Article pubs.acs.org/est
Climate Change Impact and Adaptation Assessment on Food Consumption Utilizing a New Scenario Framework Tomoko Hasegawa,*,† Shinichiro Fujimori,† Yonghee Shin,‡ Kiyoshi Takahashi,† Toshihiko Masui,† and Akemi Tanaka§ †
Center for Social & Environmental Systems Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan ‡ Climate Research Department, APEC Climate Center, 12 Centum 7-ro, Haeundae-gu, Busan, 612-020, Republic of Korea § Graduate School of Environmental Science, Hokkaido University, N10, W5, Sapporo, Hokkaido, 060-0810, Japan S Supporting Information *
ABSTRACT: We assessed the impacts of climate change and agricultural autonomous adaptation measures (changes in crop variety and planting dates) on food consumption and risk of hunger considering uncertainties in socioeconomic and climate conditions by using a new scenario framework. We combined a global computable general equilibrium model and a crop model (M-GAEZ), and estimated the impacts through 2050 based on future assumptions of socioeconomic and climate conditions. We used three Shared Socioeconomic Pathways as future population and gross domestic products, four Representative Concentration Pathways as a greenhouse gas emissions constraint, and eight General Circulation Models to estimate climate conditions. We found that (i) the adaptation measures are expected to significantly lower the risk of hunger resulting from climate change under various socioeconomic and climate conditions. (ii) population and economic development had a greater impact than climate conditions for risk of hunger at least throughout 2050, but climate change was projected to have notable impacts, even in the strong emission mitigation scenarios. (iii) The impact on hunger risk varied across regions because levels of calorie intake, climate change impacts and land scarcity varied by region.
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INTRODUCTION Several approaches have been taken in assessments of climate change impact and adaptations on agriculture and food systems, including process-based crop models, economic models and yield response functions.1 Process-based crop models have advantages in the calculation of crop responses to factors that affect growth and yield (i.e., climate, soils, and management), are useful for testing a broad range of adaptation measures. Economic models have advantages in the calculation of economic performance of agricultural sectors as a result of the equilibrium between supply and demand, take all of the major variables related to global food systems into consideration including trade balances and prices, and are easy to relate the relevant variables with socioeconomic scenarios. Yield response functions are best suited for the study of empirical relationships between observed climate and crop responses and describe well present-day crop and climate variations. Well-known studies2−6 have used economic models. Parry et 2 al. and Fischer et al.3 combined both a process-based crop model and a Computable General Equilibrium (CGE) model to analyze the impacts of climate change and socioeconomic conditions on agriculture and food systems in the future. Parry et al.2 suggested that Africa is at great risk; CO2 concentrations © 2013 American Chemical Society
stabilizing at levels of 750 ppm avoids some but not most of the risk of hunger, whereas stabilization at 550 ppm avoids most of the risk. They also found that the impact of climate change on hunger risk is greatly influenced by development pathways of income levels and technology, and not amounts of climate forcing. Fischer et al.3 suggested that agricultural techniques will be critically important in limiting potential damages resulting from climate change. Nelson et al.4 used a partial equilibrium model (IMPACT) to analyze climate change impacts at a basin level with a detailed description of irrigation practices. Hertel et al.5 analyzed the impact of climate change on crop yields in 2030 with a static CGE model. Lobel et al.6 used a global agricultural trade model and indicated that although investing the least developed areas such as SubSaharan Africa and Latin America may be most desirable for the main objectives of adaptation, it has little net effect on mitigation because production gains are offset by grater rates of land clearing in the benefited regions, which are relatively low Received: Revised: Accepted: Published: 438
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yielding and land abundant. Adaptation investments in high yielding, land scarce regions such as Asia and North America are more effective for mitigation. These existing studies have contributed to clarifying how climate change will affect the agricultural sectors and food security. However, the earlier assessments need to be updated and refined for the following reasons. Most were based on special report on emissions scenarios7 that do not account for climate mitigation and the climate scenarios of the Coupled Model Intercomparison Project phase 3 (CMIP3).8 A new set of global change scenarios has been released,9 consisting of the radiative forcing (i.e., greenhouse gas (GHG) emission) scenario of the Representative Concentration Pathways (RCPs),10−13 a state-of-the-art climate scenarios of the CMIP514 to advance knowledge of climate change and variability, and the socio-economic scenarios of the Shared Socio-Economic Pathways (SSPs).15 The RCPs and SSPs were independently developed to separate socioeconomic and climate factors. There have been no studies on food security combining them to date. Here we present a global climate change impact assessment on the agricultural sectors and food security by using the new set of scenarios (i.e., SSPs, RCPs, and CMIP5). The two main questions in this paper are: how much food consumption and population at risk of hunger in the world are improved by farmers’ autonomous adaptations to climate change? How much is the magnitude of uncertainty associated with socioeconomic conditions, climate projections within the effects of the adaptations on food security?
