Search Results (283)

Strawberry planting requires different amounts of soil water-holding capacity and fertilizer at different growth stages. Determining the stages of strawberry growth has important guiding significance for irrigation, fertilization, and picking. Quick and accurate identification of strawberry plants at different stages can provide important information for automated strawberry planting management. We propose an improved multistage identification model for strawberry based on the YOLOv9 algorithm—the ASHM-YOLOv9 model. The original YOLOv9 showed limitations in detecting strawberries at different growth stages, particularly lower precision in identifying occluded fruits and immature stages. We enhanced the YOLOv9 model by introducing the Alterable Kernel Convolution (AKConv) to improve the recognition efficiency while ensuring precision. The squeeze-and-excitation (SE) network was added to increase the network’s capacity for characteristic derivation and its ability to fuse features. Haar wavelet downsampling (HWD) was applied to optimize the Adaptive Downsampling module (Adown) of the initial model, thereby increasing the precision of object detection. Finally, the CIoU function was replaced by the Minimum Point Distance based IoU (MPDIoU) loss function to effectively solve the problem of low precision in identifying bounding boxes. The experimental results demonstrate that, under identical conditions, the improved model achieves a precision of 97.7%, a recall of 97.2%, mAP50 of 99.1%, and mAP50-95 of 90.7%, which are 0.6%, 3.0%, 0.7%, and 7.4% greater than those of the original model, respectively. The parameters, model size, and floating-point calculations were reduced by 3.7%, 5.6% and 3.8%, respectively, which significantly boosted the performance of the original model and outperformed that of the other models. Experiments revealed that the model could provide technical support for the multistage identification of strawberry planting. Full article

This study evaluates the agronomic and environmental performance of pelletized compost derived from olive mill waste as a sustainable alternative to mineral fertilizers for cultivating wheat (Triticum turgidum L.) under conventional tillage methods. A field experiment was conducted in semi-arid Spain, employing three fertilization strategies: inorganic (MAP + Urea), sewage sludge (SS), and organic compost pellets (OCP), each providing 150 kg N ha⁻¹. The parameters analyzed included wheat yield, grain quality, soil properties, and greenhouse gas (GHG) emissions. Inorganic fertilization yielded the highest productivity and nutrient uptake. However, the OCP treatment reduced grain yield by only 15%, while improving soil microbial activity and enzymatic responses. The SS and OCP treatments showed increased CO₂ and N₂O emissions compared to the control and inorganic plots. However, the OCP treatment also acted as a CH₄ sink. Nutrient use efficiency was greatest under mineral fertilization, though the OCP treatment outperformed the SS treatment. These results highlight the potential of OCP as a circular bio-based fertilizer that can enhance soil function and partially replace mineral inputs. Optimizing application timing is critical to aligning nutrient release with crop demand. Further long-term trials are necessary to evaluate their impact on the soil and improve environmental outcomes. Full article

Variable-rate nitrogen (VR-N) application allows farmers to optimize nitrogen (N) input site-specifically within field boundaries, enhancing both economic efficiency and environmental sustainability. In this study, VR-N technology was applied to durum wheat in two small-scale commercial fields (3–4 ha each) located in distinct agro-climatic zones of Thessaly, central Greece. A real-time VR-N application algorithm was used to calculate N rates based on easily obtainable near-real-time data from unmanned aerial vehicle (UAV) imagery, tailored to the crop’s actual needs. VR-N implementation was carried out using conventional fertilizer spreaders equipped to read prescription maps. Results showed that VR-N reduced N input by up to 49.6% compared to the conventional uniform-rate N (UR-N) application, with no significant impact on wheat yield or grain quality. In one of the fields, the improved gain of VR-N when compared to UR-N was 7.2%, corresponding to an economic gain of EUR 163.8 ha⁻¹, while in the second field—where growing conditions were less favorable—no considerable VR-N economic gain was observed. Environmental benefits were also notable. The carbon footprint (CF) of the wheat crop was reduced by 6.4% to 22.0%, and residual soil nitrate (NO₃⁻) levels at harvest were 13.6% to 36.1% lower in VR-N zones compared to UR-N zones. These findings suggest a decreased risk of NO₃⁻ leaching and ground water contamination. Overall, the study supports the viability of VR-N as a practical and scalable approach to improve N use efficiency (NUE) and reduce the environmental impact of wheat cultivation which could be readily adopted by farmers. Full article

