The aim of this study was to evaluate a set of recombinant inbred lines (RILs) for agronomic and physiological traits under drought conditions and to locate quantitative trait loci (QTL) associated with them. This study used a RIL population derived from a cross between drought tolerant (ILC 588) and susceptible (ILC 3279) genotypes. The population consisting of 155 RILs was grown under drought conditions in the field at Tel Hadya, Syria, in 2006 and 2007 and at Breda, Syria, in 2007. A genetic map consisting of eight linkage groups was developed using 97 simple sequence repeat (SSR) markers. The results revealed that high harvest index (HI), early flowering, and early maturity were the important attributes contributing to higher grain yield under drought. Higher stomatal conductance (gs) and cooler canopies (canopy temperature minus air temperature [Tc–Ta]) can also lead to better performance under drought conditions. Quantitative trait locus analysis identified 15 genomic regions significantly associated with various traits affecting drought tolerance in chickpea. Important QTL detected in this study included two QTL for HI explaining 38% of the total phenotypic variability of the trait, four QTL for flowering explaining 45%, and three QTL for maturity explaining 52% on a cumulative basis. Three QTL for gs and six QTL for Tc–Ta also detected explained 7 to 15% phenotypic variability individually. Two QTL (Q3‐1 and Q1‐1) on linkage group 3 (LG3) and LG1 showed effects on many traits related to drought. Hence, these regions can be further explored in future drought studies.

Three thousand two hundred and sixty-seven kabuli chickpea (Cicer arietinum L .) germplasm accessions were grown during the spring season of 1980 at Tel Hadya, the main research station of ICARDA, Syria to determine the components of seed yield. Observations were recorded on seed yield and 14 other characters . Correlation and path coefficient analyses were done to find out associations among characters and to assess the direct and indirect contribution of each character to seed yield .Large variation was observed for all the characters studied except days to flowering, days to maturity and protein content . Correlation and path coefficient analyses showed that biological yield and harvest index were the major direct contributors to seed yield . The 100-seed weight, plant height, days to flowering and maturity, canopy width, and protein content contributed to seed yield mainly through indirect effect via biological yield and harvest index . The 100-seed weight and seed yield were major contributors to biological yield . Major contributor to protein content was days to maturity . Results indicated that selection for high biological yield and harvest index would lead to high seed yield ; and selection for large seed size would lead to high biological yield. Therefore, these characters should receive the highest priority in selecting high yielding plants in chickpea breeding .

Chickpea (Cicer arietinum L.) production has remained static for the past two decades . One major limiting factor has been susceptibility of cultivars to several biotic and abiotic stresses that adversely affect yield . In recent years, cultivars resistant to Ascochyta blight (Ascochyta rabiei [Pass .] Lab.), Fusarium wilt (Fusarium oxysporum f . sp . ciceris), and cold have been bred and released in many countries . Some progress has been made in breeding for resistance to drought, insects, and cyst nematode, but not for viruses, heat, and salinity . Two or more stresses are of equal importance in most chickpea growing areas . Therefore, future efforts should be directed toward the development of cultivars with multiple-stress resistance. Proper understanding of important stresses in different countries and the genetics of resistance should lead to more systematic approaches to resistance breeding . Wild Cicer species hold promise and deserve attention in resistance breeding .

Spatial variability in field trials is a reality. A proportion of this is accounted for as inter-block variability by using block (complete or incomplete) designs. A large amount of spatial variability still remains unaccounted for, however, and this may lead to erroneous conclusions. To capture this inexplicable variation (which is mainly due to intra-block variation), yield data from a series of variety yield trials, using cereals and legumes, were analysed using various spatial models. The most suitable of these, selected on the basis of the Akaike Information Criterion, were used to assess the relative performance of genotypes. Although incomplete-block designs have been found to be effective in variety trials, spatial models have added considerable value to trials with legumes and cereals. The ‘best’ spatial models gave efficiency values of over 330% in winter-sown chickpea (Cicer arietinum), 140% in lentil (Lens esculenta), and 150% in barley (Hordeum spp.) trials. Furthermore, the use of these best models resulted in a change in the ranking of genotypes (on the basis of mean yield), which resulted, therefore, in a different set of genotypes being selected for high yield. It is recommended that: (i) incomplete block designs be used in variety trials; (ii) the Akaike Information Criterion be used to select the best spatial model; and (iii) genotypes be selected after the use of this model. The selected model would account most effectively for spatial variability in the field trials, improve selection of the most desirable genotypes and, therefore, improve the efficiency of breeding programmes.

Arguably the most important adaptive criterion in annual crops is appropriate phenology that minimizes exposure to climatic stresses and maximizes productivity in target environments. To date this has been achieved empirically by selecting among diverse genotypes in target locations. This approach is likely to become inadequate with pending climate change because selection is imposed on the outcome (flowering time) rather than the underlying mechanism (i.e. responses to daylength, ambient or vernalizing temperatures). In contrast to the cereals, in legumes the interaction between phenological mechanisms and environmental selection pressure is largely unknown. This paper addresses this shortcoming through photothermal modelling of chickpea germplasm from the world's key production areas using a meta-analysis of multi-environment trials located from 49°N to 35°S. Germplasm origin had significant effects on temperature and daylength responsiveness, the former strongly correlated to vegetative phase temperatures at the collection or development site (r = 0.8). Accordingly, temperature responses increase from winter-to spring-sown Mediterranean and Australian material, and then to north, central & southern India. Germplasm origin also affects the relationship between photoperiod and temperature response. In Eastern Mediterranean material a strong negative relationship (r = -0.77) enables temperature insensitive genotypes to compensate through a strong photoperiod response. Clearly, chickpea evolution has selected for different phenological mechanisms across the habitat range. Given that under the anticipated global warming temperature sensitive cultivars will flower relatively earlier than those responding largely to photoperiod, it is important to exploit this diversity in developing better-adapted genotypes for future cropping environments.