Agricultural Sciences: Techniques and Innovations Vol. 11

Molecular Breeding Approaches to Enhance Crop Productivity and Mitigate Climate Change: A Critical Review

R. Muthuvijayaragavan — 2026-07-23

Global agriculture faces a convergence of pressures from a growing population, shrinking arable land, and an increasingly erratic climate. Conventional plant breeding, reliant on phenotypic selection over successive generations, has delivered substantial genetic gains over the past century but is proving too slow and imprecise to keep pace with the rate of environmental change. Molecular breeding, encompassing marker-assisted selection, marker-assisted backcrossing, genomic selection, and genome editing, has emerged as a complementary and increasingly dominant paradigm for accelerating crop improvement while embedding resilience to drought, heat, salinity, and flooding. This review synthesises recent literature on the principal molecular breeding platforms applied to major staple and orphan crops, traces their contribution to yield stability under abiotic stress, and evaluates their role in climate change mitigation through traits such as nitrogen use efficiency, root architecture optimisation, and reduced input dependency. Particular attention is given to the integration of genomic selection with high-throughput phenotyping and multi-omics data, the expanding application of CRISPR-Cas-based genome editing for precision trait modification, and the use of speed breeding to compress generation intervals. Case studies drawn from rice, wheat, and maize illustrate how molecular tools have translated genetic knowledge into deployed cultivars, notably the submergence-tolerant Sub1 rice lineages and drought-tolerant maize hybrids released across sub-Saharan Africa. The review concludes that the future of climate-resilient crop improvement depends on the convergence of predictive breeding models, artificial intelligence, and coordinated policy frameworks that can translate laboratory advances into equitable field-level adoption.

Insect Pest Management in Pulses: Novel Approaches

B. L. Jat — 2026-07-23

Productivity of pulse crops remains constrained by severe infestations of insect pests, particularly pod borers, pod flies, aphids, whiteflies, leaf miners, thrips, and storage pests. Among these, Helicoverpa armigera, Maruca vitrata, Clavigralla gibbosa, Melanagromyza obtusa, and Bemisia tabaci are the most destructive species affecting chickpea, pigeonpea, mungbean, urdbean, cowpea, lentil, and field pea. This book chapter highlights recent advances and novel approaches to insect pest management in pulses. Several resistant and tolerant genotypes have been identified in different crops. Agronomic strategies have significantly reduced pest incidence and crop damage. The use of pheromone traps, light traps, and economic-threshold-based interventions has improved pest surveillance and timely decision-making. Biopesticides have demonstrated promising efficacy against major lepidopteran pests while reducing dependence on conventional insecticides. Natural enemies have also contributed substantially to pest suppression. Emerging evidence further indicates that elevated temperatures and atmospheric CO₂ may alter plant–insect interactions and influence pest dynamics in pulse ecosystems. The integration of resistant cultivars, ecological engineering, biological control agents, and safer chemistries offers a sustainable and environmentally sound strategy for effective insect pest management in pulse crops.

Mitigation of the Effects of Salt Stress in the Reproductive Phase of Rice by Proline

Lu Zaw Myo — 2026-07-23

This pot experiment evaluated the effects of salt stress and foliar application of exogenous proline on three lowland rice cultivars during the reproductive phase. Plants of CNT1, PT1 and IN35 were grown under four sodium chloride levels (0, 50, 100 and 150 mM NaCl) and received four proline concentrations (0, 50, 100 and 150 mM), applied once at the heading stage before flowering. Physiological and biochemical traits were measured after proline application, whereas yield and yield-related traits were recorded at harvest. Pollen viability and proline gene expression responses were also assessed under the imposed treatments. Salinity produced cultivar-dependent effects on chlorophyll b, total chlorophyll, starch content, pollen viability and agronomic performance. Most yield and yield-component traits declined from 50 mM NaCl, while the harvest index declined more clearly at higher salinity levels. Among the cultivars, IN35 generally maintained higher grain yield per plant, productive tillers, filled grain percentage, 1000-grain weight, straw dry weight and harvest index than CNT1 and PT1. Exogenous proline increased chlorophyll traits and improved filled grain percentage, panicle length and grain yield per plant, with responses varying according to cultivar and salinity level. Pollen viability also improved after proline application under several salt-stress conditions. Reported proline gene expression differed among cultivars and treatments, with higher expression values observed in IN35 than in the Thai cultivars under stronger salt stress. Overall, the results indicate that exogenous proline may partially reduce salt-stress effects during the reproductive phase of rice, but the response depends strongly on cultivar and stress intensity.

