Homogeneous Management Zones Research Articles

Delineating Homogeneous Management Zones for Nutrient Management in Rice Cultivated Area of Eastern India

Delineating Homogeneous Management Zones for Nutrient Management in Rice Cultivated Area of Eastern India

Mapping winter wheat crop traits dynamic change and growth performance for variable rate application using Sentinel-1 and Sentinel-2

Site specific crop management for variable rate application is extensively recognized as a method for distributing agricultural input unevenly across a field, tailored to the diverse requirement of different areas. From the previous study, this approach proven to reduce agricultural input expenses by 10 % without impacting yield and ensure environmental sustainability. This study presents a new approach to delineate management zones for precision agriculture using crop biophysical property variability assessment within winter wheat fields. A multivariate random forest framework was developed to estimate winter wheat’s biophysical properties within fields from surface reflectance and backscatters of Sentinel-1 and Sentinel-2. Combining Sentinel-1 and Sentinel-2 data resulted in more precise estimation of the green area index (R²=0.98), aboveground dry biomass (R²=0.90), plant height (R²=0.94), and leaf nitrogen content (R²=0.78). Sentinel-2 alone was particularly effective in estimating shoot density (R²=0.94). These estimates were then used to create management zones for precision agriculture, classified based on agronomic performance benchmarks. The fuzzy c-mean clustering algorithm helped generate homogeneous management zones, considering the biophysical variations within fields.The ultimate goal is to integrate these biophysical property maps and management zones into crop management workflows. This integration will assist farmers in recognizing field variability and understanding its causes. Moreover, the spatial distribution of these zones supports variable rate application, guiding farmers towards more efficient, profitable, and sustainable crop management practices.

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.

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.

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.

Evaluation of site-specific management to optimise <i>Vitis vinifera</i> L. (cv. Tannat) production in a vineyard with high heterogeneity

The spatial variability of vineyards can be characterised through precision viticulture that will allow setting the boundaries of homogeneous management zones. This study aimed to evaluate the impact of soil and plant management (site-specific management) to increase yields and improve berry quality. During three consecutive seasons, contrasting treatments designed ad hoc for two zones of vigour pre-established by NDVI were tested: high vigour zone (HV) and low vigour zone (LV). The treatments were aimed at reducing water and nitrogen supply and improving microclimatic conditions in the cluster zone in the HV zone. In the LV zone, treatments were aimed at increasing water and nitrogen supply. Leaf removal in the HV zone was the most efficient treatment to improve productivity and quality. Moreover, the water restriction improved grape quality, especially in a rainy year. The regulated deficit irrigation strategy applied in the LV zone at specific phenological stages was shown to increase vegetative growth, yield and to improve grape anthocyanins and phenols contents. The benefits of additional nitrogen supply in the LV zone on plant nitrogen status, yield, and berry composition were highly dependent on water availability. Ultimately, this study provided new insights into the relationship between water and nitrogen availability and how this determines vigour and influences yield and grape quality and influences the deviation from a “Productive Target” pattern. The use of site-specific techniques could be adjusted on a small production scale, thanks to mapping carried out with precision viticulture technologies.

Spatial Prediction Model of Plant Water Status of an Olive Orchard (Olea europaea L.) cv. Arbequina Under Semiarid Conditions in the Central Valley of Chile

The Olive (Olea europaea L.) is a typical fruit tree of Mediterranean areas characterized by high-quality oil production and high tolerance to water deficit. Due to worldwide water scarcity in Mediterranean regions, it becomes indispensable to monitor plant water status, in example, through xylem water potential (Ψx). Unfortunately, measurement is difficult to perform with high spatial resolution at field scale (> 50 measurements per hectare), due to the large amount of manpower required in the prosses which turned this technique into a high-cost solution. This situation drastically hinders its applicability in large production areas. Thus, the objective of this research is implementing a spatial prediction model of plant water status in an olive orchard, using a single Ψx measurement performed in a reference site over the orchard. The experimental site was established in 2.2 hectares of commercial olive trees in the Pencahue valley located in the Maule region (Chile) during the 2013/14 growing season. Measurements of Ψx were performed at key phenological stages of olive trees. The proposed methodology allowed to estimate the behavior of Ψx in unsampled olive trees from reference site measurements, with an average spatial error less than ±0.6 MPa and correlation of 0.8 (R2) ratifying the high spatial dependence between different sites sampled at field scale. Therefore, distribution of spatial variability would be adequate for the application of irrigation in homogeneous management zones, facilitating water management practices in clearly identified zones within the olive orchard under study.

