Lentil (Lens culinaris Medikus) is an important source of protein for people in developing countries. Aphanomyces root rot (ARR) has emerged as one of the most devastating diseases affecting lentil production. In this study, we applied two complementary quantitative trait loci (QTL) analysis approaches to unravel the genetic architecture underlying this complex trait. A recombinant inbred line (RIL) population and an association mapping population were genotyped using genotyping by sequencing (GBS) to discover novel single nucleotide polymorphisms (SNPs). QTL mapping identified 19 QTL associated with ARR resistance, while association mapping detected 38 QTL and highlighted accumulation of favorable haplotypes in most of the resistant accessions. Seven QTL clusters were discovered on six chromosomes, and 15 putative genes were identified within the QTL clusters. To validate QTL mapping and genome-wide association study (GWAS) results, expression analysis of five selected genes was conducted on partially resistant and susceptible accessions. Three of the genes were differentially expressed at early stages of infection, two of which may be associated with ARR resistance. Our findings provide valuable insight into the genetic control of ARR, and genetic and genomic resources developed here can be used to accelerate development of lentil cultivars with high levels of partial resistance to ARR.

Mechanically harvested crops must be erect (lodging resistant) to facilitate harvest. Stem lodging changes canopy structure, increases disease pressure, reduces yield, and reduces harvest efficiency in pea. A number of studies have examined the traits that cause lodging susceptibility, but the relative impact of each trait is difficult to determine. A great need exists in pea breeding to develop a working model to explain lodging resistance. This study used the flexure formula to predict the amount of lodging variation explained by some of the major traits. Datasets from pea indicate that the percent variation explained by this lodging model is ~58%, and this model can be used to predict the relative impact of an increase in load, height, stem diameter, stem wall thickness, or yield on lodging susceptibility. This study indicates that plant height is strongly correlated with lodging susceptibility, but stem diameter is positively correlated with lodging resistance. Stem wall thickness appears to have no major effect on lodging resistance, which has not been previously reported in pea. Any doubling in plant height would also double the amount of stem material, but stem stress is expected to increase fourfold. A doubling in stem diameter is expected to increase the amount of stem material by fourfold and decrease stem stress by eightfold. The results of this study indicate that plant breeders should focus on increasing basal stem diameter to increase lodging resistance.

The critical period of weed control (CPWC) for ‘Pardina’ and ‘Brewer’ lentil was determined in field experiments near Pullman, WA, in 2008 and 2009. Trial treatments were kept either weed free for periods of 0, 14, 25, 35, 45, 60, 75, or ∼90 d after emergence (DAE), or weeds were allowed to grow before removal for periods of 0, 14, 25, 35, 45, 60, 75, or ∼90 DAE. Averaged across varieties, lentil with season-long weed interference had 29.5 and 32% seed yield reduction compared to weed-free lentils in 2008 and 2009, respectively. When measured at crop maturity, a 1% loss in lentil seed yield resulted from each 5.68 g m−2of dry weed biomass. Based on a 5% yield loss threshold, the CPWC for lentil was estimated to be from 270 to 999 growing degree days (GDD), 22 to 57 DAE, or crop growth stage (CGS) 7 to the early pod stage during 2008. In 2009, the CPWC was 624 to 650 GDD, with no occurrence of a CPWC when estimated using DAE and CGS. Spiny sowthistle emerged and competed with the lentil crop later in the growing season than mayweed chamomile, indicating that mayweed chamomile may be an earlier and stronger competitor than spiny sowthistle.

Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with unmanned aerial systems were used to assess phenotypes in spring and winter pea breeding plots. The Green Red Vegetation Index (GRVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), plot volume, canopy height, and canopy coverage were extracted from RGB and multispectral information at five imaging times (between 365 to 1948 accumulated degree days/ADD after 1 May) in four winter field pea experiments and at three imaging times (between 1231 to 1648 ADD) in one spring field pea experiment. The image features were compared to ground-truth data including AGBM, lodging, leaf type, days to 50% flowering, days to physiological maturity, number of the first reproductive node, and seed yield. In two of the winter pea experiments, a strong correlation between image features and seed yield was observed at 1268 ADD (flowering). An increase in correlation between image features with the phenological traits such as days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in these winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height (r = 0.83) at 1231 ADD. In two other winter pea experiments and the spring pea experiment, the GRVI and NDVI features were significantly correlated with AGBM at flowering. When selected image features were used to develop a least absolute shrinkage and selection operator model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.60 and 0.84 in the winter and spring pea experiments, respectively. A SPOT-6 satellite image (1.5 m resolution) was also evaluated for its applicability to assess biomass and seed yield. The image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, however, the trend was not consistent. In summary, the study supports the potential of using unmanned aerial system-based imaging techniques to estimate biomass and crop performance in pea breeding programs.

To discover the quantitative trait loci (QTLs) influencing lodging resistance and other agronomic traits in pea (Pisum sativum L.), a recombinant inbred line (RIL) population was created from a cross between the variety Delta and a breeding line from a complex cross. The RIL population was grown for five site years, and phenotypic data were collected for 13 quantitative traits and seven categorical traits. Genotypic data were derived from single nucleotide polymorphism (SNP), simple sequence repeat (SSR), and cleaved amplified polymorphic sequence (CAPS) markers. A layered QTL analysis identified seven specific regions where QTLs for these traits colocated. Mendel's height gene (Le) and the semi‐leafless mutation (Af) together accounted for the majority of the variation in lodging in the pea RIL population. All of the seven regions influenced stem thickness characteristics, but not all regions were associated with lodging. The regions near the high response to photoperiod (Hr) gene, and Mendel's flower color locus (A) putatively influenced yield. Both the region near Hr and a region on upper Linkage Group (LG) I were very important for seed size. Since data were collected on 13 different traits, pleiotropic effects could also be studied, giving a unique multi‐trait insight into the effects of these QTLs.