metadata: authors: 0: author: Palazzo,Amanda
1: author: Valin,Hugo;Valin,Hugo Jean Pierre
2: author: Batka,Miroslav
3: author: Havlík,Petr
count: World
docna: 0: docna: Investment Needs for Irrigation
            Infrastructure along Different Socioeconomic Pathways
docty: Policy Research Working Paper
subtopic: Hydrology,International Trade and Trade Rules,Food Security,Crops and Crop Management Systems,Climate Change and Agriculture,Nutrition
teratopic: Water Resources,International Economics and Trade,Agriculture,Health, Nutrition and Population
abstracts: cdata!: This paper conducts an assessment of the
            global costs for expanding, upgrading, and improving
            irrigation infrastructure in developing countries, along
            different future scenarios toward 2050. It uses the GLobal
            BIOsphere Management Model, a partial equilibrium model of
            the global agricultural and forestry sectors. It examines
            the impacts of irrigation expansion on the agriculture and
            food system, from the perspective of different Sustainable
            Development Goals, in particular food security (goal 2),
            land use change and biodiversity (goal 15), greenhouse gas
            emissions (goal 13), and sustainable water use (goal 6). It
            finds that irrigation support policies improve food security
            globally and can reduce the burden on land by limiting
            expansion of cropland area. However, the effectiveness of
            irrigation to achieve a larger set of goals depends on the
            regional context. In South Asia and the Middle East and
            North Africa, the expansion of irrigation increases
            unsustainable water extraction practices. A sensitivity
            analysis is conducted to evaluate the uncertainty of the
            infrastructure costs and impacts under different
            socioeconomic developments, levels of radiative forcing and
            climate change scenarios, dietary patterns, trade openness,
            and efficiencies of irrigation systems. The findings
            indicate that irrigation systems could play an important
            role in adaptation to the most adverse climate change;
            however, increased water scarcity may also limit adaptation potentials.
repnme: repnme: Investment Needs for Irrigation
            Infrastructure along Different Socioeconomic Pathways
display_title: Investment Needs for Irrigation
            Infrastructure along Different Socioeconomic Pathways
listing_relative_url: /research/2019/02/30837452/investment-needs-irrigation-infrastructure-along-different-socioeconomic-pathways
topicv3: Agriculture and Food Security m1326205 435,Food Security m1326439 199,Water m1326522 137,Crop Production m1326627 174,Trade m1326924 698
publishtoextweb_dt: 2019-02-19T00:00:00Z
historic_topic: Water Resources,International Economics and Trade,Agriculture,Health, Nutrition and Population
no_of_pages: 65
author_keys: 0: author_key: 1359603
model_extractions: Policy Research Working Paper 8744

**Beyond the Gap**

How Countries Can Afford the Infrastructure They Need While Protecting the Planet _Background Paper_ Investment Needs for Irrigation Infrastructure along Different Socioeconomic Pathways _Amanda Palazzo_ _Hugo Valin Miroslav Batka Petr Havlík_ Sustainable Development Practice Group Office of the Chief Economist February 2019
Policy Research Working Paper 8744

# **Abstract**

This paper conducts an assessment of the global costs for expanding, upgrading, and improving irrigation infrastructure in developing countries, along different future scenarios toward 2050. It uses the GLobal BIOsphere Management Model, a partial equilibrium model of the global agricultural and forestry sectors. It examines the impacts of irrigation expansion on the agriculture and food system, from the perspective of different Sustainable Development Goals, in particular food security (goal 2), land use change and biodiversity (goal 15), greenhouse gas emissions (goal 13), and sustainable water use (goal 6). It finds that irrigation support policies improve food security globally and can reduce the burden on land by limiting expansion of cropland area. However, the effectiveness of irrigation to achieve a larger set of goals depends on the regional context. In South Asia and the Middle East and North Africa, the expansion of irrigation increases unsustainable water extraction practices. A sensitivity analysis is conducted to evaluate the uncertainty of the infrastructure costs and impacts under different socioeconomic developments, levels of radiative forcing and climate change scenarios, dietary patterns, trade openness, and efficiencies of irrigation systems. The findings indicate that irrigation systems could play an important role in adaptation to the most adverse climate change; however, increased water scarcity may also limit adaptation potentials.

This paper was commissioned by the World Bank Group’s Chief Economist office for Sustainable Development Practice Group and is a background paper for the World Bank Group’s report: _“Beyond the Gap: How Countries Can Afford the Infrastructure They Need While Protecting the Planet.”_ It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at palazzo@iiasa.ac.at.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Investment Needs for Irrigation Infrastructure along Different Socioeconomic Pathways

Amanda Palazzo<sup>a,</sup> *, Hugo Valin<sup>a</sup> , Miroslav Batka<sup>a</sup> , Petr Havlík<sup>a</sup> This paper was commissioned by the World Bank Group’s Chief Economist office for Sustainable Development and is a background paper for the World Bank Group’s report: “Beyond the Gap: How countries can afford the infrastructure they need while protecting the planet.” It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at palazzo@iiasa.ac.at.

Keywords: agriculture, irrigation, infrastructure investment, food security JEL: Q10: Agriculture Q15: Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment Q25: Renewable Resources and Conservation: Water H54: National Government Expenditures and Related Policies: Infrastructures; Other Public Investment and Capital Stock a International Institute for Applied Systems Analysis (IIASA), A‐2361, Laxenburg, Austria

*Corresponding author: Amanda Palazzo (palazzo@iiasa.ac.at). Analysis contributing to this study was partly conducted in partnership with the GEF/UNIDO/IIASA funded Integrated Solutions for Water, Energy and Land.

The results and views expressed in this document are the sole personal responsibility of the authors and do not reflect those of their institution of affiliation. Any errors or omissions remain the responsibility of the authors.

Supplementary information for this working paper can be found here: http://pure.iiasa.ac.at/id/eprint/15748/
# Regional Definitions

AFR Sub‐Saharan Africa Region Includes: Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cabo Verde, Central African Republic, Chad, Comoros, Democratic Republic of the Congo, Congo Republic, Côte d’Ivoire, Djibouti, Equatorial Guinea, Eritrea, Ethiopia, Gabon, the Gambia, Ghana, Guinea, Guinea‐Bissau, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Reunion, Rwanda, Senegal, Sierra Leone, Somalia, South Africa, Sudan, Eswatini, Tanzania, Togo, Uganda, Zambia, Zimbabwe EAP East Asia and the Pacific Region Includes: Brunei Darussalam, Cambodia, China, Fiji Islands, French Polynesia, Indonesia, People’s Democratic Republic of Korea, Republic of Korea, Lao People’s Democratic Republic, Malaysia, Mongolia, Myanmar, New Caledonia, Papua New Guinea, Philippines, Samoa, Singapore, Solomon Islands, Thailand, Timor‐Leste, Vanuatu, Vietnam ECA Europe and Central Asia Region Includes: Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyzstan, Latvia, Lithuania, Macedonia, Moldova Republic, Montenegro, Poland, Romania, Russian Federation, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine, Uzbekistan LCR Latin American and Caribbean Region Includes: Argentina, Bahamas, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Falkland Islands, French Guiana, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, Trinidad Tobago, Uruguay, República Bolivariana de Venezuela MNA Middle East and North Africa Includes: Algeria, Bahrain, Arab Republic of Egypt, Islamic Republic of Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, West Bank and Gaza, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates, Western Sahara, Republic of Yemen SAR South Asia Region Includes: Bangladesh, Bhutan, India, Nepal, Pakistan, Sri Lanka NAM North America Region Includes: United States, Puerto Rico, and Canada ADP Pacific and Asian Developed Region Includes: Australia, Japan, New Zealand EC European Community Region Includes: Austria, Belgium, Cyprus, Denmark, Finland, France, Germany, Greece, Greenland, Iceland, Ireland, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, United Kingdom 2
# Abbreviations and Acronyms

|AFOLU|Agriculture, Forestry, and Other Land Use|
|---|---|
|AQUASTAT|global water information system of the FAO; developed by the FAO Land and Water Division|
|BRICS|association of five emerging countries, Brazil, Russia, India, China and South Africa|
|CGIAR|Global partnership of 15 organizations engaged in research dedicated to reducing rural<br>poverty, increasing food security, improving human health and nutrition, and ensuring<br>sustainable management of natural resources|
|CMIP5|Coupled Model Intercomparison Project Phase 5|
|CO2|Carbon Dioxide|
|CWATM|Community Water Model; open source hydrological model developed at IIASA|
|EFR|Environmental Flow Requirement|
|EPIC|Environmental Policy Integrated Climate Model; globally gridded crop model|
|FAO|Food and Agriculture Organization of the United Nations|
|GCM|General Circulation Model|
|GDP|Gross Domestic Product|
|GFDL/GFDL‐ESM2M|General Circulation Model of NOAA Geophysical Fluid Dynamics Laboratory|
|GGCM|Globally gridded crop model|
|GHG|Greenhouse gas|
|GLOBIOM|GLobal BIOsphere Management Model; developed at IIASA|
|HadGEM/Had GEM2‐ES|General Circulation Model of Met Office Hadley Centre|
|IIASA|International Institute for Applied Systems Analysis|
|IPSL/IPSL‐CM5A‐LR|General Circulation Model of Institut Pierre‐Simon Laplace|
|ISI‐MIP|Inter‐Sectoral Impact Model Intercomparison Project|
|LPJml|Lund‐Potsdam‐Jena managed land model; dynamic vegetation model developed at the<br>Potsdam Institute for Climate Impact Research|
|LUC|Land use change|
|LULUCF|land use, land use change, and forestry|
|MER|Market Exchange Rate|
|MESSAGE|Model for Energy Supply Strategy Alternatives and their General Environmental Impact;<br>energy model developed at IIASA|
|MIROC/MIROC‐ESM‐CH|General Circulation Model for Japan Agency for Marine‐Earth Science and Technology,<br>Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute<br>for Environmental Studies|
|NEPAD|New Partnership for Africa's Development|
|NOR/NorESM1‐M|General Circulation Model for Norwegian Climate Centre|
|PCR‐GLOBWB|PCRaster GLOBal Water Balance model; hydrological model developed at Utrecht University|
|RCP|Representative Concentration Pathway|
|SDG|Sustainable Development Goal|
|SSP|Shared Socioeconomic Pathway|
|USAID|United States Agency for International Development|
|WAE|Water application efficiency|
|WFaS|Water Futures and Solutions Project|

3
# 1. Introduction

Agriculture productivity can play an important role to improve food security and achieve some of the sustainable development goals (SDGs). Intensification through irrigation is often mentioned as a good option to make progress towards achieving the SDG 2 goal (“End hunger, achieve food security and improved nutrition and promote sustainable agriculture”) and to reduce the pressure on land (SDG 15 “...sustainable use of terrestrial ecosystems”). Expansion of irrigation is also considered an adaptation option in the face of climate change, expected to strongly affect rainfed agriculture (Leclère et al., 2014a; Müller et al., 2011; Roudier et al., 2011). However, in water stressed regions, an increased role for irrigation poses challenges for water availability (SDG 6 “Ensure availability and sustainable management of water and sanitation for all”). Transforming traditional rainfed systems or upgrading water inefficient irrigation systems into productive irrigation systems will require investments that may be beyond the economic means of farmers in vulnerable regions.

In this paper we quantify the impacts and costs of investment strategies for maintaining, upgrading and expanding irrigated agriculture in developing countries, along different future scenarios towards 2050. For this purpose, we model the supply and demand of agricultural products at a high spatial resolution in an integrated approach that considers the impacts of global change (socioeconomic and climatic) on food, feed, and fiber markets using a global economic and land use model GLOBIOM (Havlik et al., 2014). We examine the impacts of related irrigation expansion on the agriculture and food system, in the perspective of different Sustainable Development Goals (SDGs), in particular food security (SDG2), land use change and biodiversity (SDG15), greenhouse gas emissions (SDG13) and water withdrawals (SDG6).

