metadata: authors: 0: author: Arndt,Channing
1: author: Farmer,William Hastings
2: author: Strzepek,Kenneth
3: author: Thurlow, James
count: Tanzania
docna: 0: docna: Climate change, agriculture and food
            security in Tanzania
docty: Policy Research Working Paper
theme: Other trade and integration
majtheme: Trade and integration
subtopic: Climate Change and Agriculture,Science of Climate Change,Climate Change and Health,Climate Change and Environment,Crops and Crop Management Systems,Nutrition,Hydrology,Food Security
teratopic: Environment,Macroeconomics and Economic Growth,Industry,Science and Technology Development
abstracts: cdata!: The consequences of climate change for
            agriculture and food security in developing countries are of
            serious concern. Due to their reliance on rain-fed
            agriculture, both as a source of income and consumption,
            many low-income countries are considered to be the most
            vulnerable to climate change. This paper estimates the
            impact of climate change on food security in Tanzania.
            Representative climate projections are used in calibrated
            crop models to predict crop yield changes for 110 districts
            in the country. The results are in turn imposed on a
            highly-disaggregated, recursive dynamic economy-wide model
            of Tanzania. The authors find that, relative to a
            no-climate-change baseline and considering domestic
            agricultural production as the principal channel of impact,
            food security in Tanzania appears likely to deteriorate as a
            consequence of climate change. The analysis points to a high
            degree of diversity of outcomes (including some favorable
            outcomes) across climate scenarios, sectors, and regions.
            Noteworthy differences in impacts across households are also
            present both by region and by income category.
geo_reg_key: 517191,119229
repnme: repnme: Climate change, agriculture and food
            security in Tanzania
display_title: Climate change, agriculture and food
            security in Tanzania
listing_relative_url: /research/2012/09/16699070/climate-change-agriculture-food-security-tanzania
topicv3: Agriculture and Food Security m1326205 435,Food Security m1326439 199,Water m1326522 137,Crop Production m1326627 174,Climate Change m1326829 567
publishtoextweb_dt: 2012-09-01T00:00:00Z
historic_topic: Environment,Macroeconomics and Economic Growth,Industry,Science and Technology Development
no_of_pages: 29
author_keys: 0: author_key: 582157
1: author_key: 1037877
2: author_key: 966215
model_extractions: WPS6188 Policy Research Working Paper 6188 Climate Change, Agriculture and Food Security in Tanzania _Channing Arndt William Farmer Kenneth Strzepek James Thurlow_ The World Bank Development Research Group Agriculture and Rural Development Team September 2012
Policy Research Working Paper 6188

# **Abstract**

The consequences of climate change for agriculture and food security in developing countries are of serious concern. Due to their reliance on rain-fed agriculture, both as a source of income and consumption, many low-income countries are considered to be the most vulnerable to climate change. This paper estimates the impact of climate change on food security in Tanzania. Representative climate projections are used in calibrated crop models to predict crop yield changes for 110 districts in the country. The results are in turn imposed on a highly-disaggregated, recursive dynamic economy-wide model of Tanzania. The authors find that, relative to a no-climate-change baseline and considering domestic agricultural production as the principal channel of impact, food security in Tanzania appears likely to deteriorate as a consequence of climate change. The analysis points to a high degree of diversity of outcomes (including some favorable outcomes) across climate scenarios, sectors, and regions. Noteworthy differences in impacts across households are also present both by region and by income category.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at channing.arndt@econ.ku.dk , whfarmer@gmail.com, strzepek@mit.edu, and thurlow@wider.unu.edu.

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

_Channing Arndt, William Farmer, Kenneth Strzepek and James Thurlow_<sup>_1_</sup> _Keywords_ : climate change; agriculture; food security; crop model; CGE model; Tanzania. JEL Codes: O440, Q180, Q540 _Acknowledgements_ : Thanks to Will Martin for leadership in the overall effort and valuable comments. Seminar participants in Dar Es Salaam and the World Bank also provided useful comments. All errors and omissions are the responsibility of the authors. The research on which this study is based was supported by the Trust Fund for Environmentally and Socially Sustainable Development (TFESSD).

