Search Results (1,749)

To address the bottlenecks of low water and fertilizer utilization efficiency and limited yield potential inherent in Henan Province’s traditional winter wheat cultivation model of “furrow irrigation + conventional row seeding”, this study delved into the synergistic regulatory mechanisms of drip irrigation combined with wide-row precision seeding. It focused on their effects on the physiological ecology and yield-quality traits of winter wheat. A two-factor experiment, encompassing “sowing method × irrigation method” will be carried out during the 2024–2025 wheat growing season, featuring four treatments: furrow irrigation + conventional row seeding (QT), drip irrigation + conventional row seeding (DT), furrow irrigation + wide-row precision seeding (QK), and drip irrigation + wide-row precision seeding (DK). Results reveal that wide-row precision seeding optimized the canopy structure, raising the leaf area index (LAI) at the heading stage by 20.19% compared to QT, thereby enhancing ventilation and light penetration and reducing plant competition. Drip irrigation, with its precise water delivery, boosted the net photosynthetic rate of the flag leaf 35 days after flowering by 62.99% relative to QT, stabilizing root water uptake and significantly delaying leaf senescence. The combined effect of the two treatments (DK treatment) synergistically improved the canopy structure and photosynthetic performance of winter wheat, prolonging the functional period of green leaves by 29.41%. It established a highly efficient photosynthetic cycle, marked by “high stomatal conductance-low intercellular CO₂ concentration-high net photosynthetic rate”. The peak net photosynthetic rate (P_n) 13 days post-flowering rose by 23.9% compared to QT. Moreover, while reducing total water consumption by 21.4%, it substantially increased water use efficiency (WUE) and irrigation water use efficiency (IWUE) by 43.2% and 14.2%, respectively, compared to the QT control. Ultimately, the DK treatment achieved a synergistic enhancement in both yield and quality: grain yield increased by 14.7% compared to QT, wet gluten content reached 35.5%, and total protein yield per unit area rose by 13.1%. This study demonstrates that coupling drip irrigation with wide-row precision seeding is an effective strategy for achieving water-saving, high-yield, and high-quality winter wheat cultivation in the Huang-Huai-Hai region. This is achieved through the synergistic optimization of canopy structure, enhanced photosynthetic efficiency, and improved WUE. These findings provide a mechanistic basis and a scalable agronomic solution for sustainable intensification of winter wheat production under water-limited conditions in major cereal-producing regions. Full article

Uncertainty-aware decision-making increasingly relies on multimodal sensing pipelines that must fuse correlated measurements, propagate uncertainty, and trigger reliable control actions. This study develops a unified mathematical framework for multimodal data fusion and Bayesian decision-making under uncertainty. The approach integrates adaptive Covariance Intersection (aCI) for correlation-robust sensor fusion, a Gaussian state–space backbone with Kalman filtering, heteroskedastic Bayesian regression with full posterior sampling via an affine-invariant MCMC sampler, and a Bayesian likelihood-ratio test (LRT) coupled to a risk-sensitive proportional–derivative (PD) control law. Theoretical guarantees are provided by bounding the state covariance under stability conditions, establishing convexity of the aCI weight optimization on the simplex, and deriving a Bayes-risk-optimal decision threshold for the LRT under symmetric Gaussian likelihoods. A proof-of-concept agro-environmental decision-support application is considered, where heterogeneous data streams (IoT soil sensors, meteorological stations, and drone-derived vegetation indices) are fused to generate early-warning alarms for crop stress and to adapt irrigation and fertilization inputs. The proposed pipeline reduces predictive variance and sharpens posterior credible intervals (up to 34% narrower 95% intervals and 44% lower NLL/Brier score under heteroskedastic modeling), while a Bayesian uncertainty-aware controller achieves 14.2% lower water usage and 35.5% fewer false stress alarms compared to a rule-based strategy. The framework is mathematically grounded yet domain-independent, providing a probabilistic pipeline that propagates uncertainty from raw multimodal data to operational control actions, and can be transferred beyond agriculture to robotics, signal processing, and environmental monitoring applications. Full article

