The study was structured in two phases. In Phase 1, all 400 spring wheat genotypes were evaluated at the seedling stage under three moisture regimes: 100%, 70%, and 40% field capacity. A completely randomized design with three replicate pots per genotype-treatment combination was used, and ten morpho-physiological traits were recorded for each line. This seedling-stage screening served as the first filter, allowing researchers to identify genotypes that maintained growth under reduced water availability before committing resources to more expensive and time-consuming field trials.

The results of Phase 1 revealed the physiological toll that drought takes on young wheat plants. Mean seedling length declined from 40.0 cm under full moisture to approximately 29 cm under drought stress, a reduction that was statistically significant at P ≤ 0.01. Perhaps more telling was the response of proline content, an osmoprotectant that plants accumulate under water stress to protect cellular structures. Proline levels increased approximately 5.5-fold under drought conditions, also significant at P ≤ 0.01. Interestingly, no statistically significant difference was observed between the two drought levels for the growth traits, suggesting that once moisture dropped below a certain threshold, additional stress did not further impair seedling growth in a measurable way.

To make sense of the ten measured traits across 400 genotypes and three moisture regimes, the researchers turned to principal component analysis, a multivariate statistical technique that condenses correlated variables into independent axes of variation. The first two principal components jointly explained approximately 40% of the seedling-stage variation across all moisture regimes, while five components were required to explain more than 70%. This relatively even distribution of variance across multiple components indicated that drought adaptation at the seedling stage is not governed by a single dominant trait but rather by a combination of independent morpho-physiological characteristics.

Building on this multivariate framework, the team developed an integrated ranking system that combined standardized trait means, PC1 and PC2 scores, and hierarchical-cluster membership. Rather than relying on any single metric, this approach synthesized information from multiple dimensions of the phenotypic data to produce a holistic assessment of each genotype’s drought performance. From this ranking, 105 lines were advanced to Phase 2 field evaluation, representing roughly a quarter of the original pool. This sequential narrowing strategy exemplifies how controlled-environment screening can serve as an efficient pre-filter for field trials, reducing the logistical burden while preserving phenotypic diversity.

Phase 2 subjected the 105 selected lines to field-stage drought treatments at two critical developmental stages: drought at anthesis and drought at grain development, alongside a full-irrigation control. These stages are particularly vulnerable points in the wheat life cycle, as water stress during flowering can reduce grain set while stress during grain filling can diminish kernel weight and overall yield. The field evaluation provided a real-world test of whether seedling-stage tolerance translates into adult-plant drought resilience, a question that has long challenged plant breeders.

The field-stage results revealed a striking reorganization of the phenotypic structure under stress. Under full irrigation, the first principal component accounted for 60.96% of the phenotypic variation, indicating that a single dominant axis governed growth differences among genotypes in the absence of stress. However, under drought at anthesis, PC1’s contribution dropped to 22.47%, and under drought at grain development it fell to 30.26%. This redistribution of phenotypic variance across a larger number of independent axes under stress suggests that drought adaptation in adult wheat plants involves multiple independent traits rather than a single dominant characteristic, and that the genetic architecture of drought response differs fundamentally between well-watered and stressed conditions.

This stage-specific reorganization of the phenotypic structure carries important implications for breeding programmes. The authors caution against reliance on single-trait or single-stage selection, arguing that the multi-trait, multi-stage nature of drought adaptation demands a more comprehensive approach. A genotype that performs well at the seedling stage may not maintain that advantage under field conditions, and traits that correlate with yield under full irrigation may become decoupled under water stress. The pronounced shift in how phenotypic variance is distributed between seedling and field stages, and between different drought timing treatments in the field, underscores the complexity of drought as a breeding target.

The 105 lines identified through this sequential multivariate profiling represent a phenotypically diverse subset of Pakistani wheat germplasm suitable as parental material for a Line × Tester crossing programme aimed at drought resilience. The Line × Tester mating design is a well-established quantitative genetics approach that allows breeders to estimate combining ability and heterosis, providing insights into which parental combinations are most likely to produce superior drought-tolerant offspring. By ensuring that the selected parents span a range of phenotypic profiles rather than clustering around a single trait optimum, the multivariate approach maximizes the genetic diversity available for recombination in the breeding programme.

The germplasm used in this study was provided by the Pakistan Agricultural Research Council, the Wheat Research Institute in Faisalabad, and the Regional Agricultural Research Institute in Bahawalpur, reflecting a collaborative effort to harness national genetic resources for climate adaptation. The research was supported by the Recep Tayyip Erdoğan University Development Foundation, and the authors acknowledge TÜBITAK for granting a fellowship under the 2221 Sabbatical Scientist Support Programme. As drought intensifies across semi-arid wheat-growing regions from South Asia to the Middle East and beyond, the sequential multivariate profiling strategy demonstrated in this study offers a practical template for other breeding programmes seeking to identify climate-resilient germplasm from large collections. The work highlights that drought tolerance is not a single destination but a journey through multiple developmental stages, each with its own set of adaptive requirements, and that successful breeding for drought resilience must embrace this complexity rather than oversimplify it.

Subject of Research: Drought tolerance screening of bread wheat germplasm using multivariate phenotypic profiling

Article Title: Multivariate phenotypic profiling identifies drought-tolerant bread wheat germplasm through sequential seedling and field-stage screening

Article References: Zeeshan, M., Alam Khan, M., Ahmad, I., Normat Saydullayevich, X., Azhar Nadeem, M., Gou, J.-Y., Shehzad Baloch, F., & Tanveer Altaf, M. (2026). Multivariate phenotypic profiling identifies drought-tolerant bread wheat germplasm through sequential seedling and field-stage screening. BMC Plant Biology. https://doi.org/10.1186/s12870-026-10121-8

Image Credits: AI Generated

DOI: 10.1186/s12870-026-10121-8

Keywords: bread wheat, drought tolerance, germplasm screening, principal component analysis, cluster analysis, phenotypic profiling, plant breeding, climate resilience, Triticum aestivum, proline content, field capacity, semi-arid regions

The stakes are enormous. More than two billion people worldwide depend on groundwater as their primary drinking source, and it supplies roughly 43 percent of global agricultural water demand. In Iran, where abstraction already exceeds natural recharge, groundwater accounts for over 55 percent of total water consumption. Population growth, intensified agriculture, urbanisation, and climate change are pushing these aquifers toward declining water tables, land subsidence, saltwater intrusion, and contamination that is often irreversible. Yet traditional monitoring — accurate as it is — remains slow, expensive, and too sparse to capture the spatial patterns that determine where problems actually emerge.

