Climate change, agriculture, and adaptation in the ... Change, Agriculture, and...4.8 Global price of rice, percentage change between 2010 and 2050, all scenarios 21 4.9 Change in

IFPRI Discussion Paper 01586

December 2016

Climate Change, Agriculture, and Adaptation in the Republic of Korea to 2050

An Integrated Assessment

Nicola Cenacchi Youngah Lim

Timothy Sulser Shahnila Islam

Daniel Mason-D’Croz Richard Robertson

Chang-Gil Kim Keith Wiebe

Environment and Production Technology Division

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

The International Food Policy Research Institute (IFPRI), established in 1975, provides evidence-based policy solutions to sustainably end hunger and malnutrition and reduce poverty. The institute conducts research, communicates results, optimizes partnerships, and builds capacity to ensure sustainable food production, promote healthy food systems, improve markets and trade, transform agriculture, build resilience, and strengthen institutions and governance. Gender is considered in all of the institute’s work. IFPRI collaborates with partners around the world, including development implementers, public institutions, the private sector, and farmers’ organizations, to ensure that local, national, regional, and global food policies are based on evidence. IFPRI is a member of the CGIAR Consortium.

AUTHORS Nicola Cenacchi ([email protected]) is a senior research analyst in the Environment and Production Technology Division of the International Food Policy Research Institute (IFPRI), Washington, DC.

Youngah Lim ([email protected]) is a research fellow in the Department of Agriculture, Food and Forestry Policy Research of the Korea Rural Economic Institute, Naju, Republic of Korea.

Timothy B. Sulser ([email protected]) is a scientist in the Environment and Production Technology Division of IFPRI, Washington, DC.

Shahnila Islam ([email protected]) is a research analyst in the Environment and Production Technology Division of IFPRI, Washington, DC.

Daniel Mason-D’Croz ([email protected]) is a scientist in the Environment and Production Technology Division of IFPRI, Washington, DC.

Richard Robertson ([email protected]) is a research fellow in the Environment and Production Technology Division of IFPRI, Washington, DC.

Chang-Gil Kim ([email protected]) is president of the Korea Rural Economic Institute, Naju, Republic of Korea.

Keith Wiebe ([email protected]) is a senior research fellow in the Environment and Production Technology Division of IFPRI, Washington, DC.

Notices 1. IFPRI Discussion Papers contain preliminary material and research results and are circulated in order to stimulate discussion and critical comment. They have not been subject to a formal external review via IFPRI’s Publications Review Committee. Any opinions stated herein are those of the author(s) and are not necessarily representative of or endorsed by the International Food Policy Research Institute. 2. The boundaries and names shown and the designations used on the map(s) herein do not imply official endorsement or acceptance by the International Food Policy Research Institute (IFPRI) or its partners and contributors.

3. This publication is available under the Creative Commons Attribution 4.0 International License (CC BY 4.0), https://creativecommons.org/licenses/by/4.0/.

Copyright 2016 International Food Policy Research Institute. All rights reserved. Sections of this material may be reproduced for personal and not-for-profit use without the express written permission of but with acknowledgment to IFPRI. To reproduce the material contained herein for profit or commercial use requires express written permission. To obtain permission, contact [email protected].

https://creativecommons.org/licenses/by/4.0/

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Contents

Abstract vii

Acknowledgments viii

Acronyms ix

1. Introduction: Food Security Challenges in the Republic of Korea 1

2. Research on Climate Change in the Republic of Korea and Rationale for This Study 3

3. Approach: The IMPACT Modeling Framework 4

4. IMPACT Model Results: Climate Change and Agricultural Adaptation in the Republic of Korea 16

5. Conclusions 44

Appendix A: Brief Summary of Climate Parameters 46

Appendix B: Modularity in Impact: Crop Models, Water Models, and Food Security Modules 47

Appendix C: Notes on the Link between DSSAT and IMPACT 58

Appendix D: Notes on Intrinsic Productivity Growth Rates and Yield Calculations 59

Appendix E: Price Effects in the IMPACT Model 60

Appendix F: Equations in the IMPACT Global Multimarket Model 61

Appendix G: Climate Change Effects on Rice Yields in East Asia and the Republic of Korea 67

Appendix H: Trends of Rice Production, Yields, Area and Demand in the Republic of Korea 70

References 73

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Tables

3.1 Rice yields: Percentage change in 2050 from reference case after technology adoption 13 3.2 Technology scenarios and their components: Socioeconomic and climate inputs, and adoption

rates 14 4.1 Net rice exports from the Republic of Korea, percentage change compared with base of no climate

change, 2050 23 4.2 Agricultural statistics on food balance for Republic of Korea, 2011, top four imports 24 4.3 Major exporters to the Republic of Korea, by product 24 4.4 Percentage change in world price of rice under technology adoption scenarios, 2050, compared

with reference of no technology adoption 35 4.5 Rice production change under climate change 42 A.1 Difference in precipitation and temperature between reference scenario (year 2000) and climate

change scenarios (around year 2050) 46

Figures

1.1 Food supply (kilocalories per capita per day), Republic of Korea, 1970 and 2011 1 3.1 Changes in maximum temperature in 2050 compared with 2000 (°C), three earth system models,

representative concentration pathway (RCP) 8.5 5 3.2 Changes in annual precipitation in 2050 compared with 2000 (millimeters), three earth system

models, representative concentration pathway (RCP) 8.5 6 3.3 IMPACT system of models: Climate, crops, and water 8 3.4 Geography of the IMPACT model 9 3.5 Map of food production units in East Asia 9 3.6 Example of logistic adoption curve beginning in 2010 and reaching a ceiling set at 80 percent in

2040 15 4.1 Scenario trends in harvested area for all crops in the Republic of Korea, between 2010 and 2050 16 4.2 Scenario trends in net imports for all crops in the Republic of Korea, between 2010 and 2050 16 4.3 Scenario trends in production for all crops in the Republic of Korea between 2010 and 2050 17 4.4 Rice yield trends in the Republic of Korea projected for 40 years, representative concentration

pathway 8.5 and HadGEM2 general circulation model 18 4.5 Exogenous rice yield growth in the Republic of Korea to 2050, incorporating changes in

productivity, climate, and water, all scenarios 19 4.6 Endogenous rice yield growth in the Republic of Korea to 2050, incorporating changes in

productivity, climate, and water, as well as market effects, all scenarios 19 4.7 Area, demand, production, and yield for rice in the Republic of Korea, percentage changes

between 2010 and 2050, all scenarios 20 4.8 Global price of rice, percentage change between 2010 and 2050, all scenarios 21 4.9 Change in harvested area, demand, production, and yield between 2010 and 2050 across no-

climate-change reference and three climate change scenarios, key crops for the Republic of Korea 22

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4.10 Net trade values for baseline suite of scenarios (no climate change plus three climate change scenarios) for selected crops, Republic of Korea, 2050 23

4.11 Percentage change in production of maize, wheat, soybeans, and vegetables between 2010 and 2050, no-climate-change reference and climate change scenarios 25

4.12 Trends of net maize exports from Brazil, all scenarios 26 4.13 Percentage change in net maize exports in 2050 for climate change scenarios, compared with no

climate change 26 4.14 Trends of net wheat exports from Australia, all scenarios 27 4.15 Percentage change in net wheat exports by 2050, compared with no climate change 27 4.16 Percentage change in net soybean exports in 2050, compared with no climate change 28 4.17 Percentage change in net vegetable exports in 2050, compared with no climate change 28 4.18 Net imports to the Republic of Korea by crop group in 2050, percentage change compared with

no climate change 28 4.19 Composition of net imports to the Republic of Korea in 2050 by crop group, percentage of total

food imports 29 4.20 Kilocalories per capita in the Republic of Korea, trend to 2050 29 4.21 Share of population at risk of hunger, Republic of Korea, to 2050 30 4.22 Share of population at risk of hunger, East Asia and Pacific region, to 2050 30 4.23 Changing kilocalorie availability from 2010 to 2050 across the baseline suite of scenarios for key

commodities 31 4.24 Shift in diet composition by kilocalories per capita for the Republic of Korea between 2010 and

2050 by crop group, percentage of total kilocalories, all scenarios 31 4.25 Percentage change in rice area, production, and yield due to technology adoption, 2050, compared

with a reference scenario without technology adoption (SSP2-HadGEM-RCP8.5 climate change scenario) 33

4.26 Percentage change in rice harvested area, production, and yield due to technology adoption in China, Japan, Democratic People’s Republic of Korea, and the Republic of Korea, 2050, compared with reference of no technology adoption, selected world regions (SSP2-Hadgem- RCP8.5 climate change scenario) 34

4.27 Net trade for rice in the Republic of Korea between 2010 and 2050 under technology adoption scenarios and a reference scenario without technology adoption (SSP2-HadGEM-RCP8.5 climate change scenario) 35

4.28 Net trade for rice in the East Asia Pacific region (SSP2-HadGEM-RCP8.5 climate change scenario) 36

4.29 Percentage change in harvested area, production, and yield between technology scenarios and NoCC reference, 2050, Republic of Korea 37

4.30 Technology adoption effects on net trade, 2050, major crop groups, Republic of Korea (SSP2-HadGEM-RCP8.5 climate change scenario) 38

4.31 Trend in per capita kilocalories from rice, Republic of Korea, across technology adoption scenarios, reference climate change scenario and no-climate-change scenario 39

4.32 Percentage change in kilocalories per capita between rice crop technology scenarios and reference scenario without technology adoption, 2050 39

4.33 Trends in per capita kilocalories, Republic of Korea and the East Asia and Pacific region, across technology adoption scenarios, reference climate change scenario and no-climate-change scenario, 2010–2050 40

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4.34 Percentage change in population at risk of hunger due to technology adoption, compared with reference of no technology adoption, 2050 (SSP2-HadGEM-RCP8.5 climate change scenario) 41

5.1 Detailed IMPACT multimarket model schematic 47 5.2 Modularity and software cost 49 5.5 IMPACT global hydrology model schematic illustrating vertical water balance of the land and

open water fraction of a grid cell 54 5.6 IMPACT water basin simulation model 56 5.7 Linking IMPACT to water models: Dynamic two-way communication year by year 57 G.1 Changes in yields across East Asia, 2050, GFDL RCP8.5, irrigated rice 67 G.2 Changes in yields across East Asia, 2050, GFDL RCP8.5, rainfed rice 67 G.3 Changes in yields across East Asia, 2050, HadGEM RCP8.5, irrigated rice 68 G.4 Changes in yields across East Asia, 2050, HadGEM RCP8.5, rainfed rice 68 G.5 Changes in yields across East Asia, 2050, IPSL RCP8.5, irrigated rice 69 G.6 Changes in yields across East Asia, 2050, IPSL RCP8.5, rainfed rice 69 H.1 Trends of rice yields in the Republic of Korea to 2050, three climate change scenarios and no-

climate-change scenario 70 H.2 Trends of rice harvested area in the Republic of Korea to 2050, three climate change scenarios and

no-climate-change scenario 71 H.3 Trends of rice production in the Republic of Korea to 2050, three climate change scenarios and no-

climate-change scenario 71 H.4 Trends of rice demand in the Republic of Korea to 2050, three climate change scenarios and no-

climate-change scenario 72

Boxes

3.1 Examples of IMPACT analysis 7 5.1 Benefits of modular design 48

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ABSTRACT

As the effects of climate change set in, and population and income growth exert increasing pressure on natural resources, food security is becoming a pressing challenge for countries worldwide. Awareness of these threats is critical to transforming concern into long-term planning, and modeling tools like the one used in the present study are beneficial for strategic support of decision making in the agricultural policy arena.

The focus of this investigation is the Republic of Korea, where economic growth has resulted in large shifts in diet in recent decades, in parallel with a decline in both arable land and agricultural production, and a tripling of agricultural imports, compared to the early 2000s. Although these are recognized as traits of a rapidly growing economy, officials and experts in the country recognize that the trends expose the Republic of Korea to climate change shocks and fluctuations in the global food market.

This study uses the IMPACT (International Model for Policy Analysis of Agricultural Commodities and Trade) economic model to investigate possible future trends of both domestic food production and dependence on food imports, as well as the effects from adoption of agricultural practices consistent with a climate change adaptation strategy. The goal is to help assess the prospects for sustaining improvements in food security and possibly inform the national debate on agricultural policy.

Results show that historical trends of harvested area and imports may continue into the future under climate change. Although crop models suggest negative long-term impacts of climate change on rice yield in the Republic of Korea, the economic model simulations show that intrinsic productivity growth and market effects have the potential to limit the magnitude of losses; rice production and yield are projected to keep growing between 2010 and 2050, with a larger boost when adoption of improved technologies is taken into consideration. At the same time, food production and net exports from the country’s major trading partners are also projected to increase, although diminished by climate change effects. In sum, these results show that kilocalorie availability will keep growing in the Republic of Korea, and although climate change may have some impact by reducing the overall availability, the effect does not appear strong enough to have significant consequences on projected trends of increasing food security.

Keywords: Republic of Korea, agriculture, international trade, food security, climate change, multimarket model, crop simulation models, IMPACT model, improved technologies, diet, kilocalories

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ACKNOWLEDGMENTS

Funding for this study was provided by the Korea Rural Economics Institute. Supplementary support was also provided by the CGIAR Research Program on Policies, Institutions, and Markets (PIM), the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS), and the Bill & Melinda Gates Foundation.

The CGIAR Research Program on Policies, Institutions, and Markets (PIM) is led by the International Food Policy Research Institute (IFPRI) and funded by CGIAR Fund Donors.

The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) is funded by the CGIAR Fund Council, Australia (ACIAR), Irish Aid, European Union, International Fund for Agricultural Development (IFAD), Netherlands, New Zealand, Switzerland, UK, USAID and Thailand.

This paper has not gone through IFPRI’s standard peer-review procedure. The opinions expressed here belong to the authors, and do not necessarily reflect those of PIM, IFPRI, CCAFS, and the CGIAR.

http://www.cgiar.org/who-we-are/cgiar-fund/fund-donors-2/

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ACRONYMS

DSSAT Decision Support System for Agrotechnology Transfer DT drought-tolerant rice varieties CSIRO Commonwealth Scientific and Industrial Research Organisation EAP East Asia and Pacific ESM earth system model FAO Food and Agriculture Organization of the United Nations FPU food production unit GCM general circulation model GDP gross domestic product GFDL Geophysical Fluid Dynamic Laboratory HadGEM Hadley Centre’s Global Environment Model HT heat-tolerant rice varieties IFPRI International Food Policy Research Institute IMPACT International Model for Policy Analysis of Agricultural Commodities and Trade IPCC Intergovernmental Panel on Climate Change IPCC AR4 Intergovernmental Panel on Climate Change Fourth Assessment Report (2007) IPCC AR5 Intergovernmental Panel on Climate Change Fifth Assessment Report (2014) IPR intrinsic productivity growth rate IPSL Institut Pierre Simon Laplace ISFM integrated soil fertility management KASMO Korea Agricultural Simulation Model KMA Korea Meteorological Administration KREI Korea Rural Economic Institute MIROC Model for Interdisciplinary Research on Climate NoCC no climate change OECD Organisation for Economic Co-operation and Development PRAG precision agriculture RCP representative concentration pathway SPAM Spatial Production Allocation Model SSP shared socioeconomic pathway

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1. INTRODUCTION: FOOD SECURITY CHALLENGES IN THE REPUBLIC OF KOREA

The Republic of Korea has experienced large shifts in diet and nutrition in recent decades as its economy has grown and as the country has opened its economy and food market through free trade agreements (Kim, Moon, and Popkin 2000). Starting from the 1970s, more animal proteins have been added to the diet and by 2011 the amount of daily calories provided by cereals dropped by about one third as the amount from other sources increased (Figure 1.1).

Figure 1.1 Food supply (kilocalories per capita per day), Republic of Korea, 1970 and 2011

Source: FAO (2016b). Notes: Cereals exclude beer, and Fruits exclude wine.

During the same period, arable land in the Republic of Korea has been declining. The agricultural sector has experienced a decrease in production of about 33 percent, while agricultural imports have grown fivefold (Kim et al. 2012). By 2014, the Republic of Korea’s agricultural imports had tripled compared with their level in the early 2000s (KREI 2015).

While indicative of a rapidly growing economy, these trends also expose domestic markets to fluctuations in the global food market and underpin questions about the future of the country’s agriculture sector as well as long-term food security in the region. To inform national agricultural policy, it is important to gain a better understanding of the possible future trends of domestic food production and dependence on food imports, and thereby assess the prospects for sustaining improvements in food security. Answers to these questions become all the more urgent when one considers the increased variability of weather events brought about by climate change and the ensuing potential impacts on food and agricultural production in both the Republic of Korea and its major trading partners.

In this context, building a stable food supply system relies on analyzing and estimating future agricultural trends and increasing the capacity of domestic production under climate change conditions—mainly through adoption of appropriate adaptation practices—while also strengthening international cooperation and trade relations to secure a reliable inflow of food commodities. This picture suggests several significant research questions:

2

• Given the current estimates of population and gross domestic product (GDP) growth, are the trends highlighted in this introduction likely to continue in the future, even under climate change conditions?

• Will climate change affect the overall ability of the Republic of Korea to produce food, especially rice, which still represents the major source of daily calories?

• Will the impacts of climate change affect the ability of the country’s trade partners to produce and export food?

• What do these estimates mean from a food security standpoint for the Republic of Korea and for the region?

Could the adoption of adaptation practices in the agricultural sector change the answers to the questions above? Could these practices improve conditions for the Republic of Korea’s agricultural sector and food security and reduce exposure to global market fluctuations? Would net trade improve, not only for rice but also for other crop groups?

In order to answer these questions, we used the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) from the International Food Policy Research Institute (IFPRI) (Robinson, et al. 2015a) to simulate future trends of agricultural supply and demand for the Republic of Korea and selected regions under three climate change scenarios. In addition, we used IMPACT to estimate how adoption of improved sustainable-intensification agricultural practices may affect productivity and food security under climate change conditions in the Republic of Korea by 2050. The IMPACT model has previously been used in many other studies at the global, regional, and national levels (see Box 3.1 for examples).

