Variabilidade espacial de parâmetros físico-químicas do solo e biofísicos de superfície em cultivo do sorgo

Bernardi, A. C. de C.; Rabello, L. M.; Inamasu, Ricardo Yassushi; Grego, C. R.; Andrade, Ricardo Guimarães

doi:10.1590/s1415-43662014000600009

Cited by 20 publications

(24 citation statements)

References 19 publications

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“…On the other hand, mapping soil properties with higher variability can be less accurate than soil properties with lower variability. Trends in the variation of soil attributes obtained in this study are consistent to those observed by Bernardi et al (2014) for several soil parameters.…”

Section: Resultssupporting

confidence: 89%

“…Remote sensing (RS) is another PA tool in which acquired images are transformed into vegetation indexes, which may be related to some crop variables (Schellberg et al, 2008;Bernardi et al, 2014;Escribano Rodríguez et al, 2014). Satellite-derived vegetation indexes have been widely used to estimate crop and grassland biomass; RS provides temporal and spatial patterns of ecosystem change and has been used to estimate the biophysical characteristics of crops and grasslands (Moges et al, 2004;Numata et al, 2007;Schellberg et al, 2008;Silva Júnior et al, 2013;Bernardi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Satellite-derived vegetation indexes have been widely used to estimate crop and grassland biomass; RS provides temporal and spatial patterns of ecosystem change and has been used to estimate the biophysical characteristics of crops and grasslands (Moges et al, 2004;Numata et al, 2007;Schellberg et al, 2008;Silva Júnior et al, 2013;Bernardi et al, 2014). Normalized difference vegetative indexes (NDVI) are commonly used to evaluate plant health, biomass, and nutrient content (Moges et al, 2004;Numata et al, 2007;Escribano Rodríguez et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

Bernardi

Grego

Andrade

et al. 2017

Rev. bras. eng. agríc. ambient.

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A B S T R A C TThe knowledge of soil property spatial variability is useful for determining the rational use of inputs, such as the site-specific application of lime and fertilizer. The objective of this study was to evaluate the vegetation index and spatial variability of physical and chemical soil properties in an integrated crop-livestock system (ICLS). Soil samples were taken from a 6.9 ha area in a regular hexagon grid at 0-0.20 m depths. Soil P, K, Ca, Mg, and cation exchange capacity -CEC; base saturation; clay and sand were analyzed. Soil electrical conductivity (ECa) was measured with a contact sensor. The site was evaluated at the end of the corn season (April) and during forage production (October) using Landsat 5 images, remote sensing techniques and a geographic information system (GIS). Results showed that the normalized difference vegetation index (NDVI) was associated with ECa and soil parameters, indicating crop and pasture variations in the ICLS. Geostatistics and GIS were effective tools for collecting data regarding the spatial variability of soil and crop indicators, identifying variation trends in the data, and assisting data interpretation to determine adequate management strategies.Variabilidade espacial de índices de vegetação e propriedades do solo em sistema de integração lavoura-pecuária R E S U M O O conhecimento da variabilidade espacial dos atributos físicos e químicos do solo é útil para o uso racional de insumos, como na aplicação localizada de corretivos e fertilizantes. Objetivou-se, nesta pesquisa, avaliar a variabilidade espacial dos índices de vegetação e atributos físicos e químicos do solo em um sistema de integração lavoura-pecuário (ILP). Uma área de 6,9 ha foi amostrada com grade regular hexagonal na profundidade de 0-0,20 m. Os valores no solo de P, K, Ca, Mg, e CTC, saturação por bases, argila e areia foram analisados. A condutividade elétrica aparente do solo (CEa) foi medida com um sensor de contato. O talhão foi avaliado no final do ciclo do milho (abril) e para a produção de pastagem (outubro) por imagens do satélite Landsat 5, utilizando-se técnicas de sensoriamento remoto e sistemas de informação geográfica (SIG). Os resultados mostraram que o índice de vegetação por diferença normalizada (NDVI) foi associado à CEa e aos parâmetros de solo indicando as variações da cultura do milho e pastagem em sistema ILP. A geoestatística e o SIG foram ferramentas eficazes para unir os dados de variabilidade espacial do solo e da cultura, indicar as tendências dos resultados e interpretar a informação para apoiar estratégias de manejo adequadas.

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Section: Resultssupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

Bernardi

Grego

Andrade

et al. 2017

Rev. bras. eng. agríc. ambient.

