Predicting spatial variation in soil nitrogen for sustainable agricultural management in Benin

  • E.E. Gongnet
  • C.E. Agbangba
  • S.A.T. Affossogbe
  • R. Glèlè Kakaï
Keywords: Bayesian Maximum Entropy, Geographical Weight Regression


Nitrogen plays an important role in plant nutrition and sustainable agriculture. The objective of this study was to assess the distribution pattern of nitrogen in arable land of Benin. A Bayesian Maximum Entropy (BME) method was used for spatial mapping. Hard data consisted of a total of 305 sampled locations of nitrogen collected at 20 cm depth across the country. Soft data were generated from environmental variables using geographical weight regression (GWR) technique. The study revealed very low (<0.03%) N concentrations across the country. The N concentrations ranged from 0 to 0.8.10-6 %, with higher concentrations in the north and low concentrations beginning from the centre toward the south. In general, low prediction errors (around 0.005) were observed across the country (0.005 to 0.04). The maximum values around 0.035 were due to low sampling density observed at the boundary. These results are important for rational management of nitrogen in fertilisation programmes in Benin.


Journal Identifiers

eISSN: 2072-6589
print ISSN: 1021-9730