lentil is a cool-season grain legume that is important for people all over the world due to its great nutritional value. The effects of heat and drought on lentil production have been amplified by global climate change. To address global food security for an increasing population, new sources of drought and heat tolerant germplasm need be introduced into lentil breeding programs. The objective of this research was to determine if NIR-based indices could be used to identify lentil accessions with resistance to heat and drought stress. Forty nine lentil accessions from the USDA and ICARDA gene banks were evaluated over two summers (2015 and 2016) in Eastern Washington, USA. Crop Scan, a hand held device, was used to collect multi-spectral data. Traditional agronomic data was also collected. These data were used to construct various indices that were used to determine the response of the crop to stress. A selection index was constructed and used to identify ten accessions with superior agronomic performance and stability when exposed to stress.

Technical Abstract: Lentil (Lens culinaris, Medik) is a cool season legume crop that experiences terminal drought and heat stresses when grown in many parts of the world, especially in North America, Australia, Southwest Asia and North Africa. Drought stress, which is often linked with high temperatures, is very damaging to grain legumes in practically all agricultural environments. The objective of this research was to determine if NIR-based indices could be used to predict lentil productivity and stability under drought and high-temperature stress conditions. In this experiment, 49 accessions from the USDA and ICARDA lentil collections were sown in an augmented design with one check cultivar(cv 'Avondale') in the Palouse region of eastern Washington in 2015 (one location) and 2016 (two locations). Spectral reflectance data were collected using a multi spectral reflectance device (Crop Scan) and green normalized difference vegetation index (GNDVI), red normalized difference vegetation index (RNDVI), photochemical reflectance index (PRI), red normalized difference vegetation index (RNDVI), water band index (WBI), and normalized water index (NWI) were calculated. Agronomic metrics including days to flowering (DTF), biomass and seed yield (SYP) data were also collected. The additive main effects and multiplicative interaction (AMMI) model was utilized to identify genotypes with consistent performance across environments. A multivariate selection index, multi-trait genotype ideotype distance (MGIDI), was used to identify stable genotypes with desirable agronomic phenotypes. The accessions Avondale, PI 368647, ILL7090, PI 518734, ILL86, ILL595, PI 533690, PI 518734, ILL1649, ILL4781 were identified as the best ten genotypes, based on productivity. Avondale, ILL7090, PI533690 were less affected by G x E interaction and selected for further evaluation.