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Chapter 7. Food Security and Food Production Systems Coordinating Lead Authors John R. Porter (Denmark), Liyong Xie (China) Lead Authors Andrew Challinor (UK), Kevern Cochrane (FAO), Mark Howden (Australia), Muhammad Mohsin Iqbal (Pakistan), David Lobell (USA), Maria Isabel Travasso (Argentina) Contributing Authors Netra Chhetri (USA / Nepal), Karen Garrett (USA), John Ingram (UK), Leslie Lipper (Italy), Nancy McCarthy (USA), Justin McGrath (USA), Daniel Smith (UK), Philip Thornton (UK), James Watson (UK), Lewis Ziska (USA) Review Editors Pramod Aggarwal (India), Kaija Hakala (Finland) Volunteer Chapter Scientist Joanne Jordan (UK)
Contents Executive Summary 7.1. Introduction and Context 7.1.1. Food Systems 7.1.2. The Current State of Food Security 7.1.3. Summary from AR4 Observed Impacts, with Detection and Attribution 7.2.1. Food Production Systems 188.8.131.52. Crop Production 126.96.36.199. Fisheries Production 22.214.171.124. Livestock Production 7.2.2. Food Security and Food Prices Assessing Impacts, Vulnerabilities, and Risks 7.3.1. Methods and Associated Uncertainties 18.104.22.168. Assessing Impacts 184.108.40.206. Treatment of Adaptation in Impacts Studies 7.3.2. Sensitivity of Food Production to Weather and Climate 22.214.171.124. Cereals and Oilseeds 220.127.116.11. Other Crops 22.214.171.124. Pests, Weeds, Diseases 220.127.116.11. Fisheries and Aquaculture 22.214.171.124. Food and Fodder Quality and Human Health 126.96.36.199. Pastures and Livestock 7.3.3. Sensitivity of Food Security to Weather and Climate 22.214.171.124. Non-Production Food Security Elements 220.127.116.11. Accessibility, Utilization, and Stability 7.3.4. Sensitivity of Land Use to Weather and Climate Projected Integrated Climate Change Impacts 7.4.1. Projected Impacts on Cropping Systems
28 October 2013
Projected Impacts on Fisheries and Aquaculture Projected impacts on Livestock Projected Impacts on Food Prices and Food Security
Adaptation and Managing Risks in Agriculture and Other Food System Activities 7.5.1. Adaptation Needs and Gaps based on Assessed Impacts and Vulnerabilities 22.214.171.124. Methods of Treating Impacts in Adaptation Studies Incremental to Transformational 220.127.116.11. Practical Regional Experiences of Adaptation, including Lessons Learned 220.127.116.11. Observed and Expected Barriers and Limits to Adaptation 184.108.40.206. Facilitating Adaptation and Avoiding Maladaptation 7.5.2. Food System Case Studies of Adaptation Examples of Successful and Unsuccessful Adaptation 7.5.3. Key Findings from Adaptations Confidence Limits, Agreement, and Level of Evidence Research and Data Gaps Food Security as a Cross-Sectoral Activity
References Chapter Box 7-1. Projected Impacts for Crops and Livestock in Global Regions and Sub-Regions under Future Scenarios Frequently Asked Questions 7.1: What factors determine food security and does low food production necessarily lead to food insecurity? 7.2: How could climate change interact with change in fish stocks, ocean acidification? 7.3: How could adaptation actions enhance food security and nutrition?
Executive Summary The effects of climate change on crop and food production are evident in several regions of the world (high confidence). Negative impacts of climate trends have been more common than positive ones. [Figures 7-2, 7-7] Positive trends are evident in some high latitude regions (high confidence). Since AR4, there have been several periods of rapid food and cereal price increases following climate extremes in key producing regions, indicating a sensitivity of current markets to climate extremes among other factors. [Figure 7-3, Table 18-4] Several of these climate extremes were made more likely as the result of anthropogenic emissions (medium confidence). [Table 18-4] Climate trends are affecting the abundance and distribution of harvested aquatic species, both freshwater and marine, and aquaculture production systems in different parts of the world. [188.8.131.52, 220.127.116.11, 7.4.2] These are expected to continue with negative impacts on nutrition and food security for especially vulnerable people, particularly in some tropical developing countries [184.108.40.206], but with benefits in other regions which become more favourable for aquatic food production (medium confidence). [184.108.40.206.3] Studies have documented a large negative sensitivity of crop yields to extreme daytime temperatures around 30C. [Chapter 5 AR4, 18.104.22.168.1] These sensitivities have been identified for several crops and regions and exist throughout the growing season (high confidence). Several studies report that temperature trends are important for determining both past and future impacts of climate change on crop yields at sub-continental to global scales (medium confidence). [7.3.2, Box 7-1] At scales of individual countries or smaller, precipitation projections remain important but uncertain factors for assessing future impacts (high confidence). [7.3.2, Box 7-1] Evidence since AR4 confirms the stimulatory effects of CO2 in most cases and the damaging effects of elevated tropospheric ozone on crop yields (high confidence). Experimental and modelling evidence indicate that interactions between CO2 and ozone, mean temperature, extremes, water and nitrogen are non-linear and difficult to predict (medium confidence). [220.127.116.11.2, Figure 7-2]
Changes in climate and CO2 concentration will enhance the distribution and increase the competiveness of agronomically important and invasive weeds (medium confidence). Rising CO2 may reduce the effectiveness of some herbicides (low confidence). The effects of climate change on disease pressure on food crops is uncertain, with evidence pointing to changed geographical ranges of pests and diseases but less certain changes in disease intensity (low confidence). [18.104.22.168] All aspects of food security are potentially affected by climate change, including food access, utilization, and price stability (high confidence). [22.214.171.124, Table 7-1] There remains limited quantitative understanding of how non-production elements of food security will be affected, and of the adaptation possibilities in these domains. Nutritional quality of food and fodder, including protein and micronutrients, is negatively affected by elevated CO2, but these effects may be counteracted by effects of other aspects of climate change (medium confidence). [184.108.40.206] Climate change will increase progressively the inter-annual variability of crop yields in many regions (medium confidence). [Figure 7-6] Without adaptation, local temperature increases in excess of about 1oC above pre-industrial is projected to have negative effects on yields for the major crops (wheat, rice and maize) in both tropical and temperate regions, although individual locations may benefit (medium confidence). [7.4, Figures 7-4,7-5,7-7] With or without adaptation, negative impacts on average yields become likely from the 2030s [Figure 7-5] with median yield impacts of 0 to -2% per decade projected for the rest of the century [Figure 7-7], and after 2050 the risk of more severe impacts increases (medium confidence). [Figure 7-5] These impacts will occur in the context of rising crop demand, which is projected to increase by about 14% per decade until 2050. [Figure 7-7] Regional chapters 22 (Africa), 23 (Europe), 24 (Asia), 27 (Central and South America) and Box 7-1 show crop production to be consistently and negatively affected by climate change in the future in low latitude countries, while climate change may have positive or negative effects in northern latitudes (high confidence). Changes in temperature and precipitation, without considering effects of CO2 will contribute to increased global food prices by 2050, with estimated increases ranging from 3-84% (medium confidence). Projections that include the effects of CO2 changes, but ignore ozone and pest and disease impacts, indicate that global price increases are about as likely as not, with a range of projected impacts from -30% to +45% by 2050. [7.4.4] Under scenarios of high levels of warming, leading to local mean temperature increases of 3-4 oC or higher, models based on current agricultural systems suggest large negative impacts on agricultural productivity and substantial risks to global food production and security (medium confidence). Such risks will be greatest for tropical countries, given the larger impacts in these regions, which are beyond projected adaptive capacity, and higher poverty rates compared to temperate regions. [7.4.1, Figures 7-4, 7-7] The average benefit, as yield difference between the adapted and non-adapted cases, of adapting crop management is equivalent to about 15 to 18% of current yields. [Figure 7-8, Table 7-2] This response is, however, extremely variable, ranging from negligible benefit from adaptation and even potential dis-benefit to very substantial (medium confidence). [220.127.116.11.1] Projected benefits of adaptation are greater for crops in temperate, rather than tropical regions [22.214.171.124.1, Figures 7-4, 7-7] (medium confidence), with some adaptation options more effective than others [Table 7-2] (medium confidence) and wheat-based systems more adaptable than other crops [Figure 7-4] (low confidence). Positive and negative yield impacts associated with local temperature increases of about 2oC above pre-industrial maintain possibilities for effective adaptation in crop production [Figures 7-4,7-5,7-8]; local warming of about 4oC and higher above pre-industrial is projected to result in differences between crop production and its population-driven demand becoming increasingly large in many regions (high confidence), thus posing very significant risks and challenges to food security (high confidence). [Figure 7-7, Table 7-3] Adaptation in fisheries, aquaculture and livestock production will potentially be strengthened by adoption of multi-level adaptive strategies to minimise negative impacts. Key adaptations for fisheries and aquaculture include policy and management to maintain ecosystems in a state that is resilient to change, enabling occupational flexibility and developing early warning systems for extreme events (medium confidence). [22.214.171.124.2] Adaptations for livestock systems centre on adjusting management to the available resources, using breeds better adapted to the
prevailing climate and removing barriers to adaptation such as improving credit access (medium confidence). [184.108.40.206.3] A range of potential adaptation options exist across all food system activities, not just in food production, but benefits from potential innovations in food processing, packaging, transport, storage and trade are insufficiently researched. [7.1, 7.5, 7.6, Figures 7-1, 7-7, 7-8] More observational evidence is needed on the effectiveness of adaptations at all levels of the food system. [7.6]
Many definitions of food security exist and these have been the subject of much debate. As early as 1992, Maxwell and Smith (1992) reviewed over 180 items discussing concepts and definitions, and more definitions have been formulated since (Defra, 2006). While many earlier definitions centred on food production, more recent definitions highlight access to food, in keeping with the 1996 World Food Summit definition (FAO, 1996) that food security is met when all people, at all times, have physical and economic access to sufficient, safe, and nutritious food to meet their dietary needs and food preferences for an active and healthy life. World-wide attention on food access was given impetus by the food price spike in 2007-08, triggered by a complex set of long- and short-term factors (von Braun and Torero, 2009). FAOs provisional estimates show that, in 2007, 75 million more people were added to the total number of undernourished relative to 20032005 (FAO, 2008); other studies report a lower number (Headey and Fan, 2010). More than enough food is currently produced per capita to feed the global population, yet about 870 million people remained hungry in 2012 (FAO et al., 2012). The questions for this chapter are how far climate and its change affect current food production systems and food security and the extent that they will do so in the future (Figure 7-1).
Food Systems
A food system is all processes and infrastructure involved in satisfying a populations food security, i.e. the gathering/catching, growing, harvesting (production aspects), storing, processing, packaging, transporting, marketing, consuming food, and disposing of food waste (non-production aspects) It includes food security outcomes of these activities related to availability and utilization of, and access to, food as well as other socioeconomic and environmental factors (Ericksen, 2008; Ericksen et al., 2010; Ingram 2011). This chapter synthesises and evaluates evidence for the impacts of climate on both production and non-production elements and their adaptation to climate change (Figure 7-1). [INSERT FIGURE 7-1 HERE Figure 7-1: Main issues of the chapter. Drivers are divided into climate and non-climate elements, affecting production and non-production elements of food systems, thereafter combining to provide food security. The thickness of the red lines is indicative of the relative availability of refereed publications on the two elements.] The impacts of climate change on food systems are expected to be widespread, complex, geographically and temporally variable, and profoundly influenced by socio-economic conditions (Vermeulen et al., 2012). Changes in food system drivers give rise to changes in food security outcomes, (medium evidence, high agreement) but often researchers only consider the impacts on the food production element of food security (Figure 7-1). Efforts to increase food production are nevertheless increasingly important as 60% more food will be needed by 2050 given current food consumption trends and assuming no significant reduction in food waste (FAO et al., 2012).
Most people on the planet currently have enough food to eat. The vast majority of undernourished people live in developing countries (medium evidence, medium agreement), when estimated based on aggregate national calorie availability and assumptions about food distribution and nutritional requirements. More precise estimates are
possible with detailed household surveys, which often show higher incidence of food insecurity than estimated by FAO. Using food energy deficit as the measure of food insecurity, Smith et al. (2006) estimated average rates of food insecurity of 59% for 12 African countries, compared to a 39% estimate from FAO for the same period (Smith et al., 2006). While there is medium evidence, medium agreement on absolute numbers, there is robust evidence, high agreement that Sub-Saharan Africa has the highest proportion of food insecure people, with an estimated regional average of 26.8% of the population undernourished in 2010-2012, and where rates over 50% can be found (FAO et al., 2012). The largest numbers of food insecure are found in South Asia, which has roughly 300 million undernourished (FAO et al., 2012. In addition to common measures of calorie availability, food security can be broadened to include nutritional aspects based on the diversity of diet including not only staple foods but also vegetables, fruits, meat, milk, eggs, and fortified foods (FAO, 2011). There is robust evidence and high agreement that lack of essential micronutrients such as zinc and vitamin A affect hundreds of millions of additional (Lopez et al., 2006; Pinstrup-Andersen, 2009). Food insecurity is closely tied to poverty; globally about 25 to 30 percent of poor people, measured using a $1 to $2 per day standard, live in urban areas (Ravallion et al., 2007; IFAD, 2010). Most poor countries have a larger fraction of people living in rural areas and poverty rates tend to be higher in rural settings (by slight margins in South Asia and Africa, and by large margins in China). In Latin America, poverty is more skewed to urban areas, with roughly two-thirds of the poor in urban areas, a proportion that has been growing in the past decade (medium evidence, medium agreement). Rural areas will continue to have the majority of poor people for at least the next few decades, even as population growth is higher in urban areas (medium evidence, medium agreement) (Ravallion et al., 2007; IFAD, 2010). The effects of price volatility are distinct from the effects of gradual price rises, for two main reasons. First, rapid shifts make it difficult for the poor to adjust their activities to favour producing higher value items. Second, increased volatility leads to greater uncertainty about the future, and can dampen willingness to invest scarce resources into productivity enhancing assets, such as fertilizer purchases in the case of farmers or rural infrastructure in the case of governments. Several factors have been found to contribute to increased price volatility: poorly articulated local markets, increased incidence of adverse weather events and greater reliance on production areas with high exposure to such risks, biofuel mandates, and increased links between energy and agricultural markets (World Bank, 2012). Vulnerability to food price volatility depends on the degree to which households and countries are net food purchasers, the level of integration into global, regional and local markets and their relative degree of volatility, which in turn is conditional upon their respective governance (HLPE, 2011; World Bank, 2012) with robust evidence and medium agreement.
Food systems as integrated drivers, activities and outcomes for food security did not feature strongly in AR4. Summary points from AR4 were that, with medium confidence, in mid- to high-latitude regions moderate warming will raise crop and pasture yields. Sight warming will decrease yields in low-latitude regions. Extreme climate and weather events will, with high confidence, reduce food production. The benefits of adaptation in crops enable avoidance of a 10-15% reduction in yield, corresponding to 1-2C local temperature increase. Adaptive capacity is projected to be exceeded in low-latitude areas with temperature increases of more than 3C. Local extinctions of particular fish species are expected with high confidence at the edges of their ranges and have serious negative impacts on fisheries with a medium confidence level.
7.2. 7.2.1.
Formal detection of impacts requires that observed changes be compared to a clearly specified baseline that characterizes behaviour in the absence of climate change (Chapter 18). For food production systems, the number and strength of non-climate drivers, such as cultivar improvement or increased use of irrigation and fertilizers in the case
of crops, make defining a clear baseline extremely difficult. Most non-climatic factors are not very well characterized in terms of spatial and temporal distributions, and the relationships between these factors and specific outcomes of interest (e.g., crop or fish production) are often difficult to quantify. Attribution of any observed changes to climate trends are further complicated by the fact that models linking climate and agriculture must, implicitly or explicitly, make assumptions about farmer behaviour. In most cases, models implicitly assume that farming practices or technologies did not adjust in response to climate over the period of interest. This assumption can be defended in some cases based on ancillary data on practices, or based on small differences between using models with and without adaptation (Schlenker and Roberts, 2009). However, in some instances the relationship between climate conditions and crop production has been shown to change over time because of management changes, such as introduction of irrigation or changes in crop varieties Zhang et al, 2008; Liu et al.,2009; Sakurai et al., 2012).
220.127.116.11. Crop Production Many studies of cropping systems have estimated impacts of observed climate changes on crop yields over the past half century, although they typically do not attempt to compare observed yields to a counterfactual baseline, and thus are not formal detection and attribution studies. These studies employ both mechanistic and statistical approaches (7.3.1), and estimate impacts by running the models with observed historical climate and then computing trends in modelled outcomes. Based on these studies, there is medium confidence that climate trends have negatively affected wheat and maize production for many regions (Figure 7-2) (medium evidence, high agreement). Since many of these regional studies are for major producers, and a global study (Lobell et al. 2011) estimated negative impacts on these crops, there is also medium confidence for negative impacts on global aggregate production of wheat and maize. Effects on rice and soybean yields have been small in major production regions and globally (Figure 7-2) (medium evidence, high agreement). There is also high confidence that warming has benefitted crop production in some high-latitude regions, such as Northeast China or the United Kingdom Jaggard et al., 2007, Supit et al., 2010; Chen et al., 2010; Gregory and Marshall, 2012). [INSERT FIGURE 7-2 HERE Figure 7-2: Summary of estimates of the impact of recent climate trends on yields for four major crops. Studies were taken from the peer-reviewed literature and used different methods (i.e., physiological process-based crop models or statistical models), spatial scales (stations, provinces, countries, or global), and time periods (median length of 29 years). Some included effects of positive CO2 trends (18.104.22.168.2) but most did not. (a) shows number of estimates with different level of impact (% yield per decade), (b) shows boxplot of estimates separated by temperate vs. tropical regions, modelling approach (process-based vs. statistical), whether CO2 effects were included, and crop. Boxplots indicate the median (vertical line), 25th-75th percentiles (box), and 10th-90th percentiles (whiskers) for estimated impacts in each category, and n indicates the number of estimates. Studies were for China (Tao et al., 2006; Tao et al., 2008; Wang et al., 2008; You et al., 2009; Chen et al., 2010; Tao et al., 2012), India (Pathak et al.,2003; Auffhammer et al., 2012), United States (Kucharik and Serbin, 2008), Mexico (Lobell et al., 2005), France (Brisson et al., 2010; Licker et al., 2013) Scotland (Gregory and Marshall, 2012), Australia (Ludwig et al., 2009), Russia (Licker et al., 2013), and some studies for multiple countries or global aggregates (Lobell and Field, 2007; Welch et al., 2010; Lobell et al., 2011). Values from all studies were converted to percentage yield change per decade. Each study received equal weighting as insufficient information was available to judge the uncertainties of each estimate.] More difficult to quantify with models is the impact of very extreme events on cropping systems, since by definition these occur very rarely and models cannot be adequately calibrated and tested. Table 18-4 lists some notable extremes over the past decade, and the impacts on cropping systems. Despite the difficulty of modeling the impacts of these events, they clearly have sizable impacts that are apparent immediately or soon after the event, and therefore not easily confused with effects of more slowly moving factors. For a subset of these events, climate research has evaluated whether anthropogenic activity has increased or decreased their likelihood (Table 18-4).
A sizable fraction of crop modelling studies were concerned with production for individual sites or provinces, spatial scales below which the changes in climate conditions are attributable to anthropogenic activity (AR5 WG1 Chapter 10). Similarly, most crop studies have focused on the past few decades, a time scale shorter than most attribution studies for climate. However, some focused on continental or global scales (Lobell and Field, 2007; You et al., 2009; Lobell et al., 2011), at which trends in several climatic variables, including average summer temperatures, have been attributed to anthropogenic activity. In particular, global temperature trends over the past few decades are attributable to human activity (AR5 WG1 Chapter 10), and the studies discussed above indicate that this warming has had significant impacts on global yield trends of some crops. In general, little work in food production or food security research has focused on determining whether climate trends affecting agriculture can be attributed to anthropogenic influence on the climate system. However, as the field of climate detection and attribution proceeds to finer spatial and temporal scales, and as agricultural modelling studies expand to broader scales, there should be many opportunities to link climate and crop studies in the next few years. Importantly, climate attribution is increasingly documented not only for measures of average conditions over growing seasons, but also for extremes. For instance, Min et al., (2011) attributed changes in rainfall extremes for 1951-99 to anthropogenic activity, and these are widely acknowledged as important to cropping systems (Rosenzweig et al., 2002). Frost damage is an important constraint on crop growth in many crops, including for various high-value crops, and significant reductions in frost occurrence since 1961 have been observed and attributed to greenhouse gas emissions in nearly every region of the world (Zwiers et al., 2011; IPCC, 2012). Increased frequency of unusually hot nights since 1961 are also attributable to human activity in most regions (AR5 WG1 Chapter 10). These events are damaging to most crops, an effect that has been observed most commonly for rice yields (Peng et al., 2004; Wassmann et al., 2009; Welch et al.,2010) as well as rice quality (Okada et al., 2011). Extremely high daytime temperatures are also damaging and occasionally lethal to crops (Porter and Gawith, 1999; Schlenker and Roberts, 2009), and trends at the global scale in annual maximum daytime temperatures since 1961 have been attributed to greenhouse gas emissions (Zwiers et al., 2011). At regional and local scales, however, trends in daytime maximum are harder to attribute to greenhouse gas emissions because of the prominent role of soil moisture and clouds in driving these trends (Christidis et al., 2005; Zwiers et al., 2011). In addition to effects of changes in climatic conditions, there are clear effects of changes in atmospheric composition on crops. Increase of atmospheric CO2 by over 100 ppm since pre-industrial times has virtually certainly enhanced water use efficiency and yields, especially for C3 crops such as wheat and rice, although these benefits played a minor role in driving overall yield trends (Amthor, 2001; McGrath and Lobell, 2011). Emissions of CO2 often are accompanied by ozone (O3) precursors that have driven a rise in tropospheric O3 that harms crop yields (Morgan et al., 2006; Mills et al., 2007; 18.104.22.168.2). Elevated O3 since pre-industrial times has very likely suppressed global production of major crops compared to what they would have been without O3 increases, with estimated losses of roughly 10% for wheat and soybean and 3-5% for maize and rice (Van Dingenen et al., 2009). Impacts are most severe over India and China (Van Dingenen et al., 2009, Avnery et al. 2011), but are also evident for soybean and maize in the United States (Fishman et al., 2010).
126.96.36.199. Fisheries Production The global average consumption of fish and other products from fisheries and aquaculture in 2010 was 18.6 kg per person per year, derived from a total production of 148.5 million tonnes, of which 86% was used for direct human consumption. The total production arose from contributions of 77.4 and 11.2 million tonnes respectively from marine and inland capture fisheries, and 18.1 and 41.7 million tonnes respectively from marine and freshwater aquaculture (FAO, 2012). Fisheries make particular contributions to food security and more than 90% of the people engaged in the sector are employed in small-scale fisheries, many of whom are found in the poorer countries of the world (Cochrane et al., 2011). The detection and attribution of impacts are as confounded in inland and marine fisheries as in terrestrial food production systems. Overfishing, habitat modification, pollution and inter-annual to decadal climate variability can all have impacts that are difficult to separate from those directly attributable to climate change.