A Technological Approach to Support Extensive Livestock Management in the Portuguese Montado Ecosystem

Extensive livestock production systems based on improved pastures under Montado of Holm oaks represent an approach in line with the challenges of sustainability and biodiversity. The increasing incorporation of technologies in the monitoring of this ecosystem allows for a better knowledge of the spatial and temporal variability and, consequently, a more economically profitable management. In this study, between July 2020 and June 2021, soil and pastures were monitored in a 20 ha Montado area located in Alentejo (Southern Portugal) and used for extensive grazing of cattle. The survey of soil apparent electrical conductivity (ECa), the application of algorithms for definition of homogeneous management zones (HMZ), the use of indices obtained from satellite imagery time series to characterize the evolution of pasture quality and the soil and pasture sampling, including the identification of bio-indicator botanical species, were the basis of this exploratory study, allowing a holistic approach to this complex soil-pasture-trees and animals ecosystem. In the near future, this knowledge could represent an important milestone in providing decision-making support systems to farm managers in terms of smart sampling, differential application of fertilizers, amendments or seeds, choosing the best spacing and density of trees in this ecosystem, promoting dynamic grazing, or identifying the animal feed supplementation needs in the critical periods of the year.

Management Zones in Pastures Based on Soil Apparent Electrical Conductivity and Altitude: NDVI, Soil and Biomass Sampling Validation

The intensification of the Montado mixed ecosystem (agro–silvo–pastoral) is a current endeavor in the context of promoting the sustainability of extensive livestock production in the Mediterranean region. Increased pasture productivity and extensive animal production involves the use of technologies to monitor spatial variability and to implement differentiated management of pasture grazing, fertilization or soil amendment. An intermediate step should lead to the identification and demarcation of areas with similar characteristics (soil and/or crop development), known as homogeneous management zones (HMZ) to implement site-specific management strategies. In this study, soil apparent electrical conductivity (ECa) and altimetry surveys were carried out in six experimental pasture fields with a non-contact electromagnetic induction sensor (EM38) associated with a Global Navigation Satellite System (GNSS) receiver. These ECa and topographic maps were used in geostatistical analyses for designing and establishing final classification maps with three HMZ (less, intermediate and more potential). The normalized difference vegetation index (NDVI), obtained from a proximal optical sensor, and soil and biomass sampling were used to validate these HMZ. From a practical perspective, these HMZ are the basis for preparation of fertilizer prescription maps and use of variable rate technology (VRT) in a Precision Agriculture project.

Geomorphology as a tool to digitize homogeneous management zones based on soil properties in the semiarid central Argentinean Pampas

In many regions of the world, detailed soil maps are not available to improve decisions on land use and soil management for sustainable agricultural production. The central Argentinean Pampas are semiarid lands that have been extensively used for livestock and crop production and there is no detailed soil map that allows to identify erosion prone areas or differentiate between different productive potentials of soils. We used geomorphological maps elaborated through a combined index of NDVI and slope derived from satellite images as a proxy for identifying different soil and land capability classes, and to delineate homogeneous land management areas. Soil pits were dug, and sampling was carried out in three landscape positions in an experimental area identified by the combined index, and soils were classified by Soil Taxonomy and Land Capability classes. Soils differed in their profile depth and water storage capacity, calcium carbonate depth, pH, available phosphorus contents, taxonomic and land capability classification among three landforms: plateau, back-slope and toe-slope. Soil types and their capability class were then extrapolated to the entire study area according to the geomorphological map. The map showed the areas of erosion prone back-slope soils and highly productive toe-slope soils, different from the plateau area that dominates the region. This allows for a more sustainable management, protecting the slopes with permanent cover, using high value crops in the toe-slope areas and a better choice of adapted crops in the plateau, which may result in improving and stabilizing farmer's incomes and reducing wind and water erosion of the slopes.

