Management zone delineation using a modified watershed algorithm

Le zonage intra-parcellaire est une méthode couramment utilisée pour gérer la variabilité intra-parcellaire. Ce concept consiste à partitionner une parcelle en zones de management selon une ou plusieurs caractéristiques du sol et/ou du couvert végétal de cette parcelle. Cet article propose une méthode de zonage originale, basée sur l'utilisation d'une méthode de segmentation d'image puissante et rapide : l'algorithme de ligne de partage des eaux. Cet algorithme d'analyse d'image a été adapté aux spécificités de l'agriculture de précision. Les performances de notre méthodes ont été testées sur des cartes biophysiques haute résolution de plusieurs champs de blé situés en Bourgogne. / Site-specific management (SSM) is a common way to manage within-field variability. This concept divides fields into site-specific management zones (SSMZ) according to one or several soil or crop characteristics. This paper proposes an original methodology for SSMZ delineation which is able to manage different kinds of crop and/or soil images using a powerful segmentation tool: the watershed algorithm. This image analysis algorithm was adapted to the specific constraints of precision agriculture. The algorithm was tested on high-resolution bio-physical images of a set of fields in France.ZONAGE;PARCELLE;TELEDETECTION;BLE;SEGMENTATION D'IMAGE;AGRICULTURE DE PRECISION;FRANCE;BOURGOGNE;PRECISION AGRICULTURE;MANAGEMENT ZONES;REMOTE SENSING;IMAGE ANALYSIS;WATERSHED SEGMENTATION

A Novel Approach for Management Zone Delineation by Classifying Spatial Multivariate Data and Analyzing Maps of Crop Yield

Precision farming has been playing a distinguished role over last few years. It encompasses the techniques of Data Mining and Information Technology into agricultural process. The acute task in classic agriculture is fertilization, which makes minerals available for crops. Site specific methods result in imbalanced management within fields which affects the crop yield. Treating the whole field as uniform area is merely heedless as it forces the farmers to use costly resources like fertilizers, pesticides etc., at greater expenses. As the field is heterogeneous, the critical task is to identify which part of the field should be considered and the percentage of fertilizer or pesticide required. In order to increase the yield productivity, concept of Management Zone Delineation (MZD) has to be adopted, which divides the agricultural field into homogeneous subfields, or zones based on the soil parameters. Precision Agriculture focuses on the utilization of Management zones (MZs). In this paper, we have collected huge data of Davanagere agricultural jurisdiction during standard farming operations which reflects the heterogeneity of agricultural field. We base our work on a new Data Mining technique called Kriging, which interpolates soil sample values for the specific region, which in turn helps in converting heterogeneous zones to homogeneous subfields

Integrated geo-referenced data and statistical analysis for dividing livestock farms into geographical zones in the Valencian Community (Spain)

The livestock sector in the Valencian Community (Spain) has experienced an increase in the intensity of farming with an increase in the number and size of pig and poultry facilities. The absence of previous environmental requirements in this region has produced a high concentration of facilities in some areas, and urban sprawl has resulted in many farms located in problematic areas close to villages or towns, residential areas and protected areas. Conflicts surrounding land use and environmental issues have been a problem in the region for many years. The initial step to solve this problem is to produce a territorial planning system to intervene and correct the current development and adapt to new European environmental regulations. The objectives of this study are to group farms with homogeneous characteristics in the Valencian Community and to characterise and search for spatial dependency patterns within the livestock sector. These objectives have the final aim of contributing basic scientific information to subsequent administrative planning decisions for livestock. This study presented methodology based on Geographic Information Systems and statistical methods for dividing livestock farms into zones and for characterising these areas. We obtained nineteen livestock geographical areas with unique characteristics (such as livestock species composition) and verified that these areas did not follow a spatial pattern.Calafat Marzal, MC.; Gallego Salguero, AC.; Quintanilla García, I. (2015). Integrated geo-referenced data and statistical analysis for dividing livestock farms into geographical zones in the Valencian Community (Spain). Computers and Electronics in Agriculture. 114:58-67. doi:10.1016/j.compag.2015.03.005586711

Clustering approaches and ensembles applied in the delineation of management classes in precision agriculture.

