Search Results (832)

This study explores the revival of cotton (Gossypium spp. L.) farming in Italy through sustainable practices, addressing economic and water-related challenges by integrating cultivar selection, arbuscular mycorrhizal fungi (AMF) inoculation, and deficit irrigation under organic farming. Field trials evaluated two widely grown Mediterranean cultivars (Armonia and ST-318) under three irrigation levels (I-100: 100% crop water requirement; I-70: 70%; I-30: 30%) across two Sicilian soil types (sandy loam vs. clay-rich). Under I-100, lint yields reached 0.99 t ha⁻¹, while severe deficit (I-30) yielded only 0.40 t ha⁻¹. However, moderate deficit (I-70) maintained 75–79% of full yields, proving a viable strategy. AMF inoculation significantly enhanced plant height (68.52 cm vs. 65.85 cm), boll number (+22.1%), and seed yield (+12.5%) (p < 0.001). Cultivar responses differed: Armonia performed better under water stress, while ST-318 thrived with full irrigation. Site 1, with higher organic matter, required 31–38% less water and achieved superior irrigation water productivity (1.43 kg m⁻³). Water stress also shortened phenological stages, allowing earlier harvests—important for avoiding autumn rains. These results highlight the potential of combining adaptive irrigation, resilient cultivars, and AMF to restore sustainable cotton production in the Mediterranean, emphasizing the importance of soil-specific management. Full article

Climate warming poses significant challenges for the sustainable management of natural water resources, making efficient planning and usage essential. This study evaluates the water requirements, irrigation demand, and rainfall deficits for two key vegetable crops, carrot and white cabbage, under projected climate scenarios RCP 4.5 and RCP 8.5 for the period 2031–2100. The analysis was conducted for Kraków and Rzeszów Counties in southern Poland using projected monthly temperature and precipitation data from the Klimada 2.0 portal. Potential evapotranspiration (ETp) during the growing season (May–October) was estimated using Treder’s empirical model and the crop coefficient method adapted for Polish conditions. The reference period for comparison was 1951–2020. The results reveal a significant upward trend in water demand for both crops, with the highest increases under the RCP 8.5 scenario–seasonal ETp values reaching up to 517 mm for cabbage and 497 mm for carrot. Rainfall deficits are projected to intensify, especially during July and August, with greater shortages in Rzeszów County compared to Kraków County. Irrigation demand varies depending on soil type and drought severity, becoming critical in medium and very dry years. These findings underscore the necessity of adapting irrigation strategies and water resource management to ensure sustainable vegetable production under changing climate conditions. The data provide valuable guidance for farmers, advisors, and policymakers in planning effective irrigation infrastructure and optimizing water-use efficiency in southern Poland. Full article

The transition to efficient production requires innovative water control techniques to maximize irrigation efficiency and minimize waste. Analyzing and optimizing irrigation practices is essential to improve water use and reduce environmental impact. The aim of the research was to identify a discrimination method to analyze the different hydration levels in baby-leaf products. The species being researched was spinach, harvested at the baby leaf stage. Utilizing a large dataset of 261 wavelengths from the hyperspectral imaging system, the feature selection minimum redundancy maximum relevance (FS-MRMR) algorithm was applied, leading to the development of a neural network-based prediction model. Finally, a mathematical classification model K-NN (k-nearest neighbors type) was developed in order to identify a transfer function capable of discriminating the hyperspectral data based on a threshold value of absolute leaf humidity. Five significant wavelengths were identified for estimating the moisture content of baby leaves. The resulting model demonstrated a high generalization capability and excellent correlation between predicted and measured data, further confirmed by the successful training, validation, and testing of a K-NN-based statistical classifier. The construction phase of the statistical classifier involved the use of the experimental dataset and the critical humidity threshold value of 0.83 (83% of leaf humidity) was considered, below which the baby-leaf crop requires the irrigation intervention. High percentages of correct classification were achieved for data within two humidity classes. Specifically, the statistical classifier demonstrated excellent performance, with 81.3% correct classification for samples below the threshold and 99.4% for those above it. The application of advanced spectral analysis and artificial intelligence methods has led to significant progress in leaf moisture analysis and prediction, yielding substantial implications for both agriculture and biological research. Full article

