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Keywords:

  • climate change;
  • grains sector;
  • productivity;
  • integrated assessment
  • C68;
  • D58;
  • F18;
  • Q17;
  • Q54

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Analysing the Effects of Variation in the Grains Sector Response
  5. 3. Discussion of Simulation Results
  6. 4. Concluding Remarks
  7. References

We examine the effects of a one degree Celsius warming globally by 2030 on the distribution of grains sector productivity responses in several major economies. An integrated assessment modelling framework, the Global Integrated Assessment Model is used in our analysis. Our results highlight that at the tails of the distribution of climate change impacts simulated in this study, there is some variation in self-sufficiency and export availability of grains products reported for specific economies. These variations could widen further if distortionary trade policies are added to the current analysis.


1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Analysing the Effects of Variation in the Grains Sector Response
  5. 3. Discussion of Simulation Results
  6. 4. Concluding Remarks
  7. References

There is general agreement within the international scientific community that the global climate has been changing and will continue to change as a result of human activity. According to the present international scientific literature, human-induced increases in the atmospheric concentration of greenhouse gases will continue to influence changes in climate across many parts of the world (IPCC, 2007, 2012). Given the projected changes in key global and regional climate variables, one of the major sectors vulnerable to climate variability and change is agriculture. Changes in water availability, water quality, temperatures and pests and diseases are all likely to have an impact on agricultural productivity. In general, agricultural productivity is considerably influenced by both temperature and precipitation. According to Lobell and Burke (2008), uncertainties related to temperature represented a greater contribution to climate change impact uncertainty than those related to precipitation for most crops and regions, and in particular, the sensitivity of crop yields to temperature is a critical source of uncertainty.

It is important to recognise that potential impacts of climate change are unlikely to be uniformly distributed around the world. Increasing temperatures may extend the growing season for crops in some areas but increase the demand for water by crops and decrease yields in other areas (Ecofys BV, 2006). Increase in CO2 concentration could have positive carbon fertilisation effects by increasing the rate of photosynthesis in some plants (Steffen and Canadell, 2005). However, higher concentration of CO2 could also reduce crop quality, by lowering the content of protein and trace elements (European Environment Agency, 2004).

Despite an increasing amount of research and analysis on the impacts of climate variability and change on the agriculture sector, there remains considerable uncertainty as to the nature and timing of the climate impacts on agriculture (see Hertel et al., 2010). This implies that potentially, there is considerable variation in agricultural sector response to climate variability and change across industries and across regions.

According to Garnaut (2011), the notion of uncertainty relating to climate change impacts refers to the fact that while one cannot be sure exactly what form the future will take, one can use the available information to assign probabilities to each possible future. One can then take a best guess of what form the future will take, although things could well turn out differently. Our ability to make this inference relies on us having a sound understanding of both the range of possible future outcomes and the likelihood of each of these outcomes. The combination of these forms the probability distribution of future outcomes (Garnaut, 2011). Garnaut (2011) points out that the question of uncertainty in relation to climate change impacts relates to the dispersion of the probability distribution around the most likely or average outcome. The principles of prudent risk management dictate that the case for action is strengthened, rather than diminished, by the fact that outcomes could turn out far worse (or better) than expected (Garnaut, 2011).

According to Valenzuela and Anderson (2011), given the great uncertainty associated with the magnitude – and in some cases the sign of potential agricultural productivity responses to climate change – analytical results ideally should include confidence bounds around them or at least high and low alternatives to the median cases presented in many studies. This article attempts to address this issue to some extent by focussing on the potential variation in the grains sector response to climate change, with a particular emphasis on major grain crops of wheat and rice. Here, the potential medium- to long-term production and trade effects of variation in grains sector responses to climate change are investigated within an integrated analytical framework encompassing climate–productivity–economic interactions.

2. Analysing the Effects of Variation in the Grains Sector Response

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Analysing the Effects of Variation in the Grains Sector Response
  5. 3. Discussion of Simulation Results
  6. 4. Concluding Remarks
  7. References

The economic and trade implications of a variation in the grains sector response to climate change are analysed here by undertaking the following scenarios:

  • 1
     Reference case (baseline) scenario: world without climate change impacts
  • 2
     Climate change scenario: impacts of a one degree Celsius warming globally by 2030 and beyond on crop productivity

