Achieving sustainable irrigation water withdrawals: global impacts on food security and land use

Unsustainable water use challenges the capacity of water resources to ensure food security and continued growth of the economy. Adaptation policies targeting future water security can easily overlook its interaction with other sustainability metrics and unanticipated local responses to the larger-scale policy interventions. Using a global partial equilibrium grid-resolving model SIMPLE-G, and coupling it with the global Water Balance Model, we simulate the consequences of reducing unsustainable irrigation for food security, land use change, and terrestrial carbon. A variety of future (2050) scenarios are considered that interact irrigation productivity with two policy interventions— inter-basin water transfers and international commodity market integration. We find that pursuing sustainable irrigation may erode other development and environmental goals due to higher food prices and cropland expansion. This results in over 800 000 more undernourished people and 0.87 GtC additional emissions. Faster total factor productivity growth in irrigated sectors will encourage more aggressive irrigation water use in the basins where irrigation vulnerability is expected to be reduced by inter-basin water transfer. By allowing for a systematic comparison of these alternative adaptations to future irrigation vulnerability, the global gridded modeling approach offers unique insights into the multiscale nature of the water scarcity challenge

Heat stress on agricultural workers exacerbates crop impacts of climate change

The direct impacts of climate change on crop yields and human health are individually well-studied, but the interaction between the two have received little attention. Here we analyze the consequences of global warming for agricultural workers and the crops they cultivate using a global economic model (GTAP) with explicit treatment of the physiological impacts of heat stress on humans' ability to work. Based on two metrics of heat stress and two labor functions, combined with a meta-analysis of crop yields, we provide an analysis of climate, impacts both on agricultural labor force, as well as on staple crop yields, thereby accounting for the interacting effect of climate change on both land and labor. Here we analyze the two sets of impacts on staple crops, while also expanding the labor impacts to highlight the potential importance on non-staple crops. We find, worldwide, labor and yield impacts within staple grains are equally important at +3 ∘C warming, relative to the 1986–2005 baseline. Furthermore, the widely overlooked labor impacts are dominant in two of the most vulnerable regions: sub-Saharan Africa and Southeast Asia. In those regions, heat stress with 3 ∘C global warming could reduce labor capacity in agriculture by 30%–50%, increasing food prices and requiring much higher levels of employment in the farm sector. The global welfare loss at this level of warming could reach $136 billion, with crop prices rising by 5%, relative to baseline

Food and environmental security in 2050: An application of gridded agricultural economic modelling

Agricultural economic models are indispensable in the analysis of broad issues affecting the farm-food-environment nexus. Many of these models have been designed accordingly to accommodate country and/or regional-level data – for example trade and economic data from the GTAP database as well as agricultural production data from UN FAO. However, it is becoming evident that agricultural economic models are far too aggregated for the analysis of localized agro-climatic issues that have broad consequences on the global farm and food system. This is particularly true for climate change. Climate-driven crop yield projections from gridded crop and climate models are quite heterogeneous within and across countries but instead of using these refined projections researchers are constrained to impose weighting methods to accommodate aggregations in existing agricultural economic models (1) (see Figure 1). This problem is also evident in the assessment of land use change impacts from agriculture wherein regional land supply elasticities are imposed and detailed biomass and soil carbon from gridded global potential vegetation models are aggregated. In this paper, we illustrate the advantages of using downscaled global model of agriculture using the gridded SIMPLE model. SIMPLE has been designed to capture the key drivers and economic responses at work in driving long run changes in the global farm and food system. The model has been validated by looking at the historical experience (2) and has been used in the assessment of food security (3) and climate change adaptation (4). We take advantage of SIMPLE’s flexibility and develop a gridded version of the model wherein crop production activities are defined at the geo-spatial level using agricultural production, area and yield data from Monfreda et al. (5). This allows us to downscale crop production from 16 regions to ~50,000 half-degree grid cells

A sensitivity analysis of the lifecycle and global land use change greenhouse gas emissions of U.S. corn ethanol fuel