Our approach models the conditions and investment required to transform rainfed cropland into highly productive and efficient irrigated cropland, taking into account the biophysical availability of water, the growing competition for water from other sectors (domestic, energy and industry) as well as the impacts that upgraded and expanded irrigation systems have on regional crop production, land use change and emissions, as well as food security and water availability. We implement the investment and support for irrigation as different levels of subsidy on the large‐scale capital costs and on‐farm capital costs. We also restrict the water used for irrigation to the quantity that is physically available after domestic and industrial demand has been satisfied. Partial subsidies for the capital costs to build large‐scale dams and water delivery systems are assumed to be publically funded in our investment support scenarios. Our approach considers four categories of irrigation investment costs– costs to expand irrigated area, costs to upgrade irrigation systems from less efficient to more efficient systems, costs to improve the overall efficiency of a less efficient system, and depreciation costs.

We look at two different levels of ambition for irrigation development, where irrigation expansion and upgrade investment in developing regions are supported by public investments: a moderate public support scenario ( _Invest_ ) on the large‐scale capital costs leading to 32% more irrigated area by 2050 in developing countries; and a high public support ( _MaxInvest_ ) scenario where large‐scale and on‐farm capital costs are heavily subsidized, enabling a 70% increase in irrigated area. Such deployment would require public investment costs of about $26 billion to $50 billion per year, in constant 2000 US dollars. These scenarios are compared to a counterfactual where no more investment in irrigation would take place. Our scenario analysis is complemented by a comprehensive uncertainty assessment investigating the roles of macroeconomic development, climate change, future diets, water efficiency and trade policies.

4
Our results show that ambitious support and investment to improve, upgrade and expand irrigated areas can have an impact on improving food security and reducing the burden on land. In our most ambitious scenario, _MaxInvest_ , 154 million hectares could be added which corresponds to a 60% increase in irrigated areas in developing countries from 2010, and the use of about 73% of the irrigable area potential in those regions (FAO, 2017). A more easily achievable scenario, _Invest_ , would expand irrigated land by 71 million hectares (+32%) and make notable improvements in food security in a number of regions but have less impact on reaching the climate and land SDGs than our ambitious support scenario.

We observe that climate change scenarios and trade policies are the most likely to affect irrigation strategies and their impacts across the SDGs. Irrigation appears as a key adaptation mechanism in regions with sufficient water supply, but climate change also limits in some areas the overall water availability and potential for irrigation expansion. In the case of trade, restrictions lead to irrigation expansion for domestic production of food in South Asia and Middle East and North Africa, in case of exchange restrictions. On the contrary, more intensive trade favors expansion of irrigated area in Latin America and the Caribbean where a share of food needs can be relocalized. Investments into increased water use efficiency is also found to lead to mixed outcomes, with higher food production levels to the benefit of food security, but limited water savings due to the rebound effect of production.

According to our results, achieving ambitious expansion of irrigation and increase in irrigated cropland productivity in developing countries would cost $50 billion per year over the next 40 years ‐‐ decomposing in $16 billion in building new irrigation infrastructure (i.e. providing access to water for irrigation and equipping rainfed cropland with irrigation equipment), $32.7 billion in depreciation costs, $0.6 billion in upgrading irrigation systems to more efficient systems (i.e. moving from gravity‐ based irrigation systems to sprinkler or drip), and $0.2 billion in general efficiency improvements of irrigation systems (i.e. improving the water application efficiency of gravity‐based irrigation systems). Approximately a quarter of the expansion costs would occur in Sub‐Saharan Africa, 32% in East Asia and the Pacific, 5% in Middle East and North Africa, and 17% in Latin America and the Caribbean, 14% in South Asia, and 10% in Europe and Central Asia.

Multiple benefits would accrue from this irrigation increase, in terms of efficiency of irrigation systems, food availability, and land sparing. However, our results also show that, for some regions, development of irrigation is by no means a panacea when confronted to the SDGs, and careful planning is needed to secure the benefits from such large investments. Unsurprisingly, investments in irrigation would lead to improvement in food availability and hence contribute to improved food security in all the considered regions (SDG2). However, the effectiveness and efficiency in achieving this goal through expanded irrigation differs substantially across regions; an investment of $4.3 billion per year would improve the food availability in Sub‐Saharan Africa by less than 1% by 2050 while an investment of $6.8 billion per year would improve the food availability in South Asia by 2.5% by 2050.

The results are even more ambiguous in terms of environmental impacts. Irrigation expansion is expected to play a land sparing role and ease protection of forests and natural land for biodiversity conservation (SDG15). While in Eastern Europe and Central Asia and in Latin America and Caribbean irrigation expansion leads to reduction in cropland area expansion with a positive effect on natural habitat protection, in Eastern Asia and Pacific and in South Asia, expansion of irrigation would lead to further expansion of cropland at the expense of natural habitats. The effect on GHG emissions (SDG13) is in most cases negative because of emissions from additional agricultural production, and in many regions also from additional conversion of natural areas including forests. Finally, the irrigation infrastructure expansion leads to substantial additional water withdrawals and in Europe and Central Asia, South Asia, and Middle East and North Africa leading to a competition with environmental water 5
flows requirement which is our proxy used for the sustainability of water withdrawals (SDG6), though the local impacts in other regions such as East Asia and Pacific may be significant.

Consideration of these multiple trade‐offs with SDGs helps to determine the regions where investments could be targeted with limited adverse impacts. According to our findings (Table 1), the most relevant regions for such large‐scale irrigation investments would be Europe and Central Asia, Middle East and North Africa, and South Asia where substantial benefits are to be expected with limited negative effects.

Table 1. Summary of the difference in impacts by indicator for Investment Scenarios relative to ZeroInvest in 2050 with color‐ coded SDG scoring

||Irrig.<br>Area|Investment<br>Cost|Crop<br>prices|Food<br>availability|GHG<br>AFOLU|Cropland|Other Nat<br>Land|Forest|Env. Flow<br>Requirem<br>ent|
|---|---|---|---|---|---|---|---|---|---|
|**Invest**|Mha|$ Billion<sup>1</sup>/<br>year|%<br>change|kcal/cap/<br>day|MtCO2eq|Mha|Mha|Mha|% of EFRs<br>at risk<sup>2</sup>|
|AFR|8.8|3.7|‐2.0|9.9|‐4.6|1.5|0.1|‐0.3|0.7|
|EAP|36.7|6.4|‐2.3|13.5|68.6|1.5|‐0.8|‐1.2|0.8|
|ECA|5.4|0.8|‐0.5|2.3|5.1|‐0.7|0.6|0.0|0.4|
|LCR|12.4|2.0|‐0.5|7.3|7.9|‐4.0|2.0|0.4|0.3|
|MNA|4.4|1.1|‐5.1|18.0|8.3|1.0|‐0.7|0.0|7.1|
|SAR|38.2|3.4|‐2.9|51.0|72.4|3.2|‐2.2|0.0|6.7|
|WLD|104.3|17.2|‐1.8|20.2|143.7|1.0|0.4|‐1.1|0.8|
|**MaxInvest**||||||||||
|AFR|22.7|10.1|‐2.2|7.7|‐10.9|‐1.3|2.3|‐0.5|2.0|
|EAP|49.4|11.3|‐3.3|34.9|67.9|1.6|‐1.9|‐1.1|2.0|
|ECA|18.5|4.7|‐1.5|8.0|7.0|‐3.7|2.8|0.0|2.6|
|LCR|43.5|8.0|‐7.3|54.1|99.0|‐5.4|8.1|‐4.9|1.6|
|MNA|5.9|1.7|‐6.5|19.7|6.9|1.0|‐0.7|0.0|7.4|
|SAR|49.6|4.8|‐5.1|71.0|71.5|5.6|‐3.1|0.0|12.2|
|WLD|187.7|40.3|‐3.8|34.2|221.4|‐4.9|10.1|‐6.5|2.1|

This working paper is structured as follows. Section 2 describes the methods we used for this analysis including the modeling framework, the irrigation investment scenarios, and the sensitivity analysis. Section 3 presents the investment needs and the composition of irrigation systems under the scenarios. Section 4 presents the impacts of the irrigation investments on the SDGs. Section 5 provides the sensitivity analysis. Finally, Section 6 concludes the working paper.

Additional results and figures are available in the supplementary information<sup>3</sup> with more details on the modeling framework, regional results, and sensitivity analysis. Figures from the supplementary information are referenced in this paper with the “SI” prefix.

> 1 Constant 2000 dollars.

> 2The total volume of environmental flow requirement (EFR) at risk to become unsustainable is calculated first by identifying pixels in which at least one month of the volume of surface water withdrawals by irrigation are in excess of the volume that should be left for the environment and then calculating the total volume of EFR in the unsustainable pixels over the entire growing period. The total volume of EFRs for unsustainable pixels is then compared to the total volume of EFRs for the region to calculate the share.

> 3 <u>http://pure.iiasa.ac.at/id/eprint/15748/</u>

6
# 2. Methods

## 2.1.Modeling framework

GLOBIOM is a partial equilibrium model representing land‐use based activities: agriculture, forestry and bioenergy sectors. The model uses a bottom‐up approach to crop, livestock, and forestry production based on detailed biophysical and cost information at the gridcell level, while demand for food and fiber are represented at the level of 30 economic regions. Markets in GLOBIOM consider bilateral trade policies and barriers as well as transportation costs (Mosnier et al., 2014). The bottom‐ up modeling approach of the model considers the biophysical environment of production under multiple management systems for 18 globally produced crop products at a gridcell‐level for four crop management systems: subsistence farming, low input rainfed, high input rainfed, and high input irrigated (Leclère et al., 2014b; von Lampe et al., 2014a). GLOBIOM dynamically models the use of cropland, pasture/grassland, managed and unmanaged forest, and other natural vegetation based on the availability of land and water and the relative profitability of each land sector given land rents and irrigation water costs and nonlinear conversion costs (Havlík et al., 2011b; Robinson et al., 2015, 2014; Schmitz et al., 2014).

GLOBIOM uses the globally gridded simulated crop yields and resource requirements (fertilizer, water for irrigation) from EPIC (Balkovič et al., 2013). The water demanded for irrigation is sourced by surface and groundwater and all irrigated areas are constrained to the water that is physically available after use for domestic and industrial purposes (Palazzo et al., 2018, 2017, Pastor et al., 2016, 2014). Section 1 of the <u>supplementary information</u> provides a more detailed description of the representation of irrigation as a crop production system within the modeling framework.

## 2.2.Scenario description

We developed and implemented several irrigation support scenarios to examine the impacts of investments in irrigation and applied the scenarios in GLBOIOM. In our scenarios, we implement the investment and support for irrigation as different levels of subsidy on the large‐scale capital costs and on‐farm capital costs. Table 2 provides an overview of components that make up the full cost of irrigation based on Rogers et al.’s (1998) conceptual overview of costing water for irrigation. Partial subsidies for the capital costs to build large‐scale dams and water delivery systems are assumed to be publically funded in our investment support scenarios.

Subsidies for capital costs from government agencies or basin authorities are still common in many developed countries (including Canada, Australia, Greece, France, Italy, and Spain) (See Table 1 in Toan, 2016). In many developing countries such as India, Pakistan, and China, all capital costs and part of the operation and maintenance costs are subsidized by state agencies and water user organizations (Toan, 2016). While Turral et al. (2010) broadly define public investment in irrigation as expenditures that create an enabling environment for producing economic output and include “irrigation and drainage development, modernization, institutional reform, improved governance, capacity building, management improvement, creation of farmer organizations, and regulatory oversight, as well as farmers’ investment in joint facilities, wells, and on‐farm water storage and irrigation equipment” (p 553). While many of these activities and expenditures have an economic cost, some are beyond the scope of what can be represented in an economic land use modeling framework and are not considered in our approach.

Additionally, we assume that a portion of the costs from the public sector include training as it is an essential component to effectively use and maintain irrigation systems and is often publicly funded, 7
as seen in some Sub‐Saharan Africa case studies (Van Koppen et al., 2005). Subsidizing the cost of water for irrigation has become common practice in developed regions like Europe and the US as a means to encourage agricultural development, though many of these policies are currently being reformed due to their environmental impacts (such as over‐extraction and water pollution, see Wichelns, 2010).

