Search Results (4,721)

Effective water resource management in agriculture is a pivotal challenge for environmental sustainability and the economic viability of crop production. The present study, conducted at the CREA research station (Monterotondo, Italy), analyzed a precision irrigation strategy based on an automated drip irrigation system with soil moisture sensors, applied to a 15-year-old high-density poplar plantation for energy production. Five treatments were compared: a non-irrigated control (T0) and four irrigation levels based on soil moisture thresholds (T1 ≤ 20%, T2 ≤ 30%, T3 ≤ 40%, T4 ≤ 50%). The aim of this study was to assess the economic feasibility of irrigated poplar plantations, considering expected increases in biomass production and related environmental impacts. The economic evaluation used the Life Cycle Costing (LCC) method, while the environmental assessment applied Life Cycle Assessment (LCA) with the AWARE indicator to quantify the water scarcity footprint. Finally, an integrated assessment using the TOPSIS multi-criteria method was performed to identify the most sustainable treatment. Over the 15-year period, T0 (no irrigation) was the preferred option (Preferred Index Pi = 1.000), followed by T3 (Pi = 0.637) and T4 (Pi = 0.586), considering equal weighting of economic and environmental impacts. Conversely, the low irrigation treatment (T1) was the least sustainable (Pi = 0.379), followed by T2 (Pi = 0.486). While irrigation appears unviable if environmental impacts are prioritized, higher biomass value can improve the economic sustainability of treatments with greater water use (T3 and T4) when economic factors dominate. Full article

Silicon (Si) plays a critical role in regulating plant physiological processes, particularly through its influence on non-enzymatic antioxidant systems and amino acid metabolism. This study aims to assess soybean performance in response to both soil and foliar Si applications under well-watered and drought conditions, with the goal of enhancing Si accumulation in plant tissues and potentially strengthening the crop’s physiological responses to water deficit stress. This is especially pertinent given that the mechanisms underlying Si fertilization and its contribution to drought tolerance in soybean remain poorly understood. Greenhouse experiments were conducted using a 3 × 2 factorial design. The factors were: (i) three foliar Si treatments: control (no Si), potassium silicate (SiK; 128 g L⁻¹ Si, 126.5 g L⁻¹ K₂O, pH 12.0), and sorbitol-stabilized potassium silicate (SiKe; 107 g L⁻¹ Si, 28.4 g L⁻¹ K₂O, 100 mL L⁻¹ sorbitol, pH 11.8); and (ii) two soil water levels: well-watered (80% field capacity) and water-restricted (40% field capacity), the latter simulating tropical dry spells. Silicon was applied to the soil via irrigation and to the leaves via foliar spraying prior to the onset water restriction. All Si solutions were adjusted to pH 7.0 with 1 M HCl immediately before application. Potassium (K) levels were standardized across treatments through supplementary applications of KCl to both soil and foliage. Biometric and physiological parameters were subsequently measured. Sorbitol-stabilized Si enhanced Si accumulation in soybean tissues and improved plant resilience under both well-watered and drought conditions by promoting key physiological traits, including increased levels of daidzein and ascorbic acid levels, along with reduced amino acid concentrations. It also improved biometric parameters such as leaf area, root development, and number of pods per plant. These findings further support the role of Si as a beneficial element in enhancing stress tolerance and contributing to sustainable agricultural practices. Full article