> 1 Arndt: University of Copenhagen (channing.arndt@econ.ku.dk); Farmer: Tufts University (whfarmer@gmail.com); Strzepek: Massachusetts Institute of Technology (strzepek@mit.edu); Thurlow: United Nations University - World Institute for Development Economics Research (thurlow@wider.unu.edu).
# **1. Introduction**

The consequences of climate change for agriculture and food security are of serious concern, not least because food supplies are already inadequate and poverty is severe in many low-income countries, particularly in Africa. Moreover, developing countries are generally considered to be most vulnerable to climate change, mainly due to their reliance on rain-fed agriculture. Previous studies linking climate change to food security have typically used agricultural crop models (see, for example, Parry et al., 2004). Their predictions range from precipitous declines in yields for major African food crops (Schlenker and Lobell, 2010) to more modest reductions (Lobell et al., 2008; Nelson et al., 2010), and even to improvements (Butt et al., 2005).

Previous studies have, however, suffered from at least one of four limitations. First, they often provide global or regional assessments. Yet climate change is expected to vary widely within continents and even countries, and so adaptation policies require higher-resolution information, possibly even at sub-national levels (Lobell et al., 2008). Secondly, despite considerable uncertainty surrounding future climate change, some studies rely on only a few climate projections (see, for example, Butt et al., 2005). Thirdly, many calibrated agronomic crop models exclude “autonomous adaptation” that may offset at least some climate change damages. Finally, previous studies typically measure direct or partial equilibrium production changes, but may exclude indirect and general equilibrium effects, including price and household income changes and inter-sectoral linkages. Since food security depends on both food availability and accessibility, it is inadequate to measure production changes without considering, for example, the impacts of climate change on households’ incomes (Parry et al., 2004; Ahmed et al., 2009).

In this paper we estimate the impact of climate change on agricultural production in Tanzania using detailed sub-national crop models. Four projections are drawn from available general circulation models (GCM) to reflect a range of possible temperature and precipitation changes by mid-century. These climate projections are then used in calibrated crop models to predict crop yield changes, which are in turn imposed on a highly-disaggregated, recursive dynamic economy-wide model of Tanzania. This model captures indirect effects and permits (some) autonomous adaptation. The economic model allows us to evaluate the availability (production) and accessibility (income) dimensions of food security. In the next section, we describe the selected climate scenarios and the crop modeling framework used to translate climate conditions into crop yields. We then describe the economy-wide model and present the results from our simulated baseline and climate change scenarios. We conclude by summarizing our results and identifying areas for further research.

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# **2. Climate Change and Agricultural Crop Yields**

# _Selecting climate change scenarios_

General Circulation Models (GCM) produce a wide range of future climate change scenarios, especially when examined at the country-level (see Solomon et al. 2007). Apart from differences in the science of modeling global climate systems, there is also uncertainty about other key variables such as how the global economy will evolve in coming decades. To account for this, GCMs typically employ different “emission scenarios” based on assumptions about future populations, technological advances, and global agreements to reduce carbon emissions.

To capture a range of possible climate change realizations, we select four projections with different temperature and precipitation outcomes averaged over all land areas of the country. The scenarios employed are presented in Table 1. In addition to temperature and precipitation deviations, the table presents the Climate Moisture Index ( _CMI_ ) (Willmott and Feddema, 1992), which is an indicator of a region’s aridity, at the national level. The CMI depends on average annual precipitation ( _P_ ) and potential evapotranspiration ( _PET_ ). A climate is classified as semi-arid (semi-humid) and then arid (humid) as _PET_ increases (decreases) relative to precipitation. The CMI is defined as: A _CMI_ of -1 is very arid whereas a _CMI_ of +1 is very humid.

