Massachusetts Institute of Technology
Joint Program on the Science and Policy of Global Change
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Report #18: Transient Climate Change and Potential Croplands of the World in the 21st Century

May 1997

¹The Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA 02543
²Joint Program on the Science and Policy of Global Change, MIT, Cambridge, MA 02139
³Institute of Arctic Biology, University of Alaska, Fairbanks, AK 99775
⁴Institute for the Study of Earth, Ocean and Space, Univ. of New Hampshire, Durham, NH 03824

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ABSTRACT: A cropland distribution model, which is based on climate, soil and topography, is applied to estimate the area and spatial distribution of global potential croplands under contemporary climate and to assess the effect of transient climate changes projected by the MIT Integrated Global System Model for assessment of climate change. The area of global potential croplands is about 32.91 x 10⁶ km ² under contemporary climate, and increases substantially over the period of 1977-2100 and differs among the three transient climate change predictions, being about +6.7% (2.20 x 10⁶ km²), +11.5% (3.78 x 10⁶ km²), and +12.5% (4.12 x 10⁶ km²) in 2100, respectively. Among twelve economic regions of the world, the Former Soviet Union and the Other OECD Countries regions have the largest increases in potential croplands, while developing countries have little increases in potential croplands. Spatial distribution of potential croplands changes considerably over time, dependent upon the transient climate change predictions.

Contents:

1. Introduction

2. Cropland Distribution Model

2.1 Climate constraints

2.2 Topographical constraints

2.3 Data for global extrapolation

2.4 Transient climate change scenarios

3. Results and Discussion

3.1 Potential croplands under contemporary climate

3.2 The effects of transient climate change on potential croplands

4. Conclusion

References

1. Introduction | Contents |

The United Nations Food and Agriculture Organization (FAO) has developed the Agro-Ecological Zone approach to assess land suitability for croplands at local and national scales, which is based on climate, soil and crop characteristics. ^[7-9] The approach has been further applied at the global scale. ^{[10, 11]} By modeling the global distribution of potential croplands under contemporary climate and under doubled CO₂ equilibrium climates generated by four atmospheric general circulation models (GCMs), Cramer and Soloman ^[11] found that climate change significantly affects the area and spatial distribution of land that can potentially be used for crop production. However, topographical constraints on cropland distribution were not included in the above studies, and consequently the area of potential croplands is likely to be substantially overestimated.

In this study, we develop a cropland distribution model that includes climate, soil and topography constraints on the distribution of potential croplands. We apply the model to estimate the area and spatial distribution of potential croplands of the world under contemporary climate. To assess the effect of transient climate change on potential croplands, we use three transient climate change predictions over the period of 1977-2100 from the MIT Global System Model for integrated assessment of climate change. ^[12] The MIT framework (Fig. 1) includes the Anthropogenic Emission Prediction and Policy Analysis (EPPA) model, ^[13] an atmospheric chemistry model, ^[14] a two-dimensional climate model (MIT 2-D L-O climate model ^[15-19]) , a terrestrial biogeochemistry model (the Terrestrial Ecosystems Model ^[19-22]) and natural emission models of N₂O and CH₄ from soils. ^[23]



Figure 1. A schematic diagram illustrating the framework and components of the MIT Global System Model for integrated assessment of climate change. The linkages and feedbacks between the component models that are currently included or under development for future inclusion are shown as solid and dashed lines, respectively.

The EPPA model projects anthropogenic emissions of CO₂ and other greenhouse gases (e.g., N₂O and CH₄) in the twelve economic regions of the world. We examine the spatial and temporal dynamics of potential croplands at the scales of the globe, economic regions and grid cells.

2. Cropland Distribution Model

2.1 Climate constraints| Contents |

We also use the climate envelope of Cramer and Soloman^[11] to define climate-based potential croplands in our study, although our procedures to calculate growing degree days and AET/PET ratios are slightly different from Cramer and Soloman. In our study, monthly mean temperature data are linearly interpolated to daily mean temperature, which are then used for calculating growing degree days. We use the water balance model of Vorosmarty et al., ^[26] which has been incorporated into our Terrestrial Ecosystem Model (TEM ^[12, 19-22] ) to estimate PET and AET. The water balance model runs at monthly time step. The input variables of the water balance model include elevation, soil texture (proportion of sand, clay and silt), monthly climate (temperature, precipitation and cloudiness) and vegetation types. Elevation is used to determine snowmelt dynamics of a grid cell (snowmelt occurs in one month if elevation is below 500 m; snowmelt occurs in two months if elevation is above 500 m). Soil texture affects the field water-holding capacity and wilting point of soils. The monthly flux PET is calculated as a function of monthly mean air temperature and solar radiation. ^[27] The flux of AET is equal to PET in wet months but is modeled as a function of rainfall, snowmelt recharge and a change of soil moisture in dry months.^[26] The ratio of AET/PET for each of twelve months is calculated. For moist tropical regions where temperature in the coldest month is greater than 15.5 degrees C, we assume that a grid cell will not be suitable for croplands if there are more than eight months in a year that have a AET/PET ratio greater than 0.70.

