Search Results (77)

Climate change has emerged as a major global concern and has substantially intensified the occurrence of abiotic stresses in plants. Among the abiotic constraints limiting crop production, drought stress is regarded as one of the most severe and pervasive challenges. To this end, developing efficient and sustainable strategies to mitigate drought has become an urgent priority in agricultural research. Current approaches to improving drought tolerance mainly include optimizing irrigation management, applying chemical regulators, and breeding drought-resistant cultivars. However, these strategies often suffer from high input costs, limited durability of effects, potential environmental risks, or restricted regional applicability, making it difficult to achieve long-term and stable drought mitigation. In recent years, a growing body of evidence has indicated that rhizosphere microorganisms play pivotal regulatory roles in plant drought adaptation, with beneficial fungi being particularly important. Nonetheless, the key processes and mechanisms by which microbiomes mediate crop adaptation to drought need to be elucidated systematically. In this review, we synthesize recent advances in the field and, against the backdrop of increasingly severe global drought, summarize the major impacts of drought stress on crop growth and physiological processes. We further systematically synthesize the key mechanisms by which beneficial fungi alleviate drought stress in crops. Finally, we outline future research directions to deepen our understanding of rhizosphere–crop–microbe interaction networks and to provide a theoretical basis for developing beneficial fungus-centered microbial biofertilizers and microbiome-mediated strategies to enhance crop drought resilience. Full article

An unmanned aerial vehicle (UAV) multi-spectral system provides a monitoring platform to rapidly obtain crop spectral information that can reflect crop nitrogen status for the generation of dynamic variable-rate nitrogen (VRN). To improve the accuracy of VRN prescription maps, a method of generating VRN prescription maps on the basis of the vegetation index was proposed, and the effects of UAV flight time and altitude on VRN prescription maps were analyzed. The experimental site was located in Dacaozhuang, Hebei Province, China, and the experimental crop was winter wheat (Lunxuan 145). The flight altitudes of the UAV system were set to 50, 70 and 90 m. The flight times were set to 8:00 a.m., 11:00 a.m., 2:00 p.m. and 5:00 p.m. local time. The flight area was 1.18 ha with a 60° rotation angle under a three-span center pivot irrigation system with an overhang. UAV flight missions were executed during the jointing, heading, and grain filling phases of winter wheat. There were 90 management zones with pie shapes in total, which were composed of a 10° angle in the rotation direction and 4 sprinklers along the lateral direction. The vegetation indices (VIs) which are closely related to crop nutrient status were selected and used to generate distribution maps, which were superimposed with the management zones to generate VRN prescription maps. The results demonstrated that the red-edge soil adjusted vegetation index (RESAVI) was relatively more sensitive to the nitrogen status of winter wheat than the other VIs were. The RESAVI distributions were stable during periods with a solar elevation angle greater than 50° (11:00 a.m.–2:00 p.m. local time), and the VRN prescription maps were similar, with the overlap percentage of the same fertilization grade being greater than 80% and the relative error of the fertilization amount being less than 5%. Compared with that at 2:00 p.m., the overlap percentage of the same fertilization grade was 56.6% in both seasons at 8:00 a.m., whereas flights at 5:00 p.m. exhibited overlaps of 70.9% and 44.6% in the 2023 and 2024 seasons, respectively. Conversely, the flight altitude had little influence on the fertilizer amount and VRN prescription maps. The difference in the amount of fertilizer used was less than 3% at different flight altitudes. The required time is half of that for a 50 m flight when the flight altitude is 70 m and one third of that when the flight altitude is 90 m. Our study recommended operating the UAV multi-spectral system at solar elevation angles greater than 50° when generating VRN prescription maps of winter wheat, and the flight height can be adjusted according to the field area and the endurance time of the UAV. Full article

