Yield Variability Research Articles

The climatic impacts on rice yield in the Indian state of Odisha: an application of Just-Pope production function and quantile regression.

The impacts of climate change on Indian agriculture are well documented. However, there is a dearth of research addressing the inter-regional diversities in the impacts. Furthermore, existing studies are mostly restricted to the impacts on mean agricultural yield, overlooking the impacts on yield variability. This study investigates climatic impacts on rice yield in Odisha, a coastal Indian state, employing district-level panel data from 1995 to 2017. This study uses the Feasible Generalized Least Squares (FGLS) and quantile regression approaches to estimate how climatic impacts are realized on mean yield, yield variability, and conditional quantiles of yield distribution. The use of the agro-climatic zone level dummies empowers the study to account for local weather and soil conditions. The results reveal substantial heterogeneity in the climatic impacts across agro-climatic zones. A marginal increase in the maximum temperature reduces rice yield by 279-325kg/ha across different agro-climatic zones, largely constituting the state's coastal region, but increases yield by 340-766kg/ha across the zones lying in the western region. Conversely, an increase in minimum temperature increases rice yield by around 211kg/ha in the coastal region, but reduces by 333-744kg/ha in the western region. In addition, an increase in precipitation reduces yield by 0.318 kg/ha in some parts of the coastal region, but increases yield by 0.268 0.881 kg/ha across other regions. Similar heterogeneity is also identified across conditional quantiles of yield distribution. The heterogeneous impacts call for strategic policy intervention specific to zones and quantiles.

Meta-analysis of legumes and groundnut production trends and variability in the Global South

This study examined the production trends and variability of grain legumes in the Global South from 2000 to 2022, with a particular emphasis on groundnut yields and regional differences. From 2000 to 2022, global legume production in the Southern Hemisphere increased by approximately 20-30%, driven by rising demand for plant-based proteins and expanded cropping areas in countries like Brazil and Argentina. Improved agricultural practices have further enhanced yields. In contrast, groundnut production experienced a more moderate growth of around 10-15%, influenced by favourable conditions and expanded cultivation in regions such as Argentina and South Africa. While demand for groundnuts remains strong, market fluctuations and competition with other crops continue to shape its production dynamics. The analysis covered a range of legumes, including common beans (Phaseolus vulgaris), cowpeas (Vigna unguiculata), pigeon peas (Cajanus cajan), groundnuts/peanuts (Arachis hypogaea), soya beans (Glycine max), bambara groundnuts (Vigna subterranea), chickpeas (Cicer arietinum), lentils (Lens culinaris), mung beans (Vigna radiata), black gram (Vigna mungo), faba beans (Vicia faba), lablab beans (Lablab purpureus), tepary beans (Phaseolus acutifolius), African yam beans (Sphenostylis stenocarpa), Kersting's groundnut (Macrotyloma geocarpum), lima beans (Phaseolus lunatus), black beans (Phaseolus vulgaris), adzuki beans (Vigna angularis), moth beans (Vigna aconitifolia), horse gram (Macrotyloma uniflorum), broad beans (Vicia faba), winged beans (Psophocarpus tetragonolobus). Regional data revealed significant differences in legume production. In Sub-Saharan Africa, cowpeas and groundnuts are vital, with cowpeas grown over 11.4 million hectares on average yielding 450 kg ha-1, and groundnuts covering 9.1 million hectares with an average yield of 1,007 kg ha-1. Chickpeas and pigeon peas dominate South Asia's production, whereas Latin America features prominent soya bean and groundnut cultivation. Oceania's legume farming is less extensive, focusing on chickpeas and mung beans. Descriptive statistics revealed that Egypt led in groundnut production with an average yield of 3,279.1 kg ha-1 and a low coefficient of variation (CV) of 4.89%, indicating stable production. Conversely, Mozambique had the lowest average yield at 322.9 kg ha-1, with a high CV of 30.23%, reflecting greater variability. The Principal Component Analysis (PCA) identified five principal components explaining 70.9% of the total variance, with the first two components (PC 1 and PC 2) accounting for 51%. Bangladesh and Brazil were major contributors to PC 1, while Algeria and Senegal influenced PC 2. These findings highlight the considerable regional variability in yields and stability in legume production. Future research should address these disparities and enhance resilience through targeted agricultural practices and policy interventions.

