Search Results (62)

Clarifying how vegetation restoration regulates wind erosion, sediment redistribution, and soil improvement is essential for ecological management in desert photovoltaic power stations. This study was conducted in a high-clearance flexible-support photovoltaic power station at the edge of the Kubuqi Desert. Three restoration measures were compared: reed mulch combined with Atriplex canescens planting along the panel front edge (M1), A. canescens planting along the panel front edge alone (M2), and reed mulch combined with grass seeding (M3). The panel front-edge zone (QY), under-panel zone (BX), and pedestal zone (JZ) were used as functional units to analyze surface sediment grain-size characteristics, soil moisture, soil nutrients, windbreak efficiency, aerodynamic roughness length, and cumulative sand-fixing efficiency. All restoration measures altered the surface sediment structure, with Mz ranging from 2.005 to 2.364 and D₀ from 1.459 to 1.935. Soil moisture ranged from 0.58% to 4.34%, with the highest value occurring in the 20–30 cm layer of QY under M1. M1 also showed higher soil organic matter in QY and JZ, reaching 1.87 and 1.16 g·kg⁻¹, respectively. Windbreak efficiency decreased with height under all measures. M1 maintained the highest and most stable values, decreasing only from 61.16% at 10 cm to 55.52% at 100 cm. The total cumulative sand-fixing efficiency was also highest under M1 (233.66%), while M2 (215.05%) and M3 (214.58%) showed comparable total effects but different zonal responses. Wind-eroded materials shifted from fine-sand dominance toward a higher relative contribution of medium sand, reflecting the reduction in finer transported fractions rather than true grain coarsening. The novelty of this study lies in linking wind-erodible sediment redistribution, soil water and nutrient responses, and windbreak–sand-fixing performance across internal functional zones of a flexible-support photovoltaic array. These results indicate that vegetation restoration in desert photovoltaic power stations should be configured by functional zone, with composite interception at the panel front edge, structural maintenance in the under-panel zone, and cover-based sand trapping in deposition-prone areas. Full article

By partitioning agricultural fields into units with similar yield-limiting factors, Management Zone (MZ) delineation provides the spatial basis for variable-rate application of inputs such as nitrogen, seed, and irrigation. To evaluate the operational implementation of MZ methodologies, this paper analyzes 137 peer-reviewed papers published between 2000 and 2025, extracting data on agronomic contexts, sensing inputs, computational workflows, and validation strategies. Our analysis reveals a clear methodological shift: while early studies relied heavily on data such as soil properties, recent literature is dominated by multisource data fusion that combines static soil proxies (e.g., apparent electrical conductivity) with dynamic remote sensing vegetation indices. Methodologically, the literature relies heavily on similarity-based clustering, specifically fuzzy c-means and k-means, often applied to raw spatial grids or Principal Component Analysis (PCA) transformations. Although machine learning and optimization-based approaches have increased in recent years, rigorous agronomic and economic validation remains limited, while internal cluster validity indices (e.g., FPI, NCE) and inferential statistical tests (e.g., ANOVA) are widely used to assess delineated zones, only 13 of the reviewed papers explicitly evaluated the economic or environmental net returns of the delineated zones. To transition MZ delineation from a classification problem to an operational decision-support tool, the current literature suggests a need to shift validation efforts away from internal clustering metrics toward multi-year yield stability assessments and direct economic cost–benefit analyses. Full article

Precision soil management is fundamental to the sustainable production of high-quality tea, yet the spatial integration of fertility and heavy metal safety remains a significant challenge. This study aimed to delineate multi-dimensional management zones (MZs) in the tea plantations of Tianmuhu, Jiangsu Province, by evaluating three clustering algorithms: K-means (KM), Fuzzy C-means (FCM), and Iterative Self-Organizing Data Analysis Technique (ISODATA). A total of 70 representative soil samples were analyzed for 10 properties. Descriptive statistics revealed pronounced spatial heterogeneity, particularly for Hg (CV = 71.04%) and P (CV = 61.83%). Pearson correlation and Principal Component Analysis (PCA) demonstrated strong synergistic relationships among organic matter (OM), nitrogen (N), and potassium (K) (r = 0.49–0.69, p < 0.01), which formed a distinct Fertility Factor on PC1. Conversely, PCA identified divergent sources for heavy metals, with Cr primarily governed by pedogenic processes (PC2), while Cd were associated with anthropogenic inputs. Guided by these distinct spatial drivers, this study separately delineated fertility and heavy metal safety MZs. The optimal number of clusters was determined by balancing statistical validity with spatial operationality via the Silhouette Coefficient (SC) and Smoothness Index (SI), with results indicating that a 2–3 zone scheme yielded the most favorable scores. Comparative analysis showed that for soil fertility, ISODATA outperformed KM and FCM by effectively capturing the high variability of P and producing statistically distinct zones (p < 0.05). For heavy metal pollution, FCM provided better partitioning by reflecting the continuous gradients of composite contaminants. Validation results showed that while 61% of the area was classified as high-fertility (ISODATA), approximately 63–75% fell into relatively higher heavy metal accumulation categories. This dual-objective zoning framework provides a scientific basis for site-specific fertilization and targeted environmental monitoring in the regional tea industry. Full article

