posted on 2005-01-01, 00:00authored byJuan Pablo Gonzalez, J. Andrew Bagnell, Simon Cook, Thomas Oberthur, Andrew Jarvis, Mauricio Rincon
Soil maps are essential resources to soil scientists and researchers in any fields related to soils, land
use, species conservation, hunger reduction, social development, etc. However, creating detailed soil maps
is an expensive and time consuming task that most developing nations cannot afford.
In recent years, there has been a significant shift towards digital representation of soil maps and
environmental variables that has created the field of predictive soil mapping (PSM), where statistical
analysis is used to create predictive models of soil properties. PSM requires less human intervention than
traditional soil mapping techniques, and relies more on computers to create models and predict properties.
However, because most of the research funds for soil research come from developed nations, the
research in this field has mostly focused in temperate zones (where most developed nations are located).
The areas of the world with more needs in terms of hunger and poverty are mostly located in the tropics,
and require different statistical models because of the unique characteristics of their weather and
environment.
Through to the v-unit/TechBridgeWorld initiative at Carnegie Mellon we were able to work with a
group of soil scientists from the International Center for Tropical Agriculture (CIAT) and develop
statistical soil models for Honduras. Thanks to this joint work, we were able to leverage the knowledge of
the soil science and computer science communities, and create a model that matches or advances the state
of the art for PSM.