Canephora coffee genotypes developed in other growing regions, with traits of interest such as drought tolerance and high coffee bean yield, need to be introduced and characterized in other locations to check adaptability. The aim of this study was to check the agronomic performance and determine the genetic parameters of the clonal canephora coffee cultivar Marilândia ES 8143, composed by twelve genotypes, developed by the Capixaba Institute of Research, Technical Assistance and Rural Extension (Instituto Capixaba de Pesquisa, Assistência Técnica e Extensão Rural—Incaper), in an irrigated system of the Central Cerrado region of Brazil. The study was conducted in the experimental areas of Embrapa Cerrados at 1050 m altitude in a center pivot irrigation system using a management system with water stress controlled for around 65 days. A randomized block experimental design was used with three replications, and each plot consisted of eight plants. The clones were planted in February 2019 and in 2021 and 2022. Phenotyping was carried out to evaluate the following traits: coffee bean yields, sieve retention percentages, plant height, canopy projection, number of pairs of plagiotropic branches, and frost damage using a scoring scale. Clone 5 stood out in mean value in the two years evaluated for bean yield. Clones 5, 6, 7, 8, and 9 had higher mean values for flat-type coffee beans in both years. Clones 1 and 5 exhibited mean values indicating good vegetative development. Clones 5 and 12 showed no visible symptoms for low air temperatures and frost effects. Highly significant differences were observed among the genotypes for all the morphoagronomic traits evaluated, and high values of heritability, genetic coefficients of variation, and selective accuracy showed conditions favorable to the selection of clones for the agronomic traits analyzed. Clones 1, 2 and 6 have values in lower groups for chlorogenic acids and caffeine, and in higher groups for protein and soluble solids, thus showing greater potential for obtaining quality beverages. Full article

Water scarcity in semi-arid regions necessitates accurate soil water modeling to optimize irrigation management. This study compares three HYDRUS-1D parameterization approaches—based on the drying-branch soil water retention curve (SWRC), wetting-branch SWRC (using Shani’s drip method), and inverse modeling—to simulating soil water content at 15 cm and 45 cm depths under center-pivot irrigation in a semi-arid region. Field experiments in three maize fields provided daily soil water, soil hydraulic, and meteorological data. Inverse modeling achieved the highest accuracy (NRMSE: 2.29–7.40%; RMSE: 0.006–0.023 cm³ cm⁻³), particularly at 15 cm depth, by calibrating van Genuchten parameters against observed water content. The wetting-branch approach outperformed the drying branch at the same depth, capturing irrigation-induced wetting processes more effectively. Statistical validation confirmed the robustness of inverse modeling in reproducing temporal patterns, while wetting-branch data improved deep-layer accuracy. The results demonstrate that inverse modeling is a reliable approach for soil water simulation and irrigation management, whereas the wetting-branch parameterization offers a practical, field-adaptable alternative. This study provides one of the first side-by-side evaluations of these three modeling approaches under real-world semi-arid conditions. Full article

Anaerobic digestate from manure, a byproduct of biogas production, is increasingly used as an organic fertilizer in circular agriculture systems. This study assessed the microbiological impact of maize fertigation with anaerobic digestate, focusing on fecal indicators (Escherichia coli, Salmonella), antibiotic resistance genes (ARGs), and integrons. The trial was conducted in a commercial maize field, where on-site manure-based anaerobic digestate was applied via center-pivot irrigation. Leaf samples were collected two days (2 dai) and four weeks (4 wai) after the last fertigation. E. coli and Salmonella were assessed by culturable methods, while ARGs and integrons were analyzed by qPCR. Results showed that E. coli (3 MPN/g) and Salmonella were detected at 2 dai but were undetectable at 4 wai and in the control condition, suggesting transient contamination. The abundance of tetW was approximately tenfold higher in digestate-treated plants than in the control, while no consistent changes were observed for the other genes. Overall, fertigation with anaerobic digestate appears to pose minimal microbiological impact within the specific conditions of this study, although it may act as a source of specific resistance determinants. Although limited by the use of single treated and control plots, this study offers preliminary insight into microbial and resistance gene dynamics in the phyllosphere, providing a basis for future replicated hypothesis-driven studies. Full article

By 2023, deforestation in the Cerrado biome surpassed 50% of its original area, primarily due to the conversion of native vegetation to pasture and agricultural land. In addition to anthropogenic pressure, climate change has intensified hydrological stress by reducing precipitation and decreasing river flows, thereby threatening water security, quality, and availability in that biome. The Annual Water Yield (AWY) model from the InVEST platform provides a tool to assess ecosystem services by estimating the balance between precipitation and evapotranspiration (ET). In this study, we applied the AWY model to the Urucuia River Basin, analyzing water yield trends from 1991 to 2020. We evaluated climate variables, land use dynamics, and river discharge data and validated the model validation using observed stream flow data. Although the model exhibited low performance in simulating observed streamflow (NSE = −0.14), scenario analyses under reduced precipitation and increased evapotranspiration (ET) revealed consistent water yield responses to climatic variability, supporting the model’s heuristic value for assessing the relative impacts of land use and climate change. The effects of deforestation on estimated water yield were limited, as land use changes resulted in only moderate shifts in basin-wide ET. This was primarily due to the offsetting effects of land conversion: while the replacement of savannas with pasture reduced ET, the expansion of agricultural areas increased it, leading to a net balancing effect. Nevertheless, other ecosystem services—such as water quality, soil erosion, and hydrological regulation—may have been affected, threatening long-term regional sustainability. Trend analysis showed a significant decline in river discharge, likely driven by the expansion of irrigated agriculture, particularly center pivot systems, despite the absence of significant trends in precipitation or ET. Full article