fed into the CGE model to understand changes in food prices and consumption with a suite of future socioeconomic scenarios; population and gross domestic product (GDP). Hunger incidence is calculated from food consumption. By using this modeling approach, we were able to complement the shortcomings of each model and connect the relevant biological and socioeconomic conditions within a unified framework to produce a global assessment of agriculture and food systems under various climate change scenarios. A similar approach has been adopted, for example, by Fischer et al.3 The estimation covers from 2005 to 2050. The world was classified into 17 regions and countries in the CGE model, and the model contains 26 commodities including 10 agricultural commodities (Table S1 and S2, Supporting Information (SI)). Scenario Settings. Figure 2 shows the scenario settings combining the three axessocioeconomic conditions; pop-
MATERIALS AND METHODS Overall Framework. Figure 1 shows the overall framework of this study. To incorporate socioeconomic and climate
Figure 2. Scenario settings. Option with/without adaptation (adap.) is only for transition and developing countries. Adaptation is considered in industrial countries for all scenarios. “NoCC”: No climate change. Present climate condition without adaptation is assumed under the NoCC condition. This figure is based on Figure 1 in van Vuuren et al. (2013).18
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ulation and GDP growths, climate conditions, and adaptation measures. We combined three socioeconomic conditions and four climate conditions according to RCP2.6, RCP4.5, RCP6.0, and RCP8.519 to examine the uncertainty of the projections. RCP2.6 is an ambitious mitigation in contract RCP8.5 is a high radiative forcing scenario. To clarify the effects of autonomous adaptation measures in transition and developing countries, we assumed scenarios with and without the adaptations in these countries for all socioeconomic and climate conditions. We assumed that adaptation measures are always available in the industrial countries. This assumption of adaptation might be simplistic and does not match the narrative conditions of the SSPs since the adaptation measures examined here require appropriate agricultural technologies and adequate long-term weather forecasts. However, by assuming no adaptation scenario for each SSP, we assessed how much the risk of hunger could be alleviated by the adaptations in the most pessimistic case. We also calculated a no climate change (NoCC) scenario for each socioeconomic condition as a reference scenario to get an overview of each world. In the NoCC case, the present climate condition was assumed for the entire period. The GHG emissions calculated from the CGE might not be exactly the same as those reported by the RCPs, particularly for high-level mitigation scenarios such as RCP4.5 and RCP2.6. Our output is similar to the emissions of RCP8.5. However, if we attempted
Figure 1. Study framework and models used.