One of the best studied areas is the North East Atlantic, where the temperature has increased rapidly in recent decades, associated with a poleward shift in distribution of fish (Perry et al., 2005; Brander, 2007; Cheung et al., 2010). There is high confidence in observations of increasing abundance of fish species in the northern extent of their ranges while decreases in abundance have occurred in the southern part (Chapter 126.96.36.199.1.1). These trends will have mixed implications for fisheries and aquaculture with some commercial species negatively and others positively affected (Cook and Heath, 2005). There is a similar well-documented example in the oceans off SE Australia with large warming trends associated with more southward incursion of the Eastern Australian Current, resulting in southward migration of marine species into the oceans around eastern Tasmania (Last et al., 2011); robust evidence, high agreement). As a further example, coral reef ecosystems provide food and other resources to more than 500 million people and with an annual value of US$5 billion or more (Hoegh-Guldberg, 2011; Munday et al., 2008). More than 60% of coral reefs are considered to be under immediate threat of damage from a range of local threats, of which overfishing is the most serious (Burke et al., 2011; Box CC-CR. Coral Reefs) and the percentage under threat rises to approximately 75% when the effect of rising ocean temperatures is added to these local impacts (Burke et al., 2011). Wilson et al. (2006) demonstrated that declines in coral reef cover typically led to declines in abundance of the majority of fish species associated with coral reefs. There is high confidence that the availability of fish and invertebrate species associated with coral reefs that are important in many tropical coastal fisheries is very likely to be reduced (Chapter 18.104.22.168.2). Other examples around the world are described in Chapter 184.108.40.206.1. These changes are impacting marine fisheries: a recent study that examined the composition of global fisheries catches according to the inferred temperature preferences of the species caught in fisheries found that there had been changes in the species composition of marine capture fisheries catches and that these were significantly related to changes in ocean temperatures (Cheung et al. 2013; Chapter 184.108.40.206). These authors noted that the relative contribution to catches by warmer water species had increased at higher latitudes while the contributions of subtropical species had decreased in the tropics. These changes have negative implications for coastal fisheries in tropical developing countries which tend to be particularly vulnerable to climate change (Cheung et al. 2013; Chapter 6.4.3; 184.108.40.206.3). There is considerably less information available on climate change impacts on fisheries and fishery resources in freshwater systems and aquaculture. Considerable attention has been given to the impacts of climate change in some African lakes but with mixed interpretations (Chapter 126.96.36.199.4.). There is evidence that increasing temperature has reduced the primary productivity of Lake Tanganyika in East Africa and a study by OReilly et al. (2003) estimated that this would have led to a decrease of approximately 30% in fish yields. However, Sarvala et al. (2006) disagreed and concluded that observed decreases in the fish catches could be explained by changed fishery practices. There has been a similar difference of opinion for Lake Kariba where Ndebele-Murisa et al., (2011) argued that a reduction in fisheries productivity had been caused by climate change while Marshall (2012) argued that the declines in fish catches can only have been caused by fishing. There is medium confidence that in India, changes in a number of climate variables including an increase in air temperature, regional monsoon variation and a regional increase in incidence of severe storms have led to changes in species composition in the River Ganga and to have reduced the availability of fish spawn for aquaculture in the river Ganga while having positive impacts on aquaculture on the plains through bringing forward and extending the breeding period of the majors carps (Vass et al. 2009).
22.214.171.124. Livestock Production In comparison to crop and fish production, considerably less work has been published on observed impacts for other food production systems, such as livestock or aquaculture, and to our knowledge nothing has been published for hunting or collection of wild foods other than for capture fisheries. The relative lack of evidence reflects a lack of study in this topic, but not necessarily a lack of real-world impacts of observed climate trends. A study of blue-tongue virus, an important ruminant disease, evaluated the effects of past and future climate trends on transmission risk, and concluded that climate changes have facilitated the recent and rapid spread of the virus into Europe (Guis et al., 2012). Ticks that carry zoonotic diseases have also likely changed distribution as a consequence of past climate trends (Chapter 23.4.2).
Food production is an important aspect of food security (7.1), and the evidence that climate change has affected food production implies some effect on food security. Yet quantifying this effect is an extremely difficult task, requiring assumptions about the many non-climate factors that interact with climate to determine food security. There is thus limited direct evidence that unambiguously links climate change to impacts on food security. One important aspect of food security is the prices of internationally traded food commodities (7.1.3). These prices reflect the overall balance of supply and demand, and the accessability of food for consumers integrated with regional to global markets. Although food prices gradually declined for most of the 20th century (FAO, 2009) since AR4 there have been several periods of rapid increases in international food prices (Figure 7-3). A major factor in recent price changes has been increased crop demand, notably via increased use in biofuel production related both to energy policy mandates and oil price fluctuations (Roberts and Schlenker, 2010; Wright, 2011; Mueller et al., 2011; Abbott et al., 2011). Yet fluctuations and trends in food production are also widely believed to have played a role in recent price changes, with recent price spikes often following climate extremes in major producers (Figure 7-3). Moreover, some of these extreme events have become more likely as a result of climate trends (Table 18-4). Domestic policy reactions can also amplify international price responses to weather events, as was the case with export bans announced by several countries since 2007 (FAO, 2008). In a study of global production responses to climate trends (Lobell, Schlenker, Costas-Roberts, 2011) estimated a price increase of 19% due to the impacts of temperature and precipitation trends on supply, or an increase of 6% once the beneficial yield effects of increased CO2 over the study period were considered. Since the price models were developed for a period ending in 2003, these estimates do not account for the policy responses witnessed in recent years which have amplified the price responses to weather. [INSERT FIGURE 7-3 HERE Figure 7-3: Since the AR4 report, international food prices have reversed historical downward trend. Plot shows history of FAO food and cereal price index (composite measures of food prices), with vertical lines indicating events when a top 5 producer of a crop had yields 25% below trend line (indicative of a seasonal climate extreme). Australia is included despite not being a top five producer, because it is an important exporter and the drops were 40% or more below trend line. Prices may have become more sensitive to weather-related supply shortfalls in recent years. At the same time, food prices are increasingly associated with the price of crude oil (blue line), making attribution of price changes to climate difficult. Thus, there is clear evidence since AR4 that prices can rise rapidly, but the role of weather in these increases remains unclear. All indices are expressed as percentage of 2002-2004 averages. Food price and crop yield data from FAO (http://www.fao.org/worldfoodsituation/foodpricesindex and http://faostat.fao.org/) and oil price data from http://www.eia.gov.]
7.3. 7.3.1.
22.214.171.124. Assessing Impacts Methods developed or extended since AR4 have resulted in more robust statements on climate impacts, both in the literature and in 7.3.2. Two particular areas, which are explored below, are improved quantification and presentation of uncertainty; and greater use of historical empirical evidence of the relationship between climate and food production. The methods used for field and controlled environment experiments remain similar to those at the time of AR4. There has been a greater use of remote sensing and geographic information systems for assessing temporal and spatial changes in land use particularly in agricultural land use for assessment of food security status (Goswami et al., 2012; Fishman et al., 2010; Thenkabail, 2009). There has also been an increase in the number of Free Air
Concentration Enrichment (FACE) studies that examine ozone instead of, or in addition to, CO2. In agriculture, FACE experiments have been used for assessing impacts of atmospheric CO2 on grain yield, quality characteristics of important crops (Erbs et al., 2010), elemental composition (Fernando et al., 2012) and diseases (Eastburn et al., 2011; Chakraborty et al., 2011). A number of meta-analyses of experimental studies, in particular FACE studies have been made since AR4. However, debate continues on the disparities between results from FACE experiments and non-FACE experiments, such as in open-top chambers or greenhouses. As reported in AR4, FACE studies tend to show lower elevated CO2 responses than non-FACE studies. Although some authors have claimed that the results of the two are statistically indistinct, others have argued that the results are only similar when the FACE experiments are grown under considerably more water stress than non-FACE experiments (Kimball, 2010; Ainsworth et al., 2008). Hence comparisons between different methodologies must take care to control for differences in water availability and microclimate. Another reason for differences between experiments may be differences in the temporal variance of CO2 i.e. whether concentrations are fluctuating or constant (Bunce, 2012). Unfortunately, the FACE experiments are carried out mostly in the USA and in China, thus limited to specific environmental conditions, which do not fully reflect tropical or sub-tropical conditions, where CO2 and soil nutrient interactions could lead to large differences in photosynthesis rate, water use and yield. Also, the number of FACE studies are still quite low, which limits statistical power when evaluating the average yield effects of elevated CO2 or interactions with temperature and moisture (7.3.2). Numerical simulation models can be used to investigate a larger number of possible environmental and management conditions than physical experiments. This, in turn, enables a broader range of statements regarding the possible response of food production systems to climate variability and change. Previous assessment reports have documented new knowledge resulting from numerical simulation of the response of food production to climate change. AR4 noted the increasing number of regional studies, which is a trend that has continued to date (Zhu et al., 2013; Craufurd et al., 2013). Since AR4, crop models have been used to examine a large number of management and environmental conditions, such as interactions among various components of food production systems (Lenz-Wiedemann et al., 2010), determination of optimum crop management practices (Soltani and Hoogenboom, 2007), vulnerability and adaptability assessments (Sultana et al., 2009), evaluation of water consumption and water use efficiency (Mo et al., 2009; Kang et al., 2009) and fostering communication between scientists, managers, policymakers and planners. The trend towards quantification of uncertainty in both climate and its impacts has continued since AR4. Novel developments include methodologies to assess the impact of climate model error on projected agricultural output, particularly for crops (Ramirez-Villegas et al., 2013, Watson and Challinor, 2013). Models that integrate crop growth models as part of broader land surface and earth systems models (Bondeau et al., 2007; Osborne et al., 2007) are also increasingly common. Ensemble techniques for climate impacts, which were in their infancy at AR4, now include the use of Bayesian methods to constrain crop model parameters (Iizumi et al., 2009; Tao et al., 2009a). It is also increasingly common to assess both bio-physical and socio-economic drivers of crop productivity within the same study (Fraser et al., 2008; Reidsma et al., 2009; Tao et al., 2009b; Challinor et al., 2010). Finally, an important recent development is the systematic comparison of results from different modelling and experimental approaches for providing insights into model uncertainties as well as to develop risk management (Challinor and Wheeler, 2008; Kang et al., 2009; Schlenker and Lobell, 2010; Rosenzweig et al., 2013). Increased quantification of uncertainty can lead to clear statements regarding climate impacts. Studies with different methods have been shown to produce convergent results for some crops and locations (Challinor et al., 2009; medium evidence, medium agreement). The methods used to describe uncertainty have also improved since AR4. The projected range of global and local temperature changes can be described by quantifying uncertainty in the temporal dimension, rather than that in temperature itself (Joshi et al., 2011), and a similar approach can be used for crop yield (Figure 7-5). Descriptions of uncertainty that present key processes and trade-offs, rather than ranges of outcome variables, have also proved to be useful tools for understanding future impacts (Thornton et al., 2009; Hawkins et al., 2012; Ruane et al., 2013). Section 7.3.2 reviews the results of such studies. A considerable body of work since AR4 has used extensive datasets of country- regional- and farm- level crop yield together with observed and/or simulated weather time series in order to assess the sensitivity of food production to weather and climate. These statistical models offer a complement to more process-based model approaches, some of which require many assumptions about soil and management practices. Process-based models, which extrapolate
based on measured interactions and mechanisms, can be used to develop a causal understanding of the empirically determined relationships in statistical models (cf Shlenker and Roberts, 2010; Lobell et al., 2013b). Although statistical models forfeit some of the process knowledge embedded in other approaches, they can often reproduce the behaviour of other models (Iglesias et al., 2000; Lobell and Burke, 2010) and can leverage within one study a growing availability of crop and weather data (Welch et al., 2010; Lobell et al., 2011). However, statistical models usually exclude the direct impact of elevated CO2, making multi-decadal prediction problematic. In determining future trends, crop models of all types can only extrapolate based on historically-determined relationships. Agro-climatic indices provide an alternative to crop models that avoid various assumptions by developing metrics, rather than providing yield predictions per se (Trnka et al., 2011). However, correlations between climate, or associated indices, and yield are not always statistically significant. The robustness of crop model results depends on data quality, model skill prediction and model complexity (Bellocchi et al., 2010). Modelling and experiments are each subject to their own uncertainties. Measurement uncertainty is a feature of field and controlled environment experiments. For example, interactions between CO2 fertilisation, temperature, soil nutrients, ozone, pests and weeds is not well understood (Soussana et al., 2010) and therefore most crop models do not include all these effects, nor broader issues of water availability, such as competition for water between industry and households (Piao et al., 2010). There are also uncertainties associated with generalizing the results of field experiments, since each one has been conducted relatively few times under a relatively small range of environmental and management conditions, and for a limited number of genotypes. This limits breadth of applicability both through limited sample size and limited representation of the diversity of genotypic responses to environment (Craufurd et al., 2013). For example, yield increases normalised by increase in CO2 have been found to vary between zero and over 30% among crop varieties (Tausz et al., 2011). The uncertainty in climate simulation is generally larger than, or sometimes comparable to, the uncertainty in crop simulation using a single model (Iizumi et al., 2011), but can be less than crop model uncertainty when multiple crop models are used (Koehler et al, 2013). There is significant uncertainty in agricultural simulation arising from climate model error. Since AR4 the choice of method for GCM bias correction has been identified as a significant source of uncertainty (Hawkins et al., 2012). There is also a contribution to uncertainty in crop model output from yield measurement error, through the calibration procedure. Yield measurements rarely have associated error bars to give an indication of accuracy. Greater access to accurate regional-scale crop yield data can lead to decreased uncertainty in projected yields (Watson and Challinor, 2013) The use of multiple crop models in impacts studies is relatively rare. Field-scale historical model intercomparisons have shown variations in yield and biomass of more than 100% (Palosuo et al., 2011). Early results from impacts studies with multiple crop models suggest that the crop model uncertainty can be larger than that caused by GCMs, due in particular to high temperature and temperature-by-CO2 interactions (Asseng et al., 2013). However, in contrast to absolute values, yield changes can be consistent across crop models (Oleson et al 2007). Given these different strengths and weaknesses, and associated dependencies, it is critical that both experimental and modelling lines of evidence, and their uncertainties, are examined carefully when drawing conclusions regarding impacts, vulnerabilities and risks. This approach to assessment is applied to each of the topics described in the rest of the chapter. The methods used for assessing impacts, vulnerabilities and risks in fisheries and aquaculture face the constraint that meaningful controlled experiments are usually not practical for fisheries in large rivers, lakes and marine environments because of the typical open and connected nature of these ecosystems. Experimentation has been used to examine responses to impacts at the scale of individual species, for example, to demonstrate the impacts of high atmospheric carbon dioxide in reducing coral calcification and growth (Hoegh-Guldberg et al. 2007) and to study the temperature tolerances of different cultured species (Ficke et al., 2007; De Silva and Soto, 2009). The far more common approach, however, is the empirical analysis of data collected in the field. This has been used to examine the effect of climate related factors on recruitment to a population, growth and population production of specific species, for example (Brander, 2010; Chapters 6 and 30). Different modelling approaches have also been used to integrate available information and assess the impacts of climate change on ecosystems and fish production at scales from national to global (Cheung et al., 2010; Fulton, 2011; Merino et al. 2012 et al.; Chapter 6.5). Efforts to assess the vulnerability of those dependent on fisheries and aquaculture have increased in recent years and range from
studies that use available information on exposure, sensitivity and adaptive capacity to provide an index of vulnerability (Allison et al., 2009; Cinner at al., 2011) to more detailed social and economic studies focused on particular communities or localities (Daw et al. 2009).
22.214.171.124. Treatment of Adaptation in Impacts Studies Adaptation occurs on a range of timescales and by a range of actors. Incremental adaptation, such as a change in crop management, can occur relatively autonomously within farming systems. It is the type of adaptation most commonly assessed in the impacts literature, and it is the only form of adaptation discussed in sections 7.3 and 7.4. Systemic and transformational adaptations are discussed in section 7.5. Methods exist to examine impacts and adaptation together in the context of non-climatic drivers (Mandryk et al., 2012), but conclusions are difficult to generalise.
188.8.131.52. Cereals and Oilseeds 18.104.22.168.1. Mean and extremes of temperature and precipitation
Both statistical and process-based models have widely been used since AR4 to assess the response of crop yield to temperature. Model results confirm the importance of known key physiological processes, such as the shortening of the time to maturity of a crop with increasing mean temperature (Iqbal et al., 2009), decline in grain set when high temperatures occur during flowering (Moriondo et al., 2011), and increased water stress at high temperatures throughout the growing cycle (Lobell et al., 2013b). Temperature responses are generally well-understood for temperatures up to the optimum temperature for crop development. The impacts of prolonged periods of temperatures beyond the optimum for development are not as well understood (Craufurd and Wheeler, 2009). For example, temperatures above 34 C after flowering appear to rapidly speed senescence in wheat (Lobell et al., 2012; Asseng et al., 2011), but many crop models do not represent this process. Crop models can be used to quantify abiotic stresses such as these, although only by hypothesizing that the functional responses to weather derived from experiments are valid at regional scales. Thus, whilst many fundamental bio-physical processes are understood at the plant or field scale, it remains difficult to quantify the extent to which these mechanisms are responsible for the observed regional-scale relationships between crop yield and weather. Despite these particular areas where specific understanding is lacking, the evidence from regional-scale statistical analyses (Schlenker and Roberts, 2009) and process-based models shows clear negative impacts of temperatures above 30-34oC on crop yields (depending on the crop and region) (high evidence, high agreement). The overall relationship between weather and yields is often crop and region specific, depending on differences in baseline climate, management and soil, and the duration and timing of crop exposure to various conditions. For example, rice yields in China have been found to be positively correlated with temperature in some regions and negatively correlated in others (Zhang et al., 2010). The trade-offs that occur in determining yield are therefore region-specific. This difference may be due to positive correlation between temperature and solar radiation in the former case, and negative correlation between temperature and water stress in the latter case. Similarly, although studies consistently show spikelet sterility in rice for daytime temperatures exceeding 33C (Jadadish et al., 2007; Wassmann et al., 2009), some statistical studies find a positive effect of daytime warming on yields because these extremes are not reached frequently enough to affect yields (Welch et al., 2010). Responses to temperature may vary according whether yields are limited by low or high temperatures. However, there is evidence that high temperatures will limit future yields even in cool environments (Semenov et al., 2012; Teixeira et al., 2013). The relative importance of temperature and water stress for crop productivity can be assessed using models, and can vary according to the criteria used for assessment (Challinor et al., 2010). There are also some cases where the sign of a correlation depends on the direction of the change. For example, Thornton et al. (2009) found that the response of crop yields to climate change in the drylands of East Africa is insensitive to increases in rainfall, since wetter
climates are associated with warmer temperatures that act to reduce yields. Since precipitation exhibits more spatial variability than temperature, temporal variations in the spatial average of precipitation tend to diminish as the spatial domain widens. As a result, precipitation becomes less important as a predictor of crop yields at broad scales (Lobell and Field, 2007; Li et al., 2010). Similarly, projected changes in precipitation from climate models tend to be more spatially variable than temperature, leading to the greater importance of projected temperatures as the spatial scale of analysis grows wider (Lobell and Burke, 2008). There is also evidence that where irrigation increases over time the influence of temperature on yields starts to dominate over that of precipitation (Hawkins et al., 2012). The impact of drought on crop yield is a more common topic of research than the impact of floods. Analysis of 66 yield impact studies for major cereals, including both pre- and post- AR4 contributions, gives broadly similar results to AR4 (Figure 7-4). Figure 7-4 shows that yields of maize and wheat begin to decline with 1-2oC of local warming in the tropics. Temperate maize and tropical rice yields are less clearly affected at these temperatures, but significantly affected with warming of 3-5oC. These data confirm AR4 findings that even slight warming will decrease yields in low-latitude regions (medium evidence, high agreement). However, whilst AR4 had few indications of yield reductions at less than 2oC of local warming, the new analysis has, in the absence of incremental adaptation, more yield decreases than increases at all temperatures. Hence, whilst AR4 concluded with medium confidence that in mid- to high-latitude regions moderate warming will raise crop yields, new knowledge suggests that temperate wheat yield decreases are about as likely as not for moderate warming. A recent global crop model intercomparison for rice, wheat and maize shows similar results to those presented here, although with less impacts on temperate rice yields (Rosenzweig et al, 2013). That study also showed that crop models without explicit nitrogen stress fail to capture the expected response. Quantitative assessments of yield changes can be found in section 7.4. Across the globe, regional variability, which has not been summarised in meta-analyses except in contributing to the spread of data (Figure 7-4), will be important in determining how climate change affects particular agricultural systems. [INSERT FIGURE 7-4 HERE Figure 7-4: Percentage simulated yield change as a function of local temperature change temperature for the three major crops and for temperate and tropical regions. Dots indicate where a known change in atmospheric CO2 was used in the study; remaining data are indicated by x. Note that differences in yield value between these symbols do not measure the CO2 fertilisation effect, since changes in other factors such as precipitation may be different between studies. Non-parameteric regressions (LOESS, span=1 and degree=1) of subsets of these data were made 500 times. These bootstrap samples are indicated by shaded bands at the 95% confidence interval. Regressions are separated according to the presence (blue) or absence (red) of simple agronomic adaptation (Table 7-2). In the case of tropical maize the central regression for absence of adaptation is slightly higher than that with adaptation. This is due to asymmetry in the data not all studies compare adaptated and non-adapted crops. Figure 7-8 presents a pair-wise adaptation comparison. Note that four of the 1048 datapoints across all six panels are outside the yield change range shown. These were omitted for clarity. Some of the studies have associated temporal baselines, with centre-points typically between 1970 and 2005. Note that local warming in cropping regions generally exceeds global mean warming (Figure 21-3). Data are taken from a review of literature: Abou-Hadid (2006); Abraha & Savage (2006); Aggarwal & Mall (2002); Alexandrov (1999); Alexandrov & Hoogenboom (2000); Alexandrov et al. (2002); Arndt et al. (2011); Brassard & Singh 2008); Brassard & Singh (2007); Butt et al. (2005); Byjesh et al. (2010); Chhetri et al. (2010); Chipanshi et al. (2003); Corobov (2002); Izaurralde et al. (2001); DeJong et al. (2001); Deryng et al. (2011); Droogers (2004); Easterling et al. (2003); El Maayar et al. (2009); El-Shaher et al. (1997); Ewert et al. (2005); Gbetibouo & Hassan (2005); Howden & Jones (2004); Iqbal et al. (2011); Izaurralde et al (2005); Jones & Thornton (2003); Kaiser (1999); Kapetanaki & Rosenweig (1997); Karim et al. (1996); Krishnan et al. (2007); Lal (2011); Lal et al. (1998); Li et al. (2011); Erda et al. (2005); Liu et al. (2010); Lobell & Ortiz-Monasterio (2007); Luo et al. (2003); Matthews & Wasmann (2003); Moya et al. (1998); Osborne et al. (2013); Piao et al. (2010); Porter & Semenov (2005); Reyenga et al. (1999); Rosenzweig et al. (1994); Rowhani et al. (2011); Sands & Edmond (2005); Schlenker & Roberts (2009); Shuang-He et al. (2011); Southworth et al. (2000); Tan et al. (2010); Tao & Zhang (2010); Tao & Zhang (2011); Tubiello et al. (2000); Thomson et al. (2005); Thornton et al. (2009); Thornton et al. (2011); Thornton et al. (2010); Tingem & Rivington (2009); Tingem et al. (2008); Walker & Schulze (2008); Winters et al. (1998); Xiao et al. (2005); Xiong et al. (2007); Yates & Strzepek (1998); Zhang & Liu (2005); Zhao et al. (2005).]