In this study several phosphorus fertilizers were evaluated under controlled production conditions using Lactuca sativa var. baby leaf and a clay-loam soil of pH 6.5 as a plant–soil model system. Various inorganic (phosphate rock, monoammonium phosphate, struvite), organic (bone meal and bone meal pelletized with compost) and organomineral fertilizers (phosphate rock, monoammonium phosphate, struvite pelletized with compost) were compared. The soil properties, crop yield, morphological aspects and metabolomics of the plants were analyzed. After 45 days of the growing cycle, the organomineral fertilizers (OMFs) composed of compost and monoammonium phosphate (OMF₂(MAP+C)) or struvite (OMF₃(STR+C)) exhibited the best yield results: 101.37 g and 83.21 g, respectively. These treatments also exhibited the best phosphorus use efficiency (PUE) results: 7.40% and 8.33%, respectively. The yield of plants treated with MAP was 56.01 g, and its PUE was 5.33%. The yield of plants treated with STR was 62.10 g and the PUE was 4.67%. Accordingly, the development of OMFs with compost had a positive effect regarding MAP and STR fertilization. Lettuce fertilized with organic bone meal fertilizers had the lowest yield and nutrient use efficiency. The non-targeted metabolic study of green tissue revealed an overactivation of the TriCarboxylic Acids-TCA cycle and amino acid biosynthesis in plants fertilized with bone meal and phosphate rock treatments, likely as a plant stress response. The overall conclusion of this work is that the development of OMFs with compost is a good strategy to increase soil P availability and, accordingly, plant P uptake and %PUE. Full article

Microelement deficiencies, often termed “hidden hunger”, represent a significant global health challenge. Optimal human health relies on adequate dietary intake of essential microelements, including selenium (Se), zinc (Zn), copper (Cu), boron (B), manganese (Mn), molybdenum (Mo), iron (Fe), nickel (Ni), and chlorine (Cl). In recent years, there has been a growing focus on vitality and longevity, which are closely associated with the sufficient intake of essential microelements. This review focuses on these nine elements, whose bioavailability in the food chain is critically determined by their geochemical behavior in soils. There is a necessity for an understanding of the sources, soil–plant transfer, and plant uptake mechanisms of these microelements, with particular emphasis on the influence of key soil properties, including pH, redox potential, organic matter content, and mineral composition. There is a dual challenge of microelement deficiencies in agricultural soils, leading to inadequate crop accumulation, and the potential for localized toxicities arising from anthropogenic inputs or geogenic enrichment. A promising solution to microelement deficiencies in crops is biofortification, which enhances nutrient content in food by improving soil and plant uptake. This strategy includes agronomic methods (e.g., fertilization, soil amendments) and genetic approaches (e.g., marker-assisted selection, genetic engineering) to boost microelement density in edible tissues. Moreover, emphasizing the need for advanced predictive modeling techniques, such as ensemble learning-based digital soil mapping, enhances regional soil microelement management. Integrating machine learning with digital covariates improves spatial prediction accuracy, optimizes soil fertility management, and supports sustainable agriculture. Given the rising global population and the consequent pressures on agricultural production, a comprehensive understanding of microelement dynamics in the soil–plant system is essential for developing sustainable strategies to mitigate deficiencies and ensure food and nutritional security. This review specifically focuses on the bioavailability of these nine essential microelements (Se, Zn, Cu, B, Mn, Mo, Fe, Ni, and Cl), examining the soil–plant transfer mechanisms and their ultimate implications for human health within the soil–plant–human system. The selection of these nine microelements for this review is based on their recognized dual importance: they are not only essential for various plant metabolic functions, but also play a critical role in human nutrition, with widespread deficiencies reported globally in diverse populations and agricultural systems. While other elements, such as cobalt (Co) and iodine (I), are vital for health, Co is primarily required by nitrogen-fixing microorganisms rather than directly by all plants, and the main pathway for iodine intake is often marine-based rather than soil-to-crop. Full article