Climate change brings considerable danger to India’s economic progress, with the agricultural sector and farmers’ livelihoods being particularly vulnerable. Himachal Pradesh is especially susceptible owing to its reliance on climate-sensitive economic activities and limited capacity to adapt to climate variability. Strengthening adaptation strategies in Himachal Pradesh is crucial for fortifying the resilience of communities reliant on environmental resources for their sustenance and economic well-being. This study examines the extent of adoption of Climate-Smart Agricultural Practices (CSAPs), identifies the factors influencing their uptake, and assesses their impact on the livelihood vulnerability of farm households in the temperate region of Himachal Pradesh. Using a multistage random sampling framework, data were collected from 432 farm households through primary surveys and secondary sources. The analysis employs descriptive statistics, a composite livelihood vulnerability index, and Ordinal Logistic and Multiple Linear Regression models. Results show higher adoption of low-cost practices such as composting, fruit-based agroforestry, crop–livestock integration, and mulching, while capital-intensive practices like micro-irrigation were limited due to financial constraints. Adoption is positively influenced by education, extension access, farming experience, financial resources, and climate information exposure. Importantly, CSAPs adoption is found to significantly reduce livelihood vulnerability, indicating enhanced resilience and reduced exposure to climate-induced risks among farm households. The findings highlight climate-smart agriculture as an effective adaptation strategy and underscore the need for policies that strengthen extension services, improve access to credit, and promote affordable climate-smart technologies to enhance resilience in vulnerable hill regions. Full article

Agricultural nonpoint source pollution is emerging as one of the increasingly serious environmental concerns all over the world. This study conducted field experiments in Zengcheng District, Guangzhou City, from 2019 to 2023 to explore the mechanisms by which different crop types, fertilization modes, and meteorological conditions affect the loss of nitrogen and phosphorus in agricultural nonpoint source pollution. In rice and corn, the CK and PK treatment groups showed significant fitting advantages, such as the R² of rice-CK reaching 0.309. MAE was 0.395, and the R² of corn-PK was as high as 0.415. For compound fertilization groups such as NPK and OF, the model fitting ability decreased, such as the R² of rice-NPK dropping to 0.193 and the R² of corn-OF being only 0.168. In addition, the overall performance of the model was limited in the modeling of total phosphorus. A relatively good fit was achieved in corn (such as NPK group R² = 0.272) and in vegetables and citrus. R² was mostly below 0.25. The results indicated that fertilization management, crop types, and meteorological conditions affected nitrogen and phosphorus losses in agricultural runoff. Cornfields under conventional nitrogen, phosphorus, and potassium fertilizer (NPK) and conventional nitrogen and potassium fertilizer treatment without phosphorus fertilizer (NK) treatments exhibited the highest nitrogen losses, while citrus fields showed elevated phosphorus concentrations under NPK and PK treatments. Organic fertilizer treatments led to moderate nutrient losses but greater variability. Organic fertilizer treatments resulted in moderate nutrient losses but showed greater interannual variability. Meteorological drivers differed among crop types. Nitrogen enrichment was mainly associated with high temperature and precipitation, whereas phosphorus loss was primarily triggered by short-term extreme weather events. Linear regression models performed well under simple fertilization scenarios but struggled with complex nutrient dynamics. Crop-specific traits such as flooding in rice fields, irrigation in corn, and canopy coverage in citrus significantly influenced nutrient migration. The findings of this study highlight that nutrient losses are jointly regulated by crop systems, fertilization practices, and meteorological variability, particularly under extreme weather conditions. These findings underscore the necessity of crop-specific and climate-adaptive nutrient management strategies to reduce agricultural nonpoint source pollution. By integrating long-term field observations with machine learning–based analysis, this study provides scientific evidence to support sustainable fertilizer management, protection of water resources, and environmentally responsible agricultural development in subtropical regions. The proposed approaches contribute to sustainable land and water resource utilization and climate-resilient agricultural systems, aligning with the goals of sustainable development in rapidly urbanizing river basins. Full article

