Studying soil nematofauna provides useful information on soil status and functioning but requires high taxonomic expertise. Near infrared reflectance (NIR) spectroscopy (NIRS) has been reported to allow fast and inexpensive determination of numerous soil attributes. Thus the present study aimed at assessing the potential of NIRS for determining the abundance and diversity of soil nematodes in a set of 103 clayey topsoil samples collected in 2005 and 2006 from agricultural soils in the highlands of Madagascar. The morphological characterization of soil nematofauna involved extraction through elutriation then counting under binoculars and identification at family or genus level using microscopy, on ca. 150-g fresh soil samples. Taxa were assigned to five trophic groups, namely bacterial feeders, fungal feeders, obligate plant feeders, facultative plant feeders, and omnivores and predators (together). In addition, four ecological indexes were calculated: the Enrichment index, Structure index, Maturity index, and Plant parasitic index. Oven-dried (40°C) < 2-mm sieved 5-g soil subsamples were scanned in the NIR range (1100-2500 nm), then spectra were fitted to nematofauna data using partial least squares regression. Depending on the sample set considered (year 2005, year 2006, or both years), NIRS prediction of total nematode abundance was accurate (ratio of standard deviation to standard error of cross validation, i.e. RPD >= 2) or acceptable (RPD >= 1.6). Predictions were accurate, acceptable, or quasi-acceptable (RPD >= 1.4) for several of the six most abundant taxa, and to a larger extent, for most trophic groups (except facultative plant feeders); but they could not be made for taxa present in a small number of samples or at low abundance. By contrast, NIRS prediction of relative abundances (in proportion of total abundance), was poor in general, as was also the prediction of ecological indexes (except for the 2006 set). On the whole, these results were less accurate than NIRS predictions of soil attributes often reported in the literature. However, though not very accurate, NIRS predictions were worthwhile considering the labor-intensity of the morphological characterization. Most of all, NIRS analyses were carried out on subsamples that were probably too small (5 g) to allow representative sampling of nematofauna. Using larger samples for NIRS (e.g. 100 g) would likely result in more accurate predictions, and is therefore recommended. Scanning un-dried samples could also help improve prediction accuracy, as morphological characterization was carried out on samples not dried after sampling. Examining wavelengths that contributed most to NIRS predictions, and chemical groups they have been assigned to, suggested that NIRS predictions regarding nematofauna depended on constituents of both nematodes and preys' food. Predictions were thus based on both nematofauna and soil organic properties reflected by nematofauna.