Precision agriculture increasingly relies on real-time data from soil sensors to optimize irrigation and nutrient application. Soil moisture and electrical conductivity (EC) are key indicators in irrigation and fertigation systems, directly affecting water-use efficiency and nutrient delivery to crops. This study evaluates the performance of an IoT-based soil-monitoring system for real-time tracking of EC and soil moisture under varied fertigation conditions in both laboratory and field scenarios. The EC sensor showed strong agreement with laboratory YSI measurements (R² = 0.999), confirming its accuracy. Column experiments were conducted in three soil types (sand, sandy loam, and loamy sand) to assess the EC and soil moisture response to fertigation. Sand showed rapid infiltration and low retention, with EC peaking at 420 µS/cm and moisture 0.33 cm³/cm³, indicating high leaching risk. Sandy loam retained the most moisture (0.35 cm³/cm³) and showed the highest EC (550 µS/cm), while loamy sand exhibited intermediate behavior. Fertilizer-specific responses showed higher EC in Calcium Ammonium Nitrate (CAN)-treated soils, while Monoammonium Phosphate (MAP) showed lower, more stable EC due to limited phosphorus mobility. Field validation confirmed that the IoT system effectively captured irrigation and fertigation events through synchronized EC and moisture peaks. These findings highlight the efficacy of IoT-based sensor networks for continuous, high-resolution soil monitoring and their potential to support precision fertigation strategies, enhancing nutrient-use efficiency while minimizing environmental losses. Full article

Crop straw chopping and returning technology has gained global implementation to enhance soil structure and fertility, facilitating increased crop yield. Nevertheless, technological adoption faces challenges from inherent limitations in machinery performance, including poor chopping and returning quality and high energy consumption. Consequently, this review first presented a theoretical framework that described the mechanical properties of straw, its fracture dynamics, interactions with airflow, and motion characteristics during the chopping process. Then, based on the straw returning process, the chopping devices were classified into five types: the chopped blade, the chopping machine, the chopping device combined with a no-tillage or reduced-tillage seeder, the chopping and ditch-burying machine, the chopping and mixing machine, and the harvester-powered chopping device. Advancements in spreading devices were also summarized. Finally, six key directions for future research were proposed: developing an intelligent field straw distribution mapping system, engineering adaptive self-regulating mechanisms for chopping and returning equipment, elucidating the mechanics and kinematics of straw in the chopping and returning process, implementing real-time quality assessment systems for straw returning operations, pioneering high forward-speed (>8 km/h) straw returning machines, and establishing context-specific straw residue management frameworks. This review provided a reference and offered support for the global application of straw returning technology. Full article

