Genetic analysis of foliar disease resistance, yield and nutritional quality traits in groundnut

A set of 340 diverse groundnut genotypes included in Genomic Selection Panel (GSP) was used to evaluate genetic parameters and trait associations for resistance to rust and late leaf spot (LLS) along with yield and nutritional quality traits. The findings revealed high genetic variability coupled with high heritability and genetic advance as percent of mean (GAM) for resistance to both the diseases and yield traits, whereas low variability for nutritional quality traits with high heritability and low GAM. Disease severity scores for rust and LLS at 90 days after sowing (DAS) were negatively associated with yield, indicating pod yield penalty, thus deploying host-resistance for rust and LLS is a good strategy to plug the pod yield losses and reduce the input cost. It is possible to simultaneously improve the number of pods per plant and hundred kernel mass that contribute to pod yield as no trade-offs were detected between them. The association of oil and protein content with pod yield showed no tradeoffs, suggesting the possibility of simultaneous improvement of pod yield either with high oil or protein content. In breeding programs that target development of groundnut varieties to meet two distinct end-uses, oil milling, and food and confectionery, selection for either high oil (for oil purpose) or high protein and low oil (food/confections) will be efficient, as an inverse association between oil and protein content was observed. The use of disease score at 90 DAS for rust and LLS is effective and optimizes resources to make selection decisions in breeding as positive association among disease severity scores at different periods (75, 90 and 105 DAS) was observed

Genotype × Environment Studies on Resistance to Late Leaf Spot and Rust in Genomic Selection Training Population of Peanut (Arachis hypogaea L.)

Foliar fungal diseases especially late leaf spot (LLS) and rust are the important production constraints across the peanut growing regions of the world. A set of 340 diverse peanut genotypes that includes accessions from gene bank of International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), elite breeding lines from the breeding program, and popular cultivars were screened for LLS and rust resistance and yield traits across three locations in India under natural and artificial disease epiphytotic conditions. The study revealed significant variation among the genotypes for LLS and rust resistance at different environments. Combined analysis of variance revealed significant environment (E) and genotype × environment (G×E) interactions for both the diseases indicating differential response of genotypes in different environments. The present study reported 31 genotypes as resistant to LLS and 66 to rust across the locations at 90 DAS with maturity duration 103 to 128 days. Twenty-eight genotypes showed resistance to both the diseases across the locations, of which 19 derived from A. cardenasii, five from A. hypogaea, and four from A. villosa. Site regression and Genotype by Genotype x Environment (GGE) biplot analysis identified eight genotypes as stable for LLS, 24 for rust and 14 for pod yield under disease pressure across the environments. Best performing environment specific genotypes were also identified. Nine genotypes resistant to LLS and rust showed 77% to 120% increase in pod yield over control under disease pressure with acceptable pod and kernel features that can be used as potential parents in LLS and rust resistance breeding. Pod yield increase as a consequence of resistance offered to foliar fungal diseases suggests the possibility of considering ‘foliar fungal disease resistance’ as a must-have trait in all the peanut cultivars that will be released for cultivation in rain fed ecologies in Asia and Africa. The phenotypic data of the present study will be used for designing genomic selection prediction models in peanut

Investigations on prevalence of aflatoxin contamination in major groundnut growing states of India, influence of soil characteristics and farmers’ level of awareness

Food safety issues are of major concern in groundnut due to aflatoxin contamination by Aspergillus flavus. Monitoring aflatoxin prevalence and understanding the factors responsible can provide useful information for devising effective management strategies. The present study focused on mapping the pre-harvest aflatoxin contamination in India along with its determining factors. A comprehensive survey was undertaken during 2012-2014 in four major groundnut growing States such as Andhra Pradesh, Gujarat, Karnataka, and Tamil Nadu. Pod (n=2434) and rhizospheric soil samples (n=1322) were collected to ascertain A. flavus populations and pre-harvest aflatoxin contamination. Further, kernel aflatoxin levels were correlated with soil organic carbon, available calcium and pH levels in the fields from where the samples were collected. Farmers’ awareness on aflatoxin problem was also determined using a semi-structured questionnaire. Our results indicate wide variations in the occurrence of pre-harvest aflatoxin contamination levels of kernels among different States (0 - 5486 ppb) and samples within States. Detectable levels of aflatoxins (>1ppb) were highest in Karnataka (70.5%), whereas it was lowest in Andhra Pradesh (32.9%). Correlation studies revealed that aflatoxin contents were positively associated with soil pH (r = 0.54-0.99) and A. flavus populations (r = 0.63 in Gujarat; r = 0.75 in Karnataka) whereas soil organic carbon and available calcium were negatively correlated with toxin levels in kernels (r = -0.99). Farmers’ awareness was considerably poor in all the States under survey. Overall, our results suggest the prevalence of aflatoxin contamination in major groundnut growing areas in India, and influence of certain edaphic factors

Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut

Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut

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Sick plants in grassland communities: a growth-defense trade-off is the main driver of fungal pathogen abundance and impact

Aboveground fungal pathogens can substantially reduce biomass production in grasslands. However, we lack a mechanistic understanding of the drivers of fungal infection and impact. Using a global change biodiversity experiment we show that the trade-off between plant growth and defense is the main determinant of fungal infection in grasslands. Nitrogen addition only indirectly increased infection via shifting plant communities towards more fast growing species. Plant diversity did not decrease infection, likely because the spillover of generalist pathogens or dominance of susceptible species counteracted dilution effects. There was also evidence that fungal pathogens reduced biomass more strongly in diverse communities. Further, fungicide altered plant-pathogen interactions beyond just removing pathogens, probably by removing certain fungi more efficiently than others. Our results show that fungal pathogens have large effects on plant functional composition and biomass production and highlight the importance of considering changes in pathogen community composition to understand their effects