National Institute of Abiotic Stress Management

This study investigated how heat stress influences HSP70 gene expression in Madgyal Sheep during grazing in hot weather. The sheep were divided into three groups—A, B, and C—and exposed to direct sunlight for zero, four, and eight hours, respectively. The effects of different heat exposure durations on HSP70 expression were examined on Days 0, 15, and 30. Throughout the experiment, the temperature-humidity index (THI) averaged 76.28±0.81. Results indicated that Groups B and C exhibited higher HSP70 mRNA expression than Group A, with no significant difference between B and C. HSP70 levels increased after 4 hours of heat exposure compared to 8 hours. Initially, HSP70 levels increase to protect cells against heat stress. As heat stress levels rise with prolonged exposure, HSP70 levels also rise, with Group C showing the highest levels. This pattern suggests that sustained heat stress elevates HSP70 to manage physiological stress. The early peak in HSP70 helps protect cells. The marked elevation of both markers in Group B reflects a strong connection between heat stress and the physiological stress response. Overall, there is a positive correlation—higher heat stress correlates with increased HSP70.

The fall armyworm [Spodoptera frugiperda (J. E. Smith)] is an invasive pest of maize, posing significant threats to crop productivity. While herbivore-induced plant volatiles (HIPVs) play a central role in indirect plant defenses, the contribution of hydrocarbon-based volatile organic compounds to S. frugiperda resistance remains underexplored. This study investigated the VOC profiles of maize seedlings infested by S. frugiperda compared to uninfested controls, and evaluated the bioactivity of selected synthetic VOCs on larval feeding performance. GC-MS analysis revealed qualitative and quantitative shifts in the maize volatilome following herbivory, with 11 VOCs-including mesitylene, cyclohexane, hexadecane, and eicosane-uniquely induced in infested plants. Hydrocarbon compounds dominated the altered profiles, suggesting their potential defensive function. To validate their bioactivity, nine synthetic hydrocarbons were applied to semi-synthetic diets, and their effects on larval development, feeding, and nutritional indices were assessed. Among them, eicosane and cyclohexane exhibited the strongest suppressive effects, significantly reducing larval weight gain, food intake, frass production, relative growth rate (RGR), relative consumption rate (RCR), and approximate digestibility (AD). Octane and pentatriacontane showed moderate inhibitory effects, while tetracosane and henicosane were largely ineffective. The results demonstrate that specific hydrocarbon-based VOCs not only correlate with herbivore attack but also directly impair pest growth and digestion. This study underscores the functional importance of herbivory-induced hydrocarbons in maize defense and identifies promising VOCs for development as biocompatible agents in sustainable pest management strategies.

Efficient nitrogen (N) and phosphorus (P) management enhances crop yield while minimizing environmental impact. Nano-fertilizers are reported to ensure sustained nutrient release, optimize nutrient absorption and improve nutrient use efficiency, leading to higher crop productivity and profitability. A two year field trial was conducted under semi-arid conditions in India to evaluate the effect of foliar-applied nano-DAP on the growth, productivity, profitability and nutrient use efficiency of rainfed maize. The treatments comprised conventional fertilizers at varying N and P levels [0, 50, 75, and 100% recommended dose of N and P] with and without foliar application of nano-DAP. Application of 100% recommended NPK (90-45–45 N-P-K kg ha -1 ) through conventional fertilizers along with foliar sprays of nano-DAP (N 100 P 100 K+ nano-DAP) resulted in higher grain yield (3058 kg ha -1 ) and economic returns (US $ 473 ha -1 ). Notably, the N 75 P 75 K + nano-DAP treatment produced yield and economic returns comparable to those obtained with 100% recommended NPK, either alone or in combination with nano-DAP. This demonstrates that two foliar sprays of nano-DAP can reduce N and P inputs by 25% without compromising on yield or profitability. Higher agronomic use efficiency of N (26.9%) and P (48.4%), apparent recovery efficiency of N (42.6%) and P (11.3%) were also observed under N 75 P 75 K + nano-DAP. Furthermore, N 75 P 75 K + nano-DAP treatment reduced greenhouse gas (GHG) emissions by 24.5% and energy consumption by 15.4%, highlighting its potential for sustainable maize production with reduced environmental impact compared to N 100 P 100 K treatments.

