Comparison measures of maps generated by geostatistical methods

Dalposso, Gustavo Henrique; Uribe-Opazo, Miguel Ángel; Mercante, Erivelto; Johann, Jerry Adriani; Borssoi, Joelmir André

doi:10.1590/s0100-69162012000100018

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…The MZs generated by the original data without interpolation and the ones generated by ID, ISD and kriging were also compared by Kappa (K) and Tau indexes (Dalposso, Uribe-Opazo, Mercante, Johann, & Borssoi, 2012). The degree of agreement was classified according to Landis and Koch (1977) for K, namely, 0 < K ≤ 0.2: no agreement; 0.2 < K ≤ 0.4: weak; 0.4 < K ≤ 0.6: moderate: 0.6 < K ≤ 0.8: strong; 0.8 < K ≤ 1: very strong.…”

Section: Methodsmentioning

confidence: 99%

<b>Data interpolation in the definition of management zones

et al. 2016

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Section: Methodsmentioning

confidence: 99%

<b>Data interpolation in the definition of management zones

et al. 2016

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“…4) The Kappa index has been used to measure the accuracy of thematic classifications (BAZZI et al, 2008;BASTIANI et al, 2012;DALPOSSO et al, 2012) and is recommended as an appropriate measure of accuracy for all elements from the error matrix. It was used to evaluate the spatial agreement among maps of more efficient MZs (Equation 6).…”

Section: Methodsmentioning

confidence: 99%

Redundant variables and the quality of management zones

et al. 2016

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Precision agriculture (PA) allows farmers to identify and address variations in an agriculture field. Management zones (MZs) make PA more feasible and economical. The most important method for defining MZs is a fuzzy C-means algorithm, but selecting the variable for use as the input layer in the fuzzy process is problematic. BAZZI et al. (2013) used Moran's bivariate spatial autocorrelation statistic to identify variables that are spatially correlated with yield while employing spatial autocorrelation. BAZZI et al. (2013) proposed that all redundant variables be eliminated and that the remaining variables would be considered appropriate on the MZ generation process. Thus, the objective of this work, a study case, was to test the hypothesis that redundant variables can harm the MZ delineation process. BAZZI This work was conducted in a 19.6-ha commercial field, and 15 MZ designs were generated by a fuzzy C-means algorithm and divided into two to five classes. Each design used a different composition of variables, including copper, silt, clay, and altitude. Some combinations of these variables produced superior MZs. None of the variable combinations produced statistically better performance that the MZ generated with no redundant variables. Thus, the other redundant variables can be discredited. The design with all variables did not provide a greater separation and organization of data among MZ classes and was not recommended.KEYWORDS: precision agriculture, spatial correlation, relative efficiency. VARIÁVEIS REDUNDANTES E A QUALIDADE DE ZONAS DE MANEJORESUMO: A agricultura de precisão proporciona aos agricultores identificar e tratar de forma adequada as variações encontradas na área agrícola. As zonas de manejo (ZMs) permitem a implantação da agricultura de precisão de forma viável e relativamente mais econômica. A forma mais importante para definir ZMs é usando o algoritmo fuzzy C-means. Um problema consiste em como selecionar a variável a ser usada como layer de entrada no processo fuzzy. Assim, o objetivo deste trabalho, foi testar a hipótese de que variáveis redundates podem prejudicar o processo de delineamento de ZMs. Este trabalho foi desenvolvido em uma área de 19,6 ha e 15 agrupamentos de ZMs foram gerados por meio do o algoritmo fuzzy C-means, dividindo-se em duas a cinco classes. Cada agrupamento usou uma composição diferente de variáveis, que são os atributos cobre, silte, argila, e altitude. Foi encontrado que algumas combinações dessas variáveis produziu melhores ZMs. Nenhuma combinação de variáveis produziu desempenho estatisticamente melhor que a ZM gerada apenas com as variáveis não redundantes. Assim, as variáveis redundantes podem ser descartadas. O agrupamento com todas as variáveis não forneceu maior separação e organização dos dados entre as classes de ZM, não sendo recomendado. PALAVRAS-CHAVE: agricultura de precisão, correlação espacial, eficiência relativa.

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“…Local influence analysis (Fig. 3 As stated by Dalposso et al (2012), influential samples can change a decision in the determination of geostatistical models or the construction of thematic maps. Regarding the sample elements of soybean productivity identified as outliers (Fig.…”

Section: Local Influence Analysismentioning

confidence: 99%

Spatial-temporal Analysis of Soybean Productivity Using Geostatistical Methods

Dalposso

Uribe-Opazo

Bastiani

2022

JAS

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To have information about the soybean productivity over the crop years is essential to define strategies to increase profits and reduce costs and most important to reduce environmental impacts. One form of monitoring is the use of Geostatistical methods, which allow us to obtain maps with more accurate predictions. In this paper, an area of 127.16 ha was studied during six crop years between 2012/2013 and 2017/2018. We found that productivity values vary between crop years, mainly due to uncontrollable climatic factors. The removal of influential points caused changes in the predicted values showed in the maps, and the use of scaled semivariograms allowed us to obtain similar maps to those obtained considering the model without influential points, then there was no need to exclude observations. The use of a model with replicates helped to identify regions where productivity was lower. The use of explanatory variables allowed us to elaborate a more accurate thematic map in the 2017/2018 crop year, which was well evidenced by the prediction standard error map.

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Comparison measures of maps generated by geostatistical methods

Cited by 5 publications

References 15 publications

<b>Data interpolation in the definition of management zones

<b>Data interpolation in the definition of management zones

Redundant variables and the quality of management zones

Spatial-temporal Analysis of Soybean Productivity Using Geostatistical Methods

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