Search Results (42)

Soil organic carbon (SOC) stocks in cacao agroecosystems are characterized by accumulating large amounts. They depend on the balance between organic matter inputs (plant residues, roots) and losses (decomposition, erosion), being closely related to climatic conditions, soil nature, vegetation type, topography, and land management practices. The objective of this study was to quantify SOC stocks (0–30 cm) and assess key soil fertility indicators across 107 georeferenced sampling locations in cacao production systems of Guamal (Meta, Colombian Llanos Piedmont). Soil pH varies between extremely acidic and moderately acidic (3.8–6.0; mean 4.57), while available P (Bray II) and exchangeable bases showed low concentrations. Organic carbon concentration averaged 1.18% and bulk density averaged 1.17 g cm⁻³. SOC stocks averaged 41.10 Mg C ha⁻¹, ranging from 7.49 to 81.55 Mg C ha⁻¹, evidencing marked spatial contrasts in carbon storage. Spearman correlations highlighted coupled soil chemical controls, including positive associations of pH with Ca²⁺ and P availability and strong negative associations of pH and P with exchangeable Al³⁺, consistent with acidity-driven fertility constraints. Principal component analysis (PCA) further identified a dominant fertility gradient structured by pH, P availability, and Ca²⁺, and a second axis related to organic carbon and cation retention. Spatial modeling using inverse distance weighting (IDW) in ArcGIS supported the visualization of SOC stock variability across the study area. Overall, the results indicate that SOC stocks in these predominantly sandy soils are strongly influenced by acidity-related constraints and heterogeneous nutrient status, underscoring the need for site-specific management to jointly enhance soil fertility and climate-mitigation potential in cacao systems. Therefore, it would be advisable in the future to address the study of differential variations in soil C storage related to chemical fertilizer application rates, especially in the long term. Full article

Urban and peri-urban agriculture (UA) plays an increasingly important role in promoting sustainable urban development, providing socioeconomic, environmental, and educational benefits. However, UA is often linked to nutrient accumulation in soils since vegetable-growing areas typically receive substantial inputs of both organic and inorganic fertilizers. This study examines soil variability in two sections of an urban allotment garden subjected to long-term manure fertilization for 12 or 16 years, with application rates up to 10–12 kg m⁻² yr⁻¹. Surface soils were analyzed for organic and inorganic carbon, total-N, available-P and -K, pH, and elemental composition using portable X-ray fluorescence (pXRF). Prolonged manure incorporation substantially enhanced soil fertility, as evidenced by increases in soil organic carbon (up to 3.78%), total-N (up to 0.38%), available-K (up to 412 mg kg⁻¹), and both total- and available-P (up to 2485 and 276 mg kg⁻¹, respectively). Marked shifts in mineral composition were also detected, including significant increases in total Ca, inorganic C (as calcium carbonate), Sr, and S. Despite the high manure inputs, no accumulation of potentially toxic elements (PTEs) was observed. However, pronounced spatial heterogeneity emerged among individual plots, with coefficients of variation reaching 58% for S and 47% for Zn, reflecting differences in fertilization intensity and management practices. Portable X-ray fluorescence (pXRF) analysis proved highly effective for detecting soil compositional changes and adequate for predicting K and P availability, highlighting its value as a rapid diagnostic tool for precision agriculture. Overall, these findings demonstrate the agronomic benefits of long-term organic fertilization while emphasizing the need for careful management to avoid nutrient imbalances and ensure sustainable practices that minimize environmental risks. Full article

Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification. Full article

Advances in sensor technology have significantly improved the efficiency and precision of agricultural spraying. Unmanned aerial vehicles (UAVs) are widely utilized for applying plant protection products (PPPs) and fertilizers, offering enhanced spatial control and operational flexibility. This study evaluated the performance of an autonomous UAV-based precision spraying system that applies variable rates based on zone levels defined in a prescription map. The system integrates real-time kinematic global navigation satellite system positioning with a proximity-triggered spray algorithm. Field experiments on a rice field were conducted to assess spray accuracy and fertilization efficacy with liquid fertilizer. Spray deposition patterns on water-sensitive paper showed that the graded strategy distinguished among zone levels, with the highest deposition in high-spray zones, moderate in medium zones, and minimal in no-spray zones. However, entry and exit deviations—used to measure system response delays—averaged 0.878 m and 0.955 m, respectively, indicating slight lags in spray activation and deactivation. Fertilization results showed that higher application levels significantly increased the grain-filling rate and thousand-grain weight (both p < 0.001), but had no significant effect on panicle number or grain count per panicle (p > 0.05). This suggests that increased fertilization primarily enhances grain development rather than overall plant structure. Overall, the system shows strong potential to optimize inputs and yields, though UAV path tracking errors and system response delays require further refinement to enhance spray uniformity and accuracy under real-world applications. Full article