The next section of this report reviews recent research on the impacts of climate change on agricultural production and the wider economy of the Republic of Korea. In doing so it also offers a rationale behind the development of the present study. Section 3 is designed to illustrate the work flow of this study and to clarify and document the structure and methodology behind the IMPACT model. Sections 4 (results) and 5 (conclusions) complete the report. The appendixes provide supplementary information on various methodological components and results.

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2. RESEARCH ON CLIMATE CHANGE IN THE REPUBLIC OF KOREA AND RATIONALE FOR THIS STUDY

According to the 2014 Korean Climate Change Assessment Report, climate change appears to be progressing rapidly across the Republic of Korea, with temperatures rising about two times faster than the global average and sea levels around Jeju rising three times faster than the global average (KMA 2014b). By reviewing the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5), the Korea Meteorological Administration (KMA) developed climate projections for the Korean Peninsula for both short- (before 2050) and long-run (after 2050) meteorological phenomena in the atmosphere, hydrosphere, and cryosphere. KMA reported that natural events such as volcanic eruption and solar radiation would have more influence on short-run climatic forecasts, until 2035, and that the global warming effect due to greenhouse gas increases would have more impact on the forecasts after 2035.

KMA (2014b) projected that the average temperatures of the Korean Peninsula may rise by 0.5°C (± 0.3℃) in the winter (0.5°C, ± 0.3℃, in the summer) from 2006 to 2024, and by 1.2°C (± 0.8℃) in the winter (1.2°C, ± 0.7℃, in the summer) from 2025 to 2049. In addition, average winter (and summer) precipitation may grow by 3.0±10.0% (2.8±7.0%) and 7.2±15.0% (5.6±5.0%) during 2006-2024 and 2025-2049, respectively. In reference to findings relevant to agricultural production, KMA (2014a) also showed that the large increase in seasonality of climatic events has already led to a higher frequency of extreme events such as heat waves, heavy snow, cold waves, drought, and heavy rain. Moreover, because of the increase in temperatures, cropping calendars have shifted, as have suitable sites for crop cultivation in response to the increasing frequency of pest and disease outbreaks. Following these findings, KMA (2014a) pointed out the necessity of developing a system of models to analyze changes in extreme climatic events, changes in agricultural productivity, shifts in the suitable time and place for crops, and outbreaks of pest and crop disease. In order to protect and stabilize agricultural production, KMA recommendations included a call for new tools to assess the impact of climate change on agriculture and the development or importation of new stress-tolerant crop varieties better able to withstand fluctuations in climate.

Although some studies have analyzed the economic effects of climate change on crop production and the agricultural sector (for example, Hwang, Kim, and Lee 2012; Park and Kwon 2011; Kim and Jeong 2010), to date only a few studies have explored the effects of climate change on the Republic of Korea’s whole national economy and trade (for example, Kim et al. 2012; Kim et al. 2014; Kwon and Lee 2013). In an effort to heed the climate change projections offered by KMA and in order to work toward developing a global trade model that would respond to projected climate change impacts on the Republic of Korea’s agricultural sector, Kim et al. (2014) and Kim et al. (2015) reviewed the available global economic models that currently assess the impacts of climate change on the national and international markets. In addition, both of these studies analyzed the possibility of representing and integrating the conditions of the local Korean agricultural markets into the county’s own global climate change assessment model. A pilot model linking the scientific crop models with a trade model (Korea Agricultural Simulation Model, or KASMO) is currently under development.

In order to develop an economic model to assess the impacts of climate change in the Korean region in the context of the global agricultural market, it is helpful to work with and understand an established model, corroborated by other researchers and international organizations, and apply it to the case of the Republic of Korea. In this vein, the present study is the first joint product of a long-term collaboration between the Korea Rural Economic Institute (KREI) and IFPRI, focused on use of the IMPACT global economic model developed at IFPRI. The goal of the collaboration is to investigate the impacts of climate change on the Republic of Korea and the global food market, and to explore the consequences of adoption of adaptation technologies across the country’s agriculture sector.

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3. APPROACH: THE IMPACT MODELING FRAMEWORK

Crops The focus of this report is rice, the Republic of Korea’s main crop in terms of harvested area, total production (in tons1) (FAO 2016a), and kilocalories consumed per person per day (FAO 2016b). Some sections will explore future trends for other crops that are important in the country as food or feed, including barley, wheat, maize, and soybeans. Maize, wheat, and soybeans are especially relevant in discussions around trade because a large proportion of the country’s consumption of these commodities is imported (Kim et al. 2012). Vegetables will also be considered because they represent an important share of daily kilocalorie intake.

Climate Scenarios To represent some of the uncertainty inherent in climate change projections, we use three climate change scenarios. The scenarios are based on results from running three climate models under a Representative Concentration Pathway (RCP) of 8.5 watts/m2 (Meinshausen et al. 2011), each of which is combined with the IPCC’s “middle of the road” GDP and population growth scenario (Shared Socioeconomic Pathway 2, or SSP2) (O’Neill et al. 2014).

The three climate models, or Earth System Models (ESMs) as defined by IPCC AR5, are as follows:

• GFDL-ESM2M (Dunne et al. 2012)—designed and maintained by the US National Oceanic and Atmospheric Administration’s Geophysical Fluid Dynamic Laboratory (GFDL) (www.gfdl.noaa.gov/earth-system-model)

• HadGEM2-ES (Jones et al. 2011)—the Hadley Centre’s Global Environment Model, version 2 (www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2)2

• IPSL-CM5A-LR (Dufresne et al. 2013)—the ESM of the Institut Pierre Simon Laplace (IPSL) (http://icmc.ipsl.fr/index.php/icmc-models/icmc-ipsl-cm5)

Figures 3.1 and 3.2 show, respectively, the changes in maximum temperature and in annual precipitation estimated according to these three scenarios across the globe. Refer to Appendix A for additional details showing changes in average maximum and minimum temperature and changes in average annual precipitation for the Republic of Korea and selected other countries.

1 Throughout the report, tons refer to metric tons. 2 For the analysis of technology adoption (described in the following sections) we focus only on a climate change scenario

based on the HadGEM climate model. See page 22 for details.

http://www.gfdl.noaa.gov/earth-system-model

http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2

http://www.metoffice.gov.uk/research/modelling-systems/unified-model/climate-models/hadgem2

http://icmc.ipsl.fr/index.php/icmc-models/icmc-ipsl-cm5

5

Figure 3.1 Changes in maximum temperature in 2050 compared with 2000 (°C), three earth system models, representative concentration pathway (RCP) 8.5

GFDL-ESM2M

IPSL-CM5A-LR

HadGEM2-ES

Source: Authors.

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Figure 3.2 Changes in annual precipitation in 2050 compared with 2000 (millimeters), three earth system models, representative concentration pathway (RCP) 8.5

GFDL-ESM2M

IPSL-CM5A-LR

HadGEM2-ES

Source: Authors.

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The IMPACT Model IMPACT is a global, multimarket economic model developed and maintained by IFPRI to examine alternative futures for global food supply, demand, trade, and prices, and food security. The model allows researchers to obtain global baseline projections of agricultural commodity supply, demand, and trade, as well as malnutrition outcomes, along with cutting-edge research results on topics such as bioenergy, climate change, changing diet/food preferences, and other themes. Box 3.1 shows some examples of studies based on the IMPACT model.

Box 3.1 Examples of IMPACT analysis

National Analysis of Food Security o Africa agriculture and climate change research monographs (Waithaka et al. 2013; Hachigonta et al.

2013; Jalloh et al. 2013) o Analysis of China (Ye et al. 2014), South Africa (Dube et al. 2013), and the United States (Tackle et

al. 2013) Regional Analysis of Food Security o Food security issues in the Arab region (Sulser et al. 2011) o “Looking Ahead: Long-Term Prospects for Africa’s Agricultural Development and Food Security”

(Rosegrant et al. 2005) o Irrigation technologies in Organisation for Economic Co-operation and Development countries

(Ignaciuk and Mason-D’Croz, 2014) Commodity Analysis o “Alternative Futures for World Cereal and Meat Consumption” (Rosegrant et al. 1999) o “Global Projections for Root and Tuber Crops to the Year 2020” (Scott et al. 2000) o “Livestock to 2020: The Next Food Revolution” (Delgado et al. 1999) Thematic and Interdisciplinary Analysis o World Water and Food to 2025: Dealing with Scarcity, joint effort of the International Food Policy

Research Institute and the International Water Management Institute (Rosegrant et al. 2002) o Food Security and Climate Change (Nelson et al. 2010) o Global assessments such as the International Assessment of Agricultural Knowledge, Science and

Technology for Development (2009), World Development Report 2008: Agriculture for Development (World Bank 2007), CGIAR’s Strategy and Results Framework (CGIAR 2009), and the Agricultural Model Intercomparison and Improvement Project (Nelson et al 2014; Wiebe et al. 2015)

Source: Adapted from Robinson, Mason-D’Croz, Islam, Sulser, et al. (2015a).

The core IMPACT multimarket economic model is linked to a number of modules, including climate models, water models (hydrology, water basin management, and water stress models), and crop simulation models (for example, the Decision Support System for Agrotechnology Transfer, or DSSAT, model) (Figure 3.3).

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Figure 3.3 IMPACT system of models: Climate, crops, and water

Source: Adapted from Robinson et al. (2015a).

Thanks in part to its modularity, IMPACT can analyze the complex relationships among factors such as population and income growth, climate variables (such as temperature and precipitation), agricultural technology development, and improvements in irrigation systems, among others. The inclusion of hydrology, water management, and water stress models helps users to estimate more accurately the effects of climatic change on agriculture. The model is designed to perform long-term scenario analysis. By building and simulating a range of possible futures (that is, scenarios), IMPACT allows users to explore evolving trends in the agricultural sector and the global food market, and use this information as a decision support tool to manage uncertainty. In this context the model has been used to assess the effects of adopting climate change adaptation technologies on agricultural productivity and food security regionally and globally (Rosegrant et al. 2014; Robinson et al. 2015b). Appendix B provides additional information on the modules of the IMPACT model as well as details on how food security indicators are calculated.

IMPACT covers 159 countries and 154 water basins. Agricultural production is analyzed at a subnational level, across 320 regions called food production units (FPUs) (Figure 3.4) (Robinson et al. 2015a). In the model, the Republic of Korea is represented by one water basin and one single FPU (Figure 3.5).

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Figure 3.4 Geography of the IMPACT model

Source: Adapted from Robinson et al. (2015a).

Figure 3.5 Map of food production units in East Asia

Source: Adapted from Robinson et al. (2015a). Notes: The basins are as follows: AMR: Amur; BRT: Brahmaputra; CHJ: Chang Jiang; GAN: Ganges; HAI: Hail He; HUA:

Hual He; HUN: Huang He; IND: Indus; JAP: Japan; LAJ: Langcang Jiang; LMO: Lower Mongolia; NKP: North Korean Peninsula; OBB: Ob; SKP: South Korean Peninsula; SON: Songhua; TWN: Taiwan; UMO: Upper Mongolia; YHE: Yili He; YRD: Yuan Red River; ZHJ: Zhu Jiang.

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Simulation of Crop Yields: From Crop Models to IMPACT In order to simulate changes in yields, area, and production for a number of commodities worldwide under different climate futures, IMPACT uses data on yield change, by crop, estimated through the DSSAT model. The link between crop model results (biophysical) and the IMPACT model involves several steps, described below.

The IMPACT model’s yield simulations begin in the year 2005, starting with 2005 yields taken from FAOSTAT.3 Early trends are calibrated to the latest data to reproduce observed historical trends, while longer-term trends rely on drivers encoded in the model. In the first step, IMPACT assumes underlying improvements in yields over time. These intrinsic productivity growth rates (IPRs) are based on historical data on productivity growth and are adjusted through expert opinion from scientists at CGIAR research centers and others to reflect future changes in input levels, improvements in management practices, and investments in agriculture. These IPRs are exogenous to the IMPACT model and are treated as part of its input data (that is, they are not solved within the IMPACT multimarket model). In addition to the IPRs, simulations through the DSSAT model provide estimates on the effects of temperature changes on crop yields, while water availability effects are captured through linked water models. The inputs from crop and water models are combined, resulting in an estimated total impact of climate change on average yields by crop and region. These shocks are then used in the IMPACT multimarket model. (Refer to Appendix C for more details on how the link between DSSAT and IMPACT is implemented.) Finally, the IMPACT multimarket model includes an endogenous link between yields and changes in output prices based on the underlying assumption that farmers will respond to changes in prices by varying the use of inputs, such as fertilizer, chemicals, and labor, which will, in turn, change yields. If the price of a crop falls, for example, there is less incentive to allocate resources to that crop and its yield will decline as a result.

By combining the biophysical yield changes from DSSAT and its linked water models with the economic relationships in IMPACT, we are able to simulate impacts that reflect the combination of biophysical effects as well as interactions with prices and other economic variables. The section below provides additional details on how yields are actually calculated inside IMPACT.

How Yields Are Calculated in IMPACT: Exogenous and Endogenous Yields Within IMPACT, yields are calculated following this general equation:

Yld = Yld_2005 x Yld_int x ClimateShock x WaterShock x PriceEffect, (1)

where Yld = final yield calculated by IMPACT; Yld_2005 = yield data based on FAOSTAT; Yld_int = yield increase factor, or IPRs (intrinsic productivity growth); ClimateShock = climate shock from DSSAT; WaterShock = water shock from the linked water models; and PriceEffect = endogenous effects of the model, that is, the solution of the IMPACT model.

All the factors in the equation, with the exclusion of price effects, are exogenous elements (that is, factors that are not part of the solution of the IMPACT model) fed into the model as necessary to the calculations. Price effects are the only endogenous factor, being part of the solution to the system of equations that make up the model. As described in the previous section, the DSSAT crop model is used to provide estimates of climate shocks, in the form of changes in yields, while linked water models are used to provide water shock effects.

3 FAOSTAT is the online database of the Food and Agriculture Organization of the United Nations (FAO). It contains statistics and data compiled by the FAO Statistics Division and is available at http://faostat3.fao.org/home/E.

http://faostat3.fao.org/home/E

11

In order to disentangle the contribution of various factors to the final IMPACT yield results, it is helpful to calculate and observe both exogenous and endogenous yields. Endogenous yields are calculated using the full equation in (1). Exogenous yields are calculated using only exogenous factors, thus following a reduced version of equation (1) without endogenous prices:

Yld = Yld_2005 x Yld_int x ClimateShock x WaterShock (2)

Refer to Appendix D for additional comments on IPRs and details on calculations of yields in IMPACT, to Appendix E for details on price effects, and to Appendix F for a summary of the equations that drive the IMPACT model.

IMPACT Simulations: Reference Suite In this study, the reference suite of IMPACT scenarios comprises four simulation scenarios:

1. SSP2-GFDL 2. SSP2-HadGEM 3. SSP2-IPSL 4. SSP2-NoCC

These are three climate change scenarios, described in the above section on climate scenarios (SSP2-GFDL, SSP2-HadGEM, SSP2-IPSL), plus a fourth scenario representing a continuation of climate conditions around the year 2005 (SSP2-NoCC, the no-climate-change scenario).

IMPACT Simulations: Technology Adoption Farmers in the Republic of Korea have expressed some preference toward new crop varieties resistant to both biotic (such as pests and diseases) and abiotic (such as droughts and floods) stresses (Kim and Jeong 2010). As a further adaptation against climate change, they have also been favoring changes in cultivation technology, for instance through adjustment in the timing of rice seeding and planting. Although several studies have underscored the necessity of assessing climate impacts on crops (for example, Kim et al. 2015; KMA 2014a; Kim et al. 2012), no studies have explored the biophysical and economic effects from adoption of adaptation technologies and practices.

In this study we simulate adoption of two agricultural practices—integrated soil fertility management (ISFM) and precision agriculture (PRAG)—and two stress-tolerant rice varieties—drought-tolerant (DT) and heat-tolerant (HT) rice—across rice cropland in the Republic of Korea, Democratic People’s Republic of Korea, Japan, and China. These chosen technologies are consistent with a sustainable intensification approach (Garnett et al. 2013) and have been recognized as tools with significant potential for climate change adaptation in that their adoption may lessen the impacts of climate change on crop yields (The Royal Society 2009; Clay 2011; Foley et al. 2011; Smith 2013). The following provides a quick summary of how these technologies are implemented at the farm level.

Precision Agriculture PRAG can be summarized as the application of the right treatment in the right place at the right time (Gebbers and Adamchuck 2010). It aims at optimizing the use of available resources, such as water and fertilizer, to increase production and profits. Farmers have always observed how agricultural productivity varies spatially—even within a single field—due to interactions between management, weather, and soil characteristics. Since the mid-1980s, an increased understanding of the determinants of yield variability, along with developments in information and automation technologies, has allowed agronomists and farmers to start quantifying and mapping the highly detailed variations in production in their fields (Bramley 2009; Gebbers and Adamchuck 2010). PRAG is a combination of tools and techniques, ranging

12

from high- to low-tech, that allow farmers to respond to changes in conditions within a field across space and time, enabling more site-specific crop management.

In a study by Rosegrant et al. (2014), PRAG was implemented in DSSAT by simulating improved planting density, optimum planting windows, and enhanced fertilizer application based on the growth stage of the crop. For additional technical details about the implementation, see Rosegrant et al. (2014, 38).

Integrated Soil Fertility Management ISFM is a field management approach that aims at improving soil fertility through the combined use of chemical fertilizers, organic input, and improved crop germplasm. In this approach, soil amendments must go hand in hand with the knowledge of how to adapt practices to local conditions in order to maximize efficiency in the use of the applied nutrients and thus increase crop productivity (Vanlauwe et al. 2010). Implicit in the definition of this approach is the understanding that chemical and organic fertilizers complement each other and that full implementation of ISFM (and therefore maximization of agronomic efficiency) is achieved only when these nutrients are combined with improved germplasm and agronomic practices that are adapted to local conditions (Vanlauwe et al. 2011).

In the study by Rosegrant et al. (2014), ISFM was implemented in DSSAT by simulating the use of both organic amendments and inorganic fertilizer. For details on the rate of manure and fertilizer application, see Rosegrant et al. (2014, 38).

