Search Results (1,075)

Understanding how irrigation water management shapes crop performance is critical for improving productivity and resource-use efficiency in peri-urban agriculture. This study investigated the socio-technical factors driving sprinkler system abandonment and assessed how irrigation water variability influences vegetable yield variability at two market gardening sites (Bogdin and 14 Yaar) in Ouagadougou, Burkina Faso. Survey data from 50 farmers and field measurements of soil properties, irrigation water application, and lettuce yield were analyzed using descriptive statistics, Spearman correlations, and principal component analysis. More than 80% of farmers had ceased using the sprinkler system within two years of installation, 76% reported major equipment failures, and 70% expressed willingness to re-adopt an improved system. Irrigation dose and yield showed considerable variability across sites (CV = 20.9–42.3% and 36.4–44.0%, respectively). At 14 Yaar, irrigation dose was strongly associated with yield (r = 0.862, p = 0.006), indicating that uneven water application was a major constraint on productivity. At Bogdin, where irrigation was more uniform, no single soil or water variable dominated yield variability. Although soil fertility variables contributed to multivariate patterns, nutrient–yield correlations were not statistically significant under the available sample size, and their potential influence on yield requires confirmation with larger datasets. Overall, operational constraints, equipment failures, and inadequate support services contributed to sprinkler system abandonment, while variability in manual water application was associated with variability in crop productivity. These findings highlight the need for irrigation strategies that are both technically robust and adapted to farmers’ realities. Full article

The rising global population has heightened food demand, placing pressure on agricultural systems, particularly in water-scarce regions such as South Africa. Smallholder farmers, essential to the sector, face climatic variability and resource constraints, necessitating innovative solutions to enhance sustainability and productivity. This study evaluates unmanned aerial vehicles (UAVs) for generating spatially explicit evapotranspiration (ET) estimates in a small-scale sugarcane field, supporting precision water management. Vegetation indices (VIs) derived from UAV-based multispectral imagery were used to predict actual ET (ET_a) and validated against eddy covariance measurements. Five models were assessed, including Normalised Difference Vegetation Index (NDVI)-based and Enhanced Vegetation Index (EVI)-based approaches. Machine learning was used to relate crop coefficients (K_c) to NDVI, enabling improved estimation. The two-band EVI (EVI2) model achieved the highest accuracy, with an R² of 0.63, an RMSE of 0.67, and an MAE of 0.52. ET-VI approaches, particularly EVI2, require lower data and technical complexity, making them suitable for smallholder systems. However, reducing dependence on in situ data remains essential to improve accessibility of remote sensing approaches for agricultural water management in resource-limited environments. These findings demonstrate the potential of UAV-based ET_a modelling to support field-scale irrigation decision-making while highlighting the need for further refinement to improve operational applicability across diverse smallholder farming contexts and beyond. Full article

Optimizing nitrogen management is essential for maintaining high spring maize yield while mitigating nitrous oxide (N₂O) emissions in irrigated areas. However, the interactive effects of total nitrogen application rate and starter nitrogen proportion on yield and N₂O emissions remain insufficiently quantified. Reliable assessment of these interactions requires well-calibrated DeNitrification–DeComposition (DNDC) simulations, yet existing calibration studies often emphasize crop parameters while neglecting soil parameters critical for soil hydrothermal dynamics and N₂O production. In this study, field data from shallow-buried drip-irrigated spring maize in Tongliao during 2024–2025 were used to conduct Extended Fourier Amplitude Sensitivity Test (EFAST) sensitivity analysis on 12 crop and 13 soil parameters of the DNDC model. Sensitive parameters were calibrated using the differential evolution algorithm, and 64 nitrogen management scenarios were simulated by combining eight total nitrogen application rates (100, 150, 200, 250, 300, 350, 400, and 450 kg N ha⁻¹) with eight starter nitrogen proportions (0%, 15%, 25%, 30%, 35%, 40%, 45%, and 50% of the total nitrogen rate). The results showed that DNDC outputs were jointly controlled by crop and soil parameters, among which maximum yield, leaf carbon-to-nitrogen ratio, stem fraction, grain carbon-to-nitrogen ratio, thermal degree days for maturity, grain fraction, soil organic carbon (SOC) decrease rate below topsoil, soil clay content, soil porosity, wilting point and depth of top soil with uniform SOC content were dominant. Compared with the conventional crop-parameter calibration, the sensitivity-screened parameter set improved the simulation of both cumulative N₂O emissions and yield. Across the 64 scenarios, cumulative N₂O emissions ranged from 0.42 to 4.87 kg [N]/ha, while simulated maize yield ranged from 1597 to 6347 kg [C]/ha. N₂O emissions increased with total nitrogen rate, whereas yield increased initially and then reached a plateau. Increasing the starter nitrogen proportion did not substantially enhance yield but increased N₂O emission risk under high nitrogen rates. Overall, the scenario with 300 kg/ha and no nitrogen applied at sowing achieved a relatively high yield of 5519 kg [C]/ha while maintaining a low cumulative N₂O emission of 0.98 kg [N]/ha and was therefore identified as the preferred trade-off strategy under shallow-buried drip irrigation. This study provides an EFAST–DNDC framework for optimizing nitrogen management to sustain spring maize yield while reducing N₂O emissions in the West Liaohe Plain. Full article