# _ZeroInvest_

_ZeroInvest_ is a scenario where there is no new investment in irrigation and therefore no expansion of irrigated areas beyond 2010 levels in developing regions. There are no investments to improve the water application efficiency of existing irrigation systems. Water used for irrigation is limited to the quantity that is physically available after domestic and industrial demand has been satisfied.

_ZeroInvest_ is used as a reference scenario but not a policy scenario.

# _Invest_

_Invest_ is a _moderate public support_ scenario where the irrigation equipment investment costs needed to expand irrigated areas or upgrade existing areas to more efficient irrigation systems in developing regions are supported by public investments, leading to 32% more irrigated area by 2050 in developing regions. Farmers are responsible for the operations and maintenance of the systems, which have been included in the model as production costs. The price of water that farmers face for irrigation use reflects the on‐farm capital costs to utilize water as well as the relative scarcity of water due to the increasing demand for water for other sectors. _Invest_ reflects a mixed‐cost sharing policy that is similar to the one used in the Fadama Project in Nigeria in which a portion of the parts and materials were subsidized (Foster and Briceno‐Garmendia, 2009).

In _Invest_ the underlying improvement in the efficiency of water application, “more crop per drop” improves by 1.5% each decade. Efficiency can be achieved through upgrades or conversion of existing flood/gravity irrigation systems to more drip and sprinkler systems or by improving the overall efficiency of flooding/gravity irrigation systems. Water used by irrigation is limited to the remaining portion of physically available water at the pixel level after domestic and industrial demand has been satisfied.

# _MaxInvest_

_MaxInvest_ is a _high public support_ scenario where ambitious investment in expanding and upgrading irrigated areas in developing regions are supported by public investment, leading to an increase of irrigated area by almost 70% by 2050 in developing regions. As with _Invest_ , farmers are responsible for the operations and maintenance of the systems, which have been included in the model as production costs. Additionally, the price of water that farmers face is subjected to scarcity impacts although some of the on‐farm capital costs are subsidized making the per unit price of water less expensive. The main difference between the _Invest_ and _MaxInvest_ scenarios is that _MaxInvest_ reflects a policy where priority is given to increasing the accessibility of water by fully subsidizing it.

In _MaxInvest_ , the underlying improvement in the efficiency of water application, “more crop per drop” improves by 3.0% each decade. Efficiency can be achieved through upgrades or conversion of existing flood/gravity irrigation systems to more drip and sprinkler systems or by improving the overall efficiency of flooding/gravity irrigation systems. Water used for irrigation is limited to the quantity that is physically available after domestic and industrial demands have been satisfied.

8
# _Regional MaxInvest_

Regional _MaxInvest_ scenarios correspond to the _MaxInvest_ scenario but investments are implemented in only one World Bank region. These scenarios are important to disentangle the interaction effects between regions in the _MaxInvest_ scenario. Water used for irrigation is limited to the quantity that is physically available after domestic and industrial demand has been satisfied.

9
Table 2. Overview of the types of costs associated with irrigation as defined by Rogers et al. (1998) and the subsidy assumptions by investment scenario

|**Type of irrigation cost**|**Reference**|**Responsible for costs**|**in****_Invest_**|**Responsible for costs in****_MaxInvest_**|
|---|---|---|---|---|
|1.Operation and maintenance|||||
|a. Energy (gasoline, diesel,<br>electricity, animal power, or<br>human power)|Sauer et al. (2010), FAO (2016)|Producer||Producer|
|b. Labor|FAO (2008), Sauer et al. (2010), FAO (2016)|(within GLOBIOM as prod|uction cost)|(within GLOBIOM as production cost)|
|c. Routine maintenance and wear‐<br>and‐tear|FAO (2008)||||
|2. Capital costs||**Large scale infrastructure**|**On‐farm**|**Large scale infrastructure**<br>**On‐farm**|
|a. Engineering, engineering<br>management|Inocencio et al. (2005), Inocencio et al. (2007), FAO<br>(2016)||||
|b. Parts and material<br>c. Interest and finance costs|Inocencio et al. (2005), Inocencio et al. (2007), FAO<br>(2008), FAO (2016), Rosegrant et al. (2017)<br>Inocencio et al. (2005), Inocencio et al. (2007), FAO|Public Sector<br>(in ex‐post investment cost<br>llti i t||Public Sector<br>(i t itt t llti i t|
|d. Training, technical assistance,<br>institutional development,<br>capacity strengthening|(2008)<br>Inocencio et al. (2005), Inocencio et al. (2007), FAO<br>(2008), Rosegrant et al. (2017)|cacuaon as expanson coss,<br>upgrade costs, and efficiency<br>costs)|Producer<br>(within GLOBIOM as<br>production cost and<br>land conversion cost)|n ex‐pos nvesmen cos cacuaon as expanson coss,<br>upgrade costs, and efficiency costs)|
|e .Administrative costs|Inocencio et al. (2005), Inocencio et al. (2007), FAO<br>(2008)||||
|f. Depreciation|Schmidhuber et al. (2009)|Public Sector<br>(in ex‐post investment cost<br>calculation as depreciation)||Public Sector<br>(in ex‐post investment cost calculation as depreciation)|
|3. Resource costs|||||
|a. Opportunity costs of alternative<br>water uses|Toan (2016)|Producer||Producer|
|b. Taxes, licenses, and<br>government levied resource fees|Van Koppen et al. (2005)|(within GLOBIOM as the|water price)|(within GLOBIOM as water price)|
|4. Environmental costs|||||
|a. Environmental damages|Toan (2016)|Quantified as a share of agricult<br>unsustainable but not modeled wi|ural water use that<br>th a monetary value|Quantified as a share of agricultural water use that<br>unsustainable but not modeled with a monetary value|

10
# 2.3.Sensitivity analysis

The investment support scenarios described above are implemented under central assumptions derived from the Shared Socioeconomic Pathway (SSP) 2: Middle of the Road (Fricko et al., 2017). In SSP2 the global population reaches 9.2 billion people (KC and Lutz, 2017) and global average GDP per capita doubles to around $16,000 (Dellink et al., 2017). We estimate crop and livestock productivity over the time period using an econometric estimate of the relationship between crop yields and GDP per capita assumptions of SSP2 (Fricko et al., 2017; Herrero et al., 2014).

The main scenarios of investment are complemented by some sensitivity analysis scenarios where some key assumptions and drivers from the SSP2 baseline are varied, to test their influence on the irrigation policy impacts. Table 3 provides an overview of the types of modeling assumptions we varied in the sensitivity analysis and a description of the alternative assumptions used. Section 5 provides the more detailed examination of the impacts of alternative assumptions for the sensitivity analysis on irrigation investments and impact of irrigation investments on the agriculture development, food security and land use change. First, we test the impact of irrigation investments under completely different socioeconomic pathways (SSPs). Our analysis considers SSP1, “Sustainability”, and SSP3, “Regional Rivalry” where the development of population and economic growth, speed of technological change in agriculture, dietary preferences and food waste management, as well as degree of globalization of agricultural commodity markets differ from the baseline (SSP2). We pay particular attention to the irrigation development storylines and the projections for water demand from domestic and industrial uses developed for the SSPs from Water Futures and Solutions Initiative (Wada et al., 2016) and Hanasaki et al. (2013).

The socioeconomic pathway SSP1 provides an alternative dietary preference scenario which moderates overconsumption and aims to reduce of the share of red meat in the diets compared to the diet preferences in the baseline (SSP2). Two additional diet scenarios are also modeled– healthy diet and healthy sustainable diet. The Healthy Diet scenario modifies current diets to set them strictly in line with the World Health Organization dietary recommendations and the Healthy Sustainable Diet scenario considers the land and water resource requirements of food items.

We evaluate the impacts of climate change using spatially explicit impacts on crop yields, irrigation water requirements which were calculated by the crop growth model EPIC (Balkovič et al., 2013) under the representative concentration pathway 2.6 and 8.5 for five global climate models (GCMs) and coupled to GLOBIOM. Additionally, the water available for irrigation is determined by the globally gridded monthly surface water availability and environmental flows is modeled by LPJmL (Palazzo et al., 2018, 2017, Pastor et al., 2016, 2014).

Over the time period, our baseline assumption is that the efficiency of irrigation systems (water application efficiency) moderately increases. We model two alternative specifications: a high water application efficiency for irrigation systems scenario with rapidly increasing irrigation water application efficiency through substitution of the inefficient systems by sprinkler and drip, and a low water application efficiency scenario with stagnating water use efficiency.

Finally, we analyze the effects of more integrated international markets (open trade) and more regionalized markets (restricted trade) and their role in irrigation investment needs.

Additionally, we combined various parameters and assumptions of the uncertainty scenarios described above to form more extreme scenarios. The goal of these “extreme scenarios” is to identify the smallest and largest expansion of irrigated areas as well as the least and greatest irrigation 11
investment costs that would be required for those areas. The details of the modeling setup and results from the extreme scenarios appear in Section 4 of the supplementary information.

Table 3. Types of assumptions and drivers varied in the sensitivity analysis

|**Type of modeling assumption**|**Change from SSP2 assumptions**|
|---|---|
|socioeconomic pathways (SSP)|SSP1 Sustainability<br>SSP3 Regional Rivalry|
|dietary patterns|Healthy Diets<br>Healthyand Sustainable Diets|
|climate change impact magnitude|HadGEM2‐ES<br>IPSL‐CM5A‐LR<br>GFDL‐ESM2M<br>MIROC‐ESM‐CHEM<br>NorESM1‐M<br>HadGEM without CO2fertilization|
|water application efficiency|High water application efficiency for irrigation<br>Low water application efficiencyfor irrigation|
|trade openness|Open trade<br>Restricted Trade|

12
# 3. Investment needs for irrigation and impacts on irrigation systems

## 3.1.Irrigation investment costs

We estimate the investment costs required to expand irrigated areas, to upgrade irrigation systems from less efficient to more efficient systems, and to improve the overall efficiency of irrigation systems. Expansion of irrigated areas involves a transformation of rainfed cropland area or the conversion from forested or other natural lands. Upgrade improvements are achieved by converting existing irrigated areas from low efficiency systems (such as flood/gravity systems) to more efficient systems (such as drip and sprinkler systems). Efficiency improvements can be achieved through improvements made to existing flood/gravity irrigation systems through land leveling, better irrigation scheduling, or improved water distribution (Miao et al., 2018). As we described in Section 1.1, the investment support scenarios are implemented as different levels of subsidy on the large‐scale capital costs and on‐farm capital costs. Farmers are responsible for the operations and maintenance of the irrigation systems, which have been included in the model as production costs.

The unit costs of surface irrigation expansion are based on new irrigation construction costs taken directly from the unit costs estimates from Inocencio et al. (2005) which are based on a review of 314 irrigation projects spanning 50 countries in Sub‐Saharan Africa, Asia, and Latin America and funded (or assisted) by the World Bank, Sub‐Saharan African Development Bank and the International Fund for Agriculture Development that were implemented from 1967 to 2003. This source distinguishes between costs of new construction and costs of irrigation rehabilitation, which are both used in the investment module depending on the situation. We also estimate the cost of upgrading existing irrigation systems from less efficient to more efficient systems based on rehabilitation unit costs. The cost of depreciation and capital replacement is the final component of the irrigation investment cost and must be borne in order to keep the existing and newly built irrigation infrastructure in good working order in the long run.

The results presented in the following section of this working paper have been aggregated and presented at World Bank regions (see Abbreviations and Acronyms). Figures in this section primarily focus on the irrigation investments in constant year 2000 US dollars from 2010 to 2050 divided into irrigated area expansion costs, system upgrade costs, system efficiency costs, and depreciation costs. Supplementary figures of investment needs are available in the supplementary information. Section 1.3 of the supplementary information provides a detailed description of the irrigation cost module.