There is further observational evidence since AR4 that response to a change in CO2 depends on plant type; C3 or C4 (DaMatta et al., 2010). The effect of increase in CO2 concentration tends to be higher in C3 plants (wheat, rice, cotton, soybean, sugar beets, and potatoes) than in C4 plants (corn, sorghum, sugarcane), because photosynthesis rates in C4 crops are less responsive to increases in ambient CO2 (Leakey, 2009). The highest fertilization responses have been observed in tuber crops, which have large capacity to store extra carbohydrates in belowground organs (Fleisher et al., 2008; Hgy and Fangmeier, 2009). There is observational evidence, new since AR4, that the response of crops to CO2 is genotype-specific (Ziska et al. 2012). For example, yield enhancment at 200 ppm additional CO2 ranged from 3 to 36 % among rice cultivars (Hasegawa et al., 2013). FACE studies have shown that the impact of elevated CO2 varies according to temperature and availability of water and nutrients, although the strong geographical bias of FACE studies towards temperate zones limits the strength of this evidence. FACE studies have shown that CO2 enhancment is limited under both low (Shimono et al., 2008) (Hasegawa et al., 2013) and high temperature. Theory suggests that water-stressed crops will respond more strongly to elevated CO2 than well-watered crops, because of CO2 induced increases in stomatal resistance. This suggests that rain-fed cropping systems will benefit more from elevated CO2 than irrigated systems. Thus both TAR and AR4 cited the expectation that rain-fed systems benefit more from elevated CO2 than systems under wetter conditions. New evidence based on historical observations supports this notion by demonstrating that the rate of yield gains in rain-fed systems is higher in dry years than in wet years (McGrath and Lobell, 2011). However, this response is not seen consistently across models and FACE meta-analyses and there is some suggestion that the relationship between water stress and assimilation may vary with spatial scale, with canopy analyses showing a reversal of the expected leaf-level dry vs wet signal (Challinor and Wheeler, 2008). Ozone in the stratosphere provides protection from lethal short-wave solar ultraviolet radiation, but in the troposphere it is a phytotoxic air pollutant. The global background concentration of ozone has increased since pre-industrial era due to anthropogenic emission of its precursors (carbon monoxide, volatile organic compounds and oxides of nitrogen), by vehicles, power plants, biomass burning and other sources of combustion. Like CO2, O3 is taken up by green leaves through stomata during photosynthesis but unlike CO2, its concentration is significantly variable depending on geographic location, elevation and extent of anthropogenic sources. Being a powerful oxidant, ozone and its secondary by-products damage vegetation by reducing photosynthesis and other important physiological functions (Mills et al., 2009; Ainsworth and McGrath, 2010). This results in stunted crop plants, inferior crop quality and decreased yields (Booker et al., 2009; Fuhrer, 2009; Vandermeiren et al., 2009; Pleijel and Uddling, 2012) and posing a growing threat to global food security (robust evidence, high agreement). The literature published since AR4 further corroborates the negative impacts of increasing concentrations of surface ozone on yield at global (Van Dingenen et al., 2009; Teixeira et al., 2011; Avnery et al., 2011a; Avnery et al., 2011b) and regional scales (Northern Hemisphere: Hollaway et al, (2011); USA: (Emberson et al., 2009; Fuhrer, 2009; Fishman et al., 2010); India (Roy et al., 2009; Rai et al., 2010; Sarkar and Agrawal, 2010); China: (Wang et al., 2007; Piao et al., 2010); Bangladesh: (Akhtar et al., 2010); Europe: (Hayes et al., 2007; Vandermeiren et al., 2009; Fuhrer, 2009). Global estimates of yield losses due to increased ozone in soybean, wheat and maize in 2000 ranged from 8.5-14%, 3.9-15% and 2.2-5.5% respectively, amounting to economic losses of $11-18 billion (Avnery et al., 2011a). Ozone may have direct effect on reproductive process, leading to reduced seed and fruit development and abortion of developing fruit (Royal Society, 2008; robust evidence, high agreement). The interactive effects of ozone with other environmental factors such as CO2, temperature, moisture and light, are important but not well understood. Generally, the ambient and increasing concentrations of O3 and CO2 individually exert counteractive effects on C3 plants (Tianhong et al., 2005; Ainsworth, 2008; Gillespie et al., 2012); but their interactive effect may compensate for each other (Ainsworth, 2008; Taub et al., 2008; Gillespie et al., 2012). However, the losses might be greater when elevated O3 combines with high temperature (Long, 2012) particularly during grain filling of wheat, when elevated ozone causes premature leaf senescence (Feng et al., 2008; Feng et al., 2011). Periods of abundant radiation and adequate water supply are favourable for both agricultural production and the formation of surface ozone; thus the effects of ozone on crops, can be difficult to detect (Long, 2012).
220.127.116.11. Other Crops Earlier flowering and maturity have been observed (robust evidence, high agreement) worldwide in grapes (Garca-Mozo et al., 2010; Duchne et al., 2010; Jorquera-Fontena and Orrego-Verdugo, 2010; Sadras and Petrie, 2011; Webb et al., 2011), apples (Fujisawa and Koyabashi, 2010; Grab and Craparo, 2011) and other perennial horticultural crops (Glenn et al., 2013). Cassava (also known as manioc) is an important source of food for many people in Africa and Latin America and recent studies suggest (medium evidence, medium agreement) that future climate should benefit its productivity as this crop is characterized by elevated optimum temperature for photosynthesis and growth, and a positive response to CO2 increases (Jarvis et al., 2012; Rosenthal and Ort, 2012; El-Sharkawy, 2012).
22.214.171.124. Pests, Weeds, Diseases As a world-wide average, yield loss in major crop species due to animal pests and (non-virus) pathogens, in the absence of any physical, biological or chemical crop protection, has been estimated at 18% and 16%, respectively (Oerke, 2006). Climate change will alter potential losses to many pests and diseases. Changes in temperature can result in geographic shifts through changes in seasonal extremes, and thus, for example, overwintering and summer survival. CO2 and ozone can either increase or decrease plant disease, and can exhibit important interactions (Chakraborty and Newton, 2011; Garrett et al., 2011), suggesting the need for system-specific risk assessment (Chakraborty et al., 2008; Eastburn et al., 2011). Interactions with landscape effects may be particularly important in forests and grasslands (Pautasso et al., 2010). The rarity of long-term studies of plant diseases and pests is a problem for the evaluation of climate change effects, but there are some examples of the potential for such analyses. On-going wheat experiments at Rothamsted Research Station UK, maintained for over 160 years, have revealed shifts in foliar wheat pathogens linked to rainfall, temperature, and SO2 emissions (Bearchell et al., 2005; Shaw et al., 2008). Wheat rust risk has been observed to respond to ENSO (Scherm and Yang, 1995). Over almost seven decades, earlier and more frequent epidemics of potato late blight, and more frequent pesticide use, were observed in Finland, associated with changing climate conditions and lack of crop rotation (Hannukkala et al., 2007). Changes in climate are expected to affect the geographic range of specific species of insects and diseases for a given crop growing region. For example, Cannon (1998) has suggested that migratory insects could colonize crops over a larger range in response to temperature increases, with subsequent reductions in yield. Climate change may also be a factor in extending the northward migration of agronomic and invasive weeds in North America (Ziska et al., 2011). Weed species also possess characteristics that are associated with long-distance seed dispersal, and it has been suggested (Hellman et al. 2008) that they may migrate rapidly with increasing surface temperatures. Predator and insect herbivores respond differently to increasing temperature, leading to possible reductions in insect predation and thus greater insect numbers. However, ecosystems are complex and insect and disease occurrence can go down as well as up. Overall, our ability to predict CO2/climate change impacts on pathogen biology and subsequent changes on yield is limited because, with few exceptions (Savary et al., 2011), experimental data are not available and analyses focus on individual diseases rather than the complete set of important diseases (medium evidence, medium agreement). Elevated CO2 can reduce yield losses due to weeds for C3 crops (soybean, wheat and rice), since many agricultural weeds are C4 species; and the C3 pathway, in general, shows a stronger response to rising carbon dioxide levels. However, both C3 and C4 weed species occur in agriculture, and there is a wide range of responses among these species to recent and projected CO2 levels (Ziska 2010). For example, in the United States, every crop, on average, competes with an assemblage of 8-10 weed species (Bridges, 1992). CO2 and climate can also affect weed demographics. For example, with field grown soybean, elevated CO2 per se appeared to be a factor in increasing the relative proportion of C3 to C4 weedy species with subsequent reductions in soybean yields (Ziska and Goins, 2006). For rice and barnyard grass (C4), increasing CO2 favoured rice, but if both temperature and CO2 increased
simultaneously, the C4 weed was favoured, primarily because higher temperatures resulted in increased seed yield loss for rice. For weeds that share physiological, morphological, or phenological traits with the crop, including those weeds that are wild relatives of the domesticated crop species, (often among the worst weeds in agronomic situations, e.g. rice and red rice) the decrease in seed yield from weeds may, be greater under elevated CO2 (Ziska, 2010). With respect to control, a number of studies have, to date, indicated a decline in herbicide efficacy in response to elevated CO2 and/or temperature for some weed species, both C3 and C4 (Archambault, 2007; Manea et al., 2011). Some of the mechanisms for this are understood, for example for the invasive plant species Canada thistle (Cirsium arvense), elevated CO2 results in a greater root biomass, thus diluting the active ingredient of the herbicide used and reducing chemical control (Ziska 2010). To date, studies on physical, cultural or biological weed control are lacking.
126.96.36.199. Fisheries and Aquaculture The natural and human processes in fisheries and aquaculture differ from mainstream agriculture and are characterized by distinct impacts and interactions related to climate change. Capture fisheries in particular, comprising the largest remaining example of harvesting natural, wild resources, are strongly influenced by global ecosystem processes. The social, economic and nutritional requirements of the growing human population are already driving heavy exploitation of capture fisheries and rapid development of aquaculture (Chapter 188.8.131.52). This trend will continue over the next 20 to 30 years at least: (Merino et al., 2012) forecast that in addition to a predicted small increase in marine fisheries production, between 71 and 117 million tonnes (MT) of fish will need to be produced by aquaculture to maintain current average per capita consumption of fish. The impacts of climate change add to and compound these threats to the sustainability of capture fisheries and aquaculture development (FAO, 2009). Expected changes in the intensity, frequency and seasonality of climate patterns and extreme events, sea level rise, glacier melting, ocean acidification and changes in precipitation with associated changes in groundwater and river flows are expected to result in significant changes across a wide range of aquatic ecosystem types and regions with consequences for fisheries and aquaculture in many places (Chapter 184.108.40.206; FAO, 2009). Ocean acidification will also have negative impacts on the culture of calcifying organisms (Chapter 18.104.22.168.4), including mollusc species of which 14.2 MT were produced by aquaculture in 2010, equivalent to 23.6% of global aquaculture production (FAO, 2012). There are also concerns that that climate change could lead to the spread of pathogens with impacts on wild and cultured aquatic resources (De Silva and Soto, 2009). Given the proximity of fishing and aquaculture sites to oceans, seas and riparian environments, extreme events can be expected to have impacts on fisheries and aquaculture with those located in low-lying areas at particular risk. The consequences of sea level rise and the expected increased frequency and intensity of storms include increased risks of loss of homes and infrastructure, increased safety risks while fishing and the loss of days at sea because of bad weather (Daw et al., 2009). In areas that experience water stress and competition for water resources, aquaculture operations and inland fisheries production will be at risk. Food production from fisheries and aquaculture will be affected by the sensitivity of the caught and cultured species to climate change and both positive and negative outcomes can be expected. Changes in marine and freshwater mean temperatures, ocean acidification, hypoxia and other climate-related changes will influence the distribution and productivity of fished and farmed aquatic species (Chapter 6.4.3; 18.104.22.168; Chapter 30.6.2). Changes in temperature extremes are also likely to have impacts. Many aquatic species are routinely subjected to large daily and seasonal fluctuations in temperature and are able to cope with them: for example, temperatures in shallow coastal habitats in the tropical Pacific can vary by more than 14oC diurnally (Pratchett et al., 2011). Nevertheless, distribution and productivity of aquatic species and communities are sensitive to changes in temperature extremes. A study on salmon populations in Washington State, USA (Mantua et al., 2010) demonstrated important impacts of seasonal variations and extremes. The study concluded that warming in winter and spring would have some positive impacts while increased summertime stream temperatures, seasonal low flows and changes in the peak and base flows would have negative impacts on the populations. Coral reefs are particularly susceptible to extremes in temperature: temperatures 1 or 2oC in excess of normal maximums for 3 to 4 weeks are sufficient to disrupt the essential relationship between endosymbiotic dino-flagellates and their coral hosts leading to coral bleaching. Large
scale bleaching of coral reefs has increased in recent decades both in intensity and frequency (Hoegh-Guldberg et al., 2007). The impacts of climate change on the fisheries and aquaculture sector will have implications for the four dimensions of food security i.e. availability of aquatic foods, stability of supply, access to aquatic foods, and utilization of aquatic products (FAO, 2009). Where climate-driven ecological changes are significant, countries and communities will need to adapt through, for example, changes in fishing and aquaculture practices and operations (126.96.36.199.2.).
22.214.171.124. Food and Fodder Quality and Human Health Food quality is any characteristic other than yield that is valuable to the producer or consumer. Examples include wheat protein and starch concentrations, which affect dough quality; amylose content in rice, which affects taste; and mineral concentrations, which affects nutrient intake of consumers. Climate change will have some adverse impacts on food quality through both biotic and abiotic stresses (Ceccarelli et al., 2010). These changes may affect crop quality by altering carbon and nutrient uptake and biochemical processes that produce secondary compounds or redistribute and store compounds during grain development and maturation. This in turn could impact human and livestock health by altering nutritional intake and/or affect economic value by altering traits valuable to processers or the consumers. Change in nitrogen concentration, a proxy for protein concentration, is the most examined quality trait and since AR4 studies have been extended to almost all the major food crops. Cereals grown in elevated CO2 show a decrease in protein (Piikki et al., 2007; Hogy et al., 2009; Erbs et al., 2010; Ainsworth and McGrath, 2010; DaMatta et al., 2010; Erda et al., 2010; Fernando et al., 2012). Meta-analysis of 228 experimental observations finds decreases between 10 to 14 % in edible portions of wheat, rice, barley and potato, but only 1.5 % in soybeans, a nitrogen-fixing legume, when grown in elevated CO2 (Taub et al., 2008). Mineral concentration of edible plant tissues are affected by growth in elevated CO 2 in a similar manner to nitrogen. Although there are numerous studies measuring mineral concentration, there are relatively few measurements for any given mineral relevant to human health. Although there were several studies published before the release of AR4, this topic was not covered in any depth in AR4. Meta-analysis of studies prior to 2002 finds that P, Ca, S, Mg, Fe, Zn, Mn and Cu decline by 2.5 - 20 % in wheat grain and leaves of numerous species in elevated CO2, but K increases insignificantly in wheat grain (Loladze, 2002; Hogy et al., 2009; Fernando et al., 2012). Since 2002, studies generally find decreases in Zn, S, P, Mg and Fe in wheat and barley grain, increase in Cu, Mo and Pb (from a limited number of studies) and mixed results for Ca and K (Hogy et al., 2009; Erbs et al., 2010; Fernando et al., 2012). Changes in mineral concentration due to elevated CO2 are determined by several factors including crop species, soil type, tissue (tubers, leaves or grain) and water status. Elevated CO2 can lower the nutritional quality of flour produced from grain cereals (Hogy et al., 2009; Erbs et al., 2010) and of cassava (Gleadow et al., 2009). When coupled with increased crop and pathogen biomass, elevated CO2 can result in increased severity of the Fusarium pseudograminearum pathogen, leading to shriveled grains with low market value (Melloy et al., 2010). Extreme temperatures and elevated CO2 concentrations reduce milling quality of rice by increasing chalkiness, but can improve taste, through, for example, reduced amylase concentration (Yang et al., 2007). Cultivars vary in their susceptibility to these processes (Ambardekar et al., 2011; Lanning et al., 2011). Overall, there is robust evidence and high agreement that elevated CO2 on its own likely results in decreased nitrogen concentrations. Combining knowledge of nitrogen and mineral studies, there is medium evidence and medium agreement that mineral concentrations will decline. The majority of these data are from wheat, with comparatively little information from key crops such as maize, rice, potato and cassava; thus magnitudes are uncertain for the species. Elevated O3 concentrations appear to have the opposite effect as elevated CO2. Meta-analysis of about 50 wheat experiments found that elevated O3 increased grain protein concentration by decreasing yield (Pleijel and Uddling, 2012). For other species, studies find both increases and decreases of N and several minerals (Taub et al., 2008), and as such no firm conclusions can be drawn, but they mostly respond similarly. Similarly, experiments examining the
effect of drought on mineral concentrations find both decreases and increases in mineral concentrations (Ghorbanian et al., 2011; Sun et al., 2011). Confidence in the impact of climate, CO2 and O3 on food quality does not imply confidence in changes regarding human health for several reasons. Post-processing of food affects nutrient concentrations, when the nutrient-rich outer layers of rice are removed, leaving the starch dense endosperm. Also, elevated CO2 can increase crop yield, thus increasing the overall yield minerals (Duval et al., 2011) and permitting greater mineral consumption. Furthermore, since calorie intake is the primary concern in many food insecure populations, even if intake of minerals is decreased, those negative effects could be outweighed by increased calorie intake. In assessing impacts on health, current diets must be considered. Decreased mineral intake will matter for those who currently do not meet, or just barley meet, requirements, but will not affect those who already exceed requirements. Little is known about combined effects of climate change factors on food quality or the economic and behavioral changes that will occur. Thus, there is little confidence regarding effects of climate change on human health through changes in nutrient composition.
188.8.131.52. Pastures and Livestock Pastures response to climate change is complex because, in addition to the direct major atmospheric and climatic drivers (CO2 concentration, temperature, and precipitation), there are important indirect interations such as plant competition, perennial growth habits, seasonal productivity, and plant-animal interactions. Projected increases in temperature and the lengthening of the growing season should extend forage production into late fall and early spring, thereby decreasing the need for accumulation of forage reserves during the winter season in USA (Izaurralde et al., 2011). In addition, water availability may play a major role in the response of pasturelands to climate change although there are differences in species response (Izaurralde et al., 2011). There is general consensus that increases in CO2 will benefit C3 species; however, warmer temperatures and drier conditions will tend to favour C4 species (Izaurralde et al., 2011; Hatfield et al., 2011; AR5 WG2 Chapter 4). While elevated atmospheric CO2 concentrations reduce sensitivity to lower precipitation in grassland ecosystems and can reduce mortality and increase recovery during severe water stress events, it is still unclear how general this result is (Soussana et al., 2010). Temperature is an important limiting factor for livestock. As productivity increases, be it increasing milk yield in dairy cattle or higher growth rates and leanness in pigs or poultry, so metabolic heat production increases and the capacity to tolerate elevated temperatures decreases (Zumbach et al., 2008; Dikmen and Hansen, 2009). Over the long term, single-trait selection for productivity will tend to result in animals with lower heat tolerance (Hoffmann, 2010). Recent work adds to previous understanding (AR4 WG2 Chapter 5) and indicates that heat stress (medium evidence, high agreement,) in dairy cows can be responsible for the increase in mortality and economic losses (Vitali et al., 2009); it affects a wide range of parameters in broilers (Feng et al, 2008); it impairs embryonic development and reproductive efficiency in pigs (Barati et al., 2008); and affects ovarian follicle development and ovulation in horses (Mortensen et al., 2009). Water stess also limits livestock systems. Climate change will affect the water resources available for livestock via impacts on runoff and groundwater (AR5 WG2 Chapter 3). Populated river basins may experience changes in river discharge, and large human and livestock populations may experience water stress such that proactive or reactive management interventions will almost certainly be required (Palmer et al., 2008). Problems of water supply for increasing livestock populations will be exacerbated by climate change in many places in sub-Saharan Africa and South Asia.
22.214.171.124. Non-Production Food Security Elements As indicated in the discussion in section 7.1.1 and Figure 7-1, food security is dependent on access and consumption patterns, food utilization and nutrition, and overall stability of the system as much as food production and availability. The overall impact of climate change on food security is considerably more complex and potentially greater than projected impacts on agricultural productivity alone. Figure 7-1 indicates the main components of food
security and their key elements. All of these will be affected by climate change to some extent. For example, climate change effects on water, sanitation and energy availability have major implications for food access and utilization as well as availability. Likewise, changes in the frequency and severity of climate extremes can affect stability of food availability and prices, with consequent impacts on access to food.
188.8.131.52. Accessibility, Utilization, and Stability 18.104.22.168.1. Climate change impacts on access
As noted in the discussion in section 7.1.3, change in the levels and volatility of food prices is a key determinant of food access. Given the hypothesis that climate change will be a contributing factor to food price increases, and hence its affordability, the vulnerability of households to reduced food access depends on their channel of food access (medium evidence, medium agreement). Table 7-1 divides households into five main categories of food access, indicating their relative impacts of food price increases. [INSERT TABLE 7-1 HERE Table 7-1: Households divided into five categories of food access, indicating the impacts for them of food price increases.] Concern about the impact of increased food prices on poverty and food security arises due to the high share of income that poor consumers spend on food, thus generating a disproportionately negative effect of price increases on this group (FAO, 2011). A study by the World Bank estimated a net increase of 44 million people in extreme poverty in low and middle income countries as a result of food price increases since June 2010 (Ivanic et al., 2011). Tthe distribution of net food buyers and net food sellers varies considerably across countries and can be expected to change with the process of economic development (Zezza et al., 2008; Aksoy et al., 2010; FAO, 2011). Changing consumption patterns associated with dietary transitions that accompany income growth, urbanization, market development, and trade liberalization determine the rate and nature of food demand growth and nutritional levels, and thus is a key determinant of global and local food security (Kearney, 2010). However the evidence base on potential climate change impacts on consumption patterns, or on other non-production elements of food security is thin, particularly when compared with the literature on climate change impacts on food production and availability. Current and future variation in the distribution and vulnerability to loss of food access across household types makes impacts assessment complex and difficult. Nonetheless, there are reasons for concern about food access due to the current high rates of food insecurity in many low income countries. Agricultural producers who are net food buyers are particularly vulnerable. Similarly, low income agricultural dependent economies that are net food importers, which are those that already have high rates of food insecurity, could experience significant losses in food access through a double negative effect on reduced domestic agricultural production and increased food prices on global markets.
There is increasing evidence of and confidence in the effect of climate change on increasing the incidence and frequency of some types of climate extreme events (IPCC, 2012), and this will have significant impacts on food security (medium evidence; medium agreement). Recent experience of global climate patterns affecting food security indicate the potential nature and magnitude of increased variability. An impact assessment of the 2010 Pakistan floods surveyed 1800 households 6 months after the floods and found that 88% of the households reported income losses of up to 50%, with significantly higher rates in rural than urban areas (Kirsch et al., 2012). The same study indicated that loss of key services such as electricity, sanitation and clean water resulted in lower standards of living even in the wake of significant relief attempts, again with significantly heavier effects on rural populations (Kirsch et al., 2012). The Russian heat wave of 2010 and subsequent export ban contributed to the more than doubling of global wheat prices by the end of the year. The degree to which these price increases affected domestic
consumers and poverty depended on national responses in importing countries, although a significant net negative effect on poverty was found (Ivanic et al., 2011). Increased incidence of climate extremes reduces incentives to invest in agricultural production, potentially offsetting positive impacts from increasing food price trends. This is particularly true for poor smallholders with limited or no access to credit and insurance. Greater exposure to climate risk, in the absence of well-functioning insurance markets, leads to: 1) greater emphasis on low-return but low-risk subsistence crops (Roe and Graham-Tomasi, 1986; Fafchamps, 1992; Heltberg and Tarp, 2002), 2) a lower likelihood of applying purchased inputs such as fertilizer (Kassie et al., 2008; Dercon and Christiansen, 2011), 3) a lower likelihood of adopting new technologies (Feder et al., 1985; Antle and Crissman., 1990), and 4) lower investments (Skees et al., 1999). All of these responses generally lead to both lower current and future farm profits (robust evidence, high agreement) (Rosenzweig and Binswanger, 1993; Hurley, 2010). It is also well documented that in many rural areas, smallholders in particular do not have the capacity to smooth consumption in the face of climate shocks, particularly generalized shocks that affect a majority of households in the same location (Dercon, 2004; Skoufias and Quisumbing, 2005; Dercon, 2006; Fafchamps, 2009; Prakash, 2011). Any increases in climate extremes will exacerbate the vulnerability of all food insecure people, including smallholders (robust evidence, high agreement). Currently, smallholders rely to a large extent on increasing labor off-farm where possible (Fafchamps, 1999; Kazianga and Udry, 2006), but also by decreasing both food consumption and non-food expenditures, such as those on education and healthcare (Skoufias and Quisumbing, 2005) (medium evidence, high agreement). Furthermore, some evidence also suggests that poorer households are more likely to reduce consumption, while wealthier households liquidate assets to cover current deficits (limited evidence, medium agreement) (Kazianga and Udry, 2006; Carter and Lybbert, 2012). Reductions in food consumption, sales of productive assets, education and healthcare can lead to long-term losses in terms of income-generation and thus to future food security (limited evidence, medium agreement) (Hoddinot and Maluccio, 2008; Skoufias and Quisumbing, 2005). Increased uncertainty of future climate conditions and increases in climate extremes will increase food insecurity unless these significant barriers to consumption and asset smoothing can be addressed (medium evidence, medium agreement).