In Europe, due to reduced availability and efficacy of active ingredients, strategies against grapevine pests based on alternative tools to synthetic pesticides need to be developed. So far, attention has been mainly focused on biological control (arthropod natural enemies and entomopathogens) and mating disruption, but other means can also help keep pests below economic injury levels. This paper aims to review information on the direct effects of farmers’ choices on grapevine pest populations, ranging from vineyard design (e.g., growing habitat, grapevine cultivar, and training system) to annual agronomic practices (e.g., fertilization, irrigation, and pruning), and specific cultural and physical methods. Information was based on the CABI Digital Library, websites and books on grapevine pests. The data presentation is based on control strategies rather than pests, as it was considered more important to focus on the mode of action of different practices and to know which pests they affect simultaneously. The widespread availability of insecticides has long led to the neglect of the potential of cultural practices, which can effectively integrate other pest control tools. Full article

The introduction of Super-High-Density (SHD) olive orchards represents a crucial innovation in modern olive growing, enhancing sustainability. However, the long-term success of these planting systems depends strongly on cultivar selection, combining suitable vegetative and reproductive traits. This three-year field study investigated key floral biology parameters—flowering phenograms, gynosterility, and self-compatibility—of ten olive cultivars grown under irrigated conditions in southern Italy: ‘Arbequina’, ‘Arbosana’, ‘Cima di Bitonto’, ‘Coratina’, ‘Don Carlo’, ‘Frantoio’, ‘Favolosa’ (=‘Fs-17’), ‘I-77’, ‘Koroneiki’, and ‘Urano’ (=‘Tosca’). Flowering phenograms varied significantly across years and cultivars, showing temporal shifts related to chilling accumulation and yield of the previous year. Early blooming cultivars (‘Arbequina’, ‘Arbosana’, and ‘Coratina’) exhibited partial flowering overlap with mid-season ones, enhancing cross-pollination opportunities. Quantitative analysis of flowering overlap revealed that most cultivar combinations exceeded the 70% threshold required for effective pollination, although specific genotypes (‘Coratina’, ‘Fs-17’, and especially ‘I-77’) showed critical mismatches, while ‘Frantoio’ and ‘Arbequina’ emerged as the most reliable pollinizers. Gynosterility exhibited statistical differences among cultivars and canopy positions: ‘I-77’ showed the highest values (71.4%), while ‘Coratina’ and ‘Cima di Bitonto’ showed the lowest ones (7.3 and 8.4%, respectively). The median portions of the canopies generally displayed a greater number of sterile flowers (29.4%), reflecting the combined effect of genetic and environmental factors such as light exposure. In the inflorescence, the majority of gynosterile flowers were concentrated in the lower part, for all canopy portions (modal value). Self-compatibility tests were performed considering a fruit set of 1% as a threshold to discriminate. For open pollination, the fruit set was highly variable among cultivars, ranging from 0.5% in ‘I-77’ to 4.7% in ‘Arbosana’. Apart from ‘I77’, all varieties achieved a fruit set greater than 1%. Instead, for the self-pollination, only ‘Arbequina’, ‘Koroneiki’, ‘Frantoio’, and ‘Cima di Bitonto’ could be identified as pseudo-self-compatible, whereas ‘Coratina’, ‘Fs-17’, and the others were clearly self-incompatible and therefore unsuitable for monovarietal orchards in areas with limited availability of pollen. By integrating self-compatibility and gynosterility data, the cultivars were ranked according to reproductive aptitude, identifying ‘Cima di Bitonto’ and ‘Frantoio’ as the most fertile genotypes, whereas ‘Don Carlo’ and particularly ‘I-77’ showed severe genetic sterility constraints. The findings underline the critical role of floral biology in defining reproductive efficiency and varietal adaptability in SHD systems. This research provides valuable insights for optimizing cultivar selection, orchard design, and management practices, contributing to the development of sustainable, climate-resilient olive production models for Mediterranean environments. Full article

