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Thousands of chemical analyses are carried out annually with the aim of recommending soil correction; however, these analyses are expensive, destructive, time-consuming, and can be harmful to the environment. As an alternative to conventional analysis methods, diffuse reflectance spectroscopy has been proposed as an option for evaluating the chemical characteristics of soil. The selection of variables has also emerged as an alternative to improve the performance of PLSR (partial least squares regression), as it decreases the root mean square error (RMSE) and increases the accuracy of the models. However, few studies have used a previous selection of variables for the construction of PLSR models to estimate the chemical characteristics of soil. In this context, the hypothesis in this study was that it is possible to calculate the liming recommendation in Oxisol based on the chemical characteristics estimated by PLSR, with a previous selection of variables using iPLS (Interval PLS). The objective was to calculate the need for liming based on chemical characteristics estimated via iPLS selection and PLSR modeling of specific wavelengths of soil reflectance. The experimental area was treated with different application rates of limestone, with and without incorporation, and phosphogypsum was applied in additional treatments. Soil assessments were carried out 5, 12, 24, and 36 months after the application of the treatments, using six layers: 0.00–0.05, 0.05–0.10, 0.10–0.20, 0.20–0.30, 0.30–0.40 and 0.40–0.60 m. Samples were subjected to conventional laboratory analyses, and spectral readings (400–2500 nm) were obtained with a spectroradiometer. The spectral curves were subjected to the iPLS variable selection method to generate PLSR models of the chemical characteristics used to calculate the liming recommendation. The chemical characteristics of the soil, such as Ca2+, sum of bases (SB), effective cation exchange capacity (CTCe), cation exchange capacity (CTC), and base saturation (BS), could be estimated, with values of R2 ranging from 0.83 to 0.92 in the calibration and validation steps, and from 0.84 to 0.90 for the prediction step (in the fourth assessment). The liming recommendation calculated based on the chemical characteristics predicted from the PLSR models showed a strong correlation (r > 0.86) with the liming recommendation calculated by conventional laboratory techniques. The fourth soil assessment yielded the best correlation coefficient (r = 0.95). [ABSTRACT FROM AUTHOR]

Studies on the use of hyperspectral remote sensing for nutrient prediction in soybean (Glycine max L.) leaves are limited, especially regarding the selection of wavelengths. Therefore, the objective of this study was to estimate macro and micronutrients in soybean leaves by employing reflectance data modeled by Partial Least Squares Regression (PLSR) from bands selected by the iPLS (Interval PLS), method with a script developed for MATLAB R2018a. The experimental area was treated with different doses of limestone, with and without incorporation, also with phosphogypsum application in additional treatments. The soybean crops of 2018/2019 and 2019/2020 had the nutritional status evaluated by a random collection of the third fully developed trefoil in the R2 (full flowering) and R5.5 (76–100% grain filling) reproductive phenological stages. The leaf samples were submitted to conventional laboratory analyses. The Ca, Mg, K, P, S, Cu, Fe, Mn, and Zn elements in the leaves were quantified through spectrometry. A field spectroradiometer performed spectral data acquisition in the visible-near infrared, short-wave infrared spectral range of soybean leaves. The spectral curves were subjected to iPLS, and PLSR models were fitted. After iPLS selection, the coefficient of determination (R2) values of PLSR models were on average 15.3% and 16.6% higher in calibration and validation, respectively, compared to classical methods. The PLSR model for Mn, which obtained R2 above 0.90 in the calibration and validation phases, stood out. The Root Mean Square Error (RMSE) decreased, on average, more than 15.0%, both in calibration and validation, after applying the iPLS selection. The models for Ca and Mn stood out with over a 30.0% decrease in RMSE, in calibration and validation. The iPLS selection was efficient in improving the performance and accuracy of the PLSR models, increasing the R2 and decreasing the RMSE, opening new possibilities for the analysis and prediction of macro and micronutrients in plants. [Display omitted] • The wavelengths selection by iPLS was efficient in improving the PLSR models. • R2 increased on average 15% and 17% in calibration and validation, respectively. • RMSE decreased on average 15% after applying the iPLS selection. • The PLSR model for Mn, which obtained R2 above 0.90 stood out. [ABSTRACT FROM AUTHOR]