In precision agriculture, the delineation of Management Zones (MZs) is essential for optimizing input use efficiency and site-specific nutrient management. MZs are established based on spatial variability derived from remote sensing data—such as Normalized Difference Vegetation Index (NDVI) from satellite or UAV-based imagery—and yield maps collected during harvest. However, the microbial community composition of the soil is often overlooked in MZ delineation. To address this gap, we investigated the soil bacterial community structure across different MZs in an arable field. The zones were delineated using NDVI data, soil profiles were described, and bulk soil samples were collected. Soil physicochemical parameters were analyzed in parallel with 16S rRNA gene amplicon sequencing to characterize bacterial community composition and diversity. The results demonstrated that soil texture and soil organic matter content were the primary drivers influencing bacterial community structure across the field. Moreover, patterns in microbial composition aligned closely with MZ delineations, indicating that microbial profiles could aid in better understanding and supporting the nutrient management practices. Our findings suggest that soil microbiological data can enhance the stability and biological relevance of MZ definitions, thereby improving resource allocation, soil health management, and overall sustainability in precision farming systems. Full article

Segmenting agricultural fields into management zones (MZ) is a core principle of precision agriculture (PA). However, the widespread adoption of PA remains limited, partly due to operational barriers in MZ segmentation. These barriers often involve the necessity for advanced programming skills and a strong statistical background, in addition to the lack of a free, integrated and straightforward tool that executes the entire workflow. Addressing this gap required the development of the open-source QGIS plugin Precision Zones. The plugin reproducibly implements the entire MZ segmentation pipeline: (i) raster layers preprocessing; (ii) dimensionality reduction via Principal Component Analysis (PCA); (iii) multivariate clustering using K-Means ++, with integrated support for determining the optimal number of zones through the Elbow and Silhouette methods; (iv) spatial filtering of MZ to mitigate noise; and (v) assessment of MZ agronomic effectiveness using statistical metrics (i.e., within-zone variance reduction). This tool enables practical MZ segmentation for a wide range of agricultural applications, eliminating the need for programming knowledge. Despite its robust architecture, as a novel tool, it has not yet been formally characterized and presented to the scientific community. Therefore, this study describes the Precision Zones plugin, address the step-by-step user decisions and presents its validation. In a reproducible case study, the plugin produced agronomically coherent MZ and reduced within-zone variability (VR%) for most soil attributes analyzed. The study concludes that Precision Zones provides a reproducible, user-friendly workflow that bridges the gap between advanced spatial analysis and practical precision agriculture applications for growers, consultants and researchers. Full article

Management zones (MZs) are a key precision agriculture strategy for managing spatial variability in crops, but conventional delineation methods are costly, time-consuming, and rely on specialized equipment. Previous studies in potato production have primarily relied on single-year NDVI or proximal soil sensor data analyses, limiting their ability to capture temporal stability and variability across multiple fields. This study addresses this gap by applying multi-year, multi-source NDVI composites to characterize spatial and temporal patterns of agricultural potential across 17 commercial potato fields at McCain’s Farm of the Future, Florenceville-Bristol, New Brunswick. A total of 230 NDVI images from Sentinel-2 and Landsat 8 (2015–2023) were processed into composite metrics (mean, standard deviation, skewness) to delineate three agricultural potential (AP) MZs. Validation was conducted using 2023 potato tuber yield and soil physicochemical properties. The results showed statistically significant correlations between NDVI metrics and key soil nutrients (total carbon: |r| < 0.19; total nitrogen: |r| < 0.28), with tuber yield (|r| < 0.41). Spatial patterns of total carbon and nitrogen corresponded with delineated MZs, and tuber yield variability partially aligned with these zones. These findings demonstrate that multi-year NDVI composites provide a cost-effective and scalable approach for mapping agricultural potential, capturing both spatial and temporal variability, and supporting data-driven management decisions in potato production systems. Full article