Increasing pressure on surface water resources in intensive agricultural regions has driven the search for sustainable alternatives for irrigation supply, especially in areas where water quality limits crop safety and export opportunities. In this context, riverbank filtration (RBF) systems offer a nature-based solution by utilizing physical, chemical, and biological processes associated with river–aquifer exchange. However, their implementation depends on suitable site selection supported by hydrogeological, geomorphological, and hydraulic criteria. This study developed an integrated methodology to identify zones with potential for implementing RBF systems in the Roldanillo–Unión–Toro irrigation district, located in northern Valle del Cauca, Colombia. This region requires irrigation water over 10,256 ha of agricultural land (mainly sugarcane, maize, grapes, and guava). We combined geophysical methods (vertical electrical soundings, 2D electrical resistivity tomography, and passive seismic), geotechnical methods (CPTu tests), and hydraulic characterization of the river reach to evaluate subsurface stratigraphy, preliminary hydrogeological suitability, inferred river–aquifer connectivity conditions, and channel stability. The evaluation covered four sectors along an approximately 21 km stretch of the Cauca River’s left-bank alluvial valley. The results revealed pronounced lateral and vertical heterogeneity of alluvial materials. However, the “El Palmar” sector was identified as the best-supported priority sector for future RBF validation, due to the presence of profile-scale evidence of potentially permeable sandy and gravelly units with intermediate resistivity values (52–61 Ω·m), favorable stratigraphic organization, and stable river-reach conditions during the field campaign. In contrast, the other three sectors (La Esperanza, Candelaria, and Cayetana) showed more fine-grained sediments with deeper permeable strata. River-flow measurements during the July 2025 field campaign indicated high discharge conditions at the evaluated reach, while river-channel observations showed active fine-sediment transport; these findings provide hydraulic and sedimentary context for the future evaluation of induced infiltration and potential clogging, but do not constitute direct evidence of river–aquifer exchange. This study highlights the value of integrated screening approaches for prioritizing candidate RBF sites in agricultural alluvial settings, while indicating that pumping tests, piezometric monitoring, hydraulic-gradient analysis, and water-quality validation remain necessary before engineering implementation. Full article

Sustainable irrigation planning under increasing water scarcity requires quantitative optimization tools to balance land and water resources. This study develops a linear programming (LP)-based framework to determine optimal cropping patterns under variable seasonal water availability in three irrigation projects in Lao PDR: Nam Tong 2 (1000 ha; ≈48.16 million m³ (MCM)), Nam Hin (80 ha; ≈0.73 MCM), and Xe Salalong (1530 ha; ≈30.80 MCM). Six major crops were analyzed for each project, with crop water requirements ranging from 4000 to 12,000 m³ ha⁻¹ and gross revenues from 1200 to 41,322 US$ ha⁻¹. Eight irrigation scenarios were constructed by combining land suitability (suitable vs. unsuitable), crop water requirement levels, and gross revenue assumptions. The model maximizes total gross revenue subject to seasonal water and land constraints. The results indicate that under limited water availability (e.g., 5.35–6.20 MCM in Nam Tong 2), crops with lower water demand (≤6000 m³ ha⁻¹) and higher economic return per unit of water are prioritized, improving water-use efficiency. As water availability increases, high-value but water-intensive crops expand until land suitability becomes the dominant constraint. Expanding irrigation on unsuitable land produces diminishing economic returns. The framework enhances the realism of irrigation planning and supports economically efficient, water-sustainable crop allocation in water-scarce regions. Full article

