Introduction:
Climate change poses unprecedented challenges to global food security, with rising temperatures and erratic precipitation threatening major cropping systems. Chickpea (Cicer arietinum L.), a protein-rich legume, offers strategic value for climate-resilient agriculture due to its drought tolerance, nitrogen fixation, and adaptability to marginal environments. This study explores the genetic and physiological bases of chickpea’s resilience, aiming to identify key mechanisms and genomic markers underpinning its adaptive performance under warming climate scenarios.

Methods:
An integrated framework combining multi-location field trials, controlled-environment assays, and genome-wide association studies (GWAS) was applied to 120 chickpea genotypes representing global diversity. Drought tolerance was evaluated using key physiological indicators such as photosynthetic efficiency, osmotic adjustment, and antioxidant responses. GWAS identified quantitative trait loci (QTLs) associated with these adaptive traits. Climate modeling projected genotype performance under 1.5–3°C warming scenarios, while rotation trials assessed soil fertility and water-use efficiency benefits.

Results:
Chickpea exhibited distinct genotype-dependent resilience mechanisms, including up to 60% higher osmolyte accumulation and over twofold enhancement in antioxidant capacity under terminal drought stress. GWAS revealed 22 significant marker–trait associations linked to stress-responsive genes, with key QTLs explaining up to 32.4% of phenotypic variance. Climate projections indicated a 15–25% yield stability advantage over conventional cereals under moderate warming. Diversified rotations improved soil nitrogen by up to 18% and water-use efficiency by 35%.

Conclusions:
This study provides a mechanistic and genomic basis for chickpea’s climate resilience, linking specific allelic variants to field-level performance. While genotype-by-environment interactions and climate projection uncertainties remain, the findings advance current understanding by offering molecular and agronomic targets for climate-adaptive breeding. Chickpea thus stands as a cornerstone crop for sustainable intensification and agricultural resilience under a changing climate.

Portugal, a leading producer of olive oil, boasts six Protected Designation of Origin (PDO) regions, each characterised by distinct olive orchard (OR) densities, ranging from traditional to super-intensive. Although the analysis of drought and aridity conditions has been widely addressed, the impacts of these factors have not been analysed in relation to OR densities. To address this gap, a new metric, the Olive Drought and Aridity Risk Index (ODAR), was developed to assess future risk levels for each OR by quantifying the combined effects of climate and soil water availability, weighted by OR density. To achieve this, drought and aridity indicators were analysed for both a historical baseline (ERA5: 1981–2000) and two future periods (2041–2060 and 2081–2100) under two anthropogenic forcing scenarios (RCP4.5 and RCP8.5). A seven-member ensemble of global climate models was employed for the projections to address inter-model variability and reduce single-model uncertainties. From Spearman’s correlation analysis, Annual Mean Aridity (AIA) was identified as the most representative indicator of climatic exposure for OR. Readily Available Soil Water (RAW, in mm) was used to represent the soil’s water-holding capacity available to olive trees. ODAR was calculated as the Euclidean distance between each future scenario/period and the historical period, using the weighted AIA and RAW indices according to OR densities. Future projections indicate that central and northern PDOs will be characterised by intermediate conditions (AIA ≈ 0.60 and RAW > 90 mm), exhibiting variable risk levels ranging from low to high. In the southern PDO, where AIA is projected to reach 0.69 and RAW is expected to fall below 60 mm, the risk will be very high, potentially jeopardising olive tree growth, fruit development, and olive oil quality. These findings underscore the need for targeted adaptation strategies to strengthen the climate resilience of olive production in Portugal’s PDO regions.

The study investigated the spatiotemporal variations of agricultural sugarcane areas in La Granja, La Carlota, Negros Occidental, from 2020 to 2024. Data on agricultural areas were delineated from Sentinel-2 satellite imagery and were cloud-masked. NDVI was computed and analyzed across sugarcane’s five (5) phenological stages: germination, tillering, stalk elongation, growth, and maturation, following the Philippine cropping calendar. Variations in NDVI have been observed across years during different phenological stages, with values ranging from -0.06 to 0.91 during the germination stage, -0.08 to 0.92 during the tillering stage, -0.08 to 0.97 during the stalk elongation stage, -0.1 to 0.99 during the grand growth stage, and -0.06 to 0.91 during the maturation stage. NDVI values were consistently high during stalk elongation and grain growth, while low values were evident during the germination and tillering periods.

