The researchers tested five nitrogen rates—0, 80, 160, 240 and 320 kg N ha−1—alongside three foliar treatments: a control, glycine betaine and zeatin. These combinations were evaluated in six barley varieties: Ramata, Placidia, Nonius, Sultan, Kıral-97 and Altıkat. The factorial field design examined the individual and combined effects of nitrogen fertilization and biostimulant treatment on growth, yield formation and agronomic nitrogen use efficiency.

Nonius recorded the highest grain yield among the six varieties, at 6.33 t ha−1, while Altıkat produced the lowest, at 5.17 t ha−1. Nitrogen rate produced a different result for total yield than for efficiency. Grain yield at 240 kg N ha−1 was reported as 116.71% higher than the unfertilized control, and the researchers identified this rate as providing the best overall balance for yield contribution.

By contrast, 80 kg N ha−1 was optimal for both agronomic nitrogen use efficiency and partial factor productivity of nitrogen. The comparison shows that the nitrogen rate associated with the highest yield response was not the same as the rate associated with the strongest efficiency measures. The full experimental range also included 320 kg N ha−1, but the abstract does not report its yield percentage or efficiency values relative to the other rates.

The findings support the tested combination of foliar biostimulants and nitrogen management as an approach for improving barley productivity and agronomic nitrogen use efficiency under rainfed field conditions, with zeatin producing the larger average grain-yield increase. Their scope remains bounded by the two-year experiment, six varieties and five nitrogen rates, and the supplied results do not establish whether the same treatment effects would occur in other environments or production systems. The research also does not show that one nitrogen rate should replace the others: 240 kg N ha−1 was associated with the strongest reported overall yield contribution, whereas 80 kg N ha−1 gave the best reported values for the two nitrogen-efficiency measures.

Within this experimental framework, the clearest result is that adding glycine betaine or zeatin improved overall measured agronomic performance, while the balance between total yield and nitrogen-use efficiency depended on the nitrogen application rate.

Subject of Research: Effects of foliar biostimulants and nitrogen rates on rainfed barley yield and agronomic nitrogen use efficiency

Article Title: Synergistic effect of biostimulants and nitrogen fertilization on crop performance of barley (Hordeum vulgare L.) under rainfed conditions

Article References: ÖZTÜRK AY, E., AKAY, H., SEZER, İ., & AY, A. (2026). Synergistic effect of biostimulants and nitrogen fertilization on crop performance of barley (Hordeum vulgare L.) under rainfed conditions. BMC Plant Biology. https://doi.org/10.1186/s12870-026-09911-x

Image Credits: AI Generated

DOI: 10.1186/s12870-026-09911-x

Keywords: barley, rainfed agriculture, biostimulants, glycine betaine, zeatin, nitrogen fertilization, grain yield, nitrogen-use efficiency, crop performance, field experiment, Synergistic, effect

The researchers drew on daily rainfall estimates from the CHIRPS satellite-based dataset and monthly soil moisture from the FLDAS Noah land-surface model, both averaged across the full administrative boundary of Gedaref State for the period 1982 to 2020. They supplemented these with annual sorghum and sesame records from the Gedaref State Ministry of Production and Economic Resources and the Mechanized Farming Corporation. Before anything else, the team checked how well the satellite rainfall product matched the available ground truth. Against a monthly station archive, CHIRPS showed a moderate seasonal correlation of 0.625, but agreement in absolute totals was limited, with a bias of minus 77.5 millimetres and a root mean square error of 130 millimetres. The authors attribute part of this discrepancy to the difference in spatial support: the station represents a single point in Gedaref town, while the satellite series averages across the entire state, including drier northern localities.

From the daily rainfall record, the researchers calculated four indicators designed to capture the main forms of within-season rainfall stress. The first was the April-to-November rainfall deficit, representing inadequate seasonal water supply. The second was the maximum number of consecutive dry days during the core June-to-September rainy period, a measure of the longest interruption in wetting. The third was onset delay, defined as the first date on or after 1 May when rainfall accumulated over three consecutive days reached 20 millimetres. The fourth was the share of seasonal rainfall delivered by the five wettest days, which captures how concentrated a season’s water supply is in a handful of violent storms. Across the record, these variables revealed striking variability: onset dates ranged from day 135 to day 240 of the year, maximum dry spells ranged from 2 to 13 days, and the five wettest days contributed between 10.2 and 21 percent of seasonal rainfall.