2.2 Topographical constraints | Contents |

For defining mountains from elevation data, we designed a pattern search algorithm within a window that has 3 x 3 grid cells. We calculate elevation differences between the center grid cell and each of its eight neighboring grid cells. We first recognize or stratify three topographical features of the world: plains with low elevation (approximately lower than 500 m in elevation); low mountains and low plateau (approximately lower than 1000 m in elevation); high mountains and high plateau (e.g., Rocky mountains in north America, Andes in south America, Himalayas mountains, Qinghai-Tibet plateau in China). Then, we apply the following criteria to decide whether the center grid cell is mountain: (1) elevation difference is greater than 400 m, when elevation of the center grid cell is less than 500 m; (2) elevation difference is greater than 600 m, when elevation of the center grid cell is within 500-1000 m; and (3) elevation difference is greater than 1200 m, when elevation of the center grid cell is more than 1000 m. If any of the eight elevation differences within a 3 x 3 window meets one of the criteria, then the center grid cell is assigned to be mountain. If a grid cell is assigned to be mountain, it will not be used for cropland, even if it meets climate constraints for croplands. We used this 3 x 3 window pattern search for all the grid cells in our global elevation database, ^[29] which has a resolution of 0.5 degrees longitude x 0.5 degrees latitude grid. The resulting global map of mountain (Fig. 2) clearly shows large mountain ranges, e.g., Rocky, Andes and Qinghai-Tibet plateau. In a visual qualitative comparison, the geographical distribution of mountains in our global map of mountains mimics well with a shaded relief image of global topography, ^[30] which was generated from an elevation data set (NOAA's ETOPO5) with a spatial resolution of 5 minutes latitude by 5 minutes longitude.

Figure 2. Spatial distribution of mountains in the world. Note that Greenland is mostly covered with ice.

2.3 Data for global extrapolation | Contents |

2.4 Transient climate change scenarios | Contents |

Figure 3. Projected changes in global annual mean atmospheric CO₂ concentration (ppmv), global annual mean temperature (degrees C), global mean daily precipitation (mm) and global annual mean cloudiness (%) over the period of 1977-2100 for the Reference, HHL and LLH scenarios by the MIT 2-D L-O climate model (see reference ^[12] for details).

The MIT 2-D L-O climate model simulates the zonally averaged climate separately over land and ocean as a function of latitude and height. It has 23 latitudinal bands, corresponding to a resolution of 7.826 degrees , and nine vertical layers. The climate outputs in the 23 latitudinal bands from the MIT 2-D L-O climate model were first linearly interpolated to 0.5 degrees resolution, and the interpolated values were then applied to all the grid cells within a 0.5 degree latitudinal band. In generating "future climate" we calculated absolute differences in monthly mean temperature and ratios in monthly precipitation and monthly mean cloudiness, using the simulated climate data in 1977 from the MIT 2-D L-O climate model as the control values. Then, we added the absolute differences in monthly mean temperature over the period of 1978-2100 to the contemporary monthly mean temperature data, and multiplied the ratios in monthly precipitation and monthly mean cloudiness over the period of 1978-2100 by the contemporary monthly precipitation and monthly mean cloudiness data, respectively.

3. Results and Discussion

3.1 Potential croplands under contemporary climate | Contents |

Potential cropland area varies substantially among the twelve economic regions of the world used in the EPPA model (Fig. 4). The ratio of potential cropland area over total land area for each of the economic regions indicates the potential of land for cultivation. The ratio varies from 11.1% in the Energy Exporting Countries (EEX) economic region to 77.7% in the Eastern European Countries (EET) economic region (Fig. 4). The EEX and the Rest of the World (ROW) economic regions have large areas of desert and semi-arid land, where there is not enough precipitation to support rain-fed agriculture. The Former Soviet Union (FSU) and the Other OECD Countries (OOE) economic regions have large areas of land in high latitudes where the temperature is too low for crops to grow. China has the largest population in the world but a low ratio of potential cropland area over total land area, because China has large areas of mountains (Fig. 2), dry land (e.g., Mongolia plateau, Gobi desert in northwestern China) and the cold Qinghai-Tibet plateau.

