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Rapid Characterization of Organic Resource Quality for Soil and Livestock Management in Tropical Agroecosystems Using Near-Infrared Spectroscopy

^a World Agroforestry Cent. (ICRAF), P.O. Box 30677-00100, Nairobi, Kenya
^b Trop. Soil Biol. and Fertil. Inst. of CIAT (TSBF-CIAT), P.O. Box 30677-00100, Nairobi, Kenya
^c The Earth Inst. at Columbia Univ., P.O. Box 1000, Palisades, NY 10964-8000

View larger version (126K): [in a new window]   Fig. 1. Portable spectrometer with high-intensity reflectance probe used for reflectance measurements. Samples are illuminated through the bottom of a glass Petri dish, and reflected light is captured from a 35-mm-diam. window through a fiber-optic cable.

View larger version (40K): [in a new window]   Fig. 2. Reflectance spectra for selected contrasting materials from the organic resource spectral library.

View larger version (33K): [in a new window]   Fig. 3. Correlation of N, lignin, and total soluble polyphenol concentrations with first derivatives of relative reflectance at different wavelengths. Lignin and total soluble polyphenol were loge–transformed before processing.

View larger version (24K): [in a new window]   Fig. 4. Validation scatter plots of actual against predicted values for (top) N and (bottom) total soluble polyphenol concentrations. Calibration models were developed with TreeNet stochastic gradient boosting using a random selection of 75% of the total number of samples and validated on the remaining 25% holdout sample. No outliers were removed from either calibration or validation data sets. Root mean square errors (RMSE) are also given.

View larger version (16K): [in a new window]   Fig. 5. Response of (top) sensitivity and (bottom) specificity to size of training data set in spectral screening tests for high N (<25 mg kg-1), low lignin (<110 mg kg-1), and low total soluble polyphenol (<40 mg kg-1) concentrations. The results are for the proportion of the total data set (n = 319) not used for training. Sensitivity is the percentage of abnormal cases correctly classified, and specificity is the percentage of abnormal cases correctly classified.