Salinity stress is a major constraint to global agriculture, reducing crop yields, degrading arable land and causing economic losses. In many regions, it is intensified by factors such as seawater intrusion, poor irrigation practices and climate-related changes in water availability. While mild salinity may not severely hinder growth, high salinity disrupts ion homeostasis, induces oxidative stress and impairs physiological processes including water and nutrient transport, leading to stunted growth and reduced biomass. Brassinolide (BL), a naturally occurring plant steroid hormone, plays a pivotal role in mitigating salinity stress by regulating ion balance, enhancing antioxidant defense systems, modulating stress-responsive gene expression (e.g., ion transporters, antioxidant enzymes) and promoting growth and development. Its exogenous application through foliar sprays, seed priming or soil treatments has shown promise in improving salt tolerance. This review outlines the mechanisms of salinity stress in plants and highlights the role of BL in alleviating its effects, with emphasis on signalling pathways, ion homeostasis, regulation of Na/H antiporters and reactive oxygen species (ROS) scavenging.

Meat is a primary source of protein consumed in the world and secondary source of protein in India. India produced 10.25 million tonnes of meat in 2023-24. Telangana is the fourth largest meat producing state accounting for 10.85% of India’s meat production. The present study attempts to analyse the growth pattern, regional variations, sources of growth and instability in production of meat from different types of meat animals (buffaloes, sheep and goats) at the district level by using Compound growth rates, Coefficient of variation, Coppock’s Instability Index (CII) and Hazell’s decomposition analysis for the time series data from 2014-15 to 2023-24 on no of animals slaughtered, yield and meat production. Between 2014-15 and 2023-24 average meat production in Telangana has increased by 120.32% from 505.05 to 1112.73 thousand tons with an average of 800.4 thousand tons. Telangna’s share is increasing in all types of meats except sheep meat where it is showing slight decreasing trend in its share. Category wise analysis shows that poultry meat occupies major share of 46.12% and 43.27% of average meat production in Telangana and India respectively. Telangana accounts for an average share of 9.8% of meat animals and 9.4% of meat production. Its share is increasing for both production and animals for all types of meats. Meat production in telangana shows increasing trend for all types of meat, milch animals and yield. Overall Telangana meat production is stable with low instability index of 2.71% which is less than 15%. This indicates high production is associated with high instability. Rangareddy emerged as leading producer of meat in Telangana accounting for 14.13% of Telangana’s average meat production. Region wise, South Telangana accounts for 50.26 % of Meat production followed by Central Telangna (35.15%) and North Telangana (16.6%). Decomposition analysis showed that animals have contributed to change in average meat production in Telangana during 2014-15 to 2023-24. In North Telangana, where production potential is more, emphasis should be on marketing infrastructure like increasing no of slaughterhouses, livestock markets, road connectivity and encouraging commercial livestock production where as other regions needs to focus on production constraints like green fodder availability. Since the buffalo meat production whose share is less in total meat is predominant in North Telangana encouraging commercial poultry in North Telangana will increase meat production.

