Agriculture, Land-Use, and Bioenergy

Land-use and land-use change, for agriculture, forestry, and other uses, are key to understanding future scenarios of global change and emissions mitigation. Land-use has been one of the largest anthropogenic sources of emissions of greenhouse gases, aerosols and short-lived species. The conversion of grasslands and forests to agricultural land results in a net emission of CO2 to the atmosphere. This conversion has been the largest of all sources of anthropogenic land use emissions historically. In the future, biomass energy crops could compete for agricultural land with traditional agricultural crops, linking land-use more directly with the energy system. Efforts to capture carbon in terrestrial reservoirs, such as forests, may place a damper on deforestation activities, and potentially lead to afforestation or reforestation activities. Interactions with crop prices may also prove important. Since land is limited, increasing the demand for land either to protect forests or to plant bioenergy crops could put upward pressure on crop prices that would not otherwise occur.

To capture these dynamics, GCAM includes a completely integrated model that allocates the land area for each of GCAM’s regions among different land uses, tracks production from these uses, and tracks carbon flows into and out of terrestrial reservoirs. The GCAM agriculture, land use, land cover, terrestrial carbon cycle module determines the demands for and production of products originating on the land, the prices of these products, the allocation of land to competing ends, the rental rate on land, and the carbon stocks and flows associated with land use.

The Agriculture and Land Use Model (AgLU)

The GCAM-3 agriculture and land use model is described in detail in two technical reports. The first addresses the economic approach, math, and code: GCAM3AGLUDocumentation.pdf. The second report describes the data, including all source data used and detailed descriptions of the methods of data processing: GCAM3AGLUDataDocumentation.pdf. The remainder of this section provides a summary of the information contained in these reports.

An economic land sharing approach is used to allocate land between alternative uses based on expected profitability, which in turn depends on the productivity of the land-based product (e.g. mass of harvestable product per ha), product price, and non-land costs of production (labor, fertilizer, etc.). The allocation of land types takes place in the model through global and regional markets for agricultural products. These markets include those for raw agricultural products as well as those for intermediate products such as poultry and beef. Demands for most agricultural products, with the exception of biomass products, are based primarily on income and population. Land allocations evolve over time through the operation of these markets, in response to changes in income, population, technology, and prices.

The boundary between managed and unmanaged ecosystems is assumed to be elastic in the GCAM. The area of land under cultivation expands and contracts with the land rental rate. Thus, increased demands for land result in higher rental rates and expansion into unmanaged ecosystems and vice versa.

Competition between alternative land uses in the GCAM is modeled using a nested logit architecture. A representative, simplified nnesting structure is depicted in Figure 1.

Figure 1. Competition for Land in GCAM

The costs of supplying agricultural products are based on regional characteristics, such as the productivity of land and the variable costs of producing the crop. Exogenous assumptions are made for the rate of increase in agricultural productivity. The productivity of land-based products is subject to change over time based on future estimates of crop productivity change.

Agricultural productivity change is aggregated by GCAM region and commodity from the FAO CROSIT database, which has 108 countries and 34 commodities. We aggregate the data for irrigated, rainfed, and total agricultural production, harvested area, and yields, but at present we only use the total. The projection years are 2005 (base year), 2030, and 2050, which allows annual productivity change rates to be calculated for each region and crop. These projections are not downscaled to AEZs, and at present there is no effort to differentiate yield improvements by AEZ within region. In the future this could be worth investigating. At present, the core model applies the median improvement rate across all crops within each region to each crop, rather than using the crop-specific yield improvements described above. This is done in order to minimize the economic distortions of differentiated yield (and therefore profit) increases. The same rate is applied to biomass as to other crops, though this and all other rates of change could be specified differently by scenario.

The AEZs within each of the 14 geopolitical GCAM regions are based on the research developed for the GTAP model using SAGE land use categories<ref>Monfreda, C., N. Ramankutty and T. W. Hertel (2007). Global Agricultural Land Use Data for Climate Change Analysis. in Economic Analysis of Land Use in Global Climate Change Policy. T. W. Hertel, S. Rose and R. S. J. Tol, Routledge.</ref>: the combination of regions and AEZs resulting in 151 distinct GCAM agriculture and land use subregions covering the globe. Within each of these 151 subregions, land is categorized into approximately a dozen types based on cover and use. Some of these types, such as tundra and desert, are not considered arable. Among arable land types, further divisions are made for lands historically in non-commercial uses such as forests and grasslands as well as commercial forestlands and croplands. Production of approximately twenty crops is currently modeled, with yields of each specific to each of the 151 subregions. The model is designed to allow specification of different options for future crop management for each crop in each subregion.

