Cpp

TMa

b

c

a

ARRAA

KSUCCH

1

irtgKva2bsdlo

h1

Ecological Indicators 67 (2016) 830–838

Contents lists available at ScienceDirect

Ecological Indicators

jo ur nal ho me page: www.elsev ier .com/ locate / ecol ind

limate change and indicators of probable shifts in the consumptionortfolios of dryland farmers in Sub-Saharan Africa: Implications forolicy

.S. Amjath-Babua,∗, Timothy J. Krupnikb, Sreejith Aravindakshanb,c,uhammad Arshada, Harald Kaechelea

Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of Socio-Economics, Eberswalder Str. 84, 15374 Müncheberg, GermanyInternational Maize and Wheat Improvement Center (CIMMYT), House 10/B, Road 53, Gulshan-2, Dhaka 1213, BangladeshFarming Systems Ecology (FSE), Wageningen University, the Netherlands

r t i c l e i n f o

rticle history:eceived 4 June 2015eceived in revised form 18 January 2016ccepted 15 March 2016vailable online 25 April 2016

eywords:econdary impactsncertainty

a b s t r a c t

Several studies estimate the immediate impact of climate change on agricultural societies in terms ofchanges in crop yields or farm income, though few studies concentrate on the immediate secondaryconsequences of climate change. This synthetic analysis uses a set of indicators to assess the repercus-sions of predicted income reductions resulting from climate change on food consumption, nutrition,health expenditure, education, and recreation in Zimbabwe, Cameroon, South Africa and Ethiopia. Wealso assess the potential decline in human development potential among smallholder dryland farmers inthese sub-Saharan African countries. In contrast to previous efforts, the current study directly integratesthe uncertainties in estimations of income changes and secondary consequences through a weighting

onsumptionlimate changeuman development

scheme. The results reveal moderate to high levels of secondary impacts which could lead to increasedvulnerability to diseases, susceptibility to nutritional disorders, deprivation of educational opportuni-ties, and ultimately to a reduction in human and societal development potential among the considerednations. The article concludes by proposing a portfolio of policy options for ameliorating the secondaryimpacts of climate change in these sub-Saharan African countries.

© 2016 Elsevier Ltd. All rights reserved.

. Climate impact assessments

The anticipated effects of climate change on dryland agriculturen Sub-Saharan Africa (SSA) tends to be higher compared to otheregions of the world, due largely to the higher baseline tempera-ures, and lower precipitation rates than found elsewhere in thelobe (O’Brien and Leichenko, 2000; Kurukulasuriya et al., 2006;otir, 2011; Müller et al., 2011). Given the projected increasedariability in precipitation and rising temperatures, considerabledverse impacts on farm production are expected (e.g., Parry et al.,004; Schlenker and Lobell, 2010), which will in turn affect the via-ility of dryland agriculture (Mendelsohn, 2008; Seo, 2010). Thisituation is compounded by the limited adaptive ability of many

ryland farmers that stems from their dependence on precipitation,

ow-income, lack of alternative livelihood options, relative absencef safety nets (e.g.: weather insurance) and poor institutional

∗ Corresponding author. Tel.: +49 33432 82416.E-mail address: amjath.babu@zalf.de (T.S. Amjath-Babu).

ttp://dx.doi.org/10.1016/j.ecolind.2016.03.030470-160X/© 2016 Elsevier Ltd. All rights reserved.

resources necessary to hedge against climate change (Thomaset al., 2007). Adaptation is nonetheless not elusive; many exampleswhere communities are adapting to the current and anticipatedeffects of climate change have been documented (Gbetibouo andHassan, 2005; Thornton et al., 2010), although such efforts may notprevent a reduction in household income derived from agriculturalpursuits (Kurukulasuriya and Mendelsohn, 2008).

Barrios et al. (2008) showed that compared to other devel-oping regions, changes in climate as measured from the 1960scan account for a large proportion of the production deficit inSSA. The prevailing climate, specifically the quantity and timingof precipitation, plays a leading role in influencing regional agri-cultural output and poverty levels. For these reasons, it is critical tounderstand the influence that climate change may have on drylandagriculture in SSA. A handful of approaches (statistical, economet-ric, and process based) are available, each of which quantifies the

impacts of climate change on rainfed agriculture in SSA in terms ofchanges in crop yields and resulting farm income, given projectionsbased on futuristic climatic scenarios. Most statistical and processbased models predict yield changes. Ricardian analysis by contrast

gical I

pisroatdvbRmu

imdpsStafrntoAnisp2aclsfa

1

mpcACipecp(instfbsss

cs

T.S. Amjath-Babu et al. / Ecolo

redicts potential changes in household income resulting from thempact on agricultural production (Mendelsohn, 2008). The use ofpatial analogues [spatial climatic analogues are those with a cur-ent climate resembling the expected future climate of a given siter region (Vermeulen et al., 2012)], which underlie the Ricardianpproach, is intuitively appealing. This analogy enables the quan-ification of temporal changes in income for farming householdsue to climatic shifts, using a cross section of data of relevantariables. Assuming that farmers of a given region have to theest of their abilities adapted to prevailing climatic conditions, theicardian approach also accounts for local adaptive measures, viz.odifying the crops or cultivars grown, adjusting planting dates, or

tilizing other changes in agronomic management (Deressa, 2007).Irrespective of the approach utilized to study the potential

mpacts of climate change, it is important to utilize the infor-ation generated to develop policy measures that may assist

ryland smallholders in adapting to the changing climate. In thisaper, we briefly discuss some of the policy measures alreadyuggested to improve the adaptive capacity of dryland farmers inSA. Most policies suggested in the literature focus on mitigatinghe primary impacts of climate change, i.e. reduced crop yieldsnd resulting food and/or income deficits. Conversely, few studiesocus on secondary impacts, resulting in a knowledge gap withespect to the expected impacts on longer-term food consumption,utrition, health expenditure, and education in SSA. We respondo this problem by analyzing the anticipated secondary impactsf climate change among dryland farmers of selected Sub-Saharanfrican countries using a set of indicators. By doing so, we provideew insight on critically important policy options to be considered

n preparing for the projected impacts of climate change formallholder dryland farming communities in SSA. Excepting com-utable general equilibrium models, or CGEs (e.g.: Calzadilla et al.,013) that analyze welfare impacts from climatic changes, therere few studies that look beyond the impacts on yield or incomehanges due to climate change in Sub-Saharan Africa. Given theimitations of using CGE models in African context, developing aet of relevant indicators to assess the secondary impacts resultingrom climate changes is critical in developing informed policyssessments and options.

