SummaryTo map resistance genes for Fusarium wilt (FW) and sterility mosaic disease (SMD) in pigeonpea, sequencing‐based bulked segregant analysis (Seq‐BSA) was used. Resistant (R) and susceptible (S) bulks from the extreme recombinant inbred lines of ICPL 20096 × ICPL 332 were sequenced. Subsequently, SNP index was calculated between R‐ and S‐bulks with the help of draft genome sequence and reference‐guided assembly of ICPL 20096 (resistant parent). Seq‐BSA has provided seven candidate SNPs for FW and SMD resistance in pigeonpea. In parallel, four additional genotypes were re‐sequenced and their combined analysis with R‐ and S‐bulks has provided a total of 8362 nonsynonymous (ns) SNPs. Of 8362 nsSNPs, 60 were found within the 2‐Mb flanking regions of seven candidate SNPs identified through Seq‐BSA. Haplotype analysis narrowed down to eight nsSNPs in seven genes. These eight nsSNPs were further validated by re‐sequencing 11 genotypes that are resistant and susceptible to FW and SMD. This analysis revealed association of four candidate nsSNPs in four genes with FW resistance and four candidate nsSNPs in three genes with SMD resistance. Further, In silico protein analysis and expression profiling identified two most promising candidate genes namely C.cajan_01839 for SMD resistance and C.cajan_03203 for FW resistance. Identified candidate genomic regions/SNPs will be useful for genomics‐assisted breeding in pigeonpea.

In order to investigate specific and general adaptation of chickpea in India, a wide range of sub-continental, Australian and Mediterranean genotypes were grown across seven sites characterizing the major chickpea growing areas over 3 years, and extensive data on plant stand, early vigour, phenology, productivity and yield components collected. High and low yielding sites were clearly separated by a range of physical and biological characters, low yield being associated with low latitude and pre-season rainfall, high temperature, early phenology, short crop duration, low biomass and fecundity. Genotype by environment interactions for yield were highly significant (P < 0.001), and accounted for more variance than that attributed to genotypes alone. Ward's hierarchical clustering indicated that the genotypes could be separated into discrete groups, comprising material specifically adapted to the north (Clusters 2 and 3) or south (Cluster 5), widely or consistently poorly adapted germplasm (Clusters 1 and 4, respectively).Cluster 5, comprising germplasm from southern and central India, was characterized by early phenology, confirming the role of drought escape in southern India. With increasing latitude Cluster 5 genotypes remained early, but had the capacity to delay maturity considerably, resulting in average, and occasionally above average yields. However, compared to well-adapted material in the north, Cluster 5 biomass was low, and the time interval between flowering and podding up to 50 days, representing repeated cycles of flowering and subsequent abortion. Clusters 2 and 3, dominated by northern Indian genotypes, were characterized by later phenology, and were able to delay the onset of flowering significantly more than the remaining germplasm at late flowering northern sites. In Cluster 3, the second highest yielding group overall, this increased both source and sink potential at productive northern sites. Cluster 2 was uniformly later than Cluster 3, and lower yielding at most sites. Cluster 1 was characterized by intermediate flowering and relatively early, responsive maturity, a phenological compromise responsible for wide adaptation, by providing sufficient drought escape in the south, and enough biomass in the north to produce above average yields in these contrasting environments. ICCV 10 from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), and 2 Indian Agricultural Research Institute (IARI) lines, BG 391 and BG 1006, were the most consistently high yielding, ranking www.elsevier.com/locate/fcr Field Crops Research 98 (2006) 230-244 in the top 10 at 10 and 8 sites, respectively. Cluster 4, comprising largely Australian cultivars, was characterized by late, unresponsive phenology and the lowest yield at each site. Crown

Pigeonpea is an important pulse crop grown predominantly in the tropical and sub-tropical regions of the world. Although pigeonpea growing area has considerably increased, yield has remained stagnant for the last six decades mainly due to the exposure of the crop to various biotic and abiotic constraints. In addition, low level of genetic variability and limited genomic resources have been serious impediments to pigeonpea crop improvement through modern breeding approaches. In recent years, however, due to the availability of next generation sequencing and high-throughput genotyping technologies, the scenario has changed tremendously. The reduced sequencing costs resulting in the decoding of the pigeonpea genome has led to the development of various genomic resources including molecular markers, transcript sequences and comprehensive genetic maps. Mapping of some important traits including resistance to Fusarium wilt and sterility mosaic disease, fertility restoration, determinacy with other agronomically important traits have paved the way for applying genomics-assisted breeding (GAB) through marker assisted selection as well as genomic selection (GS). This would accelerate the development and improvement of both varieties and hybrids in pigeonpea. Particularly for hybrid breeding programme, mitochondrial genomes of cytoplasmic male sterile (CMS) lines, maintainers and hybrids have been sequenced to identify genes responsible for cytoplasmic male sterility. Furthermore, several diagnostic molecular markers have been developed to assess the purity of commercial hybrids. In summary, pigeonpea has become a genomic resources-rich crop and efforts have already been initiated to integrate these resources in pigeonpea breeding.

The present investigation was carried out during kharif 2012 at the Agricultural Research Station, Gulbarga and other four locations located in north eastern dry zone (Zone 2) of Karnataka, to know the stability of the nineteen advanced genotypes of pigeonpea. Highly significant differences among genotypes were observed for all the characters except number of pods per plant, number of seeds per pod and seed yield per plant. The variance due to Genotype x Environment found significant for the characters like days to 80% pod maturity and number of seeds per pod. Environment (linear) interaction component was significant for all the traits. The variance due to pooled deviation (non-linear) was highly significant for all the characters except for number of seeds per pod which reflect considerable genetic diversity in the material. Out of 19 genotypes studied two entries viz., GRG-109 and GRG-107 were consistent and high yielding compared to local checks.

Fourteen pigeon pea (Cajanus cajan L.) genotypes were evaluated for their yield performance at two locations during kharif season of 2009-10 and 2010-11. A significant genotypic difference for yield character was observed. Highly significant genotype-environment interaction indicated differential response of the genotypes to the environmental changes. The stability analysis showed significance of linear component of variation for grain yield. The genotypes TJT-501 (1728.667Kg/Ha) and GRG-2009-3 (1570.000 Kg/Ha) exhibited low mean performance along with regression value nearer to unity (bi=1) and non significant deviation from regression (S 2 di=0) indicating, the high stability and wider adaptability across the different environments. The genotype ICPH-2671 (3134.833 Kg/Ha) exhibited highest mean value and regression value (bi>1) and non significant deviation (S 2 di < 0). But genotypes JKM-197 (3072.667 Kg/ha), GRG-2009 (29993.167 Kg/ha), TS-3R (2823.333 Kg / Ha) and ICP-8863 (2740.417 Kg/Ha) exhibited high mean performance but higher regression value (bi>1) and significant deviation (S 2 di < 0) value indicating adapted for high performance environments showing these genotypes are sensitive to environments and give maximum yield when inputs are not limited.