The use of sophisticated software for greenhouse microclimate analysis often requires advanced modelling expertise and significant computational effort, which may not always be available to greenhouse designers. This study proposes an integrated and modular workflow aimed at supporting greenhouse design through coupled thermal and evapotranspiration simulations. The design methodology is based on three steps. In the initial phase, the greenhouse environmental conditions are evaluated through the implementation of a dynamic thermal analysis, which is conducted by the DesignBuilder software (version 4.2). Subsequently, a plant evapotranspiration model is employed in MATLAB/Simulink (version R2025b) to evaluate crop transpiration, moisture production, and irrigation water consumption. In the final phase, the simulated moisture production is used to estimate the required ventilation rates and to support the sizing of greenhouse systems, including irrigation and HVAC components. Plant moisture production is a crucial factor in determining the sizing of greenhouse subsystems, such as the irrigation system, the ventilation rate, and the HVAC system. Nonetheless, the implementation of the evapotranspiration model necessitates a bespoke calibration to a case study. Indeed, the proposed models are more generally applicable and must be adapted to real-world applications. The methodology was applied to a small greenhouse used for the cultivation of aeroponic lettuce (Lactuca sativa cv. Romana) in a Mediterranean environment. The aim of the study was to explore the potential of the proposed integrated modelling framework to estimate annual irrigation water demand and the minimum ventilation rate required to mitigate excess moisture production, using a coupled MATLAB/Simulink implementation. The proposed approach should be interpreted as an exploratory design-support methodology rather than a fully validated predictive model, intended to investigate system behaviour under the specific conditions of the case study. Full article

Climate change intensifies hydrological variability and threatens agricultural water security. This review synthesizes literature on agricultural water system resilience under climate change through a structured critical narrative approach informed by PRISMA/SALSA reporting principles. We examine four linked domains: resilience concepts and indicators, assessment methods under uncertainty, climate impact and vulnerability evidence, and adaptation/governance pathways. The synthesis indicates a broad shift from engineering-centered water-supply approaches toward socio-ecological resilience frameworks that combine infrastructure, ecosystem processes, farmer behavior, and institutions. Methodologically, deterministic optimization is increasingly complemented by stochastic, robust, integrated-assessment, remote-sensing, and machine-learning approaches, although data requirements, uncertainty propagation, and interpretability remain important constraints. Evidence suggests that crop water demand and irrigation requirements may increase substantially under high-emission scenarios, with acute risks in arid and semi-arid regions. Effective adaptation is unlikely to rely on single technologies alone; precision irrigation, nature-based solutions, climate services, and infrastructure investments require complementary demand-side rules, water accounting, equity safeguards, and participatory governance to avoid maladaptation such as the irrigation-efficiency rebound effect. We identify priority research needs in transparent review protocols, uncertainty quantification, cross-scale governance, farmer decision-making, digital inclusion, and monitoring systems. The review provides a moderated conceptual framework and policy-oriented research agenda for strengthening agricultural water resilience. Full article

Climate change is expected to increase the frequency and severity of drought events in Europe, necessitating the identification of more water-efficient cropping systems. This study compared the evapotranspiration dynamics, water-use efficiency, and yield performance of maize (Zea mays L.) and grain sorghum (Sorghum bicolor L. Moench) under controlled field conditions using a Thornthwaite–Mather-type compensation evapotranspirometer. Three water regimes (100%, 50%, and 30% of optimal water supply) were applied during the reproductive stage, combined with weed-free and weed-infested treatments. Under moderate water deficit (50% water supply), grain sorghum maintained stable grain yield, while maize grain yield decreased by 17.98%. Under severe water deficit (30% water supply), grain yield reductions reached 36.04% in maize and 42.80% in sorghum. Grain sorghum consistently required less water and used 2.87–38.17% less water to produce 1 kg of grain compared to maize across treatments. Weed interference was associated with a lower yield and water-use efficiency in both species, while severe water deficit (70%) caused substantial declines in all measured parameters. Evapotranspiration was primarily driven by solar radiation and temperature, with reduced sensitivity under increasing water limitation. Overall, the results suggest that grain sorghum may represent a viable alternative to maize under moderate drought conditions; however, both crops require supplemental irrigation under severe water scarcity. The study highlights the importance of integrated weed management and provides novel insights into crop water-use dynamics under combined abiotic and biotic stress conditions. Full article