.1. Policy suggestions so far

The major policy suggestions from previous studies on cli-ate change adaptation in SSA are reviewed below. In this

aper, we selected dryland (rainfed) smallholder agriculture inountries spanning West/Central Africa (Cameroon), Southernfrica (Zimbabwe, South Africa), and the Horn of Africa (Ethiopia).ollier et al. (2008) suggests that where negative climate change

mpacts are anticipated, three types of adaptive policy options areossible, including (1) altering farm management in response to theffects of climate change (for example, use of irrigation or changingrop choice), (2) sectoral shifts in employment, for example step-ing out of subsistence farming and moving into wage labor, or3) relocation (e.g. migration from rural areas and increased urban-zation). However, Collier et al. (2008) caution that relocation mayot be an attractive option in SSA because of political restrictions,trong ethnic identities that may cause clashes following reloca-ion, and problems with land tenure arrangements. The potentialor stepping out of agriculture and into another sector is also limitedy the restricted absorption capacity of alternative employmentectors. The slower growth of the non-agricultural and industrialector also poses problems; as such, it cannot be expected that these

ectors can absorb an influx of former farmers with ease.

The remaining option is to encourage farmers to modify theirrop management techniques, which is the major focus of mosttudies focused on rural adaptation to climate change in SSA

ndicators 67 (2016) 830–838 831

(Stringer et al., 2009). In dryland agriculture, some technical andagronomic suggestions include improved agricultural water man-agement (installation of irrigation, use of mulching, water run-offharvesting, check-dams, some forms of conservation agriculture,contour bunds, increased application of organic materials to thesoil, and other means to improve water infiltration and soil mois-ture storage), adjustment in farm or crop management strategies,for example shifting planting dates to better coincide with rainfallor to escape heat stress, or the use of drought tolerant or less waterconsumptive crops and cultivars, etc. (Below et al., 2012; Knoxet al., 2012; Amjath-Babu et al., 2016). Several of these suggestionshave been backed by crop modeling efforts (Jones and Thornton,2003). Other options include agroforestry, crop diversification, orintegration of new enterprises to hedge against risk, for exampleintegrated crop-livestock and biologically diverse farming systems(Stringer et al., 2009; Palm et al., 2010). Conversely, irrigation iswidely acknowledged as a ‘best-bet’ strategy to avoid the nega-tive effects of climate change, although the cost of investment insufficiently large irrigation schemes is usually prohibitive, not tomention the social and managerial complexities of their opera-tion, especially where collective action may be required to optimizewater allocation and use (Collier et al., 2008; Krupnik et al., 2012).

Other more drastic options to buffer agriculture against cli-mate change include extensification of cultivation and liquidationof livestock and other assets to purchase food (Cooper et al., 2008),although both options have negative consequences, for exam-ple biodiversity loss and the undermining of household incomesecurity (Tilman et al., 2011; Tittonell, 2013). Moreover, imple-mentation of these approaches may encounter physical, social,institutional and economic obstacles. In South Africa and Ethiopia,for example, major hurdles to adaptation include a lack of creditaccess, dearth of land for expanding cultivation (especially in popu-lation dense areas), a scarcity of water for irrigation, and insufficientinformation and knowledge among farmers and policy makersalike regarding viable adaptation strategies (Bryan et al., 2009).Other studies propose agricultural risk management options (e.g.,weather forecasting and climatic information services) and safetynet mechanisms such as crop and weather index insurance. How-ever, the latter is typically more suitable to buffer against climaticvariability and weather shocks, rather than longer-term shifts inclimate (Vermeulen et al., 2012).

Given the general lack of success of implementing policy aimedat higher-yielding crop management practices and varieties inmuch of SSA (Kates, 2000; Maddison, 2007), constraints may also beencountered in the similarly complex task of encouraging uptakeof climate change adaptation policies. The high rates of poverty,poor market and transport networks are some of the myriad fac-tors that slow agricultural technology adoption in SSA (Dinar et al.,2008; Amjath-Babu et al., 2016). It is also reported that older farm-ers are less willing to experiment with new technologies (Shiferawand Holden, 1998). For example, use of heat tolerant varieties(Tingem and Rivington, 2009) and use of irrigation are not yetwidely practiced in SSA (Kurukulasuriya et al., 2006; Lobell et al.,2008; Calzadilla et al., 2013). But most importantly, the majoradaptation options suggested above are for the most part aimedat preventing more near-term adverse impacts of climate changeon crop productivity or farm income. As such, adaptation to thesecondary and potentially chronic effects of climate change – forexample health and education impacts on farming communitiesresulting from reduced yields and farm income – and on societaldevelopment, are given less emphasis.

In this paper, we address these secondary impacts of climate

change on dryland agricultural communities in SSA, by assessinghow reduced income stemming from climate variability mightaffect food consumption, the enrollment of children in educationalprograms, and human health. We identify potential policy options

8 gical I

fttnBcatsubu

2

toERfhiDZo

I

wmoaeAdtgmYipeTaT

ewmrclsptZwuctiiuciu

32 T.S. Amjath-Babu et al. / Ecolo

or reducing these secondary, societal-level impacts to minimizehe negative effect on human and societal development. Our inves-igation is limited to persistent shifts in climatic patterns and doesot consider climatic shock events such as drought or flooding.ecause of the lack of research examining the relationship betweenlimate change and these secondary consequences in SSA (Conwaynd Schipper, 2011), we respond by developing a set of indicatorshat can be used to assess the secondary impacts and frame discus-ion on appropriate policy options. This research also addresses thencertainty associated with the future impacts of climate changey presenting a novel method of communicating it by introducingncertainty weights to projected impacts.

. Methods

By moving beyond the consideration of income levels alone,his study considers the secondary implications of climate changen dryland farmers in Zimbabwe, Cameroon, South Africa andthiopia. We use a synthetic-analytical framework consideringicardian studies that estimate the relationship between dryland

arm income and climatic variables using quadratic equations thatighlight the non-linear relationship between variables. The stud-

es considered include Molua and Lambi (2006) for Cameroon,eressa (2007) for Ethiopia, Mano and Nhemachena (2007) forimbabwe and Benhin (2008) for South Africa. The general versionf the quadratic equation is:

c = ˇ1Lc + ˇ2L2 + ˇ3Oc + ˇ4O2c (1)

here Ic is the net revenue (farm income), Lc is the set of cli-atic variables (temperature and precipitation) and Oc is the set

f variables that influences farm productivity and income suchs soil quality, soil moisture level and holding capacity, etc. forach country “c”. Strict Ricardian estimates of dryland farming infrica were limited to Zimbabwe and South Africa. In Ethiopia, theataset is comprised primarily of dryland farmers (95%). We usehese coefficients as representative of dryland smallholders moreenerally. In Cameroon, dryland farmers were not segregated, butajority of the land is unirrigated (irrigated area is less than 1%;

engoh et al., 2012). Subsequently, the percentage change in farmncome (�Rc) in response to the predicted changes in mean tem-erature and rainfall (from general circulation models (GCMs)) isstimated in each study using these Ricardian equations for 2050.he coefficients of the Ricardian regressions (for climatic variables)nd the impact on income (Rc) of dryland farmers are presented inable 1.