Groundwater nitrate (NO₃⁻) contamination has emerged as a critical global environmental issue, posing serious human health risks. This study systematically investigated the hydrochemical processes, sources of NO₃⁻ pollution, the impact of land use on NO₃⁻ pollution, and drinking water safety in an urban area of southwestern China. Thirty-one groundwater samples were collected and analyzed for major hydrochemical parameters and dual isotopic composition of NO₃⁻ (δ¹⁵N-NO₃⁻ and δ¹⁸O-NO₃⁻). The groundwater samples were characterized by neutral to slightly alkaline nature, and were dominated by the Ca-HCO₃ type. Hydrochemical analysis revealed that water–rock interactions, including carbonate dissolution, silicate weathering, and cation exchange, were the primary natural processes controlling hydrochemistry. Additionally, anthropogenic influences have significantly altered NO₃⁻ concentration. A total of 19.35% of the samples exceeded the Chinese guideline limit of 20 mg/L for NO₃⁻. Isotopic evidence suggested that primary sources of NO₃⁻ in groundwater include NH₄⁺-based fertilizer, soil organic nitrogen, sewage, and manure. Spatial distribution maps indicated that the spatial distribution of NO₃⁻ concentration correlated strongly with land use types. Elevated NO₃⁻ levels were observed in areas dominated by agriculture and artificial surfaces, while lower concentrations were associated with grass-covered ridge areas. The unabsorbed NH₄⁺ from nitrogen fertilizer entered groundwater along with precipitation and irrigation water infiltration. The direct discharge of domestic sewage and improper disposal of livestock manure contributed substantially to NO₃⁻ pollution. The nitrogen fixation capacity of the grassland ecosystem led to a relatively low NO₃⁻ concentration in the ridge region. Despite elevated NO₃⁻ and F⁻ concentrations, the entropy weighted water quality index (EWQI) indicated that all groundwater samples were suitable for drinking. This study provides valuable insights into NO₃⁻ source identification and hydrochemical processes across varying land-use types. Full article

In this work, an innovative process chain is set up for the regular provision of fertilization consultation services to farmers for a variety of crops, within a precision agriculture framework. The central hub of this mechanism is a geographic information system (GIS), while a 5 × 5 m point grid is the information carrier. Potential data sources include soil samples, satellite imagery, meteorological parameters, yield maps, and agronomic information. Whenever big data are available per crop, decision-making is supported by machine learning systems (MLSs). All the map data are uploaded to a farm management information system (FMIS) for visualization and storage. The recipe maps are transmitted wirelessly to variable rate technologies (VRTs) for applications in the field. To a large degree, the process chain has been automated with programming at many levels. Currently, four different service modules based on the new process chain are available in the market. Full article

Soil organic matter (SOM) is an essential factor affecting the growth and development of crops, so the establishment of an efficient and rapid method for detecting SOM content is of great significance for crop cultivation and management. The spatial distribution map of SOM content in the study area was obtained by using the optimal model, and a distribution map of aboveground wheat biomass under different fertilization conditions was drawn. The results of this study showed that the fertilization treatments significantly increased the SOM content, and its spatial distribution showed obvious heterogeneity. By plotting the spatial distribution of SOM content and wheat growth under different fertilization conditions, it was found that the wheat biomass of fertilized fields was significantly higher than that of non-fertilized fields. Further analysis showed that there was a significant positive correlation between SOM content and wheat biomass, and a quantitative model between the two was established. This study provides scientific evidence and technical support for soil nutrient management and crop productivity enhancement in precision agriculture, as well as a reference for the application of hyperspectral imagery in agroecosystem monitoring. Full article