# [Insert Table 1]

The scenarios are labeled WET, DRY, COOL, and HOT. The COOL scenario is relative to the other scenarios, and projects a mean average temperature increase of 1.1 degrees Celsius by 2041-50 compared to 1.9 degrees Celsius in the HOT scenario. Three of the four scenarios project an increase in precipitation reflecting an analysis of climate futures for Tanzania conducted by the Tyndall Center (2010). While precipitation rises in three scenarios, the _CMI_ remains fairly constant in two of the three because rising temperatures increase _PET_ . It is of some interest to note that the WET scenario for Tanzania is the same scenario identified by the World Bank as the driest scenario globally out of all 56 possible scenarios considered for analysis (World Bank, 2010). This is a reminder that local or national conditions can differ drastically from broader global averages. Overall, the GCMs suggest that Tanzania’s climate will become warmer, although the extent of warming varies by GCM. In addition, the change in average precipitation may be positive or negative. In these senses, climate in Tanzania becomes more uncertain as a result of climate change.

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To develop a baseline “no climate change” scenario, we use historical daily climate data for 1997-2006 (i.e., mean, minimum and maximum surface temperatures and precipitation) retrieved from the NASA POWER database (Stackhouse, 2010).<sup>2</sup> A random 50-year baseline climate sequence was generated using bootstrapping techniques based on the historical annual data. In other words, one year of climate is drawn from the historical data fifty times. This technique preserves intra-annual correlations (and higher moments) but not inter-annual correlations. For the purposes of the crop modeling, the distinct wet and dry seasons in Tanzania limit the impact of inter-annual variation in soil moisture at the start of the growing seasons rendering this approach an acceptable compromise in the face of existing data limitations. Our baseline scenario therefore assumes that future weather patterns will retain the characteristics of recent historical climate variability. The principal purpose of the baseline scenario is to provide a counterfactual for the climate change scenarios.

For the purposes of projecting future climate in a given region (Tanzania), it is useful to note that the GCMs are calibrated to reasonably reproduce historical climate on a global basis and predict future climate under alternative levels of global greenhouse gases. The historical predictions of a given GCM for temperature and precipitation for a specific region of the globe, such as Tanzania, will not perfectly match the historical data. In other words, output from a given GCM for historical periods may, for example, consistently under-predict temperature and over-predict precipitation. This is also true with respect to higher order moments and time series properties of the climate series.

In sum, while the GCMs provide information with respect to long run trends in average temperatures and potential trends in average precipitation, it is less clear that the outputs of the GCMs provide useful information with respect to daily variation in climate (Schlosser, 2011). As a result, it is inappropriate to directly take the raw output from GCMs and use these in, for example, crop models for Tanzania estimated on the basis of historical data. Some manipulation of the GCM output is required. Here, we combine the long run temperature and precipitation changes predicted by each GCM with historical data on daily climate variation. Specifically, we calculate a ten-year moving average of the percentage monthly changes in temperature and precipitation between the selected climate change scenario as predicted by the GCM and a baseline no climate change scenario from the same GCM. So, the percentage change in temperature for January 2030 is the average percentage change in temperature recorded by the GCM for the periods January 2026 to January 2035. We then overlay these changes on the baseline historical scenario. For example, when the moving average from a GCM predicts a five percent increase in January temperature and a three percent decrease in precipitation for a given year, then the January baseline temperatures and precipitations (drawn from the historical record) are augmented by those percentages.

> 2 A longer historical data series would be desirable.

4
This procedure produces the four “synthetic” climate projections mentioned above (i.e., WET, DRY, COOL, and HOT). This method retains many aspects of the time series properties of historical climate variability (i.e., the historical baseline, which remains constant across all scenarios) and overlays future climate changes. It is important to point out that this procedure has implications for variability. For months with predicted increases in temperature and/or precipitation, peaks will be accentuated. Predicted temperature almost never declines; however, some months experience drying trends in nearly all GCMs and one GCM predicts an overall drying trend (see Table 1). When the GCM predicts drying, historical dry periods are deepened and tend to be extended. Based on these climate scenarios, we next assess the implications of climate change for crop production.

# _Crop and water balance models_

We use a generic crop model called CLICROP to simulate the impact of the baseline and climate change scenarios on rain-fed and irrigated crop yields and on irrigation water demand (Strzepek et al., forthcoming). CLICROP was specifically designed to capture climate change impacts since it models water stress from both insufficient and excess water supply (measured daily). Yield reduction due to retarded root growth resulting from excess water is known as “waterlogging”. Water-logging reduces yields via oxygen loss and root growth hindrance (see Sieben, 1964). The inclusion of water-logging and crop-specific parameters is an extension over simpler models, such as the FAO’s CROPWAT. Moreover, CLICROP’s daily time scale allows it to capture the shorter but higher intensity rainfall expected in Eastern Africa (Solomon et al., 2007).