Figure 4. Total land area, potential cropland area under contemporary climate, and actual cropland area in 1980 in the twelve economic regions of the world. These 12 economic regions are defined in the Anthropogenic Emission Prediction and Policy Analysis model (EPPA): DAE - Dynamics Asian Economics, EEC - European Economic Community, EET - Eastern European Countries, EEX - Energy Exporting Countries, FSU - Former Soviet Union, OOE - the Other OECD Countries, ROW - the Rest of the World, USA - United States of America. Data of actual cropland area in 1980 are aggregated from the FAO country statistics data of agricultural production, which were provided by Dr. G. Esser. Note that for Brazil, the ratio of potential cropland area over total land area is overlapped with the ratio of actual cropland area over potential cropland area.

Actual cropland areas among the twelve economic regions in 1980, which are derived from the FAO country statistics data of crop cultivation and production, ^[35] vary considerably (Fig. 4), partly because of the differences in population and agricultural practices. The ratio of actual cropland area over potential cropland area indicates the realized potential of land used for cultivation (Fig. 4). The ratio ranges from 25.0% in Brazil to 109.7% in India. Only five economic regions (Brazil, Japan, OOE, FSU, and USA) have a ratio lower than 50%. The low ratio in Brazil indicates that Brazil has a large amount of potential croplands for future use. The highest ratio in India indicates that people in India have already practiced crop cultivation in a fair amount of mountainous regions and other marginal lands. However, crop cultivation in mountains is generally very vulnerable to severe soil erosion and low slope stability in mountains, especially on long-term temporal scales.

As shown in Fig. 5, our cropland distribution model represents reasonably well the spatial distribution of global croplands as described in the map of croplands of Olson et al. ^[24] The agreement between Olson et al. and our model occurs in 6266 grid cells, about 14.35 x 10⁶ km², which accounts for 59.5% of the global cropland area estimated by Olson et al. Olson et al. includes irrigated croplands, thus, a fair amount of croplands described by Olson et al. is located in semi-arid and arid regions. We have not included global river networks in this study and thus we can not project those croplands that are completely dependent upon irrigation from river water, e.g., the Nile River (Fig. 5). A large area of croplands in the western United States is also dependent on irrigation water from aquifers.

Figure 5. The comparison between the map of croplands of Olson et al.^[24] and the map of global potential croplands under contemporary climate estimated by our cropland distribution model. In the map of croplands of Olson et al., both cultivation and secondary vegetation areas are included. Both maps have a spatial resolution of 0.5 degrees latitude x 0.5 degrees longitude.

The geographical distribution of potential croplands (Fig. 5) is closely correlated with the geographical distribution of human population (Fig. 6). Potential croplands occur mostly in those areas that have population densities of five or more people per square kilometer. Among the twelve economic regions, India and China have the highest population densities, indicating the highest pressure on potential croplands to be converted for crop cultivation. The cropland model estimates that large portions of moist tropical forests in Brazil, tropical Asian countries (e.g., Philippine, Indonesia and Malaysia) and Africa are not good for croplands due to the potential of severe soil erosion in croplands (Fig. 5). In the map of Olson et al.,^[24] there are only small areas of croplands in the moist tropical forests in Brazil but fair amounts of croplands in the tropical Asian countries where irrigated rice paddy dominates (Fig. 5). The difference between Brazil and the tropical Asian countries may be attributed primarily to much higher population pressure for crop production in the tropical Asian countries from higher population density (more than 50 people per km²; see Fig. 6) and secondarily to monsoon climate in Asia. Our cropland distribution model estimates that a large portion of eastern Amazon is suitable for croplands (Fig. 5), where extensive deforestation has been observed during the 1980s, with a fair portion of forest conversion to croplands and pasture. Skole and Tucker ^[36] estimated that deforested area in Amazon is 78,000 km² in 1978 but 230,000 km² in 1988, using high resolution LANDSAT imagery. Deforestation in tropical regions is closely driven by increases in human population and other social-economic factors across local, regional, national and international levels. ^[37-39] In this study, our cropland distribution model has not included the spatial distribution of human population. The comparison in geographical distributions between potential croplands and human population indicates that further studies are needed to quantify the relationship between cropland distribution and spatial distribution of human population. Thus, future work needs to incorporate the spatial-temporal dynamics of human population into our cropland distribution model for estimating distributions of actual croplands. Further studies are also needed to investigate the relationship between croplands and monsoon climate, especially in the above tropical Asian countries.