conditions, we combined two models: the M-GAEZ processbased crop model16 and a global CGE economic model.17 This paper seeks to provide some bounds on hunger impacts of climate change and the adaptations using a two-step modeling approach. In the first step, an ensemble of GCMs driven by different emissions scenarios is combined with the MGAEZ to understand impacts of climate change and the adaptations on crop yields. In the second step, those grid-basis changes in crop yields are aggregated into regional values and 439
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to fill those gaps, a large amount of bioenergy production would have affected the food system. Because our aim was to better understand the pure impacts of socioeconomic and climate change assumptions, we consider this issue as a topic for further study. Data on Socioeconomic and Climate Conditions. To quantify the above scenarios, we set socioeconomic and climate conditions in the models. As the socioeconomic conditions, we used future population and GDP given by SSP1 to SSP3. SSPs9 describe five representative future visions (SSP1−SSP5) indicated by two axes: mitigation challenge and adaptation challenge. SSP1 to SSP3 are respectively characterized as “sustainable” (high levels of mitigation and adaptation challenges), “middle of the road” (middle levels), and “fragmentation” (low levels). Whereas SSP1 is designed as an optimistic scenario in terms of food security, SSP3 is a pessimistic scenario. SSP2 is in the middle of the two. SSP1 assumes high levels of economic growth in industrial countries and steady human development, and moderate population growth and higher economic development in transition and developing countries. In contrast, SSP3 assumes stagnating human development, high population growth, and low economic development in developing countries and overall global economic stagnation. Original data is available on the Web site20 (Table S3 and Figure S6, SI). To clarify the uncertainty associated with different climate models, we used the eight GCMs for which all four RCP emission scenarios are available (Table S4, SI). The different GCMs are run with the same input parameters to provide uncertainty bounds. Global changes in climate conditions predicted by the models, using average data from the 1990s as a reference time period, have wide ranges in temperature (1.07− 3.83 °C), precipitation (0.16−7.10 mm/day), and solar radiation (−0.28−1.15 W/m2) in 2050 (Figure S7, SI). Regional values have even wider ranges than the global values. Model Description. The M-GAEZ model16 has been developed by ourselves based on the Global Agro-ecological Zones (GAEZ) model21 for several years. The model calculates crop yield based on climate and biological conditions, while also accounting for two specific autonomous adaptation measures in this studychanging in crop variety and planting date. We considered a wide range of crop varieties (8, 16, and 19 types for rice, wheat, and maize, respectively) that have different parameters for crop growth, such as growing period and suitable climate conditions. Crop yield was estimated for each decade based on 10-year mean monthly data of the climate conditions both for the baseline and future periods. For the baseline period (the 1990s), the crop variety and the planting date that provide the highest yield under current conditions were selected for each crop (Chapter 2, SI). Future yield was calculated depending on future adaptation assumptions. In that, the variety providing the highest yield under future climate conditions was selected in the scenario with adaptation whereas the variety providing the highest yield during the baseline period was selected for future periods in the scenario without adaptation. In the same way, the best planting date under future climate conditions was selected (for maximum yield) in the scenario with adaptation whereas the best planting date during the baseline period was selected for the future in the scenario without adaptation. As previous studies mentioned, it is clear that the CO2 fertilization will affect crop yield (i.e., the fertilization increases 8.5% in rice production in Asia in 2050s at the 1990s basis in A2
scenario16,22). In this study, CO2 fertilization effect is taken into account for all scenarios. We simply multiplied parameters that change in accordance with atmospheric CO2 concentration in the original GAEZ model21 (See Masutomi et al.16 for detail). The area of irrigation was fixed at the current level in both of the M-GAEZ and the CGE model. Multicropping area can be changed in the scenario with adaptation and is fixed at the current level in the case without adaptation in the M-GAEZ model, whereas the area of multicropping is fixed at the current level in the CGE model to avoid a duplicate consideration. In the M-GAEZ model, if multicropping generates higher crop yield than that of single-cropping under a given climate condition at a grid cell, the yield obtained by multicropping is considered to be the yield of the grid cell. Namely, the future crop yields also implicitly include the effect of change in suitable condition for multicropping due to climate change. The CGE model calculates the supply, demand, and trade of commodities in response to change in prices on the basis of population, GDP, and consumer preferences, which are assumed to correspond with socioeconomic conditions. Production functions are formulated as constant elasticity substitution (CES) functions, which have multinested structures. Main factors to change future crop yields are technology development and climate change impacts. Technology development assumption is based on the IMPACT partial equilibrium model4 (Table S5, SI) while climate change impacts is based on the M-GAEZ outcomes (rice, wheat, and maize). For oil and sugar crops, we used the mean values of rice, wheat and maize (Table S6, SI). Household consumption was formulated as a Linear Expenditure System (LES) function. The function is derived from an original function defining that spending on individual commodities has a linear relation with total consumption spending, based on the assumption that each household maximizes a “Stone-Geary” utility function subject to consumption expenditure constraint. The parameters of the LES function were calibrated by using income elasticity values.23 The income elasticity of food demand for each region and commodity was prepared from Bruinsma et al.24 (Table S7, SI). The parameters are updated recursively to realize the assumed income elasticity. Intermediate input coefficients of agricultural commodities (except for intermediate input as feed) were also adjusted according to income elasticity. We simply fixed the elasticities for the future because we have insufficient quantitative information which can be used for this study. (See section 1.2 of the SI for the interpretation of the LES function.). Trade is based on the Armington assumption. To calculate the proportion of the population at risk of hunger we used the function estimated from the proportion of the population at risk of hunger and the mean per-capita calorie intake across countries from Food Balance Sheet data25 (See the chapter 3, SI). See the chapter 1, SI for greater detail description of the CGE model.
