485
7
Food Security and
Food Production Systems
Coordinating Lead Authors:
John R. Porter (Denmark/UK), Liyong Xie (China)
Lead Authors:
Andrew J. Challinor (UK), Kevern Cochrane (South Africa), S. Mark Howden (Australia),
Muhammad Mohsin Iqbal (Pakistan), David B. 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)
This chapter should be cited as:
Porter
, J.R., L. Xie, A.J. Challinor, K. Cochrane, S.M. Howden, M.M. Iqbal, D.B. Lobell, and M.I. Travasso, 2014:
Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability.
Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea,
T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken,
P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and
New York, NY, USA, pp. 485-533.
7
486
Executive Summary ........................................................................................................................................................... 488
7.1. Introduction and Context ....................................................................................................................................... 490
7.1.1. Food Systems .................................................................................................................................................................................... 490
7.1.2. The Current State of Food Security ................................................................................................................................................... 490
7.1.3. Summary from AR4 ........................................................................................................................................................................... 491
7.2. Observed Impacts, with Detection and Attribution ................................................................................................ 491
7.2.1. Food Production Systems .................................................................................................................................................................. 491
7.2.1.1.Crop Production ................................................................................................................................................................... 491
7.2.1.2.Fisheries Production ............................................................................................................................................................. 493
7.2.1.3.Livestock Production ............................................................................................................................................................ 494
7.2.2. Food Security and Food Prices .......................................................................................................................................................... 494
7.3. Assessing Impacts, Vulnerabilities, and Risks
7.3.1. Methods and Associated Uncertainties ............................................................................................................................................. 494
7.3.1.1.Assessing Impacts ................................................................................................................................................................ 494
7.3.1.2.Treatment of Adaptation in Impacts Studies ........................................................................................................................ 497
7.3.2. Sensitivity of Food Production to Weather and Climate .................................................................................................................... 497
7.3.2.1.Cereals and Oilseeds ............................................................................................................................................................ 497
7.3.2.2.Other Crops .......................................................................................................................................................................... 499
7.3.2.3.Pests, Weeds, Diseases ......................................................................................................................................................... 500
7.3.2.4.Fisheries and Aquaculture .................................................................................................................................................... 500
7.3.2.5.Food and Fodder Quality and Human Health ....................................................................................................................... 501
7.3.2.6.Pastures and Livestock ......................................................................................................................................................... 502
7.3.3. Sensitivity of Food Security to Weather and Climate ......................................................................................................................... 502
7.3.3.1.Non-Production Food Security Elements .............................................................................................................................. 502
7.3.3.2.Accessibility, Utilization, and Stability .................................................................................................................................. 502
7.3.4. Sensitivity of Land Use to Weather and Climate ............................................................................................................................... 504
7.4. Projected Integrated Climate Change Impacts ....................................................................................................... 505
7.4.1. Projected Impacts on Cropping Systems ........................................................................................................................................... 505
7.4.2. Projected Impacts on Fisheries and Aquaculture ............................................................................................................................... 507
7.4.3. Projected Impacts on Livestock ......................................................................................................................................................... 508
Box 7-1. Projected Impacts for Crops and Livestock in Global Regions and Sub-Regions under Future Scenarios ................... 509
7.4.4. Projected Impacts on Food Prices and Food Security ....................................................................................................................... 512
Table of Contents
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7.5. Adaptation and Managing Risks in Agriculture and Other Food System Activities ............................................... 513
7.5.1. Adaptation Needs and Gaps Based on Assessed Impacts and Vulnerabilities ................................................................................... 513
7.5.1.1.Methods of Treating Impacts in Adaptation Studies—Incremental to Transformational ...................................................... 513
7.5.1.2.Practical Regional Experiences of Adaptation, Including Lessons Learned ........................................................................... 518
7.5.1.3.Observed and Expected Barriers and Limits to Adaptation ................................................................................................... 518
7.5.1.4.Facilitating Adaptation and Avoiding Maladaptation ........................................................................................................... 518
7.5.2. Food System Case Studies of Adaptation—Examples of Successful and Unsuccessful Adaptation ................................................... 518
7.5.3. Key Findings from Adaptations—Confidence Limits, Agreement, and Level of Evidence .................................................................. 519
7.6. Research and Data Gaps—Food Security as a Cross-Sectoral Activity ................................................................... 520
References ......................................................................................................................................................................... 520
Frequently Asked Questions
7.1: What factors determine food security and does low food production necessarily lead to food insecurity? ...................................... 494
7.2: How could climate change interact with change in fish stocks and ocean acidification? ................................................................. 507
7.3: How could adaptation actions enhance food security and nutrition? ............................................................................................... 514
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Chapter 7 Food Security and Food Production Systems
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Executive Summary
The effects of climate change on crop and terrestrial 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 18-3} Several of
these climate extremes were made more likely as the result of anthropogenic emissions (medium confidence). {Table 18-3}
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. {7.2.1.2, 7.3.2.4, 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 {7.3.3.2}, but with
benefits in other regions that become more favorable for aquatic food production (medium confidence). {7.5.1.1.2}
Studies have documented a large negative sensitivity of crop yields to extreme daytime temperatures around 30°C. {WGII AR4
Chapter 5, 7.3.2.1} 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 carbon dioxide (CO
2
) in most cases and the damaging effects of elevated
tropospheric ozone (O
3
) on crop yields (high confidence). Experimental and modeling evidence indicates that interactions between CO
2
and O
3
, mean temperature and extremes, water, and nitrogen are nonlinear and difficult to predict (medium confidence). {7.3.2.1, Figure 7-2}
Changes in climate and CO
2
concentration will enhance the distribution and increase the competitiveness of agronomically
important and invasive weeds (medium confidence). Rising CO
2
may reduce the effectiveness of some herbicides (low confidence). The
effects of climate change on disease pressure on food crops are uncertain, with evidence pointing to changed geographical ranges of pests and
diseases but less certain changes in disease intensity (low confidence). {7.3.2.3}
All aspects of food security are potentially affected by climate change, including food access, utilization, and price stability (high
confidence). {7.3.3.1, 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 CO
2
, but these effects may be counteracted by effects of other aspects of climate change (medium confidence).
{7.3.2.5}
For the major crops (wheat, rice, and maize) in tropical and temperate regions, climate change without adaptation will negatively
impact production for local temperature increases of 2°C or more above late-20th-century levels, although individual locations
may benefit (medium confidence). {7.4, Figure 7-4} Projected impacts vary across crops and regions and adaptation scenarios,
with about 10% of projections for the period 2030–2049 showing yield gains of more than 10% and about 10% of projections
showing yield losses of more than 25%, compared to the late 20th century. {Figure 7-5} After 2050, the risk of more severe
impacts increases. {Figure 7-5} 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).
Climate change will increase
progressively the inter-annual variability of crop yields in many regions (medium confidence). {Figure 7-6}
489
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Food Security and Food Production Systems Chapter 7
On average, agronomic adaptation improves yields by the equivalent of ~15-18% of current yields {Figure 7-8, Table 7-2}, but the
effectiveness of adaptation is highly variable (medium confidence) ranging from potential dis-benefits to negligible to very
substantial (medium confidence). {7.5.1.1.1}
Projected benefits of adaptation are greater for crops in temperate, rather than tropical, regions
(medium confidence) {7.5.1.1.1, Figures 7-4, 7-7}, with wheat- and rice-based systems more adaptable than those of maize (low confidence).
{Figure 7-4} Some adaptation options are more effective than others (medium confidence). {Table 7-2}
Global temperature increases of ~4°C or more above late-20th-century levels, combined with increasing food demand, would
pose large risks to food security globally and regionally (high confidence). Risks to food security are generally greater in low-
latitude areas. {Box 7-1, Table 7-3, Figures 7-4, 7-5, 7-7}
Changes in temperature and precipitation, without considering effects of CO
2
, will contribute to increased global food prices by
2050, with estimated increases ranging from 3 to 84% (medium confidence).
Projections that include the effects of CO
2
changes, but
ignore O
3
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}
Adaptation in fisheries, aquaculture, and livestock production will potentially be strengthened by adoption of multi-level adaptive
strategies to minimize 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 development of early warning systems for extreme
events (medium confidence). {7.5.1.1.2} Adaptations for livestock systems center 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).
{7.5.1.1.3}
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}
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Chapter 7 Food Security and Food Production Systems
7
7.1. Introduction and Context
Many definitions of food security exist, and these have been the subject
of much debate. As early as 1992, Maxwell and Smith (1992) reviewed
more than 180 items discussing concepts and definitions, and more
definitions have been formulated since (DEFRA, 2006). Whereas many
earlier definitions centered 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.” Worldwide attention on food access was given impetus
by the food “price spike” in 2007–2008, triggered by a complex set of
long- and short-term factors (FAO, 2009b; von Braun and Torero, 2009).
FAO concluded, “provisional estimates show that, in 2007, 75 million
more people were added to the total number of undernourished relative
to 2003–05” (FAO, 2008); this is arguably a low-end estimate (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 the period from 2010 to 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 to which they will
do so in the future (Figure 7-1).
