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A Ricardian analysis of the distribution of climate change impacts on agriculture across agro-ecological zones in Africa.

1. Introduction

Recent publications of the Intergovernmental Panel on Climate Change “IPCC” redirects here. For other uses, see IPCC (disambiguation).
The Intergovernmental Panel on Climate Change (IPCC) was established in 1988 by two United Nations organizations, the World Meteorological Organization (WMO) and the United Nations Environment
 (IPCC See IMS Forum. ) provide strong evidence that accumulating greenhouse gases are leading to a warming world (IPCC 2007). If these greenhouse gases and global warming global warming, the gradual increase of the temperature of the earth's lower atmosphere as a result of the increase in greenhouse gases since the Industrial Revolution.  continue unabated, they are predicted to impose serious costs to agricultural farms in low latitude that part of the earth's surface which is near the equator.

See also: Latitude
 developing countries (Kurukulasuriya et al. 2006; Seo et al. 2006; Seo and Mendelsohn 2008a, 2007). The international community needs to design an efficient mitigation MITIGATION. To make less rigorous or penal.
     2. Crimes are frequently committed under circumstances which are not justifiable nor excusable, yet they show that the offender has been greatly tempted; as, for example, when a starving man steals bread to satisfy
 program to reduce greenhouse gas greenhouse gas
n.
Any of the atmospheric gases that contribute to the greenhouse effect.



greenhouse gas 
 emissions (Nordhaus 2007). One of the substantive benefits of such a mitigation program is increased food security, especially for people living in the low latitudes (Reilly et al. 1996, McCarthy et al 2001).

Previous research has identified that climate change impacts on agriculture in developing countries will vary from place to place depending on numerous factors. Before policy makers can design appropriate policy responses, they need to have reliable indicators of how impacts will vary across the landscape. This study takes advantage of Agro-Ecological Zones (AEZs) to predict how impacts will be dispersed dis·perse  
v. dis·persed, dis·pers·ing, dis·pers·es

v.tr.
1.
a. To drive off or scatter in different directions: The police dispersed the crowd.

b.
 across Africa. The differential effects of climate change on farms in various agro-ecological zones have not yet been quantified. Specifically, we examine how climate change might affect farm net revenue in different AEZs. Not only does this research provide insight into how climate affects farmers facing different conditions, but the research will also help extrapolate extrapolate - extrapolation  climate change results from an existing sample to the continent from which they are drawn.

The study combines data about AEZs with economic farm data from a recently completed GEF/World Bank study of Africa (Dinar et al 2008). The AEZs are compiled by the Food and Agriculture Organization of the United Nations Noun 1. Food and Agriculture Organization of the United Nations - the United Nations agency concerned with the international organization of food and agriculture
FAO, Food and Agriculture Organization
 using information about climate, altitude altitude, vertical distance of an object above some datum plane, such as mean sea level or a reference point on the earth's surface. It is usually measured by the reduction in atmospheric pressure with height, as shown on a barometer or altimeter. , and soils (FAO FAO,
n See Food and Agriculture Organization.
 1978). The GEF/World Bank study measured crop choice, livestock choice, yields, gross revenues, and net revenues of nearly 10 thousand farmers (households) in 11 African countries (Kurukulasuriya et al. 2006, Kurukulasuriya and Mendelsohn 2006, Seo et al. 2006, Seo and Mendelsohn 2008a). Both the countries and the farm households were sampled to represent the various climates across Africa.

This paper differs from the earlier economic research on African agriculture in the following ways. First, it quantifies climate change impacts for each of the 16 Agro-Ecological Zones. The AEZs provide a mechanism to extrapolate from the sample to other similar locations around Africa. Second, this paper provides an analysis of net revenue that simultaneously includes both crop sector and livestock sector income for each farm. The bulk of the economic literature on agricultural impacts has focused on just crop income, although there have been a few studies on just livestock income. Third, the analysis compares the same model with and without country fixed effects.

In the next section, we discuss the basic underlying theory of Ricardian analysis. The third section describes the data followed by empirical results in the fourth section. We then use the climate parameter (1) Any value passed to a program by the user or by another program in order to customize the program for a particular purpose. A parameter may be anything; for example, a file name, a coordinate, a range of values, a money amount or a code of some kind.  estimates to predict climate change impacts over the next hundred years in the fifth section. The paper concludes with a discussion of the results and policy implications.

2. Theory

Farms in different Agro-Ecological Zones employ different farming practices. For example, dependent on the AEZ AEZ Agri-Export Zone
AEZ Acrodermatitis Enteropathica, Zinc-Deficiency Type
AEZ Alkaline Earth Zeolite
AEZ Air Exclusion Zone (aviation no-fly area) 
 they are situated in, each farmer will choose a specific farm type, irrigation irrigation, in agriculture, artificial watering of the land. Although used chiefly in regions with annual rainfall of less than 20 in. (51 cm), it is also used in wetter areas to grow certain crops, e.g., rice. , crop species, and livestock species that fit that AEZ. As some AEZs are better suited for agriculture while others are not, the average net revenues from these AEZs will differ. In our application, the Ricardian analysis is a reduced form In social science and statistics, particularlly econometrics, a reduced form equation is a method of dealing with endogeneity. A reduced form equation is defined by James Stock & Mark Watson (2007) in the following way:  regression regression, in psychology: see defense mechanism.
regression

In statistics, a process for determining a line or curve that best represents the general trend of a data set.
 of net revenue on climate, soils, economic, and institutional variables (Mendelsohn, Nordhaus, and Shaw 1994). Estimated coefficients of this model are used to measure the climate sensitivity of agriculture, and are used to predict climate change impacts in the future, given a set of future climate scenarios.

In the Ricardian technique, adaptations are implicit and endogenous endogenous /en·dog·e·nous/ (en-doj´e-nus) produced within or caused by factors within the organism.

en·dog·e·nous
adj.
1. Originating or produced within an organism, tissue, or cell.
. The Ricardian technique assumes that each farmer wishes to maximize net income subject to the exogenous Exogenous

Describes facts outside the control of the firm. Converse of endogenous.
 conditions of the farm which include climate. Assuming the farmer chooses a mix of agricultural activities that provide the highest net income and chooses each input to maximize net incomes from such activities, the resulting net revenue will be a function of just the exogenous variables:

[[pi].sup.*] = f([P.sub.q], C, W, S, [P.sub.X], [P.sub.L], [P.sub.K], [P.sub.IR]), (1)

where [pi] is net revenue, [P.sub.q] is a vector of output prices, C is a vector of climate variables, W is available water for irrigation, S is a vector of soil characteristics, [P.sub.X] is a vector of prices for the annual inputs, [P.sub.L] is a vector of prices for each type of labor, [P.sub.K] is the rental price of capital, and [P.sub.IR] is the annual cost of each type of irrigation system. In this application, net revenue includes income from both crops and livestock. This is an important distinction because most previous studies evaluated only crop income alone (or sometimes livestock income alone).

The Ricardian model estimates equation 1 econometrically by specifying a quadratic function A quadratic function, in mathematics, is a polynomial function of the form , where .