Historical land use allocation is based on data from HYDE<ref>https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=1900%3C/ref%3E for cropland, urban land, tundra, and rock/ice/desert. Managed forest and managed pasture land are calculated in each time period based on each region’s annual production, disaggregated to AEZ’s according to the estimated production of each AEZ. Unmanaged forest, unmanaged pasture, grassland, and shrubland are compiled from the HYDE data, with forest and pasture calculated as their respective values minus the managed forms in each of these lands. Historical agricultural production and harvested cropland area are taken from the FAOSTAT database for 1990 and 2005, disaggregated to the AEZ level on the basis of production and harvested area in the GTAP data. Cropping systems are divided into nine food categories (rice, wheat, corn, other grains, oil crops,sugar crops, palm fruit, roots & tubers, and miscellaneous crops) and animal production is represented by five categories (beef, dairy, pork, poultry, and other ruminants). The characterizations of animal production technologies are based on IMAGE data; subsectors are “mixed” and “pastoral”, and inputs to the systems are (1) fodder herbs and crop residues; (2) pasture and foddergrass; (3) feedcrops; and (4) scavenging and other. Under this categorization, animal feed is supplied both by pasture land and by grain and fodder crops and thus future demand for animal products impacts land allocation in GCAM.

The composition of SAGE’s 14 natural vegetation classifications is calculated in each region and AEZ for each of the five GCAM natural vegetation categories. Specifically, GCAM’s forest includes 8 SAGE categories, grassland includes 2, shrubland includes 2, tundra includes 1, and rock/ice/desert includes 2. Next, carbon contents are assigned to each of the SAGE 14 natural vegetation types, based on literature estimates<ref>Houghton, R.A. 1999. The annual net flux of carbon to the atmosphere from changes in land use 1850-1990. Tellus 51B: 298-313.</ref><ref>King, A. W., Post, W. M., and Wullschleger, S. D. 1997. The potential response of terrestrial carbon storage to changes in climate and atmospheric CO2. Climatic Change, 35(2), 199–227. Springer. Retrieved March 9, 2011, from http://www.springerlink.com/index/K528446L38325833.pdf.</ref><ref>Olson J.S., Watts J.A. and Allinson L.J. 1983 Carbon in Live Vegetation in Major World Ecosystems. Environmental Sciences Division Publication No. 1997. Oak Ridge National Laboratory, Tennessee.</ref>. The carbon density of each region/AEZ/GCAM land type is calculated as the weighted average of the carbon contents of the SAGE natural vegetation types within the GCAM natural vegetation types, for each region and AEZ. For instance, GCAM forest in USA AEZ 9 is 53% temperate needleleaf evergreen forest, 12% temperate deciduous forest, 1% boreal evergreen forest, and 34% mixed evergreen/deciduous forest. Therefore the average carbon content of USA AEZ 9 forest is (160)(0.53) + (135)(0.12) + (90)(0.01) + (103)(0.34) = 137 MgC/ha, or 13.7 kgC/m2. Similar calculations are used for vegetation carbon contents for forest, grassland, shrubland, tundra, and rock/ice/desert.

Unmanaged pasture vegetation carbon contents are set equal to the region/AEZ’s corresponding SAGE grassland carbon contents. Managed pasture carbon contents are set equal to one half of unmanaged pasture carbon contents. Managed forest vegetation carbon contents are set equal to one half of the unmanaged forest carbon contents. See below for derivation and justification of managed forest land allocation and carbon contents. Cropland vegetation carbon contents are calculated based on yields, crop moisture contents, and harvest indices.

Bioenergy Production

The supply characteristics of biomass are derived from the land-use model. The demand for biomass is derived endogenously from the energy component of the model. For example, the larger the value of carbon, the more valuable biomass is as an energy source and the greater the price the energy markets will be willing to pay for biomass. Conversely, as populations grow and incomes increase, competing demands for land may drive down the amount of land that would be available for biomass production at a given price.

