Geração de zonas de manejo para cafeicultura empregando-se sensor SPAD e análise foliar

Rodrigues, F. A.; Vieira, Luciano Baião; Queiroz, Daniel Marçal de; Santos, Nerílson Terra

doi:10.1590/s1415-43662011000800003

Cited by 32 publications

(37 citation statements)

References 22 publications

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“…According to Rodrigues Junior et al (2011), a management zone is defined as a sub-region of a field that has the same combination of production limiting factors such as yield and quality. Each management zone receives a specified dose of lime and fertilizers according to its characteristics (Fu et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Precision agriculture applied to soybean: Part III - Spatial and temporal variability of yield

Bottega¹,

Queiroz²,

Pinto³

et al. 2017

Aust J Crop Sci

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The Brazilian Savannah is an important production center of grains despite the presence of low fertility soils. In this agricultural scenario, the delineation of management zones for the application of lime and fertilizer seems to be promising for a high potential for success and may result in the reduction in production cost with an increase in the yield. The objective of this work was to evaluate the spatial and temporal variations in soybean yield in the Brazilian Savannah involving the delineation of management zones based on the apparent soil electrical conductivity (ECa). In an agricultural field in the Brazilian Savannah, 160 sampling points were taken as the references for the mapping of soybean yield in the crop seasons of 2011

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Section: Introductionmentioning

confidence: 99%

Precision agriculture applied to soybean: Part III - Spatial and temporal variability of yield

Bottega¹,

Queiroz²,

Pinto³

et al. 2017

Aust J Crop Sci

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“…This is a potentially important tool for managing spatial variability in crops and soils (Dalchiavon et al, 2012;Rodrigues Junior et al, 2011;Moral et al, 2011), apart from being a significant part of precision farming techniques (Xiaohu et al, 2016). For generating management zones, it is first necessary to collect information closely related to crop yield, and the variables chosen to define these management zones should be quick and inexpensive to measure.…”

Section: Introductionmentioning

confidence: 99%

Precision agriculture applied to soybean crop: Part II – Temporal stability of management zones

Bottega¹,

Queiroz²,

Pinto³

et al. 2017

Aust J Crop Sci

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Precision farming techniques have potential applications in the Brazilian Savannah owing to the spatial variability in crop yield. The objective of this study was to evaluate the temporal stability of management zones based on the measurement of apparent soil electrical conductivity (ECa). The study was performed in a field located in the Brazilian Savannah. During the 2011/2012 crop season, ECa was measured at 160 sampling points in a 47-ha area. ECa values were then grouped into classes using the fuzzy kmeans algorithm and fertilizers were applied to the soybean crop (BMX Potencia RR) in the 2012/2013 and 2013/2014 crop seasons according to soil analysis for each class. ECa was also measured in the 2012/2013 and 2013/2014 crop seasons and the values were grouped into classes, and the maps of apparent soil electrical conductivity classes were compared using kappa coefficients. Apparent soil electrical conductivity was significant and positively correlated with each pair of crop seasons for all three seasons analyzed. The cluster analysis indicated the presence of two management zones in 2012. These management zones were adopted for fertility management in the 2012/2013 and 2013/2014 crop seasons. A comparison of the 2012 and 2013 management zone maps, based on the kappa coefficient, showed that they remained stable. The use of kappa coefficients was found to be a promising tool in the analysis of the temporal stability of management classes.

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“…Among studies seeking to evaluate the economic viability of PA, those that incorporate management zones (MZs) aim to analyze and provide recommendations on the division of production areas into smaller MZs that are treated differently. Such practices allow farmers to use the same systems used in conventional agriculture during crop management (RODRIGUES JUNIOR et al, 2011). According to ROUDIER et al (2011), MZs simplify the representation of spatial variability in a cropping field.…”

Section: Introductionmentioning

confidence: 99%

“…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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Geração de zonas de manejo para cafeicultura empregando-se sensor SPAD e análise foliar

Cited by 32 publications

References 22 publications

Precision agriculture applied to soybean: Part III - Spatial and temporal variability of yield

Precision agriculture applied to soybean: Part III - Spatial and temporal variability of yield

Precision agriculture applied to soybean crop: Part II – Temporal stability of management zones

Redundant variables and the quality of management zones

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