Reimagining the Future of Food Systems through Agriculture 4.0: Emerging Technologies, Responsible Transformation, and Sustainability Frontiers

Aaysha Kamar — 2026-07-23

Global food systems face a convergence of pressures from population growth, climate variability, resource scarcity, and rising consumer expectations for transparency and sustainability. Agriculture 4.0, understood as the fusion of digital, biological, and robotic technologies into farming and food supply chains, has emerged as a central response to these pressures. This review synthesises the scientific literature on the core pillars of Agriculture 4.0, including the Internet of Things, artificial intelligence, robotics, digital twins, remote sensing, blockchain-enabled traceability, precision genome editing, and controlled environment agriculture, while situating these technologies within debates on responsible innovation, digital equity, and circular resource use. The review draws on peer-reviewed literature and authoritative institutional reports to examine how these technologies interact across production, processing, and consumption stages of the food system, and to what extent their promised gains in productivity and sustainability are being realised in practice. Particular attention is given to the uneven diffusion of digital tools among smallholder and resource-constrained farmers, to governance frameworks intended to guide technology development toward societal benefit, and to the persistent gap between technological capability and equitable, ethically grounded adoption. The review finds that while individual technologies show measurable gains in resource efficiency, disease detection, and supply chain transparency, systemic transformation remains constrained by infrastructural, economic, and institutional barriers, and by a fragmented evidence base linking technology adoption to long-term food security outcomes. The review concludes by outlining priority research directions, drawing overall conclusions about the trajectory of Agriculture 4.0, and identifying limitations inherent in a narrative synthesis of a rapidly evolving field.

AI-Driven Precision Agriculture: A Critical Review of Predictive Irrigation, Yield Monitoring, Smart Fertilization, and Supply Chain Integration

Valles Romero Jose Antonio — 2026-07-23

Artificial intelligence has moved from a peripheral tool to a central organising technology across the crop production cycle, reshaping how water, nutrients, yield information, and post-harvest logistics are managed. This review synthesises recent scholarship on four interlocking domains of artificial-intelligence-enabled precision agriculture: predictive irrigation scheduling, remote sensing and deep-learning-based yield monitoring, variable-rate and sensor-guided fertilization, and blockchain-artificial-intelligence convergence in agri-food supply chains. Machine learning and deep learning models, including random forests, support vector machines, convolutional neural networks, and deep reinforcement learning agents, have demonstrably improved the accuracy of evapotranspiration estimation, soil moisture prediction, and irrigation timing, while unmanned aerial vehicles and satellite-based multispectral imagery paired with convolutional and transformer architectures have advanced early yield forecasting and pest and disease detection. Variable-rate technologies coupled with artificial-intelligence-driven decision-support systems have improved nutrient-use efficiency and reduced environmental externalities associated with uniform fertilizer application, while blockchain ledgers integrated with machine learning forecasting tools have strengthened traceability, food safety assurance, and demand-driven waste reduction across agri-food value chains. Despite these advances, adoption remains markedly uneven: infrastructural, financial, and digital-literacy barriers concentrate benefits among large, well-resourced producers, particularly in high-income regions, while smallholder farmers in low- and middle-income countries risk further marginalisation. The review also identifies persistent methodological limitations, including narrow geographic representativeness of training datasets, weak external validation, and underdeveloped frameworks for data governance and algorithmic accountability. It concludes that realising the productivity, sustainability, and equity potential of artificial intelligence in agriculture depends less on further algorithmic refinement than on coordinated investment in rural digital infrastructure, interoperable data standards, and inclusive policy design.