7.1.1. Food Systems
A food system is all processes and infrastructure involved in satisfying
a population’s food security, that is, the gathering/catching, growing,
harvesting (production aspects), storing, processing, packaging,
transporting, marketing, and consuming of 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 synthesizes and
evaluates evidence for the impacts of climate on both production
and non-production elements and their adaptation to climate change
(Figure 7-1).
T
he impacts of climate change on food systems are expected to be
widespread, complex, geographi cally and temporally variable, and
profoundly influenced by socioeconomic 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 consider only 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).
7.1.2. The Current State of Food Security
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 a 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 higher than 50% can be found (FAO et al., 2012). The largest
numbers of food-insecure persons 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 people (Lopez et al., 2006; Pinstrup-
Andersen, 2009).
Food systems adapted to
ensure availability, access,
utilization, and stability
Drivers
Responses
Climate and atmosphere
Non-climate factors
Temperature
Precipitation
Carbon dioxide
Ozone...
Production aspects
Food security
Non-production aspects
Crops
Livestock
Fish...
Soil fertility
Irrigation
Fertilizers
Demography
Economics
Socio-politics...
Incomes
Processing
Transport
Storage
Retailing...
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.
491
Food Security and Food Production Systems Chapter 7
7
F
ood insecurity is closely tied to poverty; globally about 25 to 30% of
poor people, measured using a US$1 to US$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 favor 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 on their
respective governance (robust evidence, medium agreement) (HLPE,
2011; World Bank, 2012).
7.1.3. Summary from AR4
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
vary with crops and across regions and temperature changes; however,
on average, they provide approximately a 10% yield benefit when
compared with yields when no adaptation is used (WGII AR4 Section
5.5.1). Adaptive capacity is projected to be exceeded in low-latitude
areas with temperature increases of more than 3°C. Local extinctions
of particular fish species are expected at the edges of their ranges (high
confidence) and have serious negative impacts on fisheries (medium
confidence).
7.2. Observed Impacts,
with Detection and Attribution
7.2.1. Food Production Systems
Formal detection of impacts requires that observed changes be compared
to a clearly specified baseline that characterizes behavior in the absence
o
f 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 behavior.
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).
7.2.1.1. 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
(Section 7.3.1), and estimate impacts by running the models with
observed historical climate and then computing trends in modeled
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).
Because many of these regional studies are for major producers, and a
global study (Lobell et al., 2011a) 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 UK (Jaggard et al., 2007; Chen
et al., 2010; Supit et al., 2010; Gregory and Marshall, 2012).
More difficult to quantify with models is the impact of very extreme
events on cropping systems, as by definition these occur very rarely and
models cannot be adequately calibrated and tested. Table 18-3 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 (Sanchez et al. 2014) 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 18-3).
A sizable fraction of crop modeling studies were concerned with
production for individual sites or provinces, spatial scales below which
492
Chapter 7 Food Security and Food Production Systems
7
the changes in climate conditions are attributable to anthropogenic
activity (WGI AR5 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., 2011a), 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 (WGI AR5 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 modeling 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 1951–1999
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 (GHG)
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 (WGI AR5 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
Median
–10 to –5 –5 to –2.5 –2.5 to 0 Not
significant
>0 –6 –4 –2 0 2
(
N = 19)
(27)
(46)
(10)
(2)
(
54)
(18)
(10)
(13)
(12)
Yield impact of climate trend (% per decade)
(a)
(b)
Yield impact of climate trend (% per decade)
5
10
15
20
Number of estimates
0
25
Maize
Rice
Soy
Wheat
No CO
2
Yes CO
2
Process
model
Statistical
model
Te mp e rate
Tropical
Region
Model type
Crop type
CO
2
25th
75th
90th
Percentile
10th
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 carbon dioxide (CO
2
) trends (Section 7.3.2.1.2) but most did not. (a) Number of estimates with different level of impact (% yield per
decade). (b) Boxplot of estimates separated by temperate vs. tropical regions, modeling approach (process-based vs. statistical), whether CO
2
effects were included, and crop.
Boxplots indicate the median (vertical line), 25th to 75th percentiles (colored box), and 10th to 90th percentiles (white box) for estimated impacts in each category, and numbers
in parentheses indicate the number of estimates. Studies were for China (Tao et al., 2006, 2008a, 2012; Wang et al., 2008; You et al., 2009; Chen et al., 2010), India (Pathak et
al., 2003; Auffhammer et al., 2012), USA (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., 2011a). 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.
493
Food Security and Food Production Systems Chapter 7
7
e
t 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 GHG emissions
(Zwiers et al., 2011). At regional and local scales, however, trends in
daytime maximum are harder to attribute to GHG 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 CO
2
by greater than 100 ppm since preindustrial times has
virtually certainly enhanced water use efficiency and yields, especially
for C
3
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 CO
2
often are accompanied by ozone (O
3
) precursors that
have driven a rise in tropospheric O
3
that harms crop yields (Morgan
et al., 2006; Mills et al., 2007; Section 7.3.2.1.2). Elevated O
3
since
preindustrial times has very likely suppressed global production of major
crops compared to what they would have been without O
3
increases,
with estimated losses of roughly 10% for wheat and soybean and 3 to
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.
2011a,b), but are also evident for soybean and maize in the USA
(Fishman et al., 2010).
7.2.1.2. 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 interannual
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 Northeast 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, 2013). 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 (Section 30.5.1.1.1). 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
w
ell-documented example in the oceans off southeast 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 (robust evidence, high
agreement; Last et al., 2011).
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 (Munday et al., 2008; Hoegh-Guldberg, 2011).
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; see also Box CC-CR) 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 (Section 30.6.2.1.2). Other examples around the world are
described in Section 30.5.1.1.1.
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; Section 6.4.1.1).
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; Sections 6.4.3, 7.5.1.1.2).
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
(Section 22.3.3.1.4). There is evidence that increasing temperature has
reduced the primary productivity of Lake Tanganyika in East Africa and
a study by O’Reilly 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).
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7
7.2.1.3. 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 (Section 23.4.2).
7.2.2. Food Security and Food Prices
Food production is an important aspect of food security (Section 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 (Section 7.1.3). These prices reflect the overall
balance of supply and demand, and the accessibility of food for
consumers integrated with regional to global markets. Although food
prices gradually declined for most of the 20th century (FAO, 2009b) 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; Mueller et al., 2011; Wright, 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 18-3). 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 et al.,
2011a) 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 CO
2
over the study period
were considered. Because 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.
7.3. Assessing Impacts,
Vulnerabilities, and Risks
7.3.1. Methods and Associated Uncertainties
7.3.1.1. Assessing Impacts
Methods developed or extended since AR4 have resulted in more robust
statements on climate impacts, both in the literature and in Section
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 (Thenkabail et al., 2009;
Frequently Asked Questions
FAQ 7.1 | What factors determine food security and does low food production
necessarily lead to food insecurity?
O
bserved data and many studies indicate that a warming climate has a negative effect on crop production and
generally reduces yields of staple cereals such as wheat, rice, and maize, which, however, differ between regions
and latitudes. Elevated CO
2
could benefit crops yields in the short term by increasing photosynthesis rates; however,
t
here is big uncertainty in the magnitude of the CO
2
e
ffect and the significance of interactions with other factors.
Climate change will affect fisheries and aquaculture through gradual warming, ocean acidification, and changes
in the frequency, intensity, and location of extreme events. Other aspects of the food chain are also sensitive to
c
limate 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.
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Food Security and Food Production Systems Chapter 7
7
Fishman et al., 2010; Goswami et al., 2012). There has also been an
increase in the number of Free Air Concentration Enrichment (FACE)
studies that examine O
3
instead of, or in addition to, CO
2
. In agriculture,
FACE experiments have been used for assessing impacts of atmospheric
CO
2
on grain yield, quality characteristics of important crops (Erbs et
al., 2010), elemental composition (Fernando et al., 2012), and diseases
(Chakraborty et al., 2011; Eastburn 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 CO
2
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 similar
only when the FACE experiments are grown under considerably more
water stress than non-FACE experiments (Ainsworth et al., 2008; Kimball,
2010). 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 CO
2
, that is, whether concentrations are
fluctuating or constant (Bunce, 2012). Unfortunately, the FACE experiments
are carried out mostly in the USA and in China, and thus limited to
specific environmental conditions, which do not fully reflect tropical or
subtropical conditions, where CO
2
and soil nutrient interactions could
lead to large differences in photosynthesis rate, water use, and yield.
Also, the number of FACE studies is still quite low, which limits statistical
power when evaluating the average yield effects of elevated CO
2
or
interactions with temperature and moisture (Section 7.3.2).
Numerical simulation models can be used to investigate a larger number
of possible environmental and management conditions than possible
via 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
(Craufurd et al., 2013; Zhu 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 (Kang et al.,
2009; Mo et al., 2009), and fostering communication among scientists,
managers, policymakers, and planners.
The trend toward 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
200
1
990 2000
150
100
Price index
1
995 20102005
300
250
F
AO food price index
F
AO cereal price index
U
S crude oil index
Russia
wheat
Argentina
maize, soy
R
ussia
w
heat
Australia
wheat
US
maize
US wheat; India soy;
Australia wheat
A
ustralia
w
heat
US
maize
Publication of AR4
Figure 7-3 | Since the AR4, international food prices have reversed historical downward trend. The plot shows the history of FAO food and cereal price indices (composite
measures of food prices), with vertical lines indicating events when a top five 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.
