Representatividade e potencial de utilização de um banco de dados de solos do Brasil

Benedetti, Marcelo Muniz; Sparovek, Gerd; Cooper, Miguel; Curi, Nilton; Filho, Amaury de Carvalho

doi:10.1590/s0100-06832008000600036

Cited by 17 publications

(15 citation statements)

References 4 publications

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“…Nesse período, os pesquisadores receberam importante apoio e assistência técnica da FAO (Food and Agriculture Organization of the United Nations), o que tornou o país palco para os estudos em solos tropicais (CARVALHO et al, 2013;LEPSCH, 2013 profundidade do horizonte, cor úmida, granulometria, teor de óxidos, dentre outras) (BENEDETTI et al, 2008;BENEDETTI et al, 2011).…”

Section: -Levantamento De Solos No Brasil: Histórico Importância DIunclassified

Levantamento De Solos No Brasil: Métodos, Práticas E Dificuldades

Flach¹,

Corrêa²

2018

Geo.Meridionalis

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Os levantamentos de solos no Brasil são realizados a muitas décadas. Ao longo desse período, diferentes métodos e técnicas de levantamentos foram sendo utilizados, a fim de atender as demandas vigentes ou os recursos disponíveis, destacando-se o mapeamento convencional de solos, o mapeamento digital de solos e mapeamento de solos por determinação espectral. Apesar do grande número de levantamentos realizados, ainda há uma carência de informações, principalmente de informações de solos em escala de detalhe.

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Section: -Levantamento De Solos No Brasil: Histórico Importância DIunclassified

Levantamento De Solos No Brasil: Métodos, Práticas E Dificuldades

Flach¹,

Corrêa²

2018

Geo.Meridionalis

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“…Oxisols, Ultisols and Inceptisols represent 64, 19 and 6% of the agricultural area in the Goiás state, respectively. To create three soil scenarios (Oxisol, Ultisol and Inceptisol) for simulations, physical soil proprieties for each soil type were gathered from the Brazilian soil database (Benedetti et al, 2008; available at http://www.esalq.usp.br/gerd/ BrazilSoilDB 08VI05.xls). This database represents well all soil classes of Brazil regardless of their land-use (5479 profiles and 10,950 horizons throughout the country).…”

Section: Weather and Soil Datamentioning

confidence: 99%

Drought impact on rainfed common bean production areas in Brazil

Heinemann

Ramírez-Villegas

Souza

et al. 2016

Agricultural and Forest Meteorology

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a b s t r a c tCommon bean production in Goiás, Brazil is concentrated in the same geographic area, but spread across three distinct growing seasons, namely, wet, dry and winter. In the wet and dry seasons, common beans are grown under rainfed conditions, whereas the winter sowing is fully irrigated. The conventional breeding program performs all varietal selection stages solely in the winter season, with rainfed environments being incorporated in the breeding scheme only through the multi environment trials (METs) where basically only yield is recorded. As yield is the result of many interacting processes, it is challenging to determine the events (abiotic or biotic) associated with yield reduction in the rainfed environments (wet and dry seasons). To improve our understanding of rainfed dry bean production so as to produce information that can assist breeders in their efforts to develop stress-tolerant, high-yielding germplasm, we characterized environments by integrating weather, soil, crop and management factors using crop simulation models. Crop simulations based on two commonly grown cultivars (Pérola and BRS Radiante) and statistical analyses of simulated yield suggest that both rainfed seasons, wet and dry, can be divided in two groups of environments: highly favorable environment and favorable environment. For the wet and dry seasons, the highly favorable environment represents 44% and 58% of production area, respectively. Across all rainfed environment groups, terminal and/or reproductive drought stress occurs in roughly one fourth of the seasons (23.9% for Pérola and 24.7% for Radiante), with drought being most limiting in the favorable environment group in the dry TPE. Based on our results, we argue that even though drought-tailoring might not be warranted, the common bean breeding program should adapt their selection practices to the range of stresses occurring in the rainfed TPEs to select genotypes more suitable for these environments.

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“…Database compilation is a first step for PTF development, and this has been one of the main limitations in Brazil. Although information management techniques have become more accessible due to advances in informatics, most of the information recorded for Brazilian soils is still dispersed (Benedetti et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Bulk Density Prediction for Histosols and Soil Horizons with High Organic Matter Content

Beutler

Pereira

Tassinari

et al. 2017

Rev. Bras. Ciênc. Solo

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Bulk density (Bd) can easily be predicted from other data using pedotransfer functions (PTF). The present study developed two PTFs (PTF1 and PTF2) for Bd prediction in Brazilian organic soils and horizons and compared their performance with nine previously published equations. Samples of 280 organic soil horizons used to develop PTFs and containing at least 80 g kg -1 total carbon content (TOC) were obtained from different regions of Brazil. The multiple linear stepwise regression technique was applied to validate all the equations using an independent data set. Data were transformed using Box-Cox to meet the assumptions of the regression models. For validation of PTF1 and PTF2, the coefficient of determination (R 2 ) was 0.47 and 0.37, mean error -0.04 and 0.10, and root mean square error 0.22 and 0.26, respectively. The best performance was obtained for the PTF1, PTF2, Hollis, and Honeysett equations. The PTF1 equation is recommended when clay content data are available, but considering that they are scarce for organic soils, the PTF2, Hollis, and Honeysett equations are the most suitable because they use TOC as a predictor variable. Considering the particular characteristics of organic soils and the environmental context in which they are formed, the equations developed showed good accuracy in predicting Bd compared with already existing equations.

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Representatividade e potencial de utilização de um banco de dados de solos do Brasil

Cited by 17 publications

References 4 publications

Levantamento De Solos No Brasil: Métodos, Práticas E Dificuldades

Levantamento De Solos No Brasil: Métodos, Práticas E Dificuldades

Drought impact on rainfed common bean production areas in Brazil

Bulk Density Prediction for Histosols and Soil Horizons with High Organic Matter Content

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