Another question of related importance is “how reliable are thesestimates?”: to answer this question, we developed an uncertaintyeight for each of the estimates covered in this study. The threeajor sources of uncertainty include (1) estimates of regression

elations, especially because Ricardian regressions are sensitive tohanges in rainfall (Roudier et al., 2011); (2) climate model under-ying the temperature and rainfall estimates. Model uncertaintytems from sources such as limitations in understanding the com-lex climatic system, e.g.: the interaction among the global drivershat determine African climate viz. the Inter Tropical Convergenceone, El Nino – Southern Oscillation and the West African Monsoon,hich remain relatively poorly understood (Collier et al., 2008), andncertainties in climatic models themselves (model physics andalibration uncertainty, spatial resolution issues, etc.). (3) Assumedrajectories of greenhouse gas emissions. The lower predictabil-ty of the global emission trajectory (including land use changes)s a major source of uncertainty that must be dealt with. These

ncertainties should be considered as inherent features in climatehange policy assessment (Antle and Capalbo, 2010), highlight-ng the importance of and necessity for tools to represent thesencertainties.

ndicators 67 (2016) 830–838

Quantification of the uncertainty weight was performed byusing the appropriate indicators. Firstly, relative deviation of theestimated change in income predicted by the Ricardian model froman estimated deviation in production from a statistical crop modelwas calculated, following Schlenker and Lobell (2010). Relativedeviation was taken as the indicator for the uncertainty stemmingfrom the Ricardian method. Secondly, the relative deviation of tem-perature and rainfall estimates used in each study (from individualclimatic models) from the levels projected by an ensemble of cli-matic models (cf. World Bank, 2014) is assessed. The calculateddeviation was taken as an indicator of uncertainty from the selec-tion of the climatic model for Ricadian regression based assessment.Thirdly, the relative deviation in temperature and rainfall from theA2 and B1 scenarios of the GCMs (detailed below) were selectedto represent the uncertainty from the assumed trajectory (WorldBank, 2014). These values of uncertainty indicators were then stan-dardized and equally weighted to frame a composite uncertaintymeasure (∝c). ∝c was thereafter used as a weighting factor forincome change estimates from the Ricardian regressions as follows:

∝c = 1k

(∣∣∣�Rc − �Mc

�Rc

∣∣∣z+

∣∣∣∣�PA2cr − �PA2

ce

�PA2cr

∣∣∣∣z

+∣∣∣∣�TA2

cr − �TA2ce

�TA2cr

∣∣∣∣z

+∣∣∣∣�PA2

c − �PB1c

�PA2c

∣∣∣∣z

+∣∣∣∣�TA2

c − �TB1c

�TA2c

∣∣∣∣z

)(2)

where k is the number of uncertainty factors, �Rc is the Ricardiandeviation estimate of income (%) for a given country c, �Mc aremodel estimates of deviation in crop output (%) for a country c,and �PA2

cr and �TA2cr are the changes precipitation and temperature

levels predicted by the GCM model used in the Ricardian study. Fur-thermore, �PA2

ce and �TA2ce represent changes in precipitation and

temperature levels, respectively, as predicted by the GCM modelensemble. �PA2

c and �PB1c are the precipitation levels predicted by

the A2 (relatively higher emission path) and B1 (relatively loweremission path) scenario (see an explanation in IPCC, 2014) pre-dicted by the GCM model ensemble, while �TA2

c and �TB1c are

the temperature levels predicted by A2 and B1 scenario predictedby GCM model ensemble. Finally, z indicates that the values arestandardized.

It should be noted that the farm income measure used in Ricar-dian approaches can be comprised of multiple crop enterprises,unlike the crop modeling studies which tend to be based on individ-ual crops. So for the calculation of the first measure of uncertainty,maize, which is widely cultivated as a core staple and income gen-erating crop in East and Southern Africa, is used as an indicatorcrop. As such, the relative deviation in farm income (�Rc) pre-dicted by the Ricardian models is compared to relative deviationin maize yields predicted by crop models (�Mc) as a measure ofuncertainty in the Ricardian assessment. Given the fact that fam-ily labor is the major input in smallholder systems in sub-SaharanAfrica, crop yield predominantly determines income. In this study,we do not consider the influence of potential price changes onfarm income due to potential production declines resulting fromnegative climate change effects, though CGE based studies indi-cate that the agricultural commodity prices in SSA may increase3–7% by 2050s, as a result of their increasing scarcity (Calzadillaet al., 2013). Within the set of Ricardian studies, the results changeaccording to the climate model used to predict rainfall and tem-perature regimes. In order to allow a comparison, we selected theresults of two atmospheric and oceanic coupled climate models

commonly used in all studies reported in this article viz. CGCM2(Flato et al., 2000) and HADCM3 (Johns et al., 2003). To accountfor the uncertainty created by model selection, we used the rela-tive deviation of temperature and rainfall predicted by the model

T.S. Amjath-Babu et al. / Ecological Indicators 67 (2016) 830–838 833

Table 1Ricardian regressions (climate variables alone) and predictions of income impacts.

South Africa Zimbabwe Cameroon EthiopiaCoefficients Coefficients Coefficients Coefficients

Climate variablesSummer temperature −39.124** 18.93** −4495.21**

Summer temperature squared −11.8* −1.317*** −4.61** 84.85+Winter temperature 122.86** 374.26 384.48Winter temperature squared 38.55*** −2.343** −6.45 −35Spring temperature −43.73* −1740.6+Spring temperature squared 1.796 −1.186* 5.83 49.4**

Fall temperature −108.71** −23.58 6743.39***

Fall temperature squared −17.65** 3.42** 48.27 −133.4**

Winter precipitation 141.03 0.71** −1148.63***

Winter precipitation squared 0.3804*** 1.631* 2.62*** 16.11***

Spring precipitation 0.23** 656.62***

Spring precipitation squared 1.22*** −1.026* −3.22*** −2.98***

Summer precipitation 73.87*** 146.69* 1.02 112.3***

Summer precipitation squared 0.0166 −0.417*** 4.29 −0.48***

Fall precipitation −6.355** 0.09 −525.18***

Fall precipitation squared 0.16* 5.25 3.06***

Predicted change in climate derived from coupled atmospheric-oceanic GCMs 2050 2050 2050 2050CGCM2 Temperature (◦C) 3.6 3.47 3.4 3.26

Rainfall (cm) −2.64 −1.1 0 −12.02HADCM3 Temperature 3.9 3.88 3.7 3.82

Rainfall (cm) −5.28 −10.22 2.8 4.06Predicted revenue change in percentages 2050 2050 2050 2050CGCM2 −27 −38 12.5 −9.7HADCM3 −27.4 −91 −21.4 −100

Sources: Benhin (2008), Mano and Nhemachena (2007), Molua and Lambi (2006) and Deressa (2007).

