Delineation Of Management Zones Research Articles

A New Dissimilarity Metric for Anomaly Detection in Management Zones Delineation Constructed from Time-Varying Satellite Images

A field’s historical performance data are used for management zone delineation in precision agriculture, but including abnormal data leads to inappropriate zones. This paper introduces a framework incorporating historical performance data and a new Zoning Dissimilarity Metric (ZDM) to detect abnormal zoning data automatically. The methodology identifies abnormal zoning data among the field’s performance indicators extracted from satellite images to enhance the accuracy of the delineated zones. We experimented with our framework using Sentinel-2 images on 39 fields across Canada. Our experimental results, which involve both real and synthetic data, clearly demonstrate the importance of ZDM in effectively excluding abnormal data during the zone delineation process.

Learning-based multi-agent MPC for irrigation scheduling

Amid concerns about freshwater scarcity, the agricultural sector faces challenges in water conservation and optimizing crop yields, highlighting the limitations of traditional irrigation scheduling methods. To overcome these challenges, this paper introduces a unified, learning-based predictive irrigation scheduler that integrates machine learning and Model Predictive Control (MPC), while also incorporating multi-agent principles. The proposed framework incorporates a three-stage management zone delineation process, utilizing k-means clustering and hydraulic parameters estimates for optimized agro-hydrological modeling. Long Short-Term Memory (LSTM) networks are employed for accurate and computationally efficient root zone soil moisture modeling. The scheduler, formulated as a mixed-integer MPC with zone control, utilizes the identified LSTM networks to maximize root water uptake while minimizing overall water consumption and fixed irrigation costs. Additionally, the learning-based scheduler adopts a multi-agent MPC paradigm, where decentralized hybrid actor–critic agents and the concept of a limiting irrigation management zone are employed to enhance computational efficiency. Evaluating the performance on a 26.4-hectare field in Lethbridge for the 2015 and 2022 growing seasons demonstrates the superiority of the proposed scheduler over the widely-used triggered scheduling approach in terms of Irrigation Water Use Efficiency (IWUE) and total prescribed irrigation. Notably, the proposed approach achieves water savings between 7 to 23%, coupled with IWUE increases ranging from 10 to 35%.

Influence of the sampling time interval of canopy temperature on the dynamic zoning of variable rate irrigation

Generating spatial distribution maps of canopy temperature (Tc) is the basis for the dynamic management of a variable rate irrigation (VRI) system based on infrared thermometers (IRTs). To improve the accuracy of management zone delineation based on Tc maps for summer maize and winter wheat with IRTs mounted on a moving sprinkler irrigation system, the influence of sampling time interval of Tc was studied and the VRI prescription maps were compared with those obtained from unmanned aerial vehicle (UAV) thermal imaging on the same day. The experimental sites were located in Shunyi, Beijing, for summer maize and in Dacaozhuang, Hebei Province, for winter wheat. The sampling time intervals were selected as 0.5–26 min. The results demonstrated that the nRMSE of the predicted and measured values for Tc increased and that their correlation coefficient decreased with the extension of the sampling time interval. When the sampling time interval ranged from 0.5 to 5 min, the correlation coefficient and nRMSE between the predicted and measured values for Tc changed little, and the overlap percentage of the same management zones delineated with NRCT between 5 min and the minimum sampling time interval (0.5 or 1 min) was greater than 79%. For any two measuring dates of Tc, their overlap percentage changed greatly, with a maximum value of 67% for NRCT. The average irrigation amount based on the prescription maps of the IRTs system and UAV thermal imaging system on the three observation days was equal. Our results demonstrated techniques to collect IRTs data and to generate VRI prescription map based on this data, and a sampling time interval of no more than 5 minutes was recommended for Tc.

Soil sampling and sensed ancillary data requirements for soil mapping in precision agriculture I. delineation of management zones to determine zone averages of soil properties

Soil sampling and sensed ancillary data requirements for soil mapping in precision agriculture I. delineation of management zones to determine zone averages of soil properties

Summarizing soil chemical variables into homogeneous management zones – case study in a specialty coffee crop