Abstract. This paper describes an experiment performed using different approaches for spatial data clustering, aiming to assist the delineation of management classes in Precision Agriculture (PA). These approaches were established from the partitional clustering algorithm Fuzzy c-Means (FCM), traditionally used in this context, and from the hierarchical clustering algorithm HACCSpatial, especially designed for this PA task. We also performed experiments using traditional ensembles approaches from the literature, evaluating their behavior to achieve consensus solutions from individual clusterings obtained from features splitting or running one of the abovementioned algorithms. Results showed some differences between FCM and HACC-Spatial, mainly for the visualization of management classes in the form of maps. Considering the consensus clusterings provided by ensembles, it became clear the attempt to achieve an agreement result that most closely matches the original clusterings, showing us some details that may go undetected when we analyse only the individual clusterings.Geoinfo 2016

Modeling zone management in precision agriculture through Fuzzy C-Means technique at spatial database

Predict the optimal number of zones to manage tasks evolved in precision agriculture applications is challenging issue in classification tasks. Important decisions in the farm required maps of yield classes which contain relative large, similar and spatially contiguous partitions and sometimes without a priori knowledge of the field. The main goal of this study was to apply Fuzzy C-means (FCM), an unsupervised classification technique, in a geo-referenced yield and grain moisture dataset in order to find optimal number for homogeneous zones. Those data were produced by Long-Term Ecological Research in a Biological Station (KBS-LTER), Michigan, during growing season at 2008. The best results presented by this algorithm ranged from 8 to 10 zones which were validated using the indexes Partition Coefficient (PC), Classification Entropy (CE) and Dunn’s Index (DI). Even though, only two attributes were collected in the dataset, the Fuzzy C-means has shown promissing results for zone mapping

Assessment of Needs and Priorities for Precision Agriculture and Soil Testing in Arkansas

Optimum crop production is needed to meet the growing population\u27s food, water, fiber, and energy needs. However, agricultural productivity is hampered by many destabilizing factors such as pest management challenges, declining water quantity, and climate change which threaten long-term food security. Maximizing productivity will require optimizing resources through Precision Agriculture (PA) by supplying inputs based on crop needs. Precision Agriculture offers tools that can be used to optimize crop management practices globally. However, stakeholders’ perceptions and the lack of data-driven site-specific recommendations limit stakeholders’ ability to fully harness the potential benefits of PA. Identification of stakeholder needs and approaches to PA adoption practices will provide an understanding of existing bottlenecks and inform research and extension efforts that are directed at addressing stakeholders’ challenges in PA adoption and use. The goal of this research was to identify stakeholders’ perceptions of PA and farmers’ approaches to soil nutrient management in Arkansas. Two surveys - one to address each objective - were created in Qualtrics XM®. Data were collected between August 2022 and September 2023. Data were cleaned, anonymized, and analyzed using a chi-square test of homogeneity and hierarchical clustering. Stakeholders held varied but positive views about PA with much attention directed to operational planning. Stakeholders underscored the importance of core competencies such as computer skills, equipment calibration, and expertise in technology in the successful implementation of PA but, acknowledged experiencing difficulties applying these skills to their profession. In terms of soil sampling labor, row-crop and rice farmers relied heavily on their crop consultants while forage and specialty crop farmers relied more on themselves and their families. A variety of soil sampling strategies including whole-field composite, area composite, grid sampling, and zone sampling were widely adopted among row-crop and rice farmers, as well as mixed-crop farmers, forage farmers, and specialty crop farmers. Specialty crop growers did not use grid and zone sampling as much as the other operations because of the smaller farm and field sizes. Row crop and rice farmers adopted multiple grid sampling resolutions, but most adopted 1 sample ha-1 or 2.5 or more samples ha-1 sampling resolutions. Grid sampling adopters prioritized budget limitations, yield history, and irrigation strategy as the most important criteria for grid sampling adoption. The diversity in management practices used in Arkansas makes it difficult to characterize site-specific effects and develop relevant data-driven recommendations for all operations. This research provides baseline information on how research can be aligned with stakeholders’ needs for improved PA adoption in Arkansas