These four components were then combined into a single score, the Daily Rainfall-Structure Risk Index, or DRSRI, using principal component analysis. The analysis retained one component explaining 60.4 percent of the standardised variance, with coefficients of similar magnitude for all four variables, roughly 26, 25, 23 and 25 percent of the total weighting. Bootstrap resampling confirmed that no coefficient collapsed towards zero. Notably, an equal-weight version of the index correlated with the PCA version at 0.9997, meaning the sophisticated weighting made almost no practical difference to the annual ranking of risk. The index itself, however, turned out to be a powerful indicator of surface drying: higher DRSRI values coincided with lower modelled surface soil moisture anomalies in the top 10 centimetres of soil, with a correlation of minus 0.776 that was highly significant.

Yet the story took an unexpected turn when the researchers tested whether the index could actually predict soil moisture better than seasonal rainfall alone. Using a rolling-origin evaluation, in which models were trained on data from 1982 to 1996 and then progressively refitted as each new year arrived, the four rainfall-structure variables produced only a marginal and statistically unsupported improvement over seasonal rainfall, with root mean square errors of 0.544 versus 0.554 and a p-value of 0.739. The DRSRI alone performed worse than seasonal rainfall, with an RMSE of 0.681. The authors are candid about this result: the index is not a better predictor, but a better diagnostic. It tells you whether an unfavourable season was driven by low rainfall, late onset, a long dry spell, or concentrated storms, information that a single seasonal total cannot provide.

The agricultural analysis revealed perhaps the most consequential finding of the study. The clearest association was not with crop yields, as one might expect, but with the sorghum harvested-area ratio, the percentage of planted land reported as remaining productive through the season. This ratio was negatively correlated with DRSRI at minus 0.448 and positively correlated with seasonal rainfall at 0.440, both significant after false-discovery-rate correction. In contrast, no yield association for either sorghum or sesame remained statistically significant after multiple-testing correction. This distinction matters because yield measures output only from land recorded as productive, while the harvested-area ratio reflects how much planted land survived the season at all. In years with poor rainfall structure, farmers apparently lost a larger share of their planted area entirely.

The researchers also identified eight compound-risk years, 1984, 1990, 1998, 2001, 2004, 2009, 2013 and 2015, in which at least three of the four adverse rainfall conditions occurred simultaneously. In these years, modelled surface soil moisture anomalies averaged minus 0.970 standardised units compared with 0.250 in other years, and the sorghum harvested-area ratio anomaly averaged minus 0.956 versus 0.247. Both differences were highly significant. However, the classification proved sensitive to the thresholds used, with the exact set of flagged years changing under alternative percentile rules, even though the direction of the response remained stable. The authors frame this as an exploratory, impact-oriented classification rather than a formal definition of a multivariate climate extreme.

Two further findings complicate any simple narrative of worsening drought. First, a structural break detected after 1996 split the sorghum harvested-area record: the DRSRI association was strong before the break at minus 0.688 but weakened to minus 0.329 afterwards, while the seasonal rainfall association strengthened. Second, trend analysis after prewhitening and false-discovery-rate correction found no significant long-term change in any rainfall indicator between 1982 and 2020, yet the sorghum harvested-area ratio declined by about 9.2 percentage points per decade. This divergence suggests that the long-term deterioration in reported productive area was driven by factors other than rainfall, likely including the documented expansion of mechanised cultivation into more marginal clay plains, land degradation, and management or reporting changes.

The study is careful about its limits, and these caveats are instructive for anyone hoping to apply the approach elsewhere. The FLDAS soil moisture is modelled, not measured, and because CHIRPS rainfall contributes to the forcing of the FLDAS Noah model, the rainfall-moisture relationship represents internal consistency rather than independent validation. The Noah model also does not represent the cracking and preferential flow characteristic of the Vertisol clay soils that dominate Gedaref, adding further uncertainty. The state-wide averaging smooths local convective storms, meaning a wet day in the regional average does not guarantee rain on any particular field. The crop records, meanwhile, are annual administrative statistics lacking information on sowing dates, varieties, fertiliser use, pests, labour, or finance, all of which shape yields independently of rainfall.

For all these limitations, the work offers a practical template for drought monitoring in data-scarce drylands. The DRSRI can serve as a compact annual screening indicator, while its four components pinpoint the specific rainfall constraint operating in any given season. The authors suggest that early-season flags for delayed onset or extended dry spells could be overlaid with cropland and vegetation maps to direct field inspection where it is most needed. Future work, they argue, should move to pixel-level rainfall analysis within mapped cropland, compare satellite products against daily station observations, and incorporate independent soil moisture measurements. In a region where millions depend on rainfed sorghum and sesame, knowing not just how much rain fell but how it fell may prove the difference between anticipation and surprise.