The effects of the atmosphere (i.e., temperature and precipitation) are modeled indirectly in CLICROP via evapotranspiration (Allen et al., 1998) and infiltration to the soil layers (based on soil properties). Soil composition is considered at each site and is used to calculate soil moisture in each soil layer, including the moisture allowed to percolate into deep soil layers. Water balances and the upward flow of soil water are then measured. As rain falls on the given soil, certain amounts are allowed to run off, infiltrate and percolate through deep layers (in addition to the demands of evapotranspiration). Crops are then allowed to draw what water that they can from the soil layers. Crop yields are estimated using the approach proposed by Allen et al. (1998) with the additional possibility that yields are reduced when excess water results in submersion.

For this examination, the effects of CO2 fertilization are not considered in our analysis. As a result, we may overestimate yield losses caused by climate change. Recent free-air carbon enrichment (FACE) studies contradict the results of earlier closed-laboratory experiments that suggested the presence of strong positive productivity effects for major crops due to higher CO2 concentration levels (Long et al, 2006). At the same time, however, the validity of FACE results has been questioned (Tubiello et al, 2007). The debate remains unresolved at present.

5
CLICROP was run at a 1°×1° resolution (i.e., 111 square kilometer grids in a country measuring 945,000 square kilometers). Separate models were developed for the 9 major crops of Tanzania (i.e., cassava, groundnuts, maize, millet, potatoes, sorghum, soybeans, sweet potatoes and wheat). Predicted yields for each sub-national region are calculated as the sum of overlaid gridded results weighted by geographic area. CLICROP was calibrated to information on soil parameters from the FAO Soils Database (e.g., field capacity, wilting point and saturated hydraulic conductivity) (FAO-UNESCO, 2005). Regional information on crop growing seasons and planting dates were provided by Sacks et al. (forthcoming). Finally, crop locations were based on You et al. (2006) for the year 2000, and crop parameters were drawn from Allen et al. (1998) and Doorenbos and Kassam (1979). Overall, the process is detailed with more than 13 million potential simulation sets.<sup>3</sup>

# _Crop modeling results_

Although CLICROP analysis was conducted for nine crops, we focus on the results for maize for purposes of exposition. Maize is the principal food crop in Tanzania, representing 35 and 45 percent of calories consumed by poor urban and rural households, respectively (Pauw and Thurlow, 2010). Tanzanian farmers allocate about one-third of their crop land to growing maize, mainly without the use of irrigation. Deviations in dry-land maize yields therefore provide a first-cut indicator of food availability in Tanzania (Thornton et al. 2009). In addition, the implications of climate outcomes for maize are likely to be similar for sorghum and millet, making the coverage of caloric sources and land use, particularly for more vulnerable populations, even broader.

Table 2 summarizes the deviations in mean maize yields from a “no climate change” baseline scenario for the 10-year period 2041-2050. The administrative regions are grouped into similar agro-climatic zones based on Fan et al. (2010). There is a high degree of variation both across the four climate scenarios considered and across sub-national regions. For example, maize yields in the Northern Zone are projected to increase substantially in the WET scenario, but decrease by similar amounts in the HOT and DRY scenarios. Varied impacts also occur across regions but within the same scenario. For example, average maize yields in the WET scenario are projected to increase by 15 percent in Manyara in the Northern Zone, but decline by 12 percent in Tabora in the Central Zone. Nevertheless, a few regularities emerge. Maize yields are generally more favorable under the COOL and WET scenarios than under the HOT and DRY scenarios. In addition, yield declines are much more prevalent across regions and scenarios than are yield increases. In particular, under the HOT and DRY scenarios, yields rise in only a few regions and these increases are in all instances small. Finally, the coastal islands remain virtually unaffected in all climate scenarios.

> 3 The product of approximately 8500 grids times nine crops times 43 crop years times four climate scenarios.

