Prediction of Soil Erodibility by Diffuse Reflectance Spectroscopy in a Neotropical Dry Forest Biome

Samuel Ferreira Pontes, Yuri Jacques Agra Bezerra da Silva, Vanessa Martins, Cácio Luiz Boechat, Ademir Sérgio Ferreira Araújo, Jussara Silva Dantas, Ozeas S. Costa and Ronny Sobreira Barbosa ()
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Samuel Ferreira Pontes: Campus Professora Cinobelina Elvas, Universidade Federal do Piauí, km 01, Rua Manoel Gracindo, Planalto Horizonte, Bom Jesus 64900-000, Piauí, Brazil
Yuri Jacques Agra Bezerra da Silva: Campus Professora Cinobelina Elvas, Universidade Federal do Piauí, km 01, Rua Manoel Gracindo, Planalto Horizonte, Bom Jesus 64900-000, Piauí, Brazil
Vanessa Martins: Colégio Técnico de Bom Jesus, Universidade Federal do Piauí, km 01, Rua Manoel Gracindo, Planalto Horizonte, Bom Jesus 64900-000, Piauí, Brazil
Cácio Luiz Boechat: Campus Professora Cinobelina Elvas, Universidade Federal do Piauí, km 01, Rua Manoel Gracindo, Planalto Horizonte, Bom Jesus 64900-000, Piauí, Brazil
Ademir Sérgio Ferreira Araújo: Departamento de Engenharia Agrícola e Solos, Centro de Ciências Agrárias, Universidade Federal do Piauí, 3397, R. Dirce Oliveira, Teresina 64048-550, Piauí, Brazil
Jussara Silva Dantas: Centro de Ciências e Tecnologia Agroalimentar, Universidade Federal de Campina Grande, 1770, Rua Jario Vieira Feitosa, Pombal 58840-000, Paraíba, Brazil
Ozeas S. Costa: School of Earth Sciences, The Ohio State University at Mansfield, 1760 University Drive, Mansfield, OH 44906, USA
Ronny Sobreira Barbosa: Campus Professora Cinobelina Elvas, Universidade Federal do Piauí, km 01, Rua Manoel Gracindo, Planalto Horizonte, Bom Jesus 64900-000, Piauí, Brazil

Land, 2022, vol. 11, issue 12, 1-19

Abstract: The USLE and the RUSLE are two common erosion prediction models that are used worldwide, and soil erodibility (K-factor) is one parameter used to calculate them. The objectives of this study were to investigate the variability of soil-erodibility factors under different soil-texture classes and evaluate the efficiency of diffuse reflectance spectroscopy (DRS) in the near-infrared range at predicting the USLE and RUSLE K-factors using a partial least squares regression analysis. The study was conducted in Fluvisols in dry tropical forest (the Caatinga). Sampling was undertaken in the first 20 cm of soil at 80 sites distributed 15 m apart on a 70 m × 320 m spatial grid. Results show that the clay fraction is represented mainly by 2:1 phyllosilicates. Soil organic matter content is low (<0.2%), which is typical of tropical dry forests, and this is reflected in the high values of the calculated USLE and RUSLE K-factors. An empirical semivariogram was used to investigate the spatial dependence of both K-factors. Pedometric modeling showed that DRS can be used to predict both USLE (R 2 adj = 0.53; RMSE = 8.37 10 −3 t h MJ −1 mm −1 ) and RUSLE (R 2 adj = 0.58; RMSE = 6.78 10 −3 t h MJ −1 mm −1 ) K-factors.

Keywords: Caatinga; USLE; RUSLE; geostatistics; pedometrics; near infrared (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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