Subject of Research: Within-season rainfall structure, modelled surface soil moisture and agricultural response in rainfed farming systems of eastern Sudan

Article Title: Rainfall structure and modelled surface soil moisture in relation to agricultural response in rainfed lands of eastern Sudan

Article References: Abaker, M., & Elfaki, J. (2026). Rainfall structure and modelled surface soil moisture in relation to agricultural response in rainfed lands of eastern Sudan. Discover Soil, 3(1), Article 181. https://doi.org/10.1007/s44378-026-00343-5

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00343-5

Keywords: rainfall structure, soil moisture, rainfed agriculture, sorghum, sesame, drought, CHIRPS, FLDAS, Sudan, Gedaref, dry spells, compound climate risk

The analysis, published in Discover Agriculture by Ibrahim L. Kadigi of Mbeya University of Science and Technology and Lekumok Kironyi of Mwalimu Nyerere University of Agriculture and Technology, draws on the 2019/20 National Sample Census of Agriculture, Tanzania’s most comprehensive agricultural survey. The census, run by the National Bureau of Statistics, covered thousands of villages across the mainland and captured detailed records of household crop production, landholdings, and input use. From an initial pool of 2,830 sorghum-producing households, the researchers assembled an analytical dataset of 2,000 farms with complete harvest information, spanning all seven of the country’s agro-ecological zones.

What the data revealed about fertilizer adoption was stark. Only 91 farms, or 4.6 percent of the sample, reported applying organic fertilizers such as manure to their sorghum, and just 59 farms, about 3 percent, used inorganic mineral fertilizers. The remaining 92 percent of farms grew the crop on whatever nutrients the soil still held. Adoption was heavily concentrated in the Southern Highlands, where 48 of the 59 inorganic fertilizer users were found, a pattern the researchers attribute to better market integration and stronger extension support. In the Central Zone, the heart of Tanzanian sorghum production with more than 255,000 hectares under the crop, not a single sampled farm reported using mineral fertilizer.

To compare productivity across these three farming systems, the team went beyond simple averages. They employed a two-stage Monte Carlo simulation framework, a stochastic technique that converts observed farm-level yields into full probability distributions. In the first stage, yields for each fertilizer category were parameterized as empirical distributions and resampled 500 times using Latin Hypercube Sampling, an efficient method that preserves the statistical properties of the parent data with relatively few iterations. In the second stage, a multivariate empirical procedure introduced correlated randomness across the three systems, ensuring that simulated outcomes respected the real-world covariance structure of the data rather than producing implausible values such as negative yields.

The simulation was validated in two rounds against the observed census data, with goodness-of-fit tests and probability density function overlays confirming that the simulated distributions reproduced the means, spreads, and extremes of actual harvests. The researchers are careful to note what the method cannot do: because fertilizer use was not randomly assigned, the results describe associations between fertilizer categories and yield outcomes, not causal effects. Farms that choose to fertilize may differ in soil quality, management skill, or access to markets in ways the census cannot fully capture. Still, the scale of the differences is striking.

Farms using no fertilizer averaged 1.30 tonnes per hectare, with a minimum observed yield of just 0.49 tonnes. Organic fertilizer users averaged 1.38 tonnes per hectare, a marginal gain accompanied by greater variability, with a coefficient of variation of 56.4 percent compared with 47.2 percent among non-users. The researchers link this inconsistency to the uneven nutrient density and variable quality of manure and compost, which depend on storage, handling, and application rates that few farmers can control precisely. Inorganic fertilizer users, by contrast, averaged 1.94 tonnes per hectare, nearly 50 percent above non-users, with a higher minimum yield of 0.66 tonnes per hectare that suggests mineral nutrients also cushion farmers against the worst outcomes.

The probability analysis sharpened the picture further. Among non-users, 38 percent of farms fell below the national average yield of 1.0 tonne per hectare, and only 10 percent exceeded the 2.0-tonne benchmark widely regarded as the standard for economically viable sorghum production. Organic fertilizer users fared no better in the short term: 41 percent fell below 1.0 tonne, and just 8 percent cleared 2.0 tonnes, a result consistent with evidence from Kenya, Ethiopia, and Burkina Faso showing that organic inputs build soil structure and fertility over multiple seasons but rarely deliver immediate yield gains on their own. Inorganic fertilizer users told a different story. Only 19 percent fell below 1.0 tonne, while 30 percent exceeded 2.0 tonnes, a threefold increase over non-users.

When the bar was raised to the aspirational threshold of 3.0 tonnes per hectare, aligned with global productivity standards and Tanzania’s national goal of doubling cereal output by 2030, the gap widened further. Ninety percent of non-users and 92 percent of organic users remained below 2.0 tonnes, and only 1 percent and 4 percent respectively surpassed 3.0 tonnes. Among inorganic fertilizer users, however, 18 percent, nearly one farm in five, achieved yields above 3.0 tonnes, and a further 12 percent landed between 2.0 and 3.0 tonnes. The authors describe this as a tiered performance gradient: non-users trapped at subsistence levels, organic users marginally and inconsistently better, and mineral fertilizer users shifted decisively toward surplus production with commercial potential.