For the _Invest_ scenario, investment costs for all developing countries for the entire period from 2010 to 2050 total $1.1 trillion, with annual costs at about $26 billion per year (Figure 1). The total costs are split almost half and half between irrigation expansion and depreciation, with only a small portion of the cost coming from irrigation upgrades and efficiency improvement. Upgrade and efficiency improvement costs are most relevant in the East Asia and the Pacific region, totaling almost $7 billion. Of note is the fact that the type of investment required changes over time; whereas most of the expansion investments occur toward the beginning of the time period, depreciation becomes the major expense toward the end of the time period. It is worth noting, that as irrigated area increases, depreciation costs also increase over time. In terms of regional composition of the investment, the largest share of the required investment (over 40% or $444 billion) takes place in East Asia and the Pacific, followed by South Asia ($211 billion) and Sub‐Saharan Africa ($173 billion).

13
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>150<br>100 Investment type<br>a By Expansion<br>D<br>c<br>ne BD Upgrade,<br>=a BBOo efciency Maintenance :<br>ooo0o00OSAOMTOooooo COCOOCOCOOoOooooeoHKNOTO COCOOCOCOOocoeoeoHKNOTO COCOOCOCOoOeocooecocHKNOTO CO0OCOOeoooeoH§<NMOTO COCOCOoOeoooeeoeHKNOTOD CO000eooeoeoHK NOTHO CCO00oCcoooeoeoHK NOTOD FC°0°0oOoHf“eooo°oo NOT0<br>NNANNNN NNNNN NANNNN NANNNN NNNNN NNNNN NNNNN NNNNN NANNING<br><!-- End of picture text --> <!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>200<br>150<br>Investment type<br>o BB Expansion<br>=2=aE|100 I[BWL] Upgradeetficiency Maintenance . .<br>. | ll | i |<br>CSCOO00OecooeoSNOTDO COCOOCOCOSOHNOTOeeeooo C00OCOHNOTOSeeeo COCCOoOeCeoeeoHF NOTO CO00SoOSCEeeeoeHK NOTON CCOCoOCoOHFNOTONeeeoeoe C0000FSEeeCeoHFNOTO CCCCoSeeeoHK NOTO C0000SCSoCo°ooHF NOTY<br>NANNNN NANNNN NNANNNN NNNNN NNNNN ONNNNN ONNNNN NNNNN NANNING<br><!-- End of picture text -->
<!-- Start of picture text --> 60 5.0<br>¢<br>45<br>50<br>40<br>3.5<br>40<br>a= Sd 3.0 .<br>~aS><br>2 30 ° 25 5o<br>8°g<br>3$ 2.0<br>20<br>15<br>°<br>1.0<br>10 J -<br>Rosegrantet Rosegrantet Molden,2007 FAO, 2008 Schmidhuber Cosgrove & Winpenny, GLOBIOM GLOBIOM GLOBIOM<br>al., 2017 al., 2017 (High etal, 2009 = Rijsberman, 2003 Invest Maxinvest Zeroinvest<br>Investment 2000<br>Scenario)<br>Expansion B Rehabilitation / Modernization TM@ System Upgrade<br>B Efficiency Improvement & Soil Water Management @ Farmer Investment<br>@ Depreciation @ Total ® Maintenance and Operation<br>Average irrigated area increase per year [right axis]<br>2010-2050 [red] 2010-2030 [yellow] 1998-2030 [black]<br><!-- End of picture text -->
<!-- Start of picture text --> 35 25<br>30<br>2.0<br>25<br>= 20 5<br>a ES<br>2153c:<==<br>=a 1.0<br>10<br>Sa o os<br>| U<br>a : Li nthe = . = U = 40<br>You, 2008 You,2011 You,2011 Xie,2014 Rosegrant Rosegrant Molden, FAO,2008 NEPAD, Koohafkan GLOBIOM GLOBIOM GLOBIOM<br>(>086 IRR) (>12% IRR) etal, 2017 et al., 2017 2007 2003 etal,2010 = Invest Maxinvest Zeroinvest<br>(High<br>Investment<br>Scenario)<br>M Expansion @ Rehabilitation / Modernization<br>TM System Upgrade @ Efficiency Improvement<br>@ Soil Water Management & Maintenance and Operation<br>TM Depreciation @ Total<br>@ Average irrigated area increase per year [right axis] 2000-2050 [purple]<br>2010-2050 [red] 2010-2030 [yellow] 1998-2030 [black]<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>100<br>System<br>hes=- im)IY) Dripsurface irrigation inigation<br>500 al sail ul| oll nooo Bo il BB sprinter irrigation<br>CHER5888 CHEE5886 CBRE53888 CHB5888 CHBE5888 CHEE5888 CHEE58838 CHBE5338 OBE5338<br>NZ2z2 R222 NEZ2Z NRZZ2 N2Z2 NEZZ NZZF REZ NZZZ<br>ox © % S %¥ SF ®K® FS *R SFR FS RF FSR B®<br>N= N= NF NF NF NF NF NF NES<br>AFR EAP ECA LCR MNA SAR APD EC NAM<br>30<br>20<br>System<br>=wo 10 BD Zeroinvest<br>=<br>0» A a ek bl a eeee ee iI|hs |_|1) investMaxinvest<br>-10<br>-20<br>925 8225 8925 B2H BSH BL2H PLE BOHR BQH<br>€O28 g€92 S92 EOF ECE EOE EOE EOE EOE<br>a| ©FF 5OO eFF 5e@ =BF Oo5S =Ge5 =Fe= =Fee5 =fF?5S ©Fe5 =F<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>40 Total irrigation<br>— 2010<br>— 2050<br>o<br>fad<br>= System change (2010-2050)<br>on || Irrigation net expansion<br>BE System upgrade<br>|| Irrigated area not upgraded<br>|_|| | oe<br>Bo DB B @ OB OD GB DB GB GD B DB Go DB BB OG B OB<br>oO oO oO ® oO oO o oO Oo oO oO oO oO oO oO oO o oO<br>>= >c >ij >= >=} Ee> 5 omy> ix> >= Cc> >— >= >= >i >3 i=> oy— >c<br>= =e Se a — Se ~~ 3 = Be — “Sy ~~ =<br>ao [0 oO oO o oO a oJ oO<br>= = = = = = = = =<br><!-- End of picture text -->
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<!-- Start of picture text --> BARL CORN COTT POTA RICE SOYA SUGC WHEA<br>200<br>100 ><br>a<br>0 —_ o_o<br>ee al<br>-100<br>200<br>100 g<br>ise]<br>-100<br>200<br>100 m<br>$<br>52am oon a|<br>><br>s<br>= ll ii BY Zeroinvest<br>2 aaa ‘im Invest<br>2 200 BB Maxinvest<br>= fH Maxinvest Reg<br>we 100 -Q<br>0 = =. = oe ee i ail — —- va)<br>-100<br>200<br>100 z<br>><br>-100Hh ee,<br>200<br>100 a<br>3<br>an<br>-100<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>60<br>Ss<<ow<br>8<br>8<br>=< 40 Scenario.<br>£ J Delta Invest<br>=<br>&<br>o | | Delta Maxinvest<br>><br>®<br>8<br>S<br>ir 20 |<br>; I, 4 oem SHOE _ om<br>fo] Oo Oo oS Oo fo) fo] Oo i=) fo] i=] Oo fo] oS i=} Oo io] jo]<br>Oo ive} oO wo oO wo oO wo oO wo oO wo [se] wo oO wo oO wo<br>oOoN OoN OoN oON oON oOoN OoN OoN oON oN oON oN oN oN oN OoN OoN OoN<br><!-- End of picture text -->
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<!-- Start of picture text --> a) Other sectors water demand b) Industrial water only c) Domestic water only<br>3000 3000 3000<br>Region<br>Barr<br>Mi ear<br>Ml eca<br>.<br>g 2000 2000 2000 cr<br><!-- End of picture text --> <!-- Start of picture text --> SAR<br>Baro<br>1000 1000 1000 LD EC<br>nam<br>P ae 4 aa a<br>2010 2020 2030 2040 2050 2010 2020 2030 2040 2050 2010 2020 2030 2040 2050<br><!-- End of picture text -->
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<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>20 fF<br>45 Base year<br>—_ . i -- 2010<br>x A<br>8F<br>oO | Scenario<br>a 10 Oo Zerolnvest<br>—i 1) invest<br>| Maxinvest<br>0<br>2050 2050 2050 2050 2050 2050 2050 2050 2050<br><!-- End of picture text -->
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insights into population and urbanization (Jiang and O’Neill, 2015; KC and Lutz, 2017) and economic growth (Crespo Cuaresma, 2015; Dellink et al., 2017; Leimbach et al., 2015).

In Fricko et al. (2016), the full quantification of SSP2 is presented using the quantified drivers and an impact assessment modeling framework which links the economic land use model GLOBIOM with an energy demand model MESSAGE. Embedded in this future pathway for SSP2 are the drivers and assumptions, which include: population growth, per capita income growth, intrinsic technical progress for livestock yields, crop yields, and irrigation application efficiencies.

Using alternative SSPs we tested the impacts of our irrigation investment strategies under several indicators. We chose _SSP1: Sustainability_ and _SSP3: A Rocky Road_ because they represented a plausible envelope of development that would be “better” and “worse” than SSP2. Table 4 provides an overview of the global and regional population and GDP drivers for the alternative SSPs. Population growth and the economic development of the region will have impacts on future food demand, as well as the region’s investments and advancements in agricultural productivity. We use an econometric relationship of the per capita income and historical crop yields to estimate the intrinsic technological improvement in crop and livestock yields.

Table 4. Population, GDP and GDP per capita assumptions by region for SSPs

|Region|Indicator|Unit|||2050|||2100||
|---|---|---|---|---|---|---|---|---|---|
||||**_2010_**|**SSP1**|**SSP2**|**SSP3**|**SSP1**|**SSP2**|**SSP3**|
||Population|(billion people)|0.38|0.55|0.61|0.69|0.48|0.65|1.02|
|MNA|GDP|(trillion USD 2005 MER)|1.86|8.38|7.69|6.69|16.56|18.78|12.38|
||GDP per capita|(1000 USD 2005 MER)|4.88|15.14|12.54|9.66|34.82|28.79|12.09|
||Population|(billion people)|0.86|1.55|1.78|2.07|1.69|2.40|3.59|
|AFR|GDP|(trillion USD 2005 MER)|0.84|8.85|6.36|4.44|52.73|46.61|19.34|
||GDP per capita|(1000 USD 2005 MER)|0.98|5.71|3.58|2.15|31.13|19.42|5.39|
||Population|(billion people)|2.02|2.00|2.09|2.21|1.19|1.47|2.01|
|EAP|GDP|(trillion USD 2005 MER)|6.05|39.69|30.26|22.92|45.33|43.56|25.80|
||GDP per capita|(1000 USD 2005 MER)|2.99|19.83|14.45|10.38|38.08|29.66|12.86|
||Population|(billion people)|0.48|0.47|0.49|0.51|0.33|0.42|0.55|
|ECA|GDP|(trillion USD 2005 MER)|2.83|9.39|8.27|6.80|13.05|14.79|9.90|
||GDP per capita|(1000 USD 2005 MER)|5.92|20.12|16.91|13.40|39.37|35.21|17.99|
||Population|(billion people)|0.58|0.67|0.74|0.85|0.48|0.67|1.08|
|LCR|GDP|(trillion USD 2005 MER)|3.19|12.30|10.35|8.61|22.56|23.98|15.08|
||GDP per capita|(1000 USD 2005 MER)|5.46|18.24|13.96|10.09|46.59|35.79|13.97|
||Population|(billion people)|0.35|0.46|0.45|0.38|0.52|0.51|0.29|
|NAM|GDP|(trillion USD 2005 MER)|14.29|33.69|29.93|24.75|56.42|46.59|23.70|
||GDP per capita|(1000 USD 2005 MER)|41.05|72.79|66.10|65.94|108.02|90.50|80.84|
||Population|(billion people)|0.15|0.16|0.15|0.13|0.12|0.12|0.07|
|ADP|GDP|(trillion USD 2005 MER)|5.55|10.48|8.79|6.82|15.81|13.21|5.54|
||GDP per capita|(1000 USD 2005 MER)|36.21|67.00|58.38|52.86|126.62|106.47|75.85|
||Population|(billion people)|1.60|2.05|2.30|2.63|1.53|2.17|3.55|
|SAR|GDP|(trillion USD 2005 MER)|1.48|14.65|10.90|7.52|36.88|35.23|15.97|
||GDP per capita|(1000 USD 2005 MER)|0.92|7.15|4.74|2.86|24.06|16.23|4.50|
||Population|(billion people)|0.41|0.47|0.46|0.39|0.45|0.45|0.26|
|EC|GDP|(trillion USD 2005 MER)|14.17|28.62|26.11|20.00|51.28|49.31|20.47|
||GDP per capita|(1000 USD 2005 MER)|34.47|60.29|56.72|51.52|113.70|109.27|77.69|

