Search Results (537)

Popcorn (Zea mays everta) is a special type of flint maize that boasts several unique popping characteristics highly valued worldwide. Water-limiting conditions strongly influence the major popcorn quality attributes: expansion volume, popability, and nutritional composition. The objectives of this study were to identify rainfed popcorn genotypes with superior popping quality, nutritional quality, and agronomic performance. Seven diverse popcorn genotypes, including a check cultivar, were evaluated for two consecutive years (2023–2024) using a randomized complete block design with three replications at the university research farm, PMAS-AAUR. Significant genetic variations were observed across all morphological, physiological, and quality-related traits. Among the evaluated materials, Pop-2 consistently exhibited outstanding performance in key agronomic and physiological attributes as well as in popping quality, while Pop-5 and Pop-3 also showed promising potential. Overall, Pop-2, Pop-5, and Pop-3 were identified as the most suitable genotypes for cultivation and are recommended as candidates for future breeding programs targeting improved popcorn performance under rainfed conditions. Full article

Dryland, which mainly retains rain-fed agriculture, is the main type of farmland in China and widely distributed in the northern regions. Rainfall scarcity limits the development of maize at the seedling stage, which adversely affects the increase in maize yields in this region. A planting method that allows variable sowing depths based on the uneven distribution of soil moisture was proposed in this study. This site-specific planting method which fully utilizes available soil water is able to overcome the above problem. The framework of variable depth seeding suitable for this region was constructed: Within the depth range of 5.5 to 8.5 cm in the soil, maize seeds should be sown to a position with a relative soil moisture of 70%. For some drylands without such moisture conditions, seeds can be placed at the position with the highest relative soil moisture in this depth range. Taking the conventional planting method as the control group, the performance of the variable depth planting method in improving maize seedling growth was evaluated. The results showed that the proposed planting method not only increased the emergence rate and the seedling uniformity by 9.31% and 25.29%, respectively, but also raised the mean leaf number and the mean plant height in the same growth period, having a remarkable effect in improving the maize seedling traits. This planting method is easy to be embedded into precision control systems of the maize planter, and will promote the application of soil moisture-based planting technology and thus increase the yield per hectare of maize. Full article

Evapotranspiration (ET) is challenging to measure directly, motivating the use of remote sensing products as alternatives. We evaluated five high-resolution (≤1 km) global ET products (SSEBop, MOD16, ETMonitor, PMLv2, and FAO’s WaPOR) against five eddy covariance (EC) measurements in Morocco’s semi-arid Tensift Al-Haouz region, with observations spanning from 2006 to 2019. These five products were selected because they offer the finest spatial resolution (around 1 km or less) among freely downloadable global ET datasets, making them well-suited for comparison with local EC flux tower data. The study area was chosen for its reliable ground-truth EC stations, extensive knowledge of local irrigation practices, and a semi-arid climate that provides a rigorous testbed for ET model evaluation in water-limited conditions. Precipitation observations were included to assess each product’s sensitivity to soil moisture and precipitation-driven ET variations, particularly to identify which models respond to rainfall and irrigation inputs (i.e., differences between rainfed and irrigated fields). Results indicate that PMLv2 achieved the best agreement with EC (R² up to 0.65, RMSE as low as 0.4 mm/day, and PBIAS under 10% at most sites), followed by WaPOR and SSEBop which captured seasonal ET patterns (R² ~0.3–0.5) with moderate bias (~20–30%). In contrast, ETMonitor and MOD16 underperformed, showing larger errors (RMSE ~1–2.5 mm/day) and substantial underestimation biases (e.g., MOD16 PBIAS ~50–80% in irrigated sites). These findings underscore the impact of algorithmic differences and highlight PMLv2, SSEBop, and WaPOR as more reliable options for estimating ET in semi-arid agricultural regions lacking in situ measurements. Full article

