Key messageAnalysis of phenotypic data for 20 drought tolerance traits in 1–7 seasons at 1–5 locations together with genetic mapping data for two mapping populations provided 9 QTL clusters of which one present on CaLG04 has a high potential to enhance drought tolerance in chickpea improvement.AbstractChickpea (Cicer arietinum L.) is the second most important grain legume cultivated by resource poor farmers in the arid and semi-arid regions of the world. Drought is one of the major constraints leading up to 50 % production losses in chickpea. In order to dissect the complex nature of drought tolerance and to use genomics tools for enhancing yield of chickpea under drought conditions, two mapping populations—ICCRIL03 (ICC 4958 × ICC 1882) and ICCRIL04 (ICC 283 × ICC 8261) segregating for drought tolerance-related root traits were phenotyped for a total of 20 drought component traits in 1–7 seasons at 1–5 locations in India. Individual genetic maps comprising 241 loci and 168 loci for ICCRIL03 and ICCRIL04, respectively, and a consensus genetic map comprising 352 loci were constructed (http://cmap.icrisat.ac.in/cmap/sm/cp/varshney/). Analysis of extensive genotypic and precise phenotypic data revealed 45 robust main-effect QTLs (M-QTLs) explaining up to 58.20 % phenotypic variation and 973 epistatic QTLs (E-QTLs) explaining up to 92.19 % phenotypic variation for several target traits. Nine QTL clusters containing QTLs for several drought tolerance traits have been identified that can be targeted for molecular breeding. Among these clusters, one cluster harboring 48 % robust M-QTLs for 12 traits and explaining about 58.20 % phenotypic variation present on CaLG04 has been referred as “QTL-hotspot”. This genomic region contains seven SSR markers (ICCM0249, NCPGR127, TAA170, NCPGR21, TR11, GA24 and STMS11). Introgression of this region into elite cultivars is expected to enhance drought tolerance in chickpea.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-013-2230-6) contains supplementary material, which is available to authorized users.

Peanut is an important and nutritious agricultural commodity and a livelihood of many small-holder farmers in the semi-arid tropics (SAT) of world which are facing serious production threats. Integration of genomics tools with on-going genetic improvement approaches is expected to facilitate accelerated development of improved cultivars. Therefore, high-resolution genotyping and multiple season phenotyping data for 50 important agronomic, disease and quality traits were generated on the ‘reference set’ of peanut. This study reports comprehensive analyses of allelic diversity, population structure, linkage disequilibrium (LD) decay and marker-trait association (MTA) in peanut. Distinctness of all the genotypes can be established by using either an unique allele detected by a single SSR or a combination of unique alleles by two or more than two SSR markers. As expected, DArT features (2.0 alleles/locus, 0.125 PIC) showed lower allele frequency and polymorphic information content (PIC) than SSRs (22.21 alleles /locus, 0.715 PIC). Both marker types clearly differentiated the genotypes of diploids from tetraploids. Multi-allelic SSRs identified three sub-groups (K = 3) while the LD simulation trend line based on squared-allele frequency correlations (r2) predicted LD decay of 15–20 cM in peanut genome. Detailed analysis identified a total of 524 highly significant MTAs (pvalue >2.1×10–6) with wide phenotypic variance (PV) range (5.81–90.09%) for 36 traits. These MTAs after validation may be deployed in improving biotic resistance, oil/ seed/ nutritional quality, drought tolerance related traits, and yield/ yield components.

Experimental evidence is presented to show that the 18O enrichment in the leaf biomass and the mean (time-averaged) transpiration rate are positively correlated in groundnut and rice genotypes. The relationship between oxygen isotope enrichment and stomatal conductance (g(s)) was determined by altering g(s) through ABA and subsequently using contrasting genotypes of cowpea and groundnut. The Peclet model for the 18O enrichment of leaf water relative to the source water is able to predict the mean observed values well, while it cannot reproduce the full range of measured isotopic values. Further, it fails to explain the observed positive correlation between transpiration rate and 18O enrichment in leaf biomass. Transpiration rate is influenced by the prevailing environmental conditions besides the intrinsic genetic variability. As all the genotypes of both species experienced similar environmental conditions, the differences in transpiration rate could mostly be dependent on intrinsic g(s). Therefore, it appears that the delta18O of leaf biomass can be used as an effective surrogate for mean transpiration rate. Further, at a given vapour pressure difference, delta18O can serve as a measure of stomatal conductance as well.

WUE is an important yield-determining factor. Improving WUE would reduce the water requirement for a Water use efficiency (WUE) is physiologically linked to discriminaspecific yield potential and thus can help save considertion of the stable isotope of carbon (⌬ 13 C) in leaves of plant species. We determined the genetic variability in WUE by gravimetric approach able amount of irrigation water. Further, an improveand ⌬ 13 C among 34 diverse germplasm accessions of rice (Oryza sativa ment in WUE can significantly enhance total biomass L.). The leaf ⌬ 13 C ranged between 18.7 and 21.6‰, representing a production as well as yield at a given level of soil wasignificant variability and showed a strong inverse relationship with

Two pot experiments were conducted in two different seasons at the University of Agricultural Science, Bangalore, India, to study (a) the relationship between chlorophyll concentration (by measuring the leaf light-transmittance characteristics using a SPAD metre) and transpiration efficiency (TE) and (b) the effect of leaf N on chlorophyll and TE relationship in peanut. In Experiment (Expt) I, six peanut genotypes with wide genetic variation for the specific leaf area (SLA) were used. In Expt II, three non-nodulating isogenic lines were used to study the effect of N levels on leaf chlorophyll concentration-TE relationship without potential confounding effects in biological nitrogen fixation. Leaf N was manipulated by applying N fertiliser in Expt II. Chlorophyll concentration, TE (g dry matter kg 21 of H 2 O transpired, measured using gravimetric method), specific leaf nitrogen (g N m 22 , SLN), SLA (cm 2 g 21 ), carbon isotope composition (Á 13 C) were determined in the leaves sampled during the treatment period (35-55 days after sowing) in the two experiments. Results showed that the leaf chlorophyll concentration expressed as soil plant analytical development (SPAD) chlorophyll metre reading (SCMR) varied significantly among genotypes in Expt I and as a result of N application in Expt II. Changes in leaf N levels were strongly associated with changes in SCMR, TE and Á 13 C. In both the experiments, a significant positive relationship between SCMR and TE with similar slopes but differing intercepts was noticed. However, correction of TE for seasonal differences in vapour pressure deficit (VPD) between the two experiments resulted in a single and stronger relationship between SCMR and TE. There was a significant inverse relationship between SCMR and Á 13 C, suggesting a close linkage between chlorophyll concentration and Á 13 C in peanut. This study provides the first evidence for a significant positive relationship between TE and leaf chlorophyll concentration in peanut. The study also describes the effect of growing environment on the relationships among SLA, SLN and SCMR.