(dWu

nlfaseStEcR(FR

�

w‘agowDc

�

wg

* Significant at 10%.** Significant at 5%.

*** Significant at 1%.

used in Ricardian studies) from the temperature and rainfall pre-iction (median) from an ensemble of models (World Bank, 2014).e consequently selected the income impact estimates with least

ncertainty, given the two models (CGCM2 and HADCM3).We successively examine the impact of changes in food and

on-food consumption bundles, caused by the certainty equiva-ent income shifts ((1 − ˛c)�Rc), by utilizing the income elasticitiesor each country (for the year 2005). Here the assumption is thatn individual with a particular level of future income will behaveimilarly to an equally earning individual in the current time. Thelasticities (�gc) were retrieved for each country from the Unitedtates Department of Agriculture database (USDA, 2014). Elastici-ies for broad consumption categories (Food, Medical and Health,ducation, Recreation and Transport and communication) were cal-ulated from a Florida-preference independence model (Seale andegmi, 2006), while the elasticities specific to food subcategoriescereals, meat, oils and fat, fruits, etc.) were estimated using thelorida Slutsky model that assumes weak seperability (Seale andegmi, 2006). Expenditure elasticity (�gc) was then calculated as

gc = 1 + ˇgc

Wgc(3)

here ˇgc is the calculated coefficient of the consumption groupg’ in the country ‘c’ in the Florida-preference independence model,nd Wgc is the budget share of consumption for a group ‘g’ at theeometric mean price levels for a country ‘c’. Data on the sharef expenditure for the respective countries (for the year 2005)ere retrieved from the database maintained by the United Statesepartment of Agriculture (USDA, 2014). Elasticities for food sub-ategories (�fc) were calculated as(

ˇfc

)

fc = �gc 1 +

Wfc(4)

here ˇfc is the calculated coefficient of the food consumption sub-roup ‘f’ in the Florida Slutsky model of the country ‘c’ (Seale and

Regmi, 2006). Subsequently, composite indices for the indicators ofimpacts of consumption of food (�Ffc) and non-food (�NFgc) bun-dles due to climate change induced income change (uncertaintyweighted) are derived.

�NFgc = 1s

�gc [(1 − ∝c) �Rc] (5)

is indicator for the impact on non-food categories and

�Ffc = 1s

�fc [(1 − ∝c) �Rc] (6)

is the indicator for impact on food subcategories where “s” is thenumber of sub-categories.

Finally, the composite indices for climate change impact (�Fc,�NFc) on food and other consumption categories were calculatedusing expenditure shares of each subcategory as weights. In addi-tion, a composite indicator for human development impact wascalculated as the sum �Hc = �Fc + �NFc of climate change inducedimpact indicators on food and non-food categories.

3. Results and discussion

We first retrieved the percentage changes in income for theclimate change scenario for 2050 from the set of studies (withleast uncertainty) mentioned before. The estimated impact of cli-mate change shows moderate (−9.7% in Ethiopia) to high (−21.4%in Cameroon to −27% in South Africa), to very high reductions(−38% in Zimbabwe) in farm income. In comparison, the percent-age change in crop output reported by Schlenker and Lobell (2010)can be presented. The assumption here is that the change in outputis an indicator of the change in income as the major input is labor(family) under conditions of smallholder dryland farming in SSA.

Their study predicts moderate to high reductions in crop output forZimbabwe (−0.2% in Cassava to −37.7 in Maize), Cameroon (−4%in Sorghum to −18.0 in Maize), South Africa (−15.2% in Sorghumto −30.3% in Maize) and Ethiopia (−6.4% in Sorghum to −19.0% in

834 T.S. Amjath-Babu et al. / Ecological I

Tab

le

2C

alcu

lati

on

of

un

cert

ain

ty

wei

ghti

ng

fact

ors.

Cou

ntr

y

AB

S((

�R

−

�C

)/�

R)

Stan

dar

diz

edva

lue

AB

S(�

RF

(stu

dy)

−

�R

F(A

2)/�

RF

(stu

dy)

)

Stan

dar

diz

edva

lue

AB

S(�

T(s

tud

y)

−

�T

(A2)

/�T

(stu

dy)

)

Stan

dar

diz

edva

lue

AB

S(�

T(A

2)

−

�T

(B1)

/�T

(A2)

)

Stan

dar

diz

edva

lue

Dif

fere

nce

�R

F(A

2)/

�R

F(B1

)

Stan

dar

diz

edva

lue

Un

cert

ain

tym

easu

re(�

I c)*

Cer

tain

tym

easu

re(1

−

�I c

)

Zim

babw

e

0.01

0.00

0.02

0.00

0.06

0.00

0.16

0.00

1.62

1.00

0.20

0.80

Cam

eroo

n

0.19

0.38

58.4

7

1.00

0.24

0.42

0.20

0.13

0.22

0.01

0.39

0.61

Sou

th

Afr

ica

0.11

0.21

1.23

0.02

0.23

0.40

0.43

1.00

0.21

0.00

0.33

0.67

Eth

iop

ia

0.49

1.00

0.88

0.01

0.49

1.00

0.20

0.15

0.75

0.38

0.51

0.49

ndicators 67 (2016) 830–838

Maize). The expected changes in crop yields indicate that there isrobust support for probable yield decline, which will in turn resultin lowered farm income predicted by Ricardian models.

Table 2 shows the calculation of the uncertainty weights. Theresults reveal that uncertainty is low for estimates in Zimbabwealthough they are moderate for Ethiopia. This also shows the util-ity of uncertainty weighting to avoid the pitfalls of depending ona single method and model. It also allows the selection of a moreconservative estimate when a set of estimates are available. In ouranalysis, the uncertainty weights also act as a way of communi-cating uncertainty in case of climate change impact indicators. Assuch, they are of increased relevance for policy makers who need tounderstand the implications of uncertainty on their decision mak-ing process with respect to climate change adaptation and policydevelopment.