Drought- and Heat-Tolerant Rice Drought is one of the main constraints on rice yields (Bouman et al. 2007). Although rainfed environments are the most affected by changing rainfall patterns and growing competition over water resources, the impacts are also felt across water-scarce irrigated areas that depend on surface water for irrigation (Serraj et al. 2011). Between 15 million and 20 million ha of irrigated rice globally are expected to be impacted by water scarcity in the next 25 years (Bouman et al. 2007). Moreover, the projected water scarcity also means that irrigation has limited potential to alleviate drought in rainfed systems (Serraj et al. 2011). In this context, development of drought- and heat-resistant rice varieties is considered urgent. Drought resistance in rice is being pursued through a combination of genetic enhancement and improved agronomic practices to make the most efficient use of rainfall and soil moisture. As far as heat resistance is concerned, research based on marker-assisted selection and genetic modification is targeting both the enhanced fertility of flowers at high temperature and, similar to the case of drought resistance, the development of varieties with shorter duration to avoid exposure to periods of peak stress (Shah et al. 2011).

In the study by Rosegrant et al. (2014), drought-tolerant rice varieties were implemented in DSSAT by changing assumptions on root volume and water extraction capability. For additional technical details, see Rosegrant et al. (2014, 41–42).

Modeling of Agricultural Technologies: Methodological Details In order to simulate in IMPACT, the effects of adopting these agricultural technologies, we first had to identify estimates of the biophysical effects of the technologies on rice yields.4 We extracted these data from the crop model simulations in Rosegrant et al. (2014). For each chosen technology, we took the crop model estimates of yield changes (compared with a baseline of no technology adoption) across current rice cropland in the Republic of Korea and in other countries of the East Asia and Pacific (EAP) region, namely China, Japan, and the Democratic People’s Republic of Korea. The estimates of yield change were then applied as an input into the IMPACT model to represent the effects of technology adoption.

4 At this stage, we extract purely biophysical results, coming out of crop modeling and therefore independent from any

economic adjustment that may be estimated through the use of a linked economic model such as IMPACT.

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We should note here that yield changes from Rosegrant et al. (2014) were simulated in DSSAT using the Model for Interdisciplinary Research on Climate (MIROC) A1B and Commonwealth Scientific and Industrial Research Organisation (CSIRO) A1B scenarios (from IPCC AR4), but in this study they are used as inputs into a modeling framework that uses AR5 climate change scenarios. Although we cannot expect that modeling results would be exactly the same under different climate change scenarios, the emphasis of this study is on using solid peer-reviewed results about the effects of technology adoption under a spectrum of climatic conditions. The results from Rosegrant et al. (2014) fit this description.

Table 3.1 shows the rice yield results from the DSSAT crop models used as input for the IMPACT simulations. Table 3.2 shows details of the technology scenarios run in IMPACT for this report. The technology scenarios are based only on the HadGEM climate change scenario because we consider HadGEM2 to be the model that best approximates the climate change projections offered by KMA (KMA used HadGEM3-RA to project regional climate change in the Republic of Korea).

Values in Table 3.1 show the varying yield effects of technology adoption across countries. The fact that the same technology may have different effects across different countries is a direct result of the use of crop modeling. This illustrates the power of the DSSAT crop modeling suite in estimating how the interaction between highly disaggregated global data on soil, climate, technology, and crop management can affect crop growth and produce different yields at different geographic locations.

Table 3.1 Rice yields: Percentage change in 2050 from reference case after technology adoption Country Technology Percentage difference Republic of Korea PRAG 25% Republic of Korea ISFM 29% Republic of Korea DT 7% Republic of Korea HT 5% Democratic People’s Republic of Korea

PRAG 8%

Democratic People’s Republic of Korea

ISFM 10%


DT 4%


HT 10%

China PRAG 30% China ISFM 33% China DT 1% China HT 6% Japan PRAG 18% Japan ISFM 19% Japan DT 1% Japan HT 10%

Source: Authors. Notes: The numbers represent the average of DSSAT results from two general circulation models (MIROC A1B and CSIRO

A1B), from Rosegrant et al. (2014). DT = drought tolerance; HT = heat tolerance; ISFM = integrated soil fertility management; PRAG = precision agriculture.

14

Table 3.2 Technology scenarios and their components: Socioeconomic and climate inputs, and adoption rates

Scenario Climate GCM Socioeconomic scenario Technology

Maximum adoption (%)

Start adoption year

End adoption year

ISFM-NoCC NoCC n/a SSP2 ISFM 40 2010 2040

PRAG-NoCC NoCC n/a SSP2 PRAG 60 2010 2040

DT-NoCC NoCC n/a SSP2 DT 80 2010 2040

HT-NoCC NoCC n/a SSP2 HT 75 2010 2040

ISFM-HadGEM RCP8.5 HadGEM SSP2 ISFM 40 2010 2040

PRAG-HadGEM RCP8.5 HadGEM SSP2 PRAG 60 2010 2040

DT-HadGEM RCP8.5 HadGEM SSP2 DT 80 2010 2040

HT-HadGEM RCP8.5 HadGEM SSP2 HT 75 2010 2040

Source: Authors. Notes: Max adoption (%) means that, for instance, ISFM is adopted over 40 percent of the rice area by 2040. Because the simulations run to 2050, area covered by the

technology remains unchanged until 2050. All adoption follows a logistic curve. GCM = General Circulation Model; DT = drought tolerance; HadGEM = Hadley Centre’s Global Environment Model; HT = heat tolerance; ISFM = integrated soil fertility management; n/a = not applicable; NoCC = no climate change; PRAG = precision agriculture; RCP = representative concentration pathway; SSP2 = Shared Socioeconomic Pathway 2.

15

Adoption Rates in IMPACT Simulations The “maximum adoption” column in Table 3.2 shows the maximum adoption rate (that is, the ceiling) for each technology, which is assumed to be reached for all technologies in the year 2040. Adoption is considered to remain at the maximum level between 2040 and 2050. The adoption curve is logistic, and the point of inflection was set at year 2025 (see example in Figure 3.6). The numbers for maximum adoption used in this study are drawn from Rosegrant et al. (2014). The adoption pathway (logistic curve) and adoption ceiling for each technology were chosen by Rosegrant et al. (2014) based on technical feasibility (determined through the DSSAT simulations) and socioeconomic feasibility, which includes considerations such as expected profitability, scale of up-front investments, and risk-reduction value of the technology.

Figure 3.6 Example of logistic adoption curve beginning in 2010 and reaching a ceiling set at 80 percent in 2040

Source: Authors.

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4. IMPACT MODEL RESULTS: CLIMATE CHANGE AND AGRICULTURAL ADAPTATION IN THE REPUBLIC OF KOREA

Future Trends for Agricultural Production in the Republic of Korea Simulation results across all crops in the Republic of Korea indicate changes compared with the trends of the last 30–40 years described in the introduction. Although total harvested area continues to decrease (Figure 4.1) and net imports to increase (Figure 4.2) under climate change, they do so at a slower rate than under the no-climate-change (NoCC) reference.

Figure 4.1 Scenario trends in harvested area for all crops in the Republic of Korea, between 2010 and 2050

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: GFDL = Geophysical Fluid Dynamic Laboratory; HadGEM = Hadley Centre’s Global Environment Model; IPSL =

Institut Pierre Simon Laplace; NoCC = no climate change; SSP2 = Shared Socioeconomic Pathway 2.

Figure 4.2 Scenario trends in net imports for all crops in the Republic of Korea, between 2010 and 2050



17

Total production shows the largest deviation from historical trends, with all scenarios estimating an increase in production for all crops (Figure 4.3). The production trend under the IPSL scenario effectively overlaps with the trend under NoCC; in contrast, the growth in production under HadGEM is estimated to be faster than under NoCC, and under GFDL it is estimated to be slower than under NoCC. Of all commodity groups simulated in IMPACT (cereals, fruits and vegetables, oilseeds and oil products, pulses, root and tubers, sugar crops, and other crops), the only group showing consistent decline in production between 2010 and 2050 is traded oilseeds, which include groundnuts, rapeseeds, and soybeans (for details on group composition, see Robinson et al. 2015a).

Figure 4.3 Scenario trends in production for all crops in the Republic of Korea between 2010 and 2050



Climate Change Impacts on Rice and Other Key Agricultural Commodities in the Republic of Korea Rice is the main agricultural product of the Republic of Korea in terms of production (in tons) and harvested area (in ha) (FAO 2016a) and still represents the major source of daily calories (FAO 2016b). Therefore, we first consider how climate change may impact the potential of the Republic of Korea to produce rice. We are then interested in observing the effects of climate change on future rice production, yield, and area, compared with a reference scenario without climate change.

Crop modeling simulations through the DSSAT crop model show that climate change in the Republic of Korea (as projected by the HadGEM2 general circulation model under RCP8.5) provides a modest initial boost to rice yield, but the long-term effect is negative (Figure 4.4). Appendix G provides multiple maps showing the effects of climate change on rice yields across East Asia and the Republic of Korea.

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Figure 4.4 Rice yield trends in the Republic of Korea projected for 40 years, representative concentration pathway 8.5 and HadGEM2 general circulation model

Source: Authors’ Decision Support System for Agrotechnology Transfer (DSSAT) simulations. Notes: Data smoothed using lowess (0.2). HadGEM = Hadley Centre’s Global Environment Model.

The yield effects from DSSAT are used to estimate the initial climate shock (due to temperature and weather, holding everything else constant) to be used in the IMPACT model. To explore how the climate shock will affect yields in the future, however, we also need to consider changes in other factors, including technology. As noted in the methodology section, baseline improvements in technology or investments into agriculture are captured in IMPACT through intrinsic productivity growth rates (IPRs). Exogenous rice yields in the Republic of Korea are then calculated by combining IPRs with climate and water shocks to 2050.5 Exogenous yield trends are lower under the three climate change scenarios compared to the scenario without climate change (NoCC) (Figure 4.5). The underlying drivers of rice productivity represented in the IPRs are driving the increasing trend in yields seen in Figure 4.5, although the trends are slower for the climate change scenarios due to the climate and water shocks.

Final crop yields (that is, yields that are endogenous to the model)6 differ from the exogenous yields in that in addition to combining climate and water shocks from the crop and water models and productivity growth rates (that is, IPRs), they also include endogenous market effects reacting to prices7

(Figure 4.6). When the endogenous market effects are included in the simulations, the picture changes from that seen in Figures 4.4 and 4.5, with rice yields under IPSL, for example, actually rising above those in the NoCC reference scenario, suggesting that the climate shock in the Republic of Korea under IPSL is modest enough that market forces will more than make up for it in response to changing global prices and trade opportunities.

5 See page 18 for the equation for exogenous yields. 6 These are the yields we show throughout the rest of the report, because they are the standard IMPACT output. 7 See page 18 for the equation for endogenous yields.

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Figure 4.5 Exogenous rice yield growth in the Republic of Korea to 2050, incorporating changes in productivity, climate, and water, all scenarios

Source: Authors’ Decision Support System for Agrotechnology Transfer (DSSAT) simulations. Notes: GFDL = Geophysical Fluid Dynamic Laboratory; HadGEM = Hadley Centre’s Global Environment Model; IPSL =


Figure 4.6 Endogenous rice yield growth in the Republic of Korea to 2050, incorporating changes in productivity, climate, and water, as well as market effects, all scenarios

Source: Authors’ Decision Support System for Agrotechnology Transfer (DSSAT) simulations. Notes: GFDL = Geophysical Fluid Dynamic Laboratory; HadGEM = Hadley Centre’s Global Environment Model; IPSL =


Figure 4.7 shows area under rice declining between 2010 and 2050, in a continuation of recent historical trends; however, the decline is slower under the three climate change scenarios than under the reference NoCC scenario. In 2050, rice area is about 2 to 4 percent greater than in 2010, depending on the specific general circulation model (GCM). Demand for rice declines faster under all three climate change scenarios than it does under the reference scenario in response to increasing prices. Production, however,

20

grows under all scenarios but is faster under climate change. This is especially evident under HadGEM and IPSL, where, by 2050, output is between 3 and 4 percent greater than under the NoCC reference. Appendix H shows the yearly trends for rice area, demand, production, and yield in the Republic of Korea.

Figure 4.7 Area, demand, production, and yield for rice in the Republic of Korea, percentage changes between 2010 and 2050, all scenarios



Compared with other regions and crops around the globe, climate impacts (with endogenous prices effects) are relatively modest for rice in the Republic of Korea. Climate effects are stronger—and more negative—in the tropics across all crops, while higher latitudes can actually experience yield benefits under climate change, depending on the crop (especially for maize and wheat). The Republic of Korea is also likely benefiting from the climate-moderating effects in the GCMs of being mostly surrounded by ocean waters. We note that other climate-related factors such as extreme events and rising sea levels are beyond the scope of this analysis, so these may be considered as conservative estimates. We note here that previous studies have estimated more dire effects of climate change on rice production in the Republic of Korea. Section 6 addresses this specific issue.

The trends for rice area in the Republic of Korea, and consequently also for production and yield, make sense when considering that the world price of rice is projected to increase faster under climate change (Figure 4.8). Under climate change, prices are estimated to be about 13 percent, 18 percent, and 27 percent greater than the NoCC reference under GFDL, IPSL, and HadGEM, respectively. Higher prices for rice represent an incentive for farmers to continue growing rice, which is reflected in Figure 4.7, showing that rice area under the three climate change scenarios does not decrease as much as under the NoCC reference.

21

Figure 4.8 Global price of rice, percentage change between 2010 and 2050, all scenarios



While the overall harvested area of and demand for rice in the Republic of Korea are projected to decline, demand for other grains, such as barley, maize, wheat, and soybeans, is projected to increase (Figure 4.9). Figure 4.9 shows climate change effects in area, demand, production, and yield between 2010 and 2050 across selected crops. The chosen crops include those that are important in the Republic of Korea from an annual production standpoint (rice, barley, soybeans, and vegetables) and those that are important because they provide a significant share of kilocalories per capita per day (rice, maize, wheat, soybeans, and vegetables). The review includes trends for domestic production of all of these crops, even if some are mostly imported (maize, wheat, and soybeans).

As discussed above, the combination of climate shocks and market effects results in an overall increase in rice production under climate change scenarios despite decreasing demand. Barley production also increases under climate change scenarios, whereas production of maize and vegetables continues increasing between 2010 and 2050 under the climate change scenarios, but at a slower rate than under the NoCC reference scenario (Figure 4.9). Although wheat yields may increase substantially under climate change, area and total production decline across all four scenarios (Figure 4.9). This implies a continued reliance of the Republic of Korea on wheat imports, which, in the period between 2010 and 2050, are estimated to grow by between 23 and 32 percent, depending on the scenario.

22

Figure 4.9 Change in harvested area, demand, production, and yield between 2010 and 2050 across no-climate-change reference and three climate change scenarios, key crops for the Republic of Korea



Figure 4.10 shows that the terms of trade8 for rice, barley, maize, and soybeans in the Republic of Korea shift under climate change to be more favorable (that is, increasing net exports or decreasing net imports). However, net trade is less favorable for wheat and vegetables. Overall, the country remains a net exporter of rice as exports experience continuous growth under all four scenarios. Notably, by 2050, net exports of rice under climate change conditions are projected to be larger than those under the NoCC reference scenario (Table 4.1).

8 Magnitude of net imports/exports.

SSP2-NoCC SSP2-GFDL SSP2-HadGEM SSP2-IPSL

-100% 0% 100% 200%Percentage Difference




Are

MaizeBarleySoybeanRiceWheatVegetables

De


Pro


Yie


-14.0%

-14.0%

-12.9%

-44.2%

30.9%

0.6%

-23.8%

35.4%

12.5%

22.5%

-0.9%

6.4%

126.4%

-39.8%

18.8%

10.0%

13.3%

54.4%

72.9%

38.1%

27.9%

30.1%

53.4%

7.9%

-15.5%

-14.2%

-11.2%

-42.7%

24.2%

-1.3%

-25.0%

23.2%

26.6%

-1.4%

7.6%

9.3%

-30.4%

70.2%

25.5%

13.3%

48.6%

9.6%

37.0%

48.5%

27.8%

27.7%

21.6%

50.6%

-13.7%

-13.1%

-41.3%

25.0%

-9.8%

-0.8%

-26.1%

27.8%

-2.4%

8.0%

6.9%

1.0%

-28.0%

47.7%

38.6%

17.4%

52.7%

8.3%

18.2%

60.6%

24.7%

30.1%

22.8%

53.9%

-12.6%

-13.5%

-10.5%

-42.7%

25.9%

-1.2%

-25.5%

31.9%

-1.9%

7.6%

7.7%

5.8%

-32.7%

58.2%

47.4%

10.0%

17.2%

51.4%

25.6%

68.6%

27.2%

31.0%

17.5%

53.3%

23

Figure 4.10 Net trade values for baseline suite of scenarios (no climate change plus three climate change scenarios) for selected crops, Republic of Korea, 2050

Source: Authors. Notes: Negative values represent net imports, positive values net exports. GFDL = Geophysical Fluid Dynamic Laboratory;

HadGEM = Hadley Centre’s Global Environment Model; IPSL = Institut Pierre Simon Laplace; NoCC = no climate change; SSP2 = Shared Socioeconomic Pathway 2.

Table 4.1 Net rice exports from the Republic of Korea, percentage change compared with base of no climate change, 2050

Scenario Change in rice exports

SSP2-GFDL 3.8%

SSP2-HadGEM 19.8%

SSP2-IPSL 17.2%

Source: Authors Notes: GFDL = Geophysical Fluid Dynamic Laboratory; HadGEM = Hadley Centre’s Global Environment Model; IPSL =

Institut Pierre Simon Laplace; SSP2 = Shared Socioeconomic Pathway 2.

24

Impacts of Climate Change on Production and Exports from the Republic of Korea’s Trade Partners Starting in the early 2000s, the Republic of Korea began opening its economy to trade and signed a number of free trade agreements, the first of which was with Chile in 2002 (KREI 2015). Currently, maize, wheat, vegetables, and soybeans are among the country’s major imports (FAO 2016b; Kim et al. 2012, 3). Maize and wheat (and their products) are widely used as feed (Table 4.2), but they also represent 3 percent and 12 percent, respectively, of the daily per capita kilocalorie intake in the country. Soybeans represent about 2 percent of daily kilocalories, while vegetables, the third-largest import, account for another 4 percent (FAO 2016b).