Wheat is a predominant food crop of Punjab, contributing significantly towards state’s agricultural economy. However, the diverse agro-climatic zones of the state, ranging from the sub-montane regions to the plains and arid regions, exploring exclusive challenges for wheat cultivation in Punjab. To identify different varieties suitable for particular region as well as to optimize crop management practices, the comprehensive field trials were conducted aiming towards analysing the data and contributing towards the development of sustainable wheat production system in Punjab. A field experiment was conducted over two consecutive rabi seasons (2017–18 and 2018–19) at three different locations in Punjab (Ludhiana, Faridkot, and Ballowal Saunkhri). The experiment followed a factorial split plot design with three dates of sowing (D1, 25th October; D2, 15th November; D3, 5th December) and three wheat cultivars (V1, WH1105, V2, UNNAT PBW 550, V3, PBW 590) in the main plots, whereas two irrigation treatments (I1: Recommended, I2: Recommended ± weather forecast based) in the subplots. Across both seasons, the crop sown on 15th November consistently exhibited the highest tiller count (per/m2) and dry matter at harvest (g/m2) i.e. 120.0 and 388.4, 115.2 and 378.0 and 90 and 262.2 for Ludhiana, Faridkot and Ballowal Saunkhri respectively. At Ludhiana, Faridkot and Ballowal Saunkhri, the grain yield was 4.8 t/ha, 4.6 t/ha and 3.7 t/ha respectively for D2 which was significantly higher than D3 (4.2 t/ha, 3.8 t/ha and 3.0 t/ha) and at par with D1 (4.6 t/ha, 4.4 t/ha and 3.6 t/ha) for Ludhiana, Faridkot and Ballowal Saunkhri respectively). Among the cultivars, UNNAT PBW550 produced significantly more yield (4.8, 4.7 and 3.9 t/ha) than PBW 590 (4.0, 3.6 and 3.0 t/ha) for Ludhiana, Faridkot and Ballowal Saunkhri respectively. Ballowal Saunkhri recorded lowest grain yield among the different locations. Analysis indicated non-significant differences in yield under different irrigation schedules across the three sowing dates. it has been observed from the study that reduction in yield is caused with delay in sowing time of wheat, whereas scheduling irrigation based on weather forecast can be helpful in saving irrigation water and other resources.

Next-generation molecular tools with AI integration can accelerate genetic gain in sugarcane by enhancing variation, accuracy, and efficiency, enabling rapid development of high-yielding, high-quality, and climate-resilient varieties. Enhancing genetic gain is essential for sustainable sugar and bioenergy production, especially amid growing global reliance on renewable energy sources. Sugarcane and its byproducts serve as important feedstocks for both first and second-generation biofuels, and face several breeding challenges due to its genetic complexity, extended breeding cycles, and strong environmental interactions. The breeder's equation offers a quantitative framework to accelerate genetic improvement by optimizing four key components: additive genetic variation (σa), heritability (h2), selection intensity (i), and breeding cycle length (L). Additive genetic variation can be enhanced through genome-wide exploration, including genome, pan-genome, and super pangenome analyses, gene discovery, characterization, and the induction of novel variations. Precise estimation of heritability in sugarcane can be achieved through the large-scale characterization of germplasm, high-throughput phenotyping, and detailed genotype × environment interaction (G × E) studies. Selection intensity can be increased by expanding population sizes via genotypic, genomic, and invitro selection, leveraging the law of large numbers, and adopting technologies that provide greater throughput, precision, and cost efficiency. Breeding cycle time can be significantly reduced using tools, such as marker-assisted selection, genomic selection, emerging doubled haploid strategies (though still challenging in polyploid crops like sugarcane), speed breeding, transgenic approaches, and genome editing technologies like CRISPR/Cas9 (including base and prime editing), TALENs. This review provides a comprehensive overview of each component of the breeder's equation in sugarcane breeding and highlights next-generation molecular strategies and tools aligned to these components. The integration of these advanced tools with artificial intelligence holds immense potential to enhance genetic gain and accelerate the development of high-yielding, high-quality, and climate-resilient sugarcane varieties.

Abstract Genomic Selection (GS) is transforming the fields of plant and animal breeding by enabling selection based on genetic predictions without requiring direct phenotypic measurements. However, achieving practical implementation of GS is hindered by the challenge of obtaining high prediction accuracy, often compromised by experimental noise. To address these challenges, researchers have developed numerous strategies to enhance the efficiency of GS. This paper investigates the application of an innovative transfer learning method for improving GS performance. In this study, transfer learning (TL) was employed to leverage information from a proxy environment to improve predictions in a target environment. Using twelve original multi-environment data sets, we compared models with and without transfer learning based on four metrics: (i) Pearson’s correlation (COR), (ii) normalized root mean square error (NRMSE), and proportion of shared lines in the top (iii) 10 and (iv) 20% of lines (PM_10 and PM_20). The results provide compelling evidence of the benefits of transfer learning, demonstrating significant improvements in the four metrics evaluated and in all proportions of testing evaluated compared to models without transfer learning. These findings highlight the potential of transfer learning as a powerful tool to enhance predictive accuracy in genomic selection.