There are several types of bioenergy resources in GCAM, including the following: traditional bioenergy production and use, bioenergy from residues and waste products, bioenergy from crops traditionally grown for food, and purpose-grown bioenergy crops. Traditional bioenergy consists of straw, dung, fuel wood and other energy forms that are utilized in an unrefined state in the traditional sector of an economy. Traditional bioenergy use, although significant in developing nations, is a relatively small component of global energy. Traditional biomass is modeled as becoming less economically competitive as regional incomes increase over the century.

Bioenergy from residue and waste products are fuels that are consumed in the modern sectors of the economy, but which are byproducts of another activity. Examples in the model include forestry and milling by-products, crop residues in agriculture, and municipal solid waste. The availability of byproduct energy feedstocks is determined by the underlying production of primary products and the cost of collection. The total potential agricultural waste available is calculated as the total mass of the crop less the portion that is harvested for food, grains, and fibers, and the amount of biomass needed to prevent soil erosion and nutrient loss and sustain the land productivity. The amount of potential waste that is converted to bioenergy is based on the price of bioenergy. However, the bioenergy price does not affect production of the crop from which the waste is derived. For example, an increase in the price of bioenergy would increase the share of the wheat crop collected for use as bioenergy, but the higher bioenergy price would not affect the total production of wheat. Instead, the higher bioenergy price would result in higher purpose-grown energy crops.

GCAM models the production of biofuels resources that are already widely used, such as sugar and starch based ethanol production from conventional food crops such as corn and sugar, as well as biodiesel from oil crops such as soybeans and palm. While these sources may be surpassed by cellulosic biofuels in the future, they will likely continue to be an important part source of energy going forward into the next several decades. The regional production and land use requirements of these crops is modeled in GCAM along with production and land use demands from all agriculture products and forestry such as food crops and wood. Most of these crops are already grown at large scales for food and other uses around the world. For these bioenergy resources, crop yields are readily available and can be computed for each subregional AEZ from historical production and land use areas. As a result, modeling these the supply of these crops as bioenergy resources does not present any additional burden on the GCAM agriculture and land use modeling. All that is required for modeling is that links are made to refining sectors and technologies in the GCAM energy system.

Purpose-grown bioenergy refers to crops whose primary purpose is the provision of energy. These would include, for example, lignocellulosic crops such switchgrass, miscanthus, and woody poplar, as well as oil crops such as jatropha. The profitability of purpose-grown, “second-generation” bioenergy depends on the expected profitability of raising and selling that crop relative to other land-use options in GCAM. This in turn depends on numerous other model factors including: bioenergy crop productivity (which in turn depends on the character of available land as well as crop type and technology), non-energy costs of crop production, cost and efficiency of transformation of purpose-grown bioenergy crops to final energy forms (including liquids, gases, solids, electricity, and hydrogen), cost of transportation to the refinery, and the price of final energy forms. The price of final energy forms is determined endogenously as a consequence of competition between alternative energy resources, transformation technologies, and technologies to deliver end-use energy services. In other words, prices are determined so as to match demand and supplies in all energy markets.

A variety of crops could potentially be grown as bioenergy feedstocks. The productivity of those crops will depend on where they are grown—which soils they are grown in, climate characteristics and their variability, whether or not they are fertilized or irrigated, the availability of nitrogen and other minerals, ambient CO2 concentrations, and their latitude. In this analysis we assume that a generic bioenergy crop, with characteristics similar to switchgrass, can be grown in any region. Productivity is based on region-specific climate and soil characterizes and varies by a factor of three across the GCAM regions.

GCAM allows for the possibility that bioenergy could be used in the production of electric power and in combination with technologies to provide CO2 emissions captured and stored in geological reservoirs (CCS). This particular technology combination is of interest because bioenergy obtains its carbon from the atmosphere and if that carbon were to be captured and isolated permanently from the atmosphere the net effect of the two technologies would be to produce energy with negative CO2 emissions<ref>Luckow, P., M.A. Wise, J.J. Dooley, and S.H. Kim. 2010. Large-scale utilization of biomass energy and carbon dioxide capture and storage in the transport and electricity sectors under stringent CO2 concentration limit scenarios. International Journal of Greenhouse Gas Control 4: 865-877.