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Chapter 7 Food Security and Food Production Systems
7
a
gricultural 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
(Tao et al., 2008b, 2009a; Iizumi et al., 2009). It is also increasingly
common to assess both biophysical and socioeconomic drivers of crop
productivity within the same study (Fraser et al., 2008; Reidsma et al.,
2009; Challinor et al., 2010; Tao et al., 2011b). Finally, an important
recent development is the systematic comparison of results from different
modeling 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, 2014).
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., 2009a; 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 data sets of
country-, regional-, and farm-level crop yield together with observed
and/or simulated weather time series to assess the sensitivity of food
production to weather and climate (Tao et al., 2009a, 2011). 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. Schlenker and Roberts, 2009; Lobell et al., 2013a). Although
statistical models forfeit some of the process knowledge embedded in
other approaches, they can often reproduce the behavior 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., 2011b). However, statistical models usually exclude
the direct impact of elevated CO
2
, making multi-decadal prediction
problematic. In determining future trends, crop models of all types can
extrapolate only 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). Modeling and
experiments are each subject to their own uncertainties. Measurement
u
ncertainty is a feature of field and controlled environment experiments.
For example, interactions among CO
2
fertilization, temperature, soil
nutrients, O
3
, pests, and weeds are not well understood (Soussana et
al., 2010) and therefore most crop models do not include all of these
effects, or 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, as 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 normalized by increase in CO
2
have been
found to vary between zero and more than 30% among crop varieties
(Tausz et al., 2011).
Uncertainty in climate simulation is generally larger than, or sometimes
comparable to, the uncertainty in crop simulation using a single crop
model (Iizumi et al., 2011), although temperature-driven processes in
crop models have been shown to dominate the causes of uncertainty
(Koehler et al., 2013). There is significant uncertainty in agricultural
simulation arising from climate model error. Since AR4 the choice of
method for General Circulation Model (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
the simulation of mean yield and above-ground biomass of more than
60% (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-CO
2
interactions (Asseng et al., 2013). However, in
contrast to absolute values, yield changes can be consistent across crop
models (Olesen et al., 2007). Given these different strengths and
weaknesses, and associated dependencies, it is critical that both
experimental and modeling 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 CO
2
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
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Food Security and Food Production Systems Chapter 7
7
t
o examine the effect of climate-related factors on recruitment to a
population, growth, and population production of specific species, for
example (Brander, 2010; see also Chapters 6 and 30). Different modeling
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; see also Section 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., 2012) to more detailed
social and economic studies focused on particular communities or
localities (Daw et al., 2009).
7.3.1.2. Treatment of Adaptation in Impacts Studies
Adaptation occurs on a range of time scales 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
generalize.
7.3.2. Sensitivity of Food Production
to Weather and Climate
7.3.2.1. Cereals and Oilseeds
7.3.2.1.1. Mean and extremes of temperature and precipitation
Both statistical and process-based models have been used widely 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., 2013a). 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 32-34°C after flowering
appear to speed senescence rapidly in wheat (Asseng et al., 2011; Lobell
et al., 2012), but many crop models do not represent this process (Sanchez
et al., 2014). 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,
although many fundamental biophysical 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-
s
cale statistical analyses (Schlenker and Roberts, 2009) and process-based
models shows clear negative impacts of temperatures above 30°C to
34°C 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., 2008, 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 33°C
(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. (2009b) found that the response
of crop yields to climate change in the drylands of East Africa is
insensitive to increases in rainfall, as wetter climates are associated
with warmer temperatures that act to reduce yields. Because 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°C to 2°C 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°C to 5°C. These data confirm
AR4 findings that even slight warming will decrease yields in low-lati-
tude regions (medium evidence, high agreement). However, although
AR4 had few indications of yield reductions at less than 2°C of local
warming, the new analysis has, in the absence of incremental adaptation,
more yield decreases than increases at all temperatures. Hence, although
AR4 concluded with medium confidence that in mid- to high-latitude
498
Chapter 7 Food Security and Food Production Systems
7
Local mean temperature change (C°)
Rice yield change (%)
Local mean temperature change (C°)
T
ropical regions
(45)
(42)
(120)
(92)
(116)
(77)
(194)
(127)
(116)
(69)
T
emperate regions
No adaptation
With adaptation
Wheat yield change (%) Maize yield change (%)
60
12345 12345
40
20
0
–20
–40
–60
60
40
20
0
–20
–40
–60
60
40
20
0
–20
–40
–60
60
40
20
0
–20
–40
–60
60
40
20
0
–20
–40
–60
60
40
20
0
–20
–40
–60
(N = 30)
(N = 20)
Figure 7-4 | Percentage simulated yield change as a function of local temperature change for the three major crops and for temperate and tropical regions. Dots indicate where a
known change in atmospheric CO
2
was used in the study; remaining data are indicated by x. Note that differences in yield value between these symbols do not measure the CO
2
fertilization effect, as changes in other factors such as precipitation may be different between studies. Non-parametric 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
data points 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 center points
typically between 1970 and 2005. Note that local warming in cropping regions generally exceeds global mean warming (Figure 21-4). Data are taken from a review of literature:
Rosenzweig and Parry, 1994; Karim et al., 1996; El-Shaher et al., 1997; Kapetanaki and Rosenzweig, 1997; Lal et al., 1998; Moya et al., 1998; Winters et al., 1998; Yates and Strzepek,
1998; Alexandrov, 1999; Kaiser, 1999; Reyenga et al., 1999; Alexandrov and Hoogenboom, 2000; Southworth et al., 2000; Tubiello et al., 2000; DeJong et al., 2001; Izaurralde et al.,
2001; Aggarwal and Mall, 2002; Abou-Hadid, 2006; Alexandrov et al., 2002; Corobov, 2002; Chipanshi et al., 2003; Easterling et al., 2003; Jones and Thornton, 2003; Luo et al., 2003;
Matthews and Wassmann, 2003; Droogers, 2004; Howden and Jones, 2004; Butt et al., 2005; Erda et al., 2005; Ewert et al., 2005; Gbetibouo and Hassan, 2005; Izaurralde et al.,
2005; Porter and Semenov, 2005; Sands and Edmonds, 2005; Thomson et al., 2005; Xiao et al., 2005; Zhang and Liu, 2005; Zhao et al., 2005; Abraha and Savage, 2006; Brassard and
Singh, 2007, 2008; Krishnan et al., 2007; Lobell and Ortiz-Monasterio, 2007; Xiong et al., 2007; Tingem et al., 2008; Walker and Schulze, 2008; El Maayar et al., 2009; Schlenker and
Roberts, 2009; Thornton et al., 2009a, 2010, 2011; Tingem and Rivington, 2009; Byjesh et al., 2010; Chhetri et al., 2010; Liu et al., 2010; Piao et al., 2010; Tan et al., 2010; Tao and
Zhang, 2010, 2011a,b; Arndt et al., 2011; Deryng et al., 2011; Iqbal et al., 2011; Lal, 2011; Li et al., 2011; Rowhanji et al., 2011; Shuang-He et al., 2011; Osborne et al., 2013.
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Food Security and Food Production Systems Chapter 7
7
r
egions 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, 2014). 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 summarized
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.
7.3.2.1.2. Impact of carbon dioxide and ozone
There is further observational evidence since AR4 that response to a
change in CO
2
depends on plant type: C
3
or C
4
(DaMatta et al., 2010).
The effect of increase in CO
2
concentration tends to be higher in C
3
plants
(wheat, rice, cotton, soybean, sugar beets, and potatoes) than in C
4
plants (corn, sorghum, sugarcane), because photosynthesis rates in C
4
crops are less responsive to increases in ambient CO
2
(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; Högy and Fangmeier, 2009). There is
observational evidence, new since AR4, that the response of crops to CO
2
is genotype specific (Ziska et al., 2012). For example, yield enhancement
at 200 ppm additional CO
2
ranged from 3 to 36% among rice cultivars
(Hasegawa et al., 2013).
FACE studies have shown that the impact of elevated CO
2
varies
according to temperature and availability of water and nutrients,
although the strong geographical bias of FACE studies toward temperate
zones limits the strength of this evidence. FACE studies have shown that
yield enhancement by elevated CO
2
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 CO
2
than well-watered crops, because of CO
2
-induced increases in stomatal
resistance. This suggests that rain-fed cropping systems will benefit
more from elevated CO
2
than irrigated systems.
Both the Third Assessment Report (TAR) and AR4 cited the expectation
that rain-fed systems benefit more from elevated CO
2
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 versus wet signal (Challinor and Wheeler, 2008).
