Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering

Davatgar, Naser; Neishabouri, M.R.; Sepaskhah, Ali Reza

doi:10.1016/j.geoderma.2011.12.005

Cited by 120 publications

(97 citation statements)

References 30 publications

(36 reference statements)

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“…Thickness of the plow layer (Ap) was mostly in the range of 10 to 22 cm. The restriction to root growth is possible in shallow plow layers of less than 15 cm [44]. Moreover, soil volume available for nutrient uptake is limited in this condition.…”

Section: Exploratory Analysis Of Datamentioning

confidence: 99%

Assessment of Two Soil Fertility Indexes to Evaluate Paddy Fields for Rice Cultivation

et al. 2017

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Abstract:Assessing soil fertility is essential to help identify strategies with less environmental impact in order to achieve more sustainable agricultural systems. The main objective of this research was to assess two soil fertility evaluation approaches in paddy fields for rice cultivation, in order to develop a user-friendly and credible soil fertility index (SFI). The Square-Root method was used as a parametric approach, while the Joint Fuzzy Membership functions as a fuzzy method with adapted criteria definition tables, were used to compute SFI. Results indicated that both of the methods determined the major soil limiting factors for rice cultivation clearly, and soil fertility maps established using GIS (Geographic Information System) could be helpful for decision makers. The coefficients of determination (R2) for the linear regression between the two SFI values and rice yields were relatively high (0.63 and 0.61, respectively). Additionally, the two SFI were significantly correlated to each other (r = 0.68, p < 0.05). The study results demonstrated that both of the methods provide reliable and valuable information. Compared to the fuzzy method, the procedure of the parametric method is easier but may be expensive and time-consuming. However, the fuzzy method, with carefully chosen indicators, can adequately evaluate soil fertility and provide useful information for decision making.

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Section: Exploratory Analysis Of Datamentioning

confidence: 99%

Assessment of Two Soil Fertility Indexes to Evaluate Paddy Fields for Rice Cultivation

et al. 2017

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“…The spatial variability of soil properties can be influenced by the complexity of the factors related to its composition, such as climate, parent material, relief, organic structure, and time (intrinsic factors); as well as management activities such as soil preparation, fertilization, and crop rotation (extrinsic factors) (DAVATGAR et al, 2012;SILVA NETO et al, 2011;CORRÊA et al, 2009). According to Doerge (2000), agronomically speaking, it makes sense to apply nutrients and other agricultural inputs at different rates in heterogeneous fields.…”

Section: Introductionmentioning

confidence: 99%

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Betzek¹,

Souza²,

Bazzi³

et al. 2017

SCA

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The application of precision agriculture considers the values of non-sampled places by the interpolation of sample data. The accuracy with which the maps of spatial distribution of yield and the soil attributes are produced in the interpolation process influences their application and utilization. This paper aimed to compare three interpolation methods (inverse of the distance, inverse of the square distance, and ordinary kriging) in the construction of thematic maps of soybean yield and soil chemical attributes. A set of data referred to 55 sampling units for the construction maps of soybean yield and of eight soil chemical attributes, by different interpolation methods. The comparison was made based on the error matrix, by calculating the Kappa and Tau indices, beyond the relative deviation coefficient (RDC). It was noticed that the inverse of the square distance was the interpolator that less influenced the data behavior, and the best interpolation method dependent of the variability of the studied attribute. The kriging and the inverse of the square distance were considered the methods that presented the best results in the interpolation of data. Key words: Geostatistics. Precision agriculture. Spatial variability. ResumoA aplicação da agricultura de precisão considera os valores mensurados para lugares não amostrados obtidos por meio da interpolação dos dados amostrais. A precisão com que os mapas de distribuição espacial da produtividade e atributos do solo são produzidos no processo de interpolação influencia a sua aplicação e utilização. Assim, este trabalho teve como objetivo comparar três métodos de interpolação (inverso da distância, inverso do quadrado da distância e krigagem ordinária) na construção de mapas temáticos de produtividade de soja e atributos químicos do solo. Um conjunto de dados referentes a 55 unidades de amostragem foi utilizado para a construção dos mapas de oito atributos químicos do solo e da produtividade de soja, por diferentes métodos de interpolação. A comparação foi feita com base na matriz de erro, por meio do cálculo dos índices Kappa e Tau, além do coeficiente de desvio relativo

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“…Although any attribute may be related to crop yield, for DOERGE (2000), the ideal attribute is the correlation of predictable spatial information sources with yield. Clustering techniques for MZ generation include algorithms such as K-Means and Fuzzy C-Means (ILIADIS et al, 2010;VALENTE et al, 2012 andLI et al, 2013), which offer good results (VITHARANAet al, 2008;MORARI et al, 2009;MORAL et al, 2010;RODRIGUES JUNIOR et al, 2011;DAVATGAR et al, 2012;KWEON, 2012;BANSOD & PANDEY, 2013), which permit the automatic division of the studied field. In this approach, different data sources that are related to crop development factors can be used to generate MZs.…”

Section: Introductionmentioning

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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Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering

Cited by 120 publications

References 30 publications

Assessment of Two Soil Fertility Indexes to Evaluate Paddy Fields for Rice Cultivation

Assessment of Two Soil Fertility Indexes to Evaluate Paddy Fields for Rice Cultivation

Interpolation methods for thematic maps of soybean yield and soil chemical attributes

Redundant variables and the quality of management zones

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