Precision irrigation plays a crucial role in managing crop production in a sustainable and environmentally friendly manner. This study builds on the results of the GreenWaterDrone project, aiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery. To achieve this, a state-of-the-art aerial micrometeorological station (AMMS), equipped with an infrared thermal sensor, temperature–humidity sensor, and advanced multispectral and thermal cameras is mounted on an unmanned aerial system (UAS), thus minimizing crop field intervention and permanently installed equipment maintenance. Additionally, data from satellite systems and ground micrometeorological stations (GMMS) are integrated to enhance and upscale system results from the local field to the regional level. The research was conducted over two years of pilot testing in the municipality of Trifilia (Peloponnese, Greece) on pilot potato and watermelon crops, which are primary cultivations in the region. Results revealed that empirical irrigation applied to the rhizosphere significantly exceeded crop water needs, with over-irrigation exceeding by 390% the maximum requirement in the case of potato. Furthermore, correlations between high-resolution remote and proximal sensors were strong, while associations with coarser Landsat 8 satellite data, to upscale the local pilot field experimental results, were moderate. By applying a comprehensive model for upscaling pilot field results, to the overall Trifilia region, project findings proved adequate for supporting sustainable irrigation planning through simulation scenarios. The results of this study, in the context of the overall services introduced by the project, provide valuable insights for farmers, agricultural scientists, and local/regional authorities and stakeholders, facilitating improved regional water management and sustainable agricultural policies. Full article

The wheat planted at the end of the rainy season in the Cerrado suffers from a strong water deficit. A selection of genetic material with drought tolerance is necessary. In improvement programs that evaluate a large number of materials, efficient, automated, and non-destructive phenotyping is essential, which requires the use of sensors. The experiment was conducted in 2016 using a phenotyping platform, where irrigation gradients ranging from 184 (WR4) to 601 mm (WR1) were created, allowing for the comparison of four genotypes. In addition to productivity, we evaluated plant height, hectoliter weight, the number of spikes per square meter, ear length, photosynthesis, and the indices calculated by the sensors. For most morphophysiological parameters, extreme stress makes it difficult to discriminate materials. WR1 (601 mm) and WR2 (501 mm) showed similar trends in almost all variables. The data validated the phenotyping platform, which creates an irrigation gradient, considering that the results obtained, in general, were proportional to the water levels. The similar trend between sensors (NDVI, PRI, and LIFT) and morphophysiological, plant growth, and crop yield evaluations validated the use of sensors as a tool in selecting drought-tolerant wheat genotypes using a non-invasive methodology. Considering that only four genotypes were used, none showed absolute and unequivocal tolerance to drought; however, each genotype exhibited some desirable characteristics related to drought tolerance mechanisms. Full article

Water scarcity, exacerbated by climate change, population growth, and competing sectoral demands, poses a major threat to agricultural sustainability, particularly in irrigated regions such as the Nile Delta in Egypt. Addressing this challenge requires innovative approaches to evaluate irrigation performance despite the limitations in ground data availability. Traditional assessment methods are often costly, labor-intensive, and reliant on field data, limiting their scalability, especially in data-scarce regions. This paper addresses this gap by presenting a comprehensive and scalable framework that employs publicly accessible satellite data to map crop types and subsequently assess irrigation performance without the need for ground truthing. The framework consists of two parts: First, crop mapping, which was conducted seasonally between 2015 and 2020 for the four primary crops in the Nile Delta (rice, maize, wheat, and clover). The WaPOR v2 Land Cover Classification layer was used as a substitute for ground truth data to label the Landsat-8 images for training the random forest algorithm. The crop maps generated at 30 m resolution had moderate to high accuracy, with overall accuracy ranging from 0.77 to 0.80 in summer and 0.87–0.95 in winter. The estimated crop areas aligned well with national agricultural statistics. Second, based on the mapped crops, three irrigation performance indicators—adequacy, reliability, and equity—were calculated and compared with their established standards. The results reveal a good level of equity, with values consistently below 10%, and a relatively reliable water supply, as indicated by the reliability indicator (0.02–0.08). Average summer adequacy ranged from 0.4 to 0.63, indicating insufficient supply, whereas winter values (1.3 to 1.7) reflected a surplus. A noticeable improvement gradient was observed for all indicators toward the north of the delta, while areas located in the delta’s new lands consistently displayed unfavorable conditions in all indicators. This approach facilitates the identification of regions where agricultural performance falls short of its potential, thereby offering valuable insights into where and how irrigation systems can be strategically improved to enhance overall performance sustainably. Full article