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RESULTS The World without Climate Change. The global production of all five crops is expected to increase at an annual growth rate of 0.99−1.14% through 2050 in all of the three SSPs in the NoCC (Figure S8, SI). In industrial countries, the annual growth rate is of 0.64% in SSP3 and 1.10% in SSP1; the rate is lower in SSP3 because population in the countries is assumed to decrease in SSP3 but increase in SSP1. In contrast, in transition and developing countries, the annual growth rate of production under SSP3 (1.71%) is higher than those of SSP1 440
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Figure 3. (a) Per-capita calorie intake and (b) Population at risk of hunger under the three SSPs for the cases without climate change in the industrial, transition, and developing countries.
Figure 4. Percent changes in global mean calorie intake in 2050 under the SSPs and RCPs with and without adaptations from the level without climate change. Boxes and dotted lines show the uncertainty range across the 8 GCMs. Boxes represent the first-third quartile range and the plain line indicates the median; dotted lines extend to the most extreme data point which is no more than 1.5 times the interquartile range from the box; others are treated as outliers.
and pessimistic scenario, respectively. The wide range of calorie intake among socioeconomic conditions cause a wide range of risk of hunger. In 2005, 0.83 billion people were reported to be at risk of hunger.26 The global population at risk of hunger under SSP1 and SSP2 is expected to decrease to 0.38 billion and 0.54 billion in 2050, respectively, whereas the population is expected to increase to 0.95 billion under SSP3. Regional distributions of calorie intake and risk of hunger are shown in Figure S9 and Figure S10 in SI. Effects of Adaptation. Figures 4 and 5 compare changes in the global mean per-capita calorie intake and the population at risk of hunger in 2050, respectively, in cases with and without adaptation measures across all SSPs and RCPs, along with the range of uncertainty for all of the GCMs. For all the SSPs, the median changes in the calorie intake are much higher in the case with adaptations than those in the case without them even though the ranges of uncertainty partly overlap. The median changes of the population at risk of hunger are much lower in the case with adaptation measures than those in the case without them. For RCP8.5, which incorporates highest level of climate change, the median values of the calorie intake with adaptation are higher by 1.3%, 1.4%, 1.5% (41, 43, 43 kcal/
(1.28%) and SSP2 (1.47%) as a result of higher population growth. Thus, production increased in proportion to population growth. Sugar crops will likely achieve rather high production in transition and developing countries in SSP3 because of multiple effects of high base-year production and high population increase. Socioeconomic conditions are large factors in food security. For the NoCC case, in 2050, the global mean calorie intake will increase from the 2005 level (2720 kcal/person/day) by 440 kcal/person/day (16%), 330 kcal/person/day (12%), and 114 kcal/person/day (4.2%) for optimistic, middle and pessimistic scenarios, respectively. Income growth basically drives calorie intake up, particularly in developing countries. Thus, the differences among three socioeconomic conditions were primarily a result of income growth assumptions throughout income elasticity. Figure 3 shows per-capita calorie intake and the population at risk of hunger under the three socioeconomic conditions for the NoCC case. As an example, in developing countries, which have the highest income elasticity of food consumption, calorie intake in 2050 will increase to 547 kcal/ person/day (22% from 2005 level), 422 kcal/person/day (17%), and 197 kcal/person/day (8.0%) for optimistic, middle 441
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Figure 5. Percent changes in global population at risk of hunger in 2050 under the SSPs and RCPs with and without adaptations from the level without climate change. Boxes and dotted lines show the uncertainty range across the eight GCMs. Boxes represent the first-third quartile range and the plain line indicates the median; dotted lines extend to the most extreme data point which is no more than 1.5 times the interquartile range from the box; others are treated as outliers.