Genetic variability and heritability of growth, yield and quality traits in Gymnema sylvestre: an anti-diabetic medicinal herb

Genetic variability and heritability of growth, yield and quality traits in <i>Gymnema sylvestre</i>: an anti-diabetic medicinal herb

Evaluation of White Onion (Allium cepa L.) Genotypes for Growth, Yield, Quality and Resistance to Thrips and Purple Blotch Disease

Onion (Allium cepa L.), belonging to the family Alliaceae with a chromosome number 2n=16, is an essential vegetable widely cultivated for both domestic consumption and export. This study aimed to evaluate various white onion genotypes for their growth, yield, quality and resistance to pests and diseases. The experiment was conducted at the Main Agricultural Research Station, University of Agricultural Sciences, Dharwad, during the late Kharif season of 2023-24, using twenty-six genotypes arranged in a randomized complete block design. Data were collected on several growth and yield parameters, which were then analyzed. Significant variation was observed among genotypes for growth traits such as plant height, leaf length and neck diameter. The genotype 'Safed Ghavriyu' exhibited the highest plant height and number of leaves per plant, while 'Bhima Shubra' and 'Milky White' showed superior leaf length and neck diameter, respectively. Yield-related traits also varied significantly, with 'Bhima Shweta' recording the highest dry matter content and 'Safed Ghavriyu' demonstrating the highest fresh and dry weight per plant, single bulb weight and total yield per hectare. In terms of pest and disease resistance, notable differences were observed, with 'Safed Ghavriyu' showing the lowest incidence of thrips and purple blotch disease. Additionally, 'PWO-2' had the highest total soluble solids (TSS) content, while 'W-210' had the highest total sugar content. 'Milky White' exhibited the highest pyruvic acid content, contributing to its pungency. These findings underscore the significance of genetic variability and environmental influences in white onion growth and yield. The results offer valuable insights for the development of high-yielding, disease-resistant white onion varieties suited to various agro-climatic conditions in India.

Phytochemical variability in essential oils of Juniperus communis var. saxatilis populations grow wild in western Himalaya, India

Needle and berry samples of Juniperus communis L. var. saxatilis Pall. were sampled separately during post rainy season to comparatively investigate the essential oil (EO) yield and phytochemical variability in eight populations grow wild in western Himalaya, India. The EOs obtained from air-dried needle and berry samples separately by hydrodistillation method using Clevenger type apparatus varied in yield with geographic origin of populations, ranged between 2.5-3.4% and 1.8-2.8% (v/w, dried weight basis), respectively. GC-MS analysis of EO profiles resulted in identification of a total ninety-four phytochemicals, representing over 95% of total oil content. Both, needle as well as berry EO samples were dominated by monoterpene hydrocarbons α-pinene (18.52-30.65% and 14.31-27.37%), D-limonene (17.01-28.96% and 15.80-29.70%), β-myrcene (3.74-5.59% and 4.12-14.23%), followed by sesquiterpene hydrocarbons β-elemene (0.86-4.13% and 0.77-3.65%) and germacrene D (0.27-2.57% and 0.20-6.15%), respectively. The oxygenated sesquiterpenes α-cadinol (0.34-5.90% and to 5.63%) and α-bisabolol (to 3.39 and 0.95-6.71%) constituted the third most abundant group of terpenes while oxidized monoterpenes with α-terpineol (0.73-2.17% and 0.10-3.51%) and α-terpinyl acetate (2.10-4.76% and 0.05-3.54%) were the least dominant group. The principal component and hierarchical cluster analysis indicated that selective local environmental forces driving the microhabitat conditions appeared to be linked to high variability in essential oil composition of different common Juniper populations instead of their geographical proximity. The present study revealed that common Juniper essential oils from western Himalaya, India fulfil minimum requirements of European Pharmacopoeia and warrants its further exploitation as a natural resource of commercial significance for dietary and therapeutic applications.