Recent studies argue that corn ethanol fuel is becoming more sustainable and has less direct lifecycle greenhouse gas (GHG) emissions than previously estimated (Wang, Wu and Huo, 2007; Kim and Dale, 2008; Liska et al., 2008). In the U.S. Energy Independence and Security Act of 2007 (U.S. EISA) corn ethanol fuel produced from new facilities are required to have at least 20% less GHG emissions than conventional gasoline. However, it also specifies that the GHG emissions from global land use changes should be considered when assessing corn ethanol fuel emissions. Global land use change emissions from increased U.S. corn ethanol production therefore play a crucial role in determining whether this renewable fuel can meet the U.S. EISA requirement. This study examines the range of overall GHG emissions of U.S. corn ethanol fuel when the direct lifecycle and global land use change emissions are estimated using various data and assumptions. The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model by Wang (1996) is used to determine the direct lifecycle emissions of corn ethanol. These emissions are calculated using recent data on energy and chemical input usage during corn farming from Liska et al. (2008) and using new energy use and yield assumptions during ethanol production from selected studies (Wu, 2008; Liska et al., 2008; Christianson, 2008; Perrin, Fretes and Sesmero, 2009). The global land use change emissions are generated using the Land Use Change Emissions (LUCE) module. These emissions are calculated using the land use carbon emission factors and the global land use change data. The global land use change data is based on the simulation results of a special version of the Global Trade Analysis Project (GTAP) model (Hertel, 1997) by Taheripour, Hertel, and Tyner (2008) for several assumed increases in U.S. ethanol production and trade restrictions scenarios. The land use carbon emissions factors are the estimated GHG emissions incurred for each year of ethanol consumption when a hectare of forest or grassland undergo cropland conversion. These factors are calculated using the Woods Hole and the Intergovernmental Panel on Climate Change (IPCC) land carbon data and using different assumptions on soil and vegetation carbon losses during cropland conversion, wood product decay and duration of ethanol consumption. The results show that the direct lifecycle GHG emissions of corn ethanol fuel can exceed the 20% GHG reduction requirement in the U.S. EISA given the new data and assumptions during corn farming and ethanol production. However, the overall GHG emissions of corn ethanol are considerably higher due to global land use change emissions. Global land use change emissions rise with greater soil and vegetation carbon loss assumptions and when wood product decay is considered. A longer duration of ethanol consumption reduces the global land use change emissions since these emissions are distributed over a longer time period. The findings of this study indicate that it is highly uncertain if corn ethanol fuel has less GHG emissions than conventional gasoline when global land use change emissions are considered. It is possible to argue that corn ethanol fuel has more GHG emissions than conventional gasoline by increasing the soil and vegetation carbon loss assumptions, shortening the duration of ethanol consumption and using the estimates of corn ethanol direct lifecycle emissions based on previous corn farming and ethanol production data. Likewise, it is possible to conclude that corn ethanol can satisfy the 20% GHG reduction requirement in the U.S. EISA by adopting conservative assumptions on soil and vegetation carbon losses, lengthening the duration of ethanol consumption and adopting corn ethanol direct lifecycle emissions estimated using recent corn farming and ethanol production data