This study explores the potential of Precision Agriculture (PA) to optimize top-dressing nitrogen (N) fertilization in rainfed barley under drought conditions in Central Catalonia (Spain). Efficient N management is critical in Mediterranean dryland winter cereal systems, where water scarcity and environmental regulations limit fertilization strategies. Two plots (2.93 ha and 1.80 ha) were zoned using soil apparent electrical conductivity (ECa) and elevation data obtained with the VERIS 3100 ECa soil surveyor. An on-farm experimental design tested four N dose rates (0 kg N/ha, 32 kg N/ha, 64 kg N/ha, and 96 kg N/ha) across two management zones per plot. Yield data were collected using a combine harvester equipped with a yield monitor and were mapped using geostatistical methods. A linear model (ANOVA) was used to analyze barley yield (kg/ha at 13% moisture), with nitrogen rate and soil zone (management class) as explanatory factors. Results showed low average yields (~1200 kg/ha–1300 kg/ha) due to severe water stress during the 2022–2023 season. Non-fertilized plots (N0) and those receiving moderate (N64) or high fertilization (N96) achieved the best performance, with the latter likely enhancing crop N uptake during the post-stress recovery period. In contrast, low fertilization (N32) proved less effective. Marginal return analysis supported variable-rate N application only in one plot, whereas under drought conditions, a no-fertilization strategy proved more suitable in the other. Ultimately, additional trials conducted under more favourable climatic scenarios are necessary to assess and validate the effectiveness of Precision Agriculture-based fertilization strategies in rainfed barley. Full article

Synthetic Aperture Radar (SAR) has been widely applied for rice monitoring, especially in cloud-prone areas, due to its ability to penetrate clouds. However, only limited methods were developed to monitor separately irrigated rice and rainfed rice ecosystems. This study demonstrated the use of multi-temporal polarimetric dual-polarization (dual-pol) SAR (Sentinel-1B and ALOS PALSAR-2) data to monitor and discriminate the irrigated and favorable rainfed rice ecosystems in the province of Iloilo, Philippines. Key polarimetric parameters derived from H–A–α and model-based dual-pol decomposition were analyzed to characterize the rice phenology of both ecosystems. Segmented regression was performed to detect breakpoints corresponding to changes in rice phenology within each ecosystem and used to identify the parameters to use for classification. Based on the results, Sentinel-1B polarimetric parameters (entropy, anisotropy, and alpha) can capture the phenological dynamics, whereas ALOS2 polarimetric parameters were more sensitive to water conditions, as reflected in span and volume scattering. Furthermore, irrigated rice exhibited more stable and predictable scattering patterns than favorable rainfed rice. Using the Random Forest classifier, various combinations of backscatter and polarimetric parameters from Sentinel-1B and ALOS2 were tested to discriminate between the two ecosystems. The highest classification accuracy (81.81% overall accuracy; Kappa = 0.6345) was achieved using the combined backscatter (S1B VH, ALOS2 HH, and HV) and polarimetric parameters from both sensors. The results demonstrated that polarimetric parameters effectively capture phenological stages and associated scattering mechanisms, with the integration of Sentinel-1B and ALOS2 data improving the discrimination of irrigated and favorable rainfed rice systems. Full article

Climate change significantly impacts crop yields, necessitating an evaluation of its effects and the development of adaptation strategies for future potato production. This study utilized the SUBSTOR-Potato model from the DSSAT software version 4.8 and daily weather data from LARS.WG to simulate potato production under three climate change scenarios (ssps 126, 245, and 585) from 2025 to 2087 in Southern Shan State, Myanmar. High-emission scenarios are associated with extreme weather, characterized by higher temperatures and variable precipitation. The results indicated that yields would be lowest under the ssp585 scenario, with around a 25% difference between ssp126 and ssp585. Adaptation strategies, such as delaying planting dates, positively impacted yields, while early planting resulted in lower outcomes. Extending the crop cycle by adjusting harvest times helped early-planted potatoes achieve yields similar to optimally timed ones. However, increasing fertilizer use did not significantly enhance yields under climate change conditions. The study emphasizes the importance of selecting cultivars, as heat-resistant varieties struggled in lower emission scenarios. This study provides comprehensive insights into climate change impacts on potato cultivation in Southern Shan State and offers practical, cost-effective adaptation strategies applicable to similar rainfed potato systems across Southeast Asia. Full article