The continuous development of both numerical weather model outputs and remote sensing-derived products has enabled a wide range of applications across various fields, such as agricultural water management, where the need for robust gridded weather data and recurring Earth Observations (EO) is fundamental for estimating crop water requirements (CWR) and yield. This study used the latest reanalysis dataset, AgERA5, combined with the up-to-date CM SAF SARAH-3 Satellite-Based Radiation Data as meteorological inputs of the SAFY dynamic crop growth model and a one-step evapotranspiration formula for CWR and yield estimates at the farm scale of tomato crops. The Sentinel-2 (S2) estimates of Leaf Area Index (LAI) were used to force the SAFY model as soon as they became available during the growing stage, according to the satellite passages over the area of interest. The SAFY model was calibrated with ground-based weather observations and S2 LAI data on tomato crops that were collected in several farms in Campania Region (Southern Italy) during the irrigation season, which spans from April to August. To validate the method, the model estimates were compared with field observations of irrigation volumes and harvested yield from a monitored farm in the same region for the year 2021. Results demonstrated that integrating AgERA5 and CM SAF weather datasets with S2 imagery for assimilation into the SAFY model enables accurate estimates of both CWR and yield. Full article

In recent years, global agriculture has encountered several challenges exacerbated by the effects of changes in climate, such as extreme water shortages for irrigation and heat waves. Water-deficit stress adversely affects the morpho-physiology of numerous crops, including soybean (Glycine max L.), which is considered as promising crop in Bangladesh. Seaweed extract (SWE) has the potential to improve crop yield and alleviate the adverse effects of water-deficit stress. Remote and proximal sensing are also extensively utilized in estimating morpho-physiological traits owing to their cost-efficiency and non-destructive characteristics. The study was carried out to evaluate soybean morpho-physiological traits under the application of water extracts of Gracilaria tenuistipitata var. liui (red seaweed) with two varying irrigation water conditions (100% of total crop water requirement (TCWR) and 70% of TCWR). Principal component analysis (PCA) revealed that among the four treatments, the 70% irrigation + 5% (v/v) SWE and the 100% irrigation treatments overlapped, indicating that the application of SWE effectively mitigated water-deficit stress in soybeans. This result demonstrates that the foliar application of 5% SWE enabled soybeans to achieve morpho-physiological performance comparable to that of fully irrigated plants while reducing irrigation water use by 30%. Based on Pearson’s correlation matrix, a simple linear regression model was used to ascertain the relationship between unmanned aerial vehicle (UAV)-derived vegetation indices and the field-measured physiological characteristics of soybean. The Normalized Difference Red Edge (NDRE) strongly correlated with stomatal conductance (R² = 0.76), photosystem II efficiency (R² = 0.78), maximum fluorescence (R² = 0.64), and apparent transpiration rate (R² = 0.69). The Soil Adjusted Vegetation Index (SAVI) had the highest correlation with leaf relative water content (R² = 0.87), the Blue Normalized Difference Vegetation Index (bNDVI) with steady-state fluorescence (R² = 0.56) and vapor pressure deficit (R² = 0.74), and the Green Normalized Difference Vegetation Index (gNDVI) with chlorophyll content (R² = 0.73). Our results demonstrate how UAV and physiological data can be integrated to improve precision soybean farming and support sustainable soybean production under water-deficit stress. Full article

Canopy temperature is an important indicator of plants’ water status. The so-called PeriodiCT model was developed to forecast canopy temperature using ambient weather variables, providing a powerful tool for planning crop irrigation scheduling. As this model requires observed data in its parameter training before implementing the forecast, it is important to understand the data requirements in the model training such that accurate forecasts are attained. In this work, we conduct a comprehensive assessment of the PeriodiCT model in terms of sample size requirement and predictabilities across sensors in a field and across seasons for the full model and sub-models. The results show that (1) 5 days’ observations are sufficient for the full model and sub-models to achieve very high predictability, with a minimum coefficient of efficiency of 0.844 for the full model and 0.840 for the sub-model using only air temperature. The predictability decreases in the following order: full model, sub-model without radiation S, with air temperature Ta and vapor pressure VP, and with only Ta. The predictions perform reasonably well even when only one day’s observations are used. (2) The predictability into the future is very stable as the prediction steps increase. (3) The predictabilities of the full and sub-models when using a trained model from one sensor for another sensor perform comparatively well, with a minimum coefficient of efficiency of 0.719 for the full model and 0.635 for the sub-model using only air temperature. (4) The predictabilities of the sub-models without solar radiation when using trained models from one season for another season perform comparatively well, with a minimum coefficient of efficiency of 0.866 for the full model and 0.764 for the sub-model using only air temperature, although the cross-season performances are not as good as the cross-sensor performances. The importance of the predictors is in the order of air temperature, vapor pressure, wind speed, and solar radiation, while vapor pressure and wind speed have similar contributions, and solar radiation has only a marginal contribution. Full article

