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Fifty wheat genotypes were evaluated at nine diverse locations in India to identify high-yielding and stable genotypes. The analysis of variance based on additive main effects and multiplicative interaction (AMMI) indicated significant genotype, environment and genotype - environment (GE) interactions, with a total variation of 5.99, 20.23 and 73.77%, respectively. A biplot-AMMI analysis and yield stability index incorporating the AMMI stability value and yield in a single non-parametric index were used to discriminate the genotypes with highest and stable yield; the genotypes G135, G125, G104, G112 and G144 were found to be promising. Two mega environments (ME) were identified based on GGE (genotype and GE interaction) biplot analysis and the genotypes G119 and G120 and G107, G148 and G146 performed best in the mega-environments ME I and ME II, respectively. Both approaches allowed the identification of stable genotypes (G112 and G135), which can be included in the national testing program, with a view to release a new variety.

The paper compares food and straw fodder traits in wheat lines of 6 different CIMMYT wheat trials tested across 32 environments of South Asia (India, Nepal, Bangladesh and Pakistan). Experiments were conducted in the main spring wheat season with sowing time of mid-November to mid-December and harvest between March to April. Investigated and compared were grain yield (GY), straw yield (SY), straw nitrogen (N) content, neutral (NDF) and acid (ADF) detergent fiber, acid detergent lignin (ADL), and in vitro organic matter digestibility (IVOMD). Highly significant (P 60%), considerable (45%) for HI and straw N (40%), moderate for ADL (18%), and minor for NDF, ADF, and IVOMD ( Across all cultivar types only ADF was significantly (P 0.05) relationships were observed between N, ADF and IVOMD and SY. Among cultivars consistent relationships were only observed between N and GY which were significantly (P = 0.03 to 0.009) inversely correlated in five out of the six cultivar types. Within cultivars types ADF varied by at least 2.3% units in HTNM and 1st DRYT and up to 4.9% units in ESWYT. By extrapolation such cultivar-dependent differences will matter at wheat straw trading.

GxE interaction to know adaptability of 19 salt salinity tolerant barley genotypes was studied by parametric and non-parametric measures. Genotypes KB1516, RD2907 and RD2794 showed minimum environmental variance over different environments. Superiority index identified genotypes RD2907 and NDB1445 with lowest value accompanied with higher. Wricke’s measure exhibited lower values of DWRB168, DWRB165 and NDB1445. Higher values of GAI showed consistent performance of RD2907, NDB1445 and RD2552. Non-parametric measures Si(1), Si(3) and Si(6) the considered DWRB165 and DWRB168 as desirable genotypes. Thennarasu’s first measure NPi(1) found DWRB168 and NDB1445 as desirable adaptable and KB1546, RD2907 and NDB1173 were unstable genotypes. Wricke’s parameter was positively correlated with NPi(1), NPi(3) and Kang. GAI had significant positive with Pi and Kang while negative with Si(6), NPi(2) & NPi(4). Worth to mention the negative association of Pi with Si(6), NPi(2), NPi(4). Non parametric measures Si (3) Si (6) NPi (2) & NPi (4) clubbed together while Kang, Wi 2, s2i ,Si (1),Si (2) ,NPi (1) & NPi (3) joined in another cluster. Left over parametric measures were grouped in two separate clusters i.e. (bi, S2xi ,CVi),(Yield, GAI Pi) respectively. Biplot analysis based on first two principal components showed three groups among the measures.

