&lt;b&gt;Data interpolation in the definition of management zones

Schenatto, Kelyn; Souza, Eduardo Godoy de; Bazzi, Cláudio Leones; Bier, Vanderlei Arthur; Betzek, Nelson Miguel; Gavioli, Alan

doi:10.4025/actascitechnol.v38i1.27745

Cited by 20 publications

(15 citation statements)

References 30 publications

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“…The elevation attribute was selected to define the MZs for the three areas based on the results found by several authors, such as Bazzi et al (2015), Fraisse et al 2001, Jaynes et al (2005, Peralta and Costa (2013), Schenatto et al (2016), and Schepers et al (2004), which suggest that when the area is not flat, the variable elevation frequently has a spatial association with crop yield. …”

Section: Resultsmentioning

confidence: 99%

“…The data were then interpolated by ordinary Kriging in order to create a 5 × 5 m grid, with more attributes' details which, as shown by Schenatto et al (2016), is the best interpolation method to generate the sampling grid before the MZs generation process.…”

Section: Methodsmentioning

confidence: 99%

“…In the process of generating MZs, clustering methods have been widely used (FRAISSE et al, 2001;LI et al, 2007;REYNIERS et al, 2006;SCHENATTO et al, 2016;TAYLOR et al, 2003). The algorithm Fuzzy C-Means (BAZZI et al, 2013;LI et al, 2007;MORARI et al, 2009;MILNE et al, 2012;XIN-ZHONG et al, 2009) is a clustering method based on the fuzzy logic, defined by Zadeh (1965), which matches uncertainties associated with class and association boundaries (DOBERMANN et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Use of the farmer’s experience variable in the generation of management zones

Schenatto

Souza

Bazzi

et al. 2017

SCA

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In the spatial variability management of fields, the approach based on management zones (MZs) divides the area into sub-regions, which have spatially homogeneous topography and soil conditions. Such MZs should lead to the same potential yields. Farmers understand which areas of a field have high and low yields, and use of this knowledge may allow the identification of MZs in a field based on production history. The objective of the present study was to evaluate the application of farmer's experience to determine MZs. The study was conducted in three agricultural fields located in the west of the Paraná State in Brazil, and the MZs were generated considering three cases: a) without the use of the farmer's experience variable; b) with the variable of farmer's experience and the stable soil properties selected at the variable selection stage; and c) only with the farmer's experience variable. The generated MZs were evaluated using the Variance Reduction (VR) index, Fuzziness Performance Index (FPI), Modified Partition Entropy (MPE), Smooth Index (SI), and Analysis of Variance (ANOVA). The study showed that the use of farmer's experience to set MZs could be an efficient and simple tool, that it could reduce costs for the processes of setting MZs, compared to the traditional method of using stable soil variables and relief. Key words: Precision agriculture. Clustering. Farmer feeling. Management units. ResumoNo gerenciamento da variabilidade espacial das lavouras a abordagem baseada em zonas de manejo (ZMs) divide o talhão em sub-regiões, que apresentam topografia e condições do solo espacialmente homogêneas, de tal forma que tais ZMs devem conduzir aos mesmos resultados em potencial de rendimento das culturas. Os produtores têm experiência de quais áreas de um talhão apresentam altas e baixas produtividades e fazer uso dessa base de conhecimento pode permitir a identificação de ZMs em um campo com base no histórico de produção. O objetivo desse trabalho foi avaliar a eficiência de utilização da experiência do produtor na definição de ZMs. A pesquisa foi realizada em três áreas agrícolas localizadas na região Oeste do estado do Paraná/ Brasil e as ZMs foram geradas considerando

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of the farmer’s experience variable in the generation of management zones

Schenatto

Souza

Bazzi

et al. 2017

SCA

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“…10, No. 6; The altitude variation in the area and the soil penetration resistance at depth 0-0.1 m were used as the basis for the configuration of the MU's, because showed higher spatial correlation with productivity in the experimental area in the analysis performed in the agricultural years (Schenatto et al, 2016, the most recent period was also the soybean planting. Being that, the MU2 has the highest productive potential and was established in the highest region, while the MU1 is located in the lower part of the area.…”