Homogeneous management zones (HMZs) delineation is important for the application of precision agriculture because farm management decisions are based on it. Diverse soil chemical characteristics are important for the HMZs delineation. However, summarizing several variables into homogeneous zoning while taking into account the spatial distribution pattern of soil chemical characteristics is a challenge. Addressing this challenge is important to produce HMZs oriented for practical use for the farmers. In this work, 17 soil chemical variables were jointly analyzed for HMZ delineation by using indicator kriging (IK) to interpolate a soil fertility index (SFI). Soil samples were taken from a 4.5 ha area in a quasi-regular grid at 0 - 0.20 m depths in November 2019 (66 samples) and May 2021 (40 samples). Soil P, K, Ca, Mg, S, Na, H, Mn, Fe, Zn, B, cation exchange capacity, aluminum saturation, total organic carbon, base saturation, and organic matter were analyzed. In May 2021 the coffee yield was sampled together with the soil. Applying the SFI and then interpolating it using IK were effective for summarizing soil chemical variables into binary HMZs, showing a zone with higher priority for fertilization (therefore, lower general soil fertility) and another zone with low priority for fertilization. The summarizing process of several variables into binary HMZ was validated by evaluating the boxplots of each variable in each HMZ. Also, higher soil fertility areas presented a higher average coffee yield. Results indicated that joint use of SFI and IK was adequate to delineate HMZs in terms of summarizing soil fertility and separating coffee yield average variability. Delineating management zones by using the SFI approach is flexible for relatively limited sampled studies (less than one hundred samples) where machine learning and geostatistical methods may fail for lack of data.

Delineation of Soil Management Zones and Validation through the Vigour of a Fodder Crop

In Mediterranean farming systems, the semi-arid conditions and agricultural ecosystems have made site-specific management an important approach. This method aims to understand and handle the variability of soil properties and crop management, particularly through the utilization of geospatial information and accessible technology. Over three years in a 30 ha experimental field located in the Alentejo region (Portugal), crop establishment was monitored using data from soil apparent electrical conductivity (ECa), remote sensing (Sentinel-2), and in situ soil sampling. The procedure began with Step 1, involving the acquisition of soil spatial information and spatial interpolation. Subsequently, in Step 2, management zones (MZs) for soil characteristics were delineated using a combination of ECa measurements and soil analysis, and Step 3 spanned over three years of gathering meteorological data and crop remote sensing measurements. In Step 4, site-specific crop MZs were delineated by vegetation indexes (VIs). This article aims to increase the importance of in situ and remote assessments to more accurately identify areas with different productive potential. Results showed three MZs based on the percentage of sand, ECa, altimetry, exchangeable calcium, and exchangeable calcium properties, validated by crop VIs (Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE), and Normalized Difference Moisture Index (NDMI)) over time. Although there are many sensorial techniques available for site-specific management, this paper emphasizes a methodology for the farmer to identify different MZs combining remote and in situ evaluations, supporting new opportunities for a more rational use of natural resources. Based on soil parameters, three site-specific management areas could be selected. NDMI was the index that best explained the MZs created according to soil properties.

Investigation of Soil Properties Effect on Crop Yield Performance

Soil nutrient is essential for crop growth. Spatial variability of nutrients will have occurred in numerous scales, between regions, a field, particularly in soil properties. A study was carried out to observe the yield performance in an agricultural plot at Semujuk Jasin, Melaka. Using management zone delineation, the area is divided into fifteen smaller 6.8-meter × 6.8-meter plots.  A soil sample from each plot was tested in the laboratory to obtain the soil moisture, organic matter, pH level, Phosphorus, and Manganese content.  Using ArcGIS software, the result is interpolated using the Kringging method, mapped, and then compared with the crop value from each.  Plot A3 generated the lowest yield with a crop yield value of RM6.8.  This plot also has the lowest soil moisture content, which is 6.452%.  Plot A1 produces only RM65.2 for crop yield, although it has 21.505% soil moisture content, while plot C1 only produces RM47.4 for crop yield value, although it has a higher soil moisture content of 23.265%.  Regarding the soil organic matter content for both plots, plot A1 has only 1.455% organic matter content, while plot C1 has lower soil organic matter content, which is 0.823%.  Plot B2, with a low pH value (4.51), shows a poor performance of RM52.6 regarding satisfactory readings on other plots.  From the analysis made based on the experiment result, the primary limiting factor affecting crop performance is soil moisture content. Soil pH value and Organic Matter also play an essential role in determining crop performance.