The findings arrive with important caveats that the authors themselves emphasize. The census does not record fertilizer formulations, nutrient composition, application rates, or timing, so the study cannot say which products or practices drive the gains. It also cannot identify farms applying organic and mineral fertilizers together, leaving integrated soil fertility management, the approach most agronomists consider optimal, empirically untested here. The small number of fertilizer users in the sample limits precision, and simulated iterations, however sophisticated, are not a substitute for additional field observations. Confounding factors such as improved seed varieties, rainfall, and soil type may contribute to the yield differences observed.

Even so, the policy implications are difficult to ignore. Only about one in twelve Tanzanian sorghum farms uses any fertilizer at all, and the yield gap between fertilized and unfertilized fields mirrors patterns documented across Ethiopia, Nigeria, Ghana, and Zimbabwe. The study recommends expanding access to inorganic fertilizers through targeted subsidies and improved distribution, strengthening extension services so that farmers apply nutrients correctly, and integrating mineral inputs with organic amendments to secure both immediate yields and long-term soil health. For a crop that anchors food security in Tanzania’s drought-prone center, where sorghum occupies nearly a quarter of cultivated land in regions like Singida, closing even part of this gap could transform a subsistence staple into a reliable source of food, income, and climate resilience.

Subject of Research: The contribution of organic and inorganic fertilizer use to sorghum productivity in Tanzania's rainfed smallholder farming systems

Article Title: Organic and inorganic fertilizer use and sorghum productivity in Tanzania’s rainfed farming systems using national sample census of agriculture data

Article References: Kadigi, I. L., & Kironyi, L. (2026). Organic and inorganic fertilizer use and sorghum productivity in Tanzania’s rainfed farming systems using national sample census of agriculture data. Discover Agriculture, 4(1), Article 312. https://doi.org/10.1007/s44279-026-00777-z

Image Credits: AI Generated

DOI: 10.1007/s44279-026-00777-z

Keywords: sorghum, Tanzania, fertilizer, inorganic fertilizer, organic fertilizer, crop yield, smallholder farming, rainfed agriculture, Monte Carlo simulation, food security, soil fertility, National Sample Census of Agriculture

The research team, led by Priscila Belen Cano of Purdue University’s Department of Agronomy together with Gustavo Angel Maddonni of the Universidad de Buenos Aires and Ignacio Antonio Ciampitti, developed what they call a yield-based environmental regionalization framework. Rather than starting with climate maps or soil surveys, the researchers began with the crop itself. They compiled median annual maize yields for each of Indiana’s 92 counties from 2000 to 2024, drawing on insured production records from the United States Department of Agriculture Risk Management Agency. Because long-term yield integrates the cumulative effects of climate, soils, topography, and management, spatially persistent differences in productivity can be read as realized environmental differences, the authors explain.

To transform those yield patterns into coherent geographic zones, the team applied a fuzzy c-means clustering algorithm, a machine learning technique that assigns each county a degree of membership in multiple clusters rather than forcing hard boundaries. The feature matrix combined each county’s median yield with the latitude and longitude of its geographic centroid, with the coordinates weighted by a spatial factor of 1.5 to encourage geographic contiguity while preserving yield homogeneity. Selecting the optimal number of clusters required a multi-criteria evaluation using the Xie–Beni index, the Fukuyama–Sugeno index, and the Fuzzy Partition Coefficient. Because these indices favored different solutions, the researchers settled on five clusters as a balanced compromise between partition quality, model complexity, and agronomic interpretability. The result was five yield-based environmental regions spanning the state: northwest, northeast, west-central, southwest, and southeast.

The regionalization exposed a striking productivity gradient. The west-central region emerged as Indiana’s maize powerhouse, with median yields of approximately 11.2 megagrams per hectare, while the northwest region lagged at roughly 8.5 megagrams per hectare, accompanied by the greatest spatial dispersion in county yields, with a standard deviation of 2.6 megagrams per hectare compared to less than 0.7 in the other regions. The northeast, southwest, and southeast occupied intermediate positions with relatively similar median yields. Crucially, climate variables were deliberately excluded from the clustering procedure so they could later serve as an independent test of whether the yield-defined zones corresponded to genuinely distinct climatic environments.