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# [Insert Table 2]

Figures 1 and 2 provide insights into the geographic correlation of results. They show maize yields for each scenario for 110 districts in Tanzania. The darker lines show the boundaries of the larger administrative regions presented in Table 2. Once again, there is substantial regional variation in our results. For example, the COOL scenario shows significant yield increases in the Northern Zone (i.e., in the districts surrounding Mount Kilimanjaro), while yields decline slightly in the Southern Coast and Southern Highlands. By contrast, yields are damaged though-out the country in the HOT scenario, with particularly strong negative impacts in the Northern and Lake Victoria regions. The WET and DRY scenarios contrast similarly. The WET scenario shows mean yields increasing around Kilimanjaro and its southern slope while they decrease dramatically in the western regions around Lake Tanganyika.

# [Insert Figures 1 and 2]

In summary, there are strongly heterogeneous impacts across the four climate scenarios. However, as expected, there is some regional correlation in results. Climate outcomes favorable (unfavorable) to maize farmers in a particular region are also likely to favor (harm) maize farmers in neighboring regions. At the same time, geographical impacts can vary dramatically across scenarios, with some scenarios producing favorable outcomes while others resulting in pronounced negative impacts. Obviously, from a national food availability perspective, the impacts on yields in the major producing regions are more important. Maize is not equally important in all parts of the country and for all household groups (e.g., poor/non-poor and farm/non-farm). Since our objective is to evaluate the economic implications of climate change for agriculture as a whole and for broadly-defined food security, we employ, in the next section, an economy-wide model of Tanzania in order to evaluate these impacts.

# **3. Economy-wide Impacts and Food Security**

# _Economy-wide model_

The crop modeling results discussed in the previous section are passed down to a recursive dynamic computable general equilibrium (DCGE) model of mainland Tanzania, which estimates the economic impact of the baseline and climate change scenarios, including indirect or economy-wide linkages between the agricultural and nonagricultural sectors. Our model belongs to the structural-neoclassical class of CGE models (see Dervis et al., 1982). DCGE models are well-suited to analyzing climate change. First, they simulate the functioning of a market economy, including markets for labor, capital and commodities, and therefore can evaluate how changing economic conditions are mediated via prices and markets. Secondly, DCGE models ensure that all economy-wide constraints are respected, which is crucial for long-run climate change projections. Finally, CGE models contain detailed sector breakdowns and provide a 7
“simulation laboratory” for quantitatively examining how the individual impact channels of climate change influence the performance and structure of the whole economy (see Lofgren et al., 2004 for a detailed exposition of the base modeling framework adapted for this analysis).

Economic decision-making in the DCGE model is the outcome of decentralized optimization by producers and consumers within a coherent economy-wide framework. A variety of substitution mechanisms occur in response to variations in relative prices, including substitution between factors, between imports and domestic goods, and between exports and domestic sales. Production and trade function elasticities were drawn from Dimaranan (2006). The Tanzania model contains 28 activities or sectors, including 12 agricultural subsectors (see Pauw and Thurlow, 2010). Six factors of production are identified: three types of labor (unskilled, semi-skilled and skilled), agricultural land, livestock, and capital. Agricultural activities and land are distributed across the 20 administrative regions of mainland Tanzania. This sectoral and regional detail captures Tanzania’s economic structure and influences model results. Table A2 in the appendix outlines the disaggregation and regional characteristics of the model while Figure A1 provides a map of agro-climatic zones.

Climate change affects agricultural production, economic growth and household incomes in the DCGE model via predicted annual yield deviations for rain-fed crops estimated by CLICROP. The DCGE then determines how much resources should be devoted to each crop given their profitability relative to other activities. This reallocation of resources permits some autonomous adaptation by farmers and nonagricultural producers. For example, representative farmers in each region within the DCGE model allocate their land and capital between crops based on long-run rates of technical change and climate change. However, farmers are unable to anticipate weather conditions for a particular season, and so once planted, land cannot be reallocated even if weather patterns are not as expected. The representative producer in our model therefore corresponds to a “typical farmer” (see Füssel and Klein, 2006), who does not assume that historical weather patterns will persist indefinitely, but also does not have perfect foresight of future climate change. Rather they adapt their behavior based on the gradual realization of climate change.