Delineation of Productive Zones in Eastern China Based on Multiple Soil Properties

Accurate soil management has long been the focus of research in agroecology. Crop productivity can be enhanced while reducing environmental threats from excessive fertilization by fully comprehending the spatial variability of soil properties and delineating management zones (MZs). A field investigation was carried out at experimental sites outside Hefei City’s administrative districts in China to study the spatial variability of soil properties and the delineation of MZs. A total of 9601 soil samples were collected in the study area. A variety of soil properties were analyzed, including the pH, organic matter, total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, slowly released potassium, available sulfur, available boron, available copper, available zinc, available iron, and available manganese. The coefficient of variation for various soil properties exhibited a wide range, spanning from 12.2% to 100.5%. The geostatistical results show that most soil properties have moderate to strong spatial autocorrelation, and the ordinary kriging method is used to map the distribution of soil properties. The principal component analysis method was used to reduce the dimension of 13 soil properties to 4 principal components, and the fuzzy c-means clustering method was used to delineate MZs. The calculation results of the fuzzy performance index and normalized classification entropy show that the optimum number of MZs is five. In the study area, the western part exhibits the highest soil fertility, primarily attributed to its elevated organic matter content. Additionally, organic matter emerges as a key factor influencing sustainable agricultural production in this region. These results form the basis for soil managing areas outside the administrative districts of Hefei City.

Using a Non-Contact Sensor to Delineate Management Zones in Vineyards and Validation with the Rasch Model.

The production of high-quality wines is one of the primary goals of modern oenology. In this regard, it is known that the potential quality of a wine begins to be determined in the vineyard, where the quality of the grape, initially, and later that of the wine, will be influenced by the soil properties. Given the spatial variability of the fundamental soil properties related to the potential grape production, such as texture, soil organic matter content, or cation exchange capacity, it seems that a uniform management of a vineyard is not the most optimal way to achieve higher grape quality. In this sense, the delineation of zones with similar soil characteristics to implement site-specific management is essential, reinforcing the interest in incorporating technologies and methods to determine these homogeneous zones. A case study was conducted in a 3.3 ha vineyard located near Évora, south of Portugal. A non-contact sensor (DUALEM 1S) was used to measure soil apparent electrical conductivity (ECa) in the vineyard, and later, a kriged ECa map was generated. ECa and elevation maps were utilised to delineate homogeneous zones (management zones, MZs) in the field through a clustering process. MZs were validated using some soil properties (texture; pH; organic matter-OM; phosphorous-P2O5; potassium-K2O; the sum of the exchange bases-SEB; and cation exchange capacity-CEC), which were determined from 20 soil samples taken in the different MZs. Validation was also performed using Rasch measures, which were defined based on the formulation of the objective and probabilistic Rasch model, integrating the information from the aforementioned soil properties at each sampling location. The comparison of the MZs was more evident with the use of the Rasch model, as only one value was to be employed in each MZ. Finally, an additional validation was conducted using a vegetation index to consider the plant response, which was different in each MZ. The use of a non-contact sensor to measure ECa constitutes an efficient technological tool for implementing site-specific management in viticulture, which allows for the improvement of decision-making processes by considering the inherent spatial variability of the soil.

Evaluation of Variable Application Rate of Fertilizers Based on Site-Specific Management Zones for Winter Wheat in Small-Scale Farming

China is currently experiencing a severe issue of excessive fertilization. Variable rate fertilization (VRF) technology is key to solving this issue in precision agriculture, and one way to implement VRF is through management zone (MZ) delineation. This study is aimed at evaluating the feasibility and potential benefits of VRF based on site-specific MZs in smallholder farm fields. This study determined the amounts of basal and top-dressing fertilizers in different spatial units, based on soil nutrient MZs and crop growth MZs, respectively. The potential agronomic, economic, and environmental advantages of spatial variable rate fertilization were further assessed by comparing the farmer’s treatment, the expert’s treatment, and the variable rate fertilization treatment based on management zones (VR-MZ). The results showed that VR-MZ reduced the use of nitrogen (N), phosphorus (P), and potassium (K) fertilizers by 22.90–43.95%, 59.11–100%, and 8.21–100%, respectively, and it also increased the use efficiency of N, P, and K by 12.27–28.71, 89.64–176.85, and 5.48–266.89 kg/kg, respectively, without yield loss. The net incomes of VR-MZ were 15.5–449.61 USD ha−1 higher than that of traditional spatially uniform rate fertilization. Meanwhile, less nitrous oxide emission (23.50–45.81%), ammonia volatilization (19.38–51.60%), and nitrate ion leaching amounts (28.77–53.98%) were found in VR-MZ compared to those in uniform fertilization. The results suggest that the VR-MZ has great potential for saving fertilizers, significantly increasing farmers’ net income, reducing environmental pollution, and promoting the sustainable use of resources. This study provides a theoretical basis and technical support for exploring a VRF suitable for village-scale farming.

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.