Non-linear regression models for time to flowering in wild chickpea combine genetic and climatic factors.

BACKGROUND: Accurate prediction of crop flowering time is required for reaching maximal farm efficiency. Several models developed to accomplish this goal are based on deep knowledge of plant phenology, requiring large investment for every individual crop or new variety. Mathematical modeling can be used to make better use of more shallow data and to extract information from it with higher efficiency. Cultivars of chickpea, Cicer arietanum, are currently being improved by introgressing wild C. reticulatum biodiversity with very different flowering time requirements. More understanding is required for how flowering time will depend on environmental conditions in these cultivars developed by introgression of wild alleles. RESULTS: We built a novel model for flowering time of wild chickpeas collected at 21 different sites in Turkey and grown in 4 distinct environmental conditions over several different years and seasons. We propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data. By using a combination of Grammatical Evolution and Differential Evolution Entirely Parallel method, we have identified a model that reflects the influence of effects of day length, temperature, humidity and precipitation and has a coefficient of determination of R2=0.97. CONCLUSIONS: We used our model to test two important hypotheses. We propose that chickpea phenology may be strongly predicted by accession geographic origin, as well as local environmental conditions at the site of growth. Indeed, the site of origin-by-growth environment interaction accounts for about 14.7% of variation in time period from sowing to flowering. Secondly, as the adaptation to specific environments is blueprinted in genomes, the effects of genes on flowering time may be conditioned on environmental factors. Genotype-by-environment interaction accounts for about 17.2% of overall variation in flowering time. We also identified several genomic markers associated with different reactions to climatic factor changes. Our methodology is general and can be further applied to extend existing crop models, especially when phenological information is limited.

Comparison of different screening methods for the selection of Ascochyta blight disease on chickpea (Cicer arietinum L.) genotypes.

Chickpea (Cicer arietinum L.) is the second most important edible food grain legume, widely grown all over the world. However, the cultivation and production of chickpea are mainly affected by the Ascochyta blight (AB) disease, which causes losses of up to 100% in areas with high humidity and warm temperature conditions. Various screening methods are used in the selection of chickpea genotypes for resistance to AB disease. These methods are natural field condition (NFC), artificial epidemic field condition (AEC), marker-assisted selection (MAS), and real-time PCR (RT-PCR). The study was conducted with 88 chickpea test genotypes between the 2014 and 2016 growing seasons. The results of the screening were used to sort the genotypes into three categories: susceptible (S), moderately resistant (MR), and resistant (R). Using MAS screening, 13, 21, and 54 chickpea genotypes were identified as S, MR, and R, respectively. For RT-PCR screening, 39 genotypes were S, 31 genotypes were MR, and 18 genotypes were R. In the AEC method for NFC screening, 7, 17, and 64 genotypes were S, MR, and R, while 74 and 6 genotypes were S and MR, and 8 genotypes were R-AB disease. As a result of screening chickpea genotypes for AB disease, it was determined that the most effective method was artificial inoculation (AEC) under field conditions. In the study, Azkan, ICC3996, Tüb-19, and Tüb-82 were determined as resistant within all methods for Pathotype 1.

Ecology and genomics of an important crop wild relative as a prelude to agricultural innovation.

Domesticated species are impacted in unintended ways during domestication and breeding. Changes in the nature and intensity of selection impart genetic drift, reduce diversity, and increase the frequency of deleterious alleles. Such outcomes constrain our ability to expand the cultivation of crops into environments that differ from those under which domestication occurred. We address this need in chickpea, an important pulse legume, by harnessing the diversity of wild crop relatives. We document an extreme domestication-related genetic bottleneck and decipher the genetic history of wild populations. We provide evidence of ancestral adaptations for seed coat color crypsis, estimate the impact of environment on genetic structure and trait values, and demonstrate variation between wild and cultivated accessions for agronomic properties. A resource of genotyped, association mapping progeny functionally links the wild and cultivated gene pools and is an essential resource chickpea for improvement, while our methods inform collection of other wild crop progenitor species.