Table 4.2 Agricultural statistics on food balance for Republic of Korea, 2011, top four imports

Crops Production Import quantity

Export quantity

Domestic supply quantity Feed Waste Food

Food supply

Maize and products

74 7,811 86 7,799 5,182 157 622 85

Wheat and products

44 4,890 94 5,263 2,700 30 2,525 405

Vegetables, other

9,287 1,168 94 10,361 1,357 9,004 145

Soybeans 129 1,148 2 1,276 28 8 382 64 Source: FAO (2016b). Notes: 1) Data sorted by largest Import quantity. Production, imports, exports, domestic supply, feed, waste and food are all in

1000 tons. Food supply is measured in kilocalories per person per day. 2) More details on the categories used in the table and a description of the food balance sheet can be found at http://faostat3.fao.org/download/FB/*/E.

Where do these commodities come from? The major exporters of agricultural commodities to the Republic of Korea are the United States, China, Australia, Brazil, and Indonesia (KREI 2015).9 However, data on maize, wheat, soybeans, and vegetables reported in KREI (2015) show that Russia, Ukraine, and Canada also export significant quantities of grains to the Republic of Korea (Table 4.3).

Table 4.3 Major exporters to the Republic of Korea, by product Maize Wheat Soybeans Vegetables United States United States United States China Brazil Australia Brazil Ukraine Canada Russia Ukraine

Source: KREI (2015).

How will climate change affect the production and export of agricultural commodities in these countries? Figure 4.11 shows that, with the exception of maize production in the United States, all production from these major exporters to the Republic of Korea grows between 2010 and 2050 under all scenarios. The increase in maize production is slower in each country under climate change than under the NoCC reference scenario (panel [a]). The same trend is evident for wheat production in Australia and partly in Canada, whereas production of wheat in Ukraine and the United States grows substantially faster under climate change conditions than under the NoCC reference (panel [b]). Production of vegetables, which are imported to the Republic of Korea mainly from China, also grows at a faster rate under climate change conditions (panel [d]).

9 Other important exporters are New Zealand, Canada, Thailand, Chile, Malaysia, and Viet Nam.

http://faostat3.fao.org/download/FB/*/E

25

Figure 4.11 Percentage change in production of maize, wheat, soybeans, and vegetables between 2010 and 2050, no-climate-change reference and climate change scenarios

(a) Maize

(b) Wheat

(c) Soybeans

(d) Vegetables



How do these changes in production affect exports from those same countries? Net maize exports from Brazil, Russia, Ukraine, and the United States grow between 2010 and 2050 under all scenarios. However, under the GFDL scenario, Brazil becomes a net importer of maize by 2020, while under IPSL, net exports grow faster than under the other scenarios (Figure 4.12). Even considering the general upward trend for net exports out to 2050, climate change shows a large dampening effect on net exports of maize by the Republic of Korea’s major trading partners compared with the NoCC reference, especially in the HadGEM scenario (Figure 4.13).

26

Figure 4.12 Trends of net maize exports from Brazil, all scenarios



Figure 4.13 Percentage change in net maize exports in 2050 for climate change scenarios, compared with no climate change



Exports of wheat are projected to keep growing under all scenarios in Canada, the United States, and Ukraine. In Australia, net exports of wheat would decline below the 2010 levels under the GFDL climate change scenario, becoming substantially lower than the NoCC reference by 2050 (Figure 4.14). Despite the general growth, climate change is projected to reduce the net exports of wheat compared with the NoCC reference in Australia and Canada by 2050. Conversely, climate change is projected to increase net exports from Ukraine and the United States compared with the NoCC reference (Figure 4.15).

Maize

-80% -60% -40% -20% 0% 20% 40%Percentage difference

Brazil SSP2-IPSLSSP2-HadGEM

Ukraine SSP2-IPSLSSP2-GFDLSSP2-HadGEM

US SSP2-IPSLSSP2-GFDLSSP2-HadGEM

Russia SSP2-IPSLSSP2-GFDLSSP2-HadGEM

-50.0%

25.4%

-36.4%

-57.0%

-0.8%

-44.0%

-10.9%

-67.0%

-43.9%

-76.3%

-5.3%

27

Figure 4.14 Trends of net wheat exports from Australia, all scenarios



Figure 4.15 Percentage change in net wheat exports by 2050, compared with no climate change



Exports of soybeans from Brazil and the United States are estimated to continue growing under all four scenarios. Under climate change conditions, net exports of soybeans from Brazil would decrease, compared with the NoCC reference. The picture for the United States varies by GCM, with an increase in exports under GFDL, a small decrease under IPSL, and a large decrease under HadGEM (Figure 4.16). Net exports of vegetables from China are projected to increase under all three climate change scenarios (Figure 4.17).

Wheat

-60% -40% -20% 0% 20% 40% 60% 80% 100% 120%Percentage difference

Australia SSP2-GFDLSSP2-HadGEMSSP2-IPSL

Canada SSP2-GFDLSSP2-HadGEMSSP2-IPSL

Ukraine SSP2-GFDLSSP2-HadGEMSSP2-IPSL

US SSP2-GFDLSSP2-HadGEMSSP2-IPSL

-23.4%

-34.5%

-18.2%

-11.9%

-3.3%

-8.2%

102.4%

77.4%

71.2%

18.7%

41.1%

0.8%

28

Figure 4.16 Percentage change in net soybean exports in 2050, compared with no climate change



Figure 4.17 Percentage change in net vegetable exports in 2050, compared with no climate change



We also see that, despite the overall trend of increasing net imports between 2010 and 2050, climate change conditions reduce net imports to the Republic of Korea for all crop groups compared with a NoCC reference scenario (Figure 4.18). This is especially notable for fruits and vegetables and is consistent with the previous results showing projections for decreasing demand as well as increased domestic production of fruits and vegetables under climate change conditions in the Republic of Korea.

Figure 4.18 Net imports to the Republic of Korea by crop group in 2050, percentage change compared with no climate change



SSP2-GFDL SSP2-HadGEM SSP2-IPSL

-60% -40% -20% 0% 20%Percentage difference



Fruits and Vegetables

Cereals

Traded Oilseeds

Other Crops

Root and Tubers

Sugar Crops

Oilmeals

Processed Oils

Pulses

-31.9%

-5.8%

-3.0%

-1.9%

-2.3%

-0.5%

-0.1%

0.4%

1.7%

-38.6%

-14.5%

-9.8%

-4.2%

-1.7%

-2.9%

-3.0%

0.5%

1.8%

-25.8%

-13.9%

-5.9%

-2.4%

-6.2%

-1.6%

-1.7%

0.0%

4.6%

29

Although net imports decline, climate change conditions do not significantly change the composition of imports to the Republic of Korea in 2050 (Figure 4.19). Similarly, the shifts in trade do not change the composition of kilocalories per commodity in 2050.

Figure 4.19 Composition of net imports to the Republic of Korea in 2050 by crop group, percentage of total food imports



Projections for Food Security in the Republic of Korea and Its Regional Trade Partners Overall, considering climate change impacts, domestic production, and net trade, kilocalories per capita are projected to decrease by less than 3 percent under climate change in the Republic of Korea compared with a NoCC reference scenario (Figure 4.20). The share of the population at risk of hunger is already low in the Republic of Korea and will continue declining, although under climate change the decline is slightly slower (Figure 4.21). The same trend can be observed for the entire EAP region (Figure 4.22).

Figure 4.20 Kilocalories per capita in the Republic of Korea, trend to 2050



0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Percentage of total

SSP2-NoCC

SSP2-GFDL

SSP2-HadGEM

SSP2-IPSL

CommodityCerealsFruits and VegetablesOilmealsOther CropsProcessed OilsPulsesRoot and Tubers

Sugar CropsTraded Oilseeds

30

Figure 4.21 Share of population at risk of hunger, Republic of Korea, to 2050



Figure 4.22 Share of population at risk of hunger, East Asia and Pacific region, to 2050



The reason for these changes is that under climate change, estimates of kilocalories by commodity for the Republic of Korea are lower for maize and rice, which are major sources of dietary energy (Figure 4.23). But the declines in kilocalories from maize and rice are only slightly greater than they are under the NoCC reference case and are partially offset by increases for soybeans, vegetables, wheat, and other food items. In fact, roots and tubers, and cereals are the only two commodity groups

31

projected to supply a decreasing number of kilocalories over the next 40 years. This trend is observed for all four scenarios, but the reduction happens faster under climate change conditions. These results at the crop level are consistent with the observed overall decrease in kilocalories per capita under climate change compared with the NoCC scenario.

Between 2010 and 2050, the cumulative effects of changes in net trade (imports and exports) and in domestic demand and production lead to shifts in the kilocalories contributed by the modeled commodity groups (Figure 4.24). However, there are no significant changes between the NoCC and the climate change scenarios, an indication that diets shift across time (between 2010 and 2050) but do not shift significantly across scenarios.

Figure 4.23 Changing kilocalorie availability from 2010 to 2050 across the baseline suite of scenarios for key commodities



Figure 4.24 Shift in diet composition by kilocalories per capita for the Republic of Korea between 2010 and 2050 by crop group, percentage of total kilocalories, all scenarios



Impacts of New Technology Adoption on Food Security in the Republic of Korea Results in the previous sections showed that climate change may have some negative impacts on biophysical rice yield. However, when taking into consideration projections of growth for the agricultural sector and associated market effects, rice cultivation in the Republic of Korea may continue on an upward trend even under climate change. Not only do rice yields and production grow under climate change conditions, but in some cases the growth is actually projected to be larger than under the NoCC reference case.

Maize Rice Soybean Vegetables Wheat

-40% 20%Percentage change





SSP2-NoCC

SSP2-GFDL

SSP2-HadGEM

SSP2-IPSL

-13.3%

-14.9%

-17.3%

-16.6%

-21.3%

-22.5%

-23.6%

-23.0%

14.3%

12.6%

10.6%

8.5%

2.8%

2.3%

1.3%

1.8%

13.3%

13.0%

11.6%

8.0%

Kcal/capita by commodity group

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%Percentage of total value

2010

SSP2-NoCC

SSP2-GFDL

SSP2-HadGEM

SSP2-IPSL

2050

SSP2-NoCC

SSP2-GFDL

SSP2-HadGEM

SSP2-IPSL

CommodityCerealsFruits and VegetablesOther CropsProcessed OilsPulsesRoot and Tubers

Sugar CropsTraded Oilseeds

32

On the other hand, although the availability of important food imports10 still increases compared with 2010 under climate change, in some instances the net exports of these crops from producing countries decrease significantly relative to the NoCC reference case. Thus, the net effect of climate change impacts on domestic production and import-export trends is that although kilocalories per capita continue growing in the Republic of Korea, this and other food security indicators show slight deterioration under climate change, when compared with a NoCC reference baseline.

Given that rice is projected to remain the major source of kilocalories in the Republic of Korea, increasing rice yields can be a form of insurance in the face of future uncertainty and help bridge the calorie gap triggered by climate change. In addition, because the country is currently highly dependent on trade, there may be a link between the decrease in available kilocalories under climate change and the projected decrease in net imports under the same scenarios (Figure 4.18). Therefore, a few questions may be of interest:

• Could the adoption of improved technologies in rice cultivation further increase production and consumption of domestic rice?

• Could the adoption of improved technologies affect the terms of trade for the Republic of Korea—that is, would net trade grow or decline?

• Could the adoption of improved technologies in rice affect the production of other crops? • Could the adoption of improved technologies provide an increase in kilocalories per

person, in the Republic of Korea and in the region? In order to answer these questions, we built four scenarios (defined in Table 3.2 in the

methodology section), each simulating the adoption of a different improved agricultural technology or practice under a single climate change scenario (SSP2-HadGEM-RCP8.5). We project scenarios in which the four technologies—integrated soil fertility mamanagement (ISFM), precision agriculture (PRAG), drought-tolerant rice varieties (DT), and heat-tolerant rice varities (HT)—are adopted over current rice cropland across the Republic of Korea, Democratic People’s Republic of Korea, China, and Japan.

Effects of New Rice Technologies on Rice Production Figure 4.25 shows growth in rice area, production, and yields in the four countries where the improved technologies are adopted. In most regions of the world, rice production grows between 2010 and 2050, but adoption of these technologies brings additional positive changes in the adopting countries (and their region, EAP) (Figure 4.26).

10 That is, the agricultural production of food crops by countries that are known to be major exporters to the Republic of

Korea.

33

Figure 4.25 Percentage change in rice area, production, and yield due to technology adoption, 2050, compared with a reference scenario without technology adoption (SSP2-HadGEM-RCP8.5 climate change scenario)

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: HadGEM = Hadley Centre’s Global Environment Model; ISFM= Integrated soil fertility management; RCP =

representative concentration pathway; SSP2 = Shared Socioeconomic Pathway 2.

As seen earlier, in Figure 4.7, under the SSP2-HadGEM-RCP8.5 climate change scenario, rice yields are expected to grow at the same pace shown in the NoCC reference scenario. As Figures 4.25 and 4.26 show, yields with technology adoption grow faster than those under the climate change reference scenario without technology adoption and thus also faster than under the NoCC reference. Production follows the same path.

Simulation results under SSP2-HadGEM-RCP8.5 show similar results for rice demand and area, with marginal changes from the HadGEM reference under all the technology adoption scenarios. Moreover, in the section titled “Climate Change Impacts on Rice and Other Key Agricultural Commodities in the Republic of Korea,” we observed a decline in rice area and demand between 2010 and 2050 across all scenarios, including NoCC and the HadGEM reference. Therefore, the trends of decline in rice demand and area are similar across the reference scenarios and the technology adoption scenarios under climate change.

Area Production Yield

0% 20%Percentage difference



China Precision agricultureISFMHeat tolerance

Drought toleranceDem. People'sRep of Korea

Precision agricultureISFMHeat tolerance

Drought toleranceJapan Precision agriculture

ISFMHeat tolerance

Drought toleranceRepublic ofKorea

Precision agricultureISFMHeat tolerance

Drought tolerance

3.5%

2.8%

1.0%

0.2%

-1.3%

-0.9%

1.4%

0.7%

0.4%

0.2%

1.1%

0.0%

1.5%

1.3%

0.3%

1.3%

23.4%

17.8%

5.9%

1.4%

2.3%

2.2%

8.8%

3.9%

10.8%

7.5%

8.7%

0.7%

16.7%

13.2%

3.8%

6.9%

19.2%

14.6%

4.9%

1.1%

3.7%

3.2%

7.3%

3.2%

10.4%

7.3%

7.4%

0.7%

15.0%

11.8%

3.5%

5.6%

34

Figure 4.26 Percentage change in rice harvested area, production, and yield due to technology adoption in China, Japan, Democratic People’s Republic of Korea, and the Republic of Korea, 2050, compared with reference of no technology adoption, selected world regions (SSP2-Hadgem-RCP8.5 climate change scenario)

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: EAP = East Asia and Pacific; ECA = eastern Europe and central Asia; HadGEM = Hadley Centre’s Global Environment

Model; ISFM = integrated soil fertility management; LAC = Latin America and Caribbean; NA = North America; RCP = representative concentration pathway; SA = South Asia; SSA = Africa south of the Sahara; SSP2 = Shared Socioeconomic Pathway 2.

Effects of New Rice Technologies on Rice Trade In terms of the effects on prices, Table 4.4 shows that adoption of the improved technologies across the four countries is projected to reduce the world price of rice. PRAG and ISFM induce stronger changes in world rice price than do HT and DT. No change in world price was observed for other cereals of potential interest for the Republic of Korea (barley, maize, and wheat), nor for soybeans. As might be expected due to the projected increase in rice production, net trade in rice grows substantially under the technology adoption scenarios when compared with the HadGEM no-technology reference (Figure 4.27).


-10% 0% 10%Percentage difference



EAP Precision agricultureISFMHeat tolerance

Drought toleranceECA Precision agriculture

ISFMHeat tolerance

Drought toleranceLAC Precision agriculture

ISFMHeat tolerance

Drought toleranceNA Precision agriculture

ISFMHeat tolerance

Drought toleranceSA Precision agriculture

ISFMHeat tolerance

Drought toleranceSSA Precision agriculture

ISFMHeat tolerance

Drought tolerance

-0.2%

-0.1%

0.0%

0.0%

-4.9%

-3.7%

-1.3%

-0.4%

-4.6%

-3.5%

-1.3%

-0.3%

-7.3%

-5.6%

-2.0%

-0.6%

-3.1%

-2.4%

-0.9%

-0.2%

-4.0%

-3.1%

-1.1%

-0.3%

8.0%

6.1%

2.2%

0.6%

-5.8%

-4.5%

-1.6%

-0.4%

-5.9%

-4.5%

-1.6%

-0.4%

-8.1%

-6.2%

-2.3%

-0.6%

-3.9%

-3.0%

-1.1%

-0.3%

-5.2%

-4.0%

-1.4%

-0.4%

8.2%

6.2%

2.2%

0.6%

-1.0%

-0.8%

-0.3%

-0.1%

-1.3%

-1.0%

-0.4%

-0.1%

-0.8%

-0.6%

-0.2%

-0.1%

-0.8%

-0.6%

-0.2%

-0.1%

-1.2%

-0.9%

-0.3%

-0.1%

35

Table 4.4 Percentage change in world price of rice under technology adoption scenarios, 2050, compared with reference of no technology adoption

Scenario Percentage change in rice world price Precision agriculture -8.0% Integrated soil fertility management -6.2% Heat tolerance -2.2% Drought tolerance -0.6%

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations:

Figure 4.27 Net trade for rice in the Republic of Korea between 2010 and 2050 under technology adoption scenarios and a reference scenario without technology adoption (SSP2-HadGEM-RCP8.5 climate change scenario)

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: HadGEM = Hadley Centre’s Global Environment Model; RCP = representative concentration pathway; SSP2 = Shared

Socioeconomic Pathway 2.

Net exports of rice increase in the four countries where technologies are adopted (China, Japan, Democratic People’s Republic of Korea, and Republic of Korea) and in the EAP region, where trade is driven by these countries (Figure 4.28). In the Republic of Korea, the increase in rice exports relative to the reference case due to improved technology adoption ranges between +11 percent with adoption of HT rice varieties and +51 percent with PRAG.