O
3
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 O
3
has increased since the
preindustrial 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 CO
2
, O
3
is taken up by green leaves through stomata
during photosynthesis but, unlike CO
2
, its concentration is significantly
variable depending on geographic location, elevation, and extent of
anthropogenic sources. Being a powerful oxidant, O
3
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 poses 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 O
3
on yield at global
(Van Dingenen et al., 2009; Avnery et al., 2011a,b; Teixeira et al., 2011)
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, 2011; Piao et al., 2010; Bangladesh: Akhtar et al., 2010; Europe:
Hayes et al., 2007; Fuhrer, 2009; Vandermeiren et al., 2009). Global
estimates of yield losses due to increased O
3
in soybean, wheat, and
maize in 2000 ranged from 8.5 to 14%, 3.9 to 15%, and 2.2 to 5.5%
respectively, amounting to economic losses of US$11 to 18 billion (Avnery
et al., 2011a). O
3
may have a direct effect on reproductive process, leading
to reduced seed and fruit development and abortion of developing fruit
(robust evidence, high agreement; Royal Society, 2008).
The interactive effects of O
3
with other environmental factors such as
CO
2
, temperature, moisture, and light, are important but not well
understood. Generally, the ambient and increasing concentrations of
O
3
and CO
2
individually exert counteractive effects on C
3
plants (Tianhong
et al., 2005; Ainsworth et al., 2008; Gillespie et al., 2012), but their
interactive effect may compensate for each other (Ainsworth et al.,
2008; Taub et al., 2008; Gillespie et al., 2012). However, the losses might
be greater when elevated O
3
combines with high temperature (Long,
2012) particularly during grain filling of wheat, when elevated O
3
causes premature leaf senescence (Feng et al., 2008b, 2011). Periods of
abundant radiation and adequate water supply are favorable for both
agricultural production and the formation of surface O
3
; thus, the effects
of O
3
on crops can be difficult to detect (Long, 2012).
7.3.2.2. Other Crops
Earlier flowering and maturity have been observed (robust evidence,
high agreement) worldwide in grapes (Duchêne et al., 2010; García-
Mozo 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 CO
2
increases (El-Sharkawy, 2012; Jarvis et al.,
2012; Rosenthal and Ort, 2012).
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7.3.2.3. Pests, Weeds, Diseases
As a worldwide 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), but weeds produce the highest potential loss
(34%). 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. CO
2
and O
3
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. Ongoing wheat experiments at
Rothamsted Research Station UK, maintained for more than 160 years,
have revealed shifts in foliar wheat pathogens linked to rainfall,
temperature, and sulfur dioxide (SO
2
) emissions (Bearchell et al., 2005;
Shaw et al., 2008). Wheat rust risk has been observed to respond to El
Niño-Southern Oscillation (ENSO; Scherm and Yang, 1995). Over almost
7 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 CO
2
/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 CO
2
can reduce yield losses due to weeds for C
3
crops (soybean,
wheat, and rice), as many agricultural weeds are C
4
species; and the C
3
pathway, in general, shows a stronger response to rising CO
2
levels.
However, both C
3
and C
4
weed species occur in agriculture, and there is
a wide range of responses among these species to recent and projected
CO
2
levels (Ziska, 2010). For example, in the USA, every crop, on average,
competes with an assemblage of 8 to 10 weed species (Bridges, 1992).
CO
2
and climate can also affect weed demographics. For example, with
f
ield grown soybean, elevated CO
2
p
er se appeared to be a factor in
increasing the relative proportion of C
3
to C
4
weedy species with
subsequent reductions in soybean yields (Ziska and Goins, 2006). For
rice and barnyard grass (C
4
), increasing CO
2
favored rice, but if both
temperature and CO
2
increased simultaneously, the C
4
weed was
favored, 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 CO
2
(Ziska, 2010).
With respect to control, a number of studies have, to date, indicated a
decline in herbicide efficacy in response to elevated CO
2
and/or
temperature for some weed species, both C
3
and C
4
(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 CO
2
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.
7.3.2.4. Fisheries and Aquaculture
The natural and human processes in fisheries and aquaculture differ
from mainstream agriculture and are particularly vulnerable to 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 (Section 6.4.1.1). 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 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, 2009a). 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 (FAO, 2009a; see also Section 30.5.1.1).
Ocean acidification will also have negative impacts on the culture of
calcifying organisms (Section 30.6.2.1.4), including mollusc species of
which 14.2 million tonnes were produced by aquaculture in 2010,
equivalent to 23.6% of global aquaculture production (FAO, 2012).
There are also concerns 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
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Food Security and Food Production Systems Chapter 7
7
a
reas 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 (Sections 6.4.3,
7.2.1.2, 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 14°C 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°C or 2°C in
excess of normal maximums for 3 to 4 weeks are sufficient to disrupt the
essential relationship between endosymbiotic dinoflagellates 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, that is,
availability of aquatic foods, stability of supply, access to aquatic foods,
and utilization of aquatic products (FAO, 2009a). 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 (Section 7.5.1.1.2).
7.3.2.5. 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.
C
hange 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
CO
2
show a decrease in protein (Pikki et al., 2007; Högy et al., 2009;
Erbs et al., 2010; Ainsworth and McGrath, 2010; DaMatta et al., 2010;
Fernando et al., 2012). Meta-analysis of 228 experimental observations
finds decreases between 10 and 14% in edible portions of wheat, rice,
barley, and potato, but only 1.5% in soybeans, a nitrogen-fixing legume,
when grown in elevated CO
2
(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 phosphorus, calcium, sulfur, magnesium,
iron, zinc, manganese, and copper decline by 2.5 to 20% in wheat grain
and leaves of numerous species in elevated CO
2
, but potassium increases
insignificantly in wheat grain (Loladze, 2002; Högy et al., 2009;
Fernando et al., 2012). Since 2002, studies generally find decreases in
zinc, sulfur, phosphorus, magnesium, and iron in wheat and barley grain;
increase in copper, molybdenum, and lead (from a limited number of
studies); and mixed results for calcium and potassium (Högy et al., 2009;
Erbs et al., 2010; Fernando et al., 2012). Changes in mineral concentration
due to elevated CO
2
are determined by several factors including crop
species, soil type, tissue (tubers, leaves, or grain) and water status.
Elevated CO
2
can lower the nutritional quality of flour produced from
grain cereals (Högy et al., 2009; Erbs et al., 2010) and of cassava
(Gleadow et al., 2009). When coupled with increased crop and pathogen
biomass, elevated CO
2
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 CO
2
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 CO
2
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
these species.
Elevated O
3
concentrations appear to have the opposite effect as elevated
CO
2
. Meta-analysis of about 50 wheat experiments found that elevated
O
3
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.
Likewise, experiments examining the effect of drought on mineral
concentrations find both decreases and increases (Ghorbanian et al.,
2011; Sun et al., 2011).
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Chapter 7 Food Security and Food Production Systems
7
C
onfidence in the impact of climate, CO
2
,
and O
3
o
n food quality does
not imply confidence in changes regarding human health for several
reasons. Processing of food affects nutrient concentrations, when the
nutrient-rich outer layers of rice are removed, leaving the starch dense
endosperm. Also, elevated CO
2
can increase crop yield, thus increasing
the overall yield of 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 barely 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.
7.3.2.6. Pastures and Livestock
Pastures response to climate change is complex because, in addition to
the direct major atmospheric and climatic drivers (CO
2
concentration,
temperature, and precipitation), there are important indirect interactions
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 CO
2
will benefit C
3
species; however, warmer temperatures
and drier conditions will tend to favor C
4
species (Hatfield et al., 2011;
Izaurralde et al., 2011; Chapter 4). While elevated atmospheric CO
2
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
(WGII AR4 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., 2008a); 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 stress also limits livestock systems.
Climate change will affect the water resources available for livestock
via impacts on runoff and groundwater (Chapter 3). Populated river
basins may experience changes in river discharge, and large human and
l
ivestock 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.
7
.3.3. Sensitivity of Food Security to Weather and Climate
7.3.3.1. 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.
7.3.3.2. Accessibility, Utilization, and Stability
7.3.3.2.1. 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.
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).
The 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
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Food Security and Food Production Systems Chapter 7
7
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.
7.3.3.2.2. Climate change impacts on stability
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 indicates
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 health care (medium
evidence, high agreement; Skoufias and Quisumbing, 2005). 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 health care can lead to long-term
losses in terms of income generation and thus to future food security
(limited evidence, medium agreement; Skoufias and Quisumbing, 2005;
Hoddinot et al., 2008). 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).
7.3.3.2.3. Climate change impacts on utilization
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.,
Food access category Characteristics Impacts of food price increase on food access
Primarily subsistence
(
autarkic)
Subsistence farmers, herders, fi shers, and forest-dependent populations;
g
enerally low share of population (Karfakis et al., 2011)
Limited impact
F
ood producers: net sellers Generally lower share of population compared with net buyers (Aksoy and
Sid-Dimelik, 2008; Zezza et al., 2008; FAO, 2011)
P
ositive impact through increased income effect. Major benefi ciaries are those
with greatest surplus (e.g., larger, more commercialized farms) (FAO, 2011)
Food producers: net buyers Majority of poor rural households (IFAD, 2010; FAO, 2011) Ambiguous: depends on relative size of income and price effects, but generally
e
xpected to be negative due to high share of income spent on food (Ivanic and
Martin, 2008; FAO, 2011; Ivanic et al., 2011)
R
ural non-farming
households
R
ural landless: characterized by high rates of food insecurity; average share of
population for 15 low-income countries was 22% (Aksoy et al., 2010)
N
egative impact due to high share of income spent on food; however, some
limited evidence that wage increases may accompany price increases, in which
c
ase overall effects are ambiguous (Aksoy and Sid-Dikmelik, 2008; FAO, 2011)
U
rban consumers Growing share of population in most countries (IFAD, 2010) 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, 2008; Ivanic et al., 2011)
Table 7-1 | Households divided into fi ve categories of food access, indicating the impacts of food price increases.