Global changes and society’s development necessitate the improvement of water use and irrigation water saving, which require a set of water management measures to best deal with the necessary changes. This study considers the framework of the change process for water management in the Hetao Irrigation District (HID) of the Yellow River Basin. This paper presents the main measures that have been applied to ensure the sustainability and modernization of Hetao, mitigating water scarcity while maintaining land productivity and environmental value. Several components of modernization projects that have already been implemented are characterized, such as the off-farm canal distribution system, the on-farm surface irrigation, innovative crop and soil management techniques, drainage, and salinity control, including the management of autumn irrigation and advances of drip irrigation at the sector and farm levels. This characterization includes technologies, farmer training, labor needs, energy consumption, water savings, and economic aspects, based on data observed and reported in official reports. Therefore, this study integrates knowledge and analyzes the most limiting aspects in some case studies, evaluating the effectiveness of the solutions used. Full article

The Altay oasis, located at the heart of the transnational ecological conservation zone shared by China, Kazakhstan, Russia, and Mongolia, is a region with tremendous potential for water resource utilization. However, with the continued expansion of agriculture, its ecological vulnerability has become increasingly pronounced. Within this fragile balance lies a critical opportunity: efficient water resource management could pave the way for sustainable development across the entire arid oasis regions. This study uses a decision tree model based on a feature threshold to map the spatial distribution of major crops in the Altay Prefecture oasis, assess their water requirements, and identify the coupling relationships between agricultural water and land resources. Furthermore, it proposed optimization planting structure strategies under three scenarios: water-saving irrigation, cash crop orientation, and forage crop orientation. In 2023, the total planting area of major crops in Altay Prefecture was 3368 km², including spring wheat, spring maize, sunflower, and alfalfa, which consumed 2.68 × 10⁹ m³ of water. Although this area accounted for only 2.85% of the land, it consumed 26.23% of regional water resources, with agricultural water use comprising as much as 82.5% of total consumption, highlighting inefficient agricultural water use as a critical barrier to sustainable agricultural development. Micro-irrigation technologies demonstrate significant water-saving potential. The adoption of such technologies could reduce water consumption by 14.5%, thereby significantly enhancing agricultural water-use efficiency. Cropping structure optimization analysis indicates that sunflower-based planting patterns offer notable water-saving benefits. Increasing the area of sunflower cultivation by one unit can unlock a water-saving potential of 25.91%. Forage crop combinations excluding soybean can increase livestock production by 30.2% under the same level of water consumption, demonstrating their superior effectiveness for livestock system expansion. This study provides valuable insights for achieving sustainable agricultural development in arid regions under different development scenarios. Full article

Soil moisture sensor-based drip irrigation enables efficient irrigation practices by delivering the required water to plants. However, efficiency must be accompanied by uniform water management and crop growth. This study examined the effect of different irrigation amounts (IAs) per application (5.5, 55, 110, and 165 mL) on the uniformity of substrate volumetric water content (VWC) within an irrigation plot, and the corresponding effect on sweet basil growth uniformity. Sixty-four frequency domain reflectometry sensors monitored the VWC of each 440 mL pot, and drip irrigation was automatically applied at 0.3 m³·m⁻³. The 5.5 mL IA showed the highest water use efficiency; however, it also resulted in considerable non-uniform VWC (coefficient of variation, CV = 0.404). In contrast, the 110 and 165 mL IAs provided better VWC uniformity (CV = 0.073 and 0.075, respectively), suggesting that less frequent, but larger IAs improved VWC uniformity. Despite the differences in VWC uniformity among treatments, the growth and physiological responses were quite similar across the treatments. It was found that supplying 110 mL irrigation water via the soil moisture sensor-based drip irrigation system to sweet basil plants in 440 mL pots is optimal for achieving both water use efficiency and VWC uniformity. Full article