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“…Understanding the spatial variability of yield and physical properties of the soil, as well as the study of the temporal variability when properly available to farmers who adopt precision agriculture, can improve the management options and enable modern agriculture that is economically competitive and environmentally sustainable (Molin & Rabelo, 2011;Bernardi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Spatial variability of the yield and physical properties of orange (Citros sinensis [L.] Osbeck cv. Monte Parnaso) fruits

Nicolau¹,

Mercante²,

Maggi³

et al. 2017

Aust J Crop Sci

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Precision agriculture (PA) is used as a tool in the citrus industry and aims to increase production reduce costs and search for answers to explain the spatial variability of yield. The aim of this study was to model the spatial variability of yield and physical properties of fruits of orange trees in relation to the geographic quadrant where the fruits are on the trees. The experiment was carried out in a 1 ha commercial orchard of orange trees cv. Monte Parnaso. A GPS receiver and a total station were used to georeference the experimental area and the position of each sampled tree in the area, as well as the location of the geographic quadrant where the fruits sampled were on the trees. Twelve fruits were collected from each of the 13 sampled trees, resulting 39 fruits per quadrant, totalling 156 fruits each crop-year (2011 and 2012). Yield was estimated after the harvest (kg.quadrant -1 ). Physical analyses were performed for each fruit, measuring the following physical properties: equatorial diameter, total mass and juice yield. We conducted analysis of descriptive statistics and spatial variability in the data through techniques related to geostatistics. The results showed the highest spatial variability between the quarters for the juice yield and yield, and the verified juice yield was within the acceptable standard by regulatory bodies.

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“…Research on the spatial dependence structure of agricultural georeferenced variables, such as chemical and physical soil properties and crop yield, is an analysis tool which provides information to support a decision in favour of better management of production areas (BORSSOI et al, 2011;GREGO et al, 2011;URIBE-OPAZO et al, 2012;NESI et al, 2013;ASSUMPÇÃO et al, 2014;BERNARDI et al, 2014). This can be accomplished by means of geostatistical techniques that retrieve from a set of sample elements, information about the spatial variation of the phenomenon in the whole area through the construction of thematic maps of variability (DIGGLE & RIBEIRO JUNIOR, 2007).…”

Section: Introductionmentioning

confidence: 99%

Soybean yield maps using regular and optimized sample with different configurations by simulated annealing

et al. 2016

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ABSTRACT:This study aimed to compare thematic maps of soybean yield for different sampling grids, using geostatistical methods (semivariance function and kriging). The analysis was performed with soybean yield data in t ha -1 in a commercial area with regular grids with distances between points of 25x25 m, 50x50 m, 75x75 m, 100x100 m, with 549, 188, 66 and 44 sampling points respectively; and data obtained by yield monitors. Optimized sampling schemes were also generated with the algorithm called Simulated Annealing, using maximization of the overall accuracy measure as a criterion for optimization. The results showed that sample size and sample density influenced the description of the spatial distribution of soybean yield. When the sample size was increased, there was an increased efficiency of thematic maps used to describe the spatial variability of soybean yield (higher values of accuracy indices and lower values for t he sum of squared estimation error). In addition, more accurate maps were obtained, especially considering the optimized sample configurations with 188 and 549 sample points.KEYWORDS: accuracy indices, optimization, sampling grids, spatial variability. MAPAS DA PRODUTIVIDADE DA SOJA USANDO CONFIGURAÇÕES AMOSTRAISREGULARES E OTIMIZADAS PELA TÊMPERA SIMULADA RESUMO: Neste trabalho, teve-se o objetivo de estudar e comparar mapas temáticos da produtividade da soja para diferentes grades amostrais, utilizando métodos geoestatísticos (função semivariância e krigagem). A análise foi realizada com dados da produtividade de soja em t ha -1 numa área comercial com grades regulares e distâncias entre pontos de 25x25 m, 50x50 m, 75x75 m e 100x100 m, com 549; 188; 66 e 44 pontos amostrais, respectivamente, e dados obtidos pelo monitor de colheita. Também foram gerados esquemas amostrais otimizados, com o algoritmo chamado Têmpera Simulada, usando a maximização da medida de acurácia exatidão global, como critério de otimização. Foi verificado que fatores como o tamanho da amostra e a densidade amostral influenciaram na descrição da distribuição espacial da produtividade da soja feita por meio dos mapas temáticos. Quando se aumentou o tamanho amostral, houve aumento da eficiência dos mapas temáticos quanto à descrição da variabilidade espacial da produtividade da soja (maiores valores dos índices de acurácia e os menores valores para a soma quadrada do erro de estimação). Além disso, os mapas mais acurados foram obtidos considerando as configurações amostrais otimizadas com 188 e 549 pontos amostrais.PALAVRAS-CHAVE: grades amostrais, índices de acurácia, otimização, variabilidade espacial.Soybean yield maps using regular and optimized sample with different configurations by simulated annealingEng.

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Variabilidade espacial de parâmetros físico-químicas do solo e biofísicos de superfície em cultivo do sorgo

Cited by 20 publications

References 19 publications

Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

Spatial variability of vegetation index and soil properties in an integrated crop-livestock system

Spatial variability of the yield and physical properties of orange (Citros sinensis [L.] Osbeck cv. Monte Parnaso) fruits

Soybean yield maps using regular and optimized sample with different configurations by simulated annealing

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