We now focus on the potential impact of climate change andresulting income changes on food consumption among drylandfarmers of the considered sub-Saharan African countries. Table 3provides the indicators for the probable impact on food consump-tion for the countries in 2050. Here we assume that the incomeshortfall represents the consumption forgone by subsistence farm-ers (even if it is self-consumption) or consumption that will notbe made in case of market oriented farmers. It is evident thatcereals consumption will be less affected compared to other foodcategories. This is due to the comparatively lower elasticity of con-sumption. Dryland farmers in Zimbabwe may face larger decline inthe cereals consumption indicator among the countries considered,given the higher predicted impacts and higher elasticity. It shouldalso be noted that, though the predicted (uncertainty weighted)income reduction is higher for South Africa than Cameroon, thedecline in cereal consumption indicator in Cameroon is higher thanSouth Africa due to higher elasticity of cereal consumption. Sucha scenario could lead to lower caloric consumption, with conse-quent negative impacts for human health, as caloric consumption isalready lower than recommended levels in many of the farm house-holds in these countries. In case of the meat consumption indicator,sizeable declines resulting from higher consumption elasticity forZimbabwean, South African and Cameroonian dryland farmerswere observed. It is to be noted that consumption of animal sourcedprotein is already low in all the considered countries except SouthAfrica (Speedy, 2003). A potential result of reduced intake of animalsourced products may be increased incidence of anemia (Sanou andNgnie-Teta, 2012). Reduced oil and fat, as well as fruit consump-tion indicators also point toward enhanced potential deficiency offats, vitamins and minerals. These inferences could consequentlyincrease chronic malnutrition among children, as well as increasedstunting rates (Black et al., 2008; UNICEF, 2011), which is prob-lematic because the countries analyzed already have high stuntingrates among children, ranging 23.9% of the population in SouthAfrica, to 50.7% in Ethiopia (Sarma, 2011). Importantly, nutritionalimbalances can be proportionally higher for girls, women, and theelderly (Charlton and Rose, 2001), as they may buffer other mem-bers from food insufficiency by forgoing consumption in preferenceof male household members (Hadley et al., 2008). Protein-energymalnutrition can also exacerbate the immunosuppressive effectsof HIV in affected populations (Koethe and Heimburger, 2010). Inour study countries, measured HIV infection rates range from 4.6%in Cameroon to 17.3% in South Africa (AVERT, 2012). In summary,our weighted index approach indicates sizable potential impactson food consumption among all the countries considered, althoughconsiderable regional variation can be expected.

Though the impacts of climate change on access to food are fre-

quently addressed in the literature (Parry et al., 2004; Schmidhuberand Tubiello, 2007; Alderman, 2010), its secondary consequenceson other spheres of life such as access to medical and healthservices, education opportunities, recreation, etc., are given less

T.S. A

mjath-Babu

et al.

/ Ecological

Indicators 67

(2016) 830–838

835

Table 3Indicators of the impact of climatic change induced income change on food expenditure (2050).

Cereals(carbohydrates)

Meat (protein) Oils and fats Fruitsconsumption

Certaintyweighedincome impact

Impact oncerealsconsumption(carbohy-drates)

Impact on meat(protein)consumptiona

Impact oncereals oils andfatsconsumptiona

Impact onfruitsconsumptiona

Weightedindicatorimpact of foodconsumption

E S E S E S E S

Zimbabwe 0.64 0.252 0.82 0.146 0.65 0.049 0.69 0.20 −30.40 −4.90 −24.93 −19.76 −20.98 −17.64Cameroon 0.51 0.146 0.78 0.144 0.53 0.03 0.62 0.14 −13.11 −0.98 −10.22 −6.95 −8.13 −6.57South Africa 0.36 0.083 0.69 0.143 0.39 0.018 0.52 0.11 −18.18 −0.54 −12.54 −7.09 −9.45 −7.41Ethiopia 0.62 0.232 0.82 0.146 0.63 0.045 0.68 0.19 −4.76 −0.68 −3.90 −3.00 −3.24 −2.70

E – elasticity, S – share in consumption expenditure.a Weighted by expenditure share.

Table 4Indicators of the impact of climatic change induced income change on non-food expenditure and human development (2050).

Medical andHealth

Education Recreation Transport andcommunication

Certaintyweighedincome impact

Impact onmedical andhealthexpenditurea

Impact oneducationexpenditurea

Impact onrecreationexpenditurea

Impact ontransport andcommunicationexpenditurea

Weighted indicatorof expenditurecategories otherthan food

Indicator ofhumandevelopmentimpact

E S E S E S E S

Zimbabwe 4.29 0.03 0.94 0.04 2.05 0.01 1.28 0.10 −30.40 −130.42 −28.58 −62.32 −38.91 −65.06 −82.70Cameroon 1.62 0.05 0.93 0.03 2.18 0.05 1.21 0.13 −13.11 −21.23 −12.19 −28.57 −15.86 −19.46 −26.03South Africa 1.34 0.08 0.92 0.03 1.46 0.08 1.16 0.16 −18.18 −24.36 −16.73 −26.54 −21.09 −22.18 −29.59Ethiopia 3.28 0.03 0.94 0.03 5.41 0.02 1.27 0.10 −4.76 −15.61 −4.47 −25.74 −6.04 −12.97 −15.67

E – elasticity, S – share in consumption expenditure.a Weighted by expenditure share.

8 gical I

aohrstepthwlepisetbEicRvtt

lcciipwblfEoaptncffdti

eecsparmlTeioanc

increased incidence of disease renders such interventions ratherimportant. These interventions are supplementary to the global

36 T.S. Amjath-Babu et al. / Ecolo

ttention. Using the methodology described above, the indicatorsf changes in the consumption expenditure portfolio of small-older dryland farmers in these domains due to climate changeelated income losses are found in Table 4. The presented incomehares show that the share of income spent on medical expendi-ure is already quite low. These estimated indicators show thatxpenditure on medical and health services may decline over-roportionally (compared to other consumption categories) dueo the high elasticity of consumption, potentially rendering small-olders more vulnerable to diseases like malaria and tuberculosis,hich may result in further losses in productivity by limiting farm

abor (Costello et al., 2009). The high elasticity of medical and healthxpenditure in Ethiopia and Zimbabwe could make them dispro-ortionally vulnerable. Conversely, the magnitude of change in the

ndicator is the highest for South Africa and Zimbabwe. Under thiscenario, women are expected to be disproportionally affected,specially due to health needs during pregnancy, delivery, and lac-ation (Black et al., 2008). It should be noted that the number ofirths attended by skilled professionals is already low in SSA (3% inthiopia and 37% in Kenya; UNDATA, 2012). Unless adequate pol-cy support is provided, further reduction in medical expenditureould have adverse effects on efforts to improve the conditions.educed ability to pay for health services may also increase theulnerability of smallholder farmers to emerging diseases sensitiveo changes in temperature and rainfall, most notably, malaria andrypanosomiasis (Mabaso and Ndlovu, 2012; Moore et al., 2012).