Figure 6. Percent changes in regional mean per-capita calorie intake resulting from the impacts of climate change in the cases with and without adaptation in 2050 for SSP3-RCP8.5. Values represent the percent change from the level without climate change. Boxes and dotted lines show the uncertainty range across the 8 GCMs. Boxes represent the first-third quartile range and the plain line indicates the median; dotted lines extend to the most extreme data point which is no more than 1.5 times the interquartile range from the box; others are treated as outliers.
adaptation. The calorie intake in 2050 for SSP3-RCP8.5 will have a range of −0.6% (−1.0 to −0.2%) with adaptation whereas the range will be −2.1% (−2.7 to −1.0%) without adaptation. The population at risk will have a range of 3.6% (1.3−7.0%) with adaptation and 14% (6.4−20%) without adaptation. Percentage changes depend more strongly on RCPs rather than SSPs. Here we should note that calorie intake and population at risk of hunger at the NoCC cases vary greatly across SSPs (Figure 3). Therefore, absolute values of calorie intake and population at risk of hunger vary more across SSPs than across RCPs; that is, the trends of the three SSPs within any RCP vary greatly although the values across four RCPs for any given SSPs are similar. This might not be surprising because this is partly results from SSPs which have been
person/day) for SSP1 to SSP3, respectively, than those without adaptation in 2050. Those of population at risk of hunger with adaptation are lower by 9.5%, 10.4%, 10.6% (37, 56, and 101 million people) for SSP1 to SSP3, respectively, than those without adaptation. In contract, in the RCP2.6 scenario, where the climate change effect is expected to be relatively low, climate change will affect food consumption; global mean calorie intake with adaptation will be higher by 0.56%, 0.61%, 0.65% (18, 19, and 18 kcal/person/day) for SSP1 to SSP3 respectively than those without adaptation in 2050. Those of population at risk of hunger with adaptation are lower by 3.7%, 4.0%, 4.1% (14, 22, 39 million people) for SSP1 to SSP3, respectively, than those without adaptation. The ranges of uncertainty across the GCMs are much narrower in the cases with adaptation than those without 442
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Figure 7. Percent changes in regional populations at risk of hunger regulting from climate change in the case with and without adaptation in 2050 under SSP3-RCP8.5. Values represent the percent change from the level without climate change. Boxes and dotted lines show the uncertainty range across the 8 GCMs. Boxes represent the first-third quartile range and the plain line indicates the median; dotted lines extend to the most extreme data point which is no more than 1.5 times the interquartile range from the box; others are treated as outliers.
