Soil variability mapping and delineation of site-specific management zones using fuzzy clustering analysis in a Mid-Himalayan Watershed, India

Shashikumar, B. N.; Kumar, Suresh; George, K. Justin; Singh, Amarendra Pratap

doi:10.1007/s10668-022-02411-6

Cited by 10 publications

(6 citation statements)

References 55 publications

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“…The GEE platform is a very effective geospatial data analysis and processing tool, providing access to large volumes of satellite data, including Sentinel-2 data. Unlike studies such as [73,74], where field data are used to perform zonal management in maize cultivation, the GEE platform allows the obtention of accurate and up-to-date data on crop vegetative status remotely without the need to take field data [75]. This makes it possible to monitor plots that are difficult to access.…”

Section: Discussionmentioning

confidence: 99%

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

Gallardo-Romero,

Apolo-Apolo,

Martínez-Guanter

et al. 2023

Remote Sensing

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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.

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Section: Discussionmentioning

confidence: 99%

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

Gallardo-Romero,

Apolo-Apolo,

Martínez-Guanter

et al. 2023

Remote Sensing

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“…Embora existam registros da aplicação satisfatória do modelo Pentaesférico para análise de precipitação (Júnior et al, 2020; Pirani e Modarres, 2020), este não é indicado para tal finalidade (Wong e Kwon, 2021). Tal modelo apresenta mais bem resultados para mapeamento de propriedades do solo (Neto et al, 2020a;Selmy et al, 2020;Yusuf et al, 2020;Behera et al, 2021;Selmy et al, 2022;Shashikumar et al, 2022), e qualidade de águas subterrâneas (Khorrami, 2019;Said e Yurtal, 2019;Salem et al, 2022).…”

Section: Modelounclassified

“…Esta baixa performance do modelo se dá por este ser mais aplicável em outras variáveis ambientais, destacando, assim como no modelo Pentaesférico, seu maior ajuste à análise de solos (Neto et al, 2020a;Selmy et al, 2020;Yusuf et al, 2020;Selmy et al, 2022;Shashikumar et al, 2022) e de qualidade de águas subterrâneas (Khorrami, 2019;Said e Yurtal, 2019;Salem et al, 2022).…”

Section: Modelounclassified

Comparação de modelos geoestatísticos para estimativa de precipitação em Minas Gerais, Brasil, entre os anos hidrológicos de 2000 e 2021

Guimarães

Alves²

2023

Rev. Bras. Geog. Fis.

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A gestão integrada dos recursos hídricos no Brasil e no mundo tem passado por desafios provenientes de processos naturais e antrópicos. Assim, o conhecimento preciso das condições quantitativas e qualitativas dos recursos hídricos é de fundamental importância, sendo o monitoramento pluviométrico por estações pluviométricas distribuídas no estado um componente central deste, no que diz respeito ao entendimento da dinâmica espacial e temporal da precipitação. Em Minas Gerais, tais estações estão concentradas em determinadas áreas, dificultando o processo de tomada de decisão pelos agentes gestores. As técnicas de geoestatística têm se mostrado acuradas para estimativa de precipitação em espaços sem estações pluviométricas, sendo que tal trabalho visou aplicá-las no estado, nos anos hidrológicos de outubro de 2000 a setembro de 2021. Verificou-se o potencial dos modelos Circular, Gaussiano, Esférico, Tetraesférico, Pentaesférico e Exponencial, e o Grau de Dependência Espacial entre as variáveis, utilizando-se do tratamento de dados por linguagem de programação Python e R e por técnicas de geoprocessamento. Os resultados apontaram para maior aplicabilidade do modelo Gaussiano em toda série histórica, sendo sucedido pelo modelo Exponencial em períodos chuvosos, e pelo modelo Circular na estação seca. Os modelos Pentaesférico, Esférico e Tetraesférico foram os menos aplicados. Viu-se também uma relação irregular entre o desempenho dos modelos e o Grau de Dependência Espacial das variáveis.Palavras-chave: krigagem, dependência espacial, estações pluviométricas, gestão integrada dos recursos hídricos. Comparison of geostatistical models for rainfall forecast in Minas Gerais, Brazil, between 2000 and 2021 hydrological years A B S T R A C TThe integrated water resources management in Brazil and worldwide had been through challenges from anthropic activities and natural processes. In that regard, the precise knowledge of water resources quality and quantity conditions has major importance, with the rainfall monitoring network by rainfall stations as a core component for understanding the precipitation spatial and temporal dynamics. These stations are concentrated in a few sites within Minas Gerais, which makes the decision-making process difficult for the manager agents. Geostatistical analysis has been shown as an accurate method for rainfall forecast in spaces with lack of rainfall stations, and this work aimed to apply it in Minas Gerais, between October 2000 and September 2021 hydrological years. It was checked Circular, Gaussian, Spherical, Tetraspherical, Pentaspherical, and Exponential models potential, and the degree of spatial dependence, using data treatment by Python and R programming language and geoprocessing tools. The results showed that Gaussian model has greater applicability in the time series, being succeed by Exponential model in wet season, and Circular model in dry season. Pentaspherical, Spherical, and Tetraspherical models were the least applied. It was found an irregular relationship between models’ performance and the degree of spatial dependence.Keywords: kriging, degree of spatial dependence, rainfall stations, integrated water resources management.

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“…Hence, an MZ has distinctive properties that distinguish it from other zones, allowing for specific management measures. In hilly terrains, the implementation of SSMZs emerges as an effective strategy for precision farmland management, aiming to maximize crop productivity [6]. Precise soil-property distribution maps can be generated using ordinary kriging interpolation [7].…”

Section: Introductionmentioning

confidence: 99%

“…Many prior studies have utilized PCA and FCM to categorize farmland soil into distinct areas with uniform soil properties, forming the basis for sustainable soil MZs [14][15][16][17]. Recent research in similar domains has tended to focus on smaller areas (1~2000 km 2 ) or involve fewer soil-sampling points (50~500) [6,[15][16][17][18][19]. Despite the development of digital mapping research, the prediction error grows when the point density is insufficient and the interval between points is wide [20].…”

Section: Introductionmentioning

confidence: 99%

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

Tong,

Mei,

Cao

et al. 2023

Agronomy

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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.

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Soil variability mapping and delineation of site-specific management zones using fuzzy clustering analysis in a Mid-Himalayan Watershed, India

Cited by 10 publications

References 55 publications

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

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

Comparação de modelos geoestatísticos para estimativa de precipitação em Minas Gerais, Brasil, entre os anos hidrológicos de 2000 e 2021

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

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