That test came in the form of a principal component analysis of long-term climatic means, built from the Daymet gridded meteorological dataset at one-kilometer resolution. The first two principal components together explained nearly 84 percent of the total variance, and the dominant variables separating the regions were vapor pressure deficit, reference evapotranspiration, and a heat-stress index capturing cumulative degrees of daily maximum temperatures above 35 degrees Celsius. The first component discriminated between environments with longer growing seasons, higher atmospheric demand, and greater heat stress, exemplified by the southwest, and cooler environments with shorter frost-free periods and more grain-filling precipitation, exemplified by the northeast and northwest. The second component captured contrasts in diurnal temperature amplitude and solar radiation, particularly during the critical period, cleanly separating the northeast from the northwest and the southwest from the southeast.

Yields rose everywhere over the study period, but not at the same relative pace. Absolute gains ranged narrowly from 0.172 to 0.185 megagrams per hectare per year, yet in relative terms the lower-yielding northwest improved at about 2 percent per year while the high-yielding west-central region gained about 1.6 percent annually. This pattern suggests a tendency toward relative convergence in maize performance across the state, even though absolute yield gaps between regions may persist. Interannual variability also differed, with coefficients of variation around 16 to 18 percent in the higher-yielding regions and approximately 21 percent in the southern zones, hinting that southern Indiana farmers face a rockier road to consistent harvests.

To isolate the climatic drivers of year-to-year swings, the researchers first removed long-term technological trends by fitting linear regressions of yield against year for each county, retaining the residuals as a measure of pure interannual variability. They then correlated these residuals with climatic anomalies, defined as the deviation of each county-year observation from the county’s 2000 to 2024 long-term mean for each variable. The results were unambiguous. During the vegetative period, relationships were weaker and more region-dependent, though elevated vapor pressure deficit and heat-stress indices consistently tracked negative yield residuals. During grain filling, higher solar radiation anomalies were reliably beneficial. But it was the critical period, defined as fifteen days before to fifteen days after silking, that exhibited the strongest and most consistent relationships across all five regions.

Vapor pressure deficit around flowering showed the most powerful association with interannual yield variability in the northeast, southeast, and southwest regions, with correlation coefficients ranging from minus 0.44 in the northwest to minus 0.78 in the southeast. Slope values were equally telling, spanning from minus 3.24 megagrams per hectare per unit of VPD anomaly in the northwest to minus 9.53 in the southeast. In the northwest and west-central regions, the heat-stress index during the same window took precedence, though with somewhat weaker relationships, a pattern the authors attribute to the more continental thermal regime of western Indiana, where episodic extreme heat exerts a proportionally larger influence than background evaporative demand. Across every region, the worst yield outcomes clustered where high VPD and extreme heat anomalies coincided during pollination.

The physiological story behind these statistics is well grounded. Maize is most vulnerable around silking because this stage determines kernel number per plant, the foundation of final yield. Elevated vapor pressure deficit during this window reduces pollen viability, accelerates silk desiccation, and impairs pollination success, leading directly to kernel set failure. In parallel, high atmospheric demand intensifies plant water stress and triggers stomatal closure, cutting carbon assimilation and increasing kernel abortion. Extreme temperatures above 35 degrees Celsius compound these effects, while heat during grain filling reduces kernel weight and quality. The authors caution that because many of the evaluated variables are physically interrelated, their correlation analysis identifies the variables most strongly associated with yield variability rather than proving independent causal effects, and that disentangling those contributions would require multivariate approaches beyond the study’s scope.

The practical implications reach from the seed bag to the planting calendar. In the southern and eastern regions, where the frost-free period is longer, delayed sowing could shift flowering toward windows of lower atmospheric demand, though any adjustment must be coordinated with hybrid maturity selection to avoid truncating grain filling or raising late-season frost risk. In the northwest and west-central regions, where the frost-free window is tighter, choosing hybrids with enhanced drought and heat tolerance during pollination and grain filling becomes the primary management lever, tempered by the recognition that stress-tolerant hybrids may carry yield penalties in favorable years. The framework itself, built entirely from publicly available yield records and gridded climate data, is potentially transferable to the wider Corn Belt and other maize-producing regions, offering a scalable empirical route to matching agronomic strategy and breeding priorities to the environments where they will actually matter. As atmospheric drying intensifies with rising temperatures, knowing precisely where and when vapor pressure deficit strikes hardest may prove one of the most valuable tools Indiana’s farmers can hold.

