Assessment of soil property spatial variation in an Amazon pasture: basis for selecting an agronomic experimental area

Cerri, Carlos Eduardo Pellegrino; Bernoux, Martial; Chaplot, Vincent; Volkoff, Boris; Victória, Reynaldo Luiz; Melillo, Jerry M.; Paustian, Keith; Cerri, Carlos C.

doi:10.1016/j.geoderma.2004.01.027

Cited by 65 publications

(51 citation statements)

References 32 publications

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“…Esses estudos variam de centímetros para vários quilômetros de distância e permitem a construção de mapas de contorno das variáveis de interesse (Castrignanò et al, 2000;Cerri et al, 2004). Para a cultura do coco e paisagens de topo de tabuleiros costeiros inexistem pesquisas com a abordagem geoestatística.…”

Section: Introductionunclassified

Variabilidade espacial de atributos de solos em unidades de manejo em área piloto de produção integrada de coco

Gomes

Bolfe

Curi

et al. 2008

Rev. Bras. Ciênc. Solo

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RESUMOO objetivo deste trabalho foi, inicialmente, acessar as variações espaciais dos solos de parcelas experimentais de produção integrada do coco por meio do mapeamento detalhado dos solos e da construção de mapas de contorno de atributos do solo. Foram feitas observações, medições e coleta de amostras em microtrincheiras e perfis de solo. O teor de argila dos solos das microtrincheiras foi avaliado a campo pela sensação ao tato. Em todas as amostras foram realizadas análises físicas e químicas. Foram determinados granulometria (perfis), Al, Ca e Mg, acidez potencial, K e Na, pH, P disponível e C orgânico. Nas amostras dos perfis, foram determinados Fe, Al, Ti, P e Si pelo ataque sulfúrico. O Fe também foi determinado na fração argila, por extração com ditionito-citrato-bicarbonato de Na. Foi realizada difração de raios X da fração argila desferrificada. A construção de semivariogramas possibilitou a classificação e a comparação dos atributos do solo por meio do grau de dependência espacial destes. A elaboração dos mapas de contorno foi realizada pelo método interpolador de krigagem. A área mapeada corresponde a um topo amplo de tabuleiros costeiros, com uma depressão fechada no seu extremo sul. Do centro da depressão para fora dela seguem, em seqüência, Cambissolo Háplico textura argilosa, Latossolo Amarelo textura média/argilosa e Latossolo Amarelo textura média. Esses Latossolos são coesos em subsuperfície. Predominam atributos com elevado grau de heterogeneidade. Os valores médios de P, K e Ca + Mg estão influenciados por correções e adubações sistemáticas realizadas na área de plantio. Todas as variáveis analisadas apresentaram dependência espacial, expressa pelos modelos de semivariogramas. A análise da relação C 0 /(C 0 + C 1 ) revelou um grau de dependência espacial, de todas as variáveis,

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Section: Introductionunclassified

Variabilidade espacial de atributos de solos em unidades de manejo em área piloto de produção integrada de coco

Gomes

Bolfe

Curi

et al. 2008

Rev. Bras. Ciênc. Solo

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“…Furthermore, the accuracy of the interpolated map will still be dependent on the density and distribution of the original data points (Heuvelink and Bierkens 1992;McBratney et al 2000McBratney et al , 2003. Statistical models with predictive powers could potentially overcome the problem of interpolation methods (Chaplot et al 2001;Cerri et al 2004;Chen 2000Chen , 2005Sumfleth and Duttmann 2008). But empirical models derived from traditional statistical methods may hinder the real relationships between the SOC and independent data because the relationships are rarely linear in nature (Somaratne et al 2005).…”

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confidence: 99%

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

Zhao¹,

Yang²,

Benoy³

et al. 2010

Can. J. Soil. Sci.

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network models to produce soil organic carbon content distribution maps across landscapes. Can. J. Soil Sci. 90: 75Á87. Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain highresolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had significant influence on SOC distributions. The prediction of the selected two-input-node SOC model (VSP and coarse resolution soil map recorded SOC as inputs) had a correlation coefficient of 0.92 with measured values, and model predicted SOC values had 47.9% of the total points within 90.5% of the measured values and 70.6% within 91% of the measured values. The prediction of the selected four-input-node model (VSP, slope steepness, PSR and coarse resolution SOC values as inputs) had a correlation coefficient of 0.98 with measured values and model predicted SOC values had 75% of the total points within 90.5% of the measured values and 87% within 91% of the measured values. The prediction of the five-input-nodes model with soil drainage as additional input had a correlation coefficient of 0.99 with measured values, and model predicted SOC values had 87% of the total points within 90.5% of the measured values and 98% of the total points within 91% of the measured values. The calibrated SOC prediction model was used to produce a high-resolution SOC map for the Black Brook Watershed and the resulting SOC distribution map is considered to be realistic. Results indicated that DEM-derived hydrological parameters together with widely available coarse resolution soil map data could be used to produce high-resolution SOC maps with the ANN method. For personal use only.

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“…The variables that presented a more expressive nugget effect were microporosity, phosphorus, zinc, manganese and copper. Cerri et al (2004), studying the variables of a soil in the state of Rondonia, adopted sampling with distance between the points of 25 m and also obtained an elevated nugget effect in most of the variables. That indicates that there is high variability inside a small space, in a shorter distance to that practiced in the collections (Novaes Filho et al, 2007).…”

Section: Resultsmentioning

confidence: 99%

Spatial variability of soil physical and chemical aspects in a Brazil nut tree stand in the Brazilian Amazon

Guerreiro¹,

JÃonior²,

Santos³

et al. 2017

Afr. J. Agric. Res.

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The Brazil nut is considered one of the noblest trees of the Amazon biome and contains social, ecologic and economic importance to this region. The study of the spatial variance of the edaphic properties in native nut trees can direct future researches about more efficient samplings. The Geostatistics is the methodology utilized for this type of study, once that it considers the structural and random characteristics of a variable spatially distributed. This work sought to get a higher knowledge about the distribution of the nutrients in the soil, verifying the relationship with the occurrence of this species, to thereby provide subsidies to future forest management and maintenance/enlargement of the productivity in these areas. The soil samples were collected from 30 × 30 m on the line, in all of the lines in part of the study, totaling 60 samples. All of the points were georeferenced. The preparation of the samples for the sample preparation for the chemical analysis and the methods and calculations to determine the physicochemical variables studied were described by Nogueira and Souza (2005). The statistical and geostatistical analysis were conducted using the R computational environment, version 3.2.2. Most of the studied variables presented defined level. For the physical variables, there was predominance of the adjustment to the model of the gaussian variogram, follower by the spherical model. In the case of the chemical variables, there were two occurrences for each adjustment model (spherical, exponential and gaussian). The variables that best presented spatial relation with the occurrence of Brazil nut trees were the silt, clay, macroporosity, pH, phosphorus, zinc and copper.

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Assessment of soil property spatial variation in an Amazon pasture: basis for selecting an agronomic experimental area

Cited by 65 publications

References 32 publications

Variabilidade espacial de atributos de solos em unidades de manejo em área piloto de produção integrada de coco

Variabilidade espacial de atributos de solos em unidades de manejo em área piloto de produção integrada de coco

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

Spatial variability of soil physical and chemical aspects in a Brazil nut tree stand in the Brazilian Amazon

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