The core challenge the researchers faced is fundamental: groundwater is invisible from space. Unlike lakes and rivers, whose surfaces reflect light in ways satellites can decode directly, aquifers lie buried under soil and rock. The team, led by Mozhgan Yarahmadi with Ali Haghizadeh and Leila Ghasemi, circumvented this with an indirect, surrogate-based strategy. Their reasoning was that groundwater quality is tightly coupled to what happens at the surface — land use, vegetation, soil moisture, temperature, and rainfall. If satellites can see the surface, and machine learning can learn the hidden relationships between surface signals and underground chemistry, then the orbiting sensors become proxies for what lies below.

To build those proxies, the researchers turned to Sentinel-2, the European Space Agency’s twin-satellite Copernicus mission offering thirteen spectral bands at 10 to 60 metre resolution and a five-day revisit cycle. They extracted eight bands — from blue through near-infrared — for dates matching field sampling within a ten-day window, and computed five spectral indices designed to capture chlorophyll content, turbidity, and surface moisture. These were fused with climatic variables from the ERA5-Land reanalysis dataset, including precipitation and land surface temperature. Around each of five piezometer stations, a five-kilometre buffer zone averaged the pixel values, smoothing the mismatch between point-based well measurements and satellite footprints. The result was a dataset of 56 valid samples collected seasonally from 2015 to 2024, each paired with sixteen environmental predictor variables.

With the dataset assembled, the team deployed nine competing models: five base algorithms — Random Forest, XGBoost, Support Vector Regression, Multi-Layer Perceptron, and Linear Regression — and four hybrid architectures that combined them, including a Voting Ensemble averaging four base models and a Stacking Ensemble feeding base-model outputs into a Random Forest meta-model. The standouts were striking. The Voting Ensemble achieved a coefficient of determination of 0.986 for sodium at the Sabour station and 0.974 for electrical conductivity at Gosheh, while the SVR+MLP hybrid reached 0.900 for total dissolved solids at Robat. Hybrid models claimed five of the six top-performing station-model-parameter combinations, confirming that blending complementary algorithms consistently outperforms any single learner.

The pattern behind those numbers is physically meaningful. Parameters the researchers classify as optically active — sodium, electrical conductivity, total dissolved solids, and chloride — achieved the highest accuracies, because dissolved salts alter the refractive index and light absorption of water in ways Sentinel-2’s spectrometer can detect. Semi-optical parameters such as calcium, magnesium, and total hardness landed in a moderate range, while non-optical parameters like pH, sodium percentage, and sulfate proved hardest to predict, lacking any direct spectral signature. Cross-validation told an honest story: while point-specific performance soared above 0.97, stable regional performance settled between 0.52 and 0.55 for the main parameters — respectable given the modest sample size and the inherent spatial variability of aquifer chemistry.

The study’s most novel contribution lies in how it weighted the importance of each quality parameter. Rather than relying on a single importance metric, the team combined SHAP — SHapley Additive exPlanations, a technique rooted in cooperative game theory that fairly distributes each prediction among contributing features — with classical Shapley values derived from linear regression. The final weight for each parameter was the normalised average of the two. The verdict was unambiguous: electrical conductivity dominated with 50.47 percent of the total weight, followed by total dissolved solids at 32.40 percent. Total hardness and sodium percentage ranked third and fourth at 7.87 and 7.56 percent, while the remaining six parameters each fell below half a percent. In other words, just two indicators — both optically active and strongly correlated with satellite reflectance — carry more than 82 percent of the explanatory power.

The researchers then stress-tested their rankings. Seasonal analysis revealed that while conductivity and dissolved solids held steady across spring, summer, and autumn, total hardness surged in summer — climbing from 17.12 percent in spring to 21.15 percent — likely driven by higher temperatures intensifying carbonate dissolution in the alluvial aquifer. Sodium percentage also crept upward in summer, a plausible fingerprint of agricultural fertiliser use during the growing season. Bootstrap resampling with 1,000 iterations quantified the uncertainty: conductivity and dissolved solids showed the lowest coefficients of variation, around 9.7 to 9.9 percent, confirming their weights are stable and reliable, while chloride and pH carried the highest uncertainty, consistent with their weak spectral linkage.

The authors are candid about limitations. Fifty-six samples is a small dataset, and reanalysis-based climatic data may diverge from ground stations; atmospheric correction remains tricky under cloudy skies, and models trained on Khorramabad’s geology cannot simply be transplanted elsewhere without local retraining. Yet the practical implications are compelling. Because Sentinel-2 imagery and ERA5-Land data are freely available and continuously refreshed, the trained models can be extrapolated to unmonitored areas, generating spatial maps of conductivity, dissolved solids, sodium, and chloride where no wells exist. For water managers in semi-arid regions, the framework offers an interpretable, low-cost decision-support tool: prioritise conductivity and dissolved solids as headline indicators, intensify hardness monitoring during summer, and treat satellite-based assessment as a powerful complement — not a replacement — for laboratory sampling. As aquifers worldwide face mounting pressure, watching them from orbit may soon become standard practice.

Subject of Research: Prediction and weighting of groundwater quality parameters using Sentinel-2 remote sensing, hybrid machine learning models, and SHAP-game theory analysis in Khorramabad, Iran

Article Title: Groundwater Quality Parameter Weighting Using Hybrid Machine Learning and SHAP-Game Theory Based on Sentinel-2 Data (Case Study: Khorramabad, Iran)

Article References: Yarahmadi, M., Haghizadeh, A., & Ghasemi, L. (2026). Groundwater Quality Parameter Weighting Using Hybrid Machine Learning and SHAP-Game Theory Based on Sentinel-2 Data (Case Study: Khorramabad, Iran). Environmental Advances, Article 100770. https://doi.org/10.1016/j.envadv.2026.100770

Image Credits: AI Generated

DOI: 10.1016/j.envadv.2026.100770

Keywords: groundwater quality, Sentinel-2, machine learning, SHAP, game theory, remote sensing, hybrid models, Iran, water monitoring, electrical conductivity, total dissolved solids, semi-arid regions

The study’s technical foundation rests on the latest generation of global climate models, the Coupled Model Intercomparison Project Phase 6, known as CMIP6. Rather than relying on a single simulation, the team integrated outputs from five models—MRI-ESM2-0, EC-Earth3, IPSL-CM6A-LR, CESM2, and HadGEM3-GC31-LL—and downscaled them to a resolution of 0.25 degrees, fine enough to distinguish rainfall patterns across districts of northern Ghana. Because raw global models often carry systematic biases in the tropics, the researchers applied bias correction before feeding the projections into their drought assessment, a step that materially improves the reliability of regional rainfall and temperature estimates.