36

Figure 4.28 Net trade for rice in the East Asia Pacific region (SSP2-HadGEM-RCP8.5 climate change scenario)

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: HadGEM = Hadley Centre’s Global Environment Model; ISFM = integrated soil fertility management; RCP =


Effects of New Rice Technologies on the Production of Other Crops of Interest An additional question is whether the changes in rice production and yields following technology adoption in these four countries (Republic of Korea, Democratic People’s Republic of Korea, Japan, and China) may have effects on the production and trade of other crops of interest for the Republic of Korea. Because of their relevance in terms of production and harvested area (FAO 2016a), we will look at the effects for barley, soybeans, and vegetables. The latter two are also important from a kilocalorie standpoint (FAO 2016b).

Simulation results show modest effects. Area, production, and yield of these other crops decrease slightly compared with the reference when improved technologies are adopted in rice cultivation across the four countries considered. Some of the largest reductions are in the Republic of Korea (Figure 4.29). Net trade of these commodities also shifts, but not substantially.

37

Figure 4.29 Percentage change in harvested area, production, and yield between technology scenarios and NoCC reference, 2050, Republic of Korea

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: Effects in Republic of Korea of adoption of technologies in rice cultivation across four countries (Republic of Korea,

Democratic People’s Republic of Korea, Japan, and China). ISFM = integrated soil fertility management.

Effects of New Rice Technologies on Key Food Imports Adoption of the improved technologies for rice has effectively no secondary impact on the Republic of Korea’s major agricultural imports. The technology scenario results do not show any impacts on the production of maize, wheat, vegetables, and soybeans in the countries exporting to the Republic of Korea. However, technology scenarios do show some effects on net trade for some crop groups in the Republic of Korea. Technology adoption appears to make the terms of trade more favorable for cereals but less favorable for fruits and vegetables (Figure 4.30). In fact, net imports for cereals decrease slightly under technology adoption while exports increase up to about 50 percent (with PRAG); conversely, net exports of vegetables show a decrease of up to 3.4 percent (again with adoption of PRAG).


-1.5% -1.0% -0.5%Percentage difference



Barley Precision agricultureISFM

Drought toleranceHeat tolerance

Soybean Precision agricultureISFM


Vegetables Precision agricultureISFM


-1.2%

-1.0%

-1.0%

-0.3%

-0.4%

-0.3%

-0.3%

-0.1%

-1.2%

-1.1%

-1.1%

-0.2%

-1.3%

-1.1%

-1.0%

-0.3%

-0.6%

-0.5%

-0.5%

-0.1%

-1.5%

-1.2%

-1.3%

-0.3%

-0.1%

-0.1%

-0.1%

0.0%

-0.2%

-0.2%

-0.2%

0.0%

-0.2%

-0.2%

-0.2%

0.0%

38

Figure 4.30 Technology adoption effects on net trade, 2050, major crop groups, Republic of Korea (SSP2-HadGEM-RCP8.5 climate change scenario)

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: HadGEM = Hadley Centre’s Global Environment Model; ISFM = integrated soil fertility management; RCP =


Effects of New Rice Technologies on Food Security Technology adoption increases the per capita kilocalorie consumption from rice compared with the HadGEM reference scenario without technology adoption, but not compared with the NoCC reference scenario (Figure 4.31). However, even by 2050, the increase in kilocalories triggered by the technologies is small (Figure 4.32). In addition, the overall changes in kilocalories per capita11 are not affected substantially by technology adoption across the four countries (Figure 4.33).

11 These are kilocalories obtained from all food commodities.

39

Figure 4.31 Trend in per capita kilocalories from rice, Republic of Korea, across technology adoption scenarios, reference climate change scenario and no-climate-change scenario

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: HadGEM = Hadley Centre’s Global Environment Model; ISFM = integrated soil fertility management; NoCC = no

climate change.

Figure 4.32 Percentage change in kilocalories per capita between rice crop technology scenarios and reference scenario without technology adoption, 2050

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations.

2010 2020 2030 2040 2050500

550

600

650

700

750

800

850

Kilo

NoCC baselineHadGEM baseline

Drought tolerance

Heat toleranceISFMPrecision agriculture

Republic of KoreaDem. People's Rep of

Korea China Japan

0% 1% 2% 3%Percentage difference




Precision agriculture

ISFM

Heat tolerance

Drought tolerance

1.2%

0.9%

0.3%

0.1%

1.4%

1.1%

0.4%

0.1%

2.1%

1.6%

0.6%

0.1%

1.1%

0.9%

0.3%

0.1%

40

Figure 4.33 Trends in per capita kilocalories, Republic of Korea and the East Asia and Pacific region, across technology adoption scenarios, reference climate change scenario and no-climate-change scenario, 2010–2050

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: EAP = East Asia and Pacific; HadGEM = Hadley Centre’s Global Environment Model; ISFM = integrated soil fertility

management; NoCC = no climate change.

Overall, the increase in rice production due to improved technology adoption does not cause significant changes in terms of food security indicators. We see some moderate improvements in the population at risk of hunger (Figure 4.34); the changes are small due to the fact that in the Republic of Korea the share of the population at risk of hunger is already low.

EAP Republic of Korea

2010 2020 2030 2040 2050 2010 2020 2030 2040 20502,000

2,200

2,400

2,600

2,800

3,000

3,200

3,400

3,600

Kilo

NoCC baselineHadGEM baseline

Drought tolerance

Heat toleranceISFMPrecision agriculture

41

Figure 4.34 Percentage change in population at risk of hunger due to technology adoption, compared with reference of no technology adoption, 2050 (SSP2-HadGEM-RCP8.5 climate change scenario)

Source: Authors’ International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) simulations. Notes: EAP = East Asia and Pacific; HadGEM = Hadley Centre’s Global Environment Model; ISFM = integrated soil fertility

management; RCP = representative concentration pathway; SSP2 = Shared Socioeconomic Pathway 2.

Comparison between IMPACT and KASMO The trends in rice production estimated through the IMPACT model are different from those produced in the analysis by Kim et al. (2012). These authors used a crop model (CERES-Rice) and a farm decision-making and trade model, the Korea Agricultural Simulation Model (KASMO), to estimate future rice production under climate change. In Table 4.5, the category KASMO shows the results of KASMO when climatic variables are inserted directly into this model. The category CERES, on the other hand, shows results from KASMO when changes in rice yields are estimated using CERES-Rice and these data are then used as inputs into the KASMO model. Regardless of the scenario, simulations show a decline in both rice yield and production; the decline appears exacerbated under climate change.

Population at risk of hunger

-3.00% -2.50% -2.00% -1.50% -1.00% -0.50% 0.00%Percentage difference

Republic ofKorea

Drought toleranceHeat toleranceISFMPrecision agriculture

Dem.People's Repof Korea

Drought toleranceHeat toleranceISFMPrecision agriculture

China Drought toleranceHeat toleranceISFMPrecision agriculture

Japan Drought toleranceHeat toleranceISFMPrecision agriculture

EAP Drought toleranceHeat toleranceISFMPrecision agriculture

-0.1%

-0.2%

-0.2%

0.0%

-0.1%

-0.4%

-1.2%

-1.6%

0.0%

0.0%

0.0%

0.0%

-0.2%

-0.8%

-2.3%

-3.0%

-0.2%

-0.6%

-1.6%

-2.1%

42

Table 4.5 Rice production change under climate change

Category Common year 2010 2015 2020 2030 2040 2050

No climate change (KASMO)

Area (1,000 ha) 917 892 836 822 740 656 572

Yield (kg/10 a) 498 482 501 504 509 514 519

Production (1,000 t) 4,516 4,295 4,192 4,141 3,764 3,371 2,970

Import (1,000 t) 297 327 409 409 677 828 1,087

Self-sufficiency (%) 95.7 83.0 90.4 91.0 84.8 80.3 73.3

Scenario 1 (KASMO) RCP8.5

Area (1,000 ha) 917 892 837 825 732 627 522

Yield (kg/10 a) 498 482 498 495 486 473 456

Production (1,000 t) 4,516 4,295 4,166 4,087 3,560 2,967 2,379

Import (1,000 t) 297 327 409 409 855 1,180 1,601


Scenario 2 (CERES) A2

Area (1,000 ha) 917 892 836 824 730 624 519

Yield (kg/10 a) 498 482 502 495 483 470 459

Production (1,000 t) 4,516 4,295 4,195 4,082 3,524 2,937 2,379

Import (1,000 t) 297 327 409 409 887 1,205 1,598


Scenario 3 (CERES) RCP8.5

Area (1,000 ha) 917 892 836 825 726 613 501

Yield (kg/10 a) 498 482 502 492 473 453 433

Production (1,000 t) 4,516 4,295 4,195 4,062 3,429 2,776 2,168

Import (1,000 t) 297 327 409 409 970 1,344 1,779


Source: Adapted from Kim et al. (2012). Notes: KASMO = Korea Agricultural Simulation Model RCP = representative concentration pathway.

First, the scope of the two models, IMPACT and KASMO, are different. KASMO focuses specifically on the agricultural sector of the Republic of Korea. KASMO combines importing and exporting countries into a single group when simulating food trade, while IMPACT considers each country as an individual agent. KASMO takes advantage of more detailed information about the food market of the Republic of Korea and simulates the farm-level decision-making process (although not for other countries). For example, simulations performed with KASMO by Kim et al. (2012) included the effects of direct payment and free trade agreements planned by the Republic of Korea. The IMPACT model does not contain detailed trade regimes or elaborate farm-level decision-making processes, but it can quickly analyze a broad range of scenarios, and it is much broader in geography, covering the entire globe.

Second, the global climate change scenarios used in the simulations are different. Kim et al. (2012) employed data from the Korea Meteorological Administration (KMA). KMA used HadGEM3-RA to forecast future regional climate change; however, the present study uses HadGEM2-ES. The two versions of the HadGEM model are assumed to be similar, especially when focused on the Republic of Korea and even the East Asia region, but the precise differences remain to be verified. In the future we

43

hope to be able to test this hypothesis by using the same climate scenario in IMPACT that Kim et al (2012) used in KAS.MO.

Third, basic assumptions such as GDP growth, population growth, and IPRs (that is, future trends of productivity growth) are different in IMPACT and KASMO. Future research will focus on running additional simulations in IMPACT’s model framework to test how much these assumptions affect estimates of future rice production and yields.

44

5. CONCLUSIONS

Global trends of growth in population, income, and urbanization are driving up food demand and putting pressure on agricultural systems worldwide. The result has been a steady increase in the price of basic food commodities in recent years, reversing the long-term downward trends experienced in previous decades. The impacts of climate change on agricultural productivity are likely to add stress to the system, potentially driving up food prices and adding increased risks to world food markets while also increasing competition for land and water resources.

Based on data from FAO, the yield growth rate for rice in the Republic of Korea has been stagnating at least since the early 1990s (FAO 2016a). Additionally, our simulations show that by 2050, the country may face increasing precipitation of between 27 and 168 mm of rain per year, while minimum average annual temperatures may rise between 2.0°C and 3.4°C over preindustrial values (Appendix Table A.1). As the country has shifted toward becoming increasingly dependent on food imports, a concern has arisen that climate change impacts may affect the stability of the food supply and thus “exert considerable downward pressure on the economy” (Kim et al. 2012). Sustained investments in agricultural research and development will be necessary to bolster productivity trends while also helping the country prepare to face the impacts of climate change.

This study sought to answer questions on possible future trends of agricultural productivity growth as well as food supply and demand in the country under climate change, focusing on rice and other crops of interest. In order to investigate the potential returns on investments in agricultural research and development, we have also analyzed the prospective consequences of adopting yield-enhancing agricultural technologies in rice cultivation, including stress-tolerant varieties and climate-adaptive agricultural practices. As we do this, we recognize that this analysis looks at the impacts of long-term changes in productivity as affected by changing trends in average temperature and precipitation. Other important dimensions of climate change, including extreme events and sea level rise, are beyond the scope of the present analysis and represent important topics for further investigation.

Results from linked crop and economic model simulations show that historical trends of harvested area (decreasing) and imports (increasing) may continue into the future, although the pace of the trends appears slower under climate change than without climate change effects. Building on assumptions about future rates of growth in the agricultural sector, the IMPACT partial equilibrium multimarket model used in this study projects an increase in rice production and yields between 2010 and 2050 when all exogenous and endogenous effects are considered. This trend holds under scenarios with or without climate change. Even though crop models suggest negative long-term biophysical effects of climate change on rice yields in the Republic of Korea, economic model simulations show that intrinsic productivity growth and market effects (for example, price signals and trade) have the potential to limit the magnitude of losses in yields and production. Moreover, the increase in the world price of rice under climate change, combined with decreasing domestic demand, appears to drive an increase in rice production destined for export from the Republic of Korea. At the same time, food production from the country’s major trading partners increases, even under climate change. In general, net exports from those same countries also increase, although under climate change the rate of growth can at times be substantially reduced.

Given trends in domestic production as well as outcomes from trade exchanges, the number of kilocalories per capita in the Republic of Korea is projected to increase by 9 percent between 2010 and 2050, even as the composition of these calorie sources is changing across time as the diet decreases in dependence on cereals. We also estimate climate change impacts to cause less than a 3 percent reduction in this upward calorie trend as the share of population at risk of hunger in the country falls steadily to 2050. In sum, although climate change may have some impact in reducing overall kilocalorie availability in the Republic of Korea, the effect does not appear strong enough to have significant consequences on projected trends of increasing food security. This trend is common to other countries in the East Asia region.

45

Adoption of improved technologies could further increase rice production in the Republic of Korea by between 4 and 17 percent by 2050, depending on the technology. Although this increase in production may boost the availability of kilocalories per capita from rice under climate change, total kilocalories remain lower than under a scenario without climate change. There is ample opportunity for the Republic of Korea to address the challenges it faces with future climate change through wise investments in agricultural productivity that will have positive impacts on both the national food supply and, through increasing trade, on the broader agricultural sector.

It has been noted that the changes in rice production under climate change simulated in this study through the combination of DSSAT and IMPACT follow a growing trend, which is different from simulation results previously done by Kim et al. (2012) using the CERES-Rice crop model in combination with a farm-level decision support and trade model (KASMO). Results from Kim et al. (2012) show a consistent decline in rice production in the Republic of Korea under both NoCC and climate change. A number of factors may be determining these opposing trends. First, the scope of the two models, IMPACT and KASMO, is different. KASMO focuses specifically on the agricultural sector of the Republic of Korea and takes advantage of detailed information about the country’s food market, whereas IMPACT has broader geographical coverage and simulates the mechanisms of the global food market. Second, Kim et al. (2012) employed data from HadGEM3-RA to forecast future regional climate change, while the present study uses HadGEM2-ES. The two versions of the HadGEM model are assumed to be similar, but the precise differences remain to be verified. In the future we hope to be able to test this hypothesis by using the same climate scenario in IMPACT. Third, simulations in IMPACT and KASMO are informed by different assumptions on basic drivers such as GDP growth, population growth, and IPRs (that is, future trends in productivity growth). Future research will focus on running additional simulations in IMPACT’s model framework to test how much these assumptions affect estimates of future rice production and yields.

As a final note, it is important to point out that modeling results, such as those illustrated in this study, are not predictions but rather projections of possible futures, dependent on a number of assumptions. The chosen IPRs represent one of these assumptions. As for every country included in the IMPACT model, the IPRs for the Republic of Korea used in this study are based on a combination of historical trends and expert opinion, and as such they remain in line with past trends of growth for the country. Different groups of experts may see this as either a conservative or an optimistic set of assumptions. The variety of opinions and expectations about future trends in investments and developments in the country’s agricultural sector warrants future investigation of other assumptions of growth for the country. This is the type of sensitivity analysis that would add value to the country-level analysis performed in the present study.

46

APPENDIX A: BRIEF SUMMARY OF CLIMATE PARAMETERS

Table A.1 shows the annual average difference in precipitation and maximum and minimum temperatures between a reference climate, representative of conditions around the year 2000, and three climate change scenarios obtained by running the GFDL, HadGEM, and IPSL earth system models under an RCP of 8.5.

Table A.1 Difference in precipitation and temperature between reference scenario (year 2000) and climate change scenarios (around year 2050)

∆ total precipitation (mm/year) ∆ average temperature maximum (oC)

∆ average temperature minimum (oC)

Region GFDL HadGEM IPSL GFDL HadGEM IPSL GFDL HadGEM IPSL

Agr

icul

tura

l ar

ea


134.4 210.0 76.8 1.8 3.2 3.3 2.0 3.6 3.2

Republic of Korea 144.0 168.0 27.4 1.8 3.1 3.0 2.0 3.3 2.8

Japan 36.0 116.0 20.5 2.0 2.9 2.9 2.0 3.0 2.9

China 56.6 140.4 19.6 1.9 3.3 3.1 1.8 3.3 3.1

Land

are

a


131.8 196.2 66.3 1.9 3.2 3.3 2.0 3.6 3.3

Republic of Korea 147.1 174.4 23.0 1.8 3.1 3.1 2.0 3.4 2.9

Japan 41.4 128.3 23.0 2.0 2.9 2.9 2.0 3.0 2.9

China 37.6 74.1 24.7 2.1 3.4 3.2 2.0 3.3 3.4

Source: Authors. Notes: The data area shown for values either over the agricultural land, or across the total land of the country. GFDL = Geophysical Fluid Dynamic Laboratory; HadGEM =

Hadley Centre’s Global Environment Model; IPSL = Institut Pierre Simon Laplace.

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APPENDIX B: MODULARITY IN IMPACT: CROP MODELS, WATER MODELS, AND FOOD SECURITY MODULES

The sections below are extracted word for word from the IMPACT documentation report (Robinson, Mason-D’Croz, Islam, Sulser, et al. 2015).12 The text is provided to clarify the modularity aspect of the IMPACT system of models and clarify how the food security indicators (for example, population at risk of hunger) are calculated in the IMPACT model. The figures, tables, boxes, and equations maintain the numbering of the original report. --------------------------------------------------

The IMPACT model system is a network of linked models. Major components include climate models, crop models, and water models. The model system now includes a number of additional modules, and more are in development. Some of these modules are integrated into the multimarket model, and others are coded as separate modules that are linked through information flows to others. Figure 5.1, a detailed schematic of the IMPACT multimarket model, illustrates how many of these modules are interconnected. In this section, we will discuss these new modules and provide further description of the major components of the IMPACT model system such as data management and estimation, scenario specification and implementation, food security indicators, crop models, and water models.