Successful management of agricultural nitrogen (N) during crop growth has major implications for food supply, energy and environment. Need for N during crop growth is not a linear function. As an example, for corn growth, the need for N is S-shaped (sigmoid) during the growing stage, which suggests that for optimum use of the fertilizer, continual adjustment of the amount of fertilizer as the plant is growing is necessary. The most common practice is to add extra fertilizer, well beyond what the plant needs, even though the extra fertilizer results in significant environmental damage. Sustainable practice would balance crop productivity and profitability with negative environmental effects. The challenge for developing sustainability is to meet the N requirements of the growing crop with fertilizer, as well as timing of irrigation and additives application, proper dosing, and related management actions during a crop season. In this review article, we begin with the role of fertilizers in ensuring food supply for the burgeoning human population. Soil parameters, including soil biochemistry, determine the transformation of N in the fertilizer to nutrients suitable for plant uptake. There are several options for applying fertilizer to soil, and this has a bearing on the fertilizer fate. We examine current methods and technologies that farmers can use to optimize fertilizer use, keeping abreast of the temporal dependence for N needed for a growing crop. The widespread adoption of technologies for sustainable agriculture is lacking. Why such strategies are not being used extensively and a discussion of what is needed to enhance fertilizer optimization concludes the article. Full article

Abiotic stresses limit crop productivity by disrupting water relations, carbon assimilation, nutrient acquisition, membrane stability, and redox homeostasis. Partial root-zone irrigation (PRI), commonly implemented as partial root-zone drying (PRD), is often viewed as a deficit-irrigation strategy to improve water-use efficiency; however, this view underestimates the biological consequences of spatial root-zone heterogeneity. This review evaluates PRI as a spatially structured, priming-like framework for crop adaptation to abiotic stress. Available evidence indicates that localized drying and wet-side water uptake can coordinate root sensing, hydraulic–chemical signaling, abscisic acid delivery, hormone crosstalk, xylem-mediated regulation, and stomatal control. Beyond gas exchange, PRI is associated with photosynthetic maintenance, osmotic adjustment, antioxidant and redox regulation, root architectural plasticity, nutrient acquisition, and metabolic reprogramming. Evidence is strongest for drought, whereas responses to low temperature, salinity, heat-associated evaporative demand, and combined stresses remain more context-dependent. Emerging work also links PRI to rhizosphere restructuring and microbiome shifts, but the causal mechanisms and field reproducibility remain unresolved. We argue that future progress requires matched PRI–deficit-irrigation comparisons, standardized switching thresholds, shared physiological and molecular readouts across crops, high-resolution root biology, and commercially realistic field validation. This framing distinguishes conserved physiological outcomes from mechanisms that may differ among crops, genotypes, and irrigation designs. Full article