Validation of the model ensemble against observed reference datasets showed strong performance. For annual precipitation, the corrected models achieved a correlation coefficient of 0.95 with a root mean square error of 3.4 millimetres, while temperature simulations were even tighter, with correlations of 0.99 and errors ranging from 0.47 to 0.74 degrees Celsius. These figures matter because drought vulnerability modelling is only as credible as the climate inputs beneath it. A model that misrepresents the baseline climate will misrepresent the future, and the high validation scores give the projections a firmer empirical footing than many earlier regional assessments.

On top of the climate projections, the researchers deployed a battery of drought indices, each capturing a different face of water stress. The Standardized Precipitation Index tracks rainfall deficits against long-term averages; the Reconnaissance Drought Index balances precipitation against atmospheric demand; the Normalized Difference Drought Index draws on satellite reflectance to detect vegetation stress; and the Soil Moisture Index measures water actually available in the ground. By combining these indices with MODIS satellite-derived vegetation greenness, population density, and land use and land cover data within a geographic information system, the team generated composite vulnerability maps that integrate exposure, sensitivity, and adaptive capacity at the district scale.

The projections were run across three time horizons—2026 to 2050, 2051 to 2075, and 2076 to 2100—under two emissions scenarios. SSP1-2.6 represents a low-emissions pathway consistent with ambitious global mitigation, while SSP5-8.5 describes a fossil-fuel-intensive future. The spread between these scenarios frames the range of possible outcomes, and in both cases the direction of travel is the same: hotter, drier, and more drought-prone. Mean temperatures in the region are projected to rise by 2.0 to 3.9 degrees Celsius by 2100, while rainfall could decline by as much as 19 percent, a combination that compounds water stress through both reduced supply and increased evaporative demand.

The consequences for staple crops are stark. Maize, the region’s most important cereal, shows the strongest negative correlation with drought severity of any crop examined, with a correlation coefficient of −0.72 at a statistical significance level below 0.01. Mann-Kendall trend tests, applied at the 95 percent confidence level, confirm that these yield declines are part of a coherent trend rather than random year-to-year noise. Millet and sorghum, traditionally more drought-tolerant, fare somewhat better in relative terms, but the projected 25 to 60 percent yield losses across all four crops by mid-century would represent a fundamental shock to food production systems that already operate close to the margin.

The vulnerability mapping reveals why the human stakes are so high. Roughly 84 percent of the study area is classified as highly vulnerable to drought, a verdict driven less by climate exposure alone than by the region’s limited capacity to cope. More than 80 percent of agricultural production depends on rainfall with no irrigation buffer, over 49 percent of households already experience food insecurity, less than 23 percent of available cropland is under cultivation, and water bodies cover under 5 percent of the landscape. Each of these indicators points to a system with almost no slack: when the rains fail, there is little infrastructure, stored water, or diversified income to absorb the shock.

The authors are candid about the uncertainties that remain. Precipitation projections in the Sahel and West Africa are notoriously difficult, and the five CMIP6 models show divergent rainfall trajectories, with some models even displaying conflicting temperature trends in certain periods. These disagreements do not undermine the overall signal of increasing drought severity, which is consistent across the ensemble, but they do complicate the task of predicting exactly when and where the worst impacts will land. The study’s multi-model, multi-index approach is partly a response to this problem: by triangulating across models and drought metrics, the researchers aim to distinguish robust findings from model-specific artefacts.

What distinguishes this work from many climate impact studies is its insistence on connecting biophysical modelling to livelihoods. Drought indices and yield correlations describe the hazard, but the composite vulnerability maps place that hazard alongside the social and economic conditions that turn a dry spell into a hunger crisis. The framework is designed to be replicable at the district level, giving local planners a tool to identify which communities need intervention first, rather than waiting for national averages to obscure acute local distress.

The policy implications are pointed. The study calls for district-level drought preparedness planning, expansion of solar-powered irrigation to break the dependence on rainfall, index-based crop insurance to transfer residual risk, and the promotion of climate-smart agricultural practices such as drought-tolerant varieties and soil moisture conservation. It explicitly aligns these recommendations with the United Nations Office for Disaster Risk Reduction and the Sendai Framework for Disaster Risk Reduction, arguing that drought resilience in northern Ghana should be treated as a disaster risk governance priority, not merely an agricultural problem. Without such measures, the modelling suggests, a region that contributes little to global emissions will bear some of their heaviest consequences, with crop failures ripening into food insecurity for hundreds of thousands of farming households well before the middle of the century.

Subject of Research: Drought vulnerability and crop production impacts under climate change in northern Ghana

Article Title: Drought vulnerability assessment and its severe impact on crop production and livelihood of people: An empirical modelling of northern Ghana

Article References: Kofitio, T. F., Amoatey, P. K., & Asenso, E. (2026). Drought vulnerability assessment and its severe impact on crop production and livelihood of people: An empirical modelling of northern Ghana. PLOS Climate, 5(9), e0001038. https://doi.org/10.1371/journal.pclm.0001038

Image Credits: AI Generated

DOI: 10.1371/journal.pclm.0001038

Keywords: drought vulnerability, northern Ghana, CMIP6, crop yields, food security, rain-fed agriculture, climate projections, Standardized Precipitation Index, maize, Sendai Framework, climate-smart agriculture, semi-arid regions

The numbers tell a striking story. After a careful search of the Web of Science database using terms combining biochar, soil quality, and tropical or semi-arid environments, the researchers identified 666 records and retained 505 for analysis. Publications have climbed steadily since 2014, peaking at 73 articles in 2024 alone. China dominates the landscape with 183 articles and 7,257 citations, followed by the United States with 70 publications and 4,174 citations, and Pakistan with 49 articles. Intriguingly, the leading producers are not themselves defined by tropical or semi-arid climates, yet the studies they publish focus squarely on dryland and vulnerable agroecosystems, revealing a global scientific division of labor in which wealthy research powers investigate the problems of the world’s most fragile farmland.