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Chapter 7 Food Security and Food Production Systems
7
2
010). 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) (Alderman, 2010; Bloem et al., 2010;
Brinkman et al., 2010; Campbell et al., 2010; Sari et al., 2010). The
biological effects of climate change on nutrient content of foods are one
of the 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 CO
2
levels (Erda et al., 2005; Ainsworth and McGrath, 2010; 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).
7.3.4. Sensitivity of Land Use to Weather and Climate
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
s
tress will often lead to greater interannual 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, while 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 analogs 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,
Figure 7-5 | Summary of projected changes in crop yields, due to climate change over the 21st century. The figure includes projections for different emission scenarios, for
tropical and temperate regions, and for adaptation and no-adaptation cases combined. Relatively few studies have considered impacts on cropping systems for scenarios where
global mean temperatures increase by 4°C or more. For five timeframes in the near-term and long-term, data (n=1090) are plotted in the 20-year period on the horizontal axis
that includes the midpoint of each future projection period. Changes in crop yields are relative to late-20th-century levels. Data for each timeframe sum to 100%. Projections
taken from Abraha and Savage, 2006; Alexandrov and Hoogenboom, 2000; Arndt et al., 2011; Berg et al., 2013; Brassard and Singh, 2008; Brassard and 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; Müller et al., 2010; Osborne et al., 2013; Peltonen-Sainio et al., 2011; Piao et al.,
2010; Ringler et al., 2010; Rowhanji et al., 2011; Schlenker and Roberts, 2009; Shuang-He et al., 2011; Southworth et al., 2000; Tan et al., 2010; Tao & Zhang, 2010; Tao and
Zhang, 2011; Tao et al., 2009; Thornton et al., 2009; Thornton et al., 2010; Thornton et al., 2011; Tingem and Rivington, 2009; Tingem et al., 2008; Walker and Schulze, 2008;
Wang et al., 2011; Xiong et al., 2007; Xiong et al., 2009.
0 to –5%
–5 to –10%
–10 to –25%
–25 to –50%
–50 to –100%
0 to 5%
5 to 10%
10 to 25%
25 to 50%
50 to 100%
Range of yield change
increase
in yield
decrease
in yield
Color Legend
Percentage of yield projections
2010–2029 2030–2049 2090–2109
0
20
40
60
80
100
2070–20892050–2069
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Food Security and Food Production Systems Chapter 7
7
a
nd 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, 2009a). 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 that 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 toward the coast with hotter and
drier conditions, notwithstanding the positive impacts of elevated CO
2
(Nidumolu et al., 2012). Recent trends in climate have seen reductions
in cropping activity consistent with these projections (Nidumolu et al.,
2012).
7.4. Projected Integrated
Climate Change Impacts
7.4.1. Projected Impacts on Cropping Systems
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 3°C of local warming without
adaptation, even with benefits of higher CO
2
and rainfall (Figure 7-4).
Figure 7-5 shows projected impacts on mean crop yield in 20-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, 2014).
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 regions by 2050 averaged over crops, with wheat,
m
aize, 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: Müller et al. (2014) conducted gridded
simulations across the globe and reported an increase of more than 5%
in CV in 64% of 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).
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 anticipated 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
(Section 7.5).
300
200
100
0
–100
2020
2030 2040
Arid
(Berg et al., 2013)
Maize (Tao et al., 2009b)
Maize (Urban et al., 2012)
Rice (Tao and Zhang, 2013)
Wheat (Tao and Zhang, 2011a)
Wheat (Challinor et al., 2010)
Non-arid (Berg et al., 2013)
2050 2060 2070
2080
2090
Projection midpoint
Change in coefficient of variation of yield (%)
Figure 7-6 | Projected percentage change in coefficient of variation (CV) of yield for
wheat (Tao and Zhang, 2011a; Challinor et al., 2010), maize (Tao et al., 2009b; Urban
et al., 2012), rice (Tao and Zhang, 2013), and C
4
crops (arid and non-arid, Berg et al.,
2013). The data from Urban et al. (2012) show the range (mean plus and minus one
standard deviation) of percentage changes in CV. For the Challinor et al. (2010) data,
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. All other studies plot individual data points. A total of 81 data points are
plotted in the figure, although the underlying data consist of many thousands of crop
model simulations. The studies used a range of scenarios (Special Report on Emissions
Scenarios (SRES) A1B, A2, A1FI, and B1). Berg et al. (2013) is a global study of the
tropics, Urban et al. (2012) is for US maize, and the remaining data points are for
China.
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Chapter 7 Food Security and Food Production Systems
7
Relatively few studies have considered impacts on cropping systems for
scenarios where global mean temperatures increase by 4°C 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 5°C global average warming
(Thornton et al., 2011). Rötter et al. (2011) conclude that positive
effects of modest warming and increased CO
2
levels on crop yields
in Finland will be reversed at global temperatures increases of 4°C,
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 chill-hours 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 in eastern Washington (Stöckle et al., 2010) and
cherries in California (Lobell and Field, 2011), although CO
2
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, USA, Australia (Jones et al., 2005;
Wolfe et al., 2008; Cozzolino et al., 2010; 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 such as sugarcane
and coffee are expected to migrate toward more favorable zones in the
South (Pinto, 2007; Pinto et al., 2008; 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 toward 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 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 et al. (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
(N = 56)
(251)
(
186)
(132)
(293)
(
81)
75th Percentile
9
0th Percentile
1
0th Percentile
Median
25th
Percentile
−6
−
4
−2
0
2
4
6
8
14
Change in mean yield per decade (%)
−6
−
4
−2
0
2
4
6
8
1
0
Change in yield variability per decade (%)
Historical
t
rends
Coefficient of
v
ariation
All regions
(a) Impact of climate trend on mean crop yield
All regionsTemperate regionsTropical regions
No
a
daptation
With
a
daptation
No
a
daptation
With
a
daptation
Projected
P
rojectedHistorical
G
lobal demand rising
~
14% per decade to 2050 (FAO)
(b) Impact on year-to-year crop yield variability
Figure 7-7 | Boxplot summary of studies that quantify impact of climate and CO
2
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 in the
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.
507
Food Security and Food Production Systems Chapter 7
7
d
amage to plants to increase (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., 2011). An overview of crop and weed competitive studies
indicate that weeds could limit crop yields to a greater extent with rising
levels of CO
2
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
(Section 4.2.4.6). 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 (Section 7.3.2.3).
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 toward 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 to 20% (Smith et al., 2010), and +18 to 23% (Lobell et
al., 2013b) (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
i
nto productive arable land make estimates of cropping area increases
highly uncertain.
7.4.2. Projected Impacts on Fisheries and Aquaculture
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 (Section 6.4.3).
Simulation studies on skipjack and bigeye tuna in the Pacific under both
the Special Report on Emissions Scenarios (SRES) 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 only marginally. 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 tuna will decrease in
the Western Pacific and increase in the Eastern Pacific (Lehodey et al.,
2011; see also Sections 6.5.3, 30.6.2.1.1). These changes have important
implications for the future of national fishing fleets and canneries in
the Western Pacific (Bell et al., 2009). 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, seagrasses, 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. (2009), 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 to 35% in the west and 10 to 30% in the
east (Pratchett et al., 2011).
Frequently Asked Questions
FAQ 7.2 | How could climate change interact with change
in fish stocks and ocean acidification?
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 that 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 Colombia in South America, and some tropical Asian countries.
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Chapter 7 Food Security and Food Production Systems
7
B
rown 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 to 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 Australia’s 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 to 51% to a potential increase of 9 to 14%.
At the global scale, projections based on a dynamic bioclimatological
envelope model under the SRES A1B scenario suggested that climate
change could lead to an average 30 to 70% increase in fisheries yield
from high-latitude regions (>50°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 using 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% (quartile range,
4-22%) 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
s
uch 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 (Section 30.6.2.1.4).
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 among the most
vulnerable to climate change impacts on their fisheries. They included
countries in central and western Africa, Peru and Colombia in South
America, and four tropical Asian countries.
7.4.3. Projected Impacts on Livestock
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 Section 4.3.3.2, as well as in chapters for regions with prominent
livestock sectors (Sections 22.3.4.2, 23.4.2, 25.7.2.1). In North American
cattle systems, warming is expected to lengthen forage growing season
but decrease forage quality, with important variations due to rainfall
changes (Craine et al., 2010; Hatfield et al., 2011; Izaurralde 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 (André et al., 2011; Renaudeau et al., 2011).
