Climate Change and Agriculture

Climate can affect agriculture in a variety of ways. Temperature, radiation, rainfall, soil moisture and carbon dioxide (CO2) concentration are all important variables to determine agricultural productivity, and their relationships are not simply linear. Current research confirms that there are thresholds for these climate variables above which crop yields decline (Challinor et al. 2005; Proter and Semenov 2005). For example, the modeling studies discussed in recent IPCC reports indicate that moderate to medium increases in mean temperature (1–3ºC), along with associated CO2 increases and rainfall changes, are expected to benefit crop yields in temperate regions. However, in low-latitude regions, moderate temperature increases (1–2ºC) are likely to have negative yield impacts for major cereals. Warming of more than 3ºC would have negative impacts in all regions (IPCC 2007b).

The interaction of temperature increases and changing rainfall patterns determines the impact of climate change on soil moisture. With rising temperatures, both evaporation and precipitation are expected to increase. The resulting net effect on water availability would depend on which force is more dominant. The IPCC reports project that by the middle of the 21st century, water availability will increase as a result of climate change at high latitudes and in some wet tropical areas, and decrease over some dry regions at mid-latitudes and in the dry tropics (IPCC 2007b). Some regions that are already drought-prone may suffer more severe dry periods.

Increases in atmospheric CO2 concentration can have a positive impact on crops yields by stimulating plant photosynthesis and reducing the water loss via plant respiration. This carbon fertilization effect is strong for so-called C3 crops,¹ such as rice, wheat, soybeans, fine grains, legumes, and most trees, which have a lower rate of photosynthetic efficiency. For C4 crops like maize, millet, sorghum, sugarcane, and many grasses, these effects are much smaller. Other factors such as a plant's growth stage, or the application of water and nitrogen, can also impact the effect of elevated CO2 on plant yield. Recent research based on experiments with the free air concentration enrichment method suggests a much smaller CO2 fertilization effect on yield for C3 crops and little or no stimulation for C4 crops, in comparison with past estimates from studies conducted under enclosed test conditions (Long et al. 2005, 2006). Based on analysis of recent data, the IPCC reports suggest that yields may increase by 10–25% for C3 crops and by 0–10% for C4 crops when CO2 levels reach 550ppm (IPCC 2007b). However, as a number of limiting factors were not included in the modeling and experiment analysis, considerable uncertainties still surround the estimates of carbon fertilization effect.

Besides temperature and carbon concentration, some other ecological changes brought on by global warming will have an impact on agriculture. For example, the patterns of pests and diseases may change with climate change, leading to reductions in agricultural production. Moreover, agricultural productivity will be depressed by increased climate variability and increased intensity and frequency of extreme events such a drought and floods. These factors further contribute to the difficulties in estimating the impacts of climate change on agricultural productivity.

Quantitative estimates of the agricultural impact of climate change have predominantly relied on three approaches: crop simulation models, agro-ecological zone (AEZ) models, and cross-section (Ricardian) models. Crop simulation models draw on controlled experiments where crops are grown in field or laboratory settings simulating different climates and levels of CO2 in order to estimate yield responses of a specific crop variety to certain climates, and other variables of interest.² These models do not include farmer adaptation to changing climate conditions in the estimates. Consequently, their results tend to overstate the damages of climate change to agricultural production (Mendelsohn and Dinar 1999). The second approach, AEZ analysis, combines crop simulation models with land management decision analysis, and captures the changes in agro-climatic resources (Darwin et al. 1995; Fishcher et al. 2005). AEZ analysis categorizes existing lands by agro-ecological zones, which differ in the length of growing period and climatic zone. The length of growing period is defined based on temperature, precipitation, soil characteristics, and topography. The changes of the distribution of the crop zones along with climate change are tracked in AEZ models. Crop modeling and environmental matching procedures are used to identify cropspecific environmental limitations under various levels of inputs and management conditions, and provide estimates of the maximum agronomically attainable crops yields for a given land resources unit. However, as the predicted potential attainable yields from AEZ models are often much larger than current actual yields, the models may overestimate the effects of autonomous adaptation. Cline (2007) observed that AEZ studies tend to attribute excessive benefits to the warming of cold high-latitude regions, thereby overstating global gains from climate changes.

The Ricardian cross-sectional approach explores the relationship between agricultural capacity (measured by land value) and climate variables (usually temperature and precipitation) on the basis of statistical estimates from farm survey or country-level data. This approach automatically incorporates efficient climate change adaptations by farmers. The major criticisms of the Ricardian approach are its ignorance of price changes and that it fails to fully control for the impact of other variables that affect farm incomes (Mendelsohn and Dinar 1999; Cline 1996).

Cline (2007) used both Ricardian statistical models and crop models to develop a set of consensus agricultural impact estimates through the 2080s for over 100 countries. He first developed geographically detailed projections for changes in temperature and precipitation through the 2080s based on a baseline emission projection from the IPCC's Emission Scenarios. Next, these climatic change projections were applied to the agricultural impact models to assess the effects of climate change on agricultural productivity. The final consensus estimates were the weighted average of the Ricardian estimates and the crops model estimates. Table 1 [ PDF 12.1KB | 1 page ] presents the major results of Cline's estimates.

The climate models used in Cline's study predicted that under the IPCC's scenario A2,³ atmospheric concentrations of CO2 would increase to 735ppm by 2085 from a current level of 380ppm, and that global mean temperature would rise by 3.3ºC. Land areas would warm more than oceans, with the average surface temperature increasing by 5.0ºC weighting by land area and 4.4ºC weighting by farming area. By the 2080s, global agricultural productivity would decline by about 3% with carbon fertilization effect and by about 16% if the carbon fertilization effect did not materialize. These losses would be disproportionately concentrated in developing countries, which would suffer losses of 9% with carbon fertilization effect and 21% without carbon fertilization effect, in contrast to an 8% gain (with carbon fertilization effect) and 6% loss (without carbon fertilization effect) in industrial countries. The detailed estimates by country and region reported in Table 2 [ PDF 12.6KB | 1 page ] indicate that South Asia and Africa would be the two regions most harmed by climate change. In Southeast Asia, the damages of climate change to agriculture would also be severe, ranking from 15.1% for Viet Nam to 26.2% for Thailand if carbon fertilization effect did not materialize.

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