Climate change impacts on utilization may come about through changes in consumption patterns in response to shocks, as well as changes in nutrient content of food as well as food safety (medium evidence; medium agreement). Rationing consumption to prioritize calorie-rich, but nutrient poor foods is another common response (Bloem et al., 2010). The effects are a decrease in dietary quality as well as quantity, which are magnified by pre-existing vulnerabilities and lead to long term loss of health, productivity capacity and low incomes (medium evidence; medium agreement) (Bloem et al., 2010; Alderman, 2010; Brinkman et al., 2010; Campbell et al., 2010; Sari et al., 2010). The biological effects of climate change on nutrient content of foods is one of main pathways for effects on utilization. A summary of recent literature on the impacts of climate change on the composition of nutrients in food items is given in HLPE (2012). Research on grains generally shows lowering of protein content with elevated temperature and CO2 levels (Ainsworth and McGrath, 2010; Erda et al., 2005; Hatfield et al., 2011). There is good agreement that for plant-derived foods, mycotoxins are considered the key issue for food safety under climate change (Miraglia et al., 2009). The impacts of climate change on mycotoxins in the longer term are complex and region-specific; temperatures may increase sufficiently to eliminate certain mycotoxin-producing species from parts of the tropics, but in colder tropical regions and temperate zones, infections may increase (Cotty and Jaime-Garcia, 2007).
As noted in the AR4, changes in land use, for example adjusting the location of crop production, are a potential adaptation response to climate change. Studies since the AR4 have confirmed that high latitude locations will, in general, become more suitable for crops (Iqbal et al., 2009). Trnka et al. (2011), for example, examined projections
of eleven agro-climatic indices across Europe, and found that declines in frost occurrence will lead to longer growing seasons, although temperature and moisture stress will often lead to greater inter-annual variability in crop suitability. The potential influence of pests and diseases is commonly beyond the scope of such studies (Gregory et al., 2009). For tropical systems where moisture availability or extreme heat rather than frost limits the length of the growing season, there is a likelihood that the length of the growing season and overall suitability for crops will decline (medium evidence, medium agreement) (Jones and Thornton, 2009; Zhang and Cai, 2011). For example, half of the wheat-growing area of the Indo-Gangetic Plains could become significantly heat stressed by the 2050s, whilst temperate wheat environments will expand northwards as climate changes (Ortiz et al., 2008). Similarly, by 2050, the majority of African countries will experience climates over at least half of their current crop area that lie outside the range currently experienced within the country (Burke et al., 2009). The majority of these novel climates have analogues in other African countries. In mountainous regions, where temperature varies significantly across topography, changes in crop suitability can be inferred from the variation of temperature across topography. The resulting vertical zones of increasing, decreasing and unchanging suitability can be relatively robust in the face of uncertainty in future climate (Schroth et al., 2009). The interaction between water resources and agriculture is expected to become increasingly important as climate changes. For example, whilst projected changes in crop productivity in China are uncertain, even within a single emissions scenario, irrigation has significant adaptation potential (Piao et al., 2010). However, limitations to availability of water will affect this potential. Changes in water use, including increased water diversion and development to meet increasing water demand, and increased dam building will also have implications for inland fisheries and aquaculture, and therefore for the people dependent on them (Ficke et al., 2007; FAO, 2009). In the case of the Mekong River basin, a large proportion of the 60 million inhabitants are dependent in some way on fisheries and aquaculture which will be seriously impacted by human population growth, flood mitigation, increased offtake of water, changes in land use and overfishing, as well as by climate change (Brander, 2007). Ficke et al. (2007) reported that at that time there were 46 large dams planned or already under construction in the Yangtze River basin, the completion of which would have detrimental effects on those dependent on fish for subsistence and recreation. The models used in projections of land suitability and cropland expansion discussed above rely on assumptions about non-climatic constraints on crop productivity, such as soil quality and access to markets. These assumptions are increasingly amenable to testing as the climate system shifts, by comparing observed changes in cropland area with model predictions. The location of the margin between cropping land and extensive grazing in southern Australia has varied with decadal climate conditions and is projected to shift towards the coast with hotter and drier conditions, notwithstanding the positive impacts of elevated CO2 (Nidumolu et al., 2011). Recent trends in climate have seen reductions in cropping activity consistent with these projections (Nidumolu et al., 2011).
7.4. 7.4.1.
Crop yields remain the most well studied aspect of food security impacts from climate change, with many projections published since AR4. These newer studies confirm many of the patterns identified in AR4, such as negative yield impacts for all crops past 3C of local warming without adaptation, even with benefits of higher CO2 and rainfall (Figure 7-4). Figure 7-5 shows projected impacts on mean crop yield in twenty-year bins, including cases with no adaptation and a range of incremental adaptations. The data indicate that negative impacts on average yields become likely from the 2030s. Negative impacts of more than 5% are more likely than not beyond 2050 and likely by the end of the century. Some important differences by emission scenario and region are masked in Figure 7-5. From the 2080s onwards, negative yield impacts in the tropics are very likely, regardless of adaptation or emission scenario. This is consistent
with the meta-analysis of Knox et al. (2012), and a recent model intercomparison of global gridded crop models (Rosenzweig et al., 2013). [INSERT FIGURE 7-5 HERE Figure 7-5: Projected changes in crop yield as a function of time. The y-axis indicates percentage of studies and the colours denote percentage change in crop yield. The sum of absolute bar heights in each time period equals 100%. Data are plotted according to the 20-year period in which the centre point of the projection period falls. Data taken from Abraha & Savage, 2006; Alexandrov & Hoogenboom, 2000; Arndt et al., 2011; Berg et al., 2012; Brassard & Singh, 2008; Brassard & Singh, 2007; Butt et al., 2005; Calzadilla et al., 2009; Chhetri et al., 2010; Ciscar et al., 2011; Deryng et al., 2011; Giannakopoulos et al., 2009; Hermans et al., 2010; Iqbal et al., 2011; Izaurralde et al, 2005; Kim et al., 2010; Lal, 2011; Li et al., 2011; Lobell et al., 2008; Moriondo et al., 2010; Muller et al., 2010; Osborne et al., 2013; Peltonen-Sainio et al., 2011; Piao et al., 2010; Ringler et al., 2010; Rowhani et al., 2011; Schlenker & Roberts, 2009 ;Shuang-He et al., 2011; Southworth et al., 2000; Tan et al., 2010; Tao & Zhang, 2010; Tao & Zhang, 2011; Tao et al., 2009; Thornton et al., 2009; Thornton et al., 2011; Thornton et al., 2010; Tingem & Rivington, 2009 Tingem et al., 2008; Walker & Schulze, 2008; Wang et al., 2011; Xiong et al., 2009; Xiong et al., 2007.] A few studies have explicitly compared projections for different regions or crops to identify areas at most risk. Lobell et al. (2008) used a statistical crop model with 20 GCMs and identified South Asia and Southern Africa as two regions that, in the absence of adaptation, would suffer the most negative impacts on several important crops. Yields changes have also been assessed by regional meta-analyses: Knox et al. (2012) synthesized projections from 52 studies and estimated an expected 8% negative yield impact in both Africa and South Asia by 2050 averaged over crops, with wheat, maize, sorghum, and millets more affected than rice, cassava, and sugarcane. Changes in the interannual variability of yields could potentially affect stability of food availability and access. Figure 7-6 shows projected changes in the coefficient of variation (CV) of yield from some of the few studies that publish this information. The data shown are consistent with reports of CV elsewhere: Mller et al. (2013) conducted gridded simulations across the globe and reported an increase of more than 5% in CV in 64% grid cells, and a decrease of more than 5% in 29% of cases. Increases in CV can be due to reductions in mean yields and/or increases in standard deviation of yields, and often simulated changes are a combination of the two. Overall, climate change will increase crop yield variability in many regions (medium evidence, medium agreement). [INSERT FIGURE 7-6 HERE Figure 7-6: Projected percentage change in coefficient of variation (CV) of yield for wheat (gold), maize (green), rice (blue) and C4 crops (red) taken from C2010 (Challinor et al., 2010), B2013 (Berg et al., 2013), T2009 (Tao et al., 2009), TZ2013 (Tao and Zhang, 2010), TZ2012 (Tao and Zhang, 2011) and U2012 (Urban et al., 2012). U2012 and C2012 plot multiple data points: U2012 shows the range (mean plus and minus one standard deviation) of percentage changes in CV. For C2012 paired CV changes were not available, so the box shows changes in the mean CV, the mean CV plus one standard deviation, and the mean CV minus one standard deviation. 81 datapoints are plotted in the figure, although the underlying data consist of many thousands of crop model simulations. The studies used a range of scenarios (SRES A1B, A2, A1F1 and B1). B2013 is a global study of the tropics, U2012 is for US maize, and the remaining studies are for China.] Estimated impacts of both historical and future climate changes on mean yields are summarized along with projected impacts on yield variability in Figure 7-7, with all impacts expressed as the average percentage impact per decade. This comparison illustrates that future impacts are expected to be consistent with the trajectory of past impacts, with the majority of locations experiencing negative impacts while some locations benefit. Each additional decade of climate change is expected to reduce mean yields by roughly 1%, which is a small but nontrivial fraction of the antipicated roughly 14% increase in productivity per decade needed to keep pace with demand. For future projections, enough studies are available to assess differences by region and adaptation scenario, with significant adaptation effects apparent mainly in temperate systems (7.5).
[INSERT FIGURE 7-7 HERE Figure 7-7: Boxplot summary of studies that quantify impact of climate and CO2 changes on crop yields, including historical and projected impacts, mean and variability of yields, and for all available crops in temperate and tropical regions. All impacts are expressed as average impact per decade (a 10% total impact from a 50 year period of climate change would be represented as 2% per decade). References for historical impacts are given in Figure 7-2, for projected mean yields in Figure 7-5, and for yield variability in Figure 7-6. N indicates the number of estimates, with some studies providing multiple estimates. In general, decreases in mean yields and increases in yield variability are considered negative outcomes for food security. Also indicated on figure is the expected increase in crop demand of 14% per decade (Alexandratos and Bruinsma, 2012), which represents a target for productivity improvements to keep pace with demand.] Relatively few studies have considered impacts on cropping systems for scenarios where global mean temperatures increase by 4oC or more. An analysis for sub-Saharan Africa predicted overall decreases of 19% for maize yields, 68% decrease for bean yields and a small increase for fodder grass (Brachiaria decumbens) given 5oC global average warming (Thornton et al., 2011). Rtter et al. (2011) conclude that positive effects of modest warming and increased CO2 levels on crop yields in Finland will be reversed at global temperatures increases of 4oC, leading to negative yield impacts in excess of 20% in relation to current conditions. For perennial crops, winter chill accumulation that is important to many fruit and nut trees is projected to continue its decline, with for instance a 40 h per decade reduction projected for California for the period up to 2100 (Baldocchi and Wong, 2008). Averaging over three GCMs, annual winter chill loss by 2050 compared to 1970 would amount 17.7 % to 22.6 % in Egypt (Farag et al., 2010). Several studies have projected negative yield impacts of climate trends for perennial trees, including apples eastern Washington (Stckle et al., 2010) and cherries in California (Lobell and Field, 2011), although CO2 increases may offset some or all of these losses. Reductions in suitability for grapevine are expected in most of the wine producing regions (Hall and Jones, 2009; White et al., 2009; Jones et al., 2010). Wine grape production and quality will be affected in Europe, US, Australia (Jones et al., 2005; Wolfe et al., 2008; Cozzolino et al., 2010; AR5 WG2 Chapter 25), although it could be a benefit in Portugal (Santos et al., 2011) and British Columbia in Canada (Rayne et al., 2009). Important crops in Brazil like sugarcane and coffee are expected to migrate towards more favorable zones in the South (Pinto, 2007; Pinto et al., 2008; AR5 WG2 Chapter 27). Sugarcane fresh stalk mass is generally expected to gain from both warming and elevated CO 2 in Brazil (Marin et al., 2013). The suitability for coffee crops in Costa Rica, Nicaragua and El Salvador will be reduced by more than 40% (Glenn et al., 2013) while the loss of climatic niches in Colombia will force the migration of coffee crops towards higher altitudes by mid-century (Ramirez-Villegas et al., 2012). In the same way, increases in temperature will affect tea production, in particular at low altitudes (Wijeratne, et al., 2007). Consideration of pest, weed, and disease impacts are omitted from most yield projections, yet other studies have focused on projecting impacts of these biotic stressors. For pests and diseases, range expansion has been predicted for the destructive Phytophthora cinnamomi in Europe (Bergot et al., 2004) and for phoma stem canker on oilseed rape in the UK (Evans et al., 2008). Increased generations under climate change for the coffee nematode have been predicted for the coffee nematode in Brazil (Ghini et al., 2008). Walnut pests in California are predicted to experience increased numbers of generations under climate change scenarios (Luedeling et al., 2011). Luck (2011) summarized the mixed results for the qualitative effects of climate change on pathogens that cause disease of four major food crops - wheat, rice, soybean and potato - where some diseases increased in risk while others decreased under climate change scenarios. In syntheses, there is a tendency for risk of insect damage to plants to increase (Deutsch et al., 2008; Paulson et al., 2009). Typical scenario analyses are limited by simplistic assumptions, and work remains to evaluate how conclusions will change as more complete scenarios, such as those including migration and invasion patterns and other types of global change, are considered (Savary et al., 2005; Garrett et al., 2011). Effects on soil communities represent an area that needs more attention (Pritchard, 2011). Mycotoxins and pesticide residues in food are an important concern for food safety in many parts of the world, and identified as an important issue for climate change effects in Europe (Miraglia et al., 2009). Weed populations and demographics are expected to change (medium confidence), with an overall poleward migration in response to warming (Ziska et al., 2011a). An overview of crop and weed competitive studies indicate that weeds could limit crop yields to a greater extent with rising levels of carbon dioxide per se (Ziska, 2010). This
may be related to the greater degree of phenotypic and genotypic plasticity associated with weedy species relative to the uniformity inherent in large cropping systems (Chapter 22.214.171.124). Chemical control of weeds, which is the preferred management method for large-scale farms, may become less effective (limited evidence, medium agreement), with increasing economic and environmental costs (188.8.131.52). Climate change effects on productivity will alter land use patterns, both in terms of total area sown to crops and the geographic distribution of that area. For example, the suitability for potato crops is expected to increase in very high latitudes and high tropical altitudes towards 2100 (Schaefleitner et al., 2011). Given expected trends in population, incomes, bioenergy demand, and agricultural technology, global arable area is projected to increase from 2007 to 2050, with projected increases over this period of +9% (Bruinsma, 2009), +8% (Fischer et al., 2009), +10-20% (Smith et al., 2010), +18-23% (Lobell et al. 2013) (medium evidence, medium agreement). Not all such studies included the effects of global warming. Where this is the case, estimates range from a 20% increase in cropping area to a decline of 9% (Zhang and Cai, 2011), but with large regional differences (limited evidence, low agreement). Countries at northern latitudes and under the current constraint of low temperature may increase cultivated area (limited evidence, low agreement). The generally lower nutrient quality of soils and the lack of necessary infrastructure required to convert virgin land into productive arable land make estimates of cropping area increases highly uncertain.
Many studies have projected impacts of climate change on capture fisheries (Chapters 6 and 30) and only a subset of the more indicative studies at different ecological and geographical scales is included here. Overall, there is high confidence that climate change will impact on fisheries production with significant negative impacts particularly for developing countries in tropical areas, while more northerly, developed countries may experience benefits (Chapter 6.4.3). Simulation studies on skipjack and bigeye tuna in the Pacific under both the B1 and A2 scenarios indicate that catches of skipjack in the region as a whole are likely to increase by approximately 19% in 2035 compared to recent catch levels while catches of bigeye are projected to increase by 0.33%. By 2100, under the B1 scenario, catches of skipjack are projected to be 12.4% higher than recent levels but 7.5% lower under the A2 scenario, while catches of bigeye will be 8.8% and 26.7% lower under the B1 and A2 scenarios respectively. The models indicate important regional differences, with a general trend that catches of both species will decrease in the Western Pacific and increase in the Eastern Pacific (Lehodey et al, 2011; Chapters 6.5.3 and 18.104.22.168.1). These changes have important implications for the future of national fishing fleets and canneries in the western Pacific (Bell et al. 2011a) . Climate change is expected to impact directly on the productivity of coastal fisheries in the Pacific island countries and territories through increased sea surface temperature and ocean acidification and indirectly through climate-driven damage to coral reefs, mangroves, sea-grasses and intertidal flats (Pratchett et al., 2011). Extreme events such as increased severity of tropical cyclones could also impact on some species. Under both B1 and A2 emissions scenarios, the vulnerability of coastal fisheries as a whole in 2035 (as estimated through the framework described in Bell et al. (2011a)) is considered to be low. Extended to 2100, the projected impacts under the A2 emissions scenario are more severe, with reductions in coastal fisheries production by 20-35% in the west and 10-30% in the east (Pratchett et al., 2011). Brown et al. (2010) project that, under the A2 emissions scenario, primary production in the ocean around Australia will increase over the 50-year period from 2000-2050 as a result of small increases in nutrient availability from changes in ocean stratification and temperature, although the authors acknowledge considerable model uncertainty. This increase is forecast, in general, to benefit fisheries catch and value. In a complementary study, Fulton (2011) used available end-to-end models to forecast the impacts of climate change under the A2 scenario across approximately two-thirds of Australias exclusive economic zone. The results indicated that by 2060, the large-scale commercial fisheries, aided by their adaptive flexibility, would experience an overall increase of more than 90% in the value of their operations, although differing across sectors .The change in returns for the small-scale sector varied regionally from a decrease of 30-51% to a potential increase of 9-14% in another.
At the global scale, projections based on a dynamic bioclimate envelope model under the SRES A1B scenario suggested that climate change could lead to an average 3070% increase in fisheries yield from high-latitude regions (>50o N in the northern hemisphere), but a decrease of up to 40% in the tropics by 2055 compared to yields obtained in 2005 (Cheung et al., 2010). Another study used a suite of models linking physical, ecological, fisheries, and bioeconomic processes projected that, under the A1B scenario, the global yield from large fish could increase by 6% and that of the small fish used in fishmeal production by approximately 3.6%, assuming that marine fisheries and fish resources would be managed sustainably (Merino et al., 2012). There is limited information available on projected impacts on food production in inland fisheries. Xenopoulus et al. (2005) investigated the effect of climate change and water withdrawal on freshwater fish extinctions under the assumptions of two scenarios consistent with scenarios A2 and B2. They forecast that discharge would increase in between 65 and 70% of river basins in the world but it would decrease by as much as 80% in 133 rivers for which fish species data were available. In the latter group, by 2070 up to 75% of the local fish biodiversity would be headed toward extinction because of changes in climate and water consumption, with the highest rates of extinction forecast mainly in tropical and subtropical areas. These results are not directly translatable into changes in fishery production but do give cause for concern for the likely affected areas (limited evidence; low agreement). Information on future impacts on aquaculture is equally limited. Huppert et al. (2009) considered the impacts on the coast of Washington State, USA. They concluded that inundation of low-lying coastal areas from sea level rise, flooding from major storm events, and increased ocean temperatures and acidification would create significant challenges for the important shellfish aquaculture industry in the state. Inundation of existing shellfish habitats from sea level rise and increased incidence of harmful algal blooms were also contributory factors. Using a structured vulnerability framework and considering the B1 and A2 emission scenarios to project impacts on aquaculture in the tropical Pacific to 2035 and 2100, Pickering et al..(2011) concluded that production of freshwater species such as tilapia, carp and milkfish will probably benefit from the expected climate changes, while coastal enterprises are expected to encounter problems in the same time horizons, varying according to species. Aquaculture production of calcifying organisms such as molluscs will experience loss of suitable habitats through ocean acidification. This will be particularly pronounced at and in the vicinity of eastern boundary upwelling systems (Chapter 126.96.36.199.4). The food security consequences of the different impacts on capture fisheries and aquaculture are more difficult to estimate than the biological and ecological consequences. A preliminary study by Allison et al., (2009) examined the vulnerability of the economies of 132 countries to climate change impacts on fisheries in 2050 under the A1FI and B2 scenarios. Vulnerability was considered as a composite of three components: exposure to the physical effects of climate change; the sensitivity of the country to impacts on fisheries; and adaptive capacity within the country. This analysis suggested that under both scenarios several of the least developed countries were also amongst the most vulnerable to climate change impacts on their fisheries. They included countries in central and western Africa, Peru and Columbia in South America and four tropical Asian countries.