Differences in values are evident across agricultural areas at each phenological stage. Area A generally exhibited higher NDVI during germination and grand growth. Area D showed higher NDVI during tillering and elongation, reflecting optimal canopy development, while Area B displayed persistently lower NDVI throughout all stages, suggesting delayed or stressed growth. Area C often shifted earlier to lower NDVI values during the maturation stage, suggesting early senescence or harvest.

Cloud cover affected the amount of data available for each timeframe. In addition, some variabilities in the time series of NDVI calculation across different phenological stages may be attributed to varied planting dates among farmers, because sugarcane establishment does not occur uniformly across agricultural areas; some farms may already be advancing to the next stage, while others remain in earlier stages, resulting in overlapping NDVI signatures. This underscores the importance of integrating cropping calendar variations into remote sensing assessments to better align spectral observations with ground conditions.

Maize (Zea mays L.) is one of the most important cereal crops worldwide but is highly vulnerable to drought stress, which impairs growth and yield through physiological and biochemical disruptions. Plant growth-promoting rhizobacteria (PGPR) and organic amendments have been proposed as sustainable strategies to mitigate water stress. This study aimed to evaluate the effects of PGPR isolates and spent mushroom compost (SMC) on maize growth under controlled drought conditions. A total of 23 bacterial isolates were initially screened for osmotic tolerance, biofilm formation, and exopolysaccharide (EPS) production. Several isolates demonstrated strong tolerance at 20% PEG-6000 and produced significant levels of EPS, supporting their potential role in enhancing stress resilience. Germination assays revealed that inoculated seeds exhibited higher germination rates and seedling vigor indices compared with uninoculated controls. In seedling assays, selected isolates significantly promoted root elongation, shoot length, and seminal root formation, indicating improved early growth performance. Two promising isolates, identified as Bacillus velezensis strains, were further tested in greenhouse conditions in combination with SMC at 30 t/ha (SMC30). Application of SMC alone enhanced plant height, root biomass, and chlorophyll content compared with untreated controls. Notably, the combined treatment of PGPR and SMC showed additive benefits, including greater root surface area, increased dry weight, and improved recovery following drought exposure. These improvements were accompanied by higher antioxidant enzyme activities and reduced stress markers, suggesting enhanced physiological adaptation to water deficit. Overall, the results demonstrate that both PGPR and SMC independently improve maize performance under drought, and their combined use may offer a synergistic strategy for sustainable maize production in water-limited environments.

Water scarcity represents a significant challenge to citrus production in Morocco, a situation worsened by climate change. To secure the long-term sustainability and productivity of this essential agricultural sector, it is vital to implement innovative and integrated water management strategies. This study focuses on a six-year-old citrus orchard situated in a Mediterranean climate zone (Kenitra, Morocco). The orchard was equipped with LoRaWAN-enabled soil moisture sensors (TerraPrima Ladybird) and covered with geotextile black plastic mulch. The sensors, placed at depths of 20, 40, and 60 cm within the root zone, continuously monitored volumetric water content under a drip irrigation system. The data was incorporated into a daily water balance model to optimize irrigation scheduling. Concurrently, the use of plastic mulch decreased surface evaporation while improving soil moisture retention. The integrated strategy of using sensor-based irrigation management alongside soil mulching increased soil moisture levels by up to 30%, translating into an additional storage capacity of 20 to 180 m³/ha of water within the root zone. As a result, the mulch effectively reduced evaporation rates, allowing longer intervals between irrigation cycles as determined by the soil moisture sensors. This strategy not only minimized water and energy consumption but also significantly boosted fruit yields by up to 57%, along with notable improvements in fruit size and overall orchard health and efficiency. This later increased by 25%. The integration of soil moisture sensors, plastic mulch, and a Water-Energy-Food-Ecosystem nexus approach represents a promising strategy for enhancing water efficiency and overall sustainability of citrus orchards. This comprehensive approach addressed the pressing challenge of water scarcity while also promoting the long-term environmental and economic viability of the citrus industry amid evolving climate and trade dynamics.

