Low Emission Development Strategies in Agriculture


Low Emission Development Strategies in Agriculture. An Agriculture, Forestry, and Other Land Uses (AFOLU) Perspective is the title of the paper published under a Creative Commons license on Science Direct. This  review summarizes this paper.


“Resource use in many developing countries, from crop production to deforestation is responsible for the bulk of greenhouse gasses (GHG) emissions, and there are instances in which the agricultural and forestry sectors can provide low-cost climate change mitigation opportunities” (Low Emission Development Strategies in Agriculture).

Current emphasis on increasing resilience to climate change and reducing agricultural GHG emissions strengthens the support for sustainable agricultural production. Indeed, reducing losses in soil fertility, reclaiming degraded lands, and promoting synergistic interaction between crop production and forests are generally seen as good climate change policies.

To analyze the impacts of policies that target emission reduction in the agricultural sector, the article combines and reconciles the data of three models (widely accessible to the public):

1. The International Model for Policy Analysis of Agricultural Commodities and Trade Model (IMPACTRobinson et al., 2015)

a global partial equilibrium agriculture model that allows for policy and agricultural productivity investment simulations.

2. A spatially explicit model of land use choices (Li, De Pinto, Ulimwengo, You, & Robertson, 2015)

to determine the possible effects of future changes in the drivers of land use choices.

3. DeNitrification–DeComposition crop model (DNDCLi, 2007)

that estimates spatially explicit profiles of GHG emissions from cropland with varying crop genetic productivity shifters, management systems, and climate scenarios.

The proposed framework does not create barriers for the inclusion of additional input. Thus, to determine cropland and forest areas at the municipal level in Columbia use case and to increase the accuracy of the results, the authors used  the Colombian government statistics, other additional data and targeted surveys.

In particular, while for pastureland, the authors used data from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IAvH (Instituto de Investigación de Recursos Biológicos Alexander Von Humboldt ). Based on these data, the land use was classified in four categories: (1) cropland; (2) forest; (3) pasture; and (4) other land uses which include shrub and secondary vegetation.

The presented framework was further enriched with the following types of (generated or extracted from external sources) data:

  • Population density data 
  • Data on market accessibilità
  • Elevation and slope data
  • Climate data
  • Geo-referenced data
  • Yields and  domestic commodity producer prices (collected from FAOSTAT).

Though the focus of this work is on Colombia, the analytical framework can be applied to any country interested in exploring country-wide effects and the economic viability of climate change mitigation policies in agriculture.

Results show that investments in:

  • increasing the efficiency;
  • productivity of the livestock sector and
  • reducing land allocated to pasture

are preferable to alternative policies that target deforestation alone or target a reduction of emissions in crop production.

These measures would reduce deforestation and provide sufficient gains in carbon stock to offset greater emissions from increased crop production while generating higher revenues.

The paper reveals also the importance of considering the full scope of interactions among the various land uses


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