These scenarios are analysed using CSIRO’s (Commonwealth Scientific and Industrial Research Organisation) current version of the Global Integrated Assessment Model (GIAM-XP) (see Figure 1). GIAM is an integrated assessment model originally developed jointly between CSIRO and Australian Bureau of Agricultural and Resources Economics (see Garnaut, 2008; Gunasekera et al., 2008; Harman et al., 2008). It is a coupled model of a global economic module and a climate module. The economic module of GIAM is a long-run version of Global Trade and Environment Model (GTEM) developed by Australian Bureau of Agricultural and Resources Economics, which is a dynamic, multiregional and multisectoral general equilibrium model of the global economy (Pant, 2007; Clarke et al., 2009; Gurney et al., 2009). The economic module allows projections for the major human-induced factors influencing climatic conditions (such as greenhouse gas emissions) to be developed after accounting for regional and global production and consumption decisions and international trade. The economic module of GIAM used in this paper currently allows for analysis across 13 regions, 21 industries, four primary factors and six greenhouse gas emissions (see Table 1).

image

Figure 1. GIAM Analytical Framework

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Table 1. Regional, Industry, Factor and Greenhouse Emissions Coverage in GIAM
RegionsIndustriesPrimary factorsGreenhouse gases
United StatesCoalCapitalCarbon dioxide
EU 25OilLandMethane
ChinaGasLabourNitrous oxide
Former Soviet Union (FSU)Petroleum and coal productsNatural resourcesHydroflurocarbons
 Electricity Perfluorocarbons
JapanIron and steel Sulphur hexafluoride
IndiaNon-ferrous metals  
CanadaChemicals, rubber, plastics  
AustraliaOther mining  
IndonesiaNon-metallic minerals  
South AfricaManufacturing  
Other AsiaWater transport  
OPECAir transport  
Rest of World (ROW)Other transport  
 Wheat  
 Rice  
 Coarse grains  
 Other crops  
 Fishing, forestry  
 Processed food  
 Services  

The climate module of GIAM is a non-linear model for global CO2, other greenhouse gases and global temperature, commonly known as a Simple Carbon Climate Model (SCCM) (see Raupach et al., 2011). This is a globally averaged or ‘box’ model of the carbon–climate system, using well-established formulations. The model includes non-linearities in the response of terrestrial carbon assimilation to CO2, the buffering of CO2 in the ocean mixed layer, temperature responses of land–air and ocean–air CO2 exchanges, and the response of radiative forcing to gas concentrations (Raupach et al., 2011).

In the GIAM analytical framework, the GTEM module projects the greenhouse gas emissions based on economic activities. These emissions are then fed into the SCCM module. SCCM module converts the emissions into CO2 concentration levels and then into changes in temperature. In the current version of the GIAM model, changes in temperature are fed into a ‘climate–economy response function’. This response function analyses the interactions between changes in temperature and in a particular crop production activity, (e.g. grains sector responses to changes in temperature) based on a simple relationship developed using the individual crop sector productivity shocks provided under different scenarios (i.e. low, medium and high climate change effects).

The climate–economy response function estimates and translates regional/country changes in temperature (provided by the SCCM module) through time to changes in crop productivity at the agricultural industry level (e.g. wheat, rice, coarse grains in the GTEM module) in each region/country of GIAM. In essence, the grains sector climate change responses at region/country level are assumed to be a linear function of regional/country changes in average temperature (relative to 2000 level). Currently, there is a high degree of uncertainty and limited quantitative estimates about the exact nature and magnitude of medium- to long-term climate change impacts across regions and sectors including the agricultural sectors, and hence about the precise shape of functional forms and parameterisation of climate–economy response functions. Given these reasons, we have resorted to using a linear approximation of the climate–economy response function in the current study. As more empirical information becomes available about the response of crop productivity to changes in climatic conditions over time, both the functional form and parameterisation of climate–economy response functions can be revised.

Results of our analysis in this paper focus on grain self-sufficiency and export availability in key economies.

2.1 Assumptions

Potential changes in key climate variables may change crop productivity in different regions in a non-uniform manner. As indicated earlier, there is some uncertainty about the nature and extent of these potential productivity changes. In this analysis, estimates of the potential impacts of climate change on productivity of different crops across different regions from a recent study by Hertel et al. (2010) are used.

There are four agricultural industries (wheat, rice, coarse grains and other crops) in the version of the economic module used in GIAM in the present study. This is in comparison to seven agricultural industries (wheat, rice, coarse grains, oil seeds, sugar, cotton and other crops) in the Hertel et al. (2010) study. There are two key reasons for using a more aggregated agricultural industry grouping in our study. First, the focus of our study is on the grains sector (e.g. wheat and rice) given its continuing importance in many countries, particularly from the perspectives of food self-sufficiency and food security. Second, from a computational perspective, a more aggregated industry grouping in our study has enabled us to undertake a large number of modelling runs/simulations over a long time period (2005–2050) in the current analysis.

