Delineation Of Management Zones Research Articles

Integrating multisource information to delineate oasis farmland salinity management zones in southern Xinjiang, China

Soil salinization and the shortage of water resources have become important factors affecting the sustainable development of agriculture in the southern Xinjiang. Agricultural flooding in winter or spring is the main means to reduce the damage of secondary soil salinization in southern Xinjiang. However, the spatial heterogeneity of soil salinization is not considered in previous irrigation strategy. Excessive irrigation always leads to a serious waste of agricultural water resources. Irrigation zones delineation based on the degree of soil salinization is beneficial for the management of agricultural salinized land and the improvement of water use efficiency in arid areas. This study used 1599 sets of apparent electrical conductivity (ECa) data obtained before winter irrigation and after crop harvest to characterize soil salinity directly. And the spatial heterogeneity of soil salinity in the study area was analyzed directly using ECa values and statistical as well as geostatistical methods. Three main variables obtained from apparent electrical conductivity measurements (EC0.375, EC0.75, EC1.5), vegetation information (NDVI, GNDVI, SAVI), which indirectly reflected the degree of salinity, and environmental factors (Clay, Sand, Silt, DEM), which affecting the distribution of salinity, were used as auxiliary variables. Four zoning models including soil information, soil + vegetation information, soil information + environmental factors and soil + vegetation information + environmental factors were constructed. A multi-scale segmentation algorithm was used to delineate for four zone patterns and evaluate the results of management zones based on the different information. The results showed that there was a strong spatial heterogeneity of soil salinity in the study area, with the coefficients of variation of ECa all higher than 74 %. But the mean of coefficient of variation intra-zone in each zone after delineation was less than 24 %, which was significantly lower than the value before delineation. The zoning result based on only soil information was more fragmented than other zoning results, and the homogeneity intra-zone and heterogeneity among zones were both lower. This result was not desirable to the development and effective implementation of water resource allocation. Management zones delineation with additional vegetation information or environmental factors showed a better zoning result. Delineation using multisource data of soil, vegetation information and environmental factors not only had the highest homogeneity intra-zone and heterogeneity among zones, but also facilitated the implementation of variable-rate irrigation. Consequently, salinity management zoning, which integrates soil, vegetation information and environmental factors, will facilitate the implementation of efficient irrigation with variable irrigating rate.

Generation of Lime Recommendation Maps Using Traditional Thematic Maps and Management Zones

ABSTRACT The uniform management used in conventional agriculture is not the most efficient strategy for managing agricultural areas. In this way, precision agriculture (PA), based on assessments of the variability of chemical, physical, and biological attributes, seeks to optimize the activities management by site-specific management of inputs, to increase profitability in the production system, and to minimize environmental impacts. In this context, this work aimed to compare two techniques to make traditional thematic maps (TTMs) and management zones (MZs) for site-specific lime application, using data from three commercial agricultural areas (Western Paraná State, Brazil) and three methods to calculate the lime requirement (LR): (1) neutralization of exchangeable aluminum (Al3+) (NEM), (2) increase of calcium (Ca2+) and magnesium (Mg2+) (KM), and (3) base saturation method (BSM). The LR was calculated on AgDataBox-Map (ADB-Map) web platform, using AgDataBox-Map-Aplic module (ADB-Map-Aplic). ADB-Map is an application of the web platform, which aims at integrating data, software, procedures, and methodologies for Digital Agriculture. The Variance Reduction (VR%), Fuzziness Performance Index (FPI), Modified Partition Entropy (MPE), Improved Cluster Validation Index (ICVI), Smoothness Index (SI%) and Fragmentation index (FI%) indices were used to assess MZs quality, and the Kappa and global accuracy indices compared the agreement between TTMs and MZs. The results showed that the BSM method presented the best results in LR and distribution. According to the indices, MZs delineated with two classes generally (45 of the 47) showed the best results. MZs with application rates in each class calculated by the median of the lime quantities provided the lowest total LR per area. Thus, the MZs delineation allows applying lime more efficiently, reducing production costs, and allowing more sustainable use of inputs.

