03208naa a2200325 a 450000100080000000500110000800800410001902200140006002400540007410000170012824502180014526000090036330000100037252021640038265000120254665300240255865300330258265300170261565300180263270000180265070000210266870000190268970000230270870000260273170000230275770000240278070000240280470000200282877300340284821633792024-10-14       2024    bl uuuu     u00u1 u    #d  a0341-81627 ahttps://doi.org/10.1016/j.catena.2024.1079142DOI1 aBABOS, D. V.  aLaser-induced breakdown spectroscopy (LIBS) as an analytical tool in precision agriculturebEvaluation of spatial variability of soil fertility in integrated agricultural production systems.h[electronic resource]  c2024  a13 p.  aThe rapid determination of soil fertility assists in improving agricultural production and reducing environmental impacts. With a new concept of precision agriculture and digital agriculture, new demands have been generated using sensors, methods, and protocols to improve analysis time and agricultural production. In this context, multivariate calibration models (multiple linear regression) were calculated for the indirect prediction of eight parameters of soil fertility, pH (H2O and CaCl2 extractors), cation exchange capacity, sum of bases, base saturation, Ca-exchangeable, Mg-exchangeable, and labile P, using laser-induced breakdown spectroscopy (LIBS), and also, for some parameters laser-induced fluorescence spectroscopy. Soil samples from a native forest (NF) and different agricultural production systems, such as integrated crop-livestock-forest system (CLFS), integrated livestock-forest system (LFS), integrated crop-livestock system (CLS), extensive (EXT), and intensive (INT) pastures, were collected from 0 to 40 cm depth (195 samples). Calibration models with good performance (0.62 ≤ R2 values ≤ 0.87, and 1.61 ≤ residual prediction deviation values ≤ 2.86) and adequate root mean squared error of prediction (RMSEP) values were obtained for the seven fertility parameters (0.74 ≤ r values ≤ 0.97, for the validation set), except for labile P (r = 0.44 and RPD = 1.35). Through principal component analysis (PCA) it was verified the formation of two clusters, referring to samples of more fertile soils of the production systems (CLFS and LFS) and NF that present the tree variable, and the other group referring to samples of soils of low fertility (EXT, INT, and CLS). To evaluate and exemplify the use of LIBS for precision and digital agriculture, in-depth soil chemical attributes and spatial variability, maps were obtained for an analyzed area of the integrated CLFS. The LIBS technique can determine and assist in evaluating the variability of soil fertility attributes with reliability, agility (without the need for extraction procedures since the soil is thoroughly analyzed in the form of pellets), and accuracy.  aSoil pH  aDigital agriculture  aIntegrated production system  aSoil sensing  aTropical soil1 aTADINI, A. M.1 aDE MORAIS, C. P.1 aBARRETO, B. B.1 aCARVALHO, M. A. R.1 aBERNARDI, A. C. de C.1 aOLIVEIRA, P. P. A.1 aPEZZOPANE, J. R. M.1 aMILORI, D. M. B. P.1 aMARTIN NETO, L.  tCatenagv. 239, 107914, 2024.