Using a number of recently published relevant analyses of the potential responses of crop yields to climate change under different scenarios and under C fertilisation, Hertel et al. (2010) have prescribed a range of productivity estimates (low, medium and high) attributable to climate change over the period 2000–2030 for several key agricultural commodity groups. The prescribed range of productivity estimates provided by Hertel et al. (2010) cover seven agricultural industries (wheat, rice, coarse grains, oil seeds, sugar, cotton and other crops) across 34 regions/countries. However, given our focus on key grains commodities in a specified number of countries in this study, we confined our analysis to 13 regions of the current version of GIAM and three types of major grains (i.e. wheat, rice and coarse grains) which are of particular importance to specific countries. The estimates of changes in productivity used with respect to the 13 GIAM regions and the specific grains industries (for each region/country) in the modelling in this article (based on Hertel et al., 2010) are listed in Table 2. For example, for Australia and China, our productivity shock analysis due to climate change was only confined to wheat and rice, respectively (see Table 2).

Table 2. Assumed Productivity Changes (%) Attributable to Climate Change over 2000–2030 (Calibrated Against One Degree Celsius Increase)
Region (crop)LowMediumHigh
United States (wheat)−10214
EU 25 (wheat)−5719
China (rice)−12012
FSU (wheat)−5719
Japan (rice)2916
India (rice)−15−54
Canada (wheat)−5719
Australia (wheat)−5719
Indonesia (rice)0714
South Africa (coarse grains)−42−25−8
Other Asia (rice)−10−34
OPEC000
ROW (coarse grains)−22−102

According to Hertel et al. (2010), the medium-level productivity situation reflects a ‘central case’ estimate. The low productivity estimate situation reflects a world with a rapid temperature change, high sensitivity of crops to warming and a CO2 fertilisation effect at the lower end of the published estimates. The high productivity estimate situation depicts a world with relatively slow warming, low sensitivity of crops to climate change and high CO2 fertilisation. Hertel et al. (2010) highlight that these productivity estimates are intended to capture a range of plausible outcomes and can be thought of as the 5th and 95th percentile values in a distribution of potential yield impacts.

2.2 Simulation Analysis

Based on the estimates of climate change impacts on agriculture provided in Hertel et al. (2010), the effects of potential changes in productivity in a selected group of grain crops in different regions are simulated up to the year 2050 using the GIAM model. In particular, the ‘climate–economy response function’ in GIAM is employed to analyse the crops sector responses to changes in temperature. The ‘climate–economy response function’ is based on a simple relationship developed using the individual crops sector productivity shocks provided under different scenarios (low, medium and high climate change effects) by Hertel et al. (2010) calibrated to one degree Celsius change.

CSIRO’s current version of the GIAM framework allows ensemble projections where the ranges of parameter values for input factors that are defined with some uncertainty can be input to GIAM and probability distribution functions of outputs estimated, rather than the model output being just single deterministic predictions. In particular, the GIAM modelling simulations carried out in this study involved undertaking over 100 model runs/simulations randomly selected so that they cut across different regions, different crops and the different levels of shocks to productivity (high, medium and low – see Table 2). These productivity shocks representing the impacts of climate change were exogenously imposed on our ‘climate change scenario’. It is important to recognise that our ‘baseline scenario’ does not take into account any climate change impacts on productivity.

The projected agricultural productivity estimates due to climate change in Hertel et al. (2010) covered the 2000–2030 period. The projection period in GIAM simulations was 2005–2050. The main reason for extending our analysis to 2050 is to provide an indication of the likely long-term impacts of climate change in the absence of any substantial measures to undertake climate change mitigation in a comprehensive manner globally. It is assumed in the present analysis that the Hertel et al. (2010) estimates of the potential impacts of climate change on productivity of different crops across different regions will continue to 2050, in the absence of any significant climate change mitigation effort globally.

The GIAM model simulations carried out here are then used to estimate the resulting changes in production and trade in relevant grain crops in a selected group of countries. These estimates provided the basis for calculating self-sufficiency and export availability for several key food producing and trading regions.