Climate change impacts on livestock will include effects on forage and feed, direct impacts of changes in temperature and water availability on animals, and indirect effects via livestock diseases. Many of the relevant processes and projected impacts for rangelands are discussed in Chapter 126.96.36.199, as well as in chapters for regions with prominent livestock sectors (Chapters 184.108.40.206, 23.4.2, 188.8.131.52). In North American cattle systems, warming is expected to lengthen forage groing season but decrease forage quality, with important variations due to rainfall changes (Craine et al., 2010; Izaurralde et al., 2011, Hatfield et al., 2011). Simulations for French grasslands (Graux et al., 2013) and sown pastures in Tasmania (Perring et al., 2010) also project negative impacts on forage quality. Similarly, legume content of grasslands in most of southern Australia is projected to increase to the 2070s for SRES A2, with larger increases in wetter locations (Moore and Ghahramani, 2013). There is high confidence that high temperatures tend to reduce animal feeding and growth rates (Andre et al., 2011; Renandeau et al. 2011). The impacts of a changing UK climate on dairy cow production were analysed by (Wall et al., 2010), who showed that in some regions, milk yields will be reduced and mortality increased because of heat
stress throughout the current century, with annual production and mortality losses amounting to some 40 million by the 2080s under a medium-high greenhouse gas emission scenario. Existing challenges of supplying water for an increasing livestock population will be exacerbated by climate change in many places (limited evidence, high agreement). For example, Masike and Urich (2008) project that warming under A1 SRES emission scenario will cause an annual increase of more than 20% in cattle water demand by 2050 for Kgatleng District, Botswana. At the same time, there is ample scope to improve livestock water productivity considerably (Molden et al., 2010); for example, in mixed crop-livestock systems of sub-Saharan Africa via feed, water and animal management (Descheemaeker et al., 2010). Host and pathogen systems in livestock will change their ranges because of climate change (high confidence). Species diversity of some pathogens may decrease in lowland tropical areas as temperatures increase (Mills et al., 2010). The temperate regions may become more suitable for tropical vector-borne diseases such as Rift Valley fever and malaria, which are highly sensitive to climatic conditions (Rocque et al., 2008). Vector-borne diseases of livestock such as African horse sickness and bluetongue may expand their range northwards in the northern hemisphere because rising temperatures increase the development rate and winter survival of vectors and pathogens (Lancelot et al., 2008). Diseases such as West Nile virus and schistosomiasis are projected to expand into new areas (Rosenthal, 2009). The distribution, composition and migration of wild bird populations that harbour the genetic pool of Avian Influenza viruses will all be affected by climate change, although in ways that are somewhat unpredictable (Gilbert et al., 2008). The changing frequency of extreme weather events, particularly flooding, will affect diseases too. For example, outbreaks of Rift Valley fever in East Africa are associated with increased rainfall and flooding due to El Nio-Southern Oscillation events (Gummow, 2010; Pfeffer and Dobler, 2010). In general, the impacts of climate change on livestock diseases remain difficult to predict and highly uncertain (Mills et al., 2010; Tabachnick, 2010). Box 7-1 summarises impacts on a regional basis for crops and livestock. Developing countries rely heavily on climate-dependent agriculture and especially in conjunction with poverty and rapid increase in population they are vulnerable to climate change. While food insecurity is concentrated mostly in developing countries situated in the tropics (Berg et al. 2013; Ericksen et al, 2011; St. Clair and Lynch, 2010) global food supply may also be affected by heat stress in both temperate and subtropical regions (Teixeira et al. 2013). Chapter 22 identifies Africa as one of the regions most vulnerable to food insecurity. Climate change will also affect crop yields, food security and local economies in Central America, northeast Brazil and parts of the Andean region (Chapter 27) as well as in South Asia (Iqbal et al. 2009, Chapter 24). As shown in Box 7-1, in spite of uncertainties in responses at regional/national and sub-national level, there is high confidence that most developing countries will be negatively affected by climate change in the future, although climate change may have positive effects in some regions. In high latitudes (such as Russia, northern Europe, Canada, South America) global warming may increase yields and expand the growing season and acreage of agricultural crops, although yields may be low due to poor soil fertility and water shortages in some regions (Kiselev et al., 2013; Chapters 23, 24, 26, 27). Although there is slim evidence, some studies do indicate a significant increase in crops yields in some parts of China, Africa and India. Like crops, livestock is also negatively affected by climate change in almost all the continents, as evidenced by the regional chapters of Working Group 2. The dairy, meat and wool systems primarily rely on fodders, grasslands and rangelands. Climate change can impact the amount and quality of produce, profitability and reliability of production (Chapter 23, 25). Higher temperature would lead to decline in dairy production, reduced animal weight gain, stress on reproduction, increased cost of production and lower food conversion efficiency in warm regions. Disease incidence among livestock is expected to be exacerbated by climate change as most of the diseases are transmitted by vectors such as ticks and flies (Chapter 23) whose proliferation depends on climatic parameters of temperature and humidity. _____ START BOX 7-1 HERE _____ Box 7-1. Projected Impacts for Crops and Livestock in Global Regions and Sub-Regions under Future Scenarios Crop yield impacts in () correspond to () in the scenario column. - CO2, without CO2 effects; + CO2, with CO2 effects; I, irrigated; R, rainfed. In Europe: Atl., Atlantic; Cont., continental; Med., Mediterranean. pp, precipitation. N, north; E, east; W, west; S, south; C, central. [NB: See tables file for box content.]
AR4 presented a summary of food price projections based on five studies that used projected yield impacts as inputs to general or partial equilibrium models of commodity trade. Many additional projections of this type have been made since AR4, expanding the number of trade models used, the diversity of yield projections considered, and the disaggregation of prices by commodity (Hertel et al., 2010; Baldos and Hertel, 2013; Calzadilla et al. 2013; Lobell et al., 2013; Nelson et al., 2013). Many of the studies did not include CO2 effects, which is sometimes justified on the grounds that studies are concerned with worst-case scenarios, or that the bias from omitting positive CO2 effects balances the known bias from omitting negative effects of elevated O3 and increased weed and pest damage. Studies also typically ignore potential changes in yield variability (Figure 7-6) and policy responses such as export bans which have important international price effects (7.2.2). Based on the studies cited above, it is very likely that changes in temperature and precipitation, without considering effects of CO2, will lead to increased food prices by 2050, with estimated increases ranging from 3-84%. The combined effect of climate and CO2 change (but ignoring O3 and pest and disease impacts) appears about as likely as not to increase prices, with a range of projected impacts from -30% to +45% by 2050. One lesson from recent model intercomparison experiments (Nelson et al., 2013) is that the choice of economic model matters at least as much as the climate or crop model for determining price response to climate change, indicating the critical role of economic uncertainties for projecting the magnitude of price impacts. The AR4 concluded that climate changes are expected to result in higher real prices for food past 2050. This conclusion remains intact with medium confidence, albeit with a relative lack of new studies exploring price changes to 2100 or beyond. Of course, international prices are only one indicator of global food security, with the pathways by which price changes can affect food security outlined in section 7.3.3. A limited number of studies have estimated the effects of price changes on food security and related health outcomes. Nelson et al. (2009) project that, without accelerated investment in planned adaptations, climate change by 2050 would increase the number of undernourished children under the age of 5 by 20-25 million (or 17-22%), with the range including projections with and without CO2 fertilization. Lloyd et al. (2011) used the projected changes in undernourishment from Nelson (2009) to project the impact of climate change on human nutrition, estimating a relative increase in moderate stunting of 129% in 2050 compared with a future without climate change. Severe stunting was projected to increase by 23% (central Africa) to 62% (South Asia). Baldos and Hertel (2013) estimate that, compared to a baseline scenario, climate change impacts result in an increase in global malnourished population by 49 million (11%) in 2050, but that the combined effects of climate change and CO2, which result in yield increases in their study, reduces the number of malnourished. In summary, if global yields are negatively impacted by climate change, an increase in both international food prices and the global headcount of food insecure people is expected (limited evidence, high agreement). However, it is only about as likely as not that the net effect of climate and CO2 changes on global yields will be negative by 2050, but likely that such changes will occur later in the 21st century. At the same time, it is likely that socio-economic and technological trends, including changes in institutions and policies, will remain a relatively stronger driver of food security over the next few decades than climate change (Goklany, 2007; Parry et al., 2009). Importantly, all of the studies that project price impacts assume some level of on-farm agronomic adaptation, often by optimizing agronomic practices within the model. Most, but not all, also prescribe income growth rates as exogenous factors, despite the fact that incomes are heavily dependent on agriculture in many poor countries. One study that accounted for income effects found that, in countries such as Indonesia that had both a large share of poverty in agriculturally dependent households and yield impacts that were small relative to other regions, poverty was reduced by the effects of climate change (Hertel et al., 2010). However, in most countries the positive income effects of higher prices could not outweigh the costs of reduced productivity and higher food prices.
Recent work has also highlighted that productivity in many sectors besides agriculture are significantly influenced by warming, with generally negative effects of warming on economic output in tropical countries (Hsiang, 2010; Dell et al., 2012). Given the importance of incomes to food access, incorporating these effects into future estimates of food security impacts will be important. Conflict is also known to be an important factor in food security (FAO, 2010a), and evidence of climate variability effects on conflict risk (Hsiang et al., 2011) indicates a need to also consider this dimension in future work (Chapter 12). Since the impacts of climate change on food production and food security depends on multiple interacting drivers, the timing of extreme events, which are expected to become more frequent (IPCC, 2012), is critical. Extremes contribute to variability in productivity (Figure 7-6) and can form part of compound events that are driven by common external forcing (e.g. El Nio), climate system feedbacks, or causally unrelated events (IPCC, 2012). Such compound events, where extremes have simultaneous impacts in different regions, may have negative impacts on food security, particularly against the backdrop of increased food price volatility (Figure 7-3). There are very few projections of compound extreme events and interactions between multiple drivers are difficult to predict. Effective monitoring and prediction, and building resilience into food systems, are likely to be two key tools in avoiding the negative impacts resulting from these interactions (Misselhorn et al., 2010).
7.5. 7.5.1.
Adaptation and Managing Risks in Agriculture and Other Food System Activities Adaptation Needs and Gaps based on Assessed Impacts and Vulnerabilities
220.127.116.11. Methods of Treating Impacts in Adaptation Studies Incremental to Transformational The pervasiveness of climate impacts on food security and production (7.2), the commitment to future climate change from past greenhouse gas emissions (WGI SPM) and the very high likelihood of additional and likely greater climate changes from future greenhouse gas emissions (WGI SPM) means that some level of adaptation of food systems to climate change will be necessary. Here we take adaptation to mean reductions in risk and vulnerability through the actions of adjusting practices, processes and capital in response to the actuality or threat of climate change. This often involves changes in the decision environment, such as social and institutional structures, and altered technical options that can affect the potential or capacity for these actions to be realized. Adaptation can also enhance opportunities from climate change (AR4 WG2 Chapter 5; Chapter 17.2.3). These adaptations will need to be taken in the context of a range of other pressures on food security such as increasing demand as a result of population growth and increasing per capita consumption (7.1). Following the AR4, the literature on adaptation and food production has increased substantially, although there has been less focus on adaptations to food systems and on value chains: the linked sets of activities that progressively add value as inputs are converted into products the market demands. Many adaptation frameworks or approaches have been published, informing the approach in the AR4 which addressed both autonomous and planned adaptations. Autonomous adaptations are incremental changes in the existing system including through the ongoing implementation of extant knowledge and technology in response to the changes in climate experienced. They include coping responses and are reactive in nature. Planned adaptations are proactive and can either adjust the broader system or transform it (Howden et al., 2010). Adaptations can occur at a range of scales from field to policy. There is an increasing recognition in the literature that whilst many adaptation actions are local and build on past climate risk management experience, effective adaptation will often require changes in institutional arrangements and policies to strengthen the conditions favourable for effective adaptation including investment in new technologies, infrastructure, information and engagement processes (Chapters 14.3, 14.4, 15.2.4). Building adaptive capacity by decision-makers at all scales (Nelson et al., 2008) is an increasingly important part of the adaptation discourse which has also further addressed costs, benefits, barriers and limits of adaptation (Adger et al., 2009). The sector-specific nature of many adaptations means that sectors will initially be addressed separately below.
Effective adaptation of cropping could be critical in enhancing food security and sustainable livelihoods, especially in developing countries (AR4 WG2 Chapter 5, Chapter 184.108.40.206). There is increasing evidence that farmers in some regions are already adapting to observed climate changes in particular altering cultivation and sowing times, crop cultivars and species) and marketing arrangements (Chapter 184.108.40.206, Fujisawa and Koyabashi; 2010, Olesen et al., 2011) although this response is not ubiquitous (Bryan et al., 2009). There are a large number of potential adaptations for cropping systems and for the food systems of which they are part, many of them enhancements of existing climate risk management and all of which need to be embedded in the wider farm systems and community contexts. The possibility of extended growing seasons because of higher temperatures increasing growth in cooler months means that changing planting dates is a frequently identified option for cereals and oilseeds provided there is not an increase in drought at the end of the growing season (Krishnan et al., 2007; Magrin et al., 2009; Travasso et al., 2009; Deressa et al., 2009; Mary and Majule, 2009; Meza and Silva, 2009; Tingem and Rivington, 2009; Laux et al., 2010; Stckle et al., 2010; Shimono et al., 2010; Van de Geisen et al., 2010; Tao and Zhang, 2010; Olesen et al., 2011; Cho et al., 2012). Aggregated across studies, changing planting dates may increase yields by a median of 3-17% but with substantial variation (Table 7-2). Early sowing is being facilitated by improvements in machinery and by the use of techniques such as dry sowing (Passioura and Angus, 2010), seedling transplanting and seed priming and these adaptations can be integrated with varieties with greater thermal time requirements so as to maximize production benefits and to avoid late spring frosts (Tingem and Rivington, 2009; Cho et al., 2012). There can however, be practical constraints to early sowing such as seedbed condition (van Oort et al. 2012). In some situations early sowing may allow double cropping or intercropping where currently only a single crop is feasible. For example, this could occur for irrigated maize in central Chile (Meza et al., 2008) and the double crop wheat/soybean in the southern pampas of Argentina (Monzon et. al., 2007), increasing productivity per unit land although increasing nitrogen and water demand at the same time. However, in Mediterranean climates, early sowing of cereals is dependent on adequate planting rains in autumn and climate projections indicate that this may decrease in many regions (WGI SPM), limiting the effectiveness of this adaptation and possibly resulting in later sowings than are currently practiced. In such circumstances, use of short duration cultivars could be desirable so as to reduce exposure to end of season droughts and high temperature events (Orlandini et al., 2008; Walter et al., 2010). There is medium confidence that optimisation of crop varieties and planting schedules appears to be effective adaptations, increasing yields by up to 23% compared with current managementwhen aggregated across studies (Table 7-2) (medium evidence, high agreement). This flexibility in planting dates and varieties according to seasonal conditions could be increasingly important with ongoing climate change (Meza et al., 2008; Deressa et al., 2009) and especially in dealing with projections of increased climate variability (Figure 7-6). Approaches that integrate climate forecasts at a range of scales in some cases are able to better inform crop risk management (Cooper et al., 2009; Sultan et al., 2010; Baethgen, 2010, Li et al. 2010) although such forecasts are not always useable or useful (Chapter 9.4.4; Lemos and Rood, 2010; Dilling and Lemos 2011) [INSERT TABLE 7-2 HERE Table 7-2: The simulated median benefit (difference between the yield change from baseline for the adapted and non-adapted cases) for different crop management adaptations: cultivar adjustment; planting date adjustment; adjusting planting date in combination with cultivar adjustment; adjusting planting date in combination with other adaptations; irrigation optimization; fertilizer optimization; other management adaptations. The numbers in parentheses are the 25th and 75th percentile. Data points where assessed benefit of management changes are negative are not included as farmers are unlikely to intentionally adopt these. Only studies with both a no adaptation and an adaptation assessment are used. Data taken from Abraha & Savage, 2006; Aggarwal & Mall, 2002; Alexandrov, 1999; Alexandrov et al., 2002; Butt et al., 2005; Challinor et al., 2009; Corobov, 2002; DeJong et al., 2001; Deryng et al., 2011; Droogers, 2004; Easterling et al., 2003; El-Shaher et al., 1997; Ewert et al., 2005; Gbetibouo & Hassan, 2005; Howden & Jones, 2004; Kaiser, 1999; Karim et al., 1996; Lal, 2011; Lal et al., 1998; Erda et al., 2005; Matthews & Wasmann, 2003; Moya et al., 1998; Reyenga et al., 1999; Rosenzweig et al., 1994; Southworth et al., 2000; Tao & Zhang, 2011; Tubiello et al., 2000; Thornton et al., 2010; Tingem & Rivington, 2009; Xiao et al., 2005; Yates & Strzepek, 1998; Zhang & Liu, 2005.]
Warmer conditions may also allow range expansion of cropping activities polewards in regions where low temperature has been a past limitation (limited evidence, medium agreement) provided varieties with suitable daylength response are available and soil and other conditions suitable. This may particularly occur in Russia, Canada and the Scandinavian nations although the potential may be less than earlier analyses indicated due to increased climate extremes, water limitations and various institutional barriers (Alcamo et al., 2007; Dronin and Kirilenko, 2011; Kulshreshtha, 2011; Kvalvik et al., 2011; Tchebakova et al., 2011; Bindi and Olesen, 2011). In many of these cases, the northerly range expansion may only offset the reduction in southerly cropping areas and yields due to lower rainfall, water shortages and high temperatures (limited evidence, high agreement,). Improving cultivar tolerance to high temperature is a frequently identified adaptation for almost all crops and environments worldwide as high temperatures are known to reduce both yield and quality (Krishnan et al., 2007; Challinor et al., 2009; Wassmann et al., 2009; Luo et al., 2009; Shimono et al., 2010; Stckle et al., 2010) noting that a new cultivar usually takes between eight and 20 years to deliver and so it is important to be selecting cultivars for expected future climate and atmospheric conditions (Ziska et al. 2012). Improving gene conservation and access to extensive gene banks could facilitate the development of cultivars with appropriate thermal time and thermal tolerance characteristics (Mercer et al., 2008; Wassmann et al., 2009) as well as to take advantage of increasing atmospheric CO2 concentrations (Ziska et al., 2012) and respond to changing pest, disease and weed threats with these developments needing to be integrated with in situ conservation of local varieties (IAASTD, 2009). Similarly, the prospect of increasing drought conditions in many cropping regions of the world (Olesen et al., 2011) raises the need for breeding additional drought-tolerant crop varieties (Naylor et al., 2007; Mutekwa, 2009; Tao and Zhang, 2011), for enhanced storage and access to irrigation water, more efficient water delivery systems, improved irrigation technologies such as deficit irrigation, more effective water harvesting, agronomy that increases soil water retention through practices such as minimum tillage and canopy management, agroforestry, increase in soil carbon and more effective decision support (Verchot et al. 2007; Luo et al., 2009; Lioubimtseva and et al., 2009; Falloon and Betts, 2010; Piao et al., 2010; Olesen et al., 2011) amongst many other possible adaptations (Chapter 188.8.131.52.1). There is medium confidence (limited evidence, high agreement) that crop adaptations can lead to moderate yield benefits (mean of 10 to 20%) under persistently drier conditions (Deryng et al. 2011) and that irrigation optimisation for changed climate can increase yields by a median of 3.2% (Table 7-2) as well as having a range of other beneficial effects (Chapter 3.7). Diversification of activities is another climate adaptation option for cropping systems (Lioubimtseva et al., 2009; Thornton et al., 2010). For example, Reidsma and Ewert (2008) found that regional farm diversity reduces the risk that is currently associated with unfavourable climate conditions in Europe. Diversification of activities often incorporates higher value activities or those that increase efficiency of a limited resource such as through increased water use efficiency (Thomas, 2008) or to reduce risk (Seo 2010). In some cases, increased diversification outside of agriculture may be favoured (Coulthard, 2008; Mary and Majule, 2009; Mertz et al., 2009a). The above adaptations, either singly or in combination, could significantly reduce negative impacts of climate change and increase the benefit of positive changes as found in AR4 WG2 Chapter 5 (medium evidence, high agreement,). To quantify the benefits of adaptation, a meta-analysis of recent crop adaptation studies has been undertaken for wheat, rice and maize (see Figure 7-4). This meta-analysis adds more recent studies to that undertaken in the AR4 WG2 Chapter 5. It indicates that the average benefit (the yield difference between the adapted and non-adapted cases) of adapting crop management is equivalent to about 15 to 18% of current yields (Figure 7-8). This response is, however, extremely variable, ranging from negligible benefit from adaptation (even potential dis-benefit) to very substantial. The responses are dissimilar between wheat, maize and rice (Figure 7-4) with temperate wheat and tropical rice showing greater benefits of adaptation. The responses also differ markedly between adaptation management options (Table 7-2). For example, when aggregated over studies, cultivar adaptation (23%) and altering planting date in combination with other adaptations (3 to 17%) provide on average more benefit than optimizing irrigation (3.2%) or fertilisation (1%) to the new climatic conditions. These limits to yield improvements from agronomic adaptation and the increasingly overall negative crop yield impact with ongoing climate change (Figures 7-4 and 7-5) mean a substantial challenge in ensuring increases in crop production of 14% per decade given a population of nine billion people in 2050. This could be especially so for tropical wheat
and maize where impacts from increases in temperature of more than 3oC may more than offset benefits from agronomic adaptations (limited evidence, medium agreement,). [INSERT FIGURE 7-8 HERE Figure 7-8: Simulated yield benefit from adaptation calculated as the difference between the yield change from baseline (%) for paired non-adapted and adapted cases as affected by temperature and aggregated across all crops. The shaded bands at the 95% confidence interval are calculated as for Figure 7-4. Data points (n=31) where assessed benefit of management changes are negative are not included as farmers are unlikely to intentionally adopt these. Data sources are the same as for Table 7-2 and only studies that examine both a no adaptation and an adaptation scenario are used so as to avoid the issues arising from un-paired studies documented in Figure 7-4 for tropical maize.] Potential increased variability of crop production means that other climate-affected aspects of food systems such as food reserve, storage and distribution policies and systems may need to be enhanced (IAASTD, 2009, Stathers et al. 2013) (medium evidence, high agreement) along with a range of broader, value-chain issues such as provision of effective insurance markets, clarity in property rights, building adaptive capacity and developing effective participatory research cultures (Chapter 9, AR4 WG2 Chapter 5). It is notable that most of the above adaptations raised above and used in this analysis are essentially either incremental changes to existing agricultural systems or are systemic changes which integrate new aspects into current systems. Few could be considered to be transformative changes. Consequently, the potential adaptation benefits could be understated (limited evidence, medium agreement) (Rickards and Howden 2012).
Many of the resources for capture fisheries are already fully or overexploited with an estimated 30% of stocks overexploited in 2009 and 57% fully exploited (FAO, 2012). Comparable global statistics are not available for inland fisheries but the status of those stocks may not be any better. Overfishing is widely regarded as the primary pressure on marine fishery resources but other human activities including coastal and offshore mining, oil and gas extraction, coastal zone development, land-based pollution and other activities are also negatively impacting stock status and production (Rosenberg and Macleod, 2005; Cochrane et al., 2009). In inland fisheries, overfishing is also widespread, coupled with many other impacts from other human activities (Allan et al., 2005). Climate change adds another compounding influence in both cases. Adaptive responses to reduce the vulnerability of fisheries and fishing communities could include: management approaches and policies that strengthen the livelihood asset base; improved understanding of the existing response mechanisms to climate variability to assist in adaptation planning; recognising and responding to new opportunities brought about by climate change; monitoring biophysical, social and economic indicators linked to management and policy responses; and adoption of multi-sector adaptive strategies to minimise negative impacts (Allison et al., 2009; Badjeck et al., 2010; MacNeil et al., 2010). Complementary adaptive responses include occupational flexibility, changing target species and fishing operations, protecting key functional groups and the establishment of insurance schemes (Coulthard, 2008; FAO, 2009; McNeil et al., 2010; Koehn et al., 2011; Daw et al., 2009). Fishers and fish farmers will be vulnerable to extreme events such as flooding and storm surges which will require a range of adaptations including developing early warning systems for extreme events, provision of hard defences against flooding and surges, ensuring infrastructure such as ports and landing sites are protected, effective disaster response mechanisms and others (Daw et al., 2009). Governance and management of fisheries will need to follow an ecosystem approach to maximise resilience of the ecosystem, and to be adaptive and flexible to allow for rapid responses to climate induced change (Chapter 6.4.2; FAO, 2009; Daw et al., 2009). Within an ecosystem approach, habitat restoration will frequently be a desirable adaptation option, particularly in freshwater and coastal environments (Koehn et al., 2011). A wide range of management tools and strategies have been developed to manage fisheries. These are all necessary but not sufficient for adaptation to climate change in fisheries (Grafton, 2010). Grafton argued that the standard tools for fisheries
management were developed to control fishing mortality and to maintain adequate levels of recruitment to fishery stocks but without necessarily addressing the needs for resilience to change or to be able to function under changing climates. He therefore proposed that these conventional management tools must be used within processes that i) have a core objective to encourage ecosystems that are resilient to change and ii) that explicitly take into account uncertainties about future conditions and the effect of adaptation, and make use of models to explore the implications of these (Grafton, 2010). There are also opportunities for fisheries to contribute to mitigation efforts (FAO, 2009; Grafton, 2010). Aquaculture is the fastest-growing animal-food-producing sector with per capita consumption of products increasing at an average rate of 7.1% p.a. between 1980 and 2010 (FAO, 2012). Adaptive responses in aquaculture include use of improved feeds and selective breeding for higher temperature tolerance strains to cope with increasing temperatures (De Silva and Soto, 2009) and shifting to more tolerant strains of molluscs to cope with increased acidification (Huppert et al., 2009). Better planning and improved site selection to adapt to expected changes in water availability and quality; integrated water use planning that takes into account the water requirements and human benefits of fisheries and aquaculture in addition to other sectors; and improving the efficiency of water use in aquaculture operations are some of the other adaptation options (De Silva and Soto, 2009). Integrated water use planning will require making trade-offs between different land and water uses in the watershed (Mantua et al., 2010). Insurance schemes accessible to small-scale producers would help to increase their resilience (De Silva and Soto, 2009). In some near-shore locations there may be a need to shift property lines as the mean high water mark is displaced landwards by rising sea level (Huppert et al., 2009). There are no simple, generic recipes for fisheries adaptation with Bell et al. (2011b) suggesting a list of 25 separate but inter-related actions, together with supporting policies to adapt fisheries and aquaculture in the tropical Pacific to climate change (Chapter 126.96.36.199.1). These actions fall into three categories according to the primary objective: economic development and government revenue; maintaining the contribution of fish to food security; and maximising sustainable livelihoods. Actions and policies for adaptation in fisheries and aquaculture must complement those for other sectors. Similar case-by-case, integrated planning will be required in all other regions and at scales from community to regional to achieve clearly defined adaptation goals.
