New methods for estimating lime requirement to attain desirable pH values in Brazilian soils

• Autor(a) principal: Teixeira, Welldy Gonçalves
• Data de Publicação: 2020
• Outros Autores: Alvarez V., Víctor Hugo, Neves, Júlio César Lima
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: https://locus.ufv.br//handle/123456789/30141; https://doi.org/10.36783/18069657rbcs20200008
• topic: lime requirement prediction; organic matter; potential acidity; algorithm

In Brazil, empirical models are traditionally used to determine lime requirement (LR), but their reliability is doubtful in most cases, since they can lead to under- or overestimation of LR for different soil types. In this study, the most critical characteristics influencing LR were selected to develop reliable models for predicting LR that raise soil pH to optimum values for crop production in Brazil. Soil samples (n = 22) with varying proportions of clay (5-88 %) and organic matter (OM) levels (3.78-79.35 g kg-1) were used to develop the models. Organic matter and potential acidity (HAl) combined with ΔpH [target pH(H2O) - initial pH(H2O)] were the best predictor variables for estimating LR. Four models were developed (OMpH5.8, OMpH6.0, HAlpH5.8, and HAlpH6.0) for estimating LR to attain target pH values of 5.8 or 6.0 with reasonably high prediction performance (0.758≤ R2 ≤0.886). An algorithm was further developed for selecting the LR to be recommended among those estimated by the models. The proposed algorithm enables to select the minimum LR that ensure the adequate supply of Ca and Mg to plants and does not exceed the levels of soil HAl, thus preventing excessive pH increase. The new predictive models were less sensitive to predict LR high enough to meet Ca2+ and Mg2+ requirements of plants in soils containing levels of HAl lower than 5 cmolc dm-3 and OM lower than 40 g kg-1. However, they ensured an adequate supply of Ca2+ and Mg2+ to plants and avoided the overestimation of LR for most soils used in this research. Validation via an independent dataset (n = 100 samples) confirmed the good predictive performance of the models across a wide range of soil types. In summary, the proposed models can be used as good alternatives to traditional methods for predicting LR for a great variety of Brazilian soils. Further, they are versatile and may be easily deployed in soil testing laboratories, since soil pH, OM, and HAl are characteristics determined in routine analysis.