The collaboration networks uncovered by the analysis are equally revealing. Using co-authorship mapping and a metric called Total Link Strength, which quantifies the intensity of connections between countries, the team found China sitting at the epicenter of global biochar science with a link strength of 700, far ahead of the United States at 404, Australia at 290, Pakistan at 256, Saudi Arabia at 212, and Germany at 184. The thickest collaborative threads connect China with Pakistan, Saudi Arabia, Australia, and the United States. Pakistan and Saudi Arabia have emerged as regional hubs for the Middle East and South Asia, while Brazil, despite its obvious stake as a tropical nation, occupies an intermediate position with 32 publications and a comparatively modest link strength of 105, a signal that the researchers say points to an urgent need for stronger international partnerships.

At the level of individual scientists, the analysis identified Wang Qi as the most prolific author with 10 publications, acting as a bridge between research clusters, though his citation count of 88 lags behind Muhammad Arif, whose 7 papers have drawn 355 citations. This divergence illustrates a principle that echoes throughout the field: scientific impact depends not on volume but on depth and relevance. The temporal analysis of author networks between 2016 and 2022 shows a rising generation of researchers, including Zhang Dengkui, Zhou Xujiao, and Erastus Mak-Mensah, signaling renewal and expansion within a community once anchored by a smaller cadre of pioneers.

The intellectual foundations of the field rest on a handful of landmark papers. Co-citation analysis, which measures how often two works are cited together, places Laird’s 2010 study at the center with 47 connections and 1,116 citations, flanked by Agegnehu’s 2016 work and Lal’s 2009 contribution. These foundational references continue to guide new research even as recent publications gain ground, and their interdisciplinary reach, spanning soil physics, chemistry, agronomy, and environmental science, reflects the inherently cross-cutting nature of biochar research. Journals such as Science of the Total Environment, with 25 publications and 2,173 citations, and Geoderma, with just 11 articles but a remarkable 2,063 citations, serve as the primary vessels carrying this knowledge to the scientific community.

Perhaps the most consequential findings come from the keyword co-occurrence and thematic mapping, which reveal the field’s conceptual architecture. Four major clusters dominate: a core focused on biochar, soil, and carbon sequestration; an agronomic cluster built around yield, growth, and fertilizer; a production cluster examining pyrolysis temperature and physicochemical properties; and an emerging cluster centered on heavy metals, adsorption, and remediation. The temporal evolution unfolds in three distinct phases, from a characterization-focused period between 2014 and 2017, through a diversification into agricultural systems from 2018 to 2020, to a post-2021 consolidation around climate mitigation and soil sustainability.

The mechanistic evidence underpinning these themes is substantial. Meta-analyses cited in the study report an average increase of 61 percent in soil carbon following biochar application, driven by the formation of stable organo-mineral complexes, particularly in medium to fine-textured soils. Water and nitrogen use efficiency gains exceeding 14 percent have been documented, while synergistic combinations of biochar and nitrogen fertilizer have produced cumulative productivity improvements alongside reduced emissions of methane and nitrous oxide over five-year trials. Global balance studies estimate that large-scale biochar deployment could remove between 2.6 and 10.3 petagrams of carbon dioxide equivalent, potentially offsetting up to 6 percent of global emissions, a figure that positions biochar among the most promising carbon dioxide removal strategies currently available.

Why does biochar matter so much for tropical and semi-arid regions specifically? These landscapes face a punishing combination of low organic matter, high nutrient leaching, recurrent water stress, and accelerating degradation driven by intensive agriculture, deforestation, and climate change. Biochar, produced by heating biomass under oxygen-limited conditions, addresses several of these vulnerabilities simultaneously: its porous structure retains water in sandy soils, its alkalinity corrects the acidity typical of weathered tropical soils, its high cation exchange capacity holds nutrients against leaching, and its internal architecture provides habitat for beneficial microorganisms. Studies compiled in the analysis also show improved plant tolerance to salt and drought stress, mediated through better soil structure and reduced sodium uptake.

Yet the path to large-scale adoption is far from clear, and the bibliometric evidence exposes the field’s soft spots. Biochar’s properties vary enormously depending on feedstock and pyrolysis conditions, making results difficult to generalize. Long-term data on the material’s persistence and aging in soil remain scarce, methodological standardization is lacking, and the risk of contaminant introduction from poorly characterized feedstocks cannot be ignored. The viability of biochar-based carbon credits still awaits consolidated measurement, reporting, and verification frameworks. The researchers conclude that progress now depends on interdisciplinary approaches, decades-long field experiments across multiple biomes, and strengthened international collaborations that bring developing countries into the center of the research effort rather than its periphery.

The stakes could hardly be higher. As climate change intensifies and arable land continues to degrade, the world needs scalable, affordable technologies that restore soil while locking away carbon. This analysis suggests biochar has earned its place on that shortlist, but it also delivers a sober warning: without rigorous long-term science, standardized protocols, and genuine global cooperation, a technology born from ancient Amazonian dark earths risks remaining what it is today, a phenomenon of the literature rather than of the land.

Subject of Research: Bibliometric analysis of global biochar research trends for soil quality improvement in tropical and semi-arid regions

Article Title: Trends and hotspots in biochar research for soil quality improvement in tropical and semi-arid regions based on bibliometric analysis

Article References: Alves, A. B., de Souza, A. L. V., de Lima, I. C. F., da Costa, L. J., da Silva, L. C., Filho, M. F. C., de Lavôr, W. K. B., de Souza, D. C. S., da Silva, E. F., Silva, D. V., Batista, R. O., do Carmo, F. R., & de Sousa Antunes, L. F. (2026). Trends and hotspots in biochar research for soil quality improvement in tropical and semi-arid regions based on bibliometric analysis. Discover Soil, 3(1), Article 115. https://doi.org/10.1007/s44378-026-00275-0

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00275-0

Keywords: biochar, soil quality, bibliometric analysis, carbon sequestration, soil remediation, tropical agriculture, semi-arid regions, pyrolysis, soil degradation, water retention, nutrient cycling, international collaboration

The tool, called the Grassland Ecosystem Monitoring Index, or GEMI, was developed and evaluated by Avijit Ghosh of ICAR-Indian Grassland and Fodder Research Institute and colleagues, with results published in the journal Smart Agricultural Technology. Rather than relying on the familiar Normalized Difference Vegetation Index, the workhorse of vegetation monitoring that is notorious for saturating in dense canopies and being distorted by dust, haze, and bright soils, GEMI fuses two less celebrated spectral measures with a topographic variable: the Advanced Vegetation Index, the Green Leaf Index, and elevation derived from shuttle radar topography data.

