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4 results

Estimation of the Retention and Availability of Water in Soils of the State of Santa Catarina

Diego Bortolini, Jackson Adriano Albuquerque


ABSTRACT: Soil water retention and availability are important properties for agricultural production, which can be measured directly or estimated by pedotransfer functions. Some studies on this topic were carried out in Santa Catarina, Brazil. To improve the estimates, it is necessary to evaluate other properties, to analyze more soil types, as well as to use other analysis techniques such as artificial neural networks and regression trees. Thus, the objective of the study was to estimate the field capacity (FC), permanent […]

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar

Braz Calderano Filho, Helena Polivanov, César da Silva Chagas, Waldir de Carvalho Júnior, Emílio Velloso Barroso, Antônio José Teixeira Guerra, [...]


Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS […]

Remaining phosphorus estimated by pedotransfer function

Joice Cagliari, Maurício Roberto Veronez, Marcelo Eduardo Alves


Although the determination of remaining phosphorus (Prem) is simple, accurate values could also be estimated with a pedotransfer function (PTF) aiming at the additional use of soil analysis data and/or Prem replacement by an even simpler determination. The purpose of this paper was to develop a pedotransfer function to estimate Prem values of soils of the State of São Paulo based on properties with easier or routine laboratory determination. A pedotransfer function was developed by artificial neural networks (ANN) from […]

NetErosividade MG: rainfall erosivity for Minas Gerais State, Brazil

Michel Castro Moreira, Fernando Falco Pruski, Thiago Emanuel Cunha de Oliveira, Francisco de Assis de Carvalho Pinto, Demetrius David da Silva


Rainfall erosivity represents the potential of rainfall causing soil erosion. This study aimed to develop a software to estimate rainfall erosivity in the state of Minas Gerais based on Artificial Neural Networks (ANNs). The annual value of the rainfall erosivity is given by the sum of the monthly values of the erosivity indexes EI30 or KE > 25. Two methodologies were used to estimate the kinetic energy for each index. Thus, four erosivity values were evaluated for each month, resulting […]