Figure 5.1 Detailed IMPACT multimarket model schematic

Source: Authors. Notes: CGE = computable general equilibrium; GDP = gross domestic product; IMPACT = International Model for Policy

Analysis of Agricultural Commodities and Trade; IPR = intrinsic productivity growth rate.

12 This appendix contains text originally published in IFPRI Discussion Paper 01483, The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description for Version 3. It is included here with permission from the International Food Policy Research Institute (2016).

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Specifying IMPACT as a set of linked models has required major changes in the computer code, which was completely rewritten in moving from version 2 to 3. In the next section, we discuss the design of the new model code, particularly how to implement a flexible modular system.

Modularity In the redesign of IMPACT from version 2 to 3, great effort was made toward the implementation of best practices in software design not only to widen the domain of applicability of the model (that is, more commodities, countries, and features) but also to improve the quality of the model code and design. The object was to create a model design that was transparent and flexible, allowing for easier future model updates and improvements. Some elements of a modular approach had been used in IMPACT 2; however, in IMPACT 3 modularity has become a key design feature. Modularity is defined as the breaking up of software into separate and addressable components that are integrated to address specific problems (Pressman 2010). In an increasingly complex model such as IMPACT, not pursuing a modular approach risks creating monolithic software, which is difficult to understand, edit, debug, and maintain. Modular design has many benefits (Box 5.1) but requires careful design and discipline to implement.

Box 5.1 Benefits of modular design

Modular software design has many benefits that not only improve the quality of the software code but also facilitate future software development. Some of the key benefits of modular design are the following: • It facilitates breaking down complex problems into smaller and easier-to-solve subproblems. • It allows for parallel and distributed model development, with many modelers working on different

subproblems simultaneously. • It allows for different modules, in various combinations, to be used to solve different problems. • Modularity increases the readability of the model code, making it easier to understand, edit, debug,

and maintain. • It facilitates model updating. If integration is properly designed, one module can easily be replaced

with an improved module without having to update any other part of the linked model system. • It provides the ability to turn on and off modules that may not be needed for certain tasks, simplifying

the model and improving solution time. • The modules can be run in stand-alone mode, independently of the other linked modules, which

greatly facilitates development and testing of modules. • It facilitates multidisciplinary collaboration and utilization of wide-ranging expertise (for example,

collaboration across different CGIAR centers to improve modeling of water, livestock, fish, and nutrition, among others).

Source: Authors.

Modularity comes with some cost. It is necessary to develop standards of intermodule communication to integrate the different modules. The greater the number of modules, the easier each module is to create, but the more complicated it is to link them all. Developing and implementing linkage standards can be challenging and time consuming, so a balance must be achieved between developing more and simpler modules versus the time required to integrate them, a challenge illustrated well by Roger Pressman (2010) in Figure 5.2.

49

Figure 5.2 Modularity and software cost

Source: Pressman (2010, 226).

In IMPACT, we have classified modules based on the depth of their linkages. We handle module integration in three main ways, distinguished by how deeply they are integrated and the flow of information between the modules.

1. One-way information flow: This type of module integration occurs when the results of one module are inputs into another module and there are no feedback loops. We can think of these interactions as exogenous or external exchanges of information from one module to another. Data transfers between modules occur through data files, and neither module directly changes other module values. Examples of one-way information flow are crop-modeled climate shocks into the multimarket model, water flows in river basins from the global hydrology model, and postprocessing food security modules that take results of the multimarket model as inputs to estimate changes in undernourished children and risk of hunger.

2. Iterative two-way information flow: This type of coupling is needed when there needs to be some type of feedback loop. This type of integration illustrates sequential cohesion between modules, where the outputs of one module serve as inputs into the next module (van Vliet 2007) and the outputs of this second module need to be fed back as inputs into the original module. Examples of this type of integration can be seen with how the multimarket model is connected with the IMPACT water basin management and water stress models, where economic results each year serve as inputs to the water models and then the results of the water models are fed back to the multimarket model to simulate the effects of changes in irrigated water supply.

3. Dynamic and endogenous information flow: This type of integration is required when complete integration of modules is required. This reflects “content coupling, where each module directly affects the working of another module” (van Vliet 2007). This integration is needed when modules must be solved simultaneously and all information between modules must be freely shared. Examples of this type of integration are the integration of commodity demand, trade, and production, which are solved simultaneously within the multimarket model.

We have already covered in detail the dynamic and endogenous modules in IMPACT in Section 4. The rest of this section will present how other critical modules function within the IMPACT model system. The design of these modules has followed several key standards, which are outlined in Box 5.2.

50

New modules should be developed following these design standards and should aim to have a distinct research publication describing the module, its uses, and how it fits into the IMPACT model system. Modules used in stand-alone mode, treating inputs from other modules and the IMPACT multimarket model as exogenous, can be useful research tools within their own disciplines (for example, water models, detailed livestock models, land-use models).

Box 5.2 IMPACT module design requirements

A module in the IMPACT model system should be designed to • read its own parameters, • initialize its own variables, • accept variables passed to it from other modules, • pass variables computed within the module to other modules, • own its set of state variables (information hiding in software design parlance), and • be able to operate in stand-alone mode without being dynamically coupled to other modules. Source: Authors. Notes: IMPACT = International Model for Policy Analysis of Agricultural Commodities and Trade.

IMPACT Data Management and Estimation System While pursuing a modular design in IMPACT, there has been a focus on making data processing independent from the behavioral model system. The goal is that any module should have standard data requirements and that the data sources could be changed as long as they conform to deliver standard data inputs of the modules. This standardization of data inputs has allowed the breaking up of processing the IMPACT database into a series of specific data-processing modules, each focused on preparing one part of the IMPACT database. These data modules are linked into a separate IMPACT Data Management and Estimation System that provides all the data needed to implement the IMPACT model system. These data processing modules include the following:

• Food and Agriculture Organization of the United Nations (FAO) production, trade, and demand estimation program: An estimation module that uses cross-entropy estimation techniques to estimate a consistent and balanced base year database for IMPACT from FAOSTAT, AquaStat, IFPRI-SPAM, and other data sources. For more information about this module see Mason-D’Croz, Robinson, and Islam (2015)

• Population and GDP processing module: An aggregation module that takes data for population, GDP, and growth rates from a variety of sources, including the World Bank’s World Development Indicators (WDIs), UN population statistics, Central Intelligence Agency World Factbook, and the Shared Socioeconomic Pathways (SSP) database, and puts them into an IMPACT-ready format

• Price-processing module that reads in data from Organisation for Economic Co-operation and Development (OECD) Agricultural Market Access Database and maps them to IMPACT commodities

• Trade parameter–processing module that reads in data from OECD and Global Trade Analysis Project at Purdue University to IMPACT commodities and countries

• Model calibration modules that join GAMS, Excel, and Tableau to generate complex data visualizations, which are used to compare IMPACT results with historical trends and to inform model calibration to adjust IMPACT parameters in response to these trends and new expert judgment

• Climate data processing module, which reads in results from crop models aggregated to the food production unit (FPU) level and then processes them into average annual climate shocks for all IMPACT commodities

51

IMPACT Scenario Environment By designing standards for coupling of data in IMPACT, it has been possible to develop a more user-friendly interface for handling common and repetitive tasks, such as scenario design and specification, and compiling and generating result files and data visualizations. For IMPACT 3, elaborate graphical user interfaces were developed in Excel and use a Visual Basic ActionScript backend to read in data and output GAMS files that can be executed directly from Excel. This feature now allows IMPACT to be used by a wider set of users, as it no longer requires knowledge of GAMS to run IMPACT. This work builds on efforts done in IMPACT 2 to develop an Excel interface to run the model. The IMPACT Scenario Environment has two primary components: (1) the scenario development and specification tool and (2) the report and outputting tool.

The scenario development and specification tool allows users to easily specify scenario drivers to define a wide array of scenarios. This tool allows users to easily adjust assumptions on growth rates on agricultural productivity, agricultural land, population, and economic development. Users also can change assumptions in the IMPACT water models to simulate changes in irrigation infrastructure and technology. In addition, users can include various climate assumptions from processed crop model results.

The report and outputting tool allows users to easily read in IMPACT result files (in GAMS data exchange files, or GDXs) and execute a variety of standard postprocessing and outputting programs. This facilitates the process of merging, aggregating, and presenting results from one or more IMPACT scenario result files. Currently, the report and outputting tool provides the following functionality:

• Generating StatPlanet interactive web data visualizations • Generating static and publication-ready maps, line graphs, and box-whisker graphs using

R statistical package • Generating Excel pivot tables of results aggregated to user-defined aggregations of

regions and commodities

Food Security Modules Food security is an important aspect analyzed with IMPACT. Understanding the interplay of commodity production, trade, and demand is valuable, but understanding some of the potential human welfare implications of these changes is also important to better understand consequences of different scenarios. In IMPACT, there are two food security modules that were designed to give policy makers a sense of how countries were progressing toward the Millennium Development Goals (goal 1, target 2). The first module, based on work by Smith and Haddad (2000), estimates changes in child wasting (underweight) based on changes in food availability at the country level. The second module, based on work by Fischer et al. (2005), estimates changes in the share of population at risk of hunger based on changes in food availability. Both modules are examples of one-way postprocessing modules, where information from the multimarket model (food availability, population, GDP, and so forth) serves as an input to the module and the results are not fed back into the economic module.

Undernourished Children The percentage of undernourished children younger than five is estimated from the average per capita calorie consumption, female access to secondary education, the quality of maternal and child care, and health and sanitation (Rosegrant et al. 2001). Observed relationships between all of these factors were used to create the semi-log functional mathematical model, allowing an accurate estimate of the number of undernourished children to be derived from data describing the average per capita calorie consumption, female access to secondary education, quality of maternal and child care, and health and sanitation. The precise relationship used to project the percentage of undernourished children is based on a cross-country regression relationship of Smith and Haddad (2000).

52

( ) ( )

( )

tt,t0 t,t0 t,t0

t0

t,t0

25.24 ln 71.76 0.22

0.08 WAT

Percent change in undernourished childrenper capita kilocalorie availabilityRatio of female-male life exp

∆ = − × − ×∆ − ×∆

− ×∆

===

KCALUndNrsh LFER SCHKCAL

UndNrshKCALLFER

t,t0

ectancy at birthGross female secondary school enrollment rate% of population with access to safe waterDifference between time t and 2005

==

∆ =

SCHWAT

(1)

The data used in this calculation come from a variety of sources. The base values for undernourished children originally come from the World Bank’s WDIs (World Bank 2014). The base values for female-male life expectancy ratio, female secondary school enrollment, and access to safe water come from the WDIs (World Bank 2014). The projections of changes in female-male life expectancy come from the United Nations Population Prospects medium variant (United Nations 2011). The projections of changes in female secondary school enrollment and access to clean water come from the Technogarden Baseline Scenario (MA 2005).

The per capita kilocalorie availability is derived from two sources: (1) the amount of calories obtained from commodities included in the IMPACT-Food model and (2) the calories from commodities outside the model (FAO 2015).

After the percentage of undernourished children has been calculated, the total number of undernourished children is calculated as the product of equation 29, with the population of children (0–5 years old) coming from the appropriate SSP scenario (IIASA 2013).

Share at Risk of Hunger The share at risk is the percentage of the total population that is at risk of suffering from undernourishment. This calculation is based on a strong empirical correlation between the share of undernourished within the total population and the relative availability of food and is adapted from the work done by Fischer and others in the IIASA World Food System used by IIASA and FAO (Fischer et al. 2005).

2

89.63319.69

int 288.16Estimation error

Food supplyMinimum food requirement

α β εαβ

ε

= + + +== −==

=

==

ShareAtRisk RelativeKCal RelativeKCal int

KCalRelativeKCalMinKCal

KcalMinKcal (5.2)

53

It should be noted that due to the quadratic nature of this equation it is necessary to apply an upper and lower bound to the share at risk. The lower bound is defined as 0, and the upper bound is 100. Developed countries unsurprisingly have low share at risk, so for simplicity we treat all countries with less than 4 percent share at risk of hunger as if they had 0 percent share of hunger. The relative availability of food has been bounded to ensure realistic results on the quadratic curve: when the ratio of calories available to calories required, RelativeKCal, is greater than 1.7, we assume that the share at risk of hunger is effectively 0.

Crop Models The effect of climate change on crop yields starts by running the DSSAT family of crop models across a gridded representation of the world. Yield maps for groundnuts, maize, potatoes, rice, sorghum, soybeans, and wheat are compiled under both rainfed and irrigated conditions. Driving the model is a large collection of data. Some of the data represent soil characteristics and conditions as well as basic management decisions while others characterize the climatic conditions under which the crops were grown.

The climate data are maps of monthly climate data that allow the random generation of daily weather data for each location typical of what might be expected for conditions of the near recent past (2005) as well as those of the future (2050). The baseline climate information comes from Jones, Thornton, and Heinke (2009). The future climate information is derived from data processed by the Inter-sectoral Impact Model Intercomparison Project (Hempel et al. 2013; Piani, Haerter, and Coppola 2010; Weedon et al. 2011). The two datasets were combined by extracting the appropriate changes from the climate model data and imposing them on the common baseline climate. The crop models can then make projections about possible yields under the different climate circumstances.

The grid-based yields for each climate and crop combination are then aggregated within regions appropriate for the economic portions of the model. Specifically, they are computed as production-area-weighted averages using maps of production areas from the Spatial Production Allocation Model as weights (You et al. 2014). These are then used as weights in the multimarket model to estimate final yield impacts. This follows the general approach for incorporating projected yield changes from biophysical models into economics models as outlined in Müller and Robertson (2014).

Water Models The water models in the IMPACT Modeling System include (1) the IMPACT global hydrology model (IGHM) that simulates snow accumulation and melt and rainfall-runoff processes, (2) the IMPACT water basin simulation model (IWSM) that simulates operation of aggregate surface water reservoir and water supplies to economic sectors including irrigation, and (3) the IMPACT crop water allocation and stress model (ICWASM) that allocates available net irrigated water to crops and estimates the impact of water shortages on yields. These three models enable the IMPACT multimarket model to assess the effects on global food and water systems of hydroclimatic variability and change, socioeconomic change–driven water demand growth, investment in water storage and irrigation infrastructure, and technological improvements.

IGHM is driven by climate-forcing data and computes effective rainfall, potential and actual evapotranspiration, and runoff to river basins. The IGHM-simulated hydrologic outputs are then provided in a one-way link to IWSM, which optimally manages water basin storage and provides irrigated water supply in a one-way link to ICWASM, which then provides the IMPACT multimarket model with water stress–induced crop yield reductions for both irrigated and rainfed crops. The solution of IGHM depends only on climate inputs and is completely independent of the other water models and the IMPACT multimarket model. However, there is two-way communication between IWSM and the IMPACT multimarket model—the demand for water in IWSM depends on the allocation of land to crops, which is part of the solution of the IMPACT multimarket model. In turn, changes in water availability from IWSM

54

affect water allocation and stress in ICWASM. The communication between these models to capture this endogeneity is discussed below.

IGHM As described in the following schematic (Figure 5.5), IGHM is a semidistributed parsimonious model. It simulates monthly soil moisture balance, evapotranspiration, and runoff generation on each 0.5˚ latitude by 0.5˚ longitude grid cell spanning the global land surface except the Antarctic. Gridded output of hydrological fluxes—namely, effective rainfall, evapotranspiration, and runoff—are spatially aggregated to FPUs within the river basin and weighted by grid cell areas.

Figure 5.5 IMPACT global hydrology model schematic illustrating vertical water balance of the land and open water fraction of a grid cell

Source: Authors. Notes: E = evaporation (millimeters per month [mm/m]); ET = evapotranspiration (mm/m); GW = groundwater; IMPACT =

International Model for Policy Analysis of Agricultural Commodities and Trade; P = effective precipitation (mm/m); P* = precipitation (mm/m); R = total runoff (mm/m); RB = base flow (mm/m); RD = direct runoff from open water body (mm/m); RS = surface runoff (mm/m); S = soil moisture content (millimeters); T = temperature (°C); Tb = base temperature (°C), used as threshold to determine incoming precipitation as rain or snow.

The most important climatic drivers for water availability are precipitation and evaporative demand determined by net radiation at ground level, atmospheric humidity, wind speed, and temperature. In IGHM, the Priestley-Taylor equation (Priestley and Taylor 1972) is used to calculate potential evapotranspiration. Soil moisture balance is simulated for each grid cell using a single-layer water bucket. To represent subgrid variability of soil water-holding capacity, we assume it spatially varies within each grid cell, following a parabolic distribution function.

Actual evapotranspiration is determined jointly by the potential evapotranspiration and the relative soil moisture state in a grid cell. The generated runoff is divided into a surface runoff component and a deep percolation component using a partitioning factor. The base flow is linearly related to storage of the groundwater reservoir. The total runoff to the streams in a month is the sum of surface runoff and base flow.

55

IWSM

Water Demand The water demand module calculates water demand for crops, industry, households, and livestock at the FPU level. Irrigation water demand is assessed as the portion of crop water requirement not satisfied by precipitation or soil moisture based on hydrologic and agronomic characteristics. Crop water requirement is calculated for each crop using evapotranspiration and effective rainfall from IGHM. It relies on the FAO crop coefficient approach (Allen et al. 1998) to calculate water requirement for each crop every month. Irrigation demand in the FPU is calculated for a given cropping pattern after taking into account the basin efficiency of the irrigation system. The IMPACT multimarket model solves endogenously for the allocation of land to different crops while IWSM requires information about the cropping pattern to calculate irrigation water demand and hence water stress that is then an input into the multimarket model, which requires two-way communication between the models (as mentioned earlier).

Industrial water demand is modeled for the manufacturing and energy sectors using growth rates for the value-added by sector and energy production values for the electricity sector from the Emissions Prediction and Policy Analysis Model version 6 (EPPA6) of the MIT Joint Program on the Science and Policy of Global Change (Chen et al. 2015). For many countries in Africa south of the Sahara, the projected industrial water demands are substantially lower than those in IMPACT 2, suggesting an underestimation. Therefore, for countries in Africa south of the Sahara we retained the projection method of IMPACT 2 for industrial water demand, which is modeled as a nonlinear function of gross domestic production per capita and technology change.