This study develops a practical, on-farm methodology for optimizing cotton cultivation through Variable Rate Seeding (VRS), utilizing existing farm data and remote sensing, while minimizing operational interference. The methodology involved an experimental design across five rainfed cotton fields on a Brazilian commercial farm, testing four seeding rates (90%, 100%, 110%, 120%) within grid cells using a 4 × 4 Latin square design. Management zones (MZs) were defined using existing soil clay content and elevation data, augmented by twelve vegetation indices from Sentinel-2 satellite imagery and K-Means clustering. Statistical analysis evaluated plant population density’s effect on cotton yield and its association with MZs. For the 2023/2024 season, results showed no positive yield response to increasing plant density above field averages, with negative responses in many plots (e.g., 84% in Field A), suggesting potential gains from reducing rates. The association between population density effect classes and MZs was highly significant with moderate to relatively strong Cramer’s V values (up to 0.47), indicating MZs effectively distinguished response areas. Lower clay content consistently correlated with yield losses at higher densities. This work empowers farm managers to conduct their own site-specific experimentation for optimal seed populations, enhancing precision agriculture and resource efficiency. Full article

Variable Rate Seeding (VRS) in soybean (Glycine max [L.] Merr.) cultivation is a critical strategy for managing soil spatial variability, which often constrains yield. However, conventional practices that increase plant density in low-yield zones to compensate for poor fertility may unintentionally intensify intraspecific competition for already limited resources. This study addresses the need for a mechanistically sound basis for VRS prescriptions that moves beyond this counterproductive assumption. Field experiments were conducted in the Cerrado region of Brazil to evaluate different soybean population densities within management zones (MZs) delineated according to Liebig’s Law of the Minimum. This approach identified soil potassium (K) as the most growth-limiting nutrient and was used to define MZs representing distinct yield potentials. Three seeding densities were tested, Low (200,000 seeds ha⁻¹), Medium (240,000 seeds ha⁻¹), and High (280,000 seeds ha⁻¹), with particular emphasis on comparing the medium and high populations in potassium-limited zones. Results revealed that, contrary to the conventional strategy, increasing the soybean population from medium to high in low-fertility (low-K) MZs significantly decreased grain yield. This yield reduction was attributed to intensified intraspecific competition, which promoted excessive vegetative growth (increased plant height) at the expense of root development and photosynthetic efficiency. Notably, maintaining a moderate population (240,000 seeds ha⁻¹) in these low-fertility zones produced yields statistically equivalent to those in higher-fertility areas. These findings demonstrate that applying Liebig’s Law of the Minimum to identify the most limiting factor provides a robust, mechanistically sound foundation for developing VRS prescriptions. For nutrient-limited zones, the optimal and most resilient strategy is not to increase sowing density but to maintain a moderate population that balances yield potential with resource availability. Future research should investigate the phenotypic plasticity of different cultivars under this VRS strategy and assess its economic viability at a commercial scale. Full article

Understanding spatial variability is central to precision agriculture, yet terrain features are often overlooked in remote sensing workflows that inform agronomic decision-making. This work introduces a terrain-aware visual analytics approach that integrates landform classification with crop performance analysis to better support field-level decisions. Terrain features are an important contributor to yield variability, alongside environmental conditions, soil properties, and management practices. However, they are rarely integrated systematically into performance analysis and decision-making workflows—limiting the potential for terrain-aware insights in precision agriculture. Addressing this gap requires approaches that incorporate terrain attributes and landform classifications into agricultural performance analysis and management zone (MZ) delineation—ideally through visual analytics that offer interpretable insights beyond the constraints of purely data-driven methods. We introduce an interactive focus+context visualization tool that integrates multiple data layers—including terrain features, vegetation index–based performance metric, and management zones—into a unified, expressive view. The system leverages freely available remote sensing imagery and terrain data derived from Digital Elevation Models (DEMs) to evaluate crop performance and landform characteristics in support of agronomic analysis. The tool was applied to eleven agricultural fields across the Canadian Prairies under diverse environmental conditions. Fields were segmented into depressions, hilltops, and baseline areas, and crop performance was evaluated across these landform groups using the system’s interactive visualization and analytics. Depressions and hilltops consistently showed lower mean performance and higher variability (measured by coefficient of variation) compared to baseline regions, which covered an average of 82% of each field. We also subdivided baseline areas using slope and the Sediment Transport Index (STI) to investigate soil erosion effects, but field-level patterns were inconsistent and no systematic differences emerged across all sites. Expert evaluation confirmed the tool’s usability and its value for field-level decision support. Overall, the method enhances terrain-aware interpretation of remotely sensed data and contributes meaningfully to refining management zone delineation in precision agriculture. Full article