Automatic Soil Sampling Site Selection in Management Zones Using a Multi-Objective Optimization Algorithm

Precision agriculture hinges on accurate soil condition data obtained through soil testing across the field, which is a foundational step for subsequent processes. Soil testing is expensive, and reducing the number of samples is an important task. One viable approach is to divide the farm fields into homogenous management zones that require only one soil sample. As a result, these sample points must be the best representatives of the management zones and satisfy some other geospatial conditions, such as accessibility and being away from field borders and other test points. In this paper, we introduce an algorithmic method as a framework for automatically determining locations for test points using a constrained multi-objective optimization model. Our implementation of the proposed algorithmic framework is significantly faster than the conventional GIS process. While the conventional process typically takes several days with the involvement of GIS technicians, our framework takes only 14 s for a 200-hectare field to find optimal benchmark sites. To demonstrate our framework, we use time-varying Sentinel-2 satellite imagery to delineate management zones and a digital elevation model (DEM) to avoid steep regions. We define the objectives for a representative area of a management zone. Then, our algorithm optimizes the objectives using a scalarization method while avoiding constraints. We assess our method by testing it on five fields and showing that it generates optimal results. This method is fast, repeatable, and extendable.

Management Zones Delineation, Correct and Incorrect Application Analysis in a Coriander Field Using Precision Agriculture, Soil Chemical, Granular and Hydraulic Analyses, Fuzzy k-Means Zoning, Factor Analysis and Geostatistics

The objective of our investigation was to study the various effects of correct and incorrect application of fuzziness exponent, initial parameterization and fuzzy classification algorithms modeling on homogeneous management zones (MZs) delineation of a Coriandrum sativum L. field by using precision agriculture, soil chemical, granular and hydraulic analyses, fuzzy k-means zoning algorithms with statistical measures like the introduced Percentage of Management Zones Spatial Agreement (PoMZSA) (%), factor and principal components analysis (PCA) and geostatistical nutrients GIS mapping. Results of the exploratory fuzzy analysis showed how different fuzziness exponents applied to different soil parameter groups can reveal better insights for determining whether a fuzzy classification is a correct or incorrect application for delineating fuzzy MZs. In all cases, the best results were achieved by using the optimal fuzziness exponent with the full number of parameters of each soil chemical, granular and hydraulic parameter group or the maximum extracted PCAs. In each case study where the factor analysis and PCA showed optimal MZs > 2, the results of the fuzzy PoMZSA clustering metric revealed low, medium and medium to high spatial agreement, which presented a statistically significant difference between the soil parameter datasets when an arbitrary or commonly used fuzziness exponent was used (e.g., φ = 1.30 or φ = 1.50). Soil sampling and laboratory analysis are tools of major significance for performing exploratory fuzzy analysis, and in addition, the FkM Xie and Benny’s index and the introduced fuzzy PoMZSA clustering metric are valuable tools for correctly delineating management zones.

Delineating Management Zones with Different Yield Potentials in Soybean–Corn and Soybean–Cotton Production Systems

The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations.

Utilisation of Intrinsic and Extrinsic Soil Information to Derive Soil Nutrient Management Zones for Banana Production in a Smallholder Farm