Future domestic water demands are based on projections of population and income growth. In each region or basin income elasticities of demand for domestic water use are synthesized based on the literature and available estimates (de Fraiture 2007; Rosegrant, Cai, and Cline 2002). These elasticities of demand measure the propensity to consume water with respect to increases in per capita income. The elasticities also capture both direct income effects and conservation of domestic water use through technological and management change. Livestock water demand is proportional to the number of animals raised as calculated by the multimarket model.

Water Supply IWSM is a water basin management model. For FPUs where there is surface water storage capacity (for example, dams), the model specifies a single reservoir that summarizes all water storage capacity. For a given water basin that includes more than one FPU, IWSM manages storage in all those FPUs to maximize the ratio of water supply to water demand in the water basin. IWSM uses the runoff calculated by IGHM, the climatic data, and the water demands presented above to allocate available water to different uses. The schematic in Figure B.6 provides an overview of the model. In each FPU, IWSM solves for a balance between the change in the amount of water stored in the reservoirs, the entering water flows (runoff from precipitation, water from nontraditional sources such as desalination, and inflows from FPUs situated upstream), the exiting water flows (groundwater recharge from the stream, evaporation from the reservoirs, outflows to the FPU downstream or the ocean), and the water withdrawn for human use (surface water depletion). The model uses a simple hedging rule to avoid leaving empty storage for the next year.

56

Figure 5.6 IMPACT water basin simulation model

Source: Authors. Notes: FPU = food production unit; IMPACT = International Model for Policy Analysis of Agricultural Commodities and

Trade; Non-Ag = nonagricultural.

Surface water depletion added to the pumped groundwater (which is limited by the monthly capacity of tube wells and other pumps) is used to meet various water demands. The model solves by maximizing the ratio of water supplied to water demanded by water basin during a year in all FPUs. Solving for water supply in all FPUs simultaneously, IWSM assumes that linked FPUs within the same water basins are operated cooperatively, optimally allocating water between upstream and downstream demanders (qualified by imposing constraints on water delivery to downstream demanders). The model is parameterized to use available storage to smooth the distribution of water over months to avoid dramatic swings in monthly water delivery, if possible.

Following standard practice, IWSM incorporates the basic rule that nonagricultural water demands have priority over agricultural water demands. Any shortage in water supply is absorbed by agriculture first. If the shortage is larger than irrigation water demand, then livestock and domestic and industrial supplies are reduced proportionally.

ICWASM ICWASM then allocates water among crops in an area, given the economic value of the crop. We use the FAO approach (Doorenbos and Kassam 1979) to measure water stress at monthly intervals to include seasonality of water stress. Because optimizing total value of production given fixed prices leads to a tendency for specializing in high-value crops, we include a measure of risk aversion for farmers in the objective function, which preserves a diversified production structure even in case of a drought. The stress model produces a measure of yield stress for every crop—both irrigated and rainfed—in each FPU where that crop is grown. The yield stress for the base year is recorded, and the model defines for subsequent years the yield shock as the ratio of that year’s yield stress to the base year yield stress. This allows for a consistent modeling framework while making sure that the base year yields from the multimarket model dataset are preserved.

57

Linking the IMPACT Water and Multimarket Models Communication between the water models and the multimarket model is shown in Figure 5.7. In a given year, the IMPACT multimarket model is first solved assuming exogenous trends on various parameters, yielding projected production, prices, and allocation of land to crops. For this first run, expected water stress is set to the average of the previous four years, which sets harvest expectations for the allocation of land to different crops. This solution can be seen as providing projections that farmers use to make their cropping decisions.

The water demand module then calculates water demand for crops, industry, households, and livestock. Agricultural and nonagricultural water demands are then calculated as outlined above. IWSM (Figure 5.6) uses these water demands, along with river flows provided by IGHM (Figure 5.5), to provide the monthly repartition of water among FPUs given the objective function described above.

ICWASM then allocates water among crops in an area, given the economic value of the crop. The stress model produces a measure of water stress on yield for every crop—both irrigated and rainfed—in each of the FPUs and then multiplies by the temperature stress obtained from DSSAT to represent the total climate yield shock.

Finally, the new yield shocks are applied to the IMPACT multimarket model, which is solved a second time for the final equilibrium, only now assuming that the allocation of land to crops is fixed since farmers cannot change their decisions after planting. This solution yields all economic variables, including quantities and prices of outputs and inputs, and all trade flows. The model then moves to the next year, updates various parameters on trend, and starts the process again.

Figure 5.7 Linking IMPACT to water models: Dynamic two-way communication year by year

Source: Authors. Notes: FPUs = food production units; IMPACT = International Model for Policy Analysis of Agricultural Commodities and

Trade; IWSM = IMPACT water basin simulation model; ICWASM = IMPACT crop water allocation and stress model.

Given economic policy options and trends, and projected water stress based on data from previous years, the first IMPACT solution allocates land to crops (planting).

Specify domestic, industrial, and livestock water demand, and agricultural demand for water by crops, given cropping pattern from the first IMPACT solution.

Given hydrology model results, IWSM optimizes irrigation water distribution to all FPUs in a watershed over months in the year. Maximizes ratio of water demand to supply across the watershed. Calculates water shortages.

Allocates supply of available water to crops by month and calculates the impact of water stress on yields. Maximizes aggregate value of all crops and assumes concern about risk that favors maintaining historical cropping

Yield shocks affect agricultural production. Crop allocation to land is fixed. The second IMPACT model solution yields final equilibrium for current year.

IMPACT, first solution

Water demand

IWSM river basin model

ICWASM crop stress model

IMPACT, second solution

58

APPENDIX C: NOTES ON THE LINK BETWEEN DSSAT AND IMPACT

Process-based crop simulation models like DSSAT can be used to explore the effect of climate as well as agricultural technologies and practices on crop growth and yields. For instance, the models can simulate how yields may respond to varietal choice, soil management practices (for example, residue retention, tillage depth), and length of growth period. Outputs from these simulations reflect only biophysical interactions and are not affected by markets and human behavior. Process-based crop simulation models provide no insight into the availability of a variety or technology, or into how farmers respond to medium- to long-term incentives. Therefore, the next level of assessment is to consider these biophysical processes in conjunction with economic factors. The challenge is to take both the management and the climate change effects simulated in crop models and incorporate them into economic models alongside price effects and general technological progress, as well as assumptions on adaptive behavior on the part of producers.

The approach used at IFPRI is to feed into IMPACT the responses of selected crops to climate, soil, and nutrients simulated by DSSAT. The yield simulations in DSSAT are performed on a geographic grid, whereas IMPACT operates on a regional basis (in food production units, or FPUs). Therefore, the first challenge is to transform the detailed gridded crop modeling results into a form compatible with that of the economic model, which is accomplished using area-weighted average yields. The relative importance of each pixel is judged by the physical area allocated to the crop of interest by SPAM (Spatial Production Allocation Model) (You et al. 2014; You, Wood, and Wood-Sichra 2006). The SPAM areas are summed in the FPU to determine a total crop area. Next, the SPAM areas are multiplied (pixel by pixel) by the DSSAT simulated yields, providing pixel-level production information. These results are summed in the FPU to obtain the total simulated production. Based on these results, the area-weighted average yield is simply total production divided by total area. These yields are computed for all combinations of cases, and the results are then transferred to IMPACT as the “shifters” that are used in the simulations to reflect the climate change shock and the effects of technology adoption. All crop model results are applied in IMPACT using a delta method, meaning that the changes in yields (deltas) observed in the crop models/simulated yields are applied to the IMPACT yields.

We follow this approach because it allows us to capture the direction and magnitude of change, due to climate change as well as due to technologies, seen in the crop models, while maintaining the observed agricultural productivity reported in the FAOSTAT database.

59

APPENDIX D: NOTES ON INTRINSIC PRODUCTIVITY GROWTH RATES AND YIELD CALCULATIONS

Intrinsic productivity growth rates (IPRs) represent underlying improvements in yields over time. IPRs are based on historical data on productivity growth and are adjusted through expert opinion from scientists at the CGIAR research centers and others to reflect future changes in input levels, improvements in management practices, and investments in agriculture.

The text below is an excerpt, word for word, from the IMPACT documentation report (Robinson, et al. 2015).13 It provides additional details about IPRs and about how yields are calculated in IMPACT. ------------------------------------------

Crop yields are a function of commodity prices, prices of inputs, available water, climate, and exogenous trend factors. The IMPACT model includes five ways that changes in yields are achieved. First, the model assumes a scenario of underlying improvements in yields over time that, to varying degrees, continue trends observed during the past 50 to 60 years in an informed extrapolation following the concepts introduced in Evenson and Rosegrant (1995) and Evenson, Pray, and Rosegrant (1999). These long-run trends, or intrinsic productivity growth rates, are intended to reflect the expected increases in inputs, improved seeds, and improvements in management practices. These trends differ and generally are higher for developing countries, where there is considerable scope to narrow the gap in yields compared with developed countries. These intrinsic productivity growth rates are exogenous to the model, and changes in them are specified as part of the definition of different scenarios. We assume that these underlying trends vary by crop and region and that they will decline somewhat during the next 50 years as the pace of technological improvements in developed countries slows and as developing countries catch up to yields in developed countries.

Second, the IMPACT model includes a short-run (annual), endogenous response of yields to changes in both input and output prices. These yield response functions specify the change in yield as a constant elasticity function of the changes in output prices, with elasticity parameters that can vary by crop and region. The underlying assumption is that farmers will respond to changes in prices by varying the use of inputs, including inputs such as fertilizer, chemicals, and labor that will, in turn, change yields.

Third, climate is assumed to affect yields through two mechanisms. The first is through the effects of changes in temperature and weather due to climate change on crop yields for rainfed and irrigated crops, as calculated from the solution of a crop simulation model (DSSAT, see Hoogenboom et al. 2012; Jones et al. 2003) for different climate change scenarios. These crop simulations vary by crop type. The DSSAT model is run with detailed time, geographic, and crop disaggregation for different climate change scenarios that are downscaled to include weather variation in small geographic areas. This analysis gives changes in average yields due to climate change that are then averaged to generate yield shocks by crop and region (FPU) in the IMPACT model. These long-run climate scenarios generate yield shocks that are assumed to follow simple trends over time and do not consider extreme events such as droughts or floods (for more information on DSSAT see Section 5 and Appendix F).

The fourth mechanism by which climate change affects yields is through variation in water availability for agriculture year by year in different climate scenarios. This mechanism is modeled through the use of the IMPACT water models. These include (1) a global hydrology model that determines runoff to the river basins included in the IMPACT model; (2) water basin management models for each FPU that optimally allocate available water to competing nonagricultural and agricultural uses, including irrigation; and (3) a water allocation and stress model that allocates available irrigation water to crops and, when the water supply is less than demand by crop, computes the impact of the water shortage on crop yields, accounting for differences among crops and varieties. These yield shocks are then passed to the IMPACT model, affecting year-to-year crop yields.

13 This appendix contains text originally published in IFPRI Discussion Paper 01483, The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model Description for Version 3. It is included here with permission from the International Food Policy Research Institute (2016).

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APPENDIX E: PRICE EFFECTS IN THE IMPACT MODEL

The text below is an excerpt, word for word, from the IMPACT documentation report (Robinson, Mason-D’Croz, Islam, Sulser, et al. 2015).14 It is meant to provide additional and more specific details about how price effects are calculated inside the IMPACT economic model. ---------------------------------------------------------------------------------------------------------------

The world price of a commodity is the equilibrating mechanism for traded commodities—when an exogenous shock is introduced in the model, world price will adjust to clear world markets, and each adjustment is passed back to the effective producer and consumer prices via the price transmission equations. Changes in domestic prices subsequently affect commodity supply and demand, necessitating their iterative readjustments until world supply and demand balance and world net trade again equals 0. For nontraded commodities, domestic prices in each country adjust to equate supply and demand within the country.

IMPACT assumes a closed world economy—at the end of every year the world’s production must equal the world’s demand. This constraint is ensured by the following equation, where the sum of net trade over the globe must equal 0.

c,ctycty

0

Net Trade

NT

NT

=

=

∑

National production and demand for tradable commodities are linked to world markets through trade. Commodity trade by country (cty) is a function of domestic production, domestic demand, and stock change. Regions with positive net trade are net exporters, while those with negative values are net importers. This specification does not permit a separate identification of international trade by country of origin and destination—all countries export to and import from a single global market.

c,cty c,cty c,cty c,cty

Net tradeChange in stocks

NTQ

NT QSUP QD Q

t

St

S

= − −

==

Prices are endogenous in the system of equations for food and are calibrated to 2005 commodity prices (OECD Agricultural Market Access Database 2010). Prices are in constant 2005 US dollars. Domestic prices of tradable commodities are a function of world prices, adjusted by the effect of trade policy represented by taxes and tariffs, and price policies are expressed in terms of producer support estimates (PSEs), consumer support estimates (CSEs), and the cost of moving products from one market to another represented by marketing margins (MMs). Export taxes and import tariffs are drawn from data from the Global Trade Analysis Project at Purdue University and reflect trade policies at the national level (Narayanan and Walmsley 2008; International Trade Center 2006; Boumelassa, Laborde, and Mitaritonna 2009). PSEs and CSEs represent public policies to support production and consumption by creating wedges between world and domestic prices. PSEs and CSEs are based on Organization for Economic Co-operation and Development (OECD) estimates and are adjusted by expert judgment to reflect regional trade dynamics (OECD 2014). MMs reflect other factors such as transport and marketing costs of getting goods to various markets and are based on expert opinion on the quality and availability of transportation, communication, and market infrastructure.

14 This appendix contains text originally published in IFPRI Discussion Paper 01483, The International Model for Policy

Analysis of Agricultural Commodities and Trade (IMPACT): Model Description for Version 3. It is included here with permission from the International Food Policy Research Institute (2016).

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APPENDIX F: EQUATIONS IN THE IMPACT GLOBAL MULTIMARKET MODEL

Price Equations Producer prices

𝑃𝑃𝑃𝑃𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐 × (1 + 𝑀𝑀𝑀𝑀𝑀𝑀𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐) = (1 + 𝑃𝑃𝑃𝑃𝑃𝑃𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐) × ∑ 𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 , where

j = Producing activity c = Commodity(ies) produced by j cty = Country MMJ = Marketing margin to bring commodity to market PSE = Producer subsidy equivalent (ad valorem) PC = Consumer price of produced commodities

Consumer prices: Domestic price of exports and imports

𝑃𝑃𝑀𝑀𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑃𝑃𝑊𝑊𝑐𝑐 × 𝑃𝑃𝐸𝐸𝑅𝑅𝑐𝑐𝑐𝑐𝑐𝑐 × �1 + 𝑇𝑇𝑀𝑀𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐� × (1 + 𝑀𝑀𝑀𝑀𝑀𝑀𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐)

𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑃𝑃𝑊𝑊𝑐𝑐 × 𝑃𝑃𝐸𝐸𝑅𝑅𝑐𝑐𝑐𝑐𝑐𝑐 × �1 − 𝑇𝑇𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐� × (1 −𝑀𝑀𝑀𝑀𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐), where

c = Commodity cty = Country PM = Import price PE = Export price PW = World price EXR = Exchange rate of local currency to US dollars TM = Import tariff (ad valorem) TE = Export tax (ad valorem) MMM = Marketing margin from world market to domestic port (ad valorem) MME = Marketing margin from domestic port to world market (ad valorem)

Please note the following: • Consumer prices for traded goods are linked to world prices.

• Adjusted world price for imports must exceed adjusted world price for exports.

• For a country to import, the domestic import price must equal the consumer price.

𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 ≤ 𝑃𝑃𝑀𝑀𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 • For purely tradable goods, the consumer price is always equal to the import price, and the export

price equation is never used.

𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑃𝑃𝑀𝑀𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 • For exports, the domestic export price is less than or equal to the consumer price.

𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 ≥ 𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

Net price equation: Output price minus intermediate input costs

62

Note that the net price will equal the producer price of the activity whenever there are no intermediate inputs, which follows basic economic theory whereby in the long run, there are no economic profits in perfectly competitive markets.

𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑃𝑃𝑃𝑃𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐 − �1 − 𝑃𝑃𝑃𝑃𝑃𝑃𝐼𝐼𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐� × � 𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐𝑖𝑖,𝑐𝑐𝑐𝑐𝑐𝑐𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑐𝑐𝑖𝑖

Land, Area, and Yield Equations Land supply The supply of land (a factor of production) is assumed to be a function of the “scarcity value” or “shadow price index” of land.

𝑄𝑄𝑄𝑄𝑃𝑃𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 = 𝑄𝑄𝑄𝑄𝑃𝑃𝐼𝐼𝑄𝑄𝑡𝑡𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 × 𝑄𝑄𝑄𝑄𝑃𝑃𝐼𝐼𝑄𝑄𝑡𝑡2𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 × 𝑊𝑊𝑄𝑄𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝐿𝐿𝐿𝐿, where

fpu = Food production unit lnd = Land type (that is, irrigated or rainfed) QFS = Land supply QFSInt = Land supply intercept (base year land supply) QFSInt2 = Land supply exogenous growth multiplier WF = Land shadow price Lε = Land supply price elasticity

Area demand The area demand is determined by initial land allocation, previous year’s land allocation, and current own-price of commodities that can be produced on the land.

𝐴𝐴𝐴𝐴𝐴𝐴𝑎𝑎𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 = �𝐴𝐴𝐴𝐴𝐴𝐴𝑎𝑎𝐼𝐼𝑄𝑄𝑡𝑡𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 × 𝐴𝐴𝐴𝐴𝐴𝐴𝑎𝑎𝐼𝐼𝑄𝑄𝑡𝑡2𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 × 𝑊𝑊𝑄𝑄𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑊𝑊𝑊𝑊𝐿𝐿 × �

𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇0𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐�𝐴𝐴𝐿𝐿

�(1−𝐴𝐴1𝐿𝐿)

×

Area = Final crop area AreaInt = Crop area intercept (base-year crop area) AreaInt2 = Exogenous crop area growth WF = Land shadow price WFε = Elasticity of demand with respect to land price PNET = Current net price PNET0 = Base-year net price (used to index prices in equation) Aε = Elasticity of demand with respect to net price A1ε = Lagged area elasticity (serves to add addition stickiness) Area1 = Lagged crop area from previous year

Land allocation is determined by the supply of land and the crop area demand, where the sum of all crop area must equal the total land supply.