This study investigates the delineation of management zones (MZs) to support site-specific crop management by simplifying within-field variability in irrigated (54.6 ha) and rainfed (7.9 ha) sorghum and soybean fields in Brazil. Historical yield, apparent soil electrical conductivity (ECa) at 0.75 m and 1.50 m, and terrain data were analyzed using multivariate statistics to define MZs. Two clustering methods—fuzzy c-means (FCM) and hierarchical clustering—were compared for variance reduction effectiveness. Rainfed areas showed greater spatial variability (yield CV 9–12%; ECa CV 20–27%) than irrigated fields (yield CV < 7%; ECa CV ~5%). Principal component analysis (PCA) identified subsoil ECa and elevation as key variables in irrigated fields, while surface ECa and topography influenced rainfed variability. FCM produced more homogeneous zones with fewer classes, especially in irrigated fields, whereas hierarchical clustering better detected outliers but required more zones for similar variance reduction. Yield correlated strongly with slope and moisture in rainfed systems. These results emphasize aligning MZ delineation with production system characteristics—enabling variable rate irrigation in irrigated fields and promoting moisture conservation in rainfed systems. FCM is recommended for operational efficiency, while hierarchical clustering offers higher precision in complex contexts. Full article

Accurate delineation of within-field management zones (MZs) is essential for implementing precision agriculture, particularly in spatially heterogeneous environments. This study evaluates the spatiotemporal consistency and practical value of MZs derived from three complementary data sources: electromagnetic conductivity (EM38-MK2), basic soil chemical properties (pH, humus, P₂O₅, K₂O, nitrogen), and vegetation/surface indices (NDVI, SAVI, LCI, BSI) derived from Sentinel-2 imagery. Using kriging, fuzzy k-means clustering, percentile-based classification, and Weighted Overlay Analysis (WOA), MZs were generated for a five-year period (2018–2022), with 2–8 zone classes. Stability and agreement were assessed using the Cohen Kappa, Jaccard, and Dice coefficients on systematic grid samples. Results showed that EM38-MK2 and humus-weighted BSP data produced the most consistent zones (Kappa > 0.90). Sentinel-2 indices demonstrated strong alignment with subsurface data (r > 0.85), offering a low-cost alternative in data-scarce settings. Optimal zoning was achieved with 3–4 classes, balancing spatial coherence and interpretability. These findings underscore the importance of multi-source data integration for robust and scalable MZ delineation and offer actionable guidelines for both data-rich and resource-limited farming systems. This approach promotes sustainable agriculture by improving input efficiency and allowing for targeted, site-specific field management. Full article

Delineating soil management zones in cocoa cultivation areas can help optimize production and minimize ecological and environmental risks. This research assessed the spatial distribution of heavy metal concentration and soil fertility indicators in Cross River State, Nigeria, to delineate soil management zones (MZs). A total of n = 63 georeferenced, composite soil samples were collected at the 0–30 cm depth increment, air-dried, and subjected to physicochemical analysis. The soil data were subjected to principal component analysis (PCA), and the selected principal components (PCs) were used for fuzzy c-means clustering analysis to delineate the MZs. The result indicated that soil pH varied from 4.8 (strongly acidic) to 6.3 (slightly acidic), with high average organic carbon contents. The degree of contamination was low, while the ecological risk indicator (RI) of the environment under cocoa cultivation ranged from low risk (RI = 18.24) to moderate risk (RI = 287.15), with moderate risk areas mostly found in patches around the central and upper regions. Higher pH was associated with increased levels of exchangeable Ca, Mg, and K, and TN and OC. Strong spatial dependence was observed for silt, pH, OC, Mg, Zn, Cu, Pb, Cd, Cr, and DC. The result showed the first six principal components (PCs) with eigenvalues >1 accounting for 83.33% of the cumulative variance, and three MZs were derived via the selected six PCs using fuzzy c-means clustering analysis. The results of this study further indicated that MZ3 had the highest pH (6.06), TN (0.24%), OC (2.79%), exchangeable Ca (10.62 cmol/kg), Mg (4.01 cmol/kg), and K (0.12 cmol/kg). These were significantly (p < 0.05) higher than those observed in MZ2 and MZ1, and they represent the most fertile parts of the study area. Furthermore, 40.6% of the study area had marginal soil (i.e., soil under MZ2). Full article