34
<!-- Start of picture text --> a) SSP1 Industrial water demand b) SSP1 Domestic water demand<br>2500 2500<br>Region<br>AFR<br>2000 2000 4<br>Mi eae<br>% 1500 %. 1500 MlBice eca<br>g g By una<br>1000 1000 SAR<br>Baro<br>500 500 UO EC<br>) ) ———<br>2010 2020 2030 2040 2050 2010 2020 2030 2040 2050<br><!-- End of picture text --> <!-- Start of picture text --> c) SSP3 Industrial water demand d) SSP3 Domestic water demand<br>2500 2500<br>Region<br>AFR<br>2000 2000 o<br>cae<br>o 1500 o 1500 BhBice eca<br>& g Buna<br>1000 1000 SAR<br>Bj arp<br>500 500 Ll EC<br>) ty) ————<br>2010 2020 2030 2040 2050 2010 2020 2030 2040 2050<br><!-- End of picture text -->
<!-- Start of picture text --> AFR AFR AFR EAP EAP EAP ECA ECA ECA LCR LCR LCR MNA MNA MNA SAR SAR SAR<br>Zeroinvesi Invest Maxinvest Zerolnves Invest Maxinvest Zerolnves’ Invest Maxinvest Zeroinvesi Invest Maxinvest Zeroinves' Invest Maxinvest Zerolnves! Invest Maxinvest<br>200<br>150 System<br>| Surface irrigation<br>Pa | Drip irrigation Drip irrigation irrigation<br>SprinklerRainfedP irrigationa,5<br><!-- End of picture text -->

AFR AFR AFR EAP EAP EAP ECA ECA ECA LCR LCR LCR MNA MNA MNA SAR SAR SAR Zeroinvesi Invest Maxinvest Zerolnves Invest Maxinvest Zerolnves’ Invest Maxinvest Zeroinvesi Invest Maxinvest Zeroinves' Invest Maxinvest Zerolnves! Invest Maxinvest 200 150 Pa £ 100 50 0 il ii ti ii ti ti rNnoOggagga rnoagaag0agaag0 rNOa00a00 rNmOgoqgaqaq06cnoodlhUrNOgoqgaqaq06cnoodlhUrNOrNO OOOrnorno rNAaAa rNOgogdqgqgogdqgq rnNOagaaga raoa0o0a0o0 Q2Q0OQ0TNOTNO rN0200802008 0200rNOrNO 0200rnNOrnNO rNO02000200 rNnoO000000 rnNoO000000 rNO000000 2ANND ODOM NNW QDNnD NNW NNW NNW NNN NNN NHHNM NHN NHNH NNN NNW NHHN NWN NHWM NHN DAH ANH NHH NHH AHH NHH AHH HHYW NHH HAHAH HHH YDHH HDHH NHHW DHH YNDHH NHH NAV System | Surface irrigation | Drip irrigation Drip irrigation irrigation a SprinklerRainfedP . high-inputirrigationa,5 38 fa Rainfed Rainfed high-input jl Subsistence Subsistence

<!-- Start of picture text --> 100 a RainfedPP 5 38<br>fa Rainfed Rainfed high-input<br>jl Subsistence Subsistence<br>50<br>0 il ii ti ii ti ti<br>rNnoOggagga rnoagaag0agaag0 rNOa00a00 rNmOgoqgaqaq06cnoodlhUrNOgoqgaqaq06cnoodlhUrNOrNO OOOrnorno rNAaAa rNOgogdqgqgogdqgq rnNOagaaga raoa0o0a0o0 Q2Q0OQ0TNOTNO rN0200802008 0200rNOrNO 0200rnNOrnNO rNO02000200 rNnoO000000 rnNoO000000 rNO000000<br>2ANND ODOM NNW QDNnD NNW NNW NNW NNN NNN NHHNM NHN NHNH NNN NNW NHHN NWN NHWM NHN<br>DAH ANH NHH NHH AHH NHH AHH HHYW NHH HAHAH HHH YDHH HDHH NHHW DHH YNDHH NHH NAV<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>120<br>cooe ov<br>8<br>@<br>(3) .<br>a Scenario<br>2 60 I) vetta Invest<br>a<br>&<br>o | | Delta Maxinvest<br>><br>w<br>Zo<br>8 30<br>rrLeaNO -ABANO -FERNO -RAEN OO rr2ANO rRARNO -B2HREN OO —GERWw -& hoNO<br>DHND HHNHD HHH HHH HHH HHH HHH HHH HAD<br>DHD HBDHGDH DDH HHH HHH HHH HHH HBHH HOHD<br>AFR EAP ECA LCR MNA SAR APD EC NAM<br>60<br>40<br>Ee<br>vv<br>= 20 Scenario<br>& {| Delta Invest<br>xs) || Delta Maxinvest<br>3 ,e_L<br>a. |<br>-20<br>ooagDHH=- N © -ogaoa2#HHHNO -aaHHHNO@ =a24A@HHHN © -oaQaookeHHHAO -aaonhHHHNO raoowhHHHNO aohw—HHHOW OF -aodHHDNO<br>DNHDH DHH HHH HDGHHHD DGBHH HHH HHH HHH HHD<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM<br>600<br>400 Type<br>=gBD)gBD)BD) trigation<br>ooOoO BRBBBB Domesticraustria5raustria55<br>=LecDNHNDDNHNDOF OF & vrthoHDNDHHDNDHN N OO -mekHDHDHaeHDHDHaeN N ©}ae -tttHHHHHHNO NO -heHHHotHHHotNOot NOot -akeHHHHHHNO NO -AaHHHReHHHReNANReOo NANReOo Oo =-eeeHHHHHHWN WN © =-eeHADeeHADeeN N OO<br>LecDNHNDDNHNDOF thoHDNDHHDNDHN mekHDHDHaeHDHDHaeN tttHHHHHHNO heHHHotHHHotNOot akeHHHHHHNO AaHHHReHHHReNANReOo eeeHHHHHHWN eeHADeeHADeeN<br>DODD DHH HHH HHH HHH HHH HHH HBHHH BHBAHnDA<br><!-- End of picture text --> AFR EAP ECA LCR MNA SAR APD EC NAM 600 400 Type =gBD)gBD)BD) trigation ooOoO BRBBBB Domesticraustria5raustria55 =LecDNHNDDNHNDOF & vrthoHDNDHHDNDHN OO -mekHDHDHaeHDHDHaeN ©}ae -tttHHHHHHNO -heHHHotHHHotNOot -akeHHHHHHNO -AaHHHReHHHReNANReOo =-eeeHHHHHHWN © =-eeHADeeHADeeN eeOO DODD DHH HHH HHH HHH HHH HHH HBHHH BHBAHnDA AFR AFR EAP EAP ECA ECA LCR LCR MNA MNA SAR SAR Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest 400 300 Investment type type B BB Expansion S upgrade = 200 | Efficiency ao im Maintenance : il | | il | | ~-aaananannnn NMaaananannnn NMnanannnn NMnnn NM aennnnrNOanDnnnnrNOanDnnrNOanD aaannnonrNMYDnnnonrNMYDonrNMYDrNMYDYD aaanAnnF-ANMDnwDnAnnF-ANMDnwDnnF-ANMDnwDF-ANMDnwDDnwDwD aaannoanFr NMNnnnoanFr NMNnoanFr NMNn aaannaOOD-nnaOOD-OODNManan oo8annan~-NM&wDannan~-NM&wDan~-NM&wD~-NM&wD&wDwD -NOMaaanananaaananannananan wnwowo aaanannan-NMoOnannan-NMoOnan-NMoO-NMoOoO wn FrAMaonnnnnaonnnnnnnnnnnn &WYWY rAMaaannnanaaannnannnnanan wo ranananananananannanananan **a** gaanaanan

<!-- Start of picture text --> AFR AFR EAP EAP ECA ECA LCR LCR MNA MNA SAR SAR<br>Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest Delta Invest Delta Maxinvest<br>400<br>300 Investment type type<br>B BB Expansion<br>S upgrade<br>= 200 | Efficiency<br>ao im Maintenance<br>: il | | il | |<br> NM rNOanDanDnD Fr NMNn NMNnNn - NManan wn a<br>~-aaananannnn NMaaananannnn NMnanannnn NMnnn NM aennnnrNOanDnnnnrNOanDnnrNOanD aaannnonrNMYDnnnonrNMYDonrNMYDrNMYDYD aaanAnnF-ANMDnwDnAnnF-ANMDnwDnnF-ANMDnwDF-ANMDnwDDnwDwD aaannoanFr NMNnnnoanFr NMNnoanFr NMNn aaannaOOD-nnaOOD-OOD- oo8annan~-NM&wDannan~-NM&wDan~-NM&wD~-NM&wD&wDwD -NOMaaanananaaananannananan wnwowo aaanannan-NMoOnannan-NMoOnan-NMoO-NMoOoO FrAMaonnnnnaonnnnnnnnnnnn &WYWY rAMaaannnanaaannnannnnanan wo ranananananananannanananan gaanaanan<br><!-- End of picture text -->
# 5.2.Climate impacts

Climate change is considered to be a major challenge for agricultural production. The potential impacts on productivity of crops under heat and water stress can be simulated using globally gridded crop models (GGCM) that simulate the effects of increased radiative forcing from general circulation models (GCMs) on temperature, precipitation, and nutrient requirement. Changes in precipitation due to changes in radiative forcing also impact future annual and monthly stream flows which in turn impact water available for irrigation.

Climate change impacts are modeled using the bias‐corrected climate datasets produced by the five general circulation models (GCMs) (Table 5) (HadGEM2‐ES, IPSL‐CM5A‐LR, GFDL‐ESM2M, MIROC‐ ESM‐CHEM, NorESM1‐M) available from the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐ MIP)<sup>7</sup> and used in also used in Phase 5 of the Coupled Model Intercomparison Project (CMIP5),<sup>8</sup> which offers a framework to compare climate impact projections in different sectors and at different scales (Warszawski et al., 2014). The GCMs are combined with different levels of atmospheric CO2 concentrations as prescribed by the RCPs (RCP 8.5 and RCP 2.6) to reveal the impact of climate change on global changes in temperature and precipitation. Although, the scientific community has yet to reach an agreement on whether the potential benefits from increased CO2 can be taken up and used by crops, especially if temperature and precipitation reduce crop yields, the impacts of CO2 fertilization on crop yields can be modeled with the globally gridded crop model EPIC (Environmental Policy Integrated Climate model). EPIC uses these changes in temperature, precipitation, and atmospheric CO2 concentration to model the impacts on crop yields, nutrient and water requirement for each to the GCMs and RCPs. The impacts of carbon fertilization are also excluded from the crop yield impacts for one of the GCMs (HadGEM2‐ES). These data sets are provided by the fifth phase of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al., 2011).