Efficient water management is critical for sustainable dryland agriculture, especially under increasing water scarcity and climate variability. Shanxi Province, a typical dryland region in northern China characterized by pronounced climatic variability and limited soil water availability, faces severe challenges due to uneven precipitation and restricted water resources. This study aimed to evaluate the spatiotemporal dynamics of soil water resources and their coupling with crop water demand under different hydrological year types. Using daily meteorological data from 27 stations (1963–2023), we identified dry, normal, and wet years through frequency analysis. Soil water resources were assessed under rainfed conditions, and water deficits of major crops—including millet, soybean, sorghum, winter wheat, maize, and potato—were quantified during key reproductive stages. Results showed a statistically significant declining trend in seasonal precipitation during both summer and winter cropping periods (p < 0.05), which corresponds with the observed intensification of crop water stress over recent decades. Notably, more than 86% of daily rainfall events were less than 5 mm, indicating low effective rainfall. Soil water availability closely followed precipitation distribution, with higher values in the south and west. Crop-specific analysis revealed that winter wheat and sorghum had the largest water deficits in dry years, necessitating timely supplemental irrigation. Even in wet years, water regulation strategies were required to improve water use efficiency and mitigate future drought risks. This study provides a practical framework for soil water–crop demand assessment and supports precision irrigation planning in dryland farming. The findings contribute to improving agricultural water use efficiency in semi-arid regions and offer valuable insights for adapting to climate-induced water challenges. Full article

The critical nitrogen (N) dilution curve is widely used to diagnose crop N status, but no such model has been developed for sugar beet. This study evaluated the effects of irrigation amount and N rate on sugar yield, N use efficiency, and soil nitrate-N (NO₃-N) residue of drip-fertigated sugar beet in the arid southern Xinjiang of China. A reliable N nutrition index (NNI) for sugar yield was also established based on a critical N dilution curve derived from the dry matter of sugar beet. A three-year field experiment was established with six N rates (25–480 kg N ha⁻¹) and three irrigation levels based on crop evapotranspiration (ET_c) (0.6, 0.8, and 1.0 ET_c in 2019 and 2020, and 0.4, 0.6, and 0.8 ET_c in 2021). Results showed that sugar yield and N uptake increased and then generally stabilized with increasing N rate, while N use efficiency decreased. Most soil NO₃-N was mainly distributed in the 0–60 cm soil layer, but increasing irrigation amount reduced residual NO₃-N in the 0–80 cm soil layer. Additionally, the established critical N dilution curve of sugar beet was considered stable (Normalized RMSE = 16.6%), and can be used to calculate plant N requirements and further N rates during sugar beet growth. The results indicated that the optimal NNI was 0.97 under 0.6 ET_c for sugar yield production of sugar beet in this study. This study provides a basis for efficient water and N management in sugar beet production in arid and semi-arid regions globally. Full article

Widespread drought conditions have increasingly affected agricultural productivity, requiring the exploration of alternative approaches for improving crop tolerance, yield and quality, since plants adopt many physiological strategies to cope with challenging environments. This study evaluated the effects of a vegetal-derived protein hydrolysate (PH), applied via foliar spray or root drench at a concentration of 3 mL L⁻¹, on tomato plants (n = 96) under well-watered and drought-stressed conditions over a 136-day greenhouse experiment. Overall, sub-optimal irrigation significantly decreased plant dry biomass (−55.3%) and fruit production (−68.8% marketable yield), and enhanced fruit quality in terms of sugar concentration and antioxidant levels. PH treatments, regardless of the application method, did not notably influence above-ground dry biomass, yield, or fruit quality, suggesting that the intensity of drought might have limited PH effectiveness. Metabolomic analysis showed higher concentrations of stress- and quality-related metabolites in tomato fruits from plants under stress, with PH not exerting significant metabolic changes in the fruits. These findings revealed the diminished effectiveness of PHs under severe drought conditions, suggesting that drought stress level needs to be taken into consideration for optimizing biostimulant efficacy. Full article