Climate change increasingly threatens global wheat production, affecting growth, yield stability, and quality, and thereby underscoring the need for accurate yield prediction to support climate-resilient breeding. To address this challenge, we developed a new integrated framework that couples climate data correction and reconstruction with multiple-crop-model yield prediction. First, a multivariable correction super-resolution diffusion model (MCSDM) was designed to correct and reconstruct daily meteorological data from multiple CMIP6 global climate models, significantly reducing error and improving spatial resolution at the regional scale. The corrected and reconstructed datasets were then used to drive three crop models (WOFOST, AquaCrop, DSSAT), which were calibrated and validated using field experiments on representative wheat germplasms in Zhejiang Province, China. Subsequently, a multiple-crop-model weight distribution network (MCMWDN) was employed to integrate individual crop model outputs, thereby enhancing prediction robustness and accuracy. The results showed that the MCSDM substantially reduced errors in temperature, precipitation, radiation, and wind speed variables and improved resolution, while the MCMWDN achieved an R² of 0.823 and reduced the root-mean-square error by over 40% compared to single-model predictions. Yield responses varied widely among germplasms: Norin 61 maintained high and stable yields, whereas Festin and Jagger/W94-244-132 performed poorly, and Lumai 5 and Zhoumai 31 remained relatively stable under moderate scenarios. Across climate pathways, yields are projected to slightly increase under SSP1-2.6, remain stable under SSP2-4.5, and decline significantly under SSP3-7.0 and SSP5-8.5, particularly during 2071–2100. Overall, this study provides a robust and generalizable prediction framework, offering valuable guidance for breeding climate-resilient wheat varieties and informing sustainable agricultural management under future climate change.

Powdery mildew (Erysiphe necator (Schw.) Burr.) is a pathogen that threatens vineyard sustainability and profitability. This study presents a reproducible framework that integrates unsupervised machine learning with geostatistics to delineate risk and management zones in Vitis vinifera L. vineyards in Chile’s Maule Region. The workflow comprises (i) data preprocessing; (ii) dimensionality reduction via rotated principal component analysis (PCA) to synthesize multisource attributes; (iii) segmentation and dominance assessment from rotated scores to identify key agronomic factors (e.g., vigor and yield); (iv) spatial validation through factor-wise grouping and cross-validation; (v) geostatistical modeling—empirical isotropic and directional variograms, weighted least-squares fitting to extended models, model selection by cross-validation—followed by kriging of principal components and their variances; (vi) clustering-based delineation of management zones projected onto a spatial grid; and (vii) spatial interpolation and fusion to produce discretized backgrounds with contours of the dominant component. Across seasons, retained components explained at least 70% of the total variance. Silhouette coefficients of 0.47–0.56 indicated moderate-to-good separation and stable dominance patterns. Moran’s I was significant in the first two seasons, evidencing spatial dependence and interannual variation. Cross-validated isotropic ranges typically spanned 25–90 m, reaching ~170 m depending on season and component; directional analysis revealed anisotropy with predominant NE–SW (≈45°) continuity. The framework yields continuous severity/incidence maps and coherent management zones, supporting site-specific management and reduced pesticide use in precision viticulture.

Mushroom production in India is typically restricted to cooler months due to the difficulty of maintaining stable environmental conditions in open or low-cost setups. We developed and field-tested Mushroom Kothi, an IoT-enabled chamber designed to deliver real-time climate control and monitoring for smallholder mushroom farmers. Across three agro-climatic sites and two seasons, replicated trials compared Oyster mushroom (Pleurotus ostreatus) production in Mushroom Kothi versus conventional farmer practice. Continuous sensor logging of temperature, relative humidity, CO₂, light, and substrate moisture enabled automated misting and ventilation adjustments through a cloud-connected ESP32-based system. Preliminary results indicate yield gains of 15–25%, reduction in water use by 30–35%, and improved uniformity of fruit body size. Time-to-harvest was shortened by 1–3 days, while the productive window extended beyond conventional seasonal limits. Statistical analyses (two-way ANOVA, Tukey HSD) confirmed significant treatment effects (p < 0.05) across sites. In addition to production benefits, the system achieved high reliability with >95% data transmission success and reduced manual interventions, demonstrating ease of adoption in low-resource settings. By combining continuous digital monitoring with automated environmental control, Mushroom Kothi provides an affordable precision agriculture tool that enhances productivity, reduces input waste, and improves the resilience of mushroom farming. Future work will integrate predictive analytics to further support farmer decision-making.

