TY - JOUR
T1 - Prediction of soil nutrient content via pXRF spectrometry and its spatial variation in a highly variable tropical area
AU - Pelegrino, Marcelo Henrique Procópio
AU - Silva, Sérgio Henrique Godinho
AU - de Faria, Álvaro José Gomes
AU - Mancini, Marcelo
AU - Teixeira, Anita Fernanda dos Santos
AU - Chakraborty, Somsubhra
AU - Weindorf, David C.
AU - Guilherme, Luiz Roberto Guimarães
AU - Curi, Nilton
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/2
Y1 - 2022/2
N2 - Precision agriculture provides detailed information on the spatial variability of soil properties, including nutrient content, allowing for local-specific decision making. Recently, proximal sensors have been used to accurately predict soil properties, contributing to reduce costs of conventional wet-chemistry analyses for soil characterization. However, further investigations on this approach in tropical soils are needed. This work aimed to use portable X-ray fluorescence (pXRF) spectrometry data for prediction of exchangeable Ca2+ and available K+ and P contents in soils of a highly heterogeneous tropical area and evaluating its practical applications. 90 samples from soil A horizon were collected in a regular grid design, and analyzed through pXRF and for nutrient contents. Such data were split into modeling (63 samples) and validation (27 samples) datasets. Linear regression (LR), polynomial regression (PR), power regression (PwR) and stepwise multiple linear regression (SMLR) were tested for predictions. The models were used to spatially represent nutrient contents across the area and to compare the practical effects of varying regression models. PXRF elemental data provided reliable predictions of exchangeable Ca2+ and available P via SMLR and PwR, respectively, reaching root mean square errors (RMSE) of 5.66 cmolc dm−3 for Ca2+ and 9.13 mg dm−3 for P. Available K+ predictions were not successful. Different models yielded contrasting maps showing the classes of soil fertility across the area, drawing attention to the importance of testing multiple prediction models and using the best one for precision agriculture. Fusion of data from different proximal sensors may enhance available K+ predictions.
AB - Precision agriculture provides detailed information on the spatial variability of soil properties, including nutrient content, allowing for local-specific decision making. Recently, proximal sensors have been used to accurately predict soil properties, contributing to reduce costs of conventional wet-chemistry analyses for soil characterization. However, further investigations on this approach in tropical soils are needed. This work aimed to use portable X-ray fluorescence (pXRF) spectrometry data for prediction of exchangeable Ca2+ and available K+ and P contents in soils of a highly heterogeneous tropical area and evaluating its practical applications. 90 samples from soil A horizon were collected in a regular grid design, and analyzed through pXRF and for nutrient contents. Such data were split into modeling (63 samples) and validation (27 samples) datasets. Linear regression (LR), polynomial regression (PR), power regression (PwR) and stepwise multiple linear regression (SMLR) were tested for predictions. The models were used to spatially represent nutrient contents across the area and to compare the practical effects of varying regression models. PXRF elemental data provided reliable predictions of exchangeable Ca2+ and available P via SMLR and PwR, respectively, reaching root mean square errors (RMSE) of 5.66 cmolc dm−3 for Ca2+ and 9.13 mg dm−3 for P. Available K+ predictions were not successful. Different models yielded contrasting maps showing the classes of soil fertility across the area, drawing attention to the importance of testing multiple prediction models and using the best one for precision agriculture. Fusion of data from different proximal sensors may enhance available K+ predictions.
KW - Digital soil mapping
KW - Proximal sensor
KW - Random forest
KW - Regression analysis
KW - Soil fertility spatial prediction
UR - http://www.scopus.com/inward/record.url?scp=85107756492&partnerID=8YFLogxK
U2 - 10.1007/s11119-021-09825-8
DO - 10.1007/s11119-021-09825-8
M3 - Article
AN - SCOPUS:85107756492
SN - 1385-2256
VL - 23
SP - 18
EP - 34
JO - Precision Agriculture
JF - Precision Agriculture
IS - 1
ER -