Continuous use of agricultural chemicals and fertilizers, sporadic sewer overflow events, and an increase in urbanization have led to significant nutrient/pollutant loadings into the semi-arid Arroyo Colorado River basin, which is located in South Texas, U.S. Priority nutrients that require reduction include phosphorus and nitrogen and to mitigate issues of low dissolved oxygen, in some of its river segments. Consequently, the river’s potential to support aquatic life has been significantly reduced, thus highlighting the need for restoration. To achieve this restoration, a watershed protection plan was developed, comprising several preventive mitigation measures, including installing green infrastructure (GI) practices. However, for effective reduction of excessive nutrient loadings, there is a need to study the effects of different combinations of GI practices under current and future land use scenarios to guide decisions in implementing the cost-effective infrastructure while considering factors such as the existing drainage system, topography, land use, and streamflow. Therefore, this study coupled the Soil and Water Assessment Tool (SWAT) model with the System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) model to determine the effects of different combinations of GI practices on the reduction of nitrogen and phosphorus under changing land use conditions in three selected Arroyo Colorado subwatersheds. Two land use maps from the U.S. Geological Survey (USGS) Forecasting Scenarios of land use (FORE-SCE) model for 2050, namely, A1B and B1, were implemented in the coupled SWAT-SUSTAIN model in this study, where the urban area is projected to increase by 6% and 4%, respectively, with respect to the 2018 land use scenario. As expected, runoff, phosphorus, and nitrogen slightly increased with imperviousness. The modeling results showed that implementing either vegetated swales or wet ponds reduces flow and nutrients to meet the Total Maximum Daily Loads (TMDLs) targets, which cost about USD 1.5 million under current land use (2018). Under the 2050 future projected land use changes (A1B scenario), the cost-effective GI practice was implemented in vegetated swales at USD 1.5 million. In contrast, bioretention cells occupied the least land area to achieve the TMDL targets at USD 2 million. Under the B1 scenario of 2050 projected land use, porous pavements were most cost effective at USD 1.5 million to meet the TMDL requirements. This research emphasizes the need for collaboration between stakeholders at the watershed and farm levels to achieve TMDL targets. This study informs decision-makers, city planners, watershed managers, and other stakeholders involved in restoration efforts in the Arroyo Colorado basin. Full article

Controlled-release fertilizers contain polymeric coatings that modify the dynamics of phosphorus (P) release in soil. This study aimed to characterize P release from physical mixtures between conventional and controlled-release fertilizers (CRFs), quantify soil P availability, and assess agronomic responses of coffee plants during the establishment phase. Two main types of P fertilizer were evaluated: conventional monoammonium phosphate (MAP) and a blend (physical mixture of conventional MAP and controlled-release P fertilizers). Both fertilizers were applied at 0, 134, 268, and 403 kg ha⁻¹ of P₂O₅. Our findings revealed a blend longevity of 3 and 6 months. P fertilization contributed to an increase in leaf area (1134.7 cm² plant⁻¹) and shoot biomass (602.8 kg ha⁻¹) and raised P in the soil (0.061 mg dm⁻³ per kg of P₂O₅ applied). P accumulation in the coffee plants ranged between 3 and 4 kg ha⁻¹. Other macronutrient accumulations in aerial parts were of the following ranges (in kg ha⁻¹): 47–60 for N, 36–46 for K, 18–22 for Ca, 5–7 for Mg, and 3–4 for S. Micronutrients accumulated (in g ha⁻¹): 454–657 for Fe; 117–160 for B; 117–149 for Mn; 58–71 for Cu; and 34–43 for Zn. Up to 74% of the nutrients were distributed in the leaves. We concluded that the use of blends did not impose any limitation on P nutrition for coffee plants and led to biomass gains (18.9%) in plagiotropic branches. P fertilization proved essential for supporting the initial growth of coffee plants and increasing coffee leaf area and P levels in the soil and promotes adequate levels of P accumulation in plants, leading to improvements in coffee crop nutrition in the establishment phase. Full article

The Valley of Tammaro River lies between the regions of Molise and Campania in central southern Italy. The area has been inhabited since ancient times due to its fertile soil and plentiful water resources. The interest in this region is enhanced by the many urban centers and the isolated and rural building complexes that date back to the Samnite era and are connected by a road system that is still in use today. Saepinum, regarded as the symbol of Roman civilization in the Molise area (Italy), is one of these. Before becoming a Roman municipium and then a medieval and contemporary rural community, it was a Samnite trade forum and service center. A suburban villa belonging to the Gens Neratia, a family originally from the Roman municipality of Saepinum, is connected to it approximately 2 km northeast. Both sites were partially excavated, and much more can be learned from the material still available. To this end, geoelectrical studies using the tensor acquisition mode were used to conduct geophysical surveys in certain sectors. The data were processed using Data-Adaptive Probability-Based Electrical Resistivity Tomography, here adapted for the first time to Apparent Resistivity Tensor Analysis. The trace of the apparent resistivity tensor provides distortion-free maps and demonstrates that the anomalies are closely constrained on the source bodies. Full article