Realization of the goals of precision agriculture is dependent on prescribing irrigation strategies matched to spatiotemporal variations in soil moisture on commercial farms. However, the scale at which these variations occur is not well understood. A high-spatial-density network of sensors with the ability to measure and report data over the course of a growing season is needed. In this work, design of the low-profile Smart Bluetooth Stake spatiotemporal soil moisture mapping system is presented. Smart stakes use Bluetooth Low Energy to communicate 64 MHz soil moisture impedance measurements from ground level to a receiver mounted on the center-pivot irrigation boom and equipped with a rotating high-gain parabolic antenna. Smart stakes can remain in the ground throughout the entire growing season without disrupting farm operations. A system of 86 sensors was deployed on a 50-hectare commercial field near Elberta, Utah, during the final growth stage of a crop of winter wheat. Different receiver antenna configurations were tested over the course of several weeks which included two full irrigation cycles. In the high-gain antenna configuration, data was successfully collected from 75 sensors, with successful packet transmission at ranges of approximately 600 m. Enough data was collected to construct a spatiotemporal moisture map of the field over the course of an irrigation cycle. Smart Bluetooth Stakes constitute an important advance in the spatial density achievable with direct sensors for precision agriculture. Full article

Effective detection of center-pivot irrigation systems is crucial in understanding agricultural activity and managing groundwater resources for sustainable uses, especially in semi-arid regions such as North Dakota, where irrigation primarily depends on groundwater resources. In this study, we have adopted YOLOv11 to detect the center-pivot irrigation systems using multiple remote sensing datasets, including Landsat 8, Sentinel-2, and NAIP (National Agriculture Imagery Program). We developed an ArcGIS custom tool to facilitate data preparation and large-scale model execution for YOLOv11, which was not included in the ArcGIS Pro deep learning package. YOLOv11 was compared against other popular deep learning model architectures such as U-Net, Faster R-CNN, and Mask R-CNN. YOLOv11, using Landsat 8 panchromatic data, achieved the highest detection accuracy (precision: 0.98; recall: 0.91; and F1-score: 0.94) among all tested datasets and models. Spatial autocorrelation and hotspot analysis revealed systematic prediction errors, suggesting a need to adjust training data regionally. Our research demonstrates the potential of deep learning in combination with GIS-based workflows for large-scale irrigation system analysis, adopting precision agricultural technologies for sustainable water resource management. Full article

In this study, alfalfa (Medicago sativa L.) is evaluated for suitability of variable rate irrigation (VRI) by analyzing within-field variation in crop water productivity (CWP) under uniform irrigation. The objectives were to (1) measure within-field variation in crop evapotranspiration (ET), (2) quantify spatial variability of alfalfa biomass yield, and (3) assess whether a bivariate analysis of CWP and yield could inform VRI management zones. Research was conducted on a 22.6 ha center-pivot irrigated alfalfa field near Rexburg, Idaho, USA, over three harvest intervals (HIs) in 2021 and 2022. Using a water balance method at 66 field points, ET exhibited significant spatial clustering for each HI (p < 0.001 for all HIs), though spatial patterns varied among HIs. Biomass yield, measured via the quadrat method, ranged from 2.1 to 9.7 Mg ha⁻¹, with significant spatial clustering (p < 0.001 for all HIs). The CWP ranged from 0.07 to 0.54 Mg ha⁻¹ cm⁻¹, also showing significant spatial clustering (p < 0.001 for all HIs). Bivariate cluster analysis indicated 12–18% more area of the field was over-watered than under-watered, suggesting potential for optimizing irrigation with VRI. Reducing irrigation in these over-watered zones could improve CWP, supporting alfalfa as a viable candidate for VRI. Full article