Subject of Research: Environmental characterization of rainfed maize yield variability in Indiana using yield-based regionalization and climatic analysis

Article Title: Environmental characterization of maize yield variability in a major Corn Belt state

Article References: Cano, P. B., Maddonni, G. A., Gomez, F. M., Mandrini, G., Quin, D., Bourns, M. A., & Ciampitti, I. A. (2026). Environmental characterization of maize yield variability in a major Corn Belt state. Theoretical and Applied Climatology, 157(10), Article 664. https://doi.org/10.1007/s00704-026-06582-4

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06582-4

Keywords: maize, Indiana, Corn Belt, vapor pressure deficit, heat stress, yield variability, environmental regionalization, fuzzy c-means clustering, rainfed agriculture, climate variability, flowering stage, agricultural climatology

The study, conducted by Abdelmounaim Hadjira of the University of Mustapha Ben Boulaid-Batna 2, Abdelmounaim Rezki of the National Higher School of Statistics and Applied Economics, and Said Meziane of Mohamed Cherif Messaadia University, draws on annual national data spanning 1970 to 2023. The datasets combine agricultural production figures from FAOSTAT, historical climate records from the World Bank Climate Change Knowledge Portal, and socioeconomic indicators from the World Bank’s World Development Indicators. By assembling more than five decades of evidence into a single statistical framework, the researchers were able to separate short-lived fluctuations from durable long-run relationships that define the structural conditions of Algerian wheat farming.

Methodologically, the team employed an autoregressive distributed lag model of order ARDL(1,1,1,2,1,0,1), a flexible econometric specification that allows each explanatory variable to exert its influence over different time horizons. The variables included wheat production as the outcome, with rainfall, temperature, land under cereal cultivation, rural population, fertilizer consumption and carbon dioxide emissions as potential drivers. The authors first confirmed that each series displayed the appropriate statistical properties for the approach, and bounds testing procedures confirmed the existence of a stable long-run equilibrium relationship linking wheat production to its climatic and structural determinants. This is a crucial finding in itself, because it demonstrates that the variables move together over decades rather than drifting apart as unrelated trends.

Perhaps the most telling number in the study comes from the error-correction mechanism, which measures how quickly the system returns to equilibrium after a disturbance. The estimate indicates that 67.8 percent of any short-run disequilibrium in wheat production is corrected within a single year. In practical terms, this means that when a drought, a market disruption or another shock pushes Algerian wheat output away from its long-run path, the system snaps back with considerable speed, but it also means that roughly a third of any deviation carries over into the following season. The result quantifies the sensitivity of a rainfed cereal system in which farmers have limited capacity to buffer bad years through irrigation or storage.

The long-run coefficients tell a story of climatic vulnerability. Rainfall is positively associated with wheat production, an unsurprising but precisely quantified result in a country where the overwhelming majority of wheat acreage depends on precipitation rather than irrigation. Higher temperatures, by contrast, display a substantial negative long-run association with output. The finding aligns with a broad international literature, including landmark global modeling studies showing that rising temperatures reduce wheat yields, and with earlier regional research documenting the sensitivity of durum wheat across eastern Algeria’s high plateaus. Because wheat is particularly vulnerable to heat stress during flowering and grain filling, sustained warming in a semi-arid Mediterranean climate compresses the growing window and accelerates soil moisture depletion, leaving plants less able to complete their development.

Among the structural variables, land under cereal cultivation contributes positively to wheat production, confirming that expanding or maintaining the cultivated area remains a meaningful lever for output, even though yields on that land are increasingly hostage to the weather. Rural population, however, is negatively associated with production in the long run, a result the authors interpret in the context of demographic and structural change in the countryside. Fertilizer consumption shows no significant long-run effect on wheat output, a finding that may reflect the dominance of water availability as the binding constraint in rainfed systems: nutrients cannot compensate when moisture is the limiting factor for crop growth. Previous field research on nitrogen use efficiency in Algerian durum wheat has similarly emphasized that fertilizer response is tightly conditioned by water supply in semi-arid environments.

One result deserves careful handling: carbon dioxide emissions display a positive long-run coefficient in the estimated model. The authors are explicit that this should not be read as an agronomic benefit of emissions. Rather, it is a conditional statistical association within the model, more likely to reflect the broader technological, economic and structural changes that have accompanied Algeria’s development over the study period, including mechanization, input intensification and policy shifts, than any direct physiological effect of carbon dioxide on crops. The caution matters because a naive reading could suggest that a warming, higher-carbon world helps Algerian wheat, when the temperature and rainfall coefficients point in the opposite direction. International evidence supports this interpretation, with studies of the world’s top wheat-producing countries finding that six decades of warming and drought have offset the theoretical yield benefits of rising carbon dioxide concentrations.