Multi-environment wheat trials provide valuable information on the extent of genotype x environment interaction, the stability of genotypes and define and confirm agro-ecological regions through associations among sites. The All India Coordinated Crop Improvement Project on wheat evaluates candidates for release across the wheat growing regions of India. To facilitate this process the wheat area is divided into six agro-ecological zones; the northwestern plains zone (NWPZ), the northeastern plains zone (NEPZ), the central zone (CZ), the peninsular zone (PZ), the northern hills zone (NHZ) and the southern hills zone (SHZ). Factor analytic (FA) models were used to analyze the genotype x environment interaction for yield of 813 wheat genotypes evaluated at 136 locations across the six agro-ecological zones in 1307 individual advanced variety trials between 2008/09 and 2012/13. Genotype x environment interaction was firstly assessed separately within each of the six established agro-ecological zones. Key locations with a high genetic correlation with all other locations within each zone were identified. Predicted genetic values of important cultivars that were represented in a wider range of environments within each zone were estimated and highly stable genotypes were found. Genotype x environment interaction was subsequently assessed across agro-ecological zones. Only those environments where the models accounted for > 99% of the genetic variance were retained for further analysis and two smaller zones (NHZ and SHZ) with little or no genotype congruence with other agro-ecological zones were removed. Thus 476 genotypes from 488 environments were included in the analysis. Fifteen clusters of environments with similar patterns of adaptation were found. These clusters were then characterized based on zonal classification, sowing time, irrigation regime, latitude and year and three regions broadly representing the main wheat growing areas of India were identified. These regions represent a combination of the NWPZ and NEPZ defined by latitude, a central region that combines CZ locations with northern PZ locations and a southern region comprised of southern PZ sites. Further stratification of these zones was then possible based on sowing time and irrigation practice. One cluster of 29 environments had a high average genetic correlation (r = 0.75) with most other environments and production zones. These represent key locations where larger numbers of entries might be grown in future seasons as they are the best predictors of yield across cropping zones.

Predictability estimated through cross-validation approach showed moderate to high level; hence, genomic selection approach holds great potential for biofortification breeding to enhance grain zinc and iron concentrations in wheat. Wheat (Triticum aestivum L.) is a major staple crop, providing 20 % of dietary energy and protein consumption worldwide. It is an important source of mineral micronutrients such as zinc (Zn) and iron (Fe) for resource poor consumers. Genomic selection (GS) approaches have great potential to accelerate development of Fe- and Zn-enriched wheat. Here, we present the results of large-scale genomic and phenotypic data from the HarvestPlus Association Mapping (HPAM) panel consisting of 330 diverse wheat lines to perform genomic predictions for grain Zn (GZnC) and Fe (GFeC) concentrations, thousand-kernel weight (TKW) and days to maturity (DTM) in wheat. The HPAM lines were phenotyped in three different locations in India and Mexico in two successive crop seasons (2011–12 and 2012–13) for GZnC, GFeC, TKW and DTM. The genomic prediction models revealed that the estimated prediction abilities ranged from 0.331 to 0.694 for Zn and from 0.324 to 0.734 for Fe according to different environments, whereas prediction abilities for TKW and DTM were as high as 0.76 and 0.64, respectively, suggesting that GS holds great potential in biofortification breeding to enhance grain Zn and Fe concentrations in bread wheat germplasm.

Forty wheat varieties was evaluated for grain yield in the field for two successive crop seasons (2009–10 and 2010–11), with 2 dates of sowing, viz., early (last week of October) and late (first week of December). Based on the yield potential, five high yielding and five low yielding varieties were selected to study the relationship between grain yield and root characteristics along with canopy temperature depression (CTD) and flag leaf drying under early and late sown conditions. It was observed that under early sown restricted irrigation conditions the high yielding varieties utilized energy in increasing the root length and root density to deeper soil depths instead of accumulating root weight, compared to low yielding varieties. In contrast, under late sown condition, the high yielding varieties had more root weight and density in their upper soil depths, compared to low yielding varieties. Hence, it can be suggested to select wheat varieties with high root density and more root length in the lower depths of the soil for rainfed/restricted irrigation conditions to mitigate the early heat effects, and varieties having high root density and more root length in the upper layers of the soil for late sown conditions to tolerate the late or terminal heat. Further, high CTD scores at 1st stage (vegetative) and high leaf score at fourth stage (post flowering) were found to be important selection parameters, which help in reducing the evapo-transpiration and increase 1000 grain weight under early sown limited irrigation condition, and thereby increase the grain yield; whereas, CTD at 3rd stage (flowering stage) contribute to maintain the stay green trait for longer period and reduce the loss due to evapo-transpiration and in-turn increase the grain yield under late sown conditions. These traits can be used as selection tools for physiological breeding programme to increase the production and productivity of wheat.

