Land-use - COFFEE-TEA: Difference between revisions
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According to IPCC (2014), the Agriculture, Forestry and Other Land Uses (AFOLU) system was responsible for close to a quarter of total GHG emissions in 2010 which shows the importance to properly represent this sector in IAM. Another major reason for addressing the land system is the importance of this system in economic terms. Additionally, the relationship between the land and the energy systems is another major issue that needs to be addressed in IAM frameworks. | |||
Thus, the approach used in this study was to develop a non- “spatial explicit” model into the MESSAGE framework, with the object of optimizing land use in order to meet demand for food and bioenergy products. | |||
In order to create the land categories, or zones, several aspects must be considered, such as land cover, yields, soil productivity and estimates for production costs. | |||
The land cover categories considered in this model were defined based on UNEP (2015), which consist of spatial resolution of 300 m x 300 m land use information, split in the following types: cropland, crop-veg, forest, for-grass, grassland, flooded and not suited, which include regions that cannot be changed such as urban, desert and permanent ice regions. | |||
The soil productivity was addressed by the Productivity Index (PI), an ordinal measure of the productivity of a soil. The PI uses family-level Soil Taxonomy information, (SCHAETZL et al., 2012). The reason this index was used as a proxy for relative productivity is that it relies in very few additional information. However, the simplicity is exactly what creates the limitations of this analysis, since it disregards other aspects, such as water availability and climatic conditions which limit crop production. | |||
The raster dataset of soil production index used in this study has a spatial resolution of 10 x 10 km. Information with regard to soil production was obtained from the "Derived Soil Properties" of the FAO-UNESCO Soil Map of the World which contains raster information on soil properties (FAO, 2016). The soil production index considers the suitability of the best adapted crop to each soil’s condition in an area and makes a weighted average for all soils present in a pixel (NACHTERGAELE, 2003). | |||
The soil production Index ranges from 0% to 100% and was used as a proxy for productivity and relative yield. The average yields for every crop and region were taken from FAO (2015) and will be further discussed later. | |||
To better estimate the production cost of agricultural products the transportation cost of agricultural goods was incorporated in the evaluation. In this model the travel time was used as a proxy for distance, which, in turn, was used as a proxy for transportation costs. | |||
Thence all these three land aspects, land-use, soil productivity and travel time, were aggregated based on production costs and the categories which could be related to production costs: soil productivity, which accounts for relative production costs at the field, and travel time, which accounts for relative costs of transportation. | |||
So, by combining all categories of Soil Productivity and Travel Time, a matrix of relative costs for 56 cells was designed and then the cells were aggregated in 7 new cost categories. | |||
Finally, for every one of the 18 regions a two-factor land system was created, considering 7 land cover types and 7 combined cost categories. However the category with highest cost was not considered viable for agriculture. | |||
This methodology allows the assessment of the land use and land use change, whilst considering economic aspects. Thus, a supply curve for agricultural products, including bioenergy, is available within the model. | |||
Land use change basically involves the change from one land cover into another through human interference. These changes might create a net emission/removal of carbon due to variation of the carbon stock, both underground and above the ground. | |||
Revision as of 21:48, 20 February 2019
Note: The documentation of COFFEE-TEA is 'under review' and is not yet 'published'!| Corresponding documentation | |
|---|---|
| Previous versions | |
| Model information | |
| Model link | |
| Institution | COPPE/UFRJ (Cenergia), Brazil, http://www.cenergialab.coppe.ufrj.br/. |
| Solution concept | General equilibrium (closed economy) |
| Solution method | The COFFEE model is solved through Linear Programming (LP). The TEA model is formulated as a mixed complementary problem (MCP) and is solved through Mathematical Programming System for General Equilibrium -- MPSGE within GAMS using the PATH solver. |
| Anticipation | |
According to IPCC (2014), the Agriculture, Forestry and Other Land Uses (AFOLU) system was responsible for close to a quarter of total GHG emissions in 2010 which shows the importance to properly represent this sector in IAM. Another major reason for addressing the land system is the importance of this system in economic terms. Additionally, the relationship between the land and the energy systems is another major issue that needs to be addressed in IAM frameworks.
Thus, the approach used in this study was to develop a non- “spatial explicit” model into the MESSAGE framework, with the object of optimizing land use in order to meet demand for food and bioenergy products. In order to create the land categories, or zones, several aspects must be considered, such as land cover, yields, soil productivity and estimates for production costs.
The land cover categories considered in this model were defined based on UNEP (2015), which consist of spatial resolution of 300 m x 300 m land use information, split in the following types: cropland, crop-veg, forest, for-grass, grassland, flooded and not suited, which include regions that cannot be changed such as urban, desert and permanent ice regions.
The soil productivity was addressed by the Productivity Index (PI), an ordinal measure of the productivity of a soil. The PI uses family-level Soil Taxonomy information, (SCHAETZL et al., 2012). The reason this index was used as a proxy for relative productivity is that it relies in very few additional information. However, the simplicity is exactly what creates the limitations of this analysis, since it disregards other aspects, such as water availability and climatic conditions which limit crop production.
The raster dataset of soil production index used in this study has a spatial resolution of 10 x 10 km. Information with regard to soil production was obtained from the "Derived Soil Properties" of the FAO-UNESCO Soil Map of the World which contains raster information on soil properties (FAO, 2016). The soil production index considers the suitability of the best adapted crop to each soil’s condition in an area and makes a weighted average for all soils present in a pixel (NACHTERGAELE, 2003).
The soil production Index ranges from 0% to 100% and was used as a proxy for productivity and relative yield. The average yields for every crop and region were taken from FAO (2015) and will be further discussed later. To better estimate the production cost of agricultural products the transportation cost of agricultural goods was incorporated in the evaluation. In this model the travel time was used as a proxy for distance, which, in turn, was used as a proxy for transportation costs.
Thence all these three land aspects, land-use, soil productivity and travel time, were aggregated based on production costs and the categories which could be related to production costs: soil productivity, which accounts for relative production costs at the field, and travel time, which accounts for relative costs of transportation.
So, by combining all categories of Soil Productivity and Travel Time, a matrix of relative costs for 56 cells was designed and then the cells were aggregated in 7 new cost categories.
Finally, for every one of the 18 regions a two-factor land system was created, considering 7 land cover types and 7 combined cost categories. However the category with highest cost was not considered viable for agriculture.
This methodology allows the assessment of the land use and land use change, whilst considering economic aspects. Thus, a supply curve for agricultural products, including bioenergy, is available within the model. Land use change basically involves the change from one land cover into another through human interference. These changes might create a net emission/removal of carbon due to variation of the carbon stock, both underground and above the ground.