is wider in the case without adaptation than those with adaptation. The range is wide in regions such as India (IND), the rest of Asia (XSA), Turkey (TUR), and Former Soviet Union (CIS). Effects of adaptation vary by region because levels of the climate change impacts on crop yields and land scarcity and food diet vary greatly among the regions. Higher crop prices resulting from climate change impact and more severe land scarcity, will also lead to vary food consumption. In India, the rest of Asia, and the rest of Africa (XAF) where population at risk of hunger is large even in the NoCC case (SI Figure S10), the calorie intake with adaptation in 2050 are expected to be higher by 4.7%, 3.4%, and 0.6% and the populations will be lower by 31%, 16%, and 2.8%, respectively, than those without adaptation. Especially in India and the rest of Asia, effects of adaptation are relatively high because ratios of calories intake from cereals are high (57% in India and 62% in the rest of Asia in 2005); yield of wheat, which accounts for 16% and 24% of total calorie intake, will decrease 60% and 30%, respectively, under RCP8.5 without adaptation (SI Table S6); In addition, the rice yield in the rest of Asia, which accounts for 29% of total calorie intake in this area, is not expected to increase; Land scarcity in India is very severe and there is little additional land to produce cereals. Climate change impacts on the population at risk of hunger also vary by region because basic levels of calorie intake and the climate change impacts on crop yields vary among the regions. The risk of hunger is particularly sensitive to changes in calorie intake in India and the rest of Asia because the basic levels of calorie intake are low (2720 and 2380 kcal/person/day, respectively). The gradient of the function is high if calorie intake is low (Figure S5), so a decrease in crop yield resulting from climate change increased the risk of hunger.
developed to represent the extreme future visions. The median changes in calorie intake in RCP6.0 are higher than those in RCP4.5, most likely because radiative forcing and temperature in 2050 in RCP6.0 is lower than those in RCP4.5.19 There is not clear reason for that the range of uncertainty in RCP4.5 is wider than in the other RCPs. It can be happened in the case that climate conditions for a certain region where climate change impact is relatively large such as India and rest of Asia happen to have a wide range among the GCMs (see chapters 4 and 5 of SI). Since the ranges of uncertainties of climate conditions among GCMs are mostly overlapped among RCPs even at global scale (SI Figure S7), the range at regional scale may be more overlapped than global one. Sensitivity analysis on yield assumptions was conducted since yield growth assumptions could be very critical for any projections of the world food system and resulting food security. We assumed scenarios with high/middle/low yield growths for SSP2-RCP8.5 with/without adaptation. The high and low yield growths have a 0.5%/year range, which is the same as the MA assumptions,27 around the IMPACT assumptions described above. In that, the high, middle and low yield growth rates are 1.06%/year, 0.85%/year and 0.64%/ year, respectively. Figure S11 in SI compares change in global mean population at risk of hunger in 2050 across high, middle and low yield growths in cases with and without adaptations for the SSP2-RCP8.5, along with the range of uncertainty for all of the GCMs. For all the yield assumptions, the median changes in the population are much lower in the case with adaptations than those in the case without them even though the ranges of uncertainty partly overlap. We confirmed the effect of the adaptations for all the yield growths. Regional Effects of Adaptations. Figures 6 and 7 show change in regional mean calorie intake and population at risk of hunger as a resulting from applying adaptation measures from the NoCC level in the most pessimistic scenario (SSP3RCP8.5) in 2050. In all of the transition and developing countries, calorie intake and the population at risk of hunger are improved in the case with adaptation as compared to those without adaptation. The range of uncertainty among the GCMs
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DISCUSSION We analyzed the impacts of climate change and adaptation on calorie intake and population at risk of hunger. The measures significantly contributed to lowering both the risk of hunger 443
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explored the effect of autonomous adaptation to the change, as the first step. Further study needs to focus on extreme events (i.e., drought, flood, heat waves) and climate variability which are important particular in terms of food security. We considered changes in crop variety and planting dates as a process generally called “autonomous adaptation”.2 We did not take into account nonoptimal choice of crop calendars due to complex cropping patterns. In order to clarify the uncertainty caused by feasibility of autonomous adaptation in the future, we simply examined the scenarios with/without revising a planting date and crop variability. For the baseline period, we simply assumed full adjustment (optimal choice of crop cultivars and planting date for the climate condition in the baseline period), which might not be the case in many parts of the world, because we have insufficient quantitative information which can be used for this study. We could say that this assumption is not necessarily optimistic by comparing yields of the M-GAEZ and FAO data in 1990s at regional level (Chapter 2, SI). Due to this assumption, the negative impact caused by climate change on crop yield is expected to be overestimated, since the difference between the (over)estimated baseline yield and the projected future yield becomes larger. This can be also said that the effect of additional future adaptation tends to be underestimated under the assumption. In other words, we may be able to tell that we have clarified the effect of the modest level of adaptation measures, which are expected to be taken at the very least. We think that the above assumption and its potential consequence is in line with the intent of this paper, which urges that attention be directed toward effects of adaptation on food security. We examined scenarios without seasonal weather forecasts, which are necessary for finding a proper planting date. In addition, we did not take into account adaptation costs and may have underestimated the economic impact of adaptation measures. Incorporating adaptation costs as well as additional adaptations such as irrigation expansion remain as a further step of this study. Finally, since levels of climate change impacts vary by region as mentioned in the Results section, a downscaling and disaggregating of the regions could help clarify the spatial distribution of the impacts and more specific impacts in the affected regions. Study results are limited owing to these reasons, but we hope to address them in the future.