In South Africa (SA), smallholder farmers contribute significantly to food production and play an essential role in the nation’s food and nutritional security. However, there is a lack of basic understanding of the spatial variability of soil nutrients and their controlling factors in these smallholdings, which subsequently hinders their agricultural production. In this work, we assessed the spatial variability and structure of key soil nutrients required by banana fruit, identified their factors of control, and delineated management zones in a smallholder farm. We used a regular grid (50 m × 50 m) to collect a total of 27 composite samples from the 0–30 cm depth interval and analysed for soil physicochemical properties. Our classical statistics results indicated that phosphorus (P), potassium (K), calcium (Ca) and zinc (Zn) varied highly, while magnesium (Mg) and total nitrogen (TN) varied moderately across the plantation. On the other hand, geostatistics revealed that P and K were strongly spatially dependent (implying a good structure), while Mg and Zn were moderately spatially dependent (indicating a moderate structure) across the banana plantation. Soil Ca and TN contents were found to be weakly spatially dependent (meaning there was no structure) across the farm. The spatial prediction maps showed that P, Mg and Zn contents were high in the northeast part (underlain by Valsrivier) and low in the northwest part (underlain by Westleigh) of the banana plantation farm. Similarly, K and Ca were low in the northwest part (underlain by Westleigh), but they were high in the south to southwest portion (underlain by Glenrosa) of the farm. Soil TN was high in the west part (underlain by Westleigh) and low in the east-northeast part (underlain by Valsrivier) across the plantation. Three management zones (MZs) were delineated for soil P, K and Ca, while for other nutrients (Mg, Zn and TN), two MZs were delineated. The results of this study provide baseline information for site-specific management of fertilisers to supplement soil nutrients in the field to improve banana productivity.

Multiple-point statistical modeling of three-dimensional glacial aquifer heterogeneity for improved groundwater management

Quaternary glacial aquifers are important water sources for irrigation in many agricultural regions, including eastern Nebraska, USA. Quaternary glacial aquifers are heterogeneous, with juxtaposed low-permeability and high-permeability hydrofacies. Managing groundwater in such aquifers requires a realistic groundwater-flow model parameterization, and characterization of the aquifer geometry, spatial distribution of aquifer properties, and local aquifer interconnectedness. Despite its importance in considering uncertainty during decision-making, hydrofacies probabilities generated from multiple-point statistics (MPS) are not widely applied for groundwater model parameterization and groundwater management zone delineation. This study used a combination of soft data, a cognitive training image, and hard data to generate 100 three-dimensional (3D) conditional aquifer heterogeneity realizations. The most probable model (probability of hydrofacies) was then computed at node spacing of 200 × 200 × 3 m and validated using groundwater-level hydrographs. The resulting hydrofacies probability grids revealed variations in aquifer geometry, locally disconnected aquifer systems, recharge pathways, and hydrologic barriers. The profiles from hydrofacies probability at various locations show spatial variability of the streambed and aquifer connectivity. Groundwater-level hydrographs show evidence of these aquifer characteristics, verifying the general structure of the model. Using the MPS-generated 3D hydrofacies probability and hydrologic data, a novel workflow was developed in order to better define high-resolution groundwater management zones and strategies. In general, the conditional probability of hydrofacies helps improve the understanding of glacial aquifer heterogeneity, the characterization of aquifer-to-aquifer and streambed-aquifer connections, and the delineation of groundwater management zones. This MPS workflow can be adapted to other areas for modeling 3D aquifer heterogeneity using multisource data.

Multi-Parametric Approach to Management Zone Delineation in a Hazelnut Grove in Italy

The increase in high-density hazelnut (Corylus avellana) areas drives the interest in practices of precision management. This work addressed soil apparent electrical conductivity (ECa), RGB aerial (UAV) images, proximal sensing, and field scouting in delineating and validating management zones (MZs) in a 2.96 ha hazelnut grove in Italy. ECa data were fitted to a semi-variogram, interpolated (simple kriging), and clustered, resulting in two MZs that were subjected to soil analysis. RGB imagery was used to extract tree canopies from the soil background and determine two vegetation indices (VIs) of general crop status: the Visible Atmospherically Resistant Index (VARI) and the Normalized Green-Red Difference Index (NGRDI). Then, plant growth parameters were manually assessed (tree height, crown size, etc.) and a proximal VI, the Canopy Index (CI), was determined with the MECS-VINE® vertical multisensor. MZ1 was characterized by lower ECa values than MZ2. This was associated with a lower clay content (9% vs. 21% in MZ1 vs. MZ2) and organic matter content (1.03% vs. 1.51% in MZ1 vs. MZ2), indicating lower soil fertility in MZ1 vs. MZ2. Additionally, hazelnut trees had significantly smaller canopies (1.42 vs. 1.94 m2 tree−1) and slightly lower values of VARI, NGRDI, and CI in MZ1 vs. MZ2. In conclusion, our approach used ECa to identify homogeneous field areas, which showed differences in soil properties influencing tree growth. This is the premise for differential hazelnut management in view of better efficiency and sustainability in the use of crop inputs.