Extensive livestock systems occur over a huge range of biophysical and socio-ecological systems, with a consequent large range of potential adaptations. In many cases, these livestock systems are highly adapted to past climate risk, and there is high confidence that this provides a sound starting point for climate change adaptation (medium evidence, high agreement) (Thornton et al. 2009). These adaptations include matching stocking rates with pasture production, adjusting herd and water point management to altered seasonal and spatial patterns of forage production, managing diet quality (using diet supplements, legumes, choice of introduced pasture species and pasture fertility management), more effective use of silage, pasture spelling and rotation, fire management to control woody thickening, using more suitable livestock breeds or species, migratory pastoralist activities and a wide range of biosecurity activities to monitor and manage the spread of pests, weeds and diseases (Fitzgerald et al., 2008; Howden et al., 2008; Nardone et al., 2010; Ghahramani and Moore, 2013; Moore and Ghahramani, 2013). Combining adaptations can result in substantial increases in benefits in terms of production and profit when compared with single adaptations (Ghahramani and Moore, 2013; Moore and Ghahramani, 2013). In some regions, these activities can in part be informed by climate forecasts at differing time-scales to enhance opportunities and reduce risks including soil degradation (McKeon et al., 2009). Many livestock systems are integrated with or compete for land with cropping systems and one climate adaptation may be to change these relationships. For example, with increased precipitation, farmers in Africa may need to reduce their livestock holdings in favour of crops, but with rising temperatures, they may need to substitute small ruminants in place of cattle with small temperature increases or reduce stocking rates with larger temperature rises (Kabubo-Mariara, 2009; Thornton et al., 2010). As with other food systems there is a range of barriers to adaptation which could be addressed on-farm and off-farm by changes in infrastructure, establishment of functioning markets, improved access to credit, improved access to water and water management technologies, enhanced animal health services and enhanced knowledge
adoption and information systems (Howden et al., 2008; Kabubo-Mariara, 2009; Mertz et al., 2009b, Silvestri et al. 2012). Heat stress is an existing issue for livestock in some regions (robust evidence, high agreement), especially in higher productivity systems (188.8.131.52). For example, some graziers in Africa are already making changes to stock holdings in response to shorter term variations in temperatures (Chapter 188.8.131.52; Thornton et al. 2009). Breeding livestock with increased heat stress resistance is an adaptation often identified but there are usually trade-offs with productivity as well as benefits including animal welfare and so this option needs careful evaluation (Nardone et al., 2010) . Increased shade provision through trees or cost-effective structures can substantially reduce the incidence of high heat stress days, reduce animal stress and increase productivity, with spraying a less effective option (Gaughan et al. 2010; Nidumolu et al., 2011). In cooler climates, warming may be advantageous due to lesser need for winter housing and feed stocks.
Indigenous knowledge
Indigenous Knowledge (IK) has developed to cope with climate hazards contributing to food security in many parts of the world. Examples in the Americas include Alaska, where the Inuit knowledge of climate variability assured the source of food to hunters and reduced various risks (Ford 2009; Alessa et al., 2008; Weatherhead et al., 2010) down to the southern Andes where the Inca traditions of crop diversification, genetic diversity, raised bed cultivation, agroforestry, weather forecasting and water harvesting are still used in agriculture (Chapters 18.104.22.168, 184.108.40.206; Goodman-Elgar, 2008; Renard, 2011; McDowell and Hess, 2012). In Africa, weather forecasting, diversity of crops and agropastoralism strategies have been useful in the Sahel (Nyong et al., 2007). Rainwater harvesting has been a common practice in Sub-Saharan Africa (Biazin et al., 2012) to cope with dry spells and improve crop productivity, while strategies from agropastoralists in Kenya are related to drought forecasting based on the fauna, flora, moon, winds and other factors (Speranza et al., 2010). In South Africa, farmers early warming indicators of wet or dry periods in Namibia based on animals, plants and climate observations contributed to deal with climatic variability (Newsham and Thomas, 2011). In the same way, in Asia and Australia IK plays an important role to assure food security of certain groups (Salick and Ross, 2009; Marin, 2010; Speranza et al., 2010; Green et al., 2010; Kalanda et al., 2011; Pareek and Trivedi, 2011; Biazin et al., 2012), although IK and the opportunities to implement it can differ according to gender and age in some communities (Chapter 9.3.5; Rengalakshmi, 2007; Turner and Clifton, 2009; Kalanda et al., 2011) leading to distinct adaptive capacities and options. In addition to changes already occurring in climate (seasonal changes, changes in extreme events: IPCC 2012) projected changes beyond historical conditions could reduce the reliance on IK (Speranza et al., 2010; Kalanda et al., 2011; McDowell and Hess, 2012) affecting the adaptive capacity of a number of peoples globally (medium evidence, medium agreement). Moreover, there is medium confidence that some policies and regulations leading to limit the access to territories, promoting sedentarization, the substitution of traditional livelihoods, reduced genetic diversity and harvesting opportunities as well as loss of transmission of IK, may contribute to limit the adaptation to climate change in many regions (Nakashina et al., 2012) (medium evidence, medium agreement).
22.214.171.124. Practical Regional Experiences of Adaptation, including Lessons Learned Given the early stages of climate change, there are relatively few unequivocal examples of adaptation (7.5.2) additional to existing climate risk management. Where there have been management changes these have often been in response to several driving variables of which climate is only one (Chapter 184.108.40.206; Smit and Wandel, 2006; Mertz et al., 2009a; Chen et al., 2011; Odgaard and et al., 2011. The preparedness to consider adaptation even within an industry varies regionally (Battaglini et al., 2009) and in some regions there already appears to be adaptation to climate change occurring (Chapter 126.96.36.199; Fujisawa and Koyabashi, 2010; Olesen et al., 2011; Bohensky et al., 2012). Activities to build adaptive capacity to better manage climate change are more widespread (Twomlow et al., 2008) but there remain questions as to how this capacity will evolve and be maintained (Nelson et al., 2009). Crucial in this will be devolution of the decision-making process so as to integrate local, contextual information into adaptation decision-making (Nelson et al., 2008).
184.108.40.206. Observed and Expected Barriers and Limits to Adaptation Adaptation is strongly influenced by factors including institutional, technological, informational and economic and there can be barriers (restrictions that can be addressed) and limits in all these factors (Chapters 14, 15 and 16: robust evidence, high agreement). Several barriers to adaptation of food systems have been raised including inadequate information on the climate, climate impacts and on the risks and benefits of the adaptation options, lack of adaptive capacity, inadequate extension, institutional inertia, cultural acceptability, financial constraints including access to credit, insufficient fertile land, infrastructure, lack of functioning markets and insurance systems (Chapter 16; Bryan et al., 2009; Deressa et al., 2009; Kabubo-Mariara, 2009; De Bruin and Dellink, 2011, Silvestri et al. 2012). Limits to adaptation can occur for example where crop yields drop below the level required to sustain critical infrastructure such as sugar or rice mills (Park et al., 2012). In some cases, these can be effectively irreversible. Some studies have shown that access to climate information is not the principal limitation to improving decision making and it can result in perverse outcomes, increasing inequities and widening gender gaps (Coles and Scott, 2009). Incomplete adoption of adaptations may also occur. Lack of technical options can also be a barrier to adaptation. New varieties of crops or breeds of livestock provide possible core adaptations of production systems (medium evidence, high agreement; Mercer et al., 2008; Tingem and Rivington, 2009) however, there is substantial investment needed to develop these along with delays before they are available, both of which can act as adaptation barriers. This may be addressed in part by investments to improve local crop varieties or livestock breeds that are easily adopted (IAASTD, 2009). There also can be physiological limits to performance such as upper temperature limits for heat tolerance (AR4 WG2 Chapter 5).
188.8.131.52. Facilitating Adaptation and Avoiding Maladaptation Adaptation actions would usually be expected to provide benefits to farmers, the food industry along the value chain or perhaps to a broader community. However, there are possible maladaptations that arise from adapting too early or too late, by changing the incorrect elements of the food system or changing them by the incorrect amount (Chapter 14.7). A key maladaptation would be one which increased emissions of greenhouse gases, this making the underlying problem worse (Smith and Olesen, 2010); AR4 WG3 Chapter 11, robust evidence, high agreement). A recent review of agricultural climate change adaptation options found they tend to reduce greenhouse gas emissions (Smith and Olesen, 2010; Falloon and Betts, 2010) (medium evidence, medium agreement). These adaptations include measures that reduce soil erosion and loss of nutrients such as nitrogen and phosphorus, for increasing soil carbon, measures for conserving soil moisture and reducing temperature extremes by increasing vegetative cover. There is a strong focus on incremental adaptation of existing food systems in the literature since AR4 (see above) however, and this may result in large opportunity costs that could arise from not considering more systemic adaptation or more transformative change (Howden et al., 2010; Kates et al., 2012) (limited evidence, medium agreement). For example, in the USA, changes in farming systems (i.e. the combination of crops) have been assessed as providing significant adaptation benefit in terms of net farm income (Prato et al., 2010) although in other regions this might be minor (Mandryk et al. 2012). There is a need to also engage farmers, policymakers and other stakeholders in evaluating transformative, pro-active, planned adaptations such as structural changes (Mder et al., 2006; McCrum, 2009; Olesen et al., 2011). This could involve changes in land allocation and farming systems, breeding of functionally-different crop varieties, new land management techniques and new classes of service from lands such as ecosystem services (Rickards and Howden 2012). In Australia, industries including the wine, rice and peanut industries are already attempting transformative changes such as change in location so as to be early adopters of what are perceived as opportunities arising from change (Park et al., 2012). There is substantial commonality in adaptation actions within different agricultural systems. For example, changing varieties and planting times are incremental adaptations found in studies of many different cropping systems as evidenced by the sample size in the meta-analysis in this chapter. Collating information on the array of adaptation options available for farmers, their relative cost and benefit and their broad applicability could be a way of initiating
engagement with decision-makers. In the climate mitigation domain, this has been attempted using marginal abatement cost curves which identify mitigation options, their relative cost and the potential size of emission-reductions (AR4 WG3 Chapter 11). These curves can be used in setting investment priorities and informing policy discussions. The local nature of many adaptation decisions, their interactions with other highly contextual driving factors and the time and climate change-sensitive nature of adaptation decisions mean however, that global, time-independent curves are not feasible. The studies aggregated in Table 7-2 indicate that there are some options which may be more relevant and useful to consider than others. These results illustrate the potential scope and benefit of developing effective adaptation options if implemented in an adaptive management approach.
Food System Case Studies of Adaptation Examples of Successful and Unsuccessful Adaptation
Incremental, systemic and transformational adaptation to climate change is beginning to be documented, though the peer-reviewed literature largely covers vulnerability assessments and intentions to act, not adaptation actions (Berrang-Ford et al., 2010). Case 1: Incremental adaptation in the Sahel Much of the literature covers incremental, reactive adaptation, but given actors are constantly adapting to changing social and economic conditions, incremental adaptation to climate change is difficult to distinguish from other actions (Berrang-Ford et al., 2010; Speranza, 2010), and in fact is usually a response to a complex of factors. This case, of the za soil management practice in the Sahel region, is an example where a complex of factors drives local actions, and factors such as growing land scarcity and new market opportunities, rather than climate, may be the primary factors (Barbier et al., 2009; Mertz, 2009b). Inherent poor soil quality and human activities have resulted in soil degradation crusting, sealing, erosion by water and wind, and hardpan formation (Fatondji et al., 2009; Zougmor et al., 2010). Za, a traditional integrated soil and water management practice, can combat land degradation and improve yield and decrease yield variability by concentrating runoff water and organic matter in small pits (20-40 cm in diameter and 10-15 cm deep) dug manually during the dry season and combined with contour stone bunds to slow runoff. A handful of animal manure or compost is placed in each pit. By breaking the soil crust, the pits facilitate greater water infiltration, while the applied organic matter improves soil nutrient status and attracts termites, which have a positive effect on soil structure. The za technique is very labour intensive requiring some 60 days of labour per hectare. Innovations to the system, involving animal-drawn implements, can reduce labour substantially. Case 2: Mixed farming systems in Tanzania In Morogoro, Tanzania, farming households have adapted in many ways to climatic and other stresses (Paavola, 2008). They have extended cultivation through forest clearance or reducing the length of the fallow period. Intensification is under way, through change in crop choices, increased fertiliser use and irrigation, and especially greater labour inputs. Livelihood diversification has been the main adaptation strategy this has involved more non-farm income-generating activities, tapping into natural resources for subsistence and cash income (e.g. charcoal production), and has included artisanal gold and gemstone mining. Households have also altered their cropping systems, for example, by changing planting times. Migration is another frequently used strategy with farmers moving to gain land, access markets or get employment. Parents also send children to cities to work for upkeep and cash income to reduce the household numbers that need to be supported by uncertain agricultural income. While many of these strategies help in terms of the short-term needs, in the longer term they may be reducing the capacity of households to cope. For instance, land cover change interacting with climate changes has negative impacts on current and future water supplies for irrigation (Natkhin et al., 2013), and deforestation and forest degradation means faltering forest-based income sources. This will be particularly problematic to the more vulnerable groups in the community, including women and children.
Key Findings from Adaptations Confidence Limits, Agreement, and Level of Evidence
There have been many studies of crop adaptation since the AR4. In aggregate these show that adaptations to changed temperature and precipitation will bring substantial benefit, (robust evidence, high agreement) with some
adaptations (e.g. cultivar adaptation and planting date adjustment) assessed as on average being more effective than others (e.g. irrigation optimisation; 184.108.40.206.1). Most studies have assessed key farm-level adaptations such as changing planting dates and associated decisions to match evolving growing seasons and improving cultivar tolerance to high temperature, drought conditions and elevated CO2 levels. Limits to adaptation will increasing emerge for such incremental adaptations as the climate further changes, raising the need for more systemic or transformational changes (limited evidence, medium agreement,; 7.5.1, 126.96.36.199). An example of transformational change is latitudinal expansion of cold-climate cropping zones polewards but this may be largely offset by reductions in cropping production in the mid-latitudes due to rainfall reduction and temperature increase (medium confidence, limited evidence; 188.8.131.52.1). Adaptations to food systems additional to the production phase have been identified and sometimes implemented but the benefits of these have largely not been quantified. Livestock and fisheries systems also have available a large range of possible adaptations often tailored to local conditions but there is not adequate information to aggregate the possible value of these adaptations although there is high confidence (medium evidence, high agreement) that they will bring substantial benefit, particularly if implemented in combination (188.8.131.52.2, 220.127.116.11.3). Key livestock adaptations include matching stocking rates with pasture availability, water management, monitoring and managing the spread of pests, weeds and diseases, livestock breeding and adjusting to changed frequencies of heat stress and cold conditions (126.96.36.199.2). Fishery adaptations include management approaches and policies that strengthen the livelihood asset base, take a risk-based ecosystem approach to managing the resource and adoption of multi-sector adaptive strategies to minimise negative impacts. Importantly, there is an emerging recognition that existing fishery management tools and strategies are necessary but not sufficient for adaptation to climate (220.127.116.11.2). Indigenous knowledge is an important resource in climate risk management and is important for food security in many parts of the world. Climate changes may be reducing reliance on indigenous knowledge in some locations but also some policies and regulation may be limiting the contribution that indigenous knowledge can make to effective climate adaptation (medium evidence, medium agreement; 184.108.40.206.4). The focus on incremental adaptations and few studies on more systemic and transformational adaptation or adaptation across the food system mean that there may be underestimation of adaptation opportunities and benefits (limited evidence, medium agreement; 184.108.40.206). In addition to this, there is a range of limits and barriers to adaptation and many of these could be addressed by devolution of the decision-making process so as to integrate local, contextual information into adaptation decision-making. A schematic summary of these issues is given in Table 7-3. [INSERT TABLE 7-3 HERE Table 7-3: Schematic key risks for food security and the potentials for adaptation in the near- and long-term for high and low levels of warming.]
Research and data gaps reflect that most work since AR4 has continued to concentrate on food production and has not included other aspects of the food system that connect climate change to food security. Features such as food processing, distribution, access and consumption have recently become areas of research interest in their own right but only tangentially attached to climate change. Many studies either do not examine yield variability or do not report it. Closer attention should be paid to yield variability in the quantity and quality of food production, especially given observed price fluctuations associated with climate events. We expect environmental thresholds and tipping points, such as high temperatures, droughts and floods, to become more important in the future. Specific recommendations are for food production experiments in which changes in variability reflect predicted changes for given warming scenarios. Including thresholds in impact models, for especially high levels of global warming (i.e. 4-5 o C above pre-industrial), are highly likely to result in lower projections of yield, given changes in climate variability and increasing mean temperatures. Important gaps in knowledge continue to be studies of weeds, pests, and diseases, including animal diseases, in
response to climate change and how related adaptation activities can be robustly incorporated into food security assessments. Yield and other agronomic data, at a range of spatial scales, are crucial to the development, evaluation and improvement of models. Model development is currently limited by lack of data. Adaptation studies for cropping systems typically assess relatively minor agronomic management changes under future climate conditions only. Forthcoming studies should examine the impact of proposed adaptations when employed in the current climate. In this way management changes that are beneficial in a range of environments can be separated from management changes that are specificly targeted at climate change. Further, studies should be inclusive of the broader range of systemic and transformational adaptation options open to agriculture. Current forecasts of changes in distribution and productivity of marine fish species and communities are typically at global or regional scale and include adaptations to only a limited extent. Increasing the resolution to forecast impacts and changes at the national and local ecosystem scale would provide valuable information to governments and stakeholders and enable them to prepare more effectively for expected impacts on food production and security offered by fisheries. Possibilities for agronomic and breeding adaptations of food production to global warming are possible up to high levels of climate change. However, food security studies are urgently required to estimate the actual range of adaptations open to farmers and other actors in the food system and the implementation paths for these especially when possible changes in climate variability are included.
Frequently Asked Questions FAQ 7.1: What factors determine food security and does low food production necessarily lead to food insecurity? [to be placed in Section 7.2] Observed data and many studies indicate that a warming climate has a negative effect to crop production, generally reduce yields of staple cereals such as wheat, rice and maize, which, however, differs between regions and latitudes. Elevated CO2 could benefit crops yields in short term by increasing photosynthesis rates, however, there is big uncertainty in the magnitude of the CO2 effect and that interactions with other factors. Climate change will affect fisheries and aquaculture through gradual warming, ocean acidification and through changes in the frequency, intensity and location of extreme events. Other aspects of the food chain are also sensitive to climate but such impacts are much less well known. Climate-related disasters are among the main drivers of food insecurity, both in the aftermath of a disaster and in the long run. Drought is a major driver of food insecurity, and contributes to a negative impact on nutrition. Floods and tropical storms also affect food security by destroying livelihood assets. The relationship between climate change and food production depends to a large degree on when and which adaptation actions are taken. Other links in the food chain from production to consumption are sensitive to climate but such impacts are much less well known. FAQ 7.2: How could climate change interact with change in fish stocks, ocean acidification? [to be placed in Section 7.4] Millions of people rely on fish and aquatic invertebrates for their food security and as an important source of protein and some micronutrients. However, climate change will affect fish stocks and other aquatic species. For example, increasing temperatures will lead to increased production of important fishery resources in some areas but decreased production in others while increases in acidification will have negative impacts on important invertebrate species, including species responsible for building coral reefs which provide essential habitat for many fished species in these areas. The poorest fishers and others dependent on fisheries and subsistence aquaculture will be the most vulnerable to these changes, including those in small-island developing States, central and western African countries, Peru and Columbia in South America and some tropical Asian countries. FAQ 7.3: How could adaptation actions enhance food security and nutrition? [to be placed in Section 7.5] Over 70 per cent of agriculture is rain-fed. This suggests that agriculture, food security and nutrition are all highly sensitive to changes in rainfall associated with climate change. Adaptation outcomes focusing on ensuring food security under a changing climate could have the most direct benefits on livelihoods, which have multiple benefits
for food security, including: enhancing food production, access to markets and resources, and reduced disaster risk. Effective adaptation of cropping can help ensure food production and thereby contributing to food security and sustainable livelihoods in developing countries, by enhancing current climate risk management. There is increasing evidence that farmers in some regions are already adapting to observed climate changes in particular altering cultivation and sowing times and crop cultivars and species. Adaptive responses to climate change in fisheries could include: management approaches and policies that maximize resilience of the exploited ecosystems, ensuring fishing and aquaculture communities have the opportunity and capacity to respond to new opportunities brought about by climate change, and the use of multi-sector adaptive strategies to reduce the consequence of negative impacts in any particular sector. However, these adaptations will not necessarily reduce all of the negative impacts of climate change, and the effectiveness of adaptations could diminish at the higher end of warming projections.
Heat waves are periods of abnormally and uncomfortably hot weather during which the risk of heat stress on people and ecosystems is high. The number and intensity of hot days have increased markedly in the last three decades (Coumou et al., 2013) (high confidence). According to WG I, it is likely that the occurrence of heat waves has more than doubled in some locations due to human influence and it is virtually certain that there will be more frequent hot extremes over most land areas in the latter half of the 21st century. Coumou et al. (2013) predicted that, under a medium warming scenario, the number of monthly heat records will be over 12 times more common by the 2040s compared to a non-warming world. In a longer time perspective, if the global mean temperature increases to +10C or more, the habitability of large parts of the tropics and mid-latitudes will be at risk (Sherwood and Huber, 2010). Heat waves affect natural and human systems directly, often with severe losses of lives and assets as a result, and they may act as triggers for tipping points (Hughes et al., 2013). Consequently, heat waves play an important role in several key risks noted in Chapter 19 and CC-KR. Economy and Society [Ch 10, 11, 12, 13] Environmental heat stress has already reduced the global labor capacity to 90% in peak months with a further predicted reduction to 80% in peak months by 2050. Under a high warming scenario (RCP8.5), labor capacity is expected to be less than 40% of present day conditions in peak months by 2200 (Dunne et al., 2013). Adaptation costs for securing cooling capacities and emergency shelters during heat waves will be substantial. Heat waves are associated with social predicaments such as increasing violence (Anderson, 2012) as well as overall health and psychological distress and low life satisfaction (Tawatsupa et al., 2012). Impacts are highly differential with disproportional burdens on poor people, elderly people, and those who are marginalized (Wilhelmi et al., 2012). Urban areas are expected to suffer more due to the combined effect of climate and the urban heat island effect (Fischer et al., 2012). In LICs and MICs, adaptation to heat stress is severely restricted for most people in poverty and particularly those who are dependent on working outdoors in agriculture, fisheries, and construction. In small-scale agriculture, women and children are particularly at risk due to the gendered division of labor (Croppenstedt et al., 2013). The expected increase in wildfires as a result of heat waves (Pechony and Shindell, 2010) is a concern for human security, health, and ecosystems. Air pollution from wildfires already causes an estimated 339,000 premature deaths per year worldwide (Johnston et al., 2012). Human Health [Ch 11] Morbidity and mortality due to heat stress is now common all over the world (Barriopedro et al., 2011; Rahmstorf and Coumou, 2011; Nitschke et al., 2011; Diboulo et al., 2012; Hansen et al., 2012). People in physical work are at particular risk as such work produces substantial heat within the body, which cannot be released if the outside temperature and humidity is above certain limits (Kjellstrom et al., 2009). The risk of non-melanoma skin cancer from exposure to UV radiation during summer months increases with temperature (van der Leun, Jan C et al., 2008).