Maize is a water-intensive crop widely cultivated in temperate regions, where irrigation practices strongly influence its environmental performance. This study applies Life Cycle Assessment (LCA) to compare the environmental impacts of surface and drip irrigation for maize green silage production in the Po Valley (Italy), following ISO 14040/44 standards and adopting a cradle-to-farm-gate perspective. Results show that, compared to drip irrigation, surface irrigation leads to lower impacts in 14 out of 15 categories, with reductions ranging from −0.2% (marine eutrophication) to −61% (human toxicity, non-cancer), particularly for human toxicity and resource use due to lower diesel and infrastructure requirements. Conversely, drip irrigation achieves a 58% reduction in water use thanks to its higher irrigation efficiency. The single-score assessment highlights water use as the key differentiating factor, positioning drip irrigation as preferable under scenarios of water scarcity. Contribution and sensitivity analyses confirm that nitrogen fertiliser use and mechanisation are major hotspots, while yield variation (±30%) significantly affects the magnitude of results. These findings emphasise a clear trade-off: surface irrigation shows a lower environmental burden across most impact categories, whereas drip irrigation strongly reduces water scarcity impacts and provides robust, site-specific evidence to guide sustainable irrigation strategies in intensive maize systems. Full article

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 at three large farms (859, 850, and 852), this study applied the Mann–Kendall test, wavelet and cross-wavelet coherence, Pearson correlation, gray relational analysis, and principal component analysis to track the evolution of air temperature, precipitation, evaporation, sunshine duration, relative humidity, and surface temperature, and to assess their multi-scale impacts on rice, corn, and soybean yields. The region warmed and became wetter overall, with dominant periodicities near 21a and 8a. Across the three farms, yields were significantly and positively associated with precipitation and air temperature (R > 0.60). Rice yield correlated strongly and negatively with evaporation at Farm 850 (R = −0.61) and at Farm 852 (R = −0.503). At Farm 859, gray relational analysis ranked precipitation highest for rice, corn, and soybean (γ = 0.853, 0.844, and 0.826), followed by air temperature. The first two principal components explained 67.66% of the variance; PC1 (41.80%) loaded positively for air temperature, and PC2 (25.86%) for precipitation and relative humidity. Cross-wavelet coherence indicated stable coupling between yields and hydrothermal variables, with the strongest coupling for rice with precipitation and air temperature, prominent coupling for corn with air temperature and sunshine duration, and stage-dependent responses of soybean to precipitation and evaporation. These results show that long-term trends together with phase-specific oscillations jointly shape yield variability. The findings support translating phase identification and sensitive windows into crop-specific rules for sowing or transplanting arrangements, irrigation timing, and early warning, providing a quantitative basis for climate-adaptive management on the study farms and, where soils, management, and microclimate are comparable, for the wider Sanjiang Plain. Full article

In the context of global climate change and intensified water resource constraints, studying the evolution of the urban–agricultural–ecological spatial structure and the water–heat–vegetation responses driven by large-scale irrigation and drainage projects in arid and semi-arid regions is of great significance. Based on multitemporal remote sensing data from 1985 to 2015, this study takes the Inner Mongolia Hetao Plain as the research area, constructs a “multifunctionality–dynamic evolution” dual-principle classification system for urban–agricultural–ecological space, and adopts the technical process of “separate interpretation of each single land type using the maximum likelihood algorithm followed by merging with conflict pixel resolution” to improve the classification accuracy to 90.82%. Through a land use transfer matrix, a standard deviation ellipse model, surface temperature (LST) inversion, and vegetation fractional coverage (VFC) analysis, this study systematically reveals the spatiotemporal differentiation patterns of spatial structure evolution and surface parameter responses throughout the project’s life cycle. The results show the following: (1) The spatial structure follows the path of “short-term intense disturbance–long-term stable optimization”, with agricultural space stability increasing by 4.8%, the ecological core area retention rate exceeding 90%, and urban space expanding with a shift from external encroachment to internal filling, realizing “stable grain yield with unchanged cultivated land area and improved ecological quality with controlled green space loss”. (2) The overall VFC shows a trend of “central area stable increase (annual growth rate 0.8%), eastern area fluctuating recovery (cyclic amplitude ±12%), and western area local improvement (key patches increased by 18%)”. (3) The LST-VFC relationship presents spatiotemporal misalignment, with a 0.8–1.2 °C anomalous cooling in the central region during the construction period (despite a 15% VFC decrease), driven by irrigation water thermal inertia, and a disrupted linear correlation after completion due to crop phenology changes and plastic film mulching. (4) Irrigation and drainage projects optimize water resource allocation, constructing a hub regulation model integrated with the Water–Energy–Food (WEF) Nexus, providing a replicable paradigm for ecological effect assessment of major water conservancy projects in arid regions. Full article