Non-destructive diameter estimation of cabbage heads and yield prediction employing Unmanned Aerial Vehicle (UAV) imagery are superior to conventional approaches, which are labor intensive and time consuming. This approach assesses spatial variability across the field, effective allocation of resources, and supports variable application rates of fertilizer and supply chain management. Here, individual cabbage head diameters were estimated using deep learning-based pose estimation models (YOLOv8s-pose and YOLOv11s-pose) using high spatial resolution RGB images acquired from UAV 6 m during the cabbage-growing season in 2024. With a mean relative error (MRE) of 4.6% and a high mean average precision (mAP) 98.5% at 0.5, YOLOv11s-pose emerged as the best-performing model, verifying its accuracy for pragmatic agricultural use. The approximated diameter was then combined with climatic variables (temperature and rainfall) and canopy reflectance indices (normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green chlorophyll index (CIg)) that were extracted from the multispectral images with 6 m resolution and fed into AI models to develop individual cabbage head fresh weight. Among the machine learning models (MLMs) tested, CatBoost achieved the lowest Mean Squared Error (MSE = 0.025 kg/cabbage), highest R² (0.89), and outperformed other models based on the Diebold–Mariano statistical test (p < 0.05). This finding suggests that an integrated AI-powered framework enhances non-invasive and precise yield estimation in cabbage farming. Full article

Soil health has become increasingly important in recent years. The Hokkaido government initiated its original administrative strategy referred to as “Clean Agriculture” in 1991, before the concept of soil health and soil quality evolved in the 1990s. Also, Clean Agriculture has been integrated with remote sensing techniques for spatial application in arable fields. In this review paper, we summarized the scientific progress in Clean Agriculture and the management of soil health using remote sensing. One of the main pillars of Clean Agriculture is the minimal usage of chemical fertilizers and agrochemicals to increase soil fertility through the proper application of organic matter. The other two pillars are the sustainment and enhancement of the natural recycling function in agriculture and the enhancement of a stable production safe and high-quality agricultural products taking into account environmental harmony. These agronomic practices can increase soil fertility, maintain water quality, mitigate climate change, and maintain human health, and are similar to those in North America and the EU. Moreover, soil nitrogen fertility evaluated by autoclaved nitrogen (AC-N) can be estimated in large-scale fields and areas via remote sensing, which can facilitate variable nitrogen fertilization using variable-rate planters or broadcasters. Furthermore, systems comprising the growth sensor and variable-rate broadcaster can determine the additional nitrogen fertilization rates for winter wheat on the fields, which enhances soil health over relatively large areas. Further research is needed to expand the spatial utility of various Clean Agriculture techniques using multiperiod satellite images. Full article

Precision soil sampling on larger grid sizes (≥2.0 ha) is a common practice to reduce the number of soil samples and associated sampling costs. A study was conducted to evaluate the influence of different grid sizes on the depiction of spatial nutrient variability and their influence on the accuracy of variable-rate fertilizer application and total application costs. Soil sampling was conducted in nine agricultural fields using grid sizes of 0.4, 1.0, 2.0, 3.0, and 4.0 ha, and the resulting variable-rate prescription maps for lime, P, and K were spatially analyzed and compared with a reference map (generated from high-density soil sampling; approximately 2.5 samples per hectare) to determine the amount of under-, on-target, and over-application that would occur within each field. An economic analysis was conducted including the soil sampling costs, soil analysis costs, and nutrient costs to determine the effect of grid size on total application costs. Soil sampling on a 0.4 ha grid size had the best performance in depicting the spatial variability of soil pH, P, and K within the fields, and exhibited the highest application accuracy for the variable-rate prescription maps. The general trend was that the application accuracy decreased with an increase in grid size, with the potential for the under- and over-application of nutrients significantly increasing at the larger grid sizes of ≥2.0 ha. The total application cost varied among the fields as it was largely influenced by the amount of under- and over-application associated with each grid size. In most fields, the total application costs for a 0.4 ha grid size were lower or comparable to other grid sizes. In some fields, the larger grid sizes exhibited lower application costs but at the expense of reduced application accuracy. Overall, the results suggest that the smaller grid sizes of ≤1.0 ha are optimal for soil sampling in agricultural fields to ensure accurate and cost-effective variable-rate applications for site-specific nutrient management. Full article