Table 5. General Circulation Models (GCMs) used to project climate change

|GCM|Modeling group|Country|
|---|---|---|
|GFDL‐|NOAA Geophysical Fluid Dynamics Laboratory|USA|
|ESM2M|||
|HadGEM2‐<br>ES|Met Office Hadley Centre|UK|
|IPSL‐|Institut Pierre‐Simon Laplace|France|
|CM5A‐LR|||
|MIROC‐<br>ESM‐|Japan Agency for Marine‐Earth Science and Technology,<br>Atmosphere and Ocean Research Institute (The|Japan|
|CHEM|University of Tokyo), and National Institute for<br>Environmental Studies||
|NorESM1‐|Norwegian Climate Centre|Norway|
|M|||
|Source: Coupled|Model Intercomparison Project Phase 5 (https://cmip.llnl.gov/cmi|p5)|

GLOBIOM has been used for assessments of agriculture under future climate change that considered impacts of alternative adaptation strategies (Leclère et al., 2014) and the role of trade (Mosnier et al., 2014) to mitigate the impacts of climate. While the climate effects on grassland productivity are less studied than those effects on crop productivity, GLOBIOM has included these impacts to understand the economic and land use implications (Wheeler and Reynolds, 2012; Havlík et al., 2015).

For this sensitivity analysis, we have used the crop modeling simulations produced with EPIC and considered the most extreme radiative forcing (RCP 8.5), general circulation model (HadGEM GCM),

> 7 <u>https://www.isimip.org</u>

> 8 <u>https://cmip.llnl.gov/cmip5</u>

39
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<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>1200<br>Base year<br>-- 2010<br>Scenario<br>800 : 5 Zeroinvest<br>Z * F oO Invest<br>ae oO Maxinvest<br>BB Ropes HadGEM wCo2 Zeroinvest<br>| RCP8.5 HadGEM wCO2 Invest<br>400 I) RcPs.5 HadGEM wCO2 Maxinvest<br>1 BB RcPs.5 HadGEM noCO2 Zeroinvest<br>7 BM) RcPe.5 HadGEM noCo2 Invest<br>wal BBB RcPs.5 HadGEM noC02 Maxinvest<br>0<br>ssP2 ssP2 ssp2 ssP2 ssP2 SsP2<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>200<br>System<br>150 El Surface irrigation<br>oO BB dip : inigation——<br>= | Sprinkler irrigation<br>= 100<br>a Rainfed P high-input pe<br>BB Rainted high-input<br><!-- End of picture text -->

<!-- Start of picture text --> EEEEESCSSSBRERBBBSE@ooood2>2>2>222222>oo ESSCSSSSSSBRASS2OCOOOOXDOOD>e>2>2>2>2222RVE ECSSCSSCSESESBRSRRBBSY©OOCOCODODOOOD>2>2>2>2>2>222 ESSSCSCSSSES;BYRRSSSEYOOO0GDDOOODoe ee ee ee eS ELCSSSSSSESBRABSRESoo>e2>2>2>2222©oO000 ESECSSSCEESEBRBRRBBseoo222222222o O©oood<br>N°B 2NO2NO2xXS§SS8588On KX On & N°§ 2NO2ZNO2KOnSHBSFSSKX On N°§ 2NO2ZNO2aLesealesaSFSHSFES QQ°F SNOZNO2xXOnmSESSFBSxX On x N°F§ 2NO2ZNO2XOnSESSFBESxX On x N°§ 2=NO2NO02ALswalsaSHSSEFEBE<br>BESTUS BESBUS SESTUSB SERBBBB BESTUB DESBUS<br>020030 020030 a20030 agoa0g0 020030 ag00g0<br>EPEReS EPEB$ EPEGS EPEZS EPES23 EPese$<br>7 TN EO ao GN EO =a SN EO a SAE a Sn EO 5 GO EO<br>xrS iI%Ly=a£réPOEss. =S tzVLy=o£réPOEaS SeGS Ly=a£ré20 z Ss9 feLy=a2réeS x9 tye.Ly=a£réPLE ris. Ly=u£ré=FLE<br>g 8 § 8 § § $ 3 $ 3 $ 3<br>rt ret xt xt xt xt<br><!-- End of picture text --> <!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>60<br>ne]= .<br>e Scenario<br>=(3) | Detta invest<br>x<br>S40¥ || Delta Maxinvest<br>2 || Healthy Diet Invest<br><!-- End of picture text -->

<!-- Start of picture text --> a<br>&<br>= | Healthy Diet Maxinvest<br>a a Healthy & Sust Diet Invest<br>.38 20 li | | Healthy & Sust Diet Maxinvest<br>SSP2 SSP2 SSP2 SSP2 SSP2 SSP2<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>50<br>Scenario<br>ne}3 | Detta invest<br>= 25 BW vetta Maxinvest<br>5<br>aod || Healthy Diet Invest<br>2 | Healthy Diet. Maxinvest<br>a°.<br>Qa || Healthy & Sust Diet Invest-<br>6 1° in By Heattny & sust Diet Maxinvest<br>-25<br>SSP2 SSP2 SSP2 SSP2 SSP2 SSP2<br><!-- End of picture text -->

<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>400<br>Investment type<br>7)Q 200 BB Expansion<br>—5 WD Uparade<br>a= 200 BW Efficiency<br>| Maintenance.<br>EEELFESGBSEEEESee ess FEESEFSESFEBEBESESSFsh FELEFEBESEESEResess FEELEEFEBEETSSess as EFELFEELFEBESEESSESess EFEELEEEEBEEBESSSSs F<br>eta 2 a= S22 2 aS 1 oO = of = cz oa 2 DS S2tOQ eo z= S2oftA=<br>Sf >dus THFBGZSE THFBEBSE THEFBEGZGSE CHFBZGE CHLEEDBBS<br>g@2o0Og ,%®20 OgB2ogaiM. Oga2o2%320= B2o030Og 5M ga2aogaog 3? = S8=2oOOg 3?456«<br>=:,Ex8= 8| xzPEwY= zsPeo | rea 5 osztea ggEI ré2PEa3<br>322 B29 gz 322 322 B29<br>=e 2 = ag & 2 2 =o S 2 eS oR a<br>= é rz rs Ls ré ré<br>pmo® <=[icy bioca poo® 2)ia bs[4cy<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>400<br>Scenario<br>|| Delta Invest<br>| Delta Maxinvest<br>E || Healthy Diet Invest<br>a Healthy Diet Maxinvest<br>200 Ey Healthy & Sust Diet Invest<br>| Healthy & Sust Diet Maxinvest<br>SSP2 SSP2 SSP2 SSP2 SSP2 SSP2<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>200<br>System<br>150 [] Surface irrigation<br>io Drip irrigation<br>= Sprinkler irrigation<br>= 100 saan<br>Rainfed high-input<br><!-- End of picture text --> <!-- Start of picture text --> a Oo Subsistence<br>BBBBBB BRBBBB BRBBBS BRBBBB BHRBBBE BRBBBBSB<br>|®©>e2aeoo— ee — ee222G&— eeO— ®D7 ©a.©eltieet2 2 aO82 a0 etoeDO ©=©&eeetkeiekeetke=& aGo aeO29 oeDO ieO ©a©eeekekeetooJ eeOo G8 etO ©ceekerketete©>2>2oOo 222D2 © @ ©>>e2re2F2F2cecktekeeeooOo D9 2@<br>SEREESR SERBSER SERSERE CEREEA SERSERT SERS ER<br>RSEeOec eteSE NBEReOe eteBe HEE@~ OO fn BE= NMSSReOee tseBSE RESe-QeexnAte& S ASHESeOec etsse<br>Sseagas SBBRSh SBEsess SESess SBRaeSs SEBBRSs<br>ofFOr8S & of OCeRER ofOrSFRe OF OG Fa Of Orftek oF Or Eg<br>arFg@eores orFZores @2rFgors oFZeres orFgZereg orFZ@ores<br>& © 8 ° @ © 3 os 8 © Ss © ® © 8 os ® © 8 os ® cs ©<br>FE FF Ee ie Fr F Fr F fF Fr - © FR ne Fr F<br><!-- End of picture text -->

<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>15<br>zg=<br>[ay<br>8<br>= Scenario<br>=<(3) 50 1) Tradeopen Invest<br>2 a TradeOpen Maxinvest<br><!-- End of picture text --> <!-- Start of picture text --> =<br>&<br>o im TradeRestr Invest<br>a | TradeRestr Maxinvest<br>B 25<br>ir<br>| |<br>SSP2 SSP2 SSP2 SSP2 SSP2 SSP2<br><!-- End of picture text -->
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>90<br>60<br>= Scenario<br>= ith TradeOpen Invest<br>& 30 oi TradeOpen Maxinvest<br>3 ial TradeRestr Invest<br>fe) | TradeRestr Maxinvest<br>: zz a.<br>0 |<br>-30<br>SSP2 SSP2 SSP2 SSP2 SSP2 SSP2<br>AFR EAP ECA LCR MNA SAR<br>400<br>Scenario<br>| TradeOpen Invest<br>€ a TradeOpen Maxinvest<br>fl TradeRestr Invest<br>200 | TradeRestr Maxinvest<br>SSP2 SSP2 SSP2 SSP2 SSP2 SSP2<br><!-- End of picture text -->
MNA SAR <!-- Start of picture text --> AFR EAP<br><!-- End of picture text --> <!-- Start of picture text --> ECA<br><!-- End of picture text --> <!-- Start of picture text --> LCR<br><!-- End of picture text --> 400 Investment type 7)Q 300 Al Expansion; = c Upgrade 2 = a 200 i Efficiency ||<sup>Maintenance</sup> ‘ oO BD So BBR RBS BB BF 3 GBésSO-B a a oO o oO oO oO oO oO oO o o oO o o o o o o oO oO oO o o o oO -2@ez£e2 =e 2@z£ 2 2 2e2 2 2#2@ez2 2 ne =-£2@e2e2 ¢ RB Ee ¢€ FE §£€ BEE FREER §£ FEE F BEB a= 32 a= 3 2 8 = 3 es a8 = 3s a2 3 2 a8 = 32 oO 2 @& & on ae or a of fe fs Oo «££ @ & oe mo}o© OoQa a=}Cy© «D® molo£ OoOoa TtoS «my® sts®D© Oocya Tao£ oDo mo}®© Oocya Ts®£ ri«@o no}®©€& Oooa TTa§ Oo«a ao)o£€§ Oooa TaaS$ «oDoO re ge sg -F @e $$ eF 2 F eF 28 F § -e 2 eF § -F @e $$ © ( 8 £ iy @ iy £ © © © (J FE FE - Ee F od & E E Ee =
such as improvements to region’s basin and furrow irrigation systems. Efficiency improvements can be achieved through improvements made to existing flood/gravity irrigation systems through land levelling, better irrigation scheduling, or improved water distribution (Miao et al., 2018), whereas upgrade improvements are achieved by converting existing irrigated areas from low efficiency systems (such as flood/gravity systems) to more efficient systems (such as drip and sprinkler systems). These efficiency costs are needed in EAP where upgrading the existing basin and furrow irrigation systems to sprinkler and drip is impractical given that the region grows predominately rice.

Achieving an increase in water efficiency for irrigation requires additional investment, either from upgrading systems or by improving overall efficiency of a system. These costs are not incurred in scenarios such as WaterEff_Low since there is no improvement in efficiency. In each scenario the irrigation costs are not fixed (only the unit costs are fixed).

Evans and Sadler (2008) describe the methods by which irrigation efficiency improvements can be achieved: crop selection, land retirement, deficit irrigation, and water application efficiency increase. Many of these methods are endogenous behaviors within GLOBIOM that allows for the selection of appropriate crops and conversion and reversion of cropland to maximize profits. There are of course some risks associated with increasing the efficiency of irrigation systems that shift to less flexible production systems (Adamson and Loch, 2014). However, when faced with increasing water scarcity, the practice of using deficit irrigation (irrigating less area and leaving the rest of the land for rainfed area) can be used as a coping strategy even in locations with highly efficient irrigation systems (English and Raja, 1996; Palazzo and Brozović, 2014). Deficit irrigation with respect to water‐stress and impact on crop yields requires future analysis.

Return flows are not currently modeled within GLOBIOM and increasing the efficiency of irrigation may have significant impacts on how these return flows are re‐distributed throughout a basin (Grafton et al., 2018). Stronger coupling between hydrological models and GLOBIOM in future analyses may allow for the feedbacks of return flows to be captured.