High temperatures are a primary concern for wheat production in South Asia. A trial was conducted to evaluate the grain yield performance of high yielding, early maturing heat tolerant CIMMYT wheat lines, developed recently in Mexico for adaptation to high temperature stresses in South Asia. The trial, comprised of 28 entries and two checks, was grown in 13 locations across South Asia and two environments in Mexico. Each location was classified by mega environment (ME); ME1 being the temperate irrigated locations with terminal high temperature stress, and ME5 as warm, tropical, irrigated locations. Grain yield (GY), thousand kernel weight (TKW), days to heading (DH) and plant height (PH) were recorded at each location. Canopy temperature (CT) was also measured at some locations. Significant differences were observed between ME for DH, PH, GY, and TKW. The cooler ME1 locations had a mean DH of 83 days, compared to 68 days mean DH in ME5. The ME1 locations had higher mean GY of 5.26 t/ha and TKW of 41.8 g compared to 3.63 t/ha and 37.4 g, respectively, for ME5. Early heading entries (

Wheat yields globally will depend increasingly on good management to conserve rainfall and new varieties that use water efficiently for grain production. Here we propose an approach for developing new varieties to make better use of deep stored water. We focus on water-limited wheat production in the summer-dominant rainfall regions of India and Australia, but the approach is generally applicable to other environments and root-based constraints. Use of stored deep water is valuable because it is more predictable than variable in-season rainfall and can be measured prior to sowing. Further, this moisture is converted into grain with twice the efficiently of in-season rainfall since it is taken up later in crop growth during the grain-filling period when the roots reach deeper layers. We propose that wheat varieties with a deeper root system, a redistribution of branch root density from the surface to depth, and with greater radial hydraulic conductivity at depth would have higher yields in rainfed systems where crops rely on deep water for grain fill. Developing selection systems for mature root system traits is challenging as there are limited high-throughput phenotyping methods for roots in the field, and there is a risk that traits selected in the lab on young plants will not translate into mature root system traits in the field. We give an example of a breeding programme that combines laboratory and field phenotyping with proof of concept evaluation of the trait at the beginning of the selection programme. This would greatly enhance confidence in a high-throughput laboratory or field screen, and avoid investment in screens without yield value. This approach requires careful selection of field sites and years that allow expression of deep roots and increased yield. It also requires careful selection and crossing of germplasm to allow comparison of root expression among genotypes that are similar for other traits, especially flowering time and disease and toxicity resistances. Such a programme with field and laboratory evaluation at the outset will speed up delivery of varieties with improved root systems for higher yield.

Wheat is the second major staple crop, after rice, in India and Pakistan and is also gaining similar importance in Nepal and Bangladesh. Wheat production in South Asia has increased from 15 mt in 1960s to 95.5 mt during 2004–2005. It still needs to grow at the rate of 2–2.5% annually until the middle of 21st century. However, for India, recent estimations have shown a growth requirement of about 1.1%. Although the wheat improvement programs in these countries, with the active collaboration of national agricultural research centers (NARS) and CIMMYT, has made a significant progress, it is a matter of significant concern that wheat production has stagnated for last few years. Since there is little scope for increasing land area under wheat, the major challenge will be to break the yield barrier by pragmatic genetic and developmental approaches. The most serious constraints to wheat production in this region are a host of biotic and abiotic stresses. Although India has not faced any rust epidemic in the last decade, rusts continue to occupy the place of most dangerous pathogen for the region. Among the abiotic stresses, unusual warming trends during grain filling period are causing yield declines, especially in eastern and central India. There are other challenges that are specific to the highly productive rice–wheat cropping system predominant in the Indo-Gangetic plains. The total factor productivity of this system is declining due to depletion of soil organic carbon. Addition of organic matter to soil through green manuring and crop residue recycling, balanced fertilization, integrated nutrient management, diversification of rice-wheat system are some of the possible remedial measures to improve total factor productivity. The international linkages with CIMMYT needs to be strengthened more closely for developing more productive wheat genotypes and thus, achieving wheat targets in the South Asian region.