3. Discussion of Simulation Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Analysing the Effects of Variation in the Grains Sector Response
  5. 3. Discussion of Simulation Results
  6. 4. Concluding Remarks
  7. References

Climate change impacts on grains sector are likely to be influenced by the extent to which changes in key climate variables influence changes in grain productivity, grain yields and grain production costs. These in turn lead to changes in competitiveness and hence changes in overall output levels. On the demand side, the likely magnitude of the changes in demand for grains can vary depending on the degree of demand responsiveness to changes in incomes and prices in different regions for the various grain products.

As indicated earlier, the focus of the analysis in this article is on self-sufficiency (domestic production/domestic consumption) and export availability (exports/domestic production) with respect to wheat and rice, the two key food commodities that have received the most attention because of price surges during 2007–2008 (Martin and Anderson, 2012) in several major food-exporting advanced economies (United States, Canada and Australia) and food-importing developing countries (China and Indonesia). The simulation results are discussed below.

Based on the simulation results using the GIAM, the estimated wheat export availability ratios for the United States, Canada and Australia are illustrated in Figure 2 (the dotted line in the figures represent the reference case or the baseline, and the shaded areas depict the quartiles of impacts of policy shocks, i.e. assumed productivity shocks). Given that the United States, Canada and Australia are major producers and exporters of wheat, any changes in wheat sector productivity is likely to have implications for wheat export availability ratio in these countries.

image

Figure 2. Wheat Export Availability

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The modelling results shown in Figure 2 demonstrate uncertainty in changes in wheat export availability due to variation in wheat sector productivity driven by uncertainty in climate change impacts. This is reflected in deviations between the reference case and the policy scenarios. For example, in 2050, the estimated percentage change in US wheat export availability relative to the reference case level can vary from a 2 per cent increase to a 5 per cent decline due to uncertainty in climate change impacts.

Estimated changes in Canadian wheat export availability ratio (Figure 2) are broadly similar to the US results. However, the variation in wheat productivity due to climate change is relatively more favourable to Canada than for the United States, as shown by Hertel et al. (2010) (see Table 2). This could be due to its higher latitude, enhanced photosynthesis due to warming or enhanced productivity of marginal land. In 2050, the estimated percentage change in Canadian wheat export availability relative to the reference case level can vary only slightly due to uncertainty in climate change impacts, from a 0.3 per cent increase to a 0.6 per cent decline.

For Australia, in 2050, the estimated percentage change in Australian wheat export availability relative to the reference case level can vary from a 4 per cent increase to a 2 per cent decline due to uncertainty in climate change impacts. It is important to note that US, Canadian and Australian impact estimates have longer tails, implying a greater degree of variation in agricultural sector responses to climate change in these countries (Figure 2).

The comparison of the baseline scenario with the climate change scenario (i.e. the policy response scenarios in Figure 2) for the United States, Canada and Australia highlights several key points that are noteworthy. (These key points need to be interpreted within the context of the low, medium and high productivity responses (used in our GIAM modelling) and their associated positive and negative nature as illustrated in Table 2.)

First, the wheat export availability ratios for the United States, Canada and Australia in the policy response scenarios vary above and below the baseline scenario as shown in Figure 2. This reflects the (positive and negative) variation in wheat productivity responses to climate change as shown in Table 2, based on Hertel et al. (2010).

Second, the wheat export availability ratios under the policy response scenarios for the United States and Canada tend to fall below the baseline scenario in the majority of the quartiles and the median case as illustrated in Figure 2. This reflects the relatively greater decline in the estimated wheat export availability ratios due to climate change relative to the baseline case as discussed earlier. For example, in 2050, the change in US (and Canadian) wheat export availability relative to the baseline level is estimated to vary from a 2 per cent increase to a 5 per cent decline (a 0.3 per cent increase to a 0.6 per cent decline) due to uncertainty in climate change impacts.

Third, the wheat export availability ratios under the policy response scenarios for Australia tend to fall above the baseline scenario in the majority of the quartiles and the median case as illustrated in Figure 2. This reflects the relatively greater increase in the estimated wheat export availability ratio due to climate change relative to the baseline case. For example, in 2050, the change in Australian wheat export availability relative to the baseline level is estimated to vary from a 4 per cent increase to a 2 per cent decline due to uncertainty in climate change impacts as discussed earlier.

Fourth, the magnitude and the sign (positive or negative) of variation in grains sector productivity responses to climate change across regions provide some indication of the extent of confidences that can be attached to potential climate change impacts on grains sector productivity relative to the baseline scenario.