Multilayer Data and Artificial Intelligence for the Delineation of Homogeneous Management Zones in Maize Cultivation

Variable rate application (VRA) is a crucial tool in precision agriculture, utilizing platforms such as Google Earth Engine (GEE) to access vast satellite image datasets and employ machine learning (ML) techniques for data processing. This research investigates the feasibility of implementing supervised ML models (random forest (RF), the support vector machine (SVM), gradient boosting trees (GBT), classification and regression trees (CART)) and unsupervised k-means clustering in GEE to generate accurate management zones (MZs). By leveraging Sentinel-2 satellite imagery and yielding monitor data, these models calculate vegetation indices to monitor crop health and reveal hidden patterns. The achieved classification accuracy values (0.67 to 0.99) highlight the potential of GEE and ML models for creating precise MZs, enabling subsequent VRA implementation. This leads to enhanced farm profitability, improved natural resource efficiency, and reduced environmental impact.

Using an open source and resilient technology framework to generate and execute prescription maps for site-specific manure application

The development of precision farming solutions and the required network infrastructure appear to be progressing at different speeds and levels of deployment. In most of the world's rural areas, where farming takes place, network coverage and infrastructure is at a basic level. However, where digital farming solutions are applied, internet-dependent applications need backup solutions to enable the same or a comparable performance towards maintaining the functionality of the specific practice and ultimately food production. The aim is to avoid regressing to basic practices waiving advantages of established precision farming technologies. Based on a conceptual framework, defined by the authors in a previous work, site-specific slurry application was chosen as a concrete use case to implement and demonstrate fallback options to maintain efficient and reliable application even during internet outages. Initially, the differences between conventional and resilient IT infrastructure were contrasted and investigated. The generation of a prescription map (PM) based on the fusion of multiple parameters, clustered in the domains of soil, yield, and remote sensing, was performed utilizing open-source and offline usable software. For seamless transmission of the generated PMs, a new and innovative hardware platform was integrated into the machinery fleet and working process of a machinery ring (MR), which was acting as a service contractor in the inter-farm slurry application. Interoperability was considered by enabling the executing software to accept PMs of altering origin. The on- and off-line generation of PMs including data fusion, were also compared. Here the multi-parametric approach corresponded to the ground truth data, whereas the PMs generated in an online farm management information system (FMIS), using only one data source, showed deviations in management zone patterns and relative dose rates. In addition, the impact of the lack of online accessible data on the delineation of management zones was investigated and resulted in a switch from the medium to the high dose rate (DR) level in 9.1% of the field's area.

Ground-Penetrating Radar and Electromagnetic Induction: Challenges and Opportunities in Agriculture

Information on the spatiotemporal variability of soil properties and states within the agricultural landscape is vital to identify management zones supporting precision agriculture (PA). Ground-penetrating radar (GPR) and electromagnetic induction (EMI) techniques have been applied to assess soil properties, states, processes, and their spatiotemporal variability. This paper reviews the fundamental operating principles of GPR and EMI, their applications in soil studies, advantages and disadvantages, and knowledge gaps leading to the identification of the difficulties in integrating these two techniques to complement each other in soil data studies. Compared to the traditional methods, GPR and EMI have advantages, such as the ability to take non-destructive repeated measurements, high resolution, being labor-saving, and having more extensive spatial coverage with geo-referenced data within agricultural landscapes. GPR has been widely used to estimate soil water content (SWC) and water dynamics, while EMI has broader applications such as estimating SWC, soil salinity, bulk density, etc. Additionally, GPR can map soil horizons, the groundwater table, and other anomalies. The prospects of GPR and EMI applications in soil studies need to focus on the potential integration of GPR and EMI to overcome the intrinsic limitations of each technique and enhance their applications to support PA. Future advancements in PA can be strengthened by estimating many soil properties, states, and hydrological processes simultaneously to delineate management zones and calculate optimal inputs in the agricultural landscape.