The choice of ingredients is deliberate. AVI blends near-infrared and red reflectance in a cube-root transformation that dampens atmospheric scattering, making it more stable under the dusty, thin-cloud conditions that plague semi-arid rangelands. GLI, built entirely from red, green, and blue bands, excels at separating sparse green canopy from bright soil backgrounds, a persistent headache in landscapes where vegetation cover is patchy and soil noise is high. Because GLI depends only on visible bands, it could even be replicated with low-cost drones or standard digital cameras. Elevation, meanwhile, acts as a proxy for the climatic and hydrological gradients that govern productivity, moisture, and carbon cycling across terrain.

To build and test the index, the team selected the Amrit Mahal Kaval grasslands of Karnataka, a roughly 13,738-hectare semi-arid expanse where mean annual rainfall of 518 millimetres falls far short of the 1,307 millimetres lost to evaporation. Between July and December 2024, spanning the peak monsoon growing season, the researchers sampled 220 stratified random plots, each separated by at least five kilometres and fenced against grazing. They harvested and dried above-ground biomass across four campaigns, analysed soil organic carbon by wet oxidation, measured basal soil respiration through laboratory incubation, and estimated carrying capacity assuming 30 kilograms of green fodder per adult cattle unit per day.

Satellite data filled in the rest. Landsat 8 imagery was composited into cloud-free seasonal medians, Sentinel-5P TROPOMI supplied column-averaged methane and carbon monoxide concentrations, MODIS products yielded net primary productivity, and the Universal Soil Loss Equation provided erosion estimates. The researchers then regressed GEMI against all nine service parameters, from forage yield and soil moisture to methane, carbon monoxide, erosion, soil organic carbon, net primary productivity, microbial respiration, and carrying capacity.

The results were striking. GEMI explained seventy-five percent of the variation in vegetation moisture, the strongest single association, and achieved an R-squared of 0.61 for net primary productivity, 0.52 for methane, and 0.53 for carbon monoxide. For above-ground biomass, soil organic carbon, erosion, microbial respiration, and carrying capacity, the index captured between roughly twenty-nine and thirty-nine percent of the variance, all statistically significant. Crucially, when the team pitted GEMI against NDVI, EVI, SAVI, and even the raw AVI-plus-GLI combination, the composite index outperformed every rival across all nine parameters. Where NDVI could explain only about nine percent of variation in forage biomass, GEMI managed nearly thirty-nine percent.

Robustness testing added weight to the claims. A Monte Carlo uncertainty analysis with ten thousand iterations showed the index converging on a stable mean of about 0.38 with a standard deviation of roughly 0.22, while Sobol global sensitivity analysis ranked GLI as the dominant driver, followed by AVI and then elevation, with interactions between inputs accounting for barely two percent of output variance. The near-additive behaviour means managers can interpret changes in GEMI by looking at individual inputs directly, a practical advantage for operational monitoring. On the ground, forty-four percent of the grassland scored in the good range of 0.4 to 0.8, a third was moderate, seventeen percent fell into the degraded category below 0.1, and only six percent reached the very good class above 0.8, a spatial fingerprint the authors say can pinpoint degradation hotspots for timely restoration.

The mechanistic story behind the numbers is equally telling. Plots with greener canopies showed soil erosion reductions of up to eighty percent, soil organic carbon gains of up to seventy percent, and microbial respiration increases of roughly twenty-five percent compared with sparser sites. Elevation behaved as a genuine topographic constraint: below about 835 metres and above about 920 metres, ecosystem performance declined, so the index credits only the optimal altitudinal band. This inclusion of terrain explains much of GEMI’s edge, because erosion, productivity, and carbon storage depend not just on vegetation greenness but on slope, temperature, and moisture regimes that no spectral index alone can capture.

The authors are candid about limits: GEMI has so far been validated in a single semi-arid grassland during one growing season, and calibration coefficients may need adjustment before extrapolation to regions with different soils, rainfall regimes, or species composition. Multi-year evaluation and independent validation with external field data are the stated next steps. Still, the implications are considerable. For countries where field surveys are unaffordable and ecosystem service monitoring has largely stalled, a freely computable index built from open Landsat, MODIS, and Sentinel data offers a way to track fodder supply, grazing pressure, carbon sequestration, air quality, and soil health in one number, and to do so repeatedly, cheaply, and across entire landscapes.

Subject of Research: Development and evaluation of a remote sensing-based composite index for monitoring multiple grassland ecosystem services in semi-arid regions

Article Title: Development and evaluation of remote sensing-based grassland ecosystem monitoring tool for predicting provisioning, regulating, and supporting services in semi-arid regions

Article References: Ghosh, A., Das, B., Satpute, A. N., Haque, M. A., Singh, A. K., Chakroborty, A., Shukla, A. K., Biradar, N., Gupta, A. K., & Mukherjee, S. (2026). Development and evaluation of remote sensing-based grassland ecosystem monitoring tool for predicting provisioning, regulating, and supporting services in semi-arid regions. Smart Agricultural Technology, 15, Article 102556. https://doi.org/10.1016/j.atech.2026.102556

Image Credits: AI Generated

DOI: 10.1016/j.atech.2026.102556

Keywords: grasslands, remote sensing, ecosystem services, GEMI, vegetation indices, soil organic carbon, soil erosion, carrying capacity, semi-arid regions, Landsat 8, Sentinel-5P, net primary productivity

The research, conducted by Lindumusa Myeni and Nisa Ayob of North-West University, analysed daily climate records from ten weather stations spanning contrasting agroecological zones in the Free State, Gauteng, KwaZulu-Natal, Mpumalanga and North West provinces, together with district-level maize yield records spanning more than 30 growing seasons from 1993/94 to 2023/24. The five provinces together produce over 85 percent of South Africa’s maize. The authors computed rainfall- and temperature-based extreme climate indices following the Expert Team on Climate Change Detection and Indices framework, then applied Sen’s slope estimator and the Mann-Kendall test to detect trends, Pearson correlation to link extremes with yields, and stepwise multiple regression to identify the dominant climatic drivers of yield variability.