𝑄𝑄𝑄𝑄𝑃𝑃𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 = � 𝐴𝐴𝐴𝐴𝐴𝐴𝑎𝑎𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑗𝑗

Yield equations

63

Yields are determined from initial yields, exogenous assumptions on yield growth (from technology, water, and climate), and current net prices, which take into account the cost of inputs. 𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 = 𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝐼𝐼𝑄𝑄𝑡𝑡𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 × 𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝐼𝐼𝑄𝑄𝑡𝑡2𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 × 𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝑊𝑊𝑎𝑎𝑡𝑡𝑃𝑃ℎ𝑘𝑘𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 × 𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝑃𝑃𝑌𝑌𝑌𝑌𝑃𝑃ℎ𝑘𝑘𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙

× Yield = Final crop yield YieldInt = Crop yield intercept (base-year crop yield) YieldInt2 = Exogenous crop yield growth due to technology YieldWatShk = Exogenous yield shock from water stress YieldCliChk = Exogenous yield shock from climate change PNET = Current net price PNET0 = Base-year net price (used to index prices in equation) Yε = Price response with respect to net price

Yield equations for crop production with multiple technologies are calculated through nested equations, where each technology’s yield is calculated with the above equation, and then the weighted average (based on share of total area using each technology) is calculated.

𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 = � �𝑇𝑇𝐴𝐴𝑇𝑇ℎ𝑃𝑃ℎ𝑎𝑎𝐴𝐴𝐴𝐴𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙,𝑐𝑐𝑡𝑡𝑐𝑐ℎ × 𝑇𝑇𝐴𝐴𝑇𝑇ℎ𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙�𝑐𝑐𝑡𝑡𝑐𝑐ℎ

Livestock Equations Livestock animals Livestock production is treated in a similar manner to crop production, except instead of land, animals are the primary unit of production.

𝐴𝐴𝑄𝑄𝑌𝑌𝐴𝐴𝑎𝑎𝑌𝑌𝑠𝑠𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑖𝑖𝑐𝑐𝑖𝑖 = 𝐴𝐴𝑄𝑄𝑌𝑌𝐴𝐴𝑎𝑎𝑌𝑌𝐼𝐼𝑄𝑄𝑡𝑡𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑖𝑖𝑐𝑐𝑖𝑖 × 𝐴𝐴𝑄𝑄𝑌𝑌𝐴𝐴𝑎𝑎𝑌𝑌𝐼𝐼𝑄𝑄𝑡𝑡2𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑖𝑖𝑐𝑐𝑖𝑖 × � 𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇0𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐�𝐴𝐴𝑃𝑃𝐿𝐿

×

∏ � 𝑃𝑃𝐶𝐶𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝐶𝐶0𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐�𝑊𝑊𝑡𝑡𝑡𝑡𝑙𝑙𝐿𝐿

𝑓𝑓𝑡𝑡𝑡𝑡𝑙𝑙𝑖𝑖 , where

livsys = Livestock system Animals = Total number of animals AnimalInt = Animal intercept (initial number of animals) AnimalInt2 = Exogenous population growth PNET = Current net price PNET0 = Initial net price used to index price changes

� �𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃0𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐�𝑊𝑊𝑡𝑡𝑡𝑡𝑙𝑙𝐿𝐿

𝑓𝑓𝑡𝑡𝑡𝑡𝑙𝑙𝑖𝑖

= Input cost of feed raised to a feed elasticity

64

Livestock yields Livestock yields are determined through exogenous growth due to improved animals and management practices.

𝐴𝐴𝑄𝑄𝑌𝑌𝐴𝐴𝑎𝑎𝑌𝑌𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑖𝑖𝑐𝑐𝑖𝑖 = 𝐴𝐴𝑄𝑄𝑌𝑌𝐴𝐴𝑎𝑎𝑌𝑌𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝐼𝐼𝑄𝑄𝑡𝑡𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑖𝑖𝑐𝑐𝑖𝑖 × 𝐴𝐴𝑄𝑄𝑌𝑌𝐴𝐴𝑎𝑎𝑌𝑌𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝐼𝐼𝑄𝑄𝑡𝑡2𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑖𝑖𝑐𝑐𝑖𝑖

Supply Equations Supply for crops is simply the product of area and yields summed over the FPUs and land types in a country.

𝑄𝑄𝑃𝑃𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐 = ∑ �𝐴𝐴𝐴𝐴𝐴𝐴𝑎𝑎𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙 × 𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙�𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙

Similarly, livestock production is the product of animals and animal yields summed over FPUs and livestock systems.

𝑄𝑄𝑃𝑃𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐 = ∑ �𝐴𝐴𝑄𝑄𝑌𝑌𝐴𝐴𝑎𝑎𝑌𝑌𝑠𝑠𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑖𝑖𝑐𝑐𝑖𝑖 × 𝐴𝐴𝑄𝑄𝑌𝑌𝐴𝐴𝑎𝑎𝑌𝑌𝑌𝑌𝑌𝑌𝐴𝐴𝑌𝑌𝑑𝑑𝑗𝑗,𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑖𝑖𝑐𝑐𝑖𝑖�𝑓𝑓𝑖𝑖𝑖𝑖,𝑙𝑙𝑖𝑖𝑙𝑙𝑖𝑖𝑐𝑐𝑖𝑖

Country-level production of processed goods, such as oils and sugar, are determined by the following equation:

𝑄𝑄𝑃𝑃𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑄𝑄𝑃𝑃𝐼𝐼𝑄𝑄𝑡𝑡𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐 × 𝑄𝑄𝑃𝑃𝐼𝐼𝑃𝑃𝑇𝑇2𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐 × � 𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇0𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐�𝑄𝑄𝑄𝑄𝐿𝐿

× ∏ � 𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇0𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐�𝑄𝑄𝑄𝑄𝐿𝐿

𝑗𝑗𝑗𝑗≠𝑗𝑗 , where

QS = Quantity supplied QSInt = Initial quantity supplied QSInt2 = Exogenous growth rate assumption on supply

�𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇0𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐�𝑄𝑄𝑄𝑄𝐿𝐿

= Own-price response of supply based on own net price and elasticity of

supply

� �𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃𝑃𝑃𝑇𝑇0𝑗𝑗𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐�𝑄𝑄𝑄𝑄𝐿𝐿

𝑗𝑗𝑗𝑗≠𝑗𝑗

= Response to changes in prices of other activities (substitutes and complementary goods)

World supply is determined by summing the quantities supplied in each country.

𝑊𝑊𝑊𝑊𝐴𝐴𝑌𝑌𝑑𝑑𝑃𝑃𝑊𝑊𝑊𝑊𝑊𝑊𝑌𝑌𝑦𝑦𝑗𝑗 = � 𝑄𝑄𝑃𝑃𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐

Demand Equations Intermediate demand Intermediate demand is a derived demand that is based on the demand for the final goods for which these intermediate goods serve as inputs. The input-output matrix determines the proportions of inputs (c) required for each producing activity (j).

𝑄𝑄𝑄𝑄𝐼𝐼𝑄𝑄𝑡𝑡𝐴𝐴𝐴𝐴𝐴𝐴𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = � 𝐼𝐼𝐼𝐼𝑀𝑀𝑎𝑎𝑡𝑡𝐴𝐴𝑌𝑌𝑥𝑥𝑐𝑐,𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐 × 𝑄𝑄𝑃𝑃𝑗𝑗,𝑐𝑐𝑐𝑐𝑐𝑐𝑗𝑗

65

Livestock feed demand Livestock feed demand is determined by two components: (1) animal feed requirements are determined by livestock production and livestock feed requirements, and (2) price effects of feed are taken into account with the changes in consumer prices of feed raised to the feed elasticity of demand.

𝑄𝑄𝐿𝐿𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = � �𝑄𝑄𝑃𝑃𝑙𝑙𝑖𝑖𝑙𝑙𝑡𝑡𝑖𝑖𝑐𝑐𝑙𝑙𝑐𝑐𝑙𝑙,𝑐𝑐𝑐𝑐𝑐𝑐 × 𝑄𝑄𝐴𝐴𝐴𝐴𝑑𝑑𝑅𝑅𝐴𝐴𝑞𝑞𝑙𝑙𝑖𝑖𝑙𝑙𝑡𝑡𝑖𝑖𝑐𝑐𝑙𝑙𝑐𝑐𝑙𝑙,𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐�𝑙𝑙𝑖𝑖𝑙𝑙𝑡𝑡𝑖𝑖𝑐𝑐𝑙𝑙𝑐𝑐𝑙𝑙

× � �𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃0𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐�𝑊𝑊𝑡𝑡𝑡𝑡𝑙𝑙𝐿𝐿

𝑓𝑓𝑡𝑡𝑡𝑡𝑙𝑙𝑖𝑖

Household demand Household demand is determined by initial demands, income (per capita GDP), commodity prices, and consumer preferences as represented by elasticities of demand (price and income elasticities).

𝑄𝑄𝐻𝐻𝑐𝑐,ℎ,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑄𝑄𝐻𝐻𝐼𝐼𝑄𝑄𝑡𝑡𝑐𝑐,ℎ,𝑐𝑐𝑐𝑐𝑐𝑐 × 𝑊𝑊𝑇𝑇𝑝𝑝𝑄𝑄𝑃𝑃ℎ,𝑐𝑐𝑐𝑐𝑐𝑐𝐼𝐼𝑖𝑖𝑐𝑐𝐿𝐿 × 𝑃𝑃𝑊𝑊𝑊𝑊𝐻𝐻ℎ,𝑐𝑐𝑐𝑐𝑐𝑐 × ∏ � 𝑃𝑃𝐶𝐶𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝐶𝐶0𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐�𝑊𝑊𝑙𝑙𝑙𝑙𝑙𝑙𝐿𝐿

𝑐𝑐𝑐𝑐 , where

H = Household type (urban or rural) QH = Final household demand QHInt = Household demand intercept pcGDP = Per capita GDP Incε = Income demand elasticity

� �𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃0𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐�𝑊𝑊𝑙𝑙𝑙𝑙𝑙𝑙𝐿𝐿

𝑐𝑐𝑐𝑐

= Price response of demand—includes own- and cross-prices

Exogenous biofuel feedstock demand This variable is determined through exogenous growth rates that represent government mandates to encourage the production of biofuels. The equation also allows for a price response for biofuels if desired.

𝑄𝑄𝑄𝑄𝑄𝑄𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑄𝑄𝑡𝑡𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 × 𝑄𝑄𝑄𝑄𝑄𝑄𝐼𝐼𝑃𝑃𝑇𝑇2𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 × � �𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃0𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐�𝐵𝐵𝑊𝑊𝐿𝐿

𝑐𝑐𝑐𝑐

Other demand equations One equation covers other demand that grows with a trend base on per capita GDP on the lag and is not price sensitive. The equation is used only for very small demands.

𝑄𝑄𝐼𝐼𝑇𝑇𝐻𝐻𝑅𝑅𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑄𝑄𝐼𝐼𝑡𝑡ℎ𝐴𝐴𝐴𝐴1𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 ×𝑊𝑊𝑇𝑇𝑝𝑝𝑄𝑄𝑃𝑃𝑐𝑐𝑐𝑐𝑐𝑐𝑊𝑊𝑇𝑇𝑝𝑝𝑄𝑄𝑃𝑃1𝑐𝑐𝑐𝑐𝑐𝑐

The primary other demand equation is price sensitive and follows closely the food demand equation:

𝑄𝑄𝐼𝐼𝑇𝑇𝐻𝐻𝑅𝑅𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑄𝑄𝐼𝐼𝑡𝑡ℎ𝐴𝐴𝐼𝐼𝑄𝑄𝑡𝑡𝑐𝑐,ℎ,𝑐𝑐𝑐𝑐𝑐𝑐 ×𝑊𝑊𝑇𝑇𝑝𝑝𝑄𝑄𝑃𝑃ℎ,𝑐𝑐𝑐𝑐𝑐𝑐

𝑊𝑊𝑇𝑇𝑝𝑝𝑄𝑄𝑃𝑃0ℎ,𝑐𝑐𝑐𝑐𝑐𝑐

𝐼𝐼𝑖𝑖𝑐𝑐𝐿𝐿

×𝑃𝑃𝐼𝐼𝑃𝑃𝑐𝑐𝑐𝑐𝑐𝑐𝑃𝑃𝐼𝐼𝑃𝑃0𝑐𝑐𝑐𝑐𝑐𝑐

× � �𝑃𝑃𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

𝑃𝑃𝑃𝑃0𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐�𝑂𝑂𝑐𝑐ℎ𝑟𝑟𝐿𝐿

𝑐𝑐𝑐𝑐

Total demand Total demand is the sum of all the types of demand.

66

𝑄𝑄𝑄𝑄𝑐𝑐.𝑐𝑐𝑐𝑐𝑐𝑐 = � 𝑄𝑄𝐻𝐻𝑐𝑐,ℎ,𝑐𝑐𝑐𝑐𝑐𝑐ℎ

+ 𝑄𝑄𝐼𝐼𝑄𝑄𝑡𝑡𝐴𝐴𝐴𝐴𝐴𝐴𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 + 𝑄𝑄𝐿𝐿𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 + 𝑄𝑄𝑄𝑄𝑄𝑄𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 + 𝑄𝑄𝐼𝐼𝑇𝑇𝐻𝐻𝑅𝑅𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

Trade and Commodity Market Equilibrium Conditions Net trade Regional net trade for traded goods is determined by resting demand and stocks from production. Note that stocks are constant and exogenous.

𝑃𝑃𝐴𝐴𝑡𝑡𝑇𝑇𝐴𝐴𝑎𝑎𝑑𝑑𝐴𝐴𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑄𝑄𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 − 𝑄𝑄𝑄𝑄𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 − 𝑃𝑃𝑡𝑡𝑊𝑊𝑇𝑇𝑘𝑘𝑠𝑠𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

If net trade is negative, then the country is an importer of c. If net trade is positive, then the country is an exporter of c. Supply-demand balance for nontraded goods, which determines PCV Nontraded goods are not linked to world markets and must satisfy only domestic markets.

𝑄𝑄𝑃𝑃𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑄𝑄𝑄𝑄𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐

World sum of net trade This equation is applied only to traded goods. IMPACT assumes a closed world economy, meaning that there is no trade off planet. To satisfy this requirement, at the end of every year the world’s production must be equal to the world’s demand. This constraint is ensured by the following equation, where the sum of net trade over the globe must equal 0.

� 𝑃𝑃𝐴𝐴𝑡𝑡𝑇𝑇𝐴𝐴𝑎𝑎𝑑𝑑𝐴𝐴𝑐𝑐,𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐

= 0

67

APPENDIX G: CLIMATE CHANGE EFFECTS ON RICE YIELDS IN EAST ASIA AND THE REPUBLIC OF KOREA

Figure G.1 Changes in yields across East Asia, 2050, GFDL RCP8.5, irrigated rice

Source: Authors Notes: GFDL = Geophysical Fluid Dynamic Laboratory; RCP = representative concentration pathway.

Figure G.2 Changes in yields across East Asia, 2050, GFDL RCP8.5, rainfed rice

Source: Authors Notes: GFDL = Geophysical Fluid Dynamic Laboratory; RCP = representative concentration pathway.

68

Figure G.3 Changes in yields across East Asia, 2050, HadGEM RCP8.5, irrigated rice

Source: Authors Notes: HadGEM = Hadley Centre’s Global Environment Model; RCP = representative concentration pathway.

Figure G.4 Changes in yields across East Asia, 2050, HadGEM RCP8.5, rainfed rice

Source: Authors Notes: HadGEM = Hadley Centre’s Global Environment Model; RCP = representative concentration pathway.

69

Figure G.5 Changes in yields across East Asia, 2050, IPSL RCP8.5, irrigated rice

Source: Authors Notes: IPSL = Institut Pierre Simon Laplace; RCP = representative concentration pathway.

Figure G.6 Changes in yields across East Asia, 2050, IPSL RCP8.5, rainfed rice

Source: Authors Notes: IPSL = Institut Pierre Simon Laplace; RCP = representative concentration pathway.

70

APPENDIX H: TRENDS OF RICE PRODUCTION, YIELDS, AREA AND DEMAND IN THE REPUBLIC OF KOREA

Figure H.1 Trends of rice yields in the Republic of Korea to 2050, three climate change scenarios and no-climate-change scenario

Source: Authors Notes: The trend line for the HadGEM scenario is almost completely hidden behind the SSP2-NoCC line. GFDL = Geophysical

Fluid Dynamic Laboratory; HadGEM = Hadley Centre’s Global Environment Model; IPSL = Institut Pierre Simon Laplace; NoCC = no climate change; SSP2 = Shared Socioeconomic Pathway 2.

201 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050

3.0

3.5

4.0

4.5

5.0

5.5

6.0

Met

ScenarioSSP2-NoCC

SSP2-GFDL

SSP2-HadGEM

SSP2-IPSL

71

Figure H.2 Trends of rice harvested area in the Republic of Korea to 2050, three climate change scenarios and no-climate-change scenario



Figure H.3 Trends of rice production in the Republic of Korea to 2050, three climate change scenarios and no-climate-change scenario



201 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050

700

750

800

850

900

950

1000

Tho

ScenarioSSP2-NoCC

SSP2-GFDL

SSP2-HadGEM

SSP2-IPSL201 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050

3,000

3,500

4,000

4,500

5,000

Tho

ScenarioSSP2-NoCC

SSP2-GFDL

SSP2-HadGEM

SSP2-IPSL

72

Figure H.4 Trends of rice demand in the Republic of Korea to 2050, three climate change scenarios and no-climate-change scenario



201 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050

2,000

2,500

3,000

3,500

4,000

Tho

ScenarioSSP2-NoCC

SSP2-GFDL

SSP2-HadGEM

SSP2-IPSL

73

REFERENCES

Allen, R. G., L. S. Pereira, D. Raes, and M. Smith. 1998. Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper No. 56. Rome: Food and Agriculture Organization of the United Nations.

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