Detection of the Spatial Variability of Soil Salinity East of the Bitter Lake of the Suez Canal, Egypt, as a Basis for Management Zones Using Sentinel-2 Multispectral Imagery

ABSTRACT Soil salinity is one of the more serious problems that cause soil degradation, particularly in semi-arid and arid regions. In agricultural lands, it threatens plant growth and productivity, which requires specific management decisions for various inputs, such as fertilizers. Accurate prediction and mapping of soil salinity have become possible through remote sensing techniques. The focus of this research is to derive soil salinity from spectral indices measured from different bands of sentinel-2 satellite in order to delineate management zones (MZs). Four spectral salinity indices were used for the purpose of this study, using blue, green, red, and NIR bands in different combinations. Exponential regression best describes the relationship between salinity indices and soil electrical conductivity (EC) through groundtruthing. The results indicate that salinity indices 2 () and 4 () (SI2 and SI4) outperformed the other indices and could describe 70% and 71% of the variability in soil EC with prediction models of: and , respectively. The generated models were validated by normalized root mean square error and the d coefficient of agreement. The results of the best-fit models were 16.5% and 0.86 for SI2 and 16.7% and 0.84 for SI4, respectively. The box-and-whiskers diagram proposed that the field can be divided into four soil EC-dependent groups, which are considered MZs to support a variety of management decisions. This study shows that through sentinel-2 multispectral imagery, the spatial variability of soil salinity can be mapped and used as a basis for MZs within the studied region.

Spatial variability of soil properties and delineation of management zones for Suketi basin, Himachal Himalaya, India

Spatial variability of soil properties and delineation of management zones for Suketi basin, Himachal Himalaya, India

Delineation and optimization of cotton farmland management zone based on time series of soil-crop properties at landscape scale in south Xinjiang, China

Traditional farmland management approaches treat farmland as a homogeneous unit, leading to uneven distribution and waste of resources, which is contrary to precision agriculture. Management zones (MZ) delineation is an essential method of site-specific management (SSM) for accomplishing variable rate input of resources in subregions by recognizing the heterogeneity of farmland soils. Aimed at the soil salinization problem in Xinjiang, this study estimates the salt content of soil profiles and essential nutrient content using time series Sentinel-2 images and machine learning methods, and describes crop growth conditions through dynamic time warping. Subsequently, geographically weighted principal component analysis and possibilistic fuzzy C-means algorithm were employed to outline MZ based on soil-crop characteristics. Three scales (10 m, 100 m, and field scales divided by ridges) were examined to obtain the optimal scale of delineation. Soil-crop properties were estimated at a spatial resolution of 10 m using machine learning methods with R2 > 0.65. In the zoning process, the field scale was selected with accuracy and practicality in agricultural practices, with comparatively higher homogeneity within zones and variation between zones. The soils in the study area were categorized into three zones at field scale with a fuzzy performance index of 0.68, a normalized classification entropy of 0.71, and fuzzy class of 0.30. Soil function is primarily restricted by salinity and fertility. Fertilizer recommendation strategies were given for the three zones. This study uses time-series remote sensing images to obtain a high-precision estimation of soil-crop characteristics at 10 m, and optimize the scale of farmland management zoning, which provides a valuable technology and scale selection for SSM for cotton management zone delineation in saline farmland in arid and semi-arid regions.