Increase in ozone concentrations due to high temperatures affects health (Smith et al., 2010), leading to premature mortality, e.g. cardiopulmonary mortality (Smith et al., 2010). High temperatures are also associated with an increase in air-borne allergens acting as a trigger for respiratory illnesses such as asthma, allergic rhinitis, conjunctivitis, and dermatitis (Beggs, 2010). Ecosystems [Ch 4, 5, 6, 30] Tree mortality is increasing globally (Williams et al., 2012) and can be linked to climate impacts, especially heat and drought (Reichstein et al., 2013), even though attribution to climate change is difficult due to lack of time series and confounding factors. In the Mediterranean region, higher fire risk, longer fire season, and more frequent large, severe fires are expected as a result of increasing heat waves in combination with drought (Duguy et al., 2013), Box 4.2. Marine ecosystem shifts attributed to climate change are often caused by temperature extremes rather than changes in the average (Prtner and Knust, 2007). During heat exposure near biogeographical limits, even small (<0.5C) shifts in temperature extremes can have large effects, often exacerbated by concomitant exposures to hypoxia and/or elevated CO2 levels and associated acidification (Hoegh-Guldberg et al., 2007), Figure 6-5, (medium confidence) [Ch 6.3.1, 6.3.5; 30.4; 30.5; CC-MB] Most coral reefs have experienced heat stress sufficient to cause frequent mass coral bleaching events in the last 30 years, sometimes followed by mass mortality (Baker et al., 2008). The interaction of acidification and warming exacerbates coral bleaching and mortality (very high confidence).Temperate seagrass and kelp ecosystems will decline with the increased frequency of heat waves and through the impact of invasive subtropical species (high confidence). [Ch 5, 6, 30.4-30.5, CC-CR, CC-MB] Agriculture [Ch 7] Excessive heat interacts with key physiological processes in crops. Negative yield impacts for all crops past +3C of local warming without adaptation, even with benefits of higher CO2 and rainfall, are expected even in cool environments (Teixeira et al., 2011). For tropical systems where moisture availability or extreme heat limits the length of the growing season, there is a high potential for a decline in the length of the growing season and suitability for crops (medium evidence, medium agreement) (Jones and Thornton, 2009). For example, half of the wheat-growing area of the Indo-Gangetic Plains could become significantly heat-stressed by the 2050s. There is high confidence that high temperatures reduce animal feeding and growth rates (Thornton et al., 2009). Heat stress reduces reproductive rates of livestock (Hansen, 2009), weakens their overall performance (Henry et al., 2012), and may cause mass mortality of animals in feedlots during heat waves (Polley et al., 2013). In the U.S., current economic losses due to heat stress of livestock are estimated at several billion USD annually (St-Pierre et al., 2003).
Box CC-HS References Anderson, C.A., 2012: Climate Change and Violence. In: The Encyclopedia of Peace Psychology. [Christie, D.J. (ed.)]. Wiley Online Library Baker, A.C., P.W. Glynn, and B. Riegl, 2008: Climate change and coral reef bleaching: An ecological assessment of long-term impacts, recovery trends and future outlook. Estuarine, Coastal and Shelf Science, 80(4), 435-471. Barriopedro, D., E.M. Fischer, J. Luterbacher, R.M. Trigo, and R. Garca-Herrera, 2011: The hot summer of 2010: redrawing the temperature record map of Europe. Science, 332(6026), 220-224. Beggs, P.J., 2010: Adaptation to impacts of climate change on aeroallergens and allergic respiratory diseases. International Journal of Environmental Research and Public Health, 7(8), 3006-3021. Coumou, D., A. Robinson, and S. Rahmstorf, 2013: Global increase in record-breaking monthly-mean temperatures. Climatic Change, 118(3-4), 771-782. Croppenstedt, A., M. Goldstein, and N. Rosas, 2013: Gender and agriculture: inefficiencies, segregation, and low productivity traps. The World Bank Research Observer, 28(1), 79-109.
Diboulo, E., A. Sie, J. Rocklv, L. Niamba, M. Ye, C. Bagagnan, and R. Sauerborn, 2012: Weather and mortality: a 10 year retrospective analysis of the Nouna Health and Demographic Surveillance System, Burkina Faso. Global Health Action, 5(19078). Duguy, B., S. Paula, J.G. Pausas, J.A. Alloza, T. Gimeno, and R.V. Vallejo, 2013: Effects of climate and extreme events on wildfire regime and their ecological impacts. In: Regional Assessment of Climate Change in the Mediterranean. Springer, pp. 101-134. Dunne, J.P., R.J. Stouffer, and J.G. John, 2013: Reductions in labour capacity from heat stress under climate warming. Nature Climate Change, published on-line 24 February 2013, 1-4. Fischer, E., K. Oleson, and D. Lawrence, 2012: Contrasting urban and rural heat stress responses to climate change. Geophysical Research Letters, 39(3), L03705. Hansen, J., M. Sato, and R. Ruedy, 2012: Perception of climate change. Proceedings of the National Academy of Sciences, 109(37), E2415-E2423. Hansen, P.J., 2009: Effects of heat stress on mammalian reproduction. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1534), 3341-3350. Henry, B., R. Eckard, J.B. Gaughan, and R. Hegarty, 2012: Livestock production in a changing climate: adaptation and mitigation research in Australia. Crop and Pasture Science, 63(3), 191-202. Hoegh-Guldberg, O., P. Mumby, A. Hooten, R. Steneck, P. Greenfield, E. Gomez, C. Harvell, P. Sale, A. Edwards, and K. Caldeira, 2007: Coral reefs under rapid climate change and ocean acidification. Science, 318(5857), 1737-1742. Hughes, T.P., S. Carpenter, J. Rockstrm, M. Scheffer, and B. Walker, 2013: Multiscale regime shifts and planetary boundaries. Trends in Ecology & Evolution, 28(7), 389-395. Johnston, F.H., S.B. Henderson, Y. Chen, J.T. Randerson, M. Marlier, R.S. DeFries, P. Kinney, D.M. Bowman, and M. Brauer, 2012: Estimated global mortality attributable to smoke from landscape fires. Environmental Health Perspectives, 120(5), 695. Jones, P.G. and P.K. Thornton, 2009: Croppers to livestock keepers: livelihood transitions to 2050 in Africa due to climate change. Environmental Science & Policy, 12(4), 427-437. Kjellstrom, T., R. Kovats, S. Lloyd, T. Holt, and R. Tol, 2009: The direct impact of climate change on regional labor productivity. Archives of Environmental & Occupational Health, 64(4), 217-227. Nitschke, M., G.R. Tucker, A.L. Hansen, S. Williams, Y. Zhang, and P. Bi, 2011: Impact of two recent extreme heat episodes on morbidity and mortality in Adelaide, South Australia: a case-series analysis. Environ Health, 10, 42. Pechony, O. and D. Shindell, 2010: Driving forces of global wildfires over the past millennium and the forthcoming century. Proceedings of the National Academy of Sciences, 107(45), 19167-19170. Polley, H.W., D.D. Briske, J.A. Morgan, K. Wolter, D.W. Bailey, and J.R. Brown, 2013: Climate Change and North American Rangelands: Trends, Projections, and Implications. Rangeland Ecology & Management, 66(5), 493-511. Prtner, H.O. and R. Knust, 2007: Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science, 315(5808), 95-97. Rahmstorf, S. and D. Coumou, 2011: Increase of extreme events in a warming world. Proceedings of the National Academy of Sciences, 108(44), 17905-17909. Reichstein, M., M. Bahn, P. Ciais, D. Frank, M.D. Mahecha, S.I. Seneviratne, J. Zscheischler, C. Beer, N. Buchmann, and D.C. Frank, 2013: Climate extremes and the carbon cycle. Nature, 500(7462), 287-295. Sherwood, S.C. and M. Huber, 2010: An adaptability limit to climate change due to heat stress. Proceedings of the National Academy of Sciences, 107(21), 9552-9555. Smith, K.R., M. Jerrett, H.R. Anderson, R.T. Burnett, V. Stone, R. Derwent, R.W. Atkinson, A. Cohen, S.B. Shonkoff, and D. Krewski, 2010: Public health benefits of strategies to reduce greenhouse-gas emissions: health implications of short-lived greenhouse pollutants. The Lancet, 374(9707), 2091-2103. St-Pierre, N., B. Cobanov, and G. Schnitkey, 2003: Economic losses from heat stress by US livestock industries. Journal of Dairy Science, 86, E52-E77. Tawatsupa, B., V. Yiengprugsawan, T. Kjellstrom, and A. Sleigh, 2012: Heat stress, health and well-being: findings from a large national cohort of Thai adults. BMJ Open, 2(6). Teixeira, E.I., G. Fischer, H. van Velthuizen, C. Walter, and F. Ewert, 2011: Global hot-spots of heat stress on agricultural crops due to climate change. Agricultural and Forest Meteorology, 170, 206-215. Thornton, P., J. Van de Steeg, A. Notenbaert, and M. Herrero, 2009: The impacts of climate change on livestock and livestock systems in developing countries: A review of what we know and what we need to know. Agricultural Systems, 101(3), 113-127. van der Leun, Jan C, R.D. Piacentini, and F.R. de Gruijl, 2008: Climate change and human skin cancer. Photochemical & Photobiological Sciences, 7(6), 730-733. Wilhelmi, O., A. de Sherbinin, and M. Hayden, 2012: 12 Exposure to heat stress in urban environments. Ecologies and Politics of Health, 41, 219.
Williams, A.P., C.D. Allen, A.K. Macalady, D. Griffin, C.A. Woodhouse, D.M. Meko, T.W. Swetnam, S.A. Rauscher, R. Seager, and H.D. Grissino-Mayer, 2012: Temperature as a potent driver of regional forest drought stress and tree mortality. Nature Climate Change, 3, 292-297.
Anthropogenic ocean acidification and global warming share the same primary cause, which is the increase of atmospheric CO2 (Figure OA-1A; WGI, 2.2.1). Eutrophication, loss of sea ice, upwelling and deposition of atmospheric nitrogen and sulphur all exacerbate ocean acidification locally (188.8.131.52, 6.1.1, 184.108.40.206). [INSERT FIGURE OA-1 HERE Figure OA-1: A: Overview of the chemical, biological, socio-economic impacts of ocean acidification and of policy options (adapted from Turley and Gattuso, 2012). B: Multi-model simulated time series of global mean ocean surface pH (on the total scale) from CMIP5 climate model simulations from 1850 to 2100. Projections are shown for emission scenarios RCP2.6 (blue) and RCP8.5 (red) for the multi-model mean (solid lines) and range across the distribution of individual model simulations (shading). Black (grey shading) is the modelled historical evolution using historical reconstructed forcings. The models that are included are those from CMIP5 that simulate the global carbon cycle while being driven by prescribed atmospheric CO2 concentrations. The number of CMIP5 models to calculate the multi-model mean is indicated for each time period/scenario (WGI AR5 Figure 6.28). C: Effect of near future acidification (seawater pH reduction of 0.5 unit or less) on major response variables estimated using weighted random effects meta-analyses, with the exception of survival which is not weighted (Kroeker et al., 2013). The log-transformed response ratio (LnRR) is the ratio of the mean effect in the acidification treatment to the mean effect in a control group. It indicates which process is most uniformly affected by ocean acidification but large variability exists between species. Significance is determined when the 95% bootstrapped confidence interval does not cross zero. The number of experiments used in the analyses is shown in parentheses. * denotes a statistically significant effect.] Chemistry and Projections The fundamental chemistry of ocean acidification is well understood (robust evidence, high agreement). Increasing atmospheric concentrations of CO2 result in an increased flux of CO2 into a mildly alkaline ocean, resulting in a reduction in pH, carbonate ion concentration, and the capacity of seawater to buffer changes in its chemistry (very high confidence). The changing chemistry of the surface layers of the open ocean can be projected at the global scale with high accuracy using projections of atmospheric CO2 levels (Fig. CC-OA-1B). Observations of changing upper ocean CO2 chemistry over time support this linkage (WGI Table 3.2 and Figure 3.18; Figures 30.8, 30.9). Projected changes in open ocean, surface water chemistry for year 2100 based on representative concentration pathways (WGI, Figure 6.28) compared to preindustrial values range from a pH change of -0.14 unit with RCP 2.6 (421 ppm CO2, +1 C, 22% reduction of carbonate ion concentration) to a pH change of -0.43 unit with RCP 8.5 (936 ppm CO2, +3.7 C, 56% reduction of carbonate ion concentration). Projections of regional changes, especially in the highly complex coastal systems (188.8.131.52, 184.108.40.206), in polar regions (WGI 6.4.4), and at depth are more difficult but generally follow similar trends. Biological, Ecological, and Biogeochemical Impacts Investigations of the effect of ocean acidification on marine organisms and ecosystems have a relatively short history, recently analyzed in several metaanalyses (188.8.131.52, 188.8.131.52). A wide range of sensitivities to projected rates of ocean acidification exists within and across diverse groups of organisms, with a trend for greater sensitivity in early life stages (high confidence; 188.8.131.52, 188.8.131.52, 6.3.2). A pattern of positive and negative impacts emerges (high confidence; Fig. OA-1C) but key uncertainties remain in our understanding of the impacts on organisms, life histories and ecosystems. Responses can be influenced, often exacerbated by other drivers, such as warming, hypoxia, nutrient concentration, and light availability (high confidence; 22.214.171.124, 6.3.5).
Growth and primary production are stimulated in seagrass and some phytoplankton (high confidence; 22.214.171.124, 184.108.40.206-3, 30.5.6). Harmful algal blooms could become more frequent (limited evidence, medium agreement). Ocean acidification may stimulate nitrogen fixation (limited evidence, low agreement; 220.127.116.11). It decreases the rate of calcification of most, but not all, sea-floor calcifiers (medium agreement, robust evidence) such as reef-building corals (Box CC-CR), coralline algae, bivalves and gastropods reducing the competitiveness with non-calcifiers (188.8.131.52, 18.104.22.168, 126.96.36.199). Ocean warming and acidification promote higher rates of calcium carbonate dissolution resulting in the net dissolution of carbonate sediments and frameworks and loss of associated habitat (medium confidence; 22.214.171.124, 22.214.171.124, 126.96.36.199-5). Some corals and temperate fishes experience disturbances to behavior, navigation and their ability to tell conspecifics from predators (188.8.131.52). However, there is no evidence for these effects to persist on evolutionary timescales in the few groups analyzed (6.3.2). Some phytoplankton and mollusks displayed adaptation to ocean acidification in long-term experiments (limited evidence, medium agreement; 18.104.22.168), indicating that the long-term responses could be less than responses obtained in short-term experiments. However, mass extinctions in Earth history occurred during much slower rates of ocean acidification, combined with other drivers changing, suggesting that evolutionary rates are not fast enough for sensitive animals and plants to adapt to the projected rate of future change (medium confidence; 6.1.2). Projections of ocean acidification effects at ecosystem level are made difficult by the diversity of species-level responses. Differential sensitivities and associated shifts in performance and distribution will change predator-prey relationships and competitive interactions (126.96.36.199, 6.3.5-6), which could impact food webs and higher trophic levels (limited evidence, high agreement). Natural analogues at CO2 vents indicate decreased species diversity, biomass and trophic complexity of communities (Box CC-CR; 126.96.36.199, 220.127.116.11, 220.127.116.11, 30.5). Shifts in community structure have also been documented in regions with rapidly declining pH (22.214.171.124). Due to an incomplete understanding of species-specific responses and trophic interactions the effect of ocean acidification on global biogeochemical cycles is not well understood (limited evidence, low agreement) and represents an important knowledge gap. The additive, synergistic or antagonistic interactions of factors such as temperature, concentrations of oxygen and nutrients, and light are not sufficiently investigated yet. Risks, Socioeconomic Impacts and Costs The risks of ocean acidification to marine organisms, ecosystems, and ultimately to human societies, include both the probability that ocean acidification will affect fundamental physiological and ecological processes of organisms (126.96.36.199), and the magnitude of the resulting impacts on ecosystems and the ecosystem services they provide to society (Box 19-2). For example, ocean acidification under RCP4.5 to RCP8.5 will impact formation and maintenance of coral reefs (high confidence; Box CC-CR, 126.96.36.199) and the goods and services that they provide such as fisheries, tourism and coastal protection (limited evidence, high agreement; Box CC-CR, 220.127.116.11,19.5.2, 27.3.3, 30.5, 30.6). Ocean acidification poses many other potential risks, but these cannot yet be quantitatively assessed due to the small number of studies available, particularly on the magnitude of the ecological and socioeconomic impacts (19.5.2). Global estimates of observed or projected economic costs of ocean acidification do not exist. The largest uncertainty is how the impacts on lower trophic levels will propagate through the food webs and to top predators. However, there are a number of instructive examples that illustrate the magnitude of potential impacts of ocean acidification. A decrease of the production of commercially-exploited shelled mollusks (188.8.131.52) would result in a reduction of US production of 3 to 13% according to the SRES A1FI emission scenario (low confidence). The global cost of production loss of mollusks could be over 100 billion USD by 2100 (limited evidence, medium agreement). Models suggest that ocean acidification will generally reduce fish biomass and catch (low confidence) and that complex additive, antagonistic and/or synergistic interactions will occur with other environmental (warming) and human (fisheries management) factors (184.108.40.206). The annual economic damage of ocean-acidification-induced coral reef loss by 2100 has been estimated, in 2009, to be 870 and 528 billion USD, respectively for the A1 and B2 SRES emission scenarios (low confidence; 6.4.1). Although this number is small compared to global GDP, it can represent a very large GDP loss for the economies of many coastal regions or small islands that rely on the ecological goods and services of coral reefs (25.7.5, 188.8.131.52).
Mitigation and Adaptation Successful management of the impacts of ocean acidification includes two approaches: mitigation of the source of the problem (i.e. reduce anthropogenic emissions of CO2), and/or adaptation by reducing the consequences of past and future ocean acidification (22.214.171.124). Mitigation of ocean acidification through reduction of atmospheric CO2 is the most effective and the least risky method to limit ocean acidification and its impacts (18.104.22.168). Climate geoengineering techniques based on solar radiation management will not abate ocean acidification and could increase it under some circumstances (220.127.116.11). Geoengineering techniques to remove carbon dioxide from the atmosphere could directly address the problem but are very costly and may be limited by the lack of CO2 storage capacity (220.127.116.11). Additionally, some ocean-based approaches, such as iron fertilization, would only re-locate ocean acidification from the upper ocean to the ocean interior, with potential ramifications on deep-water oxygen levels (22.214.171.124; 188.8.131.52 and 30.5.7). A low-regret approach, with relatively limited effectiveness, is to limit the number and the magnitude of drivers other than CO2, such as nutrient pollution (184.108.40.206). Mitigation of ocean acidification at the local level could involve the reduction of anthropogenic inputs of nutrients and organic matter in the coastal ocean (18.104.22.168). Some adaptation strategies include drawing water for aquaculture from local watersheds only when pH is in the right range, selecting for less sensitive species or strains, or relocating industries elsewhere (126.96.36.199).
CC-OA References Kroeker K., R.C. Kordas, A. Ryan, I. Hendriks, L.Ramajo, G. Singh, C. Duarte and J.-P. Gattuso, 2013: Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Global Change Biology 19, 1884-1896. Turley C. and J.-P. Gattuso, 2012. Future biological and ecosystem impacts of ocean acidification and their socioeconomic-policy implications. Current Opinion In Environmental Sustainability 4, 278-286.
Water, energy, and food/feed/fiber are linked through numerous interactive pathways and subject to a changing climate, as depicted in Figure WE-1. The depth and intensity of those linkages vary enormously between countries, regions and production systems. Energy technologies (e.g. biofuels, hydropower, thermal power plants), transportation fuels and modes, and food products (from irrigated crops, in particular animal protein produced by feeding irrigated crops and forages) may require significant amounts of water (Sections 3.7.2, 7.3.2, 10.2, 10.3.4, 22.3.3, 25.7.2; Allan, 2003; King and Weber 2008; McMahon and Price, 2011; Macknick et al., 2012a). In irrigated agriculture, climate, irrigating procedure, crop choice and yields determine water requirements per unit of produced crop. In areas where water (and wastewater) must be pumped and/or treated, energy must be provided (Asano et al., 2006; Khan and Hanjra, 2009; USEPA, 2010; Gerten et al., 2011). While food production, refrigeration, transport and processing require large amounts of energy (Pelletier et al., 2011), a major link between food and energy as related to climate change is the competition of bioenergy and food production for land and water (Section 7.3.2, Box 25-10; Diffenbaugh et al., 2012; Skaggs et al., 2012) (robust evidence, high agreement). Food and crop wastes, and wastewater, may be used as sources of energy, saving not only the consumption of conventional non-renewable fuels used in their traditional processes, but also the consumption of the water and energy employed for processing or treatment and disposal (Schievano et al., 2009; Sung et al., 2010; Olson, 2012). Examples of this can be found in several countries across all income ranges. For example, sugar cane by-products are increasingly used to produce electricity or for cogeneration (McKendry, 2002; Kim and Dale 2004) for economic benefits, and increasingly as an option for greenhouse gas mitigation. [INSERT FIGURE WE-1 HERE Figure WE-1: The water-energy-food nexus as related to climate change. The interlinkages of supply/demand, quality and quantity of water, energy and food/feed/fiber with changing climatic conditions have implications for both adaptation and mitigation strategies.]