Swot Analysis of Artificial Intelligence Integration in Agriculture for Environmental Sustainability

İrfan İNAN — 2026-07-23

Artificial intelligence is increasingly being integrated into agricultural systems to support environmental sustainability, resource efficiency, and climate-resilient production. This review evaluates artificial intelligence-based technologies in crop and livestock production and presents a SWOT-based assessment of their integration into agriculture. A narrative and thematic approach was followed, drawing on peer-reviewed studies that directly address artificial intelligence and environmental sustainability in agriculture. The evidence was synthesised across crop production, livestock production, general environmental sustainability, and SWOT analysis dimensions. In crop production, artificial intelligence supports precision irrigation, fertilisation management, water-stress monitoring, disease detection, weed monitoring, targeted spraying, and resource-use optimisation. The reviewed evidence indicates water savings of 27.6-40% in tomato irrigation, 35.1% in a paddy and vegetable test plot, 29% in maize irrigation, and up to 70% in AI-powered solar irrigation systems. Artificial intelligence-based weed-control systems reduced herbicide or pesticide use by 10-70%, depending on crop, model, and system design. In livestock production, artificial intelligence supports feed-use prediction, feed-waste reduction, heat-stress recognition, methane-emission prediction, greenhouse-gas assessment, carbon-footprint estimation, emission-intensity reduction, and manure-management decisions. The SWOT analysis identifies resource efficiency, environmental monitoring, evidence-based decision support, and climate-resilient production management as the main strengths of artificial intelligence. The main weaknesses are data quality, model generalisation, infrastructure cost, interoperability, digital literacy, privacy, and farm-level validation. Opportunities include edge-cloud systems, low-cost IoT solutions, solar-powered irrigation, remote sensing, farmer-centred design, and AI-based emission monitoring. Threats include climate variability, unequal access to technology, data-governance risks, and environmental costs linked to the life cycle of digital technologies.

Effect of Nitrogen and Phosphorus Application on Yield and Yield Components of Wheat (Triticum aestivum L.) in Three Soils Series at New Halfa Scheme of Sudan

Badr Eldin A. Mohammed Ahmed — 2026-07-23

Phosphorus is essential for physiological processes including energy storage and transfer, respiration, photosynthesis, cell division, and cell enlargement. Nitrogen and phosphorus levels can significantly affect agronomic traits such as grains per spike, 1000-grain weight, and root biomass. A field experiment was conducted during two consecutive winter seasons (2017/2018 and 2018/2019) at three sites in the New Halfa Scheme representing the Asobri, Kashm El Girba, and Sabaat soil series. Three nitrogen levels (0, 43, and 86 kg N ha⁻¹) and three phosphorus levels (0, 40, and 80 kg P₂O₅ ha⁻¹) were evaluated for their effects on the yield of the wheat cultivar Debera. The experiment was arranged in a randomised complete block design with three replications at each location in both seasons. The measured traits were spike length, number of grains per plant, 1000-grain weight, grain yield, and harvest index. Data were analysed by analysis of variance using STATISTICS 9, and treatment means were compared using Duncan’s multiple range test at the 5% probability level. Increasing nitrogen or phosphorus levels and their interaction improved yield attributes. The highest grain yields at Shebiak and the Faculty Research Farm were 3363.5 and 2609.9 kg ha⁻¹, respectively, under 2N2P, whereas 2N1P produced the highest grain yield at Hajer (2290.6 kg ha⁻¹). Grain yield differed among soil types. Overall, applying a high nitrogen level together with phosphorus enhanced wheat production in the New Halfa Scheme.