The headline finding is unambiguous warming. Mean seasonal air temperature increased from 0.02 degrees Celsius per annum at the wetter Lydenburg station to 0.06 degrees Celsius per annum at the semi-arid stations of Klerksdorp and Vryburg, while maximum daytime temperatures rose by 0.05 to 0.11 degrees Celsius per annum across all stations. More striking still, the frequency of very hot days increased by 0.19 to 0.50 percent per annum, with the largest increases recorded at Klerksdorp, Newcastle and Carolina. In mirror image, the frequency of extreme cold days declined by 0.10 to 0.29 percent per annum at most stations. Together these trends signal a clear shift toward hotter growing-season conditions, with peak heat intensifying faster than average temperatures, a pattern the authors argue underscores why extremes, not just means, must be monitored.

Rainfall told a subtler story. Total seasonal rainfall showed highly variable and statistically non-significant trends across all stations, ranging between minus 7.08 and plus 5.91 millimetres per annum, consistent with earlier national analyses that found no coherent long-term rainfall signal. But the texture of rainfall is changing. The number of rain days declined significantly at 40 percent of stations, including Bronkhorstspruit, Estcourt, Klerksdorp and Vereeniging, at rates of 0.55 to 1.16 days per annum, implying that rain is becoming less frequent but potentially more intense, with longer dry intervals between events. Consecutive dry days increased significantly only at Vereeniging, at roughly 19 additional days per decade, while heavy rainfall indices rose significantly only at Lydenburg, where more intense downpours raise risks of runoff, erosion and waterlogging on vulnerable soils.

Perhaps most consequential for crops is the trajectory of moisture balance. The Standardized Precipitation Evapotranspiration Index, or SPEI, which captures the competition between water supply and atmospheric demand, declined significantly at 30 percent of stations, including Estcourt, Klerksdorp and Newcastle, at rates of 0.03 to 0.05 per annum. This indicates that evapotranspiration, driven largely by rising air temperatures, is increasingly outpacing precipitation, deepening water stress and drought severity. For rainfed maize, which dominates production in these semi-arid environments, such trends translate directly into greater susceptibility to prolonged dry conditions, reduced soil moisture and unstable yields. The study also noted a significant decrease in minimum nighttime temperatures at Bloemfontein, raising frost risk in that district, a reminder that warming is not spatially uniform.

Maize yields themselves varied enormously across the study municipalities. Mean yields ranged from as low as 2.42 tonnes per hectare at Vereeniging and Bloemfontein to as high as 7.49 tonnes per hectare at Estcourt, with cooler, wetter districts such as Newcastle, Lydenburg and Carolina generally outperforming drier western areas like Vryburg, Klerksdorp and Bloemfontein. Yield stability differed just as sharply: coefficients of variation spanned from 29.24 percent at Vereeniging to 54.28 percent at Vryburg, with high values at Vryburg, Newcastle and Bloemfontein pointing to strong interannual fluctuations likely driven by erratic rainfall, dry spells and temperature extremes during critical growth stages.

The correlation analysis drew a clear line between specific extremes and yield outcomes. Maize yields correlated negatively with heat indices across all municipalities, with the strongest significant relationships in semi-arid regions: at Vryburg, mean seasonal temperature correlated at r equals minus 0.64 and very hot days at r equals minus 0.62, while at Klerksdorp the corresponding values were minus 0.52 and minus 0.48. At Bethlehem, very hot days correlated at minus 0.46. By contrast, cold-related indices showed weak or non-significant correlations at most stations, suggesting cold stress is far less influential than heat. On the moisture side, yields correlated positively with total rainfall, rain days and SPEI, with SPEI reaching r equals 0.76 at Klerksdorp and 0.68 at Vryburg, confirming moisture availability as the primary limiting factor in these water-scarce districts. Consecutive dry days correlated negatively with yields at Klerksdorp, reinforcing the damage inflicted by intra-seasonal drought.

Stepwise multiple regression then quantified how much of the yield variability climate extremes can actually explain. The explanatory power of the models ranged from weak at Lydenburg, where the coefficient of determination was just 0.12, to strong at Klerksdorp and Vryburg, where it reached 0.69, meaning climate variability accounted for up to 69 percent of interannual yield fluctuations in these water-limited regions. SPEI carried large, highly significant positive coefficients at Klerksdorp and Vryburg, leading the authors to propose the drought index as a practical early-warning indicator for maize production forecasting and risk assessment. At Estcourt and Newcastle, with coefficients of determination of 0.49 and 0.67 respectively, both temperature extremes and rainfall characteristics, including amount, frequency and intensity, shaped yields in more complex ways. Where model explanatory power was low, non-climatic factors such as soils, management and technology likely dominated.

The authors stress that these relationships are scale-dependent and that coarser provincial or national analyses can obscure localised impacts, which is why district-level assessment matters for crafting adaptation. Their recommendations diverge by zone: in hot, semi-arid areas, drought- and heat-tolerant seed varieties, adjusted planting dates, conservation tillage, mulching, residue retention, cover cropping, rainwater harvesting and supplementary irrigation offer the most promise, while in wetter regions the priority is managing rainfall distribution variability, mitigating heat stress and optimising planting calendars. Climate information services, seasonal forecasts and agrometeorological advisories, they argue, can help farmers anticipate risks and act proactively rather than reactively.

The study acknowledges limitations, including the coarse resolution of station data, seasonal indices that may miss extremes during critical phenological windows, and district-level yield records that mask local variation in soils, cultivars and management. Future work, the authors suggest, should integrate high-resolution climate projections, finer-scale yield and management data, and machine learning approaches capable of capturing nonlinear climate-yield relationships. With heat extremes intensifying and moisture availability declining across the maize belt, the message for policymakers is that uniform adaptation policies will fall short; resilience must be built district by district, informed by the specific climatic constraints each farming community faces.