Essays on productivity growth in agriculture

This dissertation aims to explore the current trends in agricultural productivity and analyse its impact on the global farm and food system. Chapter 2 in this dissertation looks at the current trends in agricultural productivity in India—one most of the populous country in the world. In this chapter, productivity trends in Indian agriculture are examined by looking at changes in Total Factor Productivity—a measure which takes into account all farm outputs and inputs. Estimates in this chapter suggest that TFP growth for the 10-year period—between 1999-2000 and 2009-2010—steadily grew at the national level. Looking at the 5-year estimates, TFP growth in the early 2000s was sluggish but this poor performance was offset by sharp growth in the late 2000s. Developments at global scale ultimately affect world food production and prices. This dissertation develops a new framework for the analysis of productivity, prices, nutrition and land use in the context of a global economy. Nick-named SIMPLE, this model forms the basis for Chapters 3 and 4. In Chapter 3, projections from the SIMPLE model are validated against actual changes in key agricultural variables during the historical period 1961-2006. Given observed growths in population, incomes and total factor productivity, SIMPLE can successfully replicate historical changes in global crop production, cropland use, global crop yield and price. In Chapter 4, the implications of productivity growth for future global food security are examined using a module which calculates the headcount, prevalence and average depth of malnutrition by looking at the changes in average caloric consumption. Going forward to 2050, population growth is projected to slow down while biofuel use, per capita incomes and agricultural productivity are expected rise steadily. If TFP growth stagnates, nutritional outcomes would likely worsen, with virtually no reduction in the global headcount of malnourished persons over the 2006-2050 period. Climate change will also have significant implications for nutritional outcomes in hunger stricken regions of the world. Lastly, Chapter 5 outlines the scope for future work and identifies key areas for improvements regarding the studies documented in this dissertation

SIMPLE: a Simplified International Model of agricultural Prices, Land use and the Environment

In this paper, we document the Simplified International Model of agricultural Prices, Land use and the Environment (SIMPLE). SIMPLE is a partial equilibrium model which is designed to better understand the competing forces that influence the global farm and food system and how these drivers influence long run agricultural land use, production, prices, GHG emissions and food consumption. SIMPLE has been developed under the principle that a model should be no more complex than is absolutely necessary to understand the basic forces at work. Therefore, unlike other global models which are generally more complex and disaggregated, SIMPLE is parsimonious and tractable. Indeed, our historical validation over the period 1961-2006 confirms that SIMPLE can be used to simulate the long run changes in the global farm and food system given exogenous shocks in a few key drivers of world agriculture. Equally important is that we demonstrated how SIMPLE can be used to assess the relative contribution of each of the individual drivers to the endogenous changes in world agriculture via the numerical and the analytical decomposition tools. With these tools at hand, SIMPLE offers a more robust analysis of both historical and future long run changes in the global farm and food system

Global food security in 2050: the role of agricultural productivity and climate change

In this paper, we examine how the complexities introduced by trends in agricultural productivity and climate change affect the future of global food security. We use a partial equilibrium model of global agriculture incorporating a food security module that links changes in the average dietary energy intake to shifts in the full caloric distribution, allowing us to compute changes in the incidence, headcount and average depth of malnutrition. After validating the model against an historical period, we implement a series of future scenarios to understand the impacts of key exogenous drivers on selected food security outcomes. Our results show improvements in global food security for the period 2006–2050. Despite growing population and increased biofuel demand, baseline income growth, coupled with projected increases in agricultural productivity lead to a 24 per cent rise in global average dietary energy intake. Consequently, the incidence of malnutrition falls by 84 per cent, lifting more than half a billion people out of extreme hunger. However, these results hinge heavily on agricultural productivity growth. Without such growth, there could be a substantial setback on food security improvements. Climate change adds uncertainty to these projections, depending critically on the crop yield impacts of increasing CO2 concentrations in the atmosphere

Global food security in 2050: the role of agricultural productivity and climate change

In this paper, we examine how the complexities introduced by trends in agricultural productivity and climate change affect the future of global food security. We use a partial equilibrium model of global agriculture incorporating a food security module that links changes in the average dietary energy intake to shifts in the full caloric distribution, allowing us to compute changes in the incidence, headcount and average depth of malnutrition. After validating the model against an historical period, we implement a series of future scenarios to understand the impacts of key exogenous drivers on selected food security outcomes. Our results show improvements in global food security for the period 2006–2050. Despite growing population and increased biofuel demand, baseline income growth, coupled with projected increases in agricultural productivity lead to a 24 per cent rise in global average dietary energy intake. Consequently, the incidence of malnutrition falls by 84 per cent, lifting more than half a billion people out of extreme hunger. However, these results hinge heavily on agricultural productivity growth. Without such growth, there could be a substantial setback on food security improvements. Climate change adds uncertainty to these projections, depending critically on the crop yield impacts of increasing CO2 concentrations in the atmosphere