The characterization of soil attribute variability often requires dense sampling grids, which can be economically unfeasible. A possible solution is to perform targeted sampling based on previously collected data. The objective of this research was to develop a method for mapping soil attributes based on Management Zones (MZs) delineated from Sentinel-1 radar data. Sentinel-1 images were used to create time profiles of six indices based on VV (vertical–vertical) and VH (vertical–horizontal) backscatter in two agricultural fields. MZs were delineated by analyzing indices and VV/VH backscatter bands individually through two approaches: (1) fuzzy k-means clustering directly applied to the indices’ time series and (2) dimensionality reduction using deep-learning autoencoders followed by fuzzy k-means clustering. The best combination of index and MZ delineation approaches was compared with four soil attribute mapping methods: conventional (single composite sample), high-density uniform grid (one sample per hectare), rectangular cells (one composite sample per cell of 5 to 10 hectares), and random cells (one composite sample per cell of varying sizes). Leave-one-out cross-validation evaluated the performance of each sampling method. Results showed that combining the VV/VH index and autoencoders for MZ delineation provided more accurate soil attribute estimates, outperforming the conventional, random cells, and often the rectangular cell method. In conclusion, the proposed methodology presents scalability potential, as it does not require prior calibration and was validated on soil types commonly found across Brazil’s agricultural regions, making it suitable for integration into digital platforms for broader application in precision agriculture. Full article

Over the past quarter-century, the enhanced availability of satellite imagery, characterized by improved temporal, spectral, radiometric, and spatial resolutions, has enabled valuable insights into the spatial soil variability of annual croplands and orchards. This study investigates the impact of spatial resolution on classifying three-year, multi-temporal vegetation indices derived from satellites with coarse (30 m, Landsat 8), medium (10 m, Sentinel-2), and fine spatial resolutions (3.7 m, PlanetScope). The classification was performed using the fuzzy c-means algorithm, with the fuzziness performance index (FPI) and normalized classification entropy (NCE), which were used to determine the optimal number of management zones (MZs). Our results revealed that the Landsat 8-based NDVI images produced the highest number of clusters (nine for annual cropland and six for orchards), while the finer resolutions from PlanetScope reduced this to three clusters for both cultivation types, more accurately capturing the intra-parcel variability. Except for Landsat 8, the NDVI means of MZs generated based on Sentinel-2 and PlanetScope using the fuzzy c-means algorithm showed statistically significant differences from each other, as determined by a one-way and Welch’s ANOVA (p < 0.05). The use of PlanetScope imagery demonstrated its superiority in generating zones that reflect inherent variability, offering farmers actionable insights at a reconnaissance scale. Multi-temporal satellite imagery has proved effective in monitoring plant growth responses to edaphological soil properties. In our study, the PlanetScope satellites, which offer the highest spatial resolution, consistently produced effective zones for orchard areas. These zones have the potential to enhance farmers’ discovery of knowledge at a reconnaissance scale. With the increasing spatial resolution and enhanced spectral resolution of newer satellite sensors, using cluster analysis with insights from soil scientists promise to help farmers better understand and manage the fertility of their fields in a cost-effective manner. Full article

This study aimed to analyze yield components and maize yield cultivated at different population densities in management zones (MZs) delimited based on mapping the spatial variability of the soil’s apparent electrical conductivity (ECa). The soil ECa was measured, and two MZs were subsequently delimited, one with low ECa and the other with high ECa. In each MZ, four maize sowing densities were tested: 60,000 (D1); 80,000 (D2); 100,000 (D3); and 140,000 (D4) seeds ha⁻¹. Ear length, number of grains per ear, number of grains per row, number of rows per ear, thousand-grain weight, and yield were evaluated. The increase in sowing density in the high ECa MZ linearly reduced the values of ear diameter, number of rows per ear, number of grains per ear, and thousand-grain weight. Sowing density D3, when implemented in the low ECa MZ, showed higher values for the ear length, ear diameter, number of grains per row, number of grains per ear, and thousand-grain weight. Sowing density D2 was the one with the highest yield, regardless of the MZ where it was implemented (5628.48 kg ha⁻¹ in the high ECa management zone and 4463.63 kg ha⁻¹ in the low ECa). Full article