Water potential is an important indicator used to study water relations in plants, as it reflects the level of hydration in their tissues. There are different numerical variables that describe plant properties and can be acquired from leaf reflectance. The objective of this study was to estimate water potential in coffee plants using spectral variables. For this, a range of wavelengths that provided analytical flexibility was used. After this, machine learning techniques were employed to build data-driven models. The dataset used presents spectral characteristics (wavelength) of coffee plants, collected through the CI-710 Mini-Leaf Spectrometer equipment and also the water potential of each coffee plant, measured by the Scholander Chamber equipment. The dataset was divided into two crop management groups: irrigated and rainfed. Four machine learning techniques were implemented: Multi-Layer Perceptron (MLP), Decision Tree, Random Forest and K-Nearest Neighbor (KNN). The implementation of machine learning techniques followed two distinct strategies: regression and classification. The results indicate that the decision tree-based model demonstrated superior performance under irrigated conditions for regression tasks. In contrast, the KNN technique achieved the best performance for classification. Under rainfed conditions, the MLP model outperformed the other techniques for regression, while the Random Forest method exhibited the highest accuracy in classification tasks. While no hardware prototype was developed, the machine learning-based methods presented here suggest a possible pathway toward future intelligent, user-friendly, and accessible sensing technologies for coffee plantations. Full article

The successful establishment of young chestnut orchards is increasingly challenged by drought stress and limited irrigation availability, especially in areas with limited water access. This study evaluated the effects of ridge planting and plastic foil mulching, individually and in combination, on the early growth and stress physiology of vegetatively propagated Castanea sativa × C. crenata ‘Marsol’ trees under rainfed conditions. Over a two-year field trial, vegetative traits, photosynthetic pigments, and leaf phenolic profiles were assessed to determine treatment effects. Ridge planting combined with foil mulching significantly improved tree growth, leading to a 2.6-fold increase in leaf number and 1.6-fold increase in height compared to control (flat planting without foil). This treatment also minimized stress indicators, such as chlorosis and elevated phenolic content. Notably, the ellagitannin chestanin emerged as a dominant stress-related metabolite in the first year, suggesting its potential as an early biochemical marker of transplantation stress. Over time, a compositional shift in phenolic groups, from hydroxycinnamic acids and flavanols to flavonols and hydroxybenzoic acids, was observed, reflecting the plant’s transition from acute stress response to developmental acclimation. These results support ridge planting with foil as a practical, climate-adaptive solution for chestnut orchard establishment and highlight chestanin as a candidate marker for stress monitoring in young trees. Full article

Abiotic stresses, particularly water deficit, are major constraints to global agricultural productivity. This study aimed to evaluate physiological and proteomic responses in two sugarcane genotypes, a cross-commercial cultivar and a self-fertilization clone, subjected to water deficit stress in the field. The experiment was conducted under rain-fed conditions. Organic solutes, photosynthetic pigments, gas exchange, and the quantum efficiency of photosystem II were evaluated. Total protein was extracted using the phenol method, and the peptides were analyzed using mass spectrometry. Elevated proline levels in clone RB061047 suggest a potentially enhanced adaptive response to water-deficit stress. There were no marked differences in the photosynthetic pigments between clone RB061047 and the commercial cultivar, RB867515. Self-fertilization did not negatively affect the physiological performance of RB061047 under water-deficit conditions because the higher photosynthetic rate and the consequent more efficient use of water suggest a marked gain in biomass and productivity. The ATP synthase alpha subunit YABB2 protein, fructose-bisphosphate aldolase, and nucleoside diphosphate kinase 1 emerged as potential candidates for the development of functional molecular markers for the selection and development of new sugarcane cultivars that are more tolerant to water-deficit stress. Full article