The reduction of income resulting from climate change is alsoikely to impact the proportion of funds households expend onhild education. Enrolment in secondary and tertiary level edu-ation is expected to decline as families are likely to experiencencreased difficulty making ends meet. Note that the direct andndirect costs of education in secondary levels are 3–5 times ofrimary education in sub-Saharan Africa (Iverson, 2012). Youngomen may also be disproportionally affected, as households have

een noted to disproportionally support male children and ado-escents, especially after secondary schools (gender parity indexor upper secondary level education is 0.61 in Cameroon, 0.58 inthiopia, 0.86 in Zimbabwe in 2005, UNESCO, 2008). It is alreadybserved that households tend to utilize children and adolescentss additional farm labor as a result of climate related agriculturalroduction shortfalls, and this strategy tends to affect boys morehan girls. In addition, our data indicate that transport and commu-ication opportunities may shrink as the income loss from climatehange translates to a reduction in expenditure in these categories,or each of the countries studied. Given that most advanced medicalacilities are located in cities, the reduction in transportation expen-iture budgets combined with reduced medical expenditure pointso a widening divide for rural households that already experiencenequitable access to health facilities.

These climate change induced impacts on income, nutrition,ducation and medical expenditure are likely to negatively influ-nce the future level of human development in each country underonsideration. The probable decline in human development pos-ibilities (sum of the impacts in food and non-food categories) isresented in Table 4 for 2050. Increased malnutrition and vulner-bility to diseases, coupled with reduced ability to afford healthelated expenditure and lower capacity to seek health facilitiesay lead to a reduction in life expectancy, which is already quite

ow under Sub-Saharan conditions (Austin and McKinney, 2012).aking the indicators of impacts on income, education and lifexpectancy resulting from climate change related shifts in farmncome in to account, a moderate to high impact on human devel-pment is expected among dryland smallholder farmers if thenticipated reduction in farm income is not addressed and safety

ets are developed to avoid the secondary impacts of climatehange.

ndicators 67 (2016) 830–838

4. Conclusions and policy implications

One has to contemplate that the scenario of long term climaticshifts needs a different perspective than weather anomalies. Ourindicator based analysis points that a moderate loss in humandevelopment possibilities is likely among dryland smallholderfarmers of the Sub-Saharan African countries under consideration,given the possible income shortfall triggered by the anticipatedeffects of climate change based on the GCMs considered, even afterweighting our indicators for uncertainty. Most of the studies ana-lyzing the impact of climate change in these countries suggestadaptation measures to sustain or increase income levels. Nev-ertheless, the available estimates reveal that considerable incomechanges may occur under a new set of temperature and rainfall con-ditions even if farmers behave similarly to those who already facea similar set of rainfall and temperature i.e. spatial analogues. Evenwhen uncertainties are accounted for (using uncertainty weights),our results indicate considerable impacts to dryland farmingincome. This underscores the urgency of renewed efforts to developand extend adaptation solutions that are not currently widelyadopted in African context (e.g.: irrigation, water harvesting, etc.).To analyze the effect of income changes on consumption patterns,we subsequently considered the income elasticity coefficients withan underlying assumption that an individual with a particular levelof future income will behave similarly to an equally earning indi-vidual in the current time (human analogues). This approach couldreveal the multifaceted impacts of climate change on farmers in theSub-Saharan African countries under consideration.

In case of adaptation, as shown by a number of studies,policy makers and development implementors in SSA shouldexplore ways to sustain farm income levels by providing increasedirrigation services, popularizing heat tolerant varieties, diversifyingincome sources (through biologically integrated and diverse crop-ping systems), as well as by improving farmers’ capacity to makeuse of locally adapted but resilient farming systems. In addition,there is an overall need for increasing agricultural research andextension support. As already discussed, generating employmentopportunities beyond agriculture is also an option, though likelylong-term in nature. The insights from the current study call foradditional interventions including (1) implementing supplemen-tary feeding programs or measures such as Ethiopia’s productivesafety net programme (PSNP) where cash or food is exchanged inreturn to labor in public work projects for chronically food insecurehouseholds (Conway and Schipper, 2011), (2) reducing the cost ofeducation or providing financial assistance for students from areasnegatively affected by climate change, (3) facilitating low cost ruralhealth services or providing low cost health insurance services, and(4) increasing the efforts to improve public transportation servicesor reducing the costs of transportation. Even though similar pro-grammes could exist in the countries or are in initial phases, therecognition of secondary climate impacts widens their geographi-cal scope and changes priorities.

As there are robust indicators for a reduction in agriculturalincome, even after accounting for uncertainty, for the drylandfarmers of the countries under consideration, the overall messageof this study is that the considered countries should focus also onnational level intervention programs such as supplementary feed-ing programs (or food for work initiatives) and low cost education(or educational assistance for children in areas of high impact)and health services (or health insurance programs) to dampenthe secondary impacts of climate change on human developmentpossibilities. Given the possibility of additional effects such as

level efforts on emission mitigation to reduce the overall climatechange impacts, regional level actions to improve options of

gical I

rabtiar

R

A

A

A

A

A

B

B

B

B

B

C

C

C

C

C

C

C

D

D

F

G

H

I

I

J

J

K

K

T.S. Amjath-Babu et al. / Ecolo

elocation and to increase the ability of alternative sectors tobsorb more labor from agriculture, and farm level adaptation tooost yield and farm income. The results highlight the need toake into consideration the secondary impacts of climate changen policy frameworks in Sub-Saharan African countries and in thegendas of donor organizations in order to improve climate changeesilience of the region.

eferences

lderman, H., 2010. Safety nets can help address the risks to nutrition from increas-ing climate variability. J. Nutr. 140 (1), 148S–152S.

mjath-Babu, T.S., Krupnik, T.J., Kaechele, H., Aravindakshan, S., Sietz, D., 2016. Tran-sitioning to groundwater irrigated intensified agriculture in Sub-Saharan Africa:an indicator based assessment. Ag. Wat. Mgt. 168, 125–135.

ntle, J.M., Capalbo, S.M., 2010. Adaptation of agricultural and food systems to cli-mate change: an economic and policy perspective. Appl. Econ. Perspect. Policy32 (3), 386–416.

ustin, K.F., McKinney, L.A., 2012. Disease, war, hunger, and deprivation: a cross-national investigation of the determinants of life expectancy in less-developedand sub-Saharan African nations. Soc Perspect. 55 (3), 421–447.

