Lentil is a staple in many diets around the world and growing in popularity as a quick‐cooking, nutritious, plant‐based source of protein in the human diet. Lentil varieties are usually grown close to where they were bred. Future climate change scenarios will result in increased temperatures and shifts in lentil crop production areas, necessitating expanded breeding efforts. We show how we can use a daylength and temperature model to identify varieties most likely to succeed in these new environments, expand genetic diversity, and give plant breeders additional knowledge and tools to help mitigate these changes for lentil producers.

No abstract

In severely deficit soil, lentil (Lens culinaris Medic) crop requires micronutrients for increased production. Micronutrient management is, therefore, very important for lentil productivity but mostly ignored. This study was carried out from 2014-2015 to 2016-2017 to understand the effects of zinc (Zn), boron (B) and molybdenum (Mo) on lentil productivity, nodulation and nutrient uptake and how these elements improve soil micronutrient fertility. The experiment was laid out in randomized complete block design and the treatments were replicated thrice. Different combinations of Zn, Mo and B were contrasted with no application of micronutrients. The treatments were Zn alone (Zn), B alone (B), Mo alone (Mo), Zn combined with B (ZnB), Zn with Mo (ZnMo), B with Mo (BMo) and Zn combined with B and Mo (ZnBMo). Doses of Zn, B and Mo were 3, 2 and 1 kg hectare-1, respectively. In this trial, the highest average seed yield (1807 kg ha-1) and yield increment (44%) was obtained in ZnBMo combined application with macronutrients. Single, dual and combined application of Zn, B and Mo had significant effects on yield parameters and yield of lentil (p<0.05). The highest nutrient uptake, maximum nodulation (63.5 plant-1) and the highest protein content (26.6 %) in seed were recorded from the treatment receiving all three micronutrients. The increased lentil yield might be associated with increased nodulation and nutrient uptake by the crop under micronutrient applied treatments. The results suggest that combination of Zn, B and Mo could be applied for increased lentil production in micronutrient deficit soils.

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.

No abstract