A site-specific and dynamic modeling system for zoning and optimizing variable rate irrigation in cotton

Cotton irrigation has been rapidly expanding in west Tennessee during the past decade. Variable rate irrigation is expected to enhance water use efficiency and crop yield in this region due to the significant field-scale soil spatial heterogeneity. A detailed understanding of the soil available water content within the effective root zone is needed to optimally schedule irrigation. In addition, site-specific crop-yield mathematical relationships should be established to identify optimum irrigation management. This study aimed to design and evaluate a site-specific modeling system for zoning and optimizing variable rate irrigation in cotton. The specific objectives of this study were to investigate (i) the spatial variability of soil attributes at the field-scale, (ii) site-specific cotton lint yieldwater relationships across all soil types, and (iii) multiple zoning strategies for variable rate irrigation scenarios. The field (73 ha) was sampled and apparent soil electrical conductivity (ECa) was measured. Landsat 8 satellite data was acquired, processed, and transformed to compare indicators of vegetation and soil response to cotton lint yields, variable irrigation rates, and the spatial variability of soil attributes. Multiple modeling scenarios were developed and examined. Although experiments were performed during two wet years, supplemental irrigation enhanced cotton yield across all soil types in comparison with rain-fed conditions. However, length of cropping season and rainfall distribution remarkably affected cotton response to supplemental irrigation. Geostatistical analysis showed spatial variability in soil textural components and water content was significant and correlated to yield patterns. There was as high as four-fold difference between available water content between coarse-textured and fine-textured soils on the study site. A good agreement was observed (RMSE = 0.052 cm3 cm-3 [cubic centimeter per cubic centimeter] and r = 0.88) between predicted and observed water contents. ECa and space images were useful proximal data to investigate soil spatial variability. The site-specific water production functions performed well at predicting cotton lint yield with RMSE equal to 0.131 Mg ha-1 [megagram per hectare] and 0.194 Mg ha-1 in 2013 and 2014, respectively. The findings revealed that variable rate irrigation with pie shape zones could enhance cotton lint yield under supplemental irrigation in west Tennessee

Integrating Management Zones and Canopy Sensing to Improve Nitrogen Recommendation Algorithms

Fertilizer nitrogen use efficiency (NUE) in maize (Zea mays L.) production is historically inefficient, presenting significant environmental and economic challenges. Low NUE can be attributed to poor synchrony between soil N supply and crop demand, applying uniform rates of N fertilizer to spatially variable landscapes, and failure to account for temporal variability in crop response to N. Innovative N management strategies, including crop canopy sensing and management zones (MZ), are tools that have proven useful in increasing NUE. Several researchers have proposed that the integration of these two approaches may result in further improvements in NUE and in profitability by synthesizing both crop- and soil-based information for more robust N management. The objectives of this research were to identify soil and topographic variables that could be used to delineate MZ that appropriately characterize areas with differential crop response to N fertilizer and then to test a sensor-based N application algorithm and evaluate the potential of an integrated MZ- and sensor-based approach compared to uniform N management and to sensor-based N management alone. Management zones delineated with a field-specific approach were able to appropriately characterize the spatial variability in in-season crop response to N in all eight fields and in yield response to N in three of six fields. Sensor-based application resulted in significantly increased NUE compared to uniform N management in six of eight fields, and marginal net return was significantly increased in four of eight fields. Delineated MZ appropriately classified areas of differing NUE in six of eight fields. Results from these studies indicate that integrating field-specific MZ and sensor-based N application has potential to increase NUE and profitability compared to sensor-based or MZ-based N management approaches alone. Additional research is needed to explore how to best incorporate static soil information into a sensor-based algorithm that can be generalized for a variety of soil, climatic, and managerial factors. Advisors: Richard B. Ferguson and Joe D. Luc