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Rapid determination of lime requirement by mid‐infrared spectroscopy: A promising approach for precision agriculture
Author(s) -
Leenen Matthias,
Welp Gerhard,
Gebbers Robin,
Pätzold Stefan
Publication year - 2019
Publication title -
journal of plant nutrition and soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.644
H-Index - 87
eISSN - 1522-2624
pISSN - 1436-8730
DOI - 10.1002/jpln.201800670
Subject(s) - precision agriculture , calcareous , lime , soil test , partial least squares regression , soil water , soil science , soil organic matter , environmental science , coefficient of determination , chemistry , environmental chemistry , mineralogy , mathematics , agriculture , geology , statistics , ecology , paleontology , biology
Abstract Mid‐infrared spectroscopy (MIRS) has proven to be a cost‐effective, high throughput measurement technique for soil analysis. After multivariate calibration mid‐infrared spectra can be used to predict various soil properties, some of which are related to lime requirement (LR). The objective of this study was to test the performance of MIRS for recommending variable rate liming on typical Central European soils in view of precision agriculture applications. In Germany, LR of arable topsoils is commonly derived from the parameters organic matter content (SOM), clay content, and soil pH (CaCl 2 ) as recommended by the Association of German Agricultural Analytical and Research Institutes (VDLUFA). We analysed a total of 458 samples from six locations across Germany, which all revealed large within‐field soil heterogeneity. Calcareous topsoils were observed at some positions of three locations (79 samples). To exclude such samples from LR determination, peak height at 2513 cm −1 of the MIR spectrum was used for identification. Spectra‐based identification was accurate for carbonate contents > 0.5%. Subsequent LR derivation (LR SPP ) from MIRS‐PLSR predictions of SOM, clay, and pH (CaCl 2 ) for non‐calcareous soil samples using the VDLUFA look‐up tables was successful for all locations ( R 2 = 0.54–0.82; RMSE = 857–1414 kg CaO ha −1 ). Alternatively, we tested direct LR prediction (LR DP ) by MIRS‐PLSR and also achieved satisfactory performance ( R 2 = 0.52–0.77; RMSE = 811–1420 kg CaO ha −1 ; RPD = 1.44–2.08). Further improvement was achieved by refining the VDLUFA tables towards a stepless algorithm. It can be concluded that MIRS provides a promising approach for precise LR estimation on heterogeneous arable fields. Large sample numbers can be processed with low effort which is an essential prerequisite for variable rate liming in precision agriculture.