Marginal lands affected by salinity, water scarcity, nutrient deficiency, and climate extremes represent critical frontiers for addressing global food security challenges. Conventional staple crops often exhibit limited productivity under these abiotic stress conditions, necessitating exploration of novel and underutilized species with intrinsic resilience. This work synthesizes evidence demonstrating how integrated biotechnological approaches, high-resolution genomics, precision molecular breeding, and predictive bio-nano systems, can systematically enhance stress tolerance traits in climate-adaptive crops. High-throughput genomic mapping has identified key regulatory networks conferring drought tolerance, salinity adaptation, and nutrient-use efficiency. For instance, quinoa’s transmembrane protein genes and millet stress-responsive pathways exemplify natural adaptations that can be exploited through genomic-assisted breeding. CRISPR-based genome editing technologies have successfully enhanced abiotic stress tolerance in major cereals by targeting specific genes, such as ZmHDT103 for drought tolerance in maize and TaBAS1 for salt tolerance in wheat. Molecular breeding strategies, including marker-assisted selection and genomic selection, have demonstrated 10–20% improvements in selection efficiency for stress tolerance traits, significantly accelerating genetic gains. Predictive bio-nano systems integrating AI-driven computational models with nanoscale sensors enable real-time monitoring of soil–plant–atmosphere dynamics, facilitating proactive stress management. Empirical evidence confirms that millets, quinoa, halophytes, and orphan legumes exhibit substantial resilience in marginal environments while delivering nutritional, ecological, and economic co-benefits. However, significant challenges persist: regulatory barriers create market access uncertainties for genome-edited varieties, high implementation costs limit adoption by resource-poor farmers, and knowledge gaps remain in translating genomic insights into field-ready cultivars. This integrative framework demonstrates potential to establish resilient agroecosystems on marginal lands, yet successful implementation requires addressing regulatory harmonization, economic accessibility, and bridging the genotype-to-phenotype gap in diverse environmental contexts.

Introduction: Smallholder farmers face challenges in adopting precision agriculture due to high costs, lack of digital literacy, and limited access to advanced technologies. Existing solutions such as drones, IoT devices, and satellite imaging are effective but remain impractical in resource-limited settings. This project introduces a low-cost, farmer-friendly smart card system that delivers personalized precision agronomy advice through shared community kiosks.

Methodology: 1) Each farmer receives a Smart Agronomy Card, storing soil, land, and crop data. 2) Farmers insert the card into a solar-powered kiosk/device. 3)The device integrates the following: 4)soil data, 5) satellite weather information, and 6) AI crop growth models. 7) Weekly recommendations are delivered via text, audio (local language), or printed slips. 8)The data are then aggregated to build a digital agronomy map for community-level insights.

Tools: Smart Agronomy Cards, solar-powered shared kiosks/devices, AI-based advisory software, cloud database for soil and crop records, and weather data integration APIs (Application Programming Interfaces) are all utilized in this study.

Budget (Approximately):

(1) Smart card per farmer: $2–3. (2) Shared solar-powered kiosk: $250–300 per village. (3)Software and AI integration: $2,000–3,000 (one-time purchase). (4) Maintenance and training: minimal (community-managed).

Benefit for Farmers: Personalized advice without the need for smartphones or internet, reduced fertilizer and water costs (20–30% savings), improved crop yield, reduced pest/disease losses, accessible in their local languages (which leads to trust and easy adoption), and the community model reduces individual cost burden. One kiosk can serve 50–100 hectares of farmland. For example, a village with 200 smallholder farmers (each with 0.2–0.5 ha) can all use one kiosk.

Problems: A) Farmer literacy and digital skills. B) Limited access to kiosks. C) Data accuracy and reliability. D) Infrastructure challenges. E) Farmer trust and adoption. F) Maintenance and technical support. G) Scalability.

Time: Prototype development: 6–8 months. Field testing: 1 cropping season (4–6 months). Full deployment in a region: 1–2 years.

Results: 1) Increased input efficiency and crop yield. 2) Lower production costs for smallholder farmers. 3) Creation of a digital agronomy network at grassroots level. 4)Scalable model for national and international adoption.