Fifth, it is important to recognise that the climate change impacts illustrated in Figures 2 and 3 reflect interactions between a range of factors including the differences between the sensitivity of crop yields to a one degree Celsius change in temperature and the different levels of temperature increase across countries over time due to climate change. These interactions manifest differently in different regions. For example, when both Canada and Australia are assumed to experience the same 7 per cent productivity increase attributable to climate change (see Table 2), the median policy response is below the baseline in Canada but above the baseline in Australia, which may seem counter-intuitive. However, it is important to recognise that the 7 per cent productivity increase is an input parameter used in our modelling exercise, describing a range of productivity estimates (low, medium and high) based on Hertel et al. (2010). On the other hand, the policy responses shown in Figure 2 are outputs from our modelling exercise. It is useful to note that there is no direct mapping between the input parameters and policy responses as economic interactions such as trade and global shifts in productivity modulate the overall policy responses.

image

Figure 3. Rice Self-Sufficiency

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Sixth, the combination of differences between the sensitivity of crop yields to a change in temperature and the different levels of temperature increase due to regional climate change means that crop output responses vary with latitude and region. Consequently, relative to a baseline without climate change impacts, regions such as Canada and the United States will have greater growing capacity and will increase their exports compared to regions such as Australia. It is important to note that in our modelling, we have assumed in the baseline that the US economy represents the benchmark for productivity enhancements and other regions converge towards US productivity rates over time.

China and Indonesia are rapidly growing economies with large populations and increasing levels of urbanisation. The growing demand for food in these countries is influenced by several factors including population and income growth and changing food consumption patterns. Climate change impacts on agriculture could have important implications for food security in these countries. It is important to recognise that for rapidly growing economies such as China and Indonesia, food self-sufficiency is considered quite important.

The estimated rice self-sufficiency ratios for China and Indonesia are illustrated in Figure 3. In China, rice self-sufficiency is estimated to decline over time in baseline and in counterfactual scenarios. Estimated changes in Indonesian rice self-sufficiency (Figure 3) are broadly similar to the results reported for China in that the range of impacts (variation) in the self-sufficiency ratio is low. It is important to note that the Indonesian rice productivity shocks used in scenario analysis were between 0 and 14 per cent (see Table 2).

A key insight emerging from the estimated changes in rice self-sufficiency ratios in China and Indonesia shown in Figure 3 is that the self-sufficiency ratio is almost completely unaffected by variability due to the assumed productivity shocks coming from climate change, particularly in China.

4. Concluding Remarks

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Analysing the Effects of Variation in the Grains Sector Response
  5. 3. Discussion of Simulation Results
  6. 4. Concluding Remarks
  7. References

The scenarios examined in this article indicate how future changes in climate may affect different regions through potential impacts on grains sector productivity. There are three key messages emerging from the scenario analysis.

First, at the tails of the distribution of climate change impacts simulated in this study, there is some variation in self-sufficiency and export availability of grains commodities reported for specific regions. Such wider confidence bands are likely to be further widened if other uncertainties such as the effects of distortionary economic and trade policies are also considered. In this context, it is noteworthy to highlight the potential adverse effects of market insulating agricultural trade policies in some regions. For example, Martin and Anderson (2012) estimate that in 2007–2008 alone, insulating policies affecting the market price for rice explain 46 per cent of the rise in the world price of rice, while 28 per cent of the observed change in world wheat prices during 2005–2008 can be explained by the changes in trade policy measures that countries used to insulate from the initial price surges.

Second, based on the trade-related results (e.g. export availability) reported here, it could be argued that unrestricted global agricultural trade could be a useful mediator between regions influenced differently by climate change. Agricultural trade flows are influenced by the interaction between comparative advantage in agriculture (as determined by relative factor and resource endowments and climate/weather conditions) and a wide ranging set of national, regional and international trade policy regimes. Unrestricted international trade allows comparative advantage to be more fully exploited. Restrictions on trade risk worsening the potential impacts of climate change by hindering the ability of producers and consumers to adjust (Nelson et al., 2010).

Third, reducing the uncertainty in climate change impacts on agriculture should be a high priority for research.

The GIAM analytical framework employed in this study has several strengths that could be further utilised in future climate change-related research. These strengths include the ability of the analytical framework: to take account of the interactions between the economy and climate; to incorporate a range of plausible agricultural and other sectoral productivity outcomes due to climate change; to accommodate the changes in domestic and international production, consumption, trade and prices within an economy-wide framework; and to undertake a range of ensemble based scenario analyses to assess the impacts of climate change.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Analysing the Effects of Variation in the Grains Sector Response
  5. 3. Discussion of Simulation Results
  6. 4. Concluding Remarks
  7. References
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