This study aimed to develop a nutraceutical gummy candy enriched with hydrolysed hemp (Cannabis sativa L.) as a natural antioxidant and antihypertensive ingredient. Three European cultivars—Futura 75, Henola, and KC Zuzana—were cultivated under rainfed (RF) and irrigated (RFCI) conditions and assessed for nutritional composition and bioactivity. Henola variety showed the most favourable profile, showing the highest protein content under RFCI (29.4 g 100 g⁻¹ d.m.) and the greatest phenolic concentration under RF (15.8 µmol GAE g⁻¹ d.m.), with 35–40% higher antioxidant capacity than the other cultivars. Henola (RF) was selected for enzymatic hydrolysis with Ultraflo^® XL, which enhanced total phenolics and antioxidant capacity by 65% and 58%, respectively, stabilizing after 18 h. Incorporation of the hydrolysate (0.66%) into a pectin-based gummy significantly (p < 0.05) increased total phenolic content by 52% and antioxidant capacity by up to 60% compared with controls. After simulated digestion, bioactivity decreased by 30–45% but remained higher than controls. The incorporation of 0. 66 g of hydrolysed ingredient in 100 g of gummy increased ACE inhibition by 10% after digestion, probably associated with the peptides released during the digestion, confirming hydrolysed hemp as a stable multifunctional ingredient for plant-based nutraceutical formulations targeting oxidative stress and hypertension. Full article

Vetch (Vicia sativa L.), an important forage legume, faces increasing drought stress due to climate change. This study evaluated drought responses in 26 genotypes using both in vitro and field trials. In vitro experiments analysed seedlings grown on culture media either with 20% polyethylene glycol (PEG) to simulate drought (C20) or without PEG as a control (C0), measuring root and shoot dry weights as well as proline content. Field trials under rainfed and drought conditions assessed 100 seed weight and seed weight per plant. All traits studied exhibited high variability, with elevated coefficients of variation and broad-sense heritability. Seedling roots grown in C20 had higher dry weight than those in C0, while shoots showed the opposite trend. In C20 medium, proline content increased significantly—by 118.1% in roots and 131.1% in shoots. However, proline concentration did not correlate with field yield traits, limiting its utility as a drought tolerance marker. Principal component analysis grouped genotypes based on biomass production and drought response. Importantly, in vitro root and shoot dry weights were positively correlated with field yield traits, indicating their value as early predictors of agronomic performance and offering a useful tool for selection in vetch breeding programmes. Full article

In Mediterranean semi-arid regions, rainfed olive groves are increasingly being abandoned due to drought, low profitability, and rural depopulation. The long-term impact of abandonment on soil conditions is debated, as it may promote vegetation recovery or lead to degradation. In contrast, some farmers are adopting low-disturbance management practices that allow spontaneous vegetation to establish. These contrasting scenarios offer valuable opportunities for comparison. This study aims to develop a framework to assess the impact of different management regimes on soil health and to investigate (1) the impact of spontaneous vegetation cover (SVC) and tillage regimes on soil organic carbon (SOC), and (2) the long-term ecological dynamics of abandoned groves, through a combination of field surveys, remote sensing, and object detection. SOC was assessed using both ground-based and remote sensing-derived indicators. Vegetation cover was quantified via a grid point intercept method. Field data were integrated with a land-use monitoring framework that includes abandonment assessment through historical orthophotos and a deep learning model (YOLOv12) to detect active and abandoned olive groves. Results show that abandoned zones are richer in SOC than active ones. In particular, the active groves with SVC exhibit a mean SOC of 1%, which is higher than that of tilled groves, where SOC is 0.45%, with no apparent moisture loss. Abandoned groves can be reliably identified from aerial imagery, achieving a recall of 0.833 for abandoned patches. Our results demonstrate the potential of YOLOv12 as an innovative and accessible tool for detecting zones undergoing ecological regeneration or degradation. The study underscores the ecological and agronomic potential of spontaneous vegetation in olive agroecosystems. Full article