and uncertainty about the risk under the several socioeconomic, climate and yield conditions, even though our study dealt only with two autonomous adaptation measures. For implementation of these measures particular in developing countries, public and private investment in new crop varieties or weather and climate information systems would promote increased tolerance to water and heat stress or other relevant adverse conditions. Policies could help to address changing growing seasons and climate conditions for example, by providing farmers with opportunities to choose appropriate crop varieties and supplying them with monthly and seasonal forecasting, and early warning systems to change timing of operation. Other more adaptation measures such as technological development or irrigation could also reduce the risk of hunger in the future. We interpret the results as confirming that the effectiveness of adaptation measures is significant under the various combinations on socioeconomic, climate and yield conditions and robust regardless of the level of mitigation efforts to lower GHG emissions at least throughout 2050. Conditions will most likely change and particularly the ranges of climate change impacts will probably be different by 2100 because the RCPs project greater temperature changes during the latter period. Therefore, the benefits of climate mitigation on the agriculture could be even larger if the analysis was extended to include the entire century. Our study had some limitations that should be addressed in future research. The future ratio of bioenergy consumption to the total consumption was fixed. However, climate mitigation measures may increase the consumption of these types of crops. This assumption should be modified in future studies to correspond to the strength of mitigation measures. In addition, future changes in crop yield caused only by technological development are specified in the CGE model, although the yields might change depending on future prices of agricultural commodities, capital, and fertilizers. The irrigation area was fixed at the current level in the M-GAEZ and the CGE, whereas crop yields for the NoCC case provided by the IMPACT model include future expansion of irrigation area. We took the procedure to avoid duplicate consideration in calculating future crop yields for the CC cases. Although it is better to increase consistency of treatment of irrigation between the models, we think the current inconsistency is acceptable for the current research with the following reason. The effect of irrigation expansion on future change in crop yields is limited for the studied period (i.e., for rice, the most irrigated crop in the world, irrigated ratio to total harvested area is expected to increase from 70% to 80% from 2000 to 2050, the IMPACT data). The expansion is not expected to affect the risk of hunger strongly. Regarding with the treatment of the multicropping area, we want to note the treatment of multicropping does not greatly influence the results in terms of food security. Multicropping is an important factor particular for rice production in Southeast Asian countries such as Indonesia and Thailand where rice production is large and area of multicropping is expected to decrease under climate change. However income level of these countries is expected to increase up to the level of current industrial countries by 2050 and there will be little effect on risk of hunger. More discussion on irrigation and multicropping is needed about whether these values should be calculated endogenously within the model or whether other models of agricultural technologies are required to calculate the values. We focused only on gradual changes of climate attributes and
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ASSOCIATED CONTENT
S Supporting Information *
Additional information as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*Phone/Fax: +81-29-850-2510; e-mail: hasegawa.tomoko@ nies.go.jp. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This research was supported by the Environment Research and Technology Development Fund (A-1103) of the Ministry of the Environment, Japan, and the climate change research program of National Institute for Environmental Studies. 444
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dx.doi.org/10.1021/es4034149 | Environ. Sci. Technol. 2014, 48, 438−445