Figure 6. Geographical distribution of population density (number of people per km²) of the world in 1994 at 0.5 degrees latitude by 0.5 degrees longitude resolution. It is aggregated from the Gridded Population of the World at 5 minutes (latitude) by 5 minutes (longitude) resolution, which is released by the Consortium for International Earth Science Information Network (CIESIN) and the U.S. National Center for Geographical Information and Analysis (NCGIA) in 1995 (see reference ^[47] and the web site http://www.ciesin.org). The unsmoothed population density data from the CIESIN and NCGIA were used, which geographically cover areas from 57 degrees S to 72 degrees N.

3.2 The effects of transient climate change on potential croplands | Contents |

Figure 7. Temporal changes in the area of global potential croplands over the period of 1977-2100 under the three transient climate change predictions projected by the MIT 2-D L-O climate model.

The increase in global potential croplands is mostly attributable to increases in potential croplands of the Former Soviet Union and the Other OECD Countries economic regions (Fig. 8). The other ten economic regions have little change in the area of potential croplands. China may even have a small decrease in the area of potential croplands by the end of 21st century. The relative contributions among the 12 economic regions to the increase of global potential croplands are similar for these three climate change predictions (Fig. 8). Human population is likely to increase substantially in developing countries, e.g., China, India, and the ROW economic regions, while developed countries (e.g., USA, EEC, OOE and FSU economic regions) have a small increase in population (Fig. 9). Human populations in the Dynamic Asian Economics, the Energy Exporting Countries and Brazil economic regions (Fig. 9) also increase faster than the increase in potential croplands (Fig. 8). For developing countries, the opposite trends in population and potential cropland area indicate an increasing pressure to raise agricultural production per unit area. These developing countries, however, are under the largest constraints in energy and resources, which are essential for intensive agricultural production. Future climate change and an increase of atmospheric CO₂ concentration are also likely to affect crop production significantly. ^[40-42] In a recent study that uses comparable crop yield models with doubled CO₂ equilibrium climate changes from three GCMs (GISS, GFDL, UKMO) to assess the potential impacts of climate change on world food supply, the results suggest that doubling of atmospheric CO₂ concentration will lead to a small decrease in global crop production. ^[42] Climate change increases cereal production per hectare in the developed countries but reduces cereal production per hectare in developing countries.^[42] One important implication from the comparison among population dynamics, potential cropland area dynamics and crop production per hectare is that international trade in agricultural products will have to be substantially increased between developed countries and developing countries, in order to avoid food shortage in some countries in the 21st century.

Figure 8.Temporal changes in the areas of potential croplands in the twelve economic regions over the period of 1977-2100 under the three transient climate change predictions. The acronyms for the economic regions are the same as given in Figure 4.

Figure 9. Temporal changes of human population in the twelve economic regions over the period of 1985-2100. These projections by the Anthropogenic Emission Prediction and Policy Analysis model (EPPA) are based on the estimates of United Nations (see reference ^[48]). The acronyms for the economic regionsare the same as in Figure 4.

Geographically, potential croplands expand gradually into high latitudes of the northern hemisphere over the period of 1977-2100 (Fig. 10, 11, 12). The northward expansion of potential croplands is the largest in the `HHL' but the lowest in the `LLH' climate change predictions. Russia and Canada have the largest increases in potential cropland area, which is mostly attributed to temperature increases in high latitudes. The MIT 2-D L-O climate projected a larger temperature increase in high latitudes than in low latitudes,^[12] which is consistent with the results of 3-D GCMs. ^[43] In high latitudes where tundra and boreal forests dominate, large portions of soils are spodosols and histosols. Physical properties (e.g., water-saturation in histosols) and chemical properties (e.g., pH, cation exchange capacity) of these soils may also constrain distribution of croplands, therefore, large amounts of additional resources and inputs (e.g., drainage, lime application) are needed to make the soils suitable for crop cultivation. Kazakhstan, Uzbekistan and Turkmenistan in the Former Soviet Union economic region have large increases in potential cropland area. In the southern hemisphere, Brazil and Australia have small increases in potential cropland area. Cramer and Soloman ^[11] also projected a northward expansion of potential croplands into high latitudes under the doubled CO₂ equilibrium climates projected by the four GCMs (OSU, GISS, GFDL and UKMO). The MIT 2-D L-O climate model projects less spatial variation in climate than the 3-D GCMs because it has no variation among the grid cells along longitudes. A number of 3-D coupled atmospheric-ocean GCMs have simulated transient climate changes over time,^[44] thus it would be useful to use them to drive the cropland distribution model to examine the effects of longitude and latitude variations in climate change on potential croplands. If driven by transient climate change predictions of 3-D GCMs, our cropland distribution model may give larger spatial and temporal variations in global potential croplands. However, one limitation of these coupled atmospheric-ocean GCMs experiments ^[44] to our study here is that they provide only one transient scenario of CO₂ change over time, and most of them use 1% CO₂ increase per year over time.