The long timeframe over which climate change will unfold implies that dynamic processes are important (Arndt et al., forthcoming). The recursive dynamic specification of our CGE model allows it to capture annual changes in the rate of physical and human capital accumulation and technical change. So, for example, if climate change reduces agricultural production in a given year, it also reduces income and hence savings. This reduction in savings displaces investment and lowers production potential and economic growth. Given our long-run focus, our macroeconomic “closure” assumes that changes in aggregate absorption are proportionally distributed across nominal private and public consumption and investment via distribution neutral changes in savings rates. Government savings are flexible, tax rates are fixed, and the real exchange rate adjusts to maintain an exogenously determined current account 8
balance. In summary, our DCGE model is well suited to capture path dependent effects within a consistent macroeconomic framework.

# _Baseline scenario_

In order to estimate the economic impact of climate change for Tanzania, we first specify a baseline scenario that reflects development trends, policies and priorities in the absence of climate change. The baseline provides a reasonable trajectory for growth and structural change of the economy from 2007 to 2050 that can be used as a basis for comparison.

Economic growth in the DCGE model is determined by rates of factor accumulation and technical change. For population and labor supply, we assume that Tanzania’s population will continue to grow but at a decelerating rate (i.e., 2.0 percent today falling to 0.3 percent by 2050). We assume that the expansion of cultivated crop land will slow such that growth in agricultural production becomes increasingly dependent on the adoption of improved technologies rather than land expansion. As described earlier, the crop models use historical climate data to define year-on-year yield fluctuations in the baseline for each crop and region. Exogenous long-term agricultural productivity growth is set at 0.8 percent per year in agriculture and 1.2 percent in non-agriculture. Improvements in the education levels of Tanzania’s workforce are assumed to continue, with supply and productivity rising faster for skilled and semi-skilled workers than for unskilled workers (i.e., at 2.0 and 1.5 percent per year, respectively, compared to 0.5 percent). Under the above assumptions, Tanzania’s economy gradually develops, with agriculture’s contribution to gross domestic product (GDP) falling from 27.8 to 14.1 percent during 20072050. Overall, per capita GDP grows at an average 2.2 percent per year in the baseline, leading to significant improvements in average household welfare.

# _Economy-wide modeling results_

The DCGE model uses the crop yield results from Section 2 to estimate the economy-wide impacts of climate change. We first discuss the macroeconomic results from the model, which are summarized in Table 3. We focus on changes in “absorption”, which is the broadest measure of national welfare. Absorption tracks an economy’s use of goods for household consumption (C), investment (I), and government expenditure (G). Absorption is closely related to GDP growth. Formally, absorption (A) is defined as A=C+I+G. Recalling that GDP=C+I+G+X-M, where X is exports and M is imports, we can write that A=GDP+M-X. In other words, absorption is the volume of goods produced by the economy plus the goods that foreigners supply to the economy (imports) less the goods sent out to foreigners (exports). One advantage of measuring outcomes based on aggregate absorption is that it is less sensitive to our choice of “closure rules”, which influence the relative sizes of absorption’s C, I and G components.

# [Insert Table 3]

9
Table 3 reports average annual growth rates of real per capita absorption over the entire 2007-2050 simulation period. Changes in annual growth rates are small, with discernable reductions for only the HOT and DRY scenarios. However, even small reductions in growth rates accumulate over time. For example, by the end of the 2040s, national absorption is 0.77 and 1.7 percent below the baseline in the HOT and DRY scenarios, respectively. This is consistent with the larger and more widespread reductions in crop yields experienced under these two scenarios (see Table 2).