The impacts of a changing UK climate on dairy cow production were
analyzed 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 GHG 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 SRES A1 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
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Food Security and Food Production Systems Chapter 7
7
Box 7-1 | Projected Impacts for Crops and Livestock
in Global Regions and Sub-Regions under Future Scenarios
R
egional impacts on crops
Region Sub-region Yield impacts (%) Scenario Reference
World
• (I) Maize: –4, –7
• (R) Maize: –2, –12
•
(
I) Rice: –9.5, –12
• (R) Rice: –1, +0.07
• (I) Wheat: –10, –13
•
(
R) Wheat: –4, –10
A1B
CSIRO, MIROC
2
050
Nelson et al. (2010)
E
ast Asia
China (I) Maize:
• –10.9 to –1.4 (–7.8 to –1.6),
• –21.7 to –9.8 (–16.4 to –10.2),
•
–
32.1 to –4.3 (–26.6 to –3.9)
(R) Maize:
• –22.2 to –1.0 (–10.8 to +0.7),
• –27.6 to –7.9 (–18.1 to –5.6),
•
–
33.7 to –4.6 (–25.9 to –1.6)
(I) Rice:
•
–
18.6 to –6.1 (–10.1 to +3.3),
• –31.9 to –13.5 (–16.1 to +2.5),
•
–
40.2 to –23.6 (–19.3 to +0.18)
+1ºC, +2ºC, +3ºC
–CO
2
(+CO
2
)
Tao et al. (2011)
Eastern China Rice:
• −10 to +3 (+7.5 to +17.5),
• −26.7 to +2 (0 to +25),
• −39 to −6 (−10 to + 25)
2030, 2050, 2080
–CO
2
(+CO
2
)
Tao and Zhang (2013)
Huang-Huai-Hai Plain, China Wheat–maize: +4.5 ± 14.8, –5.8 ± 25.8 +2ºC, +5ºC Liu et al. (2010)
North China Plain
• (I) Wheat: –0.9 (+23)
• (R) Wheat: –1.9 (+28)
A1B
2085–2100
–CO
2
(+CO
2
)
MIROC
Yang et al. (2013)
Yangtze River, China
• (I) Rice: –14.8 (–3.3)
• (R) Rice: –15.2 (–4.1)
B2
2021–2050
–CO
2
(+CO
2
)
Shen et al. (2011)
South Asia
South Asia • Maize: –16
• Sorghum: –11
2050 Knox et al. (2012)
South Asia Net cereal production –4 to –10 +3ºC Lal (2011)
India Winter sorghum: up to –7, –11, –32 A2
2020, 2050, 2080
Srivastava et al. (2010)
• (I) Rice: –4, –7, –10
• (R) Rice: –6, –2.5, –2.5
A1B; A2; B1; B2
2020, 2050, 2080
+CO
2
MIROC; PRECIS/HadCM3
Kumar et al. (2013)
• Monsoon maize: –21 to 0, –35 to 0, –35 to 0
• Winter maize: –13 to +5, –50 to +5, –60 to –21
A2
2020, 2050, 2080
HadCM3
Byjesh et al. (2010)
Northeast India
• (I) Rice: –10 to +5
• (R) Rice: –35 to +5
• Maize: up to –40
• Wheat: up to –20
A1B
2030
+CO
2
PRECIS/HadCM3
Kumar et al. (2011)
Coastal India
• (I) Rice: –10 to +5
• (R) Rice: –20 to +15
• (I) Maize: –50 to –15
• (R) Maize: –35 to +10
Western Ghats, India
• (I) Rice: –11 to +5
• (R) Rice: –35 to +35
• Maize: up to –50
• Sorghum: up to –50
Pakistan Wheat: –7, –24 (Swat); +14, +23 (Chitral) +1.5ºC, +3°C Section 24.4.4.3
• Wheat: –6, –8
• Rice: –16, –19
B2, A2
2080
Iqbal et al. (2009)
P
rojected impacts for crops and livestock in global regions and sub-regions under future scenarios. Crop yield impacts in parentheses correspond to parentheticals in
the scenario column. – CO
2
= without CO
2
effects; +CO
2
= with CO
2
effects; (I) = irrigated; (R) = rainfed. ARPEGE = Action de Recherche Petite Echelle Grande Echelle;
CSIRO = Commonwealth Scientifi c and Industrial Research Organisation; ECHAM4 = European Centre for Medium Range Weather Forecasts Hamburg 4; GFDL-
C
M2.0/2 = Geophysical Fluid Dynamics Laboratory-Climate Model 2.0/2; HadCM3 = Met Offi ce Hadley Centre Climate Prediction Model 3; HIRHAM = High-Resolution
H
amburg Climate Model; MIROC = Model for Interdisciplinary Research On Climate; MPI-OM = Max Planck Institute; MRI-CGCM2.3.2 = Meteorological Research
I
nstitute of Japan Meteorological Agency-Coupled General Circulation Model 2.3.2; PRECIS = Providing Regional Climates for Impact Studies;
R
CA3 = Rossby Centre
Regional Atmospheric Model 3.
Continued next page
510
Chapter 7 Food Security and Food Production Systems
7
Box 7-1 (continued)
Region Sub-region Yield impacts (%) Scenario Reference
W
est Asia
Yarmouk Basin, Jordan • Barley: –8, +5
• Wheat: –20, +18
–20%, +20% precipitation Al-Bakri et al. (2010)
Africa
All regions • Wheat: –17
• Maize: –5
• Sorghum: –15
• Millet: –10
2050 Knox et al. (2012)
All regions Maize: –24 ± 19 2090
+5ºC
Thornton et al. (2011)
East Africa
• Maize: –3.1 to +15.0, –8.6 to +17.8
• Beans: –1.5 to +21.8, –18.1 to +23.7
A1FI; B1
2030, 2050
HadCM3; ECHam4
Thornton et al. (2010)
Sahel Millet: –20, –40 +2ºC, +3ºC Ben Mohamed (2011)
Central &
South America
Northeastern Brazil • Maize: 0 to –10
• Wheat: –1 to –14
• Rice: –1 to –10
2030 Table 27-5; Lobell et al.
(2008)
Southern Brazil
• Maize: –15
• Bean: up to +45
A2
2080
+CO
2
HadCM3
Table 27-5; Costa et al.
(2009)
Paraguay
• Wheat: +4, –9, –13 (–1, +1, –5)
• Maize: +3, +3, +8 (+3, +1, +6)
• Soybean: 0, –10, –15 (0, –15, –2)
A2 (B2)
2020, 2050, 2080
PRECIS
Table 27-5; ECLAC (2010)
C
entral America
•
W
heat: –1 to –9
• Rice: 0 to –10
2
030 Table 27-5; Lobell et al.
(2008)
• Maize: 0, 0, –10, –30
•
B
ean: –4, –19, –29, –87
• Rice: +3, –3, –14, –63
A2
2
030, 2050, 2070, 2100
Table 27-5; ECLAC (2010)
Panama Maize: –0.5, +2.4, +4.5 (–0.1, –0.8, +1.5) A2 (B1)
2020, 2050, 2080
+CO
2
Table 27-5; Ruane et al.
(2013)
Andean region
• Wheat: –14 to +2
• Barley: 0 to –13
• Potato: 0 to –5
• Maize: 0 to –5
2030 Table 27-5; Lobell et al.
(2008)
Chile
• Maize: –5% to –10%
• Wheat: –10% to –20%
A1FI
2050
+CO
2
HadCM3
Table 27-5; Meza and Silva
(2009)
Argentina
• Wheat: –16, –11 (+3, +3)
• Maize: –24, –15 (+1, 0)
• Soybean: –25, –14 (+14, +19)
A2, B2
2080
–CO
2
(+CO
2
)
PRECIS
Table 27-5; ECLAC (2010)
North America
Midwestern United States • Maize: –2.5 (–1.5)
• Soy: +1.7 (+9.1)
+0.8ºC
–CO
2
(+CO
2
)
Hatfi eld et al. (2011)
Southeastern United States
• Maize: –2.5 (–1.5)
• Soy: –2.4 (+5.0)
United States Great Plains Wheat: –4.4 (+2.4)
Northwestern United States
• Winter wheat: +19.5, +29.5
• Spring wheat: –2.2, –5.6
A1B
2040, 2080
+CO
2
Stöckle et al. (2010)
Canadian prairies
• Small grains: –48 to +18
• Oilseeds: –50 to +25
+1ºC, +2ºC, +20% precipiation,
–20% precipitation
Kulshreshtha (2011)
Europe
Boreal Wheat, maize, soybean: +34 to +54 A2, B2
2080
HadCM3/HIRHAM, ECHAM4/RCA3
Iglesias et al. (2012)
Alpine Wheat, maize, soybean: +20 to +23
Atlantic North Wheat, maize, soybean: –5 to +22
Atlantic Central Wheat, maize, soybean: +5 to +19
Atlantic South Wheat, maize, soybean: –26 to –7
Continental North Wheat, maize, soybean: –8 to +4
Continental South Wheat, maize, soybean: +11 to +33
Mediterranean North Wheat, maize, soybean: –22 to 0
Mediterranean South Wheat, maize, soybean: –27 to +5
Continued next page
511
Food Security and Food Production Systems Chapter 7
7
Box 7-1 (continued)
Region Sub-region Yield impacts (%) Scenario Reference
Australia
S
outh Wheat: –15, –12 A2; Low, high plant available
water capacity
2
080
+CO
2
CCAM
L
uo et al. (2009)
Southeast Wheat: –29 (–25) B2, A2, A1FI
2080
–
CO
2
(+CO
2
)
CCAM
Anwar et al. (2007)
Regional impacts on livestock
Region Sub-region Climate change impacts Scenarios Reference
Africa
Botswana Cost of supplying water from boreholes could increase by 23%
due to increased hours of pumping, under drier and warmer
c
onditions.