VERT, 2012. Africa HIV & AIDS Statistics. http://www.avert.org/africa-hiv-aids-statistics.htm.

arrios, S., Ouattara, B., Strobl, E., 2008. The impact of climatic change on agriculturalproduction: is it different for Africa? Food Policy 33, 287–298.

elow, T.B., Mutabazi, K.D., Kirschke, D., Franke, C., Sieber, S., Siebert, R., Tscherning,K., 2012. Can farmers’ adaptation to climate change be explained by socio-economic household-level variables? Global Environ. Chang. 22 (1), 223–235.

enhin, J.K.A., 2008. South African crop farming and climate change: an economicassessment of impacts. Global Environ. Chang. 18, 666–678.

lack, R.E., Allen, L.H., Bhutta, Z.A., Caulfield, L.E., de Onis, M., Ezzati, M., Math-ers, C., Rivera, J., 2008. Maternal and child undernutrition: global and regionalexposures and health consequences. Lancet 371, 243–260.

ryan, E., Deressa, T.T., Gbetibouo, G.A., Ringler, C., 2009. Adaptation to climatechange in Ethiopia and South Africa: options and constraints. Environ. Sci. Policy12 (4), 413–426.

alzadilla, A., Zhu, T., Rehdanz, K., Tol, R.S., Ringler, C., 2013. Economywide impactsof climate change on agriculture in Sub-Saharan Africa. Ecol. Econ. 93, 150–165.

GCM2, 2012. Canadian Centre for Climate Modelling and Analysis. http://www.cccma.ec.gc.ca/diagnostics/cgcm2/cgcm2.shtml.

harlton, K.E., Rose, D., 2001. Nutrition among older adults in Africa: the situationat the beginning of the millennium. J. Nutr. 131 (9), 2424S–2428S.

ollier, P., Conway, G., Venables, T., 2008. Climate change and Africa. Oxford Rev.Econ. Policy 24 (2), 337–353.

onway, D., Schipper, E.L.F., 2011. Adaptation to climate change in Africa: chal-lenges and opportunities identified from Ethiopia. Global Environ. Chang. 21(1), 227–237.

ooper, P.J.M., Dimes, J., Rao, K.P.C., Shapiro, B., Shiferaw, Twomlow, S., 2008. Copingbetter with current climatic variability in the rain-fed farming systems of sub-Saharan Africa: an essential first step in adapting to future climate change? Agric.Ecosyst. Environ. 126 (1), 24–35.

ostello, A., Abbas, M., Allen, A., Ball, S., Bell, S., Bellamy, R., . . ., Patterson, C., 2009.Managing the health effects of climate change: lancet and University CollegeLondon Institute for Global Health Commission. Lancet 373 (9676), 1693–1733.

eressa, T.T., 2007. Measuring the economic impact of climate change on Ethiopianagriculture: Ricardian approach. World Bank Policy Research Working Paper(4342).

inar, A., Hassan, R., Mendelsohn, R., Benhin, J., 2008. Climate change and agriculturein Africa: impact assessment and adaptation strategies. EarthScan, 224 pp.

lato, G.M., Boer, G.J., Lee, W.G., McFarlane, N.A., Ramsden, D., Reader, M.C., Weaver,A.J., 2000. The Canadian Centre for Climate Modelling and Analysis global cou-pled model and its climate. Clim. Dyn. 16 (6), 451–467.

betibouo, G.A., Hassan, R.M., 2005. Measuring the economic impact of climatechange on major South African field crops: a Ricardian approach. Global Planet.Change 47, 143–152.

adley, C., Lindstrom, D., Tessema, F., Belachew, T., 2008. Gender bias in the foodinsecurity experience of Ethiopian adolescents. Soc. Sci. Med. 66, 427–438.

PCC, 2014. Scenario Data for the Atmospheric Environment. http://www.ipcc-data.org/sim/gcm clim/SRES TAR/ddc sres emissions.html.

verson, 2012. State of girls’ education in Africa: achievements since 2000, chal-lenges and prospects for the future. http://www.plan-eu.org/content/uploads/2012/05/State-of-Girls-Education-in-Africa.pdf.

ohns, T.C., Gregory, J.M., Ingram, W.J., Johnson, C.E., Jones, A., Lowe, J.A., . . ., Tett,S.F.B., 2003. Anthropogenic climate change for 1860 to 2100 simulated withthe HadCM3 model under updated emissions scenarios. Climate Dynam. 20,583–612.

ones, P.G., Thornton, P.K., 2003. The potential impacts of climate change on maizeproduction in Africa and Latin America in 2055. Global Environ. Chang. 13 (1),

51–59.

ates, R.W., 2000. Cautionary tales: adaptation and the global poor. Clim. Change45, 5–17.

nox, J., Hess, T., Daccache, A., Wheeler, T., 2012. Climate change impacts on cropproductivity in Africa and South Asia. Environ. Res. Lett. 7 (3), 034032.

ndicators 67 (2016) 830–838 837

Koethe, J.R., Heimburger, D.C., 2010. Nutritional aspects of HIV-associated wastingin sub-Saharan Africa. Am. J. Clin. Nutr. 91 (Suppl.), 1138S–1142S.

Kotir, J.H., 2011. Climate change and variability in Sub-Saharan Africa: a review ofcurrent and future trends and impacts on agriculture and food security. Environ.Dev. Sustain. 13, 587–605.

Krupnik, T.J., Shennan, C., Settle, W.H., Demont, M., Ndiaye, A.B., Rodenburg, J., 2012.Improving irrigated rice production in the Senegal River Valley through experi-ential learning and innovation. Ag. Syst. 109, 101–112.

Kurukulasuriya, P., Mendelsohn, R., Hassan, R., Benhin, J., Deressa, T., Diop, M.,Eid, H.M., Fosu, K.Y., Gbetibouo, G., Jain, S., Mahamadou, A., Mano, R., Kabubo-Mariara, J., El-Marsafawy, S., Molua, E., Ouda, S., Ouedraogo, M., Séne, I.,Maddison, D., Seo, S.N., Dinar, A., 2006. Will African agriculture survive climatechange? World Bank Econ. Rev. 20, 367–388.