Drought is one of the main climate-induced risks threatening agricultural sustainability in semi-arid regions. Northern Kazakhstan, a key grain-producing region in Central Asia, exhibits increasing vulnerability to droughts due to climatic variability and reliance on rainfed agriculture. This study evaluates the informativeness of drought indices based on the response of agricultural vegetation to dry conditions using remote sensing-based vegetation indices across Northern Kazakhstan from 1990 to 2024. Ground-based meteorological indices—the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Hydrothermal Coefficient (HTC), and the Modified China-Z Index (MCZI)—and vegetation indices—the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Vegetation Health Index (VHI)—were analyzed using data from 11 representative meteorological stations. For the first time in Kazakhstan, the MCZI was calculated, demonstrating high sensitivity to local climate variability and strong agreement with the VHI. The SPI, MCZI, and HTC showed strong seasonal correlations with vegetation indices, whereas the SPEI had a weak correlation, limiting its applicability. The highest correlations (r ≥ 0.82) between meteorological and vegetation indices were recorded in summer, while spring and autumn were influenced by phenological and temperature factors. Persistent drying trends in the southern and southwestern areas contrasted with moderate wetting in the north. The combined use of the SPI, MCZI, HTC, and VHI proved effective for monitoring droughts. The results provide a reproducible foundation for local drought assessment and early warning systems, supporting climate-resilient agricultural planning and sustainable land and water resource management. The results also offer actionable insights to enhance adaptation strategies and support long-term agricultural and environmental sustainability in Central Asia and similar continental agroecosystems. Full article

Agroforestry systems offer clear environmental and agronomic advantages, but their effect on plant–biotic stressor interactions remains poorly understood. Specifically, the shade from companion trees can create microclimates favorable to fungal diseases on herbaceous crops. This potential drawback may offset other benefits, highlighting the urgent need for advanced plant health monitoring in these systems. This study assessed the potential of hyperspectral reflectance to detect the single and combined effects of simulated tree shading and infection by the fungal pathogen Alternaria alternata on grain sorghum (Sorghum bicolor L. Moench) under rainfed field conditions. Sorghum was grown either under full light or 50% shading conditions. Half of the plots were artificially inoculated with an A. alternata spore suspension (2 × 10⁸ CFU mL⁻¹), while the others served as controls. Leaf and ground-canopy measurements were acquired with a full range spectroradiometer (VNIR-SWIR, 400–2,400 nm) and UAV imagery covered the VIS-NIR range (400–1,000 nm) before the onset of visible symptoms. Permutational multivariate analysis of variance of leaf and ground-canopy data revealed significant effects of shading (Sh), infection (Aa), and their interaction (p < 0.05), allowing early detection of infection two days before symptom appearance, while UAV data showed only singular significant effects. Partial least squares discriminant analysis accuracy reached 78% at the leaf level, 90% at the ground-canopy level, and 74% (Sh) and 75% (Aa) at the UAV scale. Furthermore, vegetation spectral indices derived from the spectra confirmed greater physiological stress in shaded and infected plants, consistent with disease incidence assessments. Our results establish scale-specific hyperspectral reflectance spectroscopy as a powerful, non-destructive technique for early plant health surveillance in agroforestry. This advanced optical sensing capability is poised to illuminate complex stressor interactions, marking a significant step forward for precision agroforestry management. Full article

The present study aimed to determine the Genotype × Environment interaction (GEI), yield stability, and agronomic performance of 24 “Pinto” bean lines under semi-arid conditions in Central-West Mexico. All the lines possess a slow-darkening seed coat, a trait that prolongs visual quality and increases market value. The lines, which exhibit an indeterminate prostrate growth habit, were evaluated in three contrasting environments: irrigated, rainfed, and drought-stressed. A combined analysis of variance, Tukey’s test, and the additive main effects and multiplicative interaction (AMMI 2) model were applied to assess seed yield and agronomic traits. Average seed yield declined markedly across environments, from 2279 kg ha⁻¹ under irrigation to 593 kg ha⁻¹ under drought stress, with different lines performing best in each environment. AMMI 2 biplot analysis showed that the first two principal components explained 100% of GEI variability for seed yield, dry shoot biomass, total biomass, harvest index, pods per plant, and seeds per pod. Both genetic and environmental effects were significant, with notable GEI patterns. Despite pronounced environmental influence, several lines exhibited stable performance across environments. Line 11 consistently combined high yield and stability, positioning it as a strong candidate for cultivar registration and as a parent in breeding programs targeting semiarid regions. These results underscore the importance of multi-environment evaluation for identifying genotypes with broad or specific adaptation, contributing to genetic improvement and sustainable bean production under variable moisture regimes. Full article