A two-factor experiment was conducted using the cultivar ‘Xin you No. 2’ (Citrullus lanatus) to identify an efficient and green production model for drip-irrigated watermelon under plastic mulch in Southern Xinjiang. A basal organic fertilizer was applied at 2250 kg·ha⁻¹. The experimental design comprised three irrigation levels, maintaining soil moisture at 60–70% (W1), 70–80% (W2), and 80–90% (W3) of field capacity, and three nitrogen application rates: 180 (N1), 240 (N2), and 300 (N3) kg·ha⁻¹. This study systematically investigated the effects of water–nitrogen coupling on watermelon yield, quality, water use efficiency, and nitrogen partial factor productivity. The W2N2 treatment achieved the highest yield of 64,617.59 kg·ha⁻¹. Vine length, stem diameter, and dry matter accumulation increased with increasing nitrogen application under the W1 and W2 irrigation levels, but exhibited an initial increase followed by a decrease under the W3 condition. Water restriction combined with increased nitrogen application significantly enhanced the central sugar content, with the W1N3 treatment increasing it by 15.69% compared to CK. Conversely, the W1N1 treatment was most conducive to vitamin C accumulation, showing a 49.88% increase over CK. The total water consumption across the different treatments ranged from 362.12 to 493.92 mm. Both water use efficiency and irrigation water use efficiency reached their maximum values under the W1N3 treatment, at 21.94 kg·m⁻³ and 35.05 kg·m⁻³, respectively. In contrast, the highest partial factor productivity of nitrogen (NPFP) was observed under W3N1, reaching 239.33 kg·kg⁻¹. A comprehensive multi-index evaluation using the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method indicated that the W1N3 treatment achieved the highest relative closeness (0.669), identifying it as the optimal water–nitrogen combination. Full article

Biological N₂ fixation (BNF) and ratoon rice growth are biological processes that mediate N and C cycling in rice paddy ecosystems, but their contributions to paddy soil fertility have rarely been evaluated in a quantitative and unified manner. In this study, we analyzed the contribution of BNF and ratoon rice growth to soil N fertility at six rice paddy sites in four prefectures of Japan, combining 2-year field monitoring and simulation using the DNDC-Rice biogeochemistry model. Across the sites and years, ratoon rice was found to accumulate up to 30 kg N ha⁻¹ without fertilization and irrigation after main rice harvest. BNF was not measured but estimated to be 33–63 kg N ha⁻¹ yr⁻¹ at the six sites, by applying a newly built BNF model after calibration against a literature dataset. Based on the simulations using DNDC-Rice under typical local management strategies, we estimated the following contributions of BNF and ratoon rice to soil N fertility, with variations based on the climate, soil properties, and management, as follows: (a) BNF and ratoon rice contributed 4–33% and 3–23% of the N supply from soil during the main rice season, respectively. (b) While BNF contributed 3–29% of the main rice N uptake, that from ratoon rice was much lower (6% or less), presumably because the decomposition of ratoon rice residue induced N immobilization during the main rice season. (c) Although the major part of N gain by BNF was being lost via denitrification and N leaching, BNF was contributing up to 6.6% of the organic N pool at the 0–30 cm soil layer. Ratoon rice was working to save N loss by reducing N leaching, consequently contributing up to 3.3% of the soil N pool. These findings provide quantitative insights into what roles BNF and ratoon rice play in paddy soil fertility. Full article

Agricultural activities are major contributors to global greenhouse gas (GHG) emissions, with methane (CH₄) and nitrous oxide (N₂O) emissions accounting for 40% and 60% of total agricultural emissions, respectively. Therefore, developing effective emission reduction pathways in agriculture is crucial for achieving carbon budget balance. This article synthesizes the impact of farmland management practices on GHG emissions, evaluates prevalent accounting methods and their applicable scenarios, and proposes mitigation strategies based on systematic analysis. The present review (2000–2025) indicates that fertilizer management dominates research focus (accounting for over 50%), followed by water management (approximately 18%) and tillage practices (approximately 14%). Critically, the effects of these practices extend beyond GHG emissions, necessitating concurrent consideration of crop yields, soil health, and ecosystem resilience. Therefore, it is necessary to conduct joint research by integrating multiple approaches such as water-saving irrigation, conservation tillage and intercropping of leguminous crops, so as to enhance productivity and soil quality while reducing emissions. The GHG accounting framework and three primary accounting methods (In situ measurement, Satellite remote sensing, and Model simulation) each exhibit distinct advantages and limitations, requiring scenario-specific selection. Further refinement of these methodologies is imperative to optimize agricultural practices and achieve meaningful GHG reductions. Full article