Accurate hourly crop evapotranspiration (ETc) estimation is important for data-driven irrigation management support in tropical horticulture, yet existing approaches are constrained by data requirements and an inability to capture multi-scale temporal dynamics. This study proposes a Transformer encoder model for one-step-ahead hourly FAO-56 PM-derived ETc estimation in a durian orchard in Chanthaburi Province, Eastern Thailand, using 36,528 hourly meteorological observations obtained from the Visual Crossing Weather API for the orchard location over four years, with ETc computed from these inputs using the FAO-56 Penman–Monteith equation. The model employs a 168-h (7-day) look-back window, three stacked encoder blocks with multi-head self-attention ($h=8$, $d_{\mathrm{model}}=128$), and five meteorological input features (air temperature, relative humidity, solar radiation, wind speed, and ETc). A SARIMA$(2,1,2){(1,0,0)}_{24}$ model trained on the same dataset served as the statistical baseline. The Transformer achieved an RMSE of 0.0308 mm/h, MAE of 0.0188 mm/h, and $R^2$ of 0.9018 on the 168-h test set, outperforming SARIMA (RMSE = 0.0717, MAE = 0.0593, $R^2$ = 0.4688), representing a 57.0% reduction in RMSE, a 68.3% reduction in MAE, and a 92.4% improvement in $R^2$. The Transformer also achieved a daytime-only RMSE of 0.0414 mm/h vs. 0.0791 mm/h for SARIMA, and a daily cumulative ETc MAE of 0.1599 mm/day vs. 0.5901 mm/day, demonstrating superior accuracy during agronomically critical periods. The Transformer accurately reproduced both the 24-h diurnal cycle and the 7-day weekly pattern of ETc, whereas SARIMA exhibited a damped amplitude response. A recursive 168-h heuristic simulation demonstrated that the model generates physically plausible ETc patterns under an approximated meteorological scenario, suggesting the approach warrants further investigation as a component of future irrigation decision-support research. These results highlight the potential of Transformer-based deep learning for site-specific, proof-of-concept ETc estimation from meteorological inputs in tropical fruit production, pending validation across diverse sites and seasons. Full article

To measure the scale of agricultural water transfer (AWT) from the perspective of food security, this paper establishes an integrated framework for quantifying its actual scale, theoretical transferable scale, and deviation. Based on panel data from 2001 to 2023 of five cities (Anyang, Hebi, Xinxiang, Jiaozuo, Puyang) of the Wei River Basin in Henan Province, China, the actual transfer scale is derived by comparing agricultural water right allocations with net crop irrigation requirements calculated via the FAO 56 Penman Monteith formula; the theoretical transferable scale is estimated using a translog production function grounded in factor substitution theory. By contrasting the two scales, deviations and excessive transfer scenarios are identified. The results show that (1) actual AWT occurred in 59.13% of the city–year observations, exhibiting clear phase-based fluctuations, with positive transfer scale ranging from 0.046 to 13.983 × 10⁸ m³. The largest positive transfer occurred in Puyang in 2002, and Puyang, Jiaozuo, and Xinxiang were the main outflow areas. Factor combinations could release transferable water in 45.22% of the city–year observations, wherein pesticide/fertilizer, agricultural machinery, and grain sown area serve as the main substitutes. Theoretical transferable scale ranged from −60.150 to 186.374 × 10⁸ m³, with a mean of 1.718 × 10⁸ m³ and a median of −0.108 × 10⁸ m³, indicating unstable factor-substitution capacity. (2) Excessive transfer was identified when the actual transfer scale was positive and exceeded the theoretical transferable scale. Under this criterion, 47.82% of observations were excessive transfers, 11.33% were reasonable transfers, and 40.85% showed no transfer. Jiaozuo and Puyang were the core excessive transfer areas, each showing excessive transfer in 16 of the 23 years, while Xinxiang has shown a rising trend in recent years; Anyang, by contrast, effectively controls excesses through water saving technologies. The findings highlight the need for dynamic monitoring, city-specific regulation, and advanced water-saving technologies to balance water allocation with food security. Full article

Containerized ornamental plant production requires efficient irrigation strategies to balance plant quality with water and nutrient conservation. This study evaluated four leaching fraction (LF) levels (0%, 20%, 40%, and 60%) in a completely randomized design with three replications, each consisting of three pots, to determine their effects on plant growth, ornamental quality, gas exchange, water use efficiency (WUE), and macronutrient leaching in off-season potted Curcuma cv. ‘Jasmine Pink’. Irrigation volumes were determined using crop coefficient (Kc)-based estimates derived from evaporation pan measurements. The results showed that the highest LF level (60%) significantly improved several ornamental quality traits, including flower number per cluster, leaf greenness, specific leaf area, and compactness index, while also increasing aerial dry weight and improving gas exchange parameters during the flowering stage. These improvements were associated with reduced substrate electrical conductivity, indicating lower soluble salt accumulation in the root zone under higher LF treatments and more favorable conditions for plant growth. Leaching fraction is commonly used in containerized crop production to prevent excessive salt accumulation in the root zone by allowing excess irrigation water to drain from the substrate. However, increasing LF also resulted in greater irrigation water consumption and higher macronutrient losses through leachate, particularly potassium. In contrast, lower LF treatments (0–20%) improved water use efficiency and reduced nutrient losses but were associated with higher substrate electrical conductivity, suggesting greater soluble salt accumulation in the root zone. Overall, the results indicate that a higher LF (60%) provided the greatest improvement in plant growth and ornamental quality under the conditions of the present study for off-season potted Curcuma alismatifolia production, although integrated strategies may be required to reduce water and nutrient losses. These findings provide practical insights for optimizing irrigation management in container-grown ornamental crops. Full article