Land Policy in GCAM

GCAM has the capability to run several different types of land policies, including protected land cases, terrestrial pricing strategies, bioenergy taxes, bioenergy subsidies, and bioenergy constraints. These policies will have differing effects on energy use, land use, energy prices, food prices, CO2 emissions, non-CO2 emissions, and the cost of mitigation. Calvin et al. (2013) explores the trade-offs of these policies.

Protected land cases

In these cases, we set aside various amounts of non-commercial ecosystems, preventing expansion of crops and bioenergy into these lands. We have explored various levels of protection, varying from 10% to 100% of the non-commercial land area. We have also examined protecting only forests and expanding the protection to other non-commercial ecosystems (e.g., grassland, shrubs). The current default land policy in GCAM is to protect 90% of all non-commercial ecosystems.

Terrestrial pricing strategies

Efficient climate policies are those that apply an identical price to greenhouse gas emissions wherever they occur. Hence, an efficient policy is one that applies identical prices to land use change emissions and fossil and industrial emissions. Theoretically, carbon in terrestrial systems can be priced using either a flow or a stock approach. The flow approach is analogous to the pricing generally discussed for emissions in the energy sector: landowners would receive either a tax or a subsidy based on the net flow of carbon in or out of their land. If they cut down forest to grow bioenergy crops, then they would pay a tax on the CO2 emissions from the deforestation. In contrast, the stock approach applies a tax or subsidy to landowners based on the carbon content of their land. If the carbon content of the land changes, for example, by cutting forests to grow bioenergy crops, then the tax or subsidy that the landowner receives is adjusted to represent the new carbon stock in the land. The stock approach can be viewed as applying a “carbon” rental rate on the carbon in land. Both approaches have strengths and weaknesses. Real-world approaches may not be explicitly one or the other. When terrestrial pricing strategies are enabled in GCAM, the stock approach is used.

Bioenergy taxes, subsidies, and constraints

We can impose taxes, subsidies or constraints (upper or lower bounds) on bioenergy. In the case of constraints, the GCAM solver will compute the tax or subsidy needed to ensure the constraint is met.

Fertilizer

A full nitrogen (N) fertilizer module has been developed for GCAM, with production technologies in each region consistent with available data on ammonia production, and consumption downscaled to region, crop, and AEZ. In GCAM, N fertilizer is indicated in mass of fixed N. Production and consumption by country (and therefore region) are from FAO ResourceSTAT (FAOSTAT 2011a), with production totals uniformly adjusted downwards so that the global total is equal to global total consumption, excluding non-agricultural uses of N fertilizer. Production by technology is from IEA (2007), which indicates both the shares of the different production technologies used in each of eleven global regions, in addition to the energy intensity of the technologies used in each region. Consumption by region is first downscaled to crops according to a dataset put together by the International Fertilizer Industry Association (IFA) working in collaboration with the FAO (Heffer 2009), and then downscaled to AEZ on the basis of crop production, with the downscaling in the USA informed by detailed fertilizer use data from the USDA. Non-fuel costs of fertilizer production by natural gas steam reforming are calculated to return observed market prices in base years, with non-energy costs of all other technologies based on their respective technologies in the H2A model. Fertilizer input-output coefficients (kgN per kg crop) are held constant in all future periods, allowing future fertilizer demand to scale linearly with any assumed improvements in yields.

Fertilizer Mass Balances and Trade

The FAO maintains a historical time series of fertilizer production and consumption in all countries, in terms of total N. The production data includes non-agricultural use of fertilizers, and as such is 5 - 10% higher than the global consumption quantities, which include only fertilizer used for agricultural purposes. In GCAM we are only concerned with the fertilizer produced for agriculture, so all production in all regions is uniformly adjusted downwards so that global production and consumption balance. Due to the large volume of trade in the base year—the Soviet Union, for example, exports greater than 80% of the fertilizer it produces —GCAM does include trade in fertilizer, though in the same simple, exogenous fashion as trade in meat is modeled. Exporting regions are assigned an additional final demand with a fixed output for the duration of the model run, and importing countries are assigned an additional production technology that consumes no energy input.

References