Geoestatística multivariada para o delineamento de zonas de manejo para a alocação de experimentos agrícolas

Agricultural development relies heavily on experimental research and it is necessary to minimize soil variations for more accurate results. Therefore, the present work aims to quantify the spatial variability of soil attributes and to define more homogeneous Management Zones (MZs) through the multivariate geostatistics to allocate agricultural experimental areas. The experiment was carried out in Petrolina city, Pernambuco state, Brazil. Disturbed soil samples were collected following a sample grid with 56 georeferenced points in the layers of 0.00 to 0.20 m and 0.40 to 0.60 m depth. The granulometric and chemical analyzes of the soil were performed. Principal components (PC) analysis was performed. The spatial dependence of the analyzed variables and the CP was verified using experimental semivariogramas. Maps were obtained by the ordinary Kriging interpolator. The delimitation of the MZ was performed using the fuzzy c-means clustering algorithm. The results showed that in the CP the attributes related to soil acidity predominated and that the area should be delimited in three zones. The MZs delimitation using CP showed as an important tool in assisting the allocation of agricultural experiments, reducing the error arising from the heterogeneity that may occur within the experimental blocks.

Methodology for the Definition of Durum Wheat Yield Homogeneous Zones by Using Satellite Spectral Indices

One of the main questions facing precision agriculture is the evaluation of different algorithms for the delineation of homogeneous management zones. In the present study, a new approach based on the use of time series of satellite imagery, collected during two consecutive growing seasons, was proposed. Texture analysis performed using the Gray-Level Co-Occurrence Matrix (GLCM) was used to integrate and correct the sum of the vegetation indices maps (NDVI and MCARI2) and define the homogenous productivity zones on ten durum wheat fields in southern Italy. The homogenous zones identified through the method that integrates the GLCM indices with the spectral indices studied showed a greater accuracy (0.18–0.22 Mg ha−1 for ∑NDVIs + GLCM and 0.05–0.49 Mg ha−1 for ∑MCARI2s + GLCM) with respect to the methods that considered only the sum of the indices. Best results were also obtained with respect to the homogeneous zones derived by using yield maps of the previous year or vegetation indices acquired in a single day. Therefore, the survey methods based on the data collected over the entire study period provided the best results in terms of estimated yield; the addition of clustering analysis performed with the GLCM method allowed to further improve the accuracy of the estimate and better define homogeneous productivity zones of durum wheat fields.

Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy

Farmer’s management decisions and environmental factors are the main drivers for field spatial and temporal yield variability. In this study, a 22 ha field cultivated with corn for more than ten years using different prescription maps of nitrogen application rates was investigated. Prescription maps were developed based on archived yield maps, soil analysis and recently integrated with Sentinel 2 satellite images. In addition, farmer experience and availability of variable rate application (VRA) requirements had an influence on the development of the homogeneous management zones. The initial approach with VRA was quite simple, based on a simple partitioning of the field into three rectangular zones (defined mainly based on previous yield maps and farmer experience). The partitioning changed with time and knowledge, evolving to the final five irregularly shaped zones (defined based on Farm works decision support software). Furthermore, since 2010 the farmer began using soil moisture sensor for irrigation decisions. Results of the present study highlight an improvement in corn yield and a reduction in total applied nitrogen. Corn yield improved on average by 31% on a ten years basis to reach more than 14 ton/ha dm. in 2018. At the beginning of VRA, yield maps showed a high spatial variation between field zones compared to reduced variation in the following seasons. In addition, the nitrogen applied reduced by around 23% while the total yield was improving. These results showed an increase in the partial factor productivity from less than 54 to around 87 kg of corn grain per kg of nitrogen applied. This promising result shows that farmer management decisions can improve every season by continuous monitoring of crop performance, understanding field variability and taking advantage of recently developed decision support software tools.