Dynamic delineation of management zones for site-specific nitrogen fertilization in a citrus orchard

Dynamic delineation of management zones for site-specific nitrogen fertilization in a citrus orchard

Evaluating management zones and crop‐sensing relationships for improved irrigated maize nitrogen management

AbstractActive crop canopy sensors and management zones (MZ) are two methods of directing variable‐rate, in‐season nitrogen (N) fertilizer applications in maize (Zea mays L.). Researchers have suggested that integrating these two approaches may result in improved performance of sensor‐based N application algorithms through increased N use efficiency and profitability. The objectives of this research study were to (1) identify soil and topographic variables that are related to in‐season canopy reflectance and yield for soil‐based MZ delineation and (2) determine if delineated MZ can identify areas with differential crop response to N fertilizer. N ramp blocks were placed end‐to‐end in field‐length strips at eight irrigated maize fields in east central Nebraska in 2016 and 2017. Maize response to N was evaluated with in‐season canopy reflectance measurements and grain yield. Relationships between maize response variables and measured soil and topographic attributes were evaluated and used to delineate MZ. Yield response to N rate was highly variable among and within fields. Soil apparent electrical conductivity had the highest overall correlations with crop response and was used as a clustering variable in five of eight fields. Economic analysis showed a potential advantage to using soil‐based MZ compared to producer‐chosen uniform N rates in five of eight fields. Delineated MZ were able to identify areas with differential soil properties and crop response to N fertilizer. Integrating soil‐based MZ and sensor‐based N management has potential to achieve further economic benefits.

Irrigation management zone delineation and optimal irrigation scheduling for center pivot irrigation systems

In this study, a three-stage irrigation management zone delineation approach and a model-based scheduler are proposed to provide variable rate irrigation schedules for a 26.4-hectare field at the Alberta Irrigation Technology Centre in Lethbridge, Alberta, Canada. After an initial demarcation of the investigated field into quadrants according to the four different crops growing in the field, the k-means clustering approach is used to delineate each quadrant further into smaller irrigation management zones on the basis of soil hydraulic parameters, elevation of the field and the variable rate irrigation resolution of the center pivot system. For each management zone, a long short-term memory (LSTM) neural network model is then developed to characterize the soil water dynamics in the root zone. Based on the LSTM models, and based on the LSTM models, a mixed-integer model predictive controller (MPC) is designed to prescribe irrigation schedules for the various management zones in each quadrant to ensure optimal crop water uptake while minimizing water consumption and irrigation costs. The applicability of the proposed approach is illustrated in simulations. The results show that the proposed approach achieves 26% water savings compared to a triggered scheduling scheme.

Spatial Analysis of Soil Properties and Site-Specific Management Zone Delineation for the South Hail Region, Saudi Arabia

Sustainable soil management with the appropriate understanding of soil characteristics is vital in maintaining and improving agriculture soil management. The objectives of the present study are to characterize the spatial variability of soil using the GIS technique and used agglomerative hierarchical clustering (AHC) for the delineation of management zones (MZs) for precision agriculture. A total of 111 soil samples were collected from 37 soil profiles in systematic depths (0–50, 50–100, and 100–150 cm) from the South Hail region, KSA. Samples were analyzed for pH, ECe, CaCO3, available macro and micronutrients, and hydrological properties. The best fit models, using ArcGIS software, were J-Bessel for pH, Clay, bulk density (BD), and available water (AW); K-Bessel for EC and available N; Stable for CaCO3, P, K, Fe, Zn, Sand, field capacity (FC) and saturated hydraulic conductivity (Ks); Spherical for Mn and Cu; Gaussian for saturation percentage (SP); whereas exponential for permanent wilting point (PWP). The principal component analysis (PCA) resulted in six principal components (PCs) explaining 79.75% of the total variance of soil properties. The PC1 was strongly influenced by soil BD, FC, clay, PWP, Ks, and sand. PC2 was dominated by N, ECe, and CaCO3; PC3 was dominated by pH; PC4 was dominated primarily by K and P, PC5 was mainly dominated by Fe; Mn, and Cu, and PC6 was mainly dominated by SP and Zn. Based on AHC, four soil management zones (MZs) cover 77.94, 14.10, 7.11 and 0.85% of the studied area. Management zone 1 (MZ1) and Management zone 3 (MZ3) are classified as moderately saline while Management zone 2 (MZ2) is classified as highly saline soils, greater than the limiting critical value for the sensitive crops. The potential solutions to reduce salinization in the area include: reducing irrigation, moving to salt-tolerant crops or applying humic acids to fix anions and cations and eliminate them from the root zone of the plants. Treating the area with diluted sulfuric acid to remove salts and reduce ECe to less than 2 dSm−1, to get maximum productivity. This finding is diagnostic for determining the amount of fertilizer and irrigation water to be applied to soils in different management zones. Its emphasis’s the importance of site-specific management for long-term crop productivity and, as a result, reducing environmental hazards caused by uneven fertilizers and water applications.