A2, B2
2050
Section 22.3.4.2
Lowlands of Africa Reduced stocking of dairy cows, a shift from cattle to sheep and
goats, due to high temperature.
Highlands of East Africa Livestock keeping could benefi t from increased temperature.
E
ast Africa Maize stover availability per head of cattle may decrease due to
water scarcity.
South Africa Dairy yields decrease by 10–25%. A2
2
046–2065/2080–2100
ECHAM5/MPI-OM, GFDL-CM2.0/2,
MRI-CGCM2.3.2
Nesamvuni et al.
(2012)
Europe
Netherlands Dairy production affected at daily mean temperatures above 18
o
C Section 23.4.2
Italy Mortality risk to dairy cattle increased by 60% by exposure to
high air temperature and high air humidity during breeding.
French Uplands Annual grassland production system signifi cantly reduced by
4-year exposure to climatic conditions.
A2
2070
Cantarel et al. (2013)
France No impact on dairy yields. A2
1970–1999, 2020–2049, 2070–2099
ARPEGE
Graux et al. (2011)
Ireland, France Grassland dairy system increases potential of dairy production,
with increased risk of summer–autumn forage failure in France.
A1B
By the end of century
Overall Europe Spread of bluetongue virus (BTV) in sheep and ticks in cattle due
to climate warming.
2080 Graux et al. (2011)
No increase in risk of incursion of Crimean–Congo hemorrhagic
fever virus in livestock.
Section 23.4.2
Australia
Northern Australia 3
o
C increase in temperature will result in 21% reduction in forage
production for CO
2
at 350 ppm level and no change at 650 ppm
level. Changes of ±10% in rainfall were exacerbated to ±15%
change in forage production at 350 ppm CO
2
.
A1B
2030
McKeon et al. (2009)
Australia (other than Tasmania) Dairy output will decline under 1
o
C increase in temperature. A1B
2030
Section 25.7.2.1
25 sites in southern Australia Profi tability of fodder supply production declinedat most sites
due to shorter growing season.
A2
2050
Southern Australia Decline in NPP of grassland from historical climate will be 9%
in 2030, 7% in 2050, and 14% in 2070. Declines in ANPP were
larger at lower rainfall locations. Operating profi t (at constant
prices) fell by an average of 27% in 2030, 32% in 2050, and 48%
in 2070.
A2
2030, 2050, 2070
Moore and
Ghahramani (2013)
Tasmania Dairy yields increase 0.5–6.2% A1B,
ECHAM5/MPI-OM
2050
Hanslow et al. (2014)
Victoria Dairy yields decrease 1.3–6.7%
New South Wales Dairy yields decrease 1.4–6.6%
Southern Australia Dairy yields decrease 2.2–8.1%
New Zealand Change in agricultural production:
• Dairy: –2.8%, –4.3%
• Sheep and beef: –6.1%, –8.8%
2030
Global temperature change 25%,
75% of the way between lower and
upper bounds of scenarios in IPCC
2001 Third Assessment Report.
Wratt et al. (2008)
Continued next page
512
Chapter 7 Food Security and Food Production Systems
7
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 northward to 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 harbor 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
ENSO 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 summarizes 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
(St. Clair and Lynch, 2010; Ericksen et al., 2011; Berg et al., 2013) 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; see also Chapter 24). As
shown in Box 7-1, in spite of uncertainties in responses at regional/
national and subnational 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; see also 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 are
also negatively affected by climate change in almost all the continents,
as evidenced by the regional chapters of Working Group II. 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 (Chapters 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.
7.4.4. Projected Impacts on Food Prices
and Food Security
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; Calzadilla
et al., 2013; Lobell et al., 2013b; Nelson et al., 2013). Many of the studies
did not include CO
2
effects, which is sometimes justified on the grounds
that studies are concerned with “worst-case” scenarios, or that the bias
from omitting positive CO
2
effects balances the known bias from omitting
negative effects of elevated O
3
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 (Section 7.2.2).
Based on the studies cited above, it is very likely that changes in
temperature and precipitation, without considering effects of CO
2
, will
lead to increased food prices by 2050, with estimated increases ranging
from 3 to 84%. The combined effect of climate and CO
2
change (but
ignoring O
3
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., 2014) is that the choice of economic model
matters at least as much as the climate or crop model for determining
Box 7-1 (continued)
Region Sub-region Climate change impacts Scenarios Reference
Central
and South
America
A
ndean Mountain countries Beef and dairy cattle, pigs, and chickens could decrease between
0.9 and 3.2% while sheep could increase by 7%.
2
060
Hot and dry scenario
S
ection 27.3.4.1
Colombia, Venezuela, and
Ecuador
Beef cattle choice declined. 2060
Milder and wet scenario
A
rgentina and Chile Beef cattle choice increased. Future climate change
Pernambuco, Brazil Milk production and feed intake in cattle strongly affectetd. Future climate change Silva et al. (2009)
North
America
Central United States Dairy yields decrease 16–30%. Baseline CO
2
, 2× CO
2
, 3× CO
2
CGCMI/Hadley
Mader et al. (2009)
513
Food Security and Food Production Systems Chapter 7
7
p
rice 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 to 25 million (or 17 to 22%), with the range including
projections with and without CO
2
fertilization. Lloyd et al. (2011) used
the projected changes in undernourishment from Nelson et al. (2009)
to project the impact of climate change on human nutrition, estimating
a relative increase in moderate stunting of 1 to 29% in 2050 compared
with a future without climate change. Severe stunting was projected to
increase by 23% (central Africa) to 62% (South Asia).
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 CO
2
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 socioeconomic 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, 2010), 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)
a
nd can form part of compound events that are driven by common
external forcing (e.g., El Niño), 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. Adaptation and Managing Risks in
Agriculture and Other Food System
Activities
7.5.1. Adaptation Needs and Gaps
Based on Assessed Impacts and Vulnerabilities
7.5.1.1. Methods of Treating Impacts in
Adaptation Studies—Incremental to Transformational
The pervasiveness of climate impacts on food security and production
(Section 7.2), the commitment to future climate change from past GHG
emissions (WGI AR5 SPM), and the very high likelihood of additional and
likely greater climate changes from future GHG emissions (WGI AR5
SPM) mean 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 (WGII
AR4 Chapter 5; Section 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 (Section 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 that 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 while 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 favorable for effective adaptation
514
Chapter 7 Food Security and Food Production Systems
7
i
ncluding investment in new technologies, infrastructure, information,
and engagement processes (Sections 14.3-4, 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 are initially addressed separately below.
7.5.1.1.1. Cropping
Effective adaptation of cropping could be critical in enhancing food
security and sustainable livelihoods, especially in developing countries
(WGII AR4 Chapter 5; Section 9.4.3.1). 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 (Fujisawa and Koyabashi, 2010;
Olesen et al., 2011; see also Section 9.4.3.1), 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 due to 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; Deressa et al., 2009; Magrin et al., 2009; Mary and Majule,
2009; Meza and Silva, 2009; Tingem and Rivington, 2009; Travasso et
al., 2009; Laux et al., 2010; Shimono et al., 2010; Stöckle et al., 2010;
Tao and Zhang, 2010; Van de Geisen et al., 2010; Olesen et al., 2011;
Cho et al., 2012). Aggregated across studies, changing planting dates
may increase yields by a median of 3 to 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
a
daptations 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 AR5 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 optimization of crop varieties and planting schedules
appears to be effective adaptations, increasing yields by up to 23%
compared with current management when aggregated across studies
(medium evidence, high agreement; Table 7-2). 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; Baethgen, 2010; Li et al., 2010; Sultana
et al., 2010) although such forecasts are not always useable or useful
(Lemos and Rood, 2010; Dilling and Lemos, 2011; see also Section 9.4.4).
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
Frequently Asked Questions
FAQ 7.3 | How could adaptation actions enhance food security and nutrition?
More than 70% 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 contribute 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.
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owing to increased climate extremes, water limitations, and various
institutional barriers (Alcamo et al., 2007; Bindi and Olesen, 2011;
Dronin and Kirilenko, 2011; Kulshreshtha, 2011; Kvalvik et al., 2011;
Tchebakova et al., 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; Luo et al., 2009; Wassmann et al., 2009;
Shimono et al., 2010; Stöckle et al., 2010), noting that a new cultivar
usually takes between 8 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 CO
2
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, 2011a), 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; Lioubimtseva and Henebry, 2009; Luo et al., 2009;
Falloon and Betts, 2010; Piao et al., 2010; Olesen et al., 2011), among
many other possible adaptations (Sections 22.4.2, 22.4.3). 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
optimization 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
(Section 3.7).
Diversification of activities is another climate adaptation option for
cropping systems (Lioubimtseva and Henebry, 2009; Thornton et al.,
2010). For example, Reidsma and Ewert (2008) found that regional farm
diversity reduces the risk that is currently associated with unfavorable
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 and Mendelsohn, 2008; Seo, 2010; Seo et al.,
2010). In some cases, increased diversification outside of agriculture
may be favored (Coulthard, 2008; Mary and Majule, 2009; Mertz et al.,
2009a,b).