Mapping Management Zones Based on Soil Apparent Electrical Conductivity and Remote Sensing for Implementation of Variable Rate Irrigation—Case Study of Corn under a Center Pivot

Climate change, especially the trend towards global warming, will significantly affect the global hydrological cycle, leading to a general reduction of the water available for agriculture. In this scenario, it is essential that research should focus on the development of ‘water saving’ techniques and technologies. This work summarizes the methodology followed in a project for large scale implementation of variable rate irrigation (VRI) systems using center pivots in corn crop. This is based on technologies for monitoring (i) soil electrical conductivity (ECa) and altimetry, (ii) soil moisture content, (iii) vegetation indices (Normalized Difference Vegetation Index, NDVI) obtained from satellite images, and automatic pivot travel speed control technologies. ECa maps were the basis for the definition of first homogeneous management zones (HMZ) in an experimental corn field of 28 ha. NDVI time-series were used to establish the subsequent HMZ and the respective dynamic prescription irrigation maps. The main result of this study was the reduction of spatial yield variability with the VRI management in 2017 compared to the conventional irrigation management. This study demonstrates how a relatively simple approach could be designed and implemented on a large scale, which represents an important and sustainable contribution to the resolution of practical farmer issues.

Decomposition-based heuristic for the zoning and crop planning problem with adjacency constraints

This paper tackles management zone delineation and crop planning problems in an integrated precision agriculture framework. The zoning problem defines relatively homogeneous management zones regarding their soil properties, and for which specific rates of agricultural inputs are necessary. From a sustainable point of view, the crop planning problem considers cropping of species from different botanic families in adjacent zones at the same time. With this in mind, we propose a novel linear binary integer program for an integrated zoning and crop planning problem with adjacency constraints. In this model, we maximize the incomes of the crop plan subject to zoning constraints and adjacency constraints on crop families. The proposed model has a column-based formulation, and as such, we develop a decomposition-based heuristic which make use of the column generation method with column-dependent rows. The decomposition strategy involves a master problem that deals with ensuring homogeneity of the selected management zones within the field partition and ensuring that the crop plan meets adjacency policies. On the other hand, the pricing problem generates rectangular management zones whose incorporation improves the objective value of the master problem. The algorithm is implemented in JuMP, a modeling language for mathematical optimization embedded in Julia. Results from a set of instances show the relevance of the decomposition-based heuristic.

Integration of Soil Electrical Conductivity and Indices Obtained through Satellite Imagery for Differential Management of Pasture Fertilization

Dryland pastures in the Alentejo region, located in the south of Portugal, normally occupy soils that have low fertility but, simultaneously, important spatial variability. Rational application of fertilizers requires knowledge of spatial variability of soil characteristics and crop response, which reinforces the interest of technologies that facilitates the identification of homogeneous management zones (HMZ). In this work, a pasture field of about 25 ha, integrated in the Montado mixed ecosystem (agro-silvo-pastoral), was monitored. Surveys of apparent soil electrical conductivity (ECa) were carried out in November 2017 and October 2018 with a Veris 2000 XA contact sensor. A total of 24 sampling points (30 × 30 m) were established in tree-free zones to allow readings of normalized difference vegetation index (NDVI) and normalized difference water index (NDWI). Historical time series of these indices were obtained from satellite imagery (Sentinel-2) in winter and spring 2017 and 2018. Three zones with different potential productivity were defined based on the results obtained in terms of spatial variability and temporal stability of the measured parameters. These are the basis for the elaboration of differentiated prescription maps of fertilizers with variable application rate technology, taking into account the variability of soil characteristics and pasture development, contributing to the sustainability of this ecosystem.

Integrating Geophysical and Multispectral Data to Delineate Homogeneous Management Zones within a Vineyard in Northern Italy.