This study investigates the effects of sensor-based, variable-rate mineral nitrogen (N) application (VRA) in winter wheat (Triticum aestivum L.) on the spatial variability of grain yield, protein content, N uptake, N balance, and N efficiency compared with uniform N application (UA). To analyze the effects of VRA and UA on yield and N balance parameters, on-farm strip trials were conducted on heterogeneous arable fields covering an area of 49 hectares. The trials were carried out over a four-year period, from 2020 to 2023, with crops under both application methods placed in strips side-by-side. The N fertilizer requirements for growth stages (GSs) 32 and 39 were determined using an online map-overlay VRA method. This method integrated the site-specific yield potential and current plant development derived from spectral reflectance measurements using a tractor-mounted sensor system. The results show that the application of N fertilizer can be reduced by up to 38 kg ha⁻¹ yr⁻¹. The N efficiency can be increased by 15% and a significant reduction in variability of N balances can be achieved. However, the effects on yield and N efficiency are highly dependent on the specific application conditions (weather conditions, disease occurrence, and crop development). Not every field trial showed advantages of VRA over UA fertilization. Overall, the VRA system demonstrated encouraging potential, functioning as intended. However, further adjustment and optimization are required to ensure that the VRA fertilization system works robustly and reliably under on-farm conditions. Full article

Efficient management of soil nutrients is essential for optimizing crop production, ensuring sustainable agricultural practices, and addressing the challenges posed by population growth and environmental degradation. Smart agriculture, using advanced technologies, plays an important role in achieving these goals by enabling real-time monitoring and precision management of nutrients. In open-field soil cultivation, spatial variability in soil properties demands site-specific nutrient management and integration with variable-rate technology (VRT) to optimize fertilizer application, reduce nutrient losses, and enhance crop yields. Hydroponic solution cultivation, on the other hand, requires precise monitoring and control of nutrient solutions to maintain optimal conditions for plant growth, ensuring efficient use of water and fertilizers. This review aims to explore recent trends in soil and solution nutrient sensing technologies for open-field soil and facilitated hydroponic cultivation, highlighting advancements that promote efficiency and sustainability. Key technologies include electrochemical and optical sensors, Internet of Things (IoT)-enabled monitoring, and the integration of machine learning (ML) and artificial intelligence (AI) for predictive modeling. Blockchain technology is also emerging as a tool to enhance transparency and traceability in nutrient management, promoting compliance with environmental standards and sustainable practices. In open-field soil cultivation, real-time sensing technologies support targeted nutrient application by accounting for spatial variability, minimizing environmental risks such as runoff and eutrophication. In hydroponic solution cultivation, precise solution sensing ensures nutrient balance, optimizing plant health and productivity. By advancing these technologies, smart agriculture can achieve sustainable crop production, improved resource efficiency, and environmental protection, fostering a resilient food system. Full article

The priority problem in intensive viticulture is reducing pesticides, and fertilizers, and improving water-use efficiency. This is driven by global and EU regulatory efforts. This review, systematically examines 92 papers, focusing on progress in satellite solutions over time, and (pre)processing improvements of spatio-temporal and spectral resolution. The importance of the integration of satellites with ground truth data is highlighted. The results provide precise on-field adaptation strategies through the generation of prescription maps and variable rate application. This enhances sustainability and efficiency in vineyard management and reduces the environmental footprint of vineyard techniques. The effectiveness of different vegetation indices in capturing spatial and temporal variations in vine health, water content, chlorophyll levels, and overall vigor is discussed. The challenges in the use of satellite data in viticulture are addressed. Advanced satellite technologies provide detailed vineyard monitoring, offering insights into spatio-temporal variability, soil moisture, and vine health. These are crucial for optimizing water-use efficiency and targeted management practices. By integrating satellite data with ground-based measurements, viticulturists can enhance precision viticulture, reduce reliance on chemical interventions, and improve overall vineyard sustainability and productivity. Full article