Most energy production methods require significant amounts of water, either directly (e.g., crop-based energy sources and hydropower) or indirectly (e.g., cooling for thermal energy sources or other operations) (Sections 10.2.2 10.3.4, 25.7.4; van Vliet et al., 2012; Davies et al., 2013) (robust evidence, high agreement). Water for biofuels, for example, under the IEA Alternative Policy Scenario, which has biofuels production increasing to 71 EJ in 2030, has been reported by Gerbens-Leenes et al. (2012) to drive global consumptive irrigation water use from 0.5% of global renewable water resources in 2005 to 5.5% in 2030, resulting in increased pressure on freshwater resources, with potential negative impacts on freshwater ecosystems. Water is also required for mining (Section 25.7.3), processing, and residue disposal of fossil and nuclear fuels or their byproducts. Water for energy currently ranges from a few percent in most developing countries to more than 50% of freshwater withdrawals in some developed countries, depending on the country (Kenny et al., 2009; WEC, 2010). Future water requirements will depend on electricity demand growth, the portfolio of generation technologies and water management options employed (WEC, 2010; Sattler et al., 2012) (medium evidence, high agreement). Future water availability for energy production will change due to climate change (Sections 3.4, 3.5.1, 18.104.22.168) (robust evidence, high agreement). Water may require significant amounts of energy for lifting, transport and distribution and for its treatment either to use it or to depollute it. Wastewater and even excess rainfall in cities requires energy to be treated or disposed. Some non-conventional water sources (wastewater or seawater) are often highly energy intensive. Energy intensities per m3 of water vary by about a factor of 10 between different sources, e.g. locally produced potable water from ground/surface water sources vs. desalinated seawater (Box 25-2, Tables 25-6 and 25-7; Macknick et al., 2012b; Plappally and Lienhard, 2012). Groundwater (35% of total global water withdrawals, with irrigated food production being the largest user; Dll et al., 2012) is generally more energy intensive than surface water. In India, for example, 19% of total electricity use in 2012 was for agricultural purposes (Central Statistics Office, 2013), with a large share for groundwater pumping. Pumping from greater depth increases energy demand significantly electricity use (kWhr/m3 of water) increases by a factor of 3 when going from 35 to 120 m depth (Plappally and Lienhard, 2012). The reuse of appropriate wastewater for irrigation (reclaiming both water and energy-intense nutrients) may increase agricultural yields, save energy, and prevent soil erosion (Smit and Nasr, 1992; Jimenez, 1996; Wichelns et al., 2007; Raschid-Sally and Jayakody, 2008) (medium confidence). More energy efficient treatment methods enable poor quality (black) wastewater to be treated to quality levels suitable for discharge into water courses, avoiding additional fresh water and associated energy demands (Keraita et al, 2008). If properly treated to retain nutrients, such treated water may increase soil productivity, contributing to increased crop yields/food security in regions unable to afford high power bills or expensive fertilizer (Oron, 1996; Lazarova and Bahri, 2005; Redwood and Huibers, 2008; Jimenez, 2009) (high confidence). Linkages among water, energy, food/feed/fiber and climate are also strongly related to land use and management (Section 4.4.4, Box 25-10) (robust evidence, high agreement). Land degradation often reduces efficiency of water and energy use (e.g. resulting in higher fertilizer demand and surface runoff), and compromises food security (Sections 3.7.2, 4.4.4). On the other hand, afforestation activities to sequester carbon have important co-benefits of reducing soil erosion and providing additional (even if only temporary) habitat (see Box 25-10) but may reduce renewable water resources. Water abstraction for energy, food or biofuel production or carbon sequestration can also compete with minimal environmental flows needed to maintain riverine habitats and wetlands, implying a potential conflict between economic and other valuations and uses of water (Sections 25.4.3 and 25.6.2, Box 25-10) (medium evidence, high agreement). Only a few reports have begun to evaluate the multiple interactions among energy, food, land, and water and climate (McCornick et al., 2008; Bazilian et al., 2011; Bierbaum and Matson, 2013), addressing the issues from a security standpoint and describing early integrated modeling approaches. The interaction among each of these factors is influenced by the changing climate, which in turn impacts energy and water demand, bioproductivity and other factors (see Figure WE-1 and Wise et al., 2009), and has implications for security of supplies of energy, food and water, adaptation and mitigation pathways, air pollution reduction as well as the implications for health and economic impacts as described throughout this report. The interconnectivity of food/fiber, water, land use, energy and climate change, including the perhaps not yet well understood cross-sector impacts, are increasingly important in assessing the implications for adaptation/mitigation policy decisions. Fuel-food-land use-water-GHG mitigation strategy interactions, particularly related to bioresources for food/feed, power, or fuel, suggest that combined assessment of water, land type and use requirements, energy requirements and potential uses and GHG impacts often epitomize the interlinkages. For example, mitigation
scenarios described in the IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation (IPCC, 2011) indicate up to 300EJ of biomass primary energy by 2050 under increasingly stringent mitigation scenarios. Such high levels of biomass production, in the absence of technology and process/management/operations change, would have significant implications for land use, water and energy, as well as food production and pricing. Consideration of the interlinkages of energy, food/feed/fiber, water, land use and climate change is increasingly recognized as critical to effective climate resilient pathway decision making (medium evidence, high agreement), although tools to support local- and regional-scale assessments and decision-support remain very limited.
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Table 7-1: Households divided into five categories of food access, indicating the impacts of food price increases. Food Access Category Primarily subsistence (autarkic) Characteristics Subsistence farmers, herders, fishers and forest dependent populations; generally low share of population (Karfakis et. al. 2011) Generally lower share of population compared with net buyers (Aksoy and Sid-Dimelik; 2008; Zezza et al. 2008; FAO 2011) Majority of poor rural households (IFAD, 2010; FAO, 2011) Impacts of food price increase on food access Limited impact.
Positive impact through increased income effect. Major beneficiaries are those with greatest surplus (e.g. larger, more commercialized farms) (FAO 2011)
Rural landless: characterized by high rates of food insecurity; average share of population for 15 low income countries was 22% (Aksoy et. al.) Growing share of population in most countries (IFAD 2010)
Ambiguous: depends on relative size of income and price effects, but generally expected to be negative due to high share of income spent on food (Ivanic and Martin 2008; FAO, 2011; Ivanic et. al., 2011) Negative impact due to high share of income spent on food; however some limited evidence that wage increases may accompany price increases in which case overall effects are ambiguous. (FAO 2011; Aksoy and Sid-Dikmelik 2008). Negative impact by reducing food affordability. Especially vulnerable to changes in global food prices, as they are more likely to consume staple foods derived from tradable commodities (FAO, 2008a; Ivanic et al. 2011).
Urban consumers
Table 7-2: The simulated median benefit (difference between the yield change from baseline for the adapted and non-adapted cases) for different crop management adaptations: cultivar adjustment; planting date adjustment; adjusting planting date in combination with cultivar adjustment; adjusting planting date in combination with other adaptations; irrigation optimization; fertilizer optimization; other management adaptations. The numbers in parentheses are the 25th and 75th percentile. Data points where assessed benefit of management changes are negative are not included as farmers are unlikely to intentionally adopt these. Only studies with both a no adaptation and an adaptation assessment are used. Data taken from Abraha & Savage, 2006; Aggarwal & Mall, 2002; Alexandrov, 1999; Alexandrov et al., 2002; Butt et al., 2005; Challinor et al., 2009; Corobov, 2002; DeJong et al., 2001; Deryng et al., 2011; Droogers, 2004; Easterling et al., 2003; El-Shaher et al., 1997; Ewert et al., 2005; Gbetibouo & Hassan, 2005; Howden & Jones, 2004; Kaiser, 1999; Karim et al., 1996; Lal, 2011; Lal et al., 1998; Erda et al., 2005; Matthews & Wasmann, 2003; Moya et al., 1998; Reyenga et al., 1999; Rosenzweig et al., 1994; Southworth et al., 2000; Tao & Zhang, 2011; Tubiello et al., 2000; Thornton et al., 2010; Tingem & Rivington, 2009; Xiao et al., 2005; Yates & Strzepek, 1998; Zhang & Liu, 2005.Management option Cultivar adjustment (n=56) Planting date adjustment (n=19) 3 (2.1, 8.3) Planting date and cultivar adjustment (n=152) 17 (9.9, 26.1) Irrigation optimisation (n=17) Fertiliser optimisation (n=10) Other (n=9)
23 (6.8, 35.9)
1 (0.25, 4.8)
Table 7-3: Schematic key risks for food security and the potentials for adaptation in the near- and long-term for high and low levels of warming.
Box 7-1 Table Projected Impacts for Crops and Livestock in Global Regions and Sub-Regions under Future Scenarios Crop yield impacts in () correspond to () in the scenario column. - CO2, without CO2 effects; + CO2, with CO2 effects; I, irrigated; R, rainfed. In Europe: Atl., Atlantic; Cont., continental; Med., Mediterranean. pp, precipitation. N, north; E, east; W, west; S, south; C, central.Regional Impacts on Crops Region World East Asia Subregion Yield Impacts (%) Maize: up to -4 (-12) Rice: -9.5(-12) Wheat -10 (-13) Rice: -19 (+3.5); -32(+2.5);-40(+0.18) Maize: -22(-11) ;-28 (-18) ; -34(-26) Rice: !10 to +3 (+7.5 to +17.5)/ !26.7 to +2 (0 to +25)/ !39 to !6 (!10 to + 25) Wheat-maize: +4.514.8/ -5.8 25.8 IWheat : -0.9 (+23) RWheat : -1.9 (+28) IRice : -14.8 (-3.3) RRice: -15.2 (-4.1) Scenario CSIRO(MIROC) 2050 +1C; +2C;+3C - CO2(+CO2) 2030/2050/2080 - CO2 (+CO2) +2C / +5C, 2085-00 -CO2 (+CO2) 2021-2050 -CO2 (+CO2) 2050 +2C A2 2020, 2050, 2080 2020,2050,2080 +CO2 2030 +CO2 Reference Nelson et al, 2010 Chapter 24 Tao et al 2013 Chapter 24 Yang et al 2013 Chapter 24 Knox et al, 2012 Lal (2010) Chapter 24 Kumar et al 2013 Kumar et al 2011
China Eastern China China (3H Plain) North China Plain China YangtzeRiver South Asia South Asia India India India NE India Coastal India W.Ghats India Pakistan Pakistan
South Asia
Maize: -16 ; Sorghum: -11 Net cereal production -4 to -10 Sorghum winter: up to -7; -11; -32 IRice: -4,-7,-10 RRice: -6, -2.5; -2.5 IRice -10/+5RRice:-35/+5Maize:up to -40Wheat:up to-20 IRice -10/+5RRice:-20/+15IMz:-50/-15RMz:-35/+10 IRice:-11/+5RRice:-35/+35Maize,Sorghum up to -50 Monsoon Maize-21 to 0;-35 to 0 ;-35 to 0 Winter Maize -13 to +5 ; -50 to +5 ; -60 to -21 Wheat: -7 / -24 (Swat), +14 / +23 (Chitral) Wheat: -6/-8 Rice: -16/-19
Barley -8/+5 Wheat: -20/ +18 Wheat -17 Maize -5 Sorghum -15 Millet -10 Maize: -24 19 Maize: -3 to +15 ; -8.6 to +17.8 Beans: -1.5 to +21.8; -18.1 to +23.7 Millet -20/-40
Chapter24 Knox et al, 2012 Thornton et al 2009 Thornton et al 2010 Ben Mohamed 2011 Chapter27 Chapter27 Chapter 27 Chapter27 Chapter27 Chapter27 Chapter27 Chapter27 Chapter27 Hatfield et al 2011 Stockle et al 2010 Kulshrestha 2011 Iglesias et al 2012
Maize: 0 to -10, Wheat: -1 to -14, Rice : -1 to -10 Maize: -15 Bean: +45 Wheat: +4/-9/-13 (-1/+1/-5) Maize +3/+1/+6 (+3/+3/+8) Soybean: 0/10/-15 (0/-15/-2) Wheat -1 to -9 Rice: 0 to -10 Maize: 0/0/-10/-30 Bean: -4/-19/-29/-87 Rice: +3/-3/14/-63 Maize: -0.5/+2.4/+4.5 (-0.1/-0.8/+1.5) Wheat: -14/ +2 Barley: -1 / -8 Potato: 0/ -5 Maize: 0 / -14 Maize: -5% to -10% Wheat: -10% to -20% Wheat: -16/-11 (+3/+3) Maize: -24/-15 (+1/0) Soybean: -25/-14 (+14/+19) Maize: -2.5 (-1.5) Soy: +1.7 (+9.1) Maize: -2.5 (-1.5) Soy: -4.4 (+2.4) Wheat: -4.4 (+2.4) Winter Wheat: +20/+30, Spring Wheat:+7/+3 Small grains: -48 to + 18 Oilseeds: -50 to +25 +34 to +54/+20 to +23/-5 to +22 +5 to +19/-26 to -7/-8 to +4 +11 to +33/0 to -22/+5 to -27 Wheat: -15/-12 Wheat: -29 (-25)
Panama Andean region Chile Argentina North America US Midwest US SE US Gt Plains US NW Canadian Prairies Boreal /Alpine/AtlN Atl C/Atl S/ContN ContS/Med N/MedS South South-East
2040/2080 +CO2 +1C, +2C,+20% pp, -20% pp 2080 A2 B2 HadCM3/ HIRHAM ECHAM4/RCA3
Regional Impacts on Livestock Region Africa Sub-region Botswana Climate change impacts Cost of supplying water from boreholes could increase by 23% due to increased hours of pumping, under drier and warmer conditions. Reduced stocking of dairy cows, a shift from cattle to sheep and goats, due to high temperature. Livestock keeping could benefit from increased temperature. Highlands of East Africa East Africa Dairy yields decrease by 10-25%. South Africa 2046-2065/2080 2100 ECHAM5/MPIOM, GFDLCM2.0/2, MRICGCM2.3.2 (A2) Dairy production affected at daily mean temperatures above 18oC. Mortality risk to dairy cattle increased by 60%by exposure to high air temp. and high air humidity during breeding. No impact on dairy yields. ARPEGE (A2) 1970-1999, 20202049, 2070-2099 A2, 2070s French Uplands Annual grassland production system significantly reduced by four year exposure to climatic conditions. Grassland dairy system increases potential of dairy production, with increased risk of summerautumn forage failure in France. Spread of bluetongue virus (BTV) in sheep and ticks in cattle due to climate warming. No increase in risk of incursion of CrimeanCongo haemorrhagic fever virus in livestock A1B, by the end of century Graux et al. 2011 2080 Overall Europe Chapter 23 Nesamvuni et al. 2012 2050 Maize stover availability head per head of cattle may decrease due to water scarcity. 2050 Chapter 22 Chapter 22 Scenarios 2050 Reference Chapter 22
Lowlands of Africa
Chapter 22
Chapter 23
Ireland, France
Northern Australia
3oC increase will cause 4% reduction in gross value of beef, sheep and wool sector.
Chapter 25
Dairy output will decline under 1oC increase in temperature. 2050 Profitability of fodder supply production decline at most sites due to shorter growing season. Decline in NPP of grassland from historical climate will be 7% in 2030, 9% in 2050 and 14% in 2070. Operating profit to fall by 27% in 2030, 32% in 2050 and 48% in 2070. Dairy yields increase 0.5-6.2% Dairy yields decrease 1.3-6.7% Dairy yields decrease 1.4-6.6% Dairy yields decrease 2.2-8.1% National pasture production for dairy, beef and sheep will decrease by 4%. SRES A2; 2030, 2050, 2070
Andean Mountain countries Columbia Venezuela Ecuador Argentina & Chile Brazil
Beef and dairy cattle, pigs and chicken could decrease between 0.9 and 3.2% while sheep could increase by 7%. Beef cattle choice declined.
Chapter 27
North America
Central USA
Milk production and feed intake in cattle strongly affected. Dairy yields decrease 16-30%.
Silva et al., 2009 CGCMI/Hadley, Baseline CO2/2x CO2/3x CO2 Mader et al, 2009
Figure 7-1: Main issues of the chapter. Drivers are divided into climate and non-climate elements, affecting production and non-production elements of food systems, thereafter combining to provide food security. The thickness of the red lines is indicative of the relative availability of refereed publications on the two elements.
Figure 7-2: Summary of estimates of the impact of recent climate trends on yields for four major crops. Studies were taken from the peer-reviewed literature and used different methods (i.e., physiological process-based crop models or statistical models), spatial scales (stations, provinces, countries, or global), and time periods (median length of 29 years). Some included effects of positive CO2 trends (18.104.22.168.2) but most did not. (a) shows number of estimates with different level of impact (% yield per decade), (b) shows boxplot of estimates separated by temperate vs. tropical regions, modelling approach (process-based vs. statistical), whether CO2 effects were included, and crop. Boxplots indicate the median (vertical line), 25th-75th percentiles (box), and 10th-90th percentiles (whiskers) for estimated impacts in each category, and n indicates the number of estimates. Studies were for China (Tao et al., 2006; Tao et al., 2008; Wang et al., 2008; You et al., 2009; Chen et al., 2010; Tao et al., 2012), India (Pathak et al.,2003; Auffhammer et al., 2012), United States (Kucharik and Serbin, 2008), Mexico (Lobell et al., 2005), France (Brisson et al., 2010; Licker et al., 2013) Scotland (Gregory and Marshall, 2012), Australia (Ludwig et al., 2009), Russia (Licker et al., 2013), and some studies for multiple countries or global aggregates (Lobell and Field, 2007; Welch et al., 2010; Lobell et al., 2011). Values from all studies were converted to percentage yield change per decade. Each study received equal weighting as insufficient information was available to judge the uncertainties of each estimate. [Illustration to be redrawn to conform to IPCC publication specifications.]
Figure 7-3: Since the AR4 report, international food prices have reversed historical downward trend. Plot shows history of FAO food and cereal price index (composite measures of food prices), with vertical lines indicating events when a top 5 producer of a crop had yields 25% below trend line (indicative of a seasonal climate extreme). Australia is included despite not being a top five producer, because it is an important exporter and the drops were 40% or more below trend line. Prices may have become more sensitive to weather-related supply shortfalls in recent years. At the same time, food prices are increasingly associated with the price of crude oil (blue line), making attribution of price changes to climate difficult. Thus, there is clear evidence since AR4 that prices can rise rapidly, but the role of weather in these increases remains unclear. All indices are expressed as percentage of 2002-2004 averages. Food price and crop yield data from FAO (http://www.fao.org/worldfoodsituation/foodpricesindex and http://faostat.fao.org/) and oil price data from http://www.eia.gov.
Figure 7-4: Percentage simulated yield change as a function of local temperature change temperature for the three major crops and for temperate and tropical regions. Dots indicate where a known change in atmospheric CO2 was used in the study; remaining data are indicated by x. Note that differences in yield value between these symbols do not measure the CO2 fertilisation effect, since changes in other factors such as precipitation may be different between studies. Non-parameteric regressions (LOESS, span=1 and degree=1) of subsets of these data were made 500 times. These bootstrap samples are indicated by shaded bands at the 95% confidence interval. Regressions are separated according to the presence (blue) or absence (red) of simple agronomic adaptation (Table 7-2). In the case of tropical maize the central regression for absence of adaptation is slightly higher than that with adaptation. This is due to asymmetry in the data not all studies compare adaptated and non-adapted crops. Figure 7-8 presents a pairwise adaptation comparison. Note that four of the 1048 datapoints across all six panels are outside the yield change
range shown. These were omitted for clarity. Some of the studies have associated temporal baselines, with centrepoints typically between 1970 and 2005. Note that local warming in cropping regions generally exceeds global mean warming (Figure 21-3). Data are taken from a review of literature: Abou-Hadid (2006); Abraha & Savage (2006); Aggarwal & Mall (2002); Alexandrov (1999); Alexandrov & Hoogenboom (2000); Alexandrov et al. (2002); Arndt et al. (2011); Brassard & Singh 2008); Brassard & Singh (2007); Butt et al. (2005); Byjesh et al. (2010); Chhetri et al. (2010); Chipanshi et al. (2003); Corobov (2002); Izaurralde et al. (2001); DeJong et al. (2001); Deryng et al. (2011); Droogers (2004); Easterling et al. (2003); El Maayar et al. (2009); El-Shaher et al. (1997); Ewert et al. (2005); Gbetibouo & Hassan (2005); Howden & Jones (2004); Iqbal et al. (2011); Izaurralde et al (2005); Jones & Thornton (2003); Kaiser (1999); Kapetanaki & Rosenweig (1997); Karim et al. (1996); Krishnan et al. (2007); Lal (2011); Lal et al. (1998); Li et al. (2011); Erda et al. (2005); Liu et al. (2010); Lobell & Ortiz-Monasterio (2007); Luo et al. (2003); Matthews & Wasmann (2003); Moya et al. (1998); Osborne et al. (2013); Piao et al. (2010); Porter & Semenov (2005); Reyenga et al. (1999); Rosenzweig et al. (1994); Rowhani et al. (2011); Sands & Edmond (2005); Schlenker & Roberts (2009); Shuang-He et al. (2011); Southworth et al. (2000); Tan et al. (2010); Tao & Zhang (2010); Tao & Zhang (2011); Tubiello et al. (2000); Thomson et al. (2005); Thornton et al. (2009); Thornton et al. (2011); Thornton et al. (2010); Tingem & Rivington (2009); Tingem et al. (2008); Walker & Schulze (2008); Winters et al. (1998); Xiao et al. (2005); Xiong et al. (2007); Yates & Strzepek (1998); Zhang & Liu (2005); Zhao et al. (2005). [Illustration to be redrawn to conform to IPCC publication specifications.]
Figure 7-5: Projected changes in crop yield as a function of time. The y-axis indicates percentage of studies and the colours denote percentage change in crop yield. The sum of absolute bar heights in each time period equals 100%. Data are plotted according to the 20-year period in which the centre point of the projection period falls. Data taken from Abraha & Savage, 2006; Alexandrov & Hoogenboom, 2000; Arndt et al., 2011; Berg et al., 2012; Brassard & Singh, 2008; Brassard & Singh, 2007; Butt et al., 2005; Calzadilla et al., 2009; Chhetri et al., 2010; Ciscar et al., 2011; Deryng et al., 2011; Giannakopoulos et al., 2009; Hermans et al., 2010; Iqbal et al., 2011; Izaurralde et al, 2005; Kim et al., 2010; Lal, 2011; Li et al., 2011; Lobell et al., 2008; Moriondo et al., 2010; Muller et al., 2010; Osborne et al., 2013; Peltonen-Sainio et al., 2011; Piao et al., 2010; Ringler et al., 2010; Rowhani et al., 2011; Schlenker & Roberts, 2009 ;Shuang-He et al., 2011; Southworth et al., 2000; Tan et al., 2010; Tao & Zhang, 2010; Tao & Zhang, 2011; Tao et al., 2009; Thornton et al., 2009; Thornton et al., 2011; Thornton et al., 2010; Tingem & Rivington, 2009 Tingem et al., 2008; Walker & Schulze, 2008; Wang et al., 2011; Xiong et al., 2009; Xiong et al., 2007.
Figure 7-6: Projected percentage change in coefficient of variation (CV) of yield for wheat (gold), maize (green), rice (blue) and C4 crops (red) taken from C2010 (Challinor et al., 2010), B2013 (Berg et al., 2013), T2009 (Tao et al., 2009), TZ2013 (Tao and Zhang, 2010), TZ2012 (Tao and Zhang, 2011) and U2012 (Urban et al., 2012). U2012 and C2012 plot multiple data points: U2012 shows the range (mean plus and minus one standard deviation) of percentage changes in CV. For C2012 paired CV changes were not available, so the box shows changes in the mean CV, the mean CV plus one standard deviation, and the mean CV minus one standard deviation. 81 datapoints are plotted in the figure, although the underlying data consist of many thousands of crop model simulations. The studies used a range of scenarios (SRES A1B, A2, A1F1 and B1). B2013 is a global study of the tropics, U2012 is for US maize, and the remaining studies are for China. [Illustration to be redrawn to conform to IPCC publication specifications.]
Figure 7-7: Boxplot summary of studies that quantify impact of climate and CO2 changes on crop yields, including historical and projected impacts, mean and variability of yields, and for all available crops in temperate and tropical regions. All impacts are expressed as average impact per decade (a 10% total impact from a 50 year period of climate change would be represented as 2% per decade). References for historical impacts are given in Figure 7-2, for projected mean yields in Figure 7-5, and for yield variability in Figure 7-6. N indicates the number of estimates, with some studies providing multiple estimates. In general, decreases in mean yields and increases in yield variability are considered negative outcomes for food security. Also indicated on figure is the expected increase in crop demand of 14% per decade (Alexandratos and Bruinsma, 2012), which represents a target for productivity improvements to keep pace with demand.
Figure 7-8: Simulated yield benefit from adaptation calculated as the difference between the yield change from baseline (%) for paired non-adapted and adapted cases as affected by temperature and aggregated across all crops. The shaded bands at the 95% confidence interval are calculated as for Fig 7.4. Data points (n=31) where assessed benefit of management changes are negative are not included as farmers are unlikely to intentionally adopt these. Data sources are the same as for Table 7-2 and only studies that examine both a no adaptation and an adaptation scenario are used so as to avoid the issues arising from un-paired studies documented in Fig7.4 for tropical maize. [Illustration to be redrawn to conform to IPCC publication specifications.]