The impact of varying water application efficiency assumptions can be seen in the figures below. In 2050, crop production under _MaxInvest_ with higher efficiency increases global production by 0.17%, and in MNA by 2% and in SAR by 1%. Grafton et al. (2018) discuss the incentives for increasing water extraction due to the improvements in irrigation efficiency. No efficiency improvement reduces production by about the same amount. In the _ZeroInvest_ scenarios, the pattern is similar. There are very small increases in calorie availability under the high efficiency scenarios and overall less water demanded.

50
<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>200<br>System<br>150 | Surface irrigation<br>oO a Drip. irrigation=<br>x= 100 || Sprinkler irrigation<br>| Rainfed ; high-input or<br>BBR Rainfed high-input<br>50 B Subsistence<br>BRBBUERB BEBRBwY BRBBBB BRBBAR BREBBBB BUBBBSB<br>EEEEEFEESee ees FCEEECEESee ees FCELEEEEFEsee eee FCEECESECEee eS SS USEFEEEECEERSS Ss EGCEEEESee ee<br>lo} x Oo x ° x 8 x ° x oO rs ° x oO x ° x oO x ° x Oo x<br>BSHESE GSEGSE PSEESE ESeESe PSGGSe ESeeSe<br>SSSSFS SHSLES SHSHEES SHSEET SHSEES BESS EZ<br>&® BE &€ 6 BE &€ &S BF &€ S&S BRE &F S&B BE &F GS BR GB<br>P FS 3 F £§ 3 F FR 3 FP FE 3 FP FE 5 PF FB 5<br><!-- End of picture text -->

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<!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>100 System change (2010-2050)<br>LJ Irrigation net expansion<br>i System upgrade<br>f=© J Irrigated; area not upgraded<br>= 50<br>Total irrigation<br>0<br>BUBBED BEBBBG BEBBBBD BHBVSS BHBBBG BZBHEBBBS<br>oooododSeeees oooSER oeoo®dSSS ©ooooeosdSeessts @oooodsdSlee G©oooodd G©ooodeod<br>EEESSCSNEZNG2f2e_ Fle RE FESSSCECHFLHEF FESSCHFLHESSFE SESCSESSCHFLCH E FEs FCSSELSESSSeeesLOH FlOHKHF FELEFEFeesseLCHEZFLHETG<br>BSURSh NESNES NEZNE2 NESNES NESNE2 NESNE2<br>BSSEES DEGREE DSGDSH PETES DSGGSa PSaRsh<br>SPSS5SS&S of £6 PPS5SBSSSEEEB S ES£ S SSSKSESES HSSESBE £€ FHSSESSBSSHBERESBE FSFSSSESFBEHEHEEESEBE FSFESEREIESFHSSBe EEF<br>= = os 4 ae a 4 = fe = + = = ae 4 = ee 4 = so a<br><!-- End of picture text --> <!-- Start of picture text --> AFR EAP ECA LCR MNA SAR<br>500<br>400<br>Investment type<br>Q 300 Expansion<br>=@=fe} 200‘| | ii i ail PDBW|effciencyUparaceMaintenance;<br>Bo BD @ @ bo 2 G@ @ ob RB B&B @B Bo & @ @ So G B&B @ a &B G&G @<br>g¢g¢e sees £¢¢eé ce 8 gs ¢ ge g¢ge<br>&£= &€= FE= &£ ££ &E= —£= Eee* 4 —£ &meEee —£ &€ =&E &£ £€Fj &E<br>£Sefa = al BFBeefBs Ss £.BEF= = ws £BEERz= az £FEETBs Bs =BEEBs = BS<br>>é=é=é=é>é=é<br>xa = a = a = a = a caf ES<br><!-- End of picture text -->
Tables 7 and 8 summarize the spread of investment costs under the sensitivity scenarios. Generally, shifting to higher efficiency irrigation systems and significant climate impacts, such as those from RCP 8.5 increases the investment costs. Sustainable consumption and weaker climate impacts such as those from RCP 2.6 decrease the irrigation investment requirement.

Table 7. Maximum and minimum difference in the cumulative irrigation investment costs of the sensitivity scenarios compared to baseline investment scenarios by region

|||MaxI<br>|nvest<br>|||Inv<br>|est<br>||
|---|---|---|---|---|---|---|---|---|
||Minimum differe<br>irrigation investme<br>compared to_Max_|nce in<br>nt costs<br>_Invest_|Maximum diffe<br>irrigation investm<br>compared to_M_|rence in<br>ent costs<br>_axInvest_|Minimum differe<br>irrigation investme<br>compared to_Inve_|nce in<br>nt costs<br>_st_2050|Maximum diff<br>irrigation invest<br>compared to_In_|erence in<br>ment costs<br>_vest_2050|
||2050 (%)||2050 (%)||(%)||(%)||
|Region|||||||||
|AFR|RCP2p6 NOR|‐50%|WatrEff High|9%|HealthySustDiet|‐14%|WatrEff High|9%|
|EAP|RCP2p6 NOR|‐18%|WatrEff High|9%|WatrEff Low|‐11%|RCP8p5 IPSL|12%|
|ECA|RCP2p6 NOR|‐55%|RCP8p5<br>HadGem noCO2|33%|HealthySustDiet|‐23%|TradeOpen|56%|
|LCR|RCP2p6 NOR|‐60%|WatrEff High|12%|HealthySustDiet|‐22%|WatrEff High|16%|
|MNA|RCP2p6 NOR|‐26%|WatrEff High|21%|RCP2p6 NOR|‐21%|WatrEff High|16%|
|SAR|RCP8p5 HadGem<br>noCO2|‐18%|WatrEff High|8%|RCP8p5<br>HadGem noCO2|‐16%|WatrEff High|7%|
|WLD|RCP2p6 NOR|‐28%|WatrEff High|10%|WatrEff Low|‐9%|RCP8p5 IPSL|14%|

Table‐8. Maximum and minimum difference in the irrigated area of the sensitivity scenarios compared to baseline investment scenarios by region

|||Max<br>|Invest<br>|||In<br>|vest<br>||
|---|---|---|---|---|---|---|---|---|
||Minimum differe<br>irrigated areas co<br>to_MaxInvest_20|nce in<br>mpared<br>50 (%)|Maximum differ<br>irrigated areas co<br>to_MaxInvest_20|ence in<br>mpared<br>50 (%)|Minimum differe<br>irrigated areas co<br>to_Invest_2050|nce in<br>mpared<br>(%)|Maximum diffe<br>irrigated areas c<br>to_Invest_205|rence in<br>ompared<br>0 (%)|
|Region|||||||||
|AFR|RCP2p6 NOR|‐17%|WatrEff High|5%|RCP8p5 NOR|‐23%|WatrEff High|5%|
|EAP|RCP2p6 NOR|‐46%|TradeRestr|5%|HealthySustDiet|‐11%|SSP3|5%|
|ECA|SSP1|‐10%|RCP8p5<br>HadGem noCO2|9%|HealthySustDiet|‐12%|RCP8p5 IPSL|9%|
|LCR|RCP2p6 NOR|‐46%|RCP8p5<br>HadGem noCO2|33%|HealthySustDiet|‐30%|RCP8p5 IPSL|33%|
|MNA|RCP2p6 NOR|‐54%|TradeOpen|12%|HealthySustDiet|‐17%|RCP2p6 MIROC|12%|
|SAR|RCP8p5 HadGem<br>noCO2|‐13%|TradeRestr|4%|RCP8p5<br>HadGem noCO2|‐12%|RCP2p6 NOR|4%|
|WLD|RCP2p6 NOR|‐15%|TradeOpen|7%|HealthySustDiet|‐8%|RCP2p6 MIROC|7%|

54
Table‐9 Percent difference in cumulative irrigation investment costs from 2010‐2050 from Invest scenario for each sensitivity scenario by region

|**AFR**||**EAP**||**ECA**||**LCR**||**MNA**||**SAR**||**WLD**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**% difference from**|**_Invest_**|**% difference from**|**_Invest_**|**% difference from**|**_Invest_**|**% difference from****_I_**|**_nvest_**|**% difference from****_I_**|**_nvest_**|**% difference from**|**_Invest_**|**% difference from****_I_**|**_nvest_**|
|WatrEff High|10|CombinedHigh2|27|CombinedHigh2|77|CombinedHigh2|24|WatrEff High|25|CombinedHigh|18|CombinedHigh2|24|
|TradeRestr|5|CombinedHigh|20|CombinedHigh|62|CombinedHigh|18|CombinedHigh|24|CombinedHigh2|15|CombinedHigh|22|
|RCP8p5 HadGem||||||||||||||
|noCO2|3|RCP8p5 IPSL|13|RCP8p5 IPSL<br>RCP8p5 HadGem|39|WatrEff High|13|CombinedHigh2|18|RCP2p6 MIROC|10|RCP8p5 IPSL|13|
|CombinedHigh|1|WatrEff High|11|noCO2|37|RCP2p6 MIROC|6|CombinedLow|14|WatrEff High|10|WatrEff High|12|
|||RCP8p5 HadGem||||||||||||
|Invest SSP3|‐2|noCO2|9|CombinedLow2|35|TradeOpen|6|CombinedLow2|14|RCP8p5 GFDL|5|RCP8p5 GFDL|9|
|RCP2p6 GFDL|‐3|RCP2p6 MIROC|7|CombinedLow|35|RCP8p5 MIROC|4|RCP2p6 IPSL|4|RCP8p5 MIROC|5|RCP8p5 MIROC|8|
|TradeOpen|‐3|RCP8p5 MIROC|6|RCP8p5 MIROC|35|Invest SSP3|0|RCP8p5 MIROC|2|TradeRestr|4|RCP2p6 MIROC|7|
|RCP8p5 IPSL|‐4|TradeOpen|3|RCP8p5 GFDL|34|RCP8p5 NOR|0|TradeOpen|1|RCP8p5 NOR|4|RCP2p6 GFDL<br>RCP8p5 HadGem|5|
|RCP8p5 NOR|‐6|RCP2p6 GFDL|1|RCP2p6 IPSL|27|TradeRestr|0|TradeRestr|0|Invest SSP3|2|noCO2|5|
|WatrEff Low|‐6|RCP8p5 GFDL|1|RCP2p6 GFDL|26|RCP2p6 GFDL|‐2|RCP8p5 HadGEM|0|RCP8p5 IPSL|‐1|RCP2p6 IPSL|4|
|RCP8p5 GFDL|‐6|RCP8p5 NOR|‐1|WatrEff High|23|RCP2p6 IPSL|‐3|Invest SSP1|‐1|TradeOpen|‐1|TradeOpen|3|
|RCP2p6 MIROC|‐6|CombinedLow2|‐1|TradeOpen|20|RCP2p6 HadGEM|‐4|RCP8p5 IPSL|‐1|CombinedLow2|‐2|CombinedLow|1|
|RCP8p5 MIROC|‐8|CombinedLow|‐1|RCP8p5 HadGEM|20|RCP8p5 IPSL<br>|‐4|RCP8p5 GFDL|‐2|CombinedLow|‐2|CombinedLow2|1|
|||||||RCP8p5 HadGem||||||||
|CombinedHigh2|‐9|RCP2p6 IPSL|‐1|RCP2p6 MIROC|16|noCO2|‐5|RCP2p6 GFDL|‐2|RCP2p6 NOR|‐2|RCP8p5 NOR|1|
|RCP8p5 HadGEM|‐9|Invest SSP3|‐1|RCP8p5 NOR|12|RCP8p5 HadGEM|‐5|RCP2p6 MIROC|‐2|RCP2p6 IPSL|‐3|TradeRestr|0|
|RCP2p6 IPSL|‐9|TradeRestr|‐2|RCP2p6 HadGEM|3|RCP8p5 GFDL|‐9|RCP2p6 HadGEM|‐3|RCP2p6 GFDL|‐5|RCP8p5 HadGEM|‐1|
|HealthyDiet|‐10|RCP8p5 HadGEM|‐3|TradeRestr|‐8|WatrEff Low|‐12|Invest SSP3|‐5|HealthySustDiet|‐6|Invest SSP3|‐3|
|RCP2p6 HadGEM|‐15|RCP2p6 HadGEM|‐4|WatrEff Low|‐14|HealthyDiet|‐16|HealthyDiet|‐5|WatrEff Low|‐8|RCP2p6 HadGEM|‐5|
|HealthySustDiet|‐17|HealthyDiet|‐5|Invest SSP3|‐20|CombinedLow|‐19|HealthySustDiet<br>RCP8p5 HadGem|‐6|HealthyDiet|‐9|WatrEff Low|‐8|
|Invest SSP1|‐19|WatrEff Low|‐7|HealthyDiet|‐20|CombinedLow2|‐19|noCO2|‐10|RCP2p6 HadGEM|‐11|HealthyDiet|‐9|
|CombinedLow|‐23|HealthySustDiet|‐7|HealthySustDiet|‐23|HealthySustDiet|‐21|WatrEff Low|‐18|RCP8p5 HadGEM|‐12|HealthySustDiet|‐12|
|CombinedLow2|‐23|Invest SSP1|‐9|Invest SSP1|‐23|Invest SSP1|‐32|RCP8p5 NOR|‐23|Invest SSP1<br>RCP8p5 HadGem|‐13|Invest SSP1|‐15|
|RCP2p6 NOR|‐51|RCP2p6 NOR|‐18|RCP2p6 NOR|‐40|RCP2p6 NOR|‐49|RCP2p6 NOR|‐30|noCO2|‐19|RCP2p6 NOR|‐27|