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 WGII AR4 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 WGII AR4 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 fertilization (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,
7-5) mean a substantial challenge in ensuring increases in crop production
of 14% per decade given a population of 9 billion people in 2050. This
could be especially so for tropical wheat and maize, where impacts from
increases in temperature of more than 3°C may more than offset benefits
from agronomic adaptations (limited evidence, medium agreement).
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,
Management
option
Cultivar adjustment
(N = 56)
Planting date
adjustment
(N = 19)
Planting date and
cultivar adjustment
(N = 152)
Irrigation
optimization
(N = 17)
Fertilizer
optimization
(N = 10)
Other
(N = 9)
B
enefi t (%) from
using adaptation
2
3
(6.8, 35.9)
3
(2.1, 8.3)
1
7
(9.9, 26.1)
3
.2
(2, 8.2)
1
(0.25, 4.8)
6
.45
(3.2, 12.8)
Table 7-2 | The simulated median benefi t (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. N represents the number of estimates used for each adaptation. The numbers in
parentheses are the 25th and 75th percentiles. Data points where assessed benefi ts of management changes are negative are not included as farmers are unlikely to adopt these
intentionally. Only studies with both a “no adaptation” and an “adaptation” assessment are used. Data taken from Rosenzweig et al. (1994); Karim et al. (1996); El-Shaher et al.
(1997); Lal et al. (1998); Moya et al. (1998); Yates and Strzepek (1998); Alexandrov (1999); Kaiser (1999); Reyenga et al. (1999); Southworth et al. (2000); Tubiello et al. (2000);
DeJong et al. (2001); Aggarwal and Mall (2002); Alexandrov et al. (2002); Corobov (2002); Easterling et al. (2003); Matthews and Wasmann (2003); Droogers (2004); Howden
and Jones (2004); Butt et al. (2005); Erda et al. (2005); Ewert et al. (2005); Gbetibouo and Hassan (2005); Xiao et al. (2005); Zhang and Liu (2005); Abraha and Savage (2006);
Challinor et al. (2009); Tingem and Rivington (2009); Thornton et al. (2010); Deryng et al. (2011); Lal (2011); Tao and Zhang (2011b).
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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;
WGII AR4 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 that 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).
7.5.1.1.2. Fisheries
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
s
trengthen the livelihood asset base; improved understanding of the
existing response mechanisms to climate variability to assist in adaptation
planning; recognizing 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 minimize 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; Daw et al.,
2009; FAO, 2009a; MacNeil et al., 2010; Koehn et al., 2011). Fishers and
fish farmers will be vulnerable to extreme events such as flooding and
storm surges that will require a range of adaptations including developing
early warning systems for extreme events, provision of hard defenses
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 maximize resilience of the ecosystem, and to be adaptive
and flexible to allow for rapid responses to climate-induced change (Daw
et al., 2009; FAO, 2009a; see also Section 6.4.2). 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 (1) have a core objective to encourage
ecosystems that are resilient to change and (2) 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, 2009a; 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%
per year 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
12345
(N = 263)
Yield change difference (%)
6
0
8
0
4
0
2
0
0
Local mean temperature change (C°)
F
igure 7-8 | Simulated yield benefit from adaptation calculated as the difference
b
etween 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 unpaired studies documented
in Figure 7-4 for tropical maize.
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Food Security and Food Production Systems Chapter 7
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l
ocations 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. (2011) 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 (see also Section 30.6.2.1.1). 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 maximizing 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.
7.5.1.1.3. Livestock
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., 2009a). These adaptations include
matching stocking rates with pasture production; adjusting herd and
watering 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 favor
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 that 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,
2008; 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
(
Section 7.3.2.6). For example, some graziers in Africa are already
making changes to stock holdings in response to shorter term variations
in temperatures (Thornton et al., 2009a; see also 9.4.3.1). 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., 2013). In cooler climates, warming may be advantageous because
of lesser need for winter housing and feed stocks.
7.5.1.1.4. 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 ensured the source of food to hunters and reduced various
risks (Alessa et al., 2008; Ford, 2009; 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 (Goodman-Elgar, 2008;
Renard et al., 2011; McDowell and Hess, 2012; see also Sections 9.4.3.1,
27.3.4.2). 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 ensure food security of certain
groups (Salick and Ross, 2009; Green et al., 2010; Marin, 2010; Speranza
et al., 2010; Kalanda-Joshua 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 (Rengalakshmi,
2007; Turner and Clifton, 2009; Kalanda-Joshua et al., 2011; see also
Section 9.3.5), 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 indigenous knowledge
(Speranza et al., 2010; Kalanda-Joshua 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 indigenous
knowledge, may contribute to limit the adaptation to climate change
in many regions (medium evidence, medium agreement; Nakashina et
al., 2012).
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7.5.1.2. Practical Regional Experiences of Adaptation,
Including Lessons Learned
Given the early stages of climate change, there are relatively few
unequivocal examples of adaptation (Section 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 (Smit and Wandel, 2006; Mertz et al., 2009a;
Chen et al., 2011; Odgaard et al., 2011; see also Section 9.4.3.1). 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 (Fujisawa and
Koyabashi, 2010; Olesen et al., 2011; Bohensky et al., 2012; Section
9.4.3.1). 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).
7.5.1.3. 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 (robust
evidence, high agreement; Chapters 14, 15, 16). Several barriers to
adaptation of food systems have been raised including inadequate
information on the climate and 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 (Bryan et al., 2009; Deressa
et al., 2009; Kabubo-Mariara, 2009; De Bruin and Dellink, 2011, Silvestri
et al., 2012; see also Chapter 16). 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 (WGII AR4 Chapter 5).
7.5.1.4. 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
c
ommunity. 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 (Section
14.7). A key maladaptation would be one which increased emissions of
GHGs, this making the underlying problem worse (robust evidence, high
agreement; Smith and Olesen, 2010; WGIII AR4 Chapter 11). A recent
review of agricultural climate change adaptation options found they
tend to reduce GHG 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 and for increasing soil carbon, 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, however, and this may result in large
opportunity costs that could arise from not considering more systemic
adaptation or more transformative change (limited evidence, medium
agreement; Howden et al., 2010; Kates et al., 2012). 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 (Mäder et
al., 2006; McCrum et al., 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 sectors 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 that identify mitigation options, their relative
cost, and the potential size of emission reductions (WGIII AR4 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 some options 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.
7.5.2. 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
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iterature 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 et al., 2010), and in
fact is usually a response to a complex of factors. This case, of the zaï
soil management practice in the Sahel region, is an example of a complex
of factors driving 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; Zougmoré et al., 2010). Zaï, 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 to 40 cm in diameter and 10 to 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
zaï technique is very labor intensive, requiring some 60 days of labor per
hectare. Innovations to the system, involving animal-drawn implements,
can reduce labor 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 fertilizer use and irrigation, and especially greater labor 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 to markets, or employment.
P
arents 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.
7.5.3. 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 optimization; Section 7.5.1.1.1). 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 CO
2
levels. Limits to adaptation will increasingly emerge for
such incremental adaptations as the climate further changes, raising the
need for more systemic or transformational changes (limited evidence,
medium agreement; Section 7.5.1.1). 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 as a result of rainfall reduction and temperature
increase (medium confidence, limited evidence; Section 7.5.1.1.1).
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
Key risk Adaptation issues & prospects
Climatic
drivers
Risk & potential for
adaptation
Timeframe
Present
2°C
4°C
Very
low
Very
high
Medium
Near term
(2030 – 2040)
Long term
(2080 – 2100)
Reductions in mean crop yields
because of climate change and
increases in yield variability.
(high confidence)
[7.2, 7.3, 7.4, 7.5, Box 7-1]
With or without adaptation, negative impacts on average yields become likely
from the 2030s with median yield impacts of 0 to –2% per decade projected for
the rest of the century, and after 2050 the risk of more severe impacts increases.
C
OO
C
OO
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.
Carbon dioxide
fertilization
C
OO
Ocean
acidification
C
OO
Climate-related drivers of impacts
Warming
trend
Extreme
precipitation
Extreme
temperature
Level of risk & potential for adaptation
Potential for additional adaptation
to reduce risk
Risk level with
current adaptation
Risk level with
high adaptation
Drying
trend
520
Chapter 7 Food Security and Food Production Systems
7
c
ombination (Sections 7.5.1.1.2-3). 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 (Section 7.5.1.1.3). Fishery adaptations include
management approaches and policies that strengthen the livelihood asset
base, take a risk-based ecosystem approach to managing the resource,
and adopt multi-sector adaptive strategies to minimize negative impacts.
Importantly, there is an emerging recognition that existing fishery
management tools and strategies are necessary but not sufficient for
adaptation to climate (Section 7.5.1.1.2).
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;
Section 7.5.1.1.4).
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; Section 7.5.1.1). 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.
7.6. Research and Data Gaps—Food Security
as a Cross-Sectoral Activity
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 to
6°C above preindustrial), 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,
e
valuation, 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 specifically 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 a 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.
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