Subject of Research: Long-term trends in extreme climate indices and their impacts on district-level maize yields in South Africa's summer rainfall region

Article Title: Long-term changes in the climate extremes and their impacts on maize yields in the summer rainfall region of South Africa

Article References: Myeni, L., & Ayob, N. (2026). Long-term changes in the climate extremes and their impacts on maize yields in the summer rainfall region of South Africa. Theoretical and Applied Climatology, 157(10), Article 622. https://doi.org/10.1007/s00704-026-06550-y

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06550-y

Keywords: climate extremes, maize yields, South Africa, heat stress, drought, SPEI, rainfed agriculture, Mann-Kendall trend analysis, semi-arid regions, food security, adaptation strategies, agroclimatology

The Essaouira Basin sits along Morocco’s Atlantic margin, where sedimentary layers deposited over hundreds of millions of years record the opening of the central Atlantic and the subsequent uplift of the High Atlas mountains. Groundwater in the region is under mounting stress from climate change and growing demand, yet the deep structural controls on aquifer geometry have remained poorly constrained. Understanding where water-bearing layers thicken, where faults cut them apart, and where impermeable bodies seal them off is essential for managing a resource that recharges slowly in a semi-arid climate.

To build that understanding, the team processed Bouguer gravity anomaly data ranging from −65 to +35 milligals, using polynomial regression to strip away deep regional trends and isolate a residual gravity field between −38 and +19 milligals. These residual anomalies reflect density contrasts in the subsurface caused by the topography of the Paleozoic basement, the rise of Triassic salt diapirs, and lateral variations in the thickness of the sedimentary cover. Gravity data of this kind act as a large-scale density map: masses of dense basement rock pull slightly harder on a gravimeter than lighter sediments or salt, and the resulting patterns can be inverted into structural information.

Extracting usable structure from gravity data requires sharpening the edges of buried bodies. The researchers applied an improved logistic filter, a total horizontal gradient transform and the UC technique to the residual field, and together these methods resolved 13 gravity fault contacts, labelled FG1 through FG13. The mapped faults follow dominant northeast-southwest, east-west and north-south trends, echoing the tectonic history of the Atlantic rift and the later Atlas compression. Each contact marks a place where rock density changes abruptly, typically across a fault plane, and their orientations provide a first-order template for how the basin is fractured.

Gravity alone, however, cannot tell which layer is which. For that, the team turned to five seismic reflection profiles, which image subsurface layering by recording the echoes of sound waves sent into the ground. Calibrated against four boreholes, the seismic data allowed the researchers to constrain five key regional reflectors: the top of the Paleozoic basement, the Triassic-Jurassic boundary, the top of the Jurassic aquifer of Oxfordian-Kimmeridgian age, the top of the Lower Cretaceous aquifer of Barremian-Albian age, and the base of the Upper Cretaceous aquifer at the Albian-Cenomanian transition. These five surfaces define the main water-bearing packages of the basin and anchor the structural interpretation in real stratigraphy.

Integrating the gravity and seismic results revealed five negative gravity anomalies, designated N1 to N5, and three structural sub-basins, SB1 to SB3, bounded by steeply dipping faults. These sub-basins are the deep depressions where sedimentary layers accumulated to their greatest thicknesses, and they correspond closely to the zones where the Upper Jurassic, Lower Cretaceous and Upper Cretaceous aquifer units reach their maximum development. In other words, the places where the basin sank deepest over geological time are precisely the places where the largest volumes of groundwater storage now reside.

One of the most striking findings concerns salt. Seismic profiles imaged salt diapirs, walls of Triassic evaporites that have flowed upward through the overlying sediments, and these structures connect with the well-known Jbel Hadid and Jbel Amsittene salt anticlines at the surface. Because salt is essentially impermeable to water, these diapirs and anticlines act as lateral seals that compartmentalize the aquifer units into hydraulically discrete cells. Water in one cell cannot easily flow into its neighbour, which means that pumping in one compartment will not be buffered by storage in another. For water managers, this is a crucial insight: the basin is not a single connected reservoir but a mosaic of partially isolated blocks.

The structural framework also points to where the resource can best be replenished. Fault intersections, where fracture networks are densest, enhance permeability and maximize recharge potential, and the study identifies these zones as priority targets for artificial recharge schemes and strategic borehole siting. Rather than drilling blindly, planners can now use the structural synthesis map to place wells where fault-bounded structural lows coincide with thick aquifer sections and fracture-enhanced connectivity to the surface. The authors emphasize that this gravity-seismic integrated approach is transferable to comparable semi-arid basins across Morocco and North Africa, where similar structural controls likely govern hidden groundwater systems.

The significance of the work extends beyond the Essaouira Basin itself. Across North Africa, sedimentary basins host deep aquifers that sustain agriculture and urban supply, yet their structural frameworks are often known only from sparse wells. By demonstrating how residual gravity processing, edge-detection filters, seismic stratigraphy and borehole calibration can be woven into a quantitative hydrostructural model, the study offers a template for groundwater exploration under data scarcity. As climate pressures intensify across the semi-arid belt of the southern Mediterranean, mapping the deep structures that store, seal and channel groundwater may prove as important to regional water security as any new dam or desalination plant.

Subject of Research: Integrated geological and geophysical characterization of deep aquifer systems in the Essaouira Basin, western Morocco

Article Title: Hydrogeological Characterization of Deep Aquifer Systems in the Essaouira Basin, Western Morocco: Insights from Integrated Geological and Geophysical Approaches

Article References: Zbiri, A., Kchikach, A., Jaffal, M., Mrlina, J., Zappalá, S., Guernouche, M., Radwan, A. E., & Azaroual, M. (2026). Hydrogeological Characterization of Deep Aquifer Systems in the Essaouira Basin, Western Morocco: Insights from Integrated Geological and Geophysical Approaches. Natural Resources Research. https://doi.org/10.1007/s11053-026-10766-0

Image Credits: AI Generated

DOI: 10.1007/s11053-026-10766-0

Keywords: Essaouira Basin, hydrogeology, groundwater, gravity data, seismic reflection, salt diapirs, faults, aquifer geometry, Morocco, semi-arid regions, structural geology, artificial recharge

Chia (Salvia hispanica L.), a member of the mint family native to Mexico and Guatemala, has attracted worldwide attention for its exceptionally high content of omega-3 fatty acids, dietary fiber, protein, and antioxidants. Its seeds can form a mucilaginous gel when hydrated, making them popular in functional foods, beverages, and health supplements. Yet despite its nutritional pedigree, chia remains what breeders call an orphan crop: genomic resources were only recently developed, and in most producing regions, very few improved varieties exist. In India, the crop’s introduction has been limited by the absence of locally adapted cultivars and by the genetic uniformity of available germplasm, which leaves little raw material for selection and improvement.

