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Titlebook: Soil Spectral Inference with R; Analysing Digital So Alexandre M.J.-C. Wadoux,Brendan Malone,Alex B. Mc Book 2021 The Editor(s) (if applica

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發(fā)表于 2025-3-25 05:50:25 | 只看該作者
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發(fā)表于 2025-3-25 10:43:29 | 只看該作者
Materials,This chapter describes the datasets and R packages used in the book. A total of five datasets are provided and described. They originate from several studies and are made available through a book-associated R package. Most R functions used in this book are either provided in the text or available online in R packages.
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發(fā)表于 2025-3-25 15:04:31 | 只看該作者
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發(fā)表于 2025-3-25 16:44:51 | 只看該作者
Alexandre M.J.-C. Wadoux,Brendan Malone,Alex B. McNumerous step-by-step tutorials to process soil spectral data.Beginner to advanced processing of soil infrared data.Fully reproducible figures and results.Simple and intuitive explanations on using so
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發(fā)表于 2025-3-25 22:00:36 | 只看該作者
Progress in Soil Sciencehttp://image.papertrans.cn/s/image/871283.jpg
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發(fā)表于 2025-3-26 10:47:05 | 只看該作者
Soil Spectral Inference with R978-3-030-64896-1Series ISSN 2352-4774 Series E-ISSN 2352-4782
29#
發(fā)表于 2025-3-26 14:16:26 | 只看該作者
30#
發(fā)表于 2025-3-26 18:34:35 | 只看該作者
Selection of the Samples for Laboratory Analysis,cal analysis. The reason is that spectra are fast and cheap to retrieve, while a single soil analysis (e.g. for soil clay) is relatively slow, and significantly more costly, to obtain. One must select a ‘representative’ subset of soil samples to be sent to the laboratory (Daszykowski et al. .).
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