Agro-climatic classification systems for estimating the global distribution of livestock numbers and commodities

Environ Int. 2001 Sep;27(2-3):181-7. doi: 10.1016/s0160-4120(01)00080-0.

Abstract

Investment in agricultural research in developing countries is being increasingly targeted at those agro-climatic zones and issues where the economic and environmental benefits may be expected to be greatest. This first requires that the zones themselves be defined, along with information on domestic livestock numbers and commodity output within agro-climatic zones in different countries. Different methods for classifying agro-climatic zones were compared. These included methods based on estimated length of growing period (LGP) using rainfall and temperature data, the ratio of precipitation to potential evapotranspiration (PET), and on more detailed agronomic models, remote sensing data and land use information. Zonation based on LGP has already been linked to existing national livestock data. By defining agro-climatic zones and relating concentrations of livestock populations to those of humans, it is possible to make realistic estimates of livestock populations and the production of livestock commodities for most developing countries. Detailed agro-climatic analyses of Mainland East Asia and Sri Lanka have recently been undertaken using the GROWEST agronomic model. Using this model as the basis of agro-climatic classification appears to be significantly superior, particularly in temperate environments, to approaches based solely on LGP. Different ways of subdividing countries and continents into agro-climatic or agro-ecological zones (AEZs) are reviewed in this paper. In addition, we show how the numbers of production and commodities from domestic livestock can be allocated to such zones. We also indicate how some of this information can be applied.

MeSH terms

  • Agriculture / economics*
  • Animals
  • Animals, Domestic*
  • Climate*
  • Conservation of Natural Resources
  • Developing Countries*
  • Ecosystem
  • Forecasting
  • Humans
  • Rain
  • Temperature
  • Terminology as Topic