Soil is a heterogeneous medium that exhibits considerable variability in both spatial and temporal dimensions. Proper management of field variability using variable-rate fertilization (VRF) techniques is essential to maximize crop input–output ratios and resource utilization. Implementing VRF technology on a localized scale is recommended to increase crop yield, decrease input costs, and reduce the negative impact on the surrounding environment. This study assessed the agronomic and environmental viability of implementing VRF during the cultivation of summer maize using an on-the-go detector of soil total nitrogen (STN) to detect STN content in the test fields. A spatial delineation approach was then applied to divide the experimental field into multiple management zones. The amount of fertilizer applied in each zone was determined based on the sensor-detected STN. The analysis of the final yield and economic benefits indicates that plots that adopted VRF treatments attained an average summer maize grain yield of 7275 kg ha⁻¹, outperforming plots that employed uniform-rate fertilization (URF) treatments, which yielded 6713 kg ha⁻¹. Through one-way ANOVA, the yield p values of the two fertilization methods were 6.406 × 10⁻¹⁵, 5.202 × 10⁻¹⁵, 2.497 × 10⁻¹⁵, and 3.199 × 10⁻¹⁵, respectively, indicating that the yield differences between the two fertilization methods were noticeable. This led to an average yield increase of 8.37% ha⁻¹ and a gross profit margin of USD 153 ha⁻¹. In plots in which VRF techniques are utilized, the average nitrogen (N) fertilizer application rate is 627 kg ha⁻¹. In contrast, in plots employing URF methods, the N fertilizer application rate is 750 kg ha⁻¹. The use of N fertilizer was reduced by 16.4%. As a result, there is a reduction in production costs of USD 37.5 ha⁻¹, achieving increased yield while decreasing the amount of applied fertilizer. Moreover, in plots where the VRF method was applied, STN was balanced despite the reduced N application. This observation can be deduced from the variance in summer maize grain yield through various fertilization treatments in a comparative experiment. Future research endeavors should prioritize the resolution of particular constraints by incorporating supplementary soil data, such as phosphorus, potassium, organic matter, and other pertinent variables, to advance and optimize fertilization methodologies. Full article

Addressing within-field spatial variability for nitrogen (N) management to avoid over and under-use of nitrogen is crucial for optimizing crop productivity and ensuring environmental sustainability. In this study, we investigated the economic, environmental, and agronomic benefits of variable rate nitrogen application in potato (Solanum tuberosum L.). An online visible and near-infrared (vis-NIR) spectroscopy sensor was utilized to predict soil moisture content (MC), pH, total organic carbon (TOC), extractable phosphorus (P), potassium (K), magnesium (Mg), and cation exchange capacity (CEC) using a partial least squares regression (PLSR) models. The crop’s normalized difference vegetation index (NDVI) from Sentinel-2 satellite images was incorporated into online measured soil data to derive fertility management zones (MZs) maps after homogenous raster and clustering analyses. The MZs maps were categorized into high fertile (VR-H), medium–high fertile (VR-MH), medium–low fertile (VR-ML), and low fertile (VR-L) zones. A parallel strip experiment compared variable rate nitrogen (VR-N) with uniform rate (UR) treatments, adjusting nitrogen levels based on fertility zones as 50% less for VR-H, 25% less for VR-MH, 25% more for VR-ML, and 50% more for VR-L zones compared to the UR treatment. The results showed that the VR-H zone received a 50% reduction in N fertilizer input and demonstrated a significantly higher crop yield compared to the UR treatment. This implies a potential reduction in negative environmental impact by lowering fertilizer costs while maintaining robust crop yields. In total, the VR-N treatment received an additional 1.2 Kg/ha of nitrogen input, resulting in a crop yield increase of 1.89 tons/ha. The relative gross margin for the VR-N treatment compared to the UR treatment is 374.83 EUR/ha, indicating substantial profitability for the farmer. To further optimize environmental benefits and profitability, additional research is needed to explore site-specific applications of all farm resources through precision agricultural technologies. Full article

