Grain yield, adaptation and progress in breeding for early-maturing and heat-tolerant wheat lines in South Asia

Mondal, Suchismita; Singh, Ravi P.; Mason, Esten; Espino, Julio Huerta; Autrique, Enrique; Joshi, Arun Kumar

doi:10.1016/j.fcr.2016.04.017

Cited by 102 publications

(65 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We also observed that GY in both the stressed environments was negatively correlated with DTHD and positively correlated with plant height, as observed previously (Mondal et al 2016). However, nursery EYT 14–15 evaluated in the DS environment was an exception to this general trend, where GY had no correlations with DTHD and plant height, because of the warmer temperatures observed in this year.…”

Section: Discussionsupporting

confidence: 91%

“…This is due to the high temperatures and longer day length in this environment that are known to result in shorter cycles, quicker maturity, and accelerated crop development rates. These are considered as adaptations of plants to hot weather enabling them to avoid excessive stress (Zahedi and Jenner 2003; Fischer 2011; Semenov et al 2014; Trnka et al 2014; Mondal et al 2016). The plants in the stressed environments were also shorter than in the optimal environment (average height reduction of 15 cm, and 35.5 cm in the DS and HS environments, respectively).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

et al. 2018

View full text Add to dashboard Cite

Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center’s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress–resilience within years.Electronic supplementary materialThe online version of this article (10.1007/s00122-018-3206-3) contains supplementary material, which is available to authorized users.

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Under future climatic scenarios, global climate models predict an increase in the intensity and magnitude of high-temperature episodes (Asseng et al, 2011;Lobell et al, 2012;Gourdji et al, 2012Gourdji et al, , 2013Pachauri and Meyer, 2014;Pask et al, 2014). Short episodes of high temperatures (>30°C) coinciding with a specific developmental stage or long-duration exposure to high temperatures in wheat growing regions have been reported from China, the United States, and India (Mondal et al, 2013;Tack et al, 2015;Liu et al, 2016;Mondal et al, 2016). Recently, a modeling exercise without accounting for CO 2 fertilization concluded that a 1°C increase in global temperature would lead to a decline in global wheat production ranging between 4.1 and 6.4% (Liu et al, 2016).…”

Section: Researchmentioning

confidence: 99%

Quantifying the Impact of Heat Stress on Pollen Germination, Seed Set, and Grain Filling in Spring Wheat

et al. 2019

View full text Add to dashboard Cite

Exposure to temperatures ≥30°C during flowering and grain filling stages can negatively affect seed set and seed weight in spring wheat (Triticum aestivum L.). The screening of a large set of germplasm under hot wheat growing environments (Indo‐Gangetic Plain in India) led to the identification of promising heat‐tolerant genotypes. The selected set of 28 diverse spring wheat genotypes were exposed to heat stress (34/16°C day/night temperatures) for 10 d during flowering and for 30 d during grain filling to quantify genetic variability in pollen germination, photosynthesis, and yield parameters under controlled‐environment conditions. Pollen grains collected immediately at anthesis (between 0530 and 0630 h) were incubated on liquid in vitro pollen germination media. Averaged across wheat genotypes, a significant reduction in pollen germination (39.9%, P < 0.001) was recorded from plants exposed to heat stress. Heat stress for 10 d during flowering induced significant reduction in seed number (15.4 and 23.0%) and seed weight (32.3 and 34.6%) on main and primary spikes, respectively, compared with the control. Heat stress during grain filling had a more pronounced impact on seed weight (16 and 22%) than seed number (2.7 and 9.3%) in main and primary spikes, respectively. Genotypes KSG025 and KSG1214 with higher seed number, seed weight, and harvest index and appreciable pollen germination under heat stress were identified as candidate donors for simultaneously enhancing flowering and post‐flowering heat tolerance in spring wheat.

show abstract

“…Among these, only a few have discussed sugar beet and wheat GHG emissions [22,23]. However, only a few reports are available on wheat production; therefore, quantification of energy consumption and GHG emissions in Pakistan is very important Wheat is one of the world's major staple crops and a source of 20% of daily protein to approximately 4.5 billion people, with yearly production of 730 million tons worldwide [24,25]. In Pakistan, wheat is a chief staple food and it dominates all crops in acreage (8740 thousand hectares) and production (25,195 thousand tons), throughout the country ( Figure 1) and also contributes 1.6% and 8.9% to the country's GDP and value-added agriculture, respectively [26].…”

Section: Introductionmentioning

confidence: 99%

Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis

Ilahi

Raza

et al. 2019

Sustainability

View full text Add to dashboard Cite

Energy is a major component in enhancing agricultural productivity for the rapidly growing world population. From that fact, a comprehensive analysis of energy inputs and outputs is required to conserve energy for future generations without threatening the food supply. Therefore, this study was performed in wheat production across important cropping zones of Punjab, Pakistan. In this study, the energy use pattern of wheat production was analyzed, and the degrees of technical efficiency of Decision Making Units (DMUs) were examined using Data Envelopment Analysis (DEA). Based on the results of the DEA analysis, the inefficient energy inputs were identified and further explored with the core objective of a significant reduction of excess valuable resources. Data were collected from conducting a face-to-face questionnaire of 200 farmers. The farms for sample were chosen randomly by a stratified normal approach. The results disclosed that the input energy of 34,430.98 MJ ha−1 was used up for wheat production with an output energy of 48,267.05 MJ ha−1. Energy use efficiency, specific energy, energy productivity, and net energy gain in wheat production were calculated as 1.4 MJ kg−1, 9.27 MJ kg−1, 0.10 MJ kg−1 and 13,836.07 MJ kg−1, respectively. The average technical, pure technical, and scale efficiency of DMUs were 0.668, 0.776, and 0.828, respectively, and 0.74% of consulted DMUs were functioning at decreasing returns to scale. Additionally, the significant energy consumption belongs to fertilizer, and diesel fuel, which contribute 65% of the total energy input. If these inputs are applied and managed in line with ours optimize value (29,388.5 MJ ha−1) could save 14.65% resources, which will eventually add the equal quantity in wheat-yield. The total Greenhouse Gas (GHG) emissions were calculated to be 866.43 kg CO2-eq ha−1. In conclusion, the results of the present study suggest that there is sensible capacity for enhancing the energy efficiency of wheat production in Pakistan by accompanying the recommendations for economical energy management, sustainable and efficient use of energy is extremely encouraged.

show abstract

Grain yield, adaptation and progress in breeding for early-maturing and heat-tolerant wheat lines in South Asia

Cited by 102 publications

References 29 publications

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat

Quantifying the Impact of Heat Stress on Pollen Germination, Seed Set, and Grain Filling in Spring Wheat

Optimization Approach for Improving Energy Efficiency and Evaluation of Greenhouse Gas Emission of Wheat Crop using Data Envelopment Analysis

Contact Info

Product

Resources

About