The study’s most forward-looking contribution comes from Dynamic ARDL simulations, a technique that allows researchers to trace how wheat production would respond over time to hypothetical changes in the driving variables while holding the rest of the system at its long-run equilibrium. The simulations show that a positive rainfall shock generates persistent increases in wheat production, with the gains accumulating and remaining in place over the simulated horizon. A positive temperature shock, by contrast, produces sustained declines in output that deepen over time rather than fading. The asymmetry is sobering for a country on the front line of Mediterranean climate change, where climate projections generally point toward hotter conditions and more erratic precipitation. If the simulated dynamics hold, each increment of warming locks in a durable production penalty, while rainfall gains, though valuable, must repeatedly be re-earned season by season.

The implications for policy are direct. The authors argue that their findings underscore the importance of strengthening climate adaptation policies through improved water management, heat-resilient production practices and sustainable land-use strategies. For Algeria, where wheat imports weigh heavily on the national budget and food security is a standing political priority, the analysis suggests that adaptation investments, from supplemental irrigation and drought-tolerant varieties to adjusted planting calendars and soil moisture conservation, are not optional refinements but structural necessities. The rapid error-correction speed identified in the study is encouraging in one respect, indicating that the production system can absorb and recover from shocks, but the simulation evidence makes clear that recovery to a declining long-run trend is small consolation.

The authors are equally candid about the limits of their analysis. The model relies on annual, national-level data, which cannot capture the regional and seasonal heterogeneity that defines Algerian agriculture, from the subhumid coastal plains to the semi-arid high plateaus where most wheat is grown. Seasonal climate indicators, such as rainfall concentrated in the critical winter and spring growth phases, would likely sharpen the picture, and nonlinear modeling approaches could reveal thresholds beyond which heat and drought effects escalate sharply. The researchers call for future work employing subnational datasets, seasonal indicators and nonlinear methods to provide a more comprehensive assessment. For now, the study stands as a rigorous quantitative confirmation of what Algerian farmers have long known in their bones: in this corner of the semi-arid Mediterranean, wheat lives and dies by the rain, and the heat is a creditor whose bill only grows.

Subject of Research: Climate shocks and wheat production dynamics in Algeria analyzed with ARDL and Dynamic ARDL simulations

Article Title: How do climate shocks shape wheat production in Algeria? Evidence from dynamic autoregressive distributed lag simulations

Article References: Hadjira, A., Rezki, A., & Meziane, S. (2026). How do climate shocks shape wheat production in Algeria? Evidence from dynamic autoregressive distributed lag simulations. Theoretical and Applied Climatology, 157(10), Article 661. https://doi.org/10.1007/s00704-026-06590-4

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06590-4

Keywords: wheat production, Algeria, climate change, ARDL model, Dynamic ARDL simulations, rainfall, temperature, rainfed agriculture, food security, semi-arid climate, error correction, climate adaptation

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 research team, led by Romaric Serge Lokossou of the National Institute of Agricultural Research of Benin and the University of Parakou, set up a rainfed field experiment on a hillside with a punishing 22 percent slope in the village of Koutagou. The region’s soils are thin, sandy, gravel-laden and notoriously erodible, products of the Atacora mountain chain where only about a third of the land is arable. Rainfall arrives in a five-month wet season totaling roughly 1,100 to 1,200 millimeters per year, and when it comes, it often arrives violently, sending water and topsoil cascading down the slopes. Despite decades of intervention by NGOs and government projects dating back to the 1960s, Boukombé still records some of the lowest crop yields in Benin and ranks among the country’s poorest districts.

The experiment compared three soil and water conservation techniques in a split-plot design with three replicates. The main treatments were rectangular honeycomb arrangements, circular honeycomb arrangements, and ordinary contour stone rows as the control. Honeycomb structures are built from stones gathered nearby and shaped into connected cells, or alveoli, that partition the field into small basins. Each cell acts as a miniature reservoir, slowing runoff, trapping sediment, and giving water time to infiltrate rather than race downhill. The researchers crossed these conservation structures with four fertilization regimes: the full recommended dose of NPK and urea, two reduced microdosing options at roughly 72 percent and 36 percent of the recommended dose, and an unfertilized control.

Over two growing seasons, 2012 and 2013, the team measured runoff and soil loss after every erosive rainfall event using bounded runoff plots, tracked soil moisture gravimetrically at two-week intervals, and harvested sorghum biomass and grain from central rows of each plot. The rainfall record itself told a story of climatic stress: 468.8 millimeters fell during the 2012 season and 483.35 millimeters in 2013, but 2012 was punctuated by dry spells lasting five to thirteen days during critical crop stages, while 2013 delivered rain more favorably distributed across the sorghum’s flowering period.