India is the second largest producer of wheat in the world, with production hovering around 68–75 million tons for past few years. The latest estimated demand for wheat production for the year 2020 is approximately 87.5 million tons, or about 13 million tons more than the record production of 75 million tons harvested in crop season 1999–2000. Since 2000, India has struggled to match that record production figure and thus faces a critical challenge in maintaining food security in the face of its growing population. The current major challenges facing future wheat production in India are increasing heat stress; dwindling water supplies for irrigation; a growing threat of new virulence of diseases such as wheat rusts (yellow, brown, and black) and leaf blight; continuous adoption of rice-wheat systems on around 11 million hectares; changes in urbanization patterns, and demand for better quality wheat. In addition, the threat posed by the new stem rust race Ug99 can not be underestimated. The wide gap (around 2.5 t/ha) between the potential and harvested yield in the eastern Gangetic Plains also cries out for solutions. Addressing issues related to different stresses will require harnessing genes discovered in landraces and wild relatives following conventional as well as non-conventional approaches. For effective technology delivery in areas that suffer from poor linkages with farmers, participatory research needs to be strengthened. The future germplasm requirements from a dependable collaborator such as CIMMYT are largely being dictated by the above factors.

There are many socioeconomic and technological constraints that affect the production of wheat and other staple cereals in South Asia. Wheat production is one of the economic mainstays in South Asia, but the yield gap between farmers’ fields and experimental yields is wide across the region. For the last 3 years, CIMMYT and the CAZS-NR have been collaborating with farmers, NARS, and other South Asian partners to promote improved wheat varieties and new resource conservation technologies (RCTs) in farmers’ fields. Participation fostered among farmers, scientists, extension specialists, NGOs and the private sector included variety selection (PVS), and evaluation of agronomic practices. Through PVS, several farmer-preferred technologies have been identified including wheat varieties for adverse conditions in eastern Uttar Pradesh (India) and for boron deficiency in parts of Nepal. There has been considerable improvement in the access of farmers to new varieties and technologies in the rural areas. Yield increases (15–70%) have been achieved by resource-poor farmers over the existing varieties through the adoption of new varieties and RCTs. The farmers have also made substantial cost savings and achieved higher yields through resource-conserving agronomic techniques such as zero till. Seed of the new farmer-selected cultivars has been multiplied by groups of collaborating farmers and widely distributed.

In this investigation 23 genotypes of wheat were tested for stability in 19 locations of North Western plains of the country, Yield data generated from the trials were analysed using AMMI analysis. The distribution of genotype by AMMI revealed that the genotypes 10,13, 20,12,15 and 14 scattered close to the origin, indicating minimal interaction of these genotypes with environments. Studied environments explained 57.2% of the total variation, whereas G and GxE captured 6.2% and 24.3%, respectively. First two principal components (PC1 and PC2) were used to create a 2-dimensional GGE biplot and explained 26.4% and 14.3% of GGE sum of squares (SS), respectively. Environments of Karnal, Ludhiana and Gurdaspur fall in same sector with genotypes 23 & 16. The spearman correlations calculated based on ranks by stability methods varied from positive value 0.97 to negative correlation of 0.759. The cultivar superiority estimate (Pi) maintained negative correlation with other estimates ranking.