Impact of technology bundling on rice productivity: Insights from participatory on-farm trials in the Philippines

ContextWhile the individual benefits of technologies like alternate wetting and drying (AWD), site-specific nutrient management (SSNM), mechanical transplanting, laser-guided land leveling (LLL), and herbicide application are well-documented, limited information exists on their combined effects on rice productivity and profitability. ObjectivesThis study hypothesized that integrating resource-use efficient technologies could offer compounded benefits in yields and reduced production costs. The study aimed to assess the cost-effectiveness and grain yields of bundled technologies for rice, evaluating whether bundling could enhance yields, reduce production costs, and improve gross margin under farmers' field conditions. MethodsOn-farm participatory field trials were conducted over two dry seasons in the Philippines. Technology combinations were grouped as treatments (T) to reflect increasing bundling levels: T1 (farmers' practice (FP) with continuous flooding or CF), T2 (CF with mechanical transplanting, SSNM, and pre-emergence herbicide or PH), T3 (T2 with LLL), T4 (FP with AWD), T5 (AWD, mechanical transplanting, SSNM, and PH), and T6 (T5 with LLL). ResultsBundles involving LLL (T3) significantly increased the unit cost of production (P < 0.05) but did not result in proportional yield or gross margin increases. The outcome was attributed to exposure to less fertile soil after cut-and-fill operations and the high rental fee of LLL. However, the AWD in T6 with LLL mitigated these effects, resulting in no significant impact on yield. T5 balanced production costs and yield, offering a more economically viable approach than T3. The variability in yields and gross margins across treatments suggests that the expected benefits of higher yields and reduced costs from bundling technologies were inconsistent, with some treatments showing no advantages over farmers’ practices. ConclusionThe bundles combining AWD, SSNM, mechanical transplanting, and pre-emergence herbicide offered a balanced approach to cost-efficiency and productivity. Bundles involving LLL have increased production costs without corresponding yield or gross margin gains. Implications of the studyThe results underscore the complexity of bundled agricultural technologies, particularly in the context of smallholder systems. Benefits vary significantly depending on the technology combination, environmental conditions, and management practices, emphasizing the need for site-specific approaches when introducing new technologies. The findings provide valuable insights for bundling rice farming technologies with similar agroecosystems like the study site to enhance resource use management.

A New Proposal for Soybean Plant Stand: Variation Based on the Law of the Minimum.

The hypothesis of this study is that it is possible to determine the plant stand in the soybean (Glycine max L. Merril) crop based on the spatial variability of management units, which are limiting factors in maximizing crop yield. Our objectives were as follows: (I) to evaluate the relationship between soil physical and chemical attributes to establish potential management units for variable-rate seeding; (II) to propose a method for varying plant stands based on the law of minimum soil nutrients; an (III) to relate the interaction between different plant stands on soybean grain yield, taking into account the interaction between the spatial variability of the mapped attributes. Field experiments were carried out on two plots over two agricultural years. The areas were seeded by randomly varying the soybean stand across strips in the first year. The most limiting soil nutrient was established and used, together with the soil CEC, to determine management units (MUs), which were also used to seed soybeans in VRT (Variable Rate Technology) in the same plots in the second year. MUs with the lowest restriction for maximizing yield were sown in the second year with the lowest plant stand. Data were processed using multivariate statistics. Our findings reveal that it is possible to establish MUs for seeding soybeans with different stands following the spatial variability of limiting soil nutrients according to the law of the minimum and thus increase the crop grain yield. Spatial variability of potassium (K) in the plot, identified as limiting, affected the spatial variability of grain yield. Decreasing plant stands in MUs with the lowest limitation level increases yield. However, increasing the stand in MUs with a higher limitation level can lead to increased intraspecific competition, affecting yield as well as increasing input costs.

Genetic Studies on Variability, Heritability and Genetic Gain of Yield and Yield Related Traits in Foxtail Millets (Setaria italic L.)