The hydrological results were unambiguous. Circular honeycomb arrangements reduced cumulative runoff by 25.05 percent compared with contour stone rows, while rectangular honeycombs achieved a 17.65 percent reduction. Soil loss told an even more dramatic story: the circular honeycomb plots lost 9,206 kilograms per hectare of sediment over the study period, against 19,580 kilograms per hectare under conventional stone rows, a reduction of just over 52 percent. Rectangular honeycombs cut soil loss by 38.55 percent. Event-by-event analysis showed the pattern held across most significant storms, and in several rainfall events the combination of honeycomb structures with fertilizer produced measurably less runoff and sediment than either factor alone.

Soil moisture, the lifeblood of rainfed farming in semi-arid zones, followed the same hierarchy. Circular honeycomb plots retained the most water in both seasons, averaging 13.63 percent moisture in 2012 and 15.53 percent in 2013, exceeding the other treatments by 8.52 to 10.52 percent. The authors attribute this advantage to the architecture of the honeycomb itself. Where a contour stone row is a single linear barrier, a honeycomb field is a dense network of intersecting stone walls enclosing many small catchment cells. More barriers mean more opportunities to interrupt flow, and more enclosed space means more residence time for water to soak into the root zone. The researchers note that honeycomb geometry echoes principles celebrated in engineering and mathematics, where the hexagonal honeycomb is recognized as the most stable structure in nature, and that similar cellular designs have proven superior in applications from mechanical engineering to solar thermal collectors.

The agronomic payoff was substantial. In 2012, circular honeycomb plots produced 2,086 kilograms per hectare of sorghum biomass, 52.38 percent more than the other conservation treatments, and grain yields of 451 kilograms per hectare against 240 under contour stone rows, an 87.42 percent advantage. In the wetter, better-distributed 2013 season, circular honeycomb grain yields reached 1,569 kilograms per hectare, 68.56 percent above the control. Fertilization amplified these gains. The recommended dose roughly tripled biomass relative to unfertilized plots, but critically, the microdosing options, which use far less fertilizer, performed nearly as well. Microdosing option 1 at 166.7 kilograms of NPK per hectare matched or exceeded the full recommended dose in several comparisons, confirming the technique’s promise for resource-poor farmers who cannot afford or access full fertilizer rates.

The synergy between conservation structures and nutrient management is central to the study’s message. Fertilized sorghum develops denser canopies and more extensive root systems, which shield the soil from raindrop impact and anchor it against scouring, further suppressing runoff and erosion. In turn, the honeycomb cells conserve the moisture that fertilizer needs to translate into grain. Sorghum, a drought-tolerant staple in Boukombé used for food and for the traditional beer known as Tchoucoutou, is ideally suited to exploit this combination, drawing water and nutrients from the soil with its dense, branched root architecture. The interaction between circular honeycombs and microdosed fertilizer produced the strongest plant development observed in the trial, suggesting a low-cost pathway to intensification on lands where conventional approaches have repeatedly failed.

The authors are careful to frame the results as a beginning rather than an endpoint. The circular honeycomb emerged as the most promising option for steep semi-arid hillsides, but they caution that its adoption hinges on questions the experiment did not address: the labor required to build and maintain cellular stone walls, the economics relative to linear stone rows, farmer acceptance, long-term performance, and water-use efficiency under different rainfall regimes. Still, in a region where water is the single most limiting factor for crop production and where erosion rates exceed the natural pace of soil formation, the study provides something rare and valuable: hard field evidence that a technique developed by local farmers, refined over centuries on the slopes of the Atacora chain, can beat the imported alternatives. As climate variability intensifies across the Sahelian margins, the humble honeycomb may prove to be one of the most elegant pieces of climate adaptation infrastructure already in the ground.

Subject of Research: Field evaluation of indigenous honeycomb stone arrangements for soil erosion control, soil moisture conservation, and sorghum productivity under fertilizer microdosing on steep hillsides in northwestern Benin

Article Title: Endogenous honeycomb stone arrangements reduce soil erosion, improve soil moisture, and sorghum productivity under fertilizer microdosing on steep lands of Boukombé in northwestern Benin

Article References: Lokossou, R. S., Akponikpè, P. B. I., Moutouama, F. T., Likpètè, D. D., Djènontin, J. A., Fatondji, D., & Baco, N. M. (2026). Endogenous honeycomb stone arrangements reduce soil erosion, improve soil moisture, and sorghum productivity under fertilizer microdosing on steep lands of Boukombé in northwestern Benin. Discover Soil, 3(1), Article 146. https://doi.org/10.1007/s44378-026-00301-1

Image Credits: AI Generated

DOI: 10.1007/s44378-026-00301-1

Keywords: honeycomb stone arrangements, soil and water conservation, soil erosion, runoff, soil moisture, sorghum, fertilizer microdosing, Benin, steep lands, rainfed agriculture, indigenous knowledge, semi-arid farming

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