Mobile drip irrigation (MDI) systems integrate the technological advantages of center-pivot irrigation (CPI) systems and drip irrigation systems, boasting a high water-saving potential. To further enhance water use efficiency in alfalfa production in northern China, this preliminary study verified the accuracy of the HYDRUS-2D soil water movement numerical model through field experiments. Using the numerical model, four drip-line installation distances (60, 75, 90, and 105 cm), three deficit irrigation thresholds (45–50% FC, 55–60% FC, and 65–70% FC), and four irrigation depths (70% W, 85% W, 100% W, and 115% W) were set to simulate root water uptake, soil surface evaporation, total irrigation amount, and deep percolation during the entire growth cycle of alfalfa, respectively. Objective functions were constructed according to the simulation results, and the NSGA-II algorithm was used for multi-objective optimization of the deficit irrigation schedule. The preliminary results indicated that HYDRUS-2D can accurately simulate soil water movement under MDI systems, as the RMSE values of soil water content at all measured depths were less than 0.021 cm³/cm³, with the NRMSE values being below 23.3%, and the MAE values below 0.014 cm³/cm³. Increasing the deficit irrigation threshold from F1 to F3 enhanced root water uptake by 12.24–15.34% but simultaneously increased the total irrigation amount, soil surface evaporation (by up to 29.58%), and the risk of deep percolation; similar trends were observed with increasing irrigation depth. The drip-line installation distance had no significant impact on irrigation performance. The NSGA-II multi-objective optimization algorithm was used to obtain Pareto-optimal solutions that balance conflicting objectives. For this case study, a drip-line installation distance of 105 cm, a deficit irrigation threshold of 50–55% FC, and an irrigation depth of 112% W were recommended to achieve balance among the various optimization objectives. This study provides a preliminary framework for optimizing MDI systems and irrigation strategies. However, since a deeper root distribution (>80 cm) was not investigated in this study, future research incorporating deeper root zones is required for developing more comprehensive irrigation scheduling suitable for typical alfalfa cultivation scenarios. Full article

Increasing water scarcity for agricultural irrigation demands options to maximize yield with available water. Alfalfa (Medicago sativa) is a valuable crop in arid and semiarid regions and is considered a major user of irrigation water. Consequently, an area of established alfalfa was center-pivot-irrigated over two years according to one of four irrigation regimes, each with three replicates as strip plots. These were started after the last of the six harvests of the year, after seeding: winter-irrigated and throughout the growing season (winter full), winter-irrigated and terminated after the 4th harvest (winter limited), irrigated from mid-April, when canal water typically becomes available, and throughout the remainder of the growing season (season full), or typically-irrigated until the 4th harvest (season limited). Annual dry matter yield (DMY) was increased using winter irrigation compared to season-only irrigation (10.34, 8.94, 8.67, and 6.54 Mg ha⁻¹ for winter full, winter limited, season full, and season limited, respectively, p < 0.0001, SEM 0.45). Irrigation termination after the fourth harvest with no winter irrigation significantly reduced annual applied water use efficiency (AAWUE) compared to all other treatments (9.08, 8.59, 8.82, and 7.38 kg DMY ha⁻¹ mm⁻¹ for winter full, winter limited, season full, and season limited, respectively; p < 0.0098, SEM = 0.38). Winter irrigation to fill the soil profile, followed by late summer irrigation termination, is feasible for increasing alfalfa productivity over season-only irrigation. Full article

The 204-million-hectare Brazilian tropical savanna (Cerrado biome), located in the central part of Brazil, constitutes the main region of food and natural fiber production in the country. An important part of this production is based on center pivot irrigation. Existing studies evaluating the spatio-temporal dynamics of center pivots in Brazil do not consider their retraction. This study aimed to evaluate the expansion and retraction of center pivots in the Cerrado biome in the period 1985–2020. We relied on the data produced by the MapBiomas Irriga project. In this period, the area occupied by center pivots increased from 47 thousand hectares in 1985 to 1.2 million hectares in 2020, mostly concentrated in the states of Minas Gerais, Goiás, São Paulo, and Bahia, confirming previous reports available in the literature. Among the 13 irrigation poles recognized by the National Water Agency (ANA), the Oeste Baiano (Bahia State) and the São Marcos (Goiás State) presented the largest areas of center pivots (173,048 ha and 101,725 ha, respectively). We also found that 76% of the center pivots are concentrated in the regions with low water availability (0.01–0.45 mm day⁻¹). Within this 16-year period (2005–2020), more than 10% of center pivots found in 2005 were either abandoned or converted into rain-fed crop production. The results of this study can provide an important foundation for public policies directed toward the sustainable use of water resources by different consumers. Full article