The present work comprised of 40 foxtail millet advanced breeding lines obtained from small millet scheme, MARS, Dharwad. These lines with checks viz., DHFT-109-3 and HMT-100-1 were evaluated in replicated yield trial at millet scheme, MARS, Dharwad, during rabi 2020-21 for assessing genetic variability, heritability, and genetic gain among 17 traits. Wide range of variation provides an ample scope for selection of superior and desired advanced breeding lines by the plant breeders for further improvement of these characters. The phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV) for all the traits under study. In the present study foxtail millet advanced breeding lines exhibited moderate PCV and GCV for the most important economic traits viz., panicle length (18.01 and 15.85 % 15.85% and18.01%), panicle girth (15.25% and 15.45%), productive tiller per plant (18.44% and 19.40%) and straw yield per hectare (19.92% and 21.39%). The high phenotypic coefficient of variation and genotypic coefficient of variation were observed for characters viz., specific leaf area (21.08% and 22.44%), specific leaf weight (20.52% and 26.72%), grain yield per plant (9.32% and 10.99 %), grain yield per plot (8.85 % and 10.49%) and straw yield per ha (19.92 and 21.39). Most of the traits viz., plant height(79 %), flag leaf length(98%), flag leaf width ( 80%), number of productive tillers per plant (90%), panicle length(98%), panicle girth (97 %), SPAD at 50 per cent flowering (92%), test weight (63%), specific leaf area (88%), specific leaf weight (87%), grain yield per plant(71%) and straw yield per hectare (87%) showed the high estimates of broad sense heritability (h2 b) indicating the reliability of the estimates for variation between advanced breeding lines and effectiveness of selection.

Selenium Biofortification of Allium Species

Allium species have great potential in the production of functional food via selenium biofortification. This review is devoted to the specificity of Allium plant biofortification with Se, including the genetic peculiarities, effect of the chemical form of the microelement, methods of supply, sulfur and AMF effects, and hormonal regulation. The biosynthesis of methylated Se amino acids and the beneficial effect of Se treatment on secondary metabolite accumulation and plant yield are discussed. Special attention is paid to the production of functional foods based on Allium plants enriched in different ways: bread with leek leaf powder, Allium microgreens and seedlings, and ‘Black garlic’ biofortified with Se. Further focus is provided to the high variability of Allium crop yield and quality under Se supply governed by genetic factors and environmental stresses, and to the need for plant growth technology optimization to obtain the predicted nutritional characteristics of the derived functional product with high anti-carcinogenic activity.

AGRIBUSINESS STRATEGIES FOR MITIGATING RISKS IN THE TOMATO VALUE-CHAIN: FROM SOKOTO FIELDS TO MARKETS

This study examined the risks, attitudes, and management strategies of 100 tomato value chain actors (45 farmers and 55 marketers) aged 30-60 years in Sokoto Metropolis, Nigeria. A multi-stage sampling technique was employed to select respondents An interview schedule was used for the data collection, while descriptive statistics were used to analyze the data. The socioeconomic results reveal that all the respondents were male, mostly middle-aged (mean of 42 years), married. About 72% of the marketers are members of cooperative associations while none of the farmers were members. About 67% of the farmers and 33% of the marketers have a secondary occupation. Results revealed that farmers predominantly encountered pests and diseases infestation (mean score 5.00), yield variability (mean score 4.69), and price instability (mean score 4.69) as major risks. Marketers, on the other hand, cited perishability (mean score 4.98), price instability (mean score 4.96), and payment defaults (mean score 4.84) as critical challenges. While pest and disease control, crop diversification, and income diversification were common among farmers, marketers primarily relied on price negotiation, improved storage, and efficient transportation. The study concludes that enhancing extension services, promoting cooperatives, and improving infrastructure are crucial to mitigating risks and boosting the tomato value chain's sustainability in Sokoto.

Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards.