The purpose of this study was to construct a map of expected fertilization rates for nitrogen (N) and phosphorus (P₂O₅) based on measurements of components in soil samples and to identify the spatial variabilities of four lots of a salt-affected paddy field in Korea. Four salt-affected paddy field lots in Korea were divided into 30 sectors for collecting soil samples. They were then analyzed for soil organic matter (SOM), silicon dioxide (SiO₂), total nitrogen (TN), and available phosphorus (Av.P₂O₅) in accordance with international standards. Expected fertilization rates of N and P₂O₅ were developed as prescription standards for the application of fertilizer to paddy fields. They were derived using a model of the fertilization rates of N and P₂O₅. To determine the presence of spatial correlation and continuity in the given fields, a spherical variogram was used. Based on the spherical model with the application of a regular kriging interpolation, maps of the contents of TN and Av.P₂O₅ as well as the expected fertilization rates of N and P₂O₅ at each sector of $1\times 1$ m² were developed. The expected fertilization rate of N at each sector appeared in the range of min. 10.0 g to max. 25.7 g, while that of P₂O₅ appeared in the range of min. 0.68 g to max. 8.46 g. Full article

Nitrogen fertilizer is one of the essential nutrients for wheat growth and development, and it plays an important role in increasing and stabilizing wheat yield. Future climate change will affect wheat growth, development, and yield, since climate change will also alter nitrogen cycles in farmland. Therefore, further research is needed to understand the response of wheat yield and nitrogen losses to climate change during cultivation. In this study, we investigate the wheat-producing region in the middle and lower reaches of the Yangtze River in China, one of the leading wheat-producing areas, by employing a random forest model using wheat yield records from agricultural meteorological observation stations and spatial data on wheat yield, nitrogen application rate, and nitrogen losses. The model predicts winter wheat yield and nitrogen losses in the middle and lower reaches of the Yangtze River based on CMIP6 meteorological data and related environmental variables, under SSP126 and SSP585 emission scenarios. The results show that future climate change (temperature and precipitation changes) will decrease winter wheat yield by 2~4% and reduce total nitrogen losses by 0~5%, but in other areas, the total nitrogen losses will increase by 0~5% and the N leaching losses per unit of yield will increase by 0~10%. The results of this study can provide a theoretical basis and reference for optimizing nitrogen application rates, increasing yield, and reducing nitrogen losses in wheat cultivation under climate change conditions. Full article

The remote sensing of the biophysical and biochemical parameters of crops facilitates the preparation of application maps for variable-rate nitrogen fertilization. According to comparative studies of machine learning algorithms, Gaussian process regression (GPR) can outperform more popular methods in the prediction of crop status from hyperspectral data. The present study evaluates GPR model accuracy in the context of spring wheat dry matter, nitrogen content, and nitrogen uptake estimation. Models with the squared exponential covariance function were trained on images from two hyperspectral cameras (a frenchFabry–Pérot interferometer camera and a push-broom scanner). The most accurate predictions were obtained for nitrogen uptake (${\mathrm{R}}^2=0.75–0.85$, ${\mathrm{RPD}}_{\mathrm{P}}=2.0–2.6$). Modifications of the basic workflow were then evaluated: the removal of soil pixels from the images prior to the training, data fusion with apparent soil electrical conductivity measurements, and replacing the Euclidean distance in the GPR covariance function with the spectral angle distance. Of these, the data fusion improved the performance while predicting nitrogen uptake and nitrogen content. The estimation accuracy of the latter parameter varied considerably across the two hyperspectral cameras. Satisfactory nitrogen content predictions (${\mathrm{R}}^2>0.8$, ${\mathrm{RPD}}_{\mathrm{P}}>2.4$) were obtained only in the data-fusion scenario, and only with a high spectral resolution push-broom device capable of capturing longer wavelengths, up to 1000 $\mathrm{n}$$\mathrm{m}$, while the full-frame camera spectral limit was 790 $\mathrm{n}$$\mathrm{m}$. The prediction performance and uncertainty metrics indicated the suitability of the models for precision agriculture applications. Moreover, the spatial patterns that emerged in the generated crop parameter maps accurately reflected the fertilization levels applied across the experimental area as well as the background variation of the abiotic growth conditions, further corroborating this conclusion. Full article