Quantifying changes in soil properties greatly benefits our understanding of soil management and sustainable land use, especially in the context of strong anthropogenic activities and climate change. This study investigated the effects of long-term reclamation on soil properties in an artificial oasis region with a cultivation history of more than 50 years. Critical soil properties were measured at 77 sites, and a total of 462 soil samples were collected down to a depth of 1 m, which captures both surface and subsurface processes that are critical for long-term cultivation effects. Thirteen critical soil properties were analyzed, among which four properties—soil organic carbon (SOC), total phosphorus (TP), pH, and ammonium nitrogen (NH₄⁺)—were selected for detailed analysis due to their ecological significance and low intercorrelation. By comparing cultivated soils with nearby desert soils, this study found that semicentennial cultivation led to significant improvements in soil properties, including increased concentrations of SOC, NH₄⁺, and TP, as well as reduced pH throughout the soil profile, indicating improved fertility and reduced alkalinity. Further analysis suggested that environmental factors—including temperature, clay content, evaporation differences between surface and subsurface layers, sparse vegetation cover, cotton root distribution, as well as prolonged irrigation and fertilization—collectively contributed to the enhancement of SOC decomposition and the reduction of soil alkalinity. Furthermore, three-dimensional digital soil mapping was performed to investigate the effects of long-term cultivation on the distributions of soil properties at unvisited sites. The soil depth functions were separately fitted to model the vertical variation in the soil properties, including the exponential function, power function, logarithmic function, and cubic polynomial function, and the parameters were extrapolated to unvisited sites via the quantile regression forest (QRF), boosted regression tree, and multiple linear regression techniques. The QRF technique yielded the best performance for SOC (R² = 0.78 and RMSE = 0.62), TP (R² = 0.79 and RMSE = 0.12), pH (R² = 0.78 and RMSE = 0.10), and NH₄⁺ (R² = 0.71 and RMSE = 0.38). The results showed that depth function coupled with machine learning methods can predict the spatial distribution of soil properties in arid areas efficiently and accurately. These research conclusions will lead to more effective targeted measures and guarantees for local agricultural development and food security. Full article

Crop rotation affects soil pH by disturbing H⁺ production and consumption within soil–crop systems, primarily through fertilization, irrigation, cropping, and harvest. Studies have shown that crop rotation improves soil organic matter prediction. However, simply incorporating crop rotation may not significantly improve soil pH prediction, because the spatial variability in soil pH is lower and the way crop rotation influences pH is different. To quantify the extent to which crop rotation improves soil pH mapping, we introduced the strategy of grouping soil samples by crop rotation and modeling separately. We chose a typical multiple-cropping region suffering soil acidification in Southern China, where the complex crop rotation was mapped by Sentinel-1/2 time series and a legend featuring three main systems (i.e., paddy, vegetable, and orchard) and nine subsystems. This crop rotation map was then combined with other variables to derive multiple combinations and predict soil pH. Based on the best combination, we further assessed the grouping strategy. The results showed that simply incorporating crop rotation in one joint model was useful but could not obtain the expected accuracy, with a root mean squared error (RMSE) of 0.66 and an R² of 0.36. The individual statistical accuracies were quite low for the vegetable and orchard rotations, with an RMSE of 0.77/0.70 and an R² of 0.30/−0.04. Grouping soil samples by crop rotation significantly enhanced soil pH predictability with a decrease in the RMSE of 15% and an increase in the R² of 53%. The results proved that grouping by crop rotation can fit and optimize the sub-models after learning the characteristics of the rotation subsamples, offering a way for improving digital mapping of soil pH over heterogeneous agricultural landscapes. Full article