Future climate impacts on crop production in Bhutan

Understanding the relationship between weather extremes and crop productivity is the backbone of risk assessments on food security. The former is paramount in countries (i.e., Bhutan) where there are a limited number of impact assessments in agriculture. The analysis performed in this work highlights the trends in agricultural production under a changing climate and the attribution of yield change to a specific climatic hazard. We improve the understanding between the two (climate and yields) by applying an FAO eco-physiological model (PyAEZ) and by relying on state-of-the-art downscaled climate projections (CORDEX-CORE). We analyze climate change impacts on ten crops (maize, foxtail millet, buckwheat, wheat, rice, common beans, cabbage, white potatoes, carrots, and citrus) at national level. The main simulation findings point to a higher yield gain and loss under rainfed conditions as opposed to irrigated conditions by the end-century (2070-2099) compared to the baseline period (2010-2039), particularly under RCP 8.5 for white potatoes (+17.7%), wheat (+15.5%), cabbage (+12.8%), buckwheat (-6.6%), and citrus (-5.8%). The irrigation potential to modulate climate change impacts is reflected in a yield gain of +43.4% on average for all crops and RCP’s when compared to rainfed conditions. On average, weather extremes explain 28% and 33% of the yield variability over time, respectively under RCP’s 2.6 and 8.5. Overall, this study supports smallholder farmers, decision-makers, and project developers to implement adaptation solutions that minimize growing weather extremes such as heat-stress and dry-spells.

Stress-Inducible Expression of HvABF2 Transcription Factor Improves Water Deficit Tolerance in Transgenic Barley Plants.

Barley (Hordeum vulgare L.), a major cereal crop grown in arid and semi-arid regions, faces significant yield variability due to drought and heat stresses. In this study, the HvABF2 gene, encoding an ABA-dependent transcription factor, was cloned using specific primers from water deficit-stressed barley seedlings. Gene expression analysis revealed high HvABF2 expression in developing caryopses and inflorescences, with significant induction under stress conditions. The HvABF2 coding sequence was utilized to generate transgenic barley plants with both stress-inducible and constitutive expression, driven by the rice SNAC1 and maize Ubiquitin promoters, respectively. Selected transgenic barley lines, along with control lines, were subjected to water deficit-stress experiments at seedling and flag leaf stages under controlled and greenhouse conditions. The transgenic lines exhibited higher relative water content and stomatal resistance under stress compared to control plants. However, constitutive overexpression of HvABF2 led to growth retardation under well-watered conditions, resulting in reduced plant height, grain weight, and grain number. In contrast, stress-inducible expression mitigated these effects, demonstrating improved drought tolerance without adverse growth impacts. This study highlights that the stress-inducible expression of HvABF2, using the SNAC1 promoter, effectively improves drought tolerance while avoiding the negative pleiotropic effects observed with constitutive expression.

Evaluation of meteorological drought and its impact on crop yield over Afar region, northeast Ethiopia

Drought is a natural hazard caused by a prolonged precipitation deficit that cannot meet human, livestock, and environmental demands. In Ethiopia, frequent and severe droughts increasingly affect the socio-economic and environmental sectors. This study evaluates the current and future projected meteorological drought and its impact on crop yield over the Afar region in northeast Ethiopia. We used surface stations, satellite climate estimates, downscaled atmospheric reanalysis, and regional climate model datasets. We evaluated the occurrence of drought using the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) calculated at 3-month and 12-month time scales. The drought vs. regional cereal yield is correlated to explain yield variability in the region. Results showed that more intense droughts were analyzed in 1984, 1985, 2002, 2008, 2009, 2010, 2015, and 2016. Among these years, 1984, 2002, 2008, 2009, and 2015 were the driest years across all locations in the study area. The regression of SPI and SPEI with yield showed that the indices significantly explained (r2 = 0.56 for SPI and 0.18 for SPEI) the observed yield variation. Spatially, more intense drought prevails over the northern, northwestern, and southwestern parts of Afar, where these parts are more prone to severe drought. The projected drought pattern showed increases in the intensity and frequency of drought in the middle and end of the century. The findings of this study are helpful for stakeholders working on drought mitigation in the region. Keywords: Climate dataset, meteorological drought indices, drought projection, pastoral community.