Micro- and nanoplastics (MPs/NPs) are increasingly recognized as persistent contaminants in agricultural systems, where repeated inputs from mulch films, biosolids, composts, irrigation water, and atmospheric deposition create sustained exposure pathways for crops. Although various studies report effects on crop growth and physiology, mechanistic interpretation remains limited because outcomes vary widely across experiments and are often discussed without appropriate attention to exposure context, particle properties, and evidentiary strength. This review advances an agroecosystem-centered, evidence-aware framework for interpreting how MPs/NPs influence crops from environmental entry to plant phenotype. We argue that crop responses cannot be inferred from polymer identity alone, but must be interpreted through the interacting effects of particle size, morphology, surface chemistry, weathering state, aggregation behavior, co-contaminant associations, and exposure matrix. Within this framework, crop responses are organized along a mechanistic chain linking environmental entry and plant contact, interface behavior at root and leaf surfaces, conditional barrier crossing and transport, ROS-centered stress signaling with hormonal and ionic regulation, and downstream effects on germination, root function, photosynthesis, biomass, productivity, and quality-related traits. Particular emphasis is placed on distinguishing surface association, supported internalization, and supported systemic translocation, because these categories carry distinct implications for edible-tissue occurrence, crop quality, and food-system relevance. Current evidence suggests that the soil–plant–food pathway is plausible and increasingly supported, but its interpretation remains constrained by uneven analytical rigor and limited field realism. Future progress will require realistic agricultural exposure designs, stronger polymer-specific confirmation, and closer integration of mechanistic evidence with agronomic and food-system endpoints. Full article

Evapotranspiration (ET) is a fundamental process within the water cycle and the agricultural water balance, optimizing resource allocation, maintaining soil health, and enhancing ecosystem resilience to climate change. Because ET represents a primary consumptive use of irrigation on agricultural lands, enhancing water-use efficiency and sustainable water management requires accurate estimation of evapotranspiration to support long-term sustainability and productivity. This study offers an effective means to visualize spatial and temporal patterns of reference evapotranspiration (ETo) across various vegetation management practices. This study examined the impacts of agroforestry buffers (ABs), grass buffers (GBs), biofuel crops in an agroforestry watershed (BCa), and biofuel crops in a grass buffer watershed (BCg) on ETo, compared to a corn (Zea mays L.)–soybean (Glycine max L.) rotation (RC) for claypan soil in Northern Missouri, USA. The experimental watersheds were located at the Greenley Memorial Research Center, Missouri, USA. Campbell Scientific sensors and Photosynthetically Active Radiation (PAR) smart sensors were installed to measure net radiation, anemometers, humidity, and air temperature. All instruments were mounted on masts at a height of 2 m above ground level in crop, tree, grass, and biofuel areas. Measured meteorological data were recorded hourly from April to October during 2017 and 2018. Daily ETo predictions were calculated using the Penman–Monteith model. These ETo predictions were displayed across the landscape using Python-based GIS for selected dates (each Saturday) for the watersheds. The methodology was implemented using the software programs of Python 2.7.10 and ArcGIS 10.3.1. The results indicated that ETo increased by 11%, 17%, 18%, and 25% in 2017, and by 7%, 9%, 14%, and 20% in 2018 for AB, BCa, BCg, and GB, respectively, compared to RC management. This process may improve soil water recharge in perennial management systems. Accurate estimation of ET in agricultural regions is critical for understanding water balance, hydrological and ecosystem processes, and climate variability. Given that agriculture constitutes the majority of global water consumption, precise ET estimation is particularly significant for sustainable water management, especially in regions experiencing water scarcity. These outcomes may support effective planning and management of agricultural water resources by enabling optimized irrigation and agricultural production. Full article