Section: Soil Anmentioning

confidence: 99%

Application of Plant Densities in Management Units in the Soybean Cultivation

Machado¹,

Maggi²,

Souza³

et al. 2018

JAS

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The application of management units (MU's) aims to make economically viable to precision agriculture, making the technique accessible to a greater number of producers. Using MU's, the experimental area is divided into plots with different productive potentials. In this context, the objective of the experiment was to verify the effectiveness of the area division in MU's and to define the soybean plant density that provides higher productive efficiency in each MU. For the formation of MU's it was used the altitude variation and the soil penetration resistance 0-0.1 m in the experimental area, being that the area was divided into 2 MU's, called MU1 and MU2, and each MU was composed of 8 plots. At planting, 2 plant densities were applied, 214 000 and 257 000 plants ha -1 , and each density was applied in 4 plots per MU, using row spacing of 0.70 m. In relation to productivity, there was a significant difference, applying the t-Student test, between MU's, and the MU2, unit with higher productive potential, located in the highest part in the area, achieved higher productivity; and there was an effect, using the Tukey test, on the application of the 2 different plant densities in the MU's, being that the densities of 214 000 and 257 000 plants ha -1 reached, respectively, higher productivity in MU2 and MU1.

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“…The indices available in the fclustindex function of the e1071 package were also used: fuzzy hypervolume (Fhv), xie beni (Xb), partition entropy (Pe), where the lowest value indicates the best division or most similarity among the groups, and the average partition density (Apd) and partition density (Pd) indices, where the best division corresponds to the highest value (MEYER et al, 2015). Additionally, the cluster validation index (CVI) was used, where the smallest value indicates the best division (SCHENATTO et al, 2016). (HAIR JR. et al, 2009).…”

Section: Data Groupingmentioning

confidence: 99%

Defining Management Zones Based on Soil Attributes and Soybean Productivity

et al. 2017

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-Demarcating soil management zones can be useful, for instance, delimiting homogeneous areas and selecting attributes that are generally correlated with plant productivity, but doing so involves several different steps. The objective of this study was to identify the chemical and physical attributes of soil and soybean plants that explain crop productivity, in addition to suggesting and testing a methodological procedure for defining soil management zones. The procedure consisted of six steps: sample collection, data filtering, variable selection, interpolation, grouping, and evaluation of management zones. The samples were collected in an experimental area of 12.5 ha cultivated with soybean during the 2013/14 crop in Dystrophic Red Latosol, in Mato Grosso, Brazil. A total of 117 pairs of plant and soil samples were collected. Student's t-test was used (α = 0.02) to verify that the number of samples was adequate for correlation analysis. Results showed that only the P and Mn content in the grains explained (based on R 2 values) the variation in soybean grain productivity the area. Based on the interpolation of these contents by ordinary kriging, the fuzzy C-means algorithm was used to separate them into groups by similarity. Division into two groups was the best option, which could be differentiated by Mann-Whitney test (P < 0.05), resulting in a map with 10 management zones.Keywords: Glycine max L.. Direct seeding. Precision agriculture. DEFINIÇÃO DE ZONAS DE MANEJOS A PARTIR DE ATRIBUTOS DO SOLO E PRODUTIVIDADE DE SOJARESUMO -Zonas de manejo do solo são usadas, por exemplo, para delimitar áreas homogêneas, selecionando atributos que no geral correlacionam com a produtividade das plantas, mas, defini-las requer diferentes etapas. Objetivou-se neste trabalho identificar atributos químicos e físicos do solo e de plantas de soja que explicaram a produtividade de grãos da cultura e, também, sugerir e testar um procedimento metodológico para definir zonas de manejo do solo. O procedimento consistiu de seis etapas: coleta de amostras, filtragem dos dados, seleção das variáveis, interpolação, agrupamento e avaliação das zonas de manejo. As amostras foram coletadas em uma área experimental de 12,5 ha, cultivada com soja na safra 2013/14, em um Latossolo Vermelho Distrófico, em Mato Grosso, onde foram coletados 117 pares de amostras de plantas e de solo. Utilizando-se o teste de Student (α = 0,02), verificou-se que o número de amostras foi adequado para a análise de correlação. Entretanto, apenas os teores de P e Mn dos grãos explicaram (R 2 ) a variação da produtividade de grãos de soja na área. Com base na interpolação destes teores por krigagem ordinária utilizou-se o algoritmo fuzzy C-means para separá-los em grupos por similaridade, em que a divisão em 2 grupos foi a melhor opção, que diferiram pelo teste de Mann-Whitney (P < 0,05), resultando em um mapa com 10 zonas de manejo. Palavras-chave:Glycine max L.. Plantio direto. Agricultura de precisão.

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<b>Data interpolation in the definition of management zones

Cited by 20 publications

References 30 publications

Use of the farmer’s experience variable in the generation of management zones

Use of the farmer’s experience variable in the generation of management zones

Application of Plant Densities in Management Units in the Soybean Cultivation

Defining Management Zones Based on Soil Attributes and Soybean Productivity

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