Using Unmanned Aerial Systems Technology to Characterize the Dynamics of Small-Scale Maize Production Systems for Precision Agriculture

Precision agriculture (PA) utilizes spatial and temporal variability to improve the sustainability and efficiency of farming practices. This study used high-resolution imagery from UAS to evaluate maize yield variability across three fields in Ghana: Sombolouna, Tilli, and Yendi, exploiting the potential of UAS technology in PA. Initially, excess green index (EGI) classification was used to differentiate between bare soil, dead vegetation, and thriving vegetation, including maize and weeds. Thriving vegetation was further classified into maize and weeds, and their corresponding rasters were developed. Normal difference red edge (NDRE) was applied to assess maize health. The Jenks natural breaks algorithm classified maize rasters into low, medium, and high differential yield zones (DYZs). The percentage of bare spaces, maize, weed coverages, and total maize production was determined. Significant variations in field conditions showed Yendi had 34% of its field as bare, Tilli had the highest weed coverage at 22%, and Sombolouna had the highest maize crop coverage at 73.9%. Maize yields ranged from 860 kg ha−1 in the low DYZ to 4900 kg ha−1 in the high DYZ. Although yields in Sombolouna and Tilli were similar, both fields significantly outperformed Yendi. Scenario analysis suggested that enhancing management practices to elevate low DYZs to medium levels could increase production by 2.1%, while further improvements to raise low and medium DYZs to high levels could boost productivity by up to 20%.

Evaluation of the variability yield component characters and kinship relationships of several introduced local gogo rice cultivars

Abstract Rice is a food crop that produces rice and acts as a source of energy for people in Indonesia. Increasing the genetic diversity of rice plants is important for further development in the field of rice plant breeding. The purpose of this study was to find out information about the diversity of yield component characters and kinship relationships of several local gogo rice cultivars introduced from Padang Lawas Regency. This study was conducted using an experimental method without a design. Then continued with the identification of quantitative characters of gogo rice plants. The genetic material used was 19 local upland rice cultivars introduced from Padang Lawas Regency. The study was conducted at the Screen House, Faculty of Agriculture, Universitas Sumatera Utara. The results of the study showed that all observed yield component characters had diversity because they had different results in each cultivar. The results of the UPGMA phylogenetics showed that the Sibahopotali cultivar had the furthest similarity coefficient value, namely 0.70. Close similarities were found in the Sibahopo Pinang cultivar with Siperak with a coefficient value of 0.90.

Simulation of Intra-seasonal Climatic Variability, Potential Yield and Yield Gap of Sugarcane for the South Gujarat Region, India

The validated CANEGRO (DSSAT v4.7) model was used to simulate the intra-seasonal climatic variation viz. temperature (-3oC to +3oC) and rainfall (-25% to +25%) variability in sugarcane yield and quantifying the yield gap of sugarcane at Navsari, Bharuch, Surat and Valsad districts of south Gujarat region under the various management options included dates of planting, irrigations and fertilizers application and compared with the reported yield of the districts. The cane yield increased with an increase in rainfall and a decrease in temperature. In contrast, the cane yield decreased with a decrease in rainfall and an increase in temperature. The extent of effect was found to vary with crop growth stages. The potential yield of sugarcane simulated by the model in different districts was found to vary between 107.0 to 114.7 t ha-1, while the reported yield in these districts varied between 68.1 to 72.0 t ha-1. This resulted in a total yield gap between 38.9 to 45.5 t ha-1 in different districts. The highest yield gap was in Navsari district while the lowest yield gap was found in Bharuch district.

Climate-adaptative management strategies for soybean production under ENSO scenarios in Southern Brazil: An in-silico analysis of crop failure risk

CONTEXTSoybeans (Glycine max L.) are a crucial crop for global food security, and the state of Rio Grande do Sul (RS), Brazil, plays a significant role. However, climate instability, particularly water stress (WS), is a major concern in this region, causing large interannual yield variability. OBJECTIVEThis study aims to address this issue using an in-silico approach to: i) characterize WS and spatial patterns and their frequency within ENSO events; and ii) explore climate-adaptative management strategies such as planting dates and maturity groups to mitigate the risk of crop failure and maximize seed yield and profits. METHODSCrop growth simulations were performed testing three soybean maturity groups (MG, 5.0, 5.8, and 6.4) and eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in RS, Brazil, using APSIM Next Generation. Failure risk was calculated as the percentage of simulations that yielded less than the economic break-even soybean yield in a given scenario. RESULTS AND CONCLUSIONSThe simulated yields were clustered into four regions: Northeast, North, Central, and Southwest. Four WS seasonal patterns were then defined (no stress, early stress, late stress, and whole season stress). On average, WS reduced yields up to 2 Mg ha-1 (∼50 % relative to the maximum). WS varied among regions, with the SW experiencing more frequent and severe stress (up to 50 % of whole season stress during La Nina). ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events to increased stress. The MG 5.0 resulted in a higher probability of failure risk in all regions. Early planting dates resulted in the highest yield variability (up to 5 Mg ha-1). Climate-adaptative management strategies, such as optimizing planting dates and maturity groups, resulted in a 15 % reduction in crop failure. SIGNIFICANCEOur findings provide valuable insights for developing targeted approaches to enhance soybean yield stability, thereby increasing the resilience of agriculture in the face of future climate uncertainties.