The Ogallala aquifer, underlying eight states from South Dakota to Texas, is practically non-recharging south of Nebraska, and groundwater withdrawals for irrigation have lowered the aquifer in western Kansas. Subsequent well-yield declines encourage deficit irrigation, greater reliance on precipitation, and producing profitable drought-tolerant crops like upland cotton (Gossypium hirsutum (L.)). Our objective was to evaluate deficit irrigated cotton growth, yield, and water productivity (CWP) in northwest, west-central, and southwest Kansas in relation to El Niño southern oscillation (ENSO) phase effects on precipitation and growing season cumulative thermal energy (CGDD). Using the GOSSYM crop growth simulator with actual 1961–2000 location weather records partitioned by the ENSO phase, we modeled crop growth, yield, and evapotranspiration (ET) for irrigation capacities of 2.5, 3.75, and 5.0 mmd⁻¹ and periods of 4, 6, and 8 weeks. Regardless of location, the ENSO phase did not influence CGDD, but precipitation and lint yield decreased significantly in southwest Kansas during La Niña compared with the Neutral and El Niño phases. Simulated lint yields, ET, CWP, and leaf area index (LAI) increased with increasing irrigation capacity despite application duration. Southwestern Kansas producers may use ENSO phase information with deficit irrigation to reduce groundwater withdrawals while preserving desirable cotton yields. Full article

Water use efficiency measures are generally recommended to reduce water use. Yet, flood irrigation practices in high-elevation mountain valleys of the Colorado River Basin headwaters generate return flows, which support late-season streamflow and groundwater recharge. Return flows support the ecosystem and provide recreational benefits. This study provides a framework for quantifying how land-use changes and associated return flow patterns affect the economic value of water across uses in a hydrologically connected, shallow alluvial aquifer system. This study first investigates how return flow patterns could change under three alternatives to flood irrigation: an increased use of center pivots, increased residential development, and conversion to pasture. The brown trout was used as an indicator species to track eco-hydrology, return flow, and capacity for recreational activities under each alternative. Estimates from the non-market valuation literature coupled with predicted changes in brown trout productivity approximate associated changes to recreational angler value. Recreational angler values are highest under the flood irrigation alternative. The inclusion of recreational angler values with agricultural values alters the magnitude of returns but not the rankings. These results highlight the potential heterogeneity of conclusions to be drawn regarding water use efficiency, depending on the economic value of water in different uses and the degree of hydrologic connectivity. This study also highlights data gaps and modeling needs for conducting similar future analyses. Full article

Mechanical properties of vegetables or crop materials play a noteworthy part in designing new related implements. These properties can be extracted from force–deformation curves. Several factors, such as soil preparation, irrigation, and pre- and post-harvest treatments influence them. The core objective of this investigation work was to analyze force–deformation curves obtained from compression, penetration, and shear tests of potatoes (Spunta cv.) produced with three tillage implements. The potatoes cv. Spunta were cultivated in loamy sand soil under the center-pivot irrigation system. The tillage implements used were a disc harrow plow, chisel plow, and moldboard plow. The trials were performed at a constant planting depth (15 cm) below the soil and a single plowing speed of 5.4 km/h. All data were expressed as an average of five replicates ± standard deviation. The force–deformation curves analysis showed that the modulus of elasticity for potatoes cv. Spunta ranged from 4.32 to 5.8 N/mm, the bioyield force ranged from 84.25 to 114.12 N, and rupture forces ranged from 100.90 to 139.78 N. Furthermore, the results showed that the average values of the elastic and plastic ranges were 3.0 and 2.1 mm, respectively. The mean value of hardness was 1671.53 N·mm. No significant differences were observed with respect to the two planting seasons, but tillage implements had a significant impact on the characteristics extracted from the compression tests. The mean of the maximum forces required to penetrate the potato during the penetration stage were 41.24 N, 44.86 N, and 47.16 N for potatoes produced with the disc harrow plow, chisel plow, and moldboard plow, respectively. Similarly, the means of the maximum forces required to cut the potato in the shear stage were 724.38 N, 761 N, and 773.43 N for the disc harrow plow, chisel plow, and moldboard plow, respectively. The force–deformation curves showed that additional information might be required to obtain a complete description of the potato quality necessary to harvest potatoes cv. Spunta using harvesting and handling equipment with reduced economic loss. An extensive study of the soil characteristics and the above-mentioned properties is also recommended. The results obtained about the mechanical characteristics of potatoes cv. Spunta can be useful in providing information that aids in designing potato harvesting machines and in potato products factories. Full article