Smallholder farmers in semi-arid regions worldwide face persistent water scarcity, declining soil fertility, and increasing climate variability, which constrain food production. This study investigated soil and water management practices and their effects on soil health, crop productivity, and adoption among smallholder vegetable farmers in a semi-arid area in Kenya. A mixed-methods approach was employed, combining survey data from 397 farmers with a randomized field experiment. Results showed that hand watering (88.7%) and manure application (95.5%) were prevalent, while only 5.7% of farmers used drip irrigation. Compost and mulch treatments significantly improved soil organic carbon (p = 0.03), available water capacity (p = 0.01), and gravimetric moisture content (p = 0.02), with soil moisture conservation practices strongly correlated with higher yields in leafy green vegetables (R = 0.62). Despite these benefits, adoption was hindered by high water costs (42.6%) and unreliable sources (25.7%). Encouragingly, 96.2% of respondents expressed willingness to pay for improved water systems if affordable and dependable. The findings stress the need for integrated water–soil strategies supported by inclusive policy, infrastructure investment, and gender-responsive training to enhance resilience and productivity in smallholder farming under water-scarce conditions across sub-Saharan Africa and other regions globally, contributing to global sustainability targets such as SDG 6, 12 and 15. Full article

Addressing the challenges of low resource-use efficiency and supply–demand mismatch in Hami melon production, this study investigated the interactive effects of irrigation and fertilization to identify an optimal regime that balances yield, water conservation, and resource-use efficiency (i.e., water use efficiency and fertilizer partial factor productivity). A greenhouse experiment was conducted in Hami, Xinjiang, employing a two-factor design with five irrigation levels (W1–W5: 60–100% of full irrigation) and three fertilization levels (F1–F3: 80–100% of standard rate), replicated three times. Growth parameters, yield, water use efficiency (WUE), and partial factor productivity of fertilizer (PFP) were evaluated and comprehensively analyzed using the entropy-weighted TOPSIS method, regression analysis, and the NSGA-II multi-objective genetic algorithm. Results demonstrated that irrigation volume was the dominant factor influencing growth and yield. The W4F3 treatment (90% irrigation with 100% fertilization) achieved the optimal outcome, yielding 75.74 t ha⁻¹—a 9.71% increase over the control—while simultaneously enhancing WUE and PFP. Both the entropy-weighted TOPSIS evaluation (C = 0.998) and regression analysis (optimal irrigation level at w = 0.79, ~90% of full irrigation) identified W4F3 as superior. NSGA-II optimization further validated this, generating Pareto-optimal solutions highly consistent with the experimental optimum. The model-predicted optimal regime for greenhouse Hami melon in Xinjiang is an irrigation amount of 3276 m³ ha⁻¹ and a fertilizer application rate of 814.8 kg ha⁻¹. This regime facilitates a 10% reduction in irrigation water and a 5% reduction in fertilizer input without compromising yield, alongside significantly improved resource-use efficiencies. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Blueberries (Vaccinium spp.) are highly sensitive to winter chilling fulfillment, growing degree days above 7 °C (GDD7), and water balance (WB). By integrating a climate-based natural suitability index (CNSI), three-dimensional kernel density estimation, traditional and spatial Markov chains, and optimal geographic detector analysis, this study examines the spatiotemporal evolution and driving mechanisms of blueberry climatic suitability realization in 19 major producing provinces in China during 2008–2023. Results show that CNSI exhibits a stable and moderately right-skewed distribution, with partial convergence and a narrowing interprovincial gap. Suitability realization is highest in the middle and lower Yangtze River rice-growing belt, whereas the northern dryland belt and the southern subtropical mountainous belt show persistent mismatches between climatic potential and production advantages. Markov results reveal path dependence and moderate mobility, with “low–low lock-in” and “high–high club” phenomena reinforced under neighborhood effects. GeoDetector results indicate that effective facility irrigation and fertilizer input are dominant factors explaining spatial variation in CNSI, while comprehensive transportation accessibility and agricultural labor act as stable complements. Interaction analysis suggests that multi-factor synergies, particularly irrigation-centered combinations, yield strong dual-factor enhancement and near-nonlinear enhancement. These findings highlight the importance of aligning climatic suitability with adaptive infrastructure investment and region-specific management to promote sustainable production-share advantages in China’s blueberry industry. Full article