The impact of weather patterns on inter-annual crop yield variability

Inter-annual variations in crop production have significant implications for global food security, economic stability, and environmental sustainability. Existing crop yield prediction models primarily using meteorological variables may not adequately encapsulate the full breadth of weather influences on crop development processes, such as compound or extreme events. Incorporating weather patterns into crop models could provide a more comprehensive understanding of the environmental conditions affecting growth, enabling more accurate and earlier yield predictions.Our study examines 30 distinct UK Met Office weather patterns (MO30) based on mean sea level pressure. We investigate their association with weather conditions that limit winter wheat yield in the UK (1990–2020). Blocked, negative North Atlantic Oscillation (NAO) patterns create the highest risk of temperatures that are below optimal for crop yield. However, the connection between weather patterns and yield is complex, with differing effects at a regional scale and even at which point in the growth cycle they appear. It was found that anticyclonic weather patterns during sowing, emergence, vernalisation, anthesis, and grain filling exhibit a relationship with good crop yields with a Spearman correlation coefficient of up to 0.55 for a single weather pattern (WP3 during vernalisation in South East England), whilst cyclonic patterns can help during the terminal spikelet phenological phase. The strongest positive correlations were during sowing, emergence, and vernalisation, whilst the largest negatives were observed in anthesis and grain filling.The potential of combining weather patterns with existing crop simulation models to produce earlier and more accurate yield predictions is shown. This would enable effective crop management and climate mitigation strategies, critical to strengthening food security. Projected changes in weather pattern occurrences in the late 21st century will likely reduce crop yields. This is due to increased cyclonic weather patterns, which bring warmer, wetter conditions during the wheat's vernalisation stage, followed by warmer, drier conditions during the anthesis and grain-filling phases.

Climate-Related Risks and Agricultural Yield Assessment in the Senegalese Groundnut Basin

Climate change and variability pose significant threats to agricultural production, particularly in regions heavily dependent on rainfed agriculture like Senegal. The problem addressed in this study revolves around the impact of climate-related risks on agricultural yields in the Senegalese Groundnut Basin as a key agricultural region. Daily rainfall, temperatures, and yield over 1991–2020 were used. The data were analyzed using multiple regression, trend analysis, and correlation approaches. The results indicate that the overall seasonal precipitation increases over time (98 mm in the north and 103 mm in the south). However, we found that the south Groundnut Basin has a much slower seasonal precipitation rate than the northern zone. Our results also show that the northern zone exhibits a more consistent and predictable growing season, with onset and offset, in contrast with the southern zone, which shows higher variability. The analysis further reveals that both the northern and southern zones are experiencing a warming trend, with the southern zone showing a more pronounced increase in maximum temperatures (+0.7 °C) than to the northern zone (+0.4 °C). Estimates from the regression analysis revealed that total seasonal precipitation and maximum temperature positively and significantly influence groundnut, millet, and maize yields in the northern and southern zones. All the other weather-related parameters have different influences depending on the zone. These findings highlight the heterogeneous nature of the study area and the significant role climatic factors play in crop yield variability in the Groundnut Basin. Understanding these influences is crucial for developing targeted agricultural strategies and climate adaptation measures to mitigate risks and enhance regional productivity. The study provides valuable insights for policymakers and farmers aiming to improve crop resilience and sustain agricultural outputs amidst changing climatic conditions.