We measure the total economic damages caused by climate change via the agricultural sector as the cumulative loss or deviation in national absorption from the baseline using a five percent annual discount rate. The largest damages occur in the DRY scenario, where the total discounted loss throughout the 2007-2050 period amounts to US$13 billion (measured in 2007 prices). This amount is two-thirds of Tanzania’s GDP in 2007. By contrast, total discounted absorption rises in the WET scenario by US$3 billion suggesting possible gains from climate change for Tanzania.<sup>4</sup> Table 3 also decomposes economic damages across time periods. Despite the escalating biophysical effects of climate change on agricultural yields towards the end of our simulation period, applying a five percent discount rate means that a significant share of the economic costs or benefits of climate change will accrue over the next two decades. This is because crop yield reductions are often temporary during a bad year and can immediately rebound in the subsequent year if the season’s climate improves. In other words, crop yield losses in a given year are usually temporary, unlike damages to assets which may have lasting effects, such as roads damaged by flooding. This partly explains why agricultural damages are more evenly distributed across time periods. However, if we did not discount effects further into the future, then the costs or benefits of climate change would be larger and more heavily weighted towards the middle of the century. Moreover, it should be noted that most GCMs predict a pronounced aggravation of climate change impacts during the second half of the century. Were the time horizon of our analysis extended beyond 2050, then later periods would begin to exhibit progressively stronger impacts.

One of the advantages of CGE models is their ability to decompose national impacts to the sector and regional levels. Table 4 reports deviations in real GDP from the baseline in 20462050 for different sectors. Since agriculture is our only impact channel through which climate effects economic growth, it is not surprising that this sector exhibits the largest changes in our four scenarios. However, agriculture provides important inputs into downstream sectors, such as agro-processing. For example, agricultural GDP is 11.5 percent below the baseline in the DRY scenario by the end of the 2040s. This reduces the supply of raw inputs (e.g., grain) to

> 4 Other impacts, such as increased frequency and intensity of flooding events, were not modeled for the case of Tanzania. The impacts of these events are potentially large (Arndt et al., forthcoming) and could easily overwhelm the benefits of enhanced moisture to crops.

10
downstream agro-processing sectors (e.g. milling), causing their GDP to contract by 7.8 percent. However, not all sectors are adversely affected, even in the DRY scenario. For example, food imports increase in order to offset declining domestic production in the DRY scenario (see Table 3). Accordingly, some traders in the service sector benefit from higher demand for their services. Despite the expansion of services, forestry and fishery, which are only indirectly affected by climate change in our analysis, the net effect of climate change is a significant reduction in national GDP in the HOT and DRY scenarios and a slight decrease/increase in the COOL and WET scenarios, respectively.

# [Insert Table 4]

Table 5 presents deviations in real agricultural GDP from the baseline for the different regions in the model. It should be noted that the DCGE model is for mainland Tanzania only, and so does not reflect changes on the coastal islands (i.e., Pemba and Zanzibar). Almost all regions are adversely affected in the HOT and DRY scenarios, with particularly large reductions in agricultural GDP in the Northern and Central Zones and around Lake Victoria. These regions represent a large share of Tanzania’s agricultural sector, and so a drop in their production has national-level implications. Similarly, while overall agricultural production rises in the WET scenario, it hides significant regional variation. While production increases in the Northern Zone and Northern Coast, it falls in most other regions, including around Lake Victoria. There are also differences in regional outcomes even within agro-climatic zones, such as within Southern Coast. Such pronounced regional variation underscores the need for sub-national assessments, especially for designing policy responses to climate change.

# [Insert Table 5]

Households in the DCGE model are affected by climate change via changes in both agricultural incomes and consumer prices. Households can adapt to these changes by reallocating their resources (e.g., land, labor and capital) towards less-affected sectors or occupations (e.g., non-farm activities). However, if agricultural production falls as a result of climate change then consumer prices for agricultural products will likely increase. Producers may then allocate more of their resources towards climate change affected sectors in order to take advantage of higher prices. This will certainly be the case for farmers in regions that are less adversely affected by climate change. Households’ adaptation decisions therefore involve production and demand considerations, both of which are captured in a general equilibrium model.

Table 6 reports deviations in households’ real food consumption expenditure from baseline by 2046-2050. Changes in food consumption in each scenario are less pronounced than changes in agricultural GDP. Two factors drive this result. First, Tanzania is able to import food to replace falling domestic supplies. For example, the 11.5 percent decline in national agricultural production in the DRY scenario (see Table 4) is partially offset by a 37.1 percent increase in net food imports (see Table 3), leaving national food consumption to fall by 8.0 11
percent (see Table 6). Secondly, the model assumes that transport systems are sufficiently developed in Tanzania by 2050 that food is effectively traded in national markets. This means that falling production and excess demand in certain regions can be supplied by producers in other regions. In this way, market forces will distribute changes in national food consumption across regions and household groups.