The research team confronted this bottleneck with induced mutagenesis, a technique that uses physical agents such as gamma irradiation to create random changes in the plant genome. Two chia genotypes, CHIAmpion W-83 and Nira Black Chia-1, were exposed to gamma rays, and the resulting mutant populations were advanced through successive generations to allow the genome to stabilize and recessive traits to surface. From this material, six stabilized mutant lines emerged, each carrying distinct and heritable alterations that were subsequently evaluated under field conditions for qualitative traits, phenology, plant architecture, yield components, and seed yield.

The phenotypic diversity recovered from the mutagenized populations was striking. The mutants displayed altered pigmentation patterns, crinkled leaves, chlorosis, and modified panicle architecture, all visible signs that gamma irradiation had effectively rewritten portions of the chia genome. Similar macro-mutations have long served as valuable tools in crop genetics, and in chia they provide the first tangible evidence that mutation breeding can function as a practical diversification strategy for the species. Because chia’s natural gene pool is so constrained, the ability to manufacture new variation in a single generation represents a significant technical advance for breeders working with limited germplasm.

Quantitative traits showed equally meaningful variation. Flowering time, maturity duration, plant height, branching pattern, panicle length, test weight, and seed yield all differed significantly among the mutant lines, giving breeders a palette of characters from which to assemble improved varieties. The most successful line, designated Mutant 94-1, combined early flowering and early maturity with superior branching, longer panicles, and the highest seed yield recorded among the mutants, outperforming its own parental line. In rainfed agriculture, where the growing season is dictated by erratic monsoon rainfall rather than irrigation, early maturity is a particularly prized trait: it allows a crop to complete its life cycle before terminal drought sets in, effectively escaping the worst of water stress.

A second line, Mutant 74-1-5, also demonstrated improved yield potential alongside a desirable plant architecture, reinforcing the conclusion that beneficial agronomic mutations can be recovered at useful frequencies in chia. The remaining four mutants, while not top performers for yield, were highlighted as trait-specific genetic resources that will support downstream research. Mutants 94-1 and 125-1 offer material for studying pigmentation, Mutant 148-1-2 provides a platform for investigating leaf morphology, Mutant 31-1-1 sheds light on chlorophyll expression, and Mutant 80-1 carries distinctive inflorescence shape characteristics. Each of these lines could serve as a genetic reference point for mapping the genes underlying the corresponding traits, especially now that reference genome assemblies and gene expression atlases for chia have become available to the research community.

The strategic significance of the work extends beyond the laboratory. India’s rainfed regions, which account for a large share of the country’s cultivated area, are increasingly vulnerable to climate variability, and agricultural planners are actively searching for hardy, high-value crops that can diversify dryland farming systems. Chia fits this profile in several respects. Previous research, including satellite-based observations, has suggested that chia can use less water than many other crops in warm climates, and field trials at ICAR institutes have examined its performance under deficit irrigation in semi-arid conditions. Technical bulletins describing cultivation practices for chia have also been released, indicating that the institutional groundwork for scaling the crop is already in place. What has been missing is genetic material tailored to Indian conditions, and the new mutant lines directly address that gap.

The study also reinforces the broader relevance of induced mutagenesis in modern plant breeding. For crops with narrow genetic bases, limited crossable relatives, or long generation times, mutation breeding offers a shortcut to diversity that does not involve transgenic methods and can therefore move more easily through regulatory channels in many countries. Historically, induced mutations have contributed thousands of officially released varieties worldwide, spanning cereals, legumes, and oilseeds. Applying the same toolkit to chia, a crop newly introduced to Indian agriculture, is a textbook example of how the method can accelerate domestication and adaptation of emerging species. The authors note that the identified mutants constitute elite breeding materials for developing improved chia varieties suited to Indian agro-ecological conditions and for accelerating future genetic studies in the crop.

From a technical standpoint, the pipeline used by the researchers is instructive. Mutagenesis was followed by careful generational advancement, which is essential because mutations induced in the first generation are frequently heterozygous or chimeric. Only after several generations of selfing do mutant phenotypes become fixed and reliably observable. The subsequent field characterization of the six stabilized lines, covering both qualitative descriptors and quantitative agronomic traits, mirrors the evaluation protocols used in variety development, meaning that the mutant lines are not merely curiosities but candidates for direct integration into breeding programs. Lines such as Mutant 94-1 could be tested in multi-location trials, crossed with other genotypes to pyramid favorable traits, or used as parents in varietal development aimed specifically at rainfed and semi-arid environments.

For consumers and farmers alike, the implications are compelling. A domestically adapted chia variety could open a new nutraceutical value chain for Indian dryland farmers, offering a high-margin crop alternative in regions where traditional options are increasingly unreliable. Meanwhile, the diverse mutant collection gives Indian plant scientists a homegrown resource for exploring the genetics of omega-3 accumulation, mucilage production, drought response, and flowering time in a species whose molecular biology is only now being decoded. What began as a flash of gamma radiation in a treated seed lot may ultimately help transform an ancient Aztec staple into a modern pillar of climate-resilient Indian agriculture.

Subject of Research: Gamma irradiation-induced genetic improvement of chia (Salvia hispanica L.) for rainfed agriculture in India

Article Title: Characterization of Novel Mutants of Chia (Salvia hispanica L.): A Prospective and Potential Crop for Indian Rainfed Agro-Ecologies

Article References: Santosh, H. B., Boraiah, K. M., Sarkar, B., Alam, B. K., Lakshmi, N. J., Salini, K., Harisha, C. B., Basavaraj, P. S., Halli, H. M., Kate, P., & Reddy, K. S. (2026). Characterization of Novel Mutants of Chia (Salvia hispanica L.): A Prospective and Potential Crop for Indian Rainfed Agro-Ecologies. Indian Journal of Genetics and Plant Breeding. https://doi.org/10.1007/s44489-026-00043-y

Image Credits: AI Generated

DOI: 10.1007/s44489-026-00043-y

Keywords: chia, Salvia hispanica, mutation breeding, gamma irradiation, rainfed agriculture, genetic variability, seed yield, nutraceutical crop, plant breeding, semi-arid regions, omega-3 fatty acids, mutants