@article{fdi:010089839,
  title = {{Q}uantification of soil organic carbon in particle size fractions using a near-infrared spectral library in {W}est {A}frica},
  author = {{C}ambou, {A}ur{\'e}lie and {H}oussoukp{\`e}vi, {I}.{A}. and {C}hevallier, {T}iphaine and {M}oulin, {P}. and {R}akotondrazafy, {N}.{M}. and {F}onkeng, {E}.{E}. and {H}armand, {J}.{M}. and {A}holoukp{\`e}, {H}.{N}.{S}. and {A}madji, {G}.{L}. and {T}abi, {F}.{O}. and {C}hapuis {L}ardy, {L}ydie and {B}arth{\`e}s, {B}ernard},
  editor = {},
  language = {{ENG}},
  abstract = {{P}article size fractionation enables a better understanding of soil organic carbon ({C}) dynamics since it separates fractions that differ in composition, residence time and function. {H}owever, this method is time-consuming and tedious; thus, its use has been greatly limited. {O}ur objective was to evaluate the ability of an existing soil spectral library ({SSL}) from different regions of {W}est {A}frica to predict the {C} amount in the fractions (g{C} kg-1 soil) of the samples in a new target set from {B}enin. {T}he {SSL} included 181 samples from five countries, and the target set included 94 samples (depth inf. or eg. 40 cm), most of which were coarse-textured; near-infrared reflectance ({NIR}) spectra were collected for 2 mm sieved samples (non-fractionated samples). {T}he predicted variables were the {C} amounts in the non-fractionated soil and in the < 20, 20-50, and > 50 µm fractions ({F}<20, {F}20-50, and {F}>50, respectively). {D}ifferent methods were tested to optimize the predictions: (i) {SSL} enrichment with 10 or 15 samples selected from the target set (spiking) and replicated six times (i.e. extra-weighted); (ii) locally weighted (local) partial least squares regression ({PLSR}), which is calibration by the spectral neighbours with the highest weights attributed to closest neighbours, and was compared to 'global' (i.e., common) {PLSR}, where all calibration samples equally contribute; and (iii) spectrum pretreatments (e.g., smoothing, centring, derivatization). {I}n addition, the intermediate precision of the conventional data (standard error of laboratory; {SEL}int) was estimated through triplicate fractionation of three samples carried out by three operators (one per replicate). {W}hen the {SSL} alone was used for calibration, the predictions were inaccurate for the {C} amounts in the non-fractionated soil and in {F}<20; however, the predictions were accurate for the {C} amounts in {F}20-50 and {F}>50, with minimal benefit from the local {PLSR} over the global {PLSR} in general. {F}or the non-fractionated soil, {F}<20, {F}20-50 and {F}>50, the ratios of performance to the interquartile range in the validation set, {RPIQVAL}, were 1.6-1.8, 1.6-1.7, 1.9 and 1.9-2.1, respectively. {C}alibration with {SSL} spiked (i.e., completed with spiking samples) yielded an increase in {RPIQVAL} from 33 to 56% for the {C} amount in the non-fractionated soil and {F}<20 and from 0 to 20 % for {F}20-50 and {F}>50 ({RPIQVAL} reached 2.4-2.5, 2.2-2.3, 1.9-2.0 and 2.1-2.3, respectively), and the benefit of local {PLSR} was still limited. {T}he {SEL}int was based on a few samples and thus only provided a rough estimation; this estimate represented at least 65% of the prediction error for the {C} amounts in the fractions. {T}herefore, the {SEL}int needs to be determined more extensively to both improve the model accuracy and refine the interpretation of the predictions based on {NIR} spectra. {T}his library should be enriched with samples from other sites to represent other soil types.},
  keywords = {{AFRIQUE} {DE} {L}'{OUEST} ; {BURKINA} {FASO} ; {TOGO} ; {CAMEROUN} ; {BENIN} ; {CONGO} {BRAZZAVILLE}},
  booktitle = {},
  journal = {{G}eoderma},
  volume = {443},
  numero = {},
  pages = {116818 [17 ]},
  ISSN = {0016-7061},
  year = {2024},
  DOI = {10.1016/j.geoderma.2024.116818},
  URL = {https://www.documentation.ird.fr/hor/fdi:010089839},
}