Figure 10. Changes in the spatial distribution of global potential croplands over the period of 1977-2100 under the Reference transient climate change prediction. Percentages in the color legend represent the number of years a grid cell is suitable for cropland over the 124-year simulation in the study.

Figure 11. Changes in the spatial distribution of global potential croplands over the period of 1977-2100 under the HHL transient climate change prediction. Percentages in the color legend represent the number of years a grid cell is suitable for cropland over the 124-year simulation in the study.

Figure 12. Changes in the spatial distribution of global potential croplands over the period of 1977-2100 under the LLH transient climate change prediction. Percentages in the color legend represent the number of years a grid cell is suitable for cropland over the 124-year simulation in the study.

The potential expansion of croplands into high latitudes in the northern hemisphere in the 21st century may have a significant impact on the global carbon budget. Total carbon storage (vegetation carbon plus reactive soil organic carbon) under contemporary climate at 315 ppmv CO₂ has a bimodal distribution along 0.5 degree latitudinal bands (Fig. 13). Of 1658.6 PgC (10¹⁵ g C) total carbon storage of the terrestrial biosphere, ^[19] 423.6 Pg (25.5%) is stored within the tropical latitudes (10 degrees S to 10 degrees N) where tropical forests dominate, and 420.8 PgC (25.4%) is stored within the 45 degrees N to 65 degrees N latitude band, where boreal forests and tundra dominate. In tropical forest ecosystems, vegetation carbon is substantially higher than reactive soil organic carbon, while a large portion of carbon in boreal forests and tundra is stored in soils. In calculating the effects of land cover and land use changes on the historical and present global carbon budget, ^{[45, 46]} tropical deforestation plays a dominant role in CO₂ emissions from land use and land cover changes. Conversion of boreal forests and tundra into croplands may also result in a large loss of carbon in vegetation and soils, and will affect carbon flux and storage in the terrestrial biosphere. Our study implies that the effort to stabilize atmospheric CO₂ concentration in the next centuries may also need to take into consideration the constraints of land use and land cover changes at the mid- to high- latitudes of the northern hemisphere.

Figure 13. Latitudinal distribution of total carbon storage (TOC) and reactive soil organic carbon (SOC) for contemporary climate with 315 ppmv CO₂ along 0.5 degree resolution latitudinal bands, as estimated by the Terrestrial Ecosystem Model (see reference [19]). Total carbon storage estimated by TEM is the sum of vegetation carbon and reactive soil organic carbon. Estimates of reactive soil organic carbon in a grid cell exclude inert soil organic carbon. Vegetation carbon is the difference between total carbon storage (TOC) and reactive soil organic carbon (SOC).

4. Conclusion | Contents |

The incorporation of a global large river network and spatial distribution of human population into the cropland distribution model may improve our estimate of croplands under contemporary climate and provide a basis to assess the effects of water supply (particularly in semi-arid and arid regions) and population dynamics on agriculture in the future. Further work is also needed to model or delineate spatial distributions of various crops on the globe ^[10] and to estimate yields of crops. A land use model, which uses potential croplands and social-economic constraints, needs to be developed to define the area and spatial distribution of actual croplands on the globe. The linkage of a land cover and land use change model, a global terrestrial biogeochemistry model (e.g., TEM) and a climate model will allow us to assess the impacts of land cover and land use changes on global carbon and nitrogen cycling as well as the feedbacks of land cover and land use changes to atmosphere and climate over time (e.g., net CO₂ fluxes, albedo and roughness).

Acknowledgments

We thank B.L. Turner II, R.G. Prinn, J.D. Henry and R. Eckaus for invaluable discussion and comments on the work and earlier versions of the manuscript. G. Esser provided actual cropland data at country level in 1980 from FAO. This study was supported by the Joint Program on the Science and Policy of Global Change at the Massachusetts Institute of Technology (CE-S-462041), the National Institute of Global Environmental Changes of the Department of Energy (No:901214-HAR), and the Earth Observing Systems Program of NASA (NAGW-2669).

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