# [Insert Table 6]

The impact of climate change on incomes and food security therefore depends on three household characteristics. First, climate change has region-specific implications, with some regions benefitting from improved conditions while others are adversely affected. Secondly, climate change will affect crops differently, and so changes in households’ agricultural incomes will depend on their cropping patterns and their ability to reallocate farm resources between farm activities. Finally, agriculture generates only part of households’ incomes and food comprises only part of their consumption basket. Climate change will therefore affect households differently based on their income and consumption patterns.

In sum, despite endogenous market-based adaptation, there are still significant differences in outcomes across household groups and regions. For example, lower-income households experience larger declines in per capita food consumption than higher-income households in the COOL and DRY scenarios. This is because poorer households are typically more reliant on agriculture for their livelihoods, and because they spend a larger share of their incomes on food. Likewise, while all regions around Lake Victoria experience similar reductions in agricultural GDP (see Table 5), household food consumption declines by 10.5 percent in Kagera and by only 6.7 percent in Shinyanga. This is because households in Shinyanga region are less dependent on agricultural incomes, and are more heavily engaged in non-farm activities than are households in Kagera. Finally, food consumption amongst non-farm households also declines in the DRY scenario, due to rising food prices and falling real incomes (i.e., due to falling demand for nonagricultural products).

# **4. Conclusions**

Relative to a no climate change baseline and considering domestic agricultural production as the principal channel of impact, food security in Tanzania appears likely to deteriorate as a consequence of climate change. This relative decline comes about through reductions in agricultural production, principally food production, due to increases in temperature and changes in rainfall patterns. In the DRY scenario, average agricultural production levels are more than 10 percent below the levels of a hypothetical no climate change scenario by mid-century. This reduced productive capacity also limits growth in exports and growth in household incomes 12
hence reducing the overall capacity of the economy to obtain and distribute food from international markets. It is important to point out that the results do not point to an absolute decline in the levels of food security indicators, such as total agricultural production and household purchasing power. Rather, the rate of improvement in these indicators in three out of four scenarios is reduced. In addition, in one scenario, projected changes in climate are favorable to agricultural production and food security. Overall, the analysis points to a high degree of diversity of outcomes across climate scenarios, sectors, and regions.

The methodology applied is well-suited to considering the implications of climate change for growth, development, and ultimately food security. However, while the economic modeling framework is comprehensive, the treatment of climate change within the modeling framework is not. Climate change could impact food security through numerous additional channels beyond reductions in potential yields. These additional channels merit particular attention in future research. We will briefly discuss three. First, the increase in intensity of rainfall due to climate change has the potential to increase the frequency and intensity of flooding events (Arndt et al, 2011). As recent events in Pakistan illustrate, flooding can be highly destructive. Not only does flooding cause a spike in food insecurity in the short run, it frequently wipes out economic infrastructure, such as transport networks, with potentially long term implications for production and growth (Chinowsky, 2011). Second, the results presented reiterate the importance of long run accumulation (Arndt et al., forthcoming). If rates of growth decline even slightly over long periods of time, this decline eventually leads to significant economic impacts. In this context, it is important to recall that the (assumed) underlying rate of agricultural productivity growth is the same across all climate scenarios. It is certainly conceivable that climate change could reduce the expected rate of underlying agricultural productivity growth for any given level of effort devoted to new technology generation and adoption. Finally, all world prices are assumed to be constant. If the climate outcomes in the DRY scenario also resulted in reduced (increased) production globally, then the impacts of the reduction in agricultural production would be magnified (mitigated) by increased (decreased) prices for food commodities on world markets.

Overall, while significant progress has been registered, we as a field remain at nascent stages in our understanding of the implications of climate change – across the multiplicity of possible dimensions, and especially for food security in vulnerable low-income countries like Tanzania.

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