Proximal and Remote Sensing Data Integration to Assess Spatial Soil Heterogeneity in Wild Blueberry Fields

Wild blueberries (Vaccinium angustifolium Ait.) are often cultivated uniformly despite significant within-field variations in topography and crop density. This study was conducted to relate apparent soil electrical conductivity (ECa), topographic attributes, and multi-spectral satellite imagery to fruit yield and soil attributes and evaluate the potential of site-specific management (SSM) of nutrients. Elevation and ECa at multiple depths were collected from two experimental fields (referred as FieldUnd, FieldFlat) in Normandin, Quebec, Canada. Soil samples were collected at two depths (0–0.05 m and 0.05–0.15 m) and analyzed for a range of soil properties. Statistical analyses of fruit yield, soil, and sensor data were used to characterize within-field variability. Fruit yield showed large variability in both fields (CVUnd = 54.4%, CVFlat = 56.5%), but no spatial dependence. However, several soil attributes showed considerable variability and moderate to strong spatial dependence. Elevation and the shallowest depths of both the Veris (0.3 m) and DUALEM (0.54 m) ECa sensors showed moderate to strong spatial dependence and correlated significantly to most soil properties in both study sites, indicating the feasibility of SSM. In place of management zone delineation, a quadrant analysis of the shallowest ECa depth vs. elevation provided four sensor combinations (scenarios) for theoretical field conditions. ANOVA and Tukey–Kramer’s post hoc test showed that the greatest differentiation of soil properties in both fields occurred between the combinations of high ECa/low elevation versus low ECa/high elevation. Vegetation indices (VIs) obtained from satellite data showed promise as a biomass indicator, and bare spots classified with satellite imagery in FieldUnd revealed significantly distinct soil properties. Combining proximal and multispectral data predicted within-field variations of yield-determining soil properties and offered three theoretical scenarios (high ECa/low elevation; low ECa/high elevation; bare spots) on which to base SSM. Future studies should investigate crop response to fertilization between the identified scenarios.

Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates

CONTEXTCotton is an economically important crop in Australia that requires high resource application, particularly that of nitrogen (N) fertilizers. Determining optimal N fertilizer rates that reach both economic and environmental objectives is a key challenge in cotton systems because of the inherent within-field variability and relatively low N fertilizer use efficiency (NFUE). OBJECTIVEThis study aimed to model optimal N fertilizer rates by accounting for within-field variability through management zone (MZ) delineation across a cotton field in Queensland, Australia. METHODSMZs were delineated using satellite-derived normalized difference vegetation index (NDVI) and the crop simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), was automatically calibrated with a grid-search optimization algorithm and validated across two cotton seasons. A total of 336 different N fertilizer scenarios were subsequently evaluated at pre-planting and top-dressing to observe the effect on profit margin and NFUE. RESULTS AND CONCLUSIONSThe MZ delineation analysis determined that within-field variability could be best represented by two MZs, one of which displaying subsoil constraints due to high carbonate concentrations. The use of the auto-calibration algorithm led to a successful validation of the model with a Wilmott d-index of agreement ranging between 0.75 (soil nitrate) and 0.96 (aboveground biomass and plant N), respectively. The subsequent N scenario simulations indicated that by reducing N fertilizer rates by 80 and 30 kg N ha−1 across the two MZs, respectively, compared to the current industry average, profits could be maximized through maintaining yields while reducing N inputs. SIGNIFICANCEOverall, these results demonstrate the potential of combining remote sensing-derived MZs and crop model auto-calibration techniques to support the cotton industry in achieving improved resource efficiency and profit margins.