Water scarcity and poor soil fertility are major limiting factors constraining agricultural production in the arid and semi-arid regions of Northwest China. Water–nitrogen synergistic regulation is an important approach to improving crop growth and enhancing agricultural productivity. In this study, four irrigation levels—severe water deficit (W1: 45–65% θ_f), moderate water deficit (W2: 55–70% θ_f), mild water deficit (W3: 65–80% θ_f), and full irrigation (W4: 75–90% θ_f)—and four nitrogen application rates—no nitrogen (N0, 0 kg·ha⁻¹), low nitrogen (N1, 80 kg·ha⁻¹), medium nitrogen (N2, 160 kg·ha⁻¹), and high nitrogen (N3, 240 kg·ha⁻¹)—were established to systematically analyze the effects of water–nitrogen coupling on osmotic adjustment substances, yield, and forage quality of alfalfa (Medicago sativa L.) leaves. The results showed that: (1) Proline (Pro) content increased significantly with intensified water deficit, with W1 being 82.29% higher than W4 on average. Soluble protein (SP) and soluble sugar (SS) contents increased with increasing water availability, with their average values under W4 being 26.50% and 36.92% higher than those under W1, respectively. Increasing nitrogen application significantly improved the accumulation of osmotic adjustment substances, with Pro reaching the lowest value at N2, SP peaking at N2, and SS peaking at N3. (2) Yield increased significantly with higher irrigation, and increased first and then decreased with nitrogen application. Yield under W4 was 94.20% higher than under W1, and N2 increased yield by 12.45–50.65% compared with other nitrogen levels. (3) Under the W4N2 treatment, crude protein (CP) content and relative feed value (RFV) increased by 34.54% and 51.10%, respectively, compared with W1N0, while acid detergent fiber (ADF) and neutral detergent fiber (NDF) decreased by 28.74% and 24.44%, respectively. (4) Correlation analysis indicated that Pro content was significantly positively correlated with ADF and NDF but negatively correlated with yield, CP, and RFV. In contrast, SP and SS contents were significantly negatively correlated with ADF and NDF and positively correlated with yield, CP, and RFV. (5) Principal component analysis identified that the combination of full irrigation (W4: 75–90% θ_f) and medium nitrogen application (N2, 160 kg·ha⁻¹) optimizes both yield and forage quality by balancing osmotic adjustment substances. Full article

In arid regions, irrigation is essential for sustaining crop production, but irrigation water often contains high levels of salts that may reduce yields. This study aimed to evaluate crop responses to irrigation water with salinity levels exceeding 4 g/L (≈6.25 dS/m). A large-scale field survey was conducted across several Tunisian governorates, covering a wide range of crops and production systems. Irrigation water salinity and corresponding crop yields were recorded and analyzed to determine tolerance patterns under real farming conditions. Results indicate that, even under high salinity conditions, several cropssuch as carrot (Daucus carota), barley (Hordeum vulgare), and tomato (Solanum lycpersicum), can maintain high yields, highlighting their potential for saline irrigation in arid regions. These findings provide valuable insights for irrigation management, crop selection, and the development of sustainable agricultural practices in arid environments. Full article