Kurukulasuriya, P., Mendelsohn, R., 2008. How will climate change shift agroecolog-ical zones and impact African agriculture? World bank Policy Research WorkingPaper 4717. The World Bank.

Lobell, D.B., Burke, M.B., Tebaldi, C., Mastrandrea, M.D., Falcon, W.P., Naylor, R.L.,2008. Prioritizing climate change adaptation needs for food security in 2030.Science 319 (5863), 607–610.

Mabaso, M.L.H., Ndlovu, N.C., 2012. Critical review of research literature on climate-driven malaria epidemics in sub-Saharan Africa. Public Health 126, 909–919.

Maddison, D.J., 2007. The Perception of and Adaptation to Climate Change in Africa,World Bank Policy Research Working Paper No. 4308. The World Bank.

Mano, R., Nhemachena, C., 2007. Assessment of the Economic Impacts of ClimateChange on Agriculture in Zimbabwe, Policy Research Working Paper 4292. TheWorld Bank.

Mendelsohn, R., 2008. The impact of climate change on agriculture in developingcountries. J. Natural Resour. Policy Res. 1, 5–19.

Molua, E., Lambi, C., 2006. The economic impact of climate change on agriculture inCameroon, CEEPA Discussion Paper No. 17, Centre for Environmental Economicsand Policy in Africa, University of Pretoria.

Moore, S., Shrestha, S., Tomlinson, K.W., Vuong, H., 2012. Predicting the effect ofclimate change on African trypanosomiasis: integrating epidemiology with par-asite and vector biology. J. R. Soc. Interface 9, 817–830.

Müller, C., Cramer, W., Hare, W.L., Lotze-Campen, H., 2011. Climate change risks forAfrican agriculture. Proc. Natl. Acad. Sci. U. S. A. 108, 4313–4315.

O’Brien, K.L., Leichenko, R.M., 2000. Double exposure: assessing the impacts ofclimate change within the context of economic globalization. Global Environ.Chang. 10 (3), 221–232.

Palm, C.A., Smukler, S.M., Sullivan, C.C., Mutuo, P.K., Nyadzi, G.I., Walsh, M.G., 2010.Identifying potential synergies and trade-offs for meeting food security andclimate change objectives in sub-Saharan Africa. PNAS 107, 19661–19666.

Parry, M.L., Rosenzweig, C., Iglesias, A., Livermore, M., Fischer, G., 2004. Effects ofclimate change on global food production under SRES emissions and socio-economic scenarios. Global Environ. Chang. 14 (1), 53–67.

Roudier, P., Sultan, B., Quirion, P., Berg, A., 2011. The impact of future climate changeon West African crop yields: what does the recent literature say? Global Environ.Chang. 21, 1073–1083.

Sanou, D., Ngnie-Teta, I., 2012. Risk Factors for Anemia in Preschool Children inSub-Saharan Africa. http://cdn.intechweb.org/pdfs/30546.pdf.

Sarma, D., 2011. An Analysis of the Nutritional and Agricultural Trends in FortyCountries of High Burden. Bioversity International, Rome.

Schmidhuber, J., Tubiello, F.N., 2007. Global food security under climate change. Proc.Natl. Acad. Sci. U. S. A. 104 (50), 19703–19708.

Schlenker, W., Lobell, D.B., 2010. Robust negative impacts of climate change onAfrican agriculture. Environ. Res. Lett. 5, 1–5.

Seale, J.L., Regmi, A., 2006. Modeling international consumption patterns. Rev.Income Wealth 52, 603–624.

Seo, S.N., 2010. Is an integrated farm more resilient against climate change? A micro-econometric analysis of portfolio diversification in African agriculture. FoodPolicy 35, 32–40.

Shiferaw, B., Holden, S.T., 1998. Resource degradation and adoption of land conser-vation technologies in the Ethiopian Highlands: a case study in Andit Tid, NorthShewa. Agric. Econ. 18 (3), 233–247.

Speedy, A.W., 2003. Global production and consumption of animal source foods. J.Nutr. 133 (11), 4048S–4053S.

Stringer, L.C., Dyer, J.C., Reed, M.S., Dougill, A.J., Twyman, C., Mkwambisi, D., 2009.Adaptations to climate change, drought and desertification: local insights toenhance policy in southern Africa. Environ. Sci. Policy 12 (7), 748–765.

Thomas, D.S., Twyman, C., Osbahr, H., Hewitson, B., 2007. Adaptation to climatechange and variability: farmer responses to intra-seasonal precipitation trendsin South Africa. Clim. change 83 (3), 301–322.

Thornton, P.K., Jones, P.G., Alagarswamy, G., Andresen, J., Herrero, M., 2010. Adaptingto climate change: agricultural system and household impacts in East Africa.Agric. Syst. 103, 73–82.

Tingem, M., Rivington, M., 2009. Adaptation for crop agriculture to climate change inCameroon: turning on the heat. Mitig. Adapt. Strat. Global Change 14, 153–168.

Tilman, D., Balzer, C., Hill, J., Befort, B.L., 2011. Global food demand and thesustainable intensification of agriculture. Proc. Natl. Acad. Sci. U. S. A. 108,20260–20264.

Tittonell, P., 2013. Livelihood strategies, resilience and transformability in African

agroecosystems. Agric. Syst. 126, 3–14.

UNDATA, 2012. Births attended by skilled health personnel, percentage. http://data.un.org/Data.aspx?d=MDG&f=seriesRowID%3A570.

UNESCO, 2008. Regional overview: sub-Saharan Africa. http://en.unesco.org/gem-report/sites/gem-report/files/157229E.pdf.

8 gical I

U

U

V

World Bank, 2014. Climate Change Knowledge Portal, http://sdwebx.worldbank.org/climateportal/.

38 T.S. Amjath-Babu et al. / Ecolo

NICEF, 2011. Exploring the impact of climate change on children in South Africa.UNICEF South Africa, Pretoria.

SDA, 2014. International Food Consumption Patterns. http://www.ers.usda.gov/data-products/international-food-consumption-patterns.aspx.

ermeulen, S.J., Aggarwal, P.K., Ainslie, A., Angelone, C., Campbell, B.M., Challinor,A.J., . . ., Wollenberg, E., 2012. Options for support to agriculture and food securityunder climate change. Environ. Sci. Policy 15 (1), 136–144.

ndicators 67 (2016) 830–838

Yengoh, G.T., Brogaard, S., Olsson, L., 2012. Crop water requirements in Cameroon’sSavanna zones under climate change scenarios and adaptation needs. In:Sharma, P. (Ed.), Crop Production Technologies. , ISBN 978-953-307-787-1.