55
Table10. Percent difference in cumulative irrigation investment costs from 2010‐2050 from _MaxInvest_ scenario for each sensitivity scenario by region

|**AFR**||**EAP**||**ECA**||**LCR**||**MNA**||**SAR**||**WLD**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**% difference from****_I_**|**_nvest_**|**% difference from****_I_**|**_nvest_**|**% difference from****_I_**|**_nvest_**|**% difference from****_I_**|**_nvest_**|**% difference from****_I_**|**_nvest_**|**% difference from**|**_Invest_**|**% difference from**|**_Invest_**|
|CombinedHigh|14|CombinedHigh2|19|CombinedHigh2|130|CombinedHigh2|31|CombinedHigh|22|CombinedHigh|22|CombinedHigh<br>CombinedHigh|27|
|WatrEff High|10|RCP8p5 IPSL|17|CombinedHigh|113|CombinedHigh|28|WatrEff High|19|CombinedHigh2|16|2|27|
|Invest SSP3|8|CombinedHigh<br>RCP8p5 HadGem|17|TradeOpen|72|WatrEff High|19|CombinedHigh2|16|RCP2p6 MIROC|10|RCP8p5 IPSL|18|
|TradeRestr|6|noCO2|16|CombinedLow|62|RCP2p6 MIROC|17|CombinedLow|12|WatrEff High|9|RCP2p6 MIROC|13|
|CombinedHigh2|5|WatrEff High|12|CombinedLow2|62|RCP8p5 MIROC|12|CombinedLow2|12|RCP2p6 NOR|9|WatrEff High|12|
|RCP8p5 MIROC<br>RCP8p5 HadGem|4|RCP2p6 MIROC|10|RCP8p5 GFDL|60|TradeOpen|0|TradeOpen|2|TradeRestr|2|RCP8p5 MIROC|11|
|noCO2|3|RCP2p6 IPSL|8|RCP8p5 IPSL|60|Invest SSP3|0|RCP2p6 IPSL|2|RCP8p5 MIROC|1|RCP8p5 GFDL|11|
|RCP2p6 NOR|3|RCP2p6 GFDL|6|RCP8p5 MIROC<br>RCP8p5 HadGem|55|RCP8p5 IPSL<br>RCP8p5 HadGem|‐1|HealthyDiet|‐1|Invest SSP3|1|RCP2p6 GFDL|10|
|TradeOpen|1|RCP8p5 MIROC|6|noCO2|50|noCO2|‐1|RCP2p6 HadGEM|‐1|RCP2p6 GFDL|0|RCP2p6 IPSL<br>RCP8p5<br>HadGem|9|
|RCP2p6 GFDL|0|CombinedLow|5|RCP2p6 IPSL|47|RCP8p5 NOR|‐2|TradeRestr|‐2|RCP8p5 NOR|0|noCO2|7|
|RCP2p6 MIROC|‐1|CombinedLow2|5|RCP2p6 GFDL|44|RCP2p6 NOR|‐3|RCP2p6 GFDL|‐2|RCP8p5 GFDL|‐1|CombinedLow<br>CombinedLow|7|
|RCP8p5 NOR|‐2|Invest SSP3|5|RCP2p6 MIROC|44|TradeRestr|‐3|HealthySustDiet|‐3|TradeOpen|‐4|2|7|
|HealthyDiet|‐4|RCP8p5 NOR|5|RCP8p5 HadGem|38|RCP8p5 HadGem|‐7|Invest SSP3|‐3|RCP2p6 IPSL|‐6|TradeOpen|5|
|RCP2p6 HadGEM|‐5|RCP8p5 HadGem|4|WatrEff High|32|RCP8p5 GFDL|‐8|Invest SSP1|‐4|HealthyDiet|‐6|RCP2p6 NOR|4|
|RCP8p5 GFDL|‐6|TradeOpen|4|RCP8p5 NOR|25|RCP2p6 GFDL|‐9|RCP8p5 HadGem<br>RCP8p5 HadGem|‐6|RCP8p5 IPSL|‐7|RCP8p5 NOR|3|
|RCP8p5 HadGem|‐6|TradeRestr|2|RCP2p6 NOR|20|RCP2p6 IPSL|‐10|noCO2|‐8|HealthySustDiet|‐8|Invest SSP3<br>RCP8p5|2|
|RCP8p5 IPSL|‐8|RCP2p6 HadGEM|0|RCP2p6 HadGEM|14|RCP2p6 HadGEM|‐14|RCP2p6 MIROC|‐8|WatrEff Low|‐8|<br>HadGem|1|
|RCP2p6 IPSL|‐10|RCP2p6 NOR|‐2|TradeRestr|‐13|WatrEff Low|‐17|RCP8p5 MIROC|‐8|CombinedLow|‐10|TradeRestr<br>RCP2p6|0|
|WatrEff Low|‐10|RCP8p5 GFDL|‐3|WatrEff Low|‐22|CombinedLow|‐19|RCP8p5 IPSL|‐10|CombinedLow2|‐10|HadGEM|‐1|
|HealthySustDiet|‐16|HealthyDiet|‐8|Invest SSP3|‐24|CombinedLow2|‐19|RCP8p5 GFDL|‐13|Invest SSP1|‐12|HealthyDiet<br>HealthySustDie|‐8|
|CombinedLow|‐23|HealthySustDiet|‐10|HealthyDiet|‐26|HealthyDiet|‐21|WatrEff Low|‐18|RCP2p6 HadGEM|‐12|t|‐11|
|CombinedLow2|‐23|WatrEff Low|‐15|Invest SSP1|‐28|HealthySustDiet|‐27|RCP8p5 NOR|‐24|RCP8p5 HadGem<br>RCP8p5 HadGem|‐17|WatrEff Low|‐12|
|Invest SSP1|‐23|Invest SSP1|‐15|HealthySustDiet|‐30|Invest SSP1|‐30|RCP2p6 NOR|‐26|noCO2|‐18|Invest SSP1|‐14|

56
Table 11 Irrigation infrastructure investment costs as a percent of the regional GDP discounted at 6% (%)

|**Scenario**|**Year**|AFR|EAP|ECA|LCR|MNA|SAR|WLD|
|---|---|---|---|---|---|---|---|---|
||2020|0.43|0.19|0.05|0.10|0.13|0.37|0.07|
||2030|0.36|0.13|0.04|0.08|0.11|0.27|0.06|
|Invest|2040|0.31|0.11|0.04|0.07|0.09|0.22|0.06|
||2050|0.27|0.10|0.04|0.07|0.08|0.19|0.05|
||2020|0.72|0.29|0.18|0.19|0.16|0.53|0.11|
||2030|0.68|0.20|0.17|0.19|0.14|0.37|0.10|
|MaxInvest|2040|0.65|0.17|0.15|0.19|0.12|0.29|0.09|
||2050|0.58|0.15|0.14|0.18|0.10|0.25|0.09|

Table 12 Cumulative irrigation infrastructure investment costs as a percent of the cumulative regional GDP from 2010‐2030 and 2030‐2050, calculated as an average of the ratio of costs to GDP undiscounted (%)

|Scenario|Year|AFR|EAP|ECA|LCR|MNA|SAR|WLD|
|---|---|---|---|---|---|---|---|---|
|Invest|2010‐2030<br>2030‐2050|0.36<br>0.16|0.13<br>0.05|0.04<br>0.03|0.08<br>0.04|0.10<br>0.04|0.27<br>0.08|0.06<br>0.03|
|MIt|2010‐2030|0.68|0.20|0.17|0.19|0.13|0.37|0.10|
|axnves|2030‐2050|0.45|0.07|0.09|0.16|0.05|0.10|0.06|

57
# 6. Conclusion

Achieving ambitious expansion of irrigation and increase in irrigated cropland productivity in developing countries where large‐scale and on‐farm capital costs are heavily subsidized would cost $50 billion per year over the next 40 years and bring 154 million hectares of irrigated area into production by 2050. The construction of new irrigation infrastructure (i.e. providing access to water for irrigation and equipping rainfed cropland with irrigation equipment) would cost $16 billion per year, $32.7 billion per year would be needed for depreciation costs, $0.6 billion for upgrading irrigation systems to more efficient systems (i.e. moving from gravity‐based irrigation systems to sprinkler or drip), and $0.2 billion for general efficiency improvements of irrigation systems (i.e. improving the water application efficiency of gravity‐based irrigation systems).

Benefits from such large investments are not distributed equally across regions or development goals. Irrigating 73% of the FAO defined potentially irrigable area would lead to improvement in food availability and hence contribute to improved food security in all the considered regions (SDG2). However, the effectiveness and efficiency in achieving this goal through expanded irrigation differs substantially across regions; an investment of $4.3 billion per year would improve the food availability in Sub‐Saharan Africa by less than 1% by 2050 while an investment of $6.8 billion per year would improve the food availability in South Asia by 2.5% by 2050.

Expanding irrigated areas by 70% in developing countries would lead to significant impacts in the environmental flows and the biodiversity in inland waters (SDG6). Ambitious irrigation support could lead to an increase in the share of surface water withdrawals for irrigation at risk to become unsustainable. Most notably, in South Asia and the Middle East and North Africa the share of surface water withdrawals at risk to become unsustainable could reach 51% and 66% by 2050 respectively. Historically in many regions, increases in production have come from the expansion of cropland area rather than through intensification or yield improvements (Byerlee et al., 2014; Fischer et al., 2014; Hillocks, 2002). Irrigation investments could save about 5 Mha of cropland from being converted and 10.1 Mha of other natural land, however forest area could decrease by 6.5 Mha (SDG15).

Irrigation can be used to help regions to adapt to the food security challenges from climate change (SDG2 and SDG13). By 2050, the impacts from climate change will reduce global calorie availability especially in South Asia where calorie availability will drop by 4% compared to baseline in 2050. Investments in irrigation can help increase calorie availability in nearly all developing regions (by 80 kcal/cap/day in South Asia and 30 kcal/cap/day globally under the most ambitious investment). Achieving this level of food security will cost less annually under climate change by $495 million under moderate investment and $1.7 billion under ambitious investment.

Our scenario analysis was complemented by a comprehensive uncertainty assessment which examined the role of macroeconomic development, climate change, future diets, water efficiency and trade policies. We observed that climate change scenarios and trade policies are the most likely to affect irrigation strategies and their impacts across the SDGs.

We find that multiple benefits would accrue from large‐scale irrigation investments, in terms of efficiency of irrigation systems, food availability, and land sparing and our multi‐criteria analysis finds that the most relevant regions for investment would be Europe and Central Asia, Middle East and North Africa, and South Asia where substantial benefits are to be expected with limited negative effects.

58