Irrigation practices and nitrogen (N) addition play pivotal roles in wheat production, and their rational coordination can significantly enhance N, phosphorus (P), and potassium (K) use efficiency and yield of wheat. However, the comprehensive effects of irrigation practices and N addition rates on N, P, and K accumulation and utilization and yield of wheat in dryland remain unclear. A field experiment with two irrigation practices (W0, zero-irrigation and W1, one-off irrigation), and four N addition rates (0, 120, 180, and 240 kg N ha⁻¹, represented by N0, N120, N180, and N240, respectively) was conducted in 2021–2022 and 2023–2024. Compared to W0N0, W1N180 significantly increased wheat grain yield, spike number, and grains per spike by 46.4%, 35.9%, and 18.9%, respectively. Wheat yield and N, P, and K accumulation reached the maximum value at N180 or N240. One-off irrigation significantly improved the uptake efficiency and fertilizer partial factor productivity for N, P, and K, whereas increased N addition enhanced these parameters specifically for P and K. However, N180 treatment increased N uptake efficiency, N fertilizer partial factor productivity, P internal efficiency, and K internal efficiency by 22.2%, 31.1%, 9.4%, and 5.9%, respectively, compared to N240 under one-off irrigation. In addition, W1N180 significantly increased above-ground N, P, and K accumulation by 45.8%, 52.8%, and 51.8%, as well as pre-anthesis N and P translocation by 48.5% and 47.0%, respectively, compared to W0N120. Consequently, the W1N180 strategy not only improved wheat yield but also optimized N, P, and K accumulation, pre-anthesis N and P translocation, and nutrient use efficiency. Therefore, one-off irrigation combined with N180 can be recommended for enhancing wheat yield and nutrient use efficiency in dryland. Full article

This study evaluates the impact of a decision support system (DSS) on the sustainability of potato production in Cyprus. Field experiments were conducted over three consecutive years (2022–2024), comparing conventional farming with smart farming (SF) guided by the DSS. Thirteen sustainability indicators were assessed, revealing clear trade-offs between input-use efficiency and yield stability. While SF significantly reduced input use—fertilizers, pesticides, irrigation water, and labor—average yields declined by 21%. These results provide new evidence on the environmental and economic implications of SF in high-input farming systems, supporting efforts to promote more sustainable agricultural practices in Cyprus and beyond. Full article

This study examined how different fertilisation strategies (mineral, compost, vermicompost and non-fertilised control) influence vine nutrient status, must composition and wine chemical characteristics over two consecutive seasons (2024–2025) in two semi-arid Mediterranean vineyards, one deficit-irrigated and other rainfed. Compost and vermicompost were produced from winery residues, in line with a circular management approach. Organic fertilisation improved vine nitrogen and potassium levels, particularly at veraison, with cumulative effects observed over time. Musts from fertilised vines (mineral, compost and vermicompost) exhibited higher levels of yeast-assimilable nitrogen (YAN) and pH, as well as lower titratable acidity, compared to the control group (without fertilization). Wines obtained from these treatments exhibited higher ethanol content and modified acidity parameters, with compositional changes being more evident in the rainfed vineyard. Analysis of volatile compounds revealed that organic fertilisers, particularly vermicompost, promoted the formation of esters, higher alcohols, and terpenes linked to grape metabolism and fermentation. These results demonstrate that organic amendments derived from winery waste can serve as efficient nutrient sources, thereby enhancing the nutritional balance of vines and the composition of wines, while also promoting sustainable and circular practices in viticulture. Full article

Availability of irrigation water during growing seasons in the Republic of South Africa (RSA) remains a significant concern. Persistent droughts and unpredictable rainfall patterns attributed to climate change, coupled with an increasing population, have exacerbated irrigation water scarcity. Globally, treated wastewater has been utilised as an irrigation water source; however, despite global advances in the usage of treated wastewater, its suitability for irrigation in RSA remains a contentious issue. Considering this uncertainty, this review article aims to unravel the South African scenario on the suitability of treated wastewater for irrigation purposes and highlights the potential environmental impacts and public health risks. The review synthesised literature in the last two decades (2000–present) using Web of Science, ScienceDirect, ResearchGate, and Google Scholar databases. Findings reveal that treated wastewater can serve as a viable irrigation source in the country, enhancing various soil parameters, including nutritional pool, organic carbon, and fertility status. However, elevated levels of salts, heavy metals, and microplastics in treated wastewater resulting from insufficient treatment of wastewater processes may present significant challenges. These contaminants might induce saline conditions and increase heavy metals and microplastics in soil systems and water bodies, thereby posing a threat to public health and potentially causing ecological risks. Based on the reviewed literature, irrigation with treated wastewater should be implemented on a localised and pilot basis. This review aims to influence policy-making decisions regarding wastewater treatment plant structure and management. Stricter monitoring and compliance policies, revision of irrigation water standards to include emerging contaminants such as microplastics, and intensive investment in wastewater treatment plants in the country are recommended. With improved policies, management, and treatment efficiency, treated wastewater can be a dependable, sustainable, and practical irrigation water source in the country with minimal public health risks. Full article