Irrigation effects on processing tomato have been comprehensively studied, whereas the integrated effects of irrigation and agronomic measures lack systematic investigations. This study employed a two-year field experiment to investigate the interactive effects of irrigation, fertilizer, and tillage practices on the crop growth, total yield, and fruit quality of processing tomato. The experimental treatments comprised three irrigation levels (full irrigation, mild water deficit, and moderate water deficit), combined with two fertilizer strategies (synthetic fertilizer only and partial substitution of synthetic fertilizer with manure), and two tillage practices (ridge planting and flat planting). It was found that the partial organic fertilizer substitution and the ridge planting significantly improved the total tomato yield by 13.11% and 75.54% on average, respectively, compared to the synthetic fertilizer application and flat planting, although they led to more salt accumulation in the top soil layer. However, the extent of the increase greatly varied over different irrigation levels and years. The mild water deficit led to a yield increase of 9.22% compared to full irrigation, while the moderate water deficit resulted in an obvious yield loss of 25.95%. Moreover, the ridge planting, the partial organic fertilizer substitution, and water deficit had strong positive effects on the fruit quality and the tillage–irrigation interaction had strong effects on the fruit quality, but it showed negligible effects on the tomato yield. In contrast, the tomato yield was very sensitive to the fertilizer–irrigation interaction, while the fruit quality showed nonsignificant sensitivity to the tillage–irrigation interaction. Finally, the combination of ridge planting, partial organic fertilizer substitution, and a mild water deficit was highlighted as a sustainable cropping production system for processing tomato to achieve an enhanced total yield and fruit quality. Full article

The degradation of vegetation cover and the vulnerability of urban market gardening systems to climate risks are a major challenge for food security in peri-urban areas. This study analyzes the spatio-temporal dynamics of vegetation using the NDVI and assesses its correspondence with producers’ perceptions of hydroclimatic impacts. NDVIs were extracted from the MODIS MOD13Q1v6.1 product via Google Earth Engine, with a spatial resolution of 250 m × 250 m and a temporal resolution of 16 days, then processed in Python v3.14.0 using the xarray library. Additionally, 369 producers in Grand Nokoué were surveyed about the risks of flooding, drought, and heat waves, as well as the adaptation strategies they implement. The results reveal a decline in areas with a moderate to high NDVI (between 0.41 and 0.81) and an expansion of areas with a low or very low NDVI (below 0.41), reflecting increased fragmentation and degradation of vegetation cover. Producers’ perceptions confirm this vulnerability and reveal different strategies depending on the type of crop and risk, including irrigation, temporary abandonment of plots, agroforestry, and the adoption of resilient crops. These observations highlight the need to implement targeted policies and appropriate agroecological practices in order to strengthen the resilience of urban market gardening systems to extreme climate risks. Full article

Rice (Oryza sativa L.) is one of the most important crops globally. However, its production faces significant challenges due to climate change, reduced arable land, and increased demand. In this context, the present study conducted a systematic literature review (SLR) on technological advances in rice production in Latin America. Recognized scientific databases were consulted, and rigorous inclusion and exclusion criteria were applied to synthesize current knowledge on the subject. The results show that the main innovations include genetically improving varieties with greater resistance to biotic and abiotic stresses; implementing advanced water management techniques, such as intermittent irrigation; and applying biofertilizers and organic amendments to improve soil fertility. Additionally, precision agriculture tools, such as remote sensing and artificial intelligence-based modeling, have optimized crop monitoring and input efficiency. Brazil, Mexico, and Colombia are the main generators of rice production technologies in the region. Despite the progress made, challenges remain regarding the adoption of these innovations by producers, highlighting the need for comprehensive policies to facilitate technology transfer. This review establishes a foundation for researchers and policymakers interested in the sustainable development of rice production in Latin America. Full article

Cotton (Gossypium hirsutum L.) is a primary global commercial crop, and accurate monitoring of its growth and yield prediction are essential for optimizing water management. This study integrates leaf area index (LAI) data derived from unmanned aerial system (UAS) imagery into the Decision Support System for Agrotechnology Transfer (DSSAT) model to improve cotton growth simulation and yield estimation. The results show that the normalized difference vegetation index (NDVI) exhibited higher estimation accuracy for the cotton LAI during the squaring stage (R² = 0.56, p < 0.05), whereas the modified triangle vegetation index (MTVI) and enhanced vegetation index (EVI) demonstrated higher and more stable accuracy in the flowering and boll-setting stages (R² = 0.64 and R² = 0.76, p < 0.05). After assimilating LAI data, the optimized DSSAT model accurately represented canopy development and yield variation under different irrigation levels. Compared with the DSSAT, the assimilated model reduced yield prediction error from 40–52% to 3.6–6.3% under 30%, 60%, and 90% irrigation. These findings demonstrate that integrating UAS-derived LAI data with the DSSAT substantially enhances model accuracy and robustness, providing an effective approach for precision irrigation and sustainable cotton management. Full article

Legume green manure incorporation offers a potential pathway for sustainable cropping in arid irrigated areas. This study aimed to determine whether water and nitrogen inputs could be concurrently reduced without compromising maize productivity under this practice. A two-year field experiment (2024–2025) was conducted using a split-plot design with three irrigation levels (I1: 4045, I2: 3240, I3: 2430 m³·ha⁻¹) and three nitrogen rates (N1: 360, N2: 288, N3: 216 kg·ha⁻¹). Compared with conventional management (I1N1), 20% co-reduction in water and nitrogen (I2N2) maintained stable leaf area index (LAI), net photosynthetic rate (P_n), transpiration rate (T_r), DM, and GY, while significantly increasing water use efficiency (WUE) by 7.6% and nitrogen use efficiency for grain yield (NUtEg) by 11.7%. Excessive water reduction (I3) or nitrogen reduction (N3) significantly inhibited growth and reduced yield (p < 0.05). Soil water content under I2N2 did not differ significantly from I1N1 in the 0–110 cm profile, and soil total nitrogen remained higher at silking.) Structural equation model (SEM) revealed SWC and STN indirectly affected P_n and T_r via regulating LAI and SPAD (path coefficients: 0.48–0.62), which drove DM accumulation and determined GY (R² = 0.81). These short-term results suggest that moderate water-nitrogen reduction with green manure can sustain yield while improving resource efficiency, offering a promising practice for arid irrigated maize systems, though longer-term validation is needed. Full article

Drought stress is one of the major constraints limiting crop productivity, primarily through oxidative damage, pigment degradation, and metabolic imbalance. Nanostructured selenium particles (SeNPs) have recently attracted attention for their potential to enhance plant tolerance to abiotic stress. In this study, green-synthesized SeNPs, with a main hydrodynamic size distribution in the range of 90–100 nm, were foliar applied to broccoli (Brassica oleracea var. italica) plants grown under well-watered (100% water holding capacity) and drought (50% water holding capacity) conditions at concentrations of 0, 10, 20 and 50 ppm. Drought stress significantly decreased chlorophyll a and b, total chlorophyll, and carotenoids, while increasing malondialdehyde (MDA) and proline levels, confirming oxidative stress and membrane damage. SeNPs treatments partially mitigated these effects by enhancing pigment stability, increasing carotenoid content, and reducing both MDA and proline accumulation. Phenolic and flavonoid responses exhibited a dose-dependent pattern with the highest stimulation at 50 ppm under drought and moderate enhancement at 10 ppm under optimal irrigation. Antioxidant capacity assays demonstrated that SeNPs modulate plant redox metabolism, in a context-dependent manner, particularly under water deficit. Peroxidase (POD) activity was also significantly induced under drought stress, mainly at 20 ppm. These results indicate that foliar-applied SeNPs can influence physiological and biochemical responses associated with drought tolerance in broccoli. The observed effects are consistent with nanoparticle–leaf surface interactions contributing to redox regulation and stress adaptation, rather than implying direct nanoparticle internalization. Full article