Soil electrical conductivity (EC) maps obtained through proximal soil sensing (i.e., geophysical data) are usually considered to delineate homogeneous site-specific management zones (SSMZ), used in Precision Agriculture to improve crop production. The recent literature recommends the integration of geophysical soil monitoring data with crop information acquired through multispectral (VIS-NIR) imagery. In non-flat areas, where topography can influence the soil water conditions and consequently the crop water status and the crop yield, considering topography data together with soil and crop data may improve the SSMZ delineation. The objective of this study was the fusion of EC and VIS-NIR data to delineate SSMZs in a rain-fed vineyard located in Northern Italy (Franciacorta), and the assessment of the obtained SSMZ map through the comparison with data acquired by a thermal infrared (TIR) survey carried out during a hot and dry period of the 2017 agricultural season. Data integration is performed by applying multivariate statistical methods (i.e., Principal Component Analysis). The results show that the combined use of soil, topography and crop information improves the SSMZ delineation. Indeed, the correspondence between the SSMZ map and the CWSI map derived from TIR imagery was enhanced by including the NDVI information.

Integrated versus hierarchical approach for zone delineation and crop planning under uncertainty

This paper considers the problem of zone delineation management and crop planning. The problem consists of selecting which crops to plant in different management zones in order to minimize the total costs subjected to a given demand requirement. From a hierarchical point of view, the process starts by generating a partition of an agricultural field into homogeneous management zones, according to a given soil property. Then, the best crop rotation must be assigned to each management zone, applying agronomic practices in a site-specific manner in each zone. This hierarchical approach establishes two decision making levels of planning. At each level, a two-stage stochastic optimization model is proposed, representing the uncertain behavior of a soil property and crop yields by using a finite set of scenarios. Next, we combined them into a new two-stage stochastic program, solving an integrated approach by simultaneously determining an optimal zoning and allocation. Results from a set of evaluated instances showed the relevance of the proposed methodology and the benefits of the hierarchical approach over the integrated one.

Spatial variability of the physical quality of soil from management zones

The uniform management of soil, without considering the local soil spatial variability of its properties, may accelerate its degradation process. The objectives of this study were to: (i) delineate homogeneous management zones (HMZs) using the multivariate analysis approach, and (ii) evaluate the effect of uniform soil management on the physical–hydraulic attributes of these HMZ. The study was carried out in two fields: field 1 (F1) of 312 ha and field 2 (F2) of 297 ha. After collection of soil samples (grid approach) and laboratory analyses, principal component and cluster analyses were applied to define the HMZ in each field. After a new visit to the fields to confirm the variation of HMZ, soil profile samples were collected for soil quality analysis. The HMZ corresponded to soil spatial variability at the mineralogy level. The HMZ were defined by the soil physical attributes bulk density (Bd) and structural porosity in fields where soil spatial variability had greater homogeneity (F1), and by chemical attributes when it was more complex (F2). Considering uniform management, in F1, a marked reduction in structural porosity was observed in 46% of the 312 ha. F2 had an accentuated reduction in structural porosity of 26% and an increase in Bd in the other 24% of the surface layer (0.00–0.30 m). These results confirm the hypothesis that uniform management can increase the soil degradation and potential erosion, and that dividing the field into HMZs can result in a more adequate and sustainable soil management.

Delineating robust rectangular management zones based on column generation algorithm

This paper considers a management zone delineation problem in a precision agriculture framework that defines a partition of a field into rectangular and homogeneous management zones according to certain vegetation or soil indexes. Modeling the problem as a robust optimization model accounts for spatial and temporal variability of the index considered, representing this variability according to a finite set of sampled realizations (scenarios). The model assumes that the complete enumeration of all the possible rectangular management zones is known. To deal with this assumption, we propose a strategy based on a column generation algorithm to exploit the structure of the model. To solve the model and implement the strategy, an algebraic modeling language and state of the art MIP software was used. Results from the modeling approach and the computational strategy were obtained by solving different instances of a real case-study. We show that the robust optimization model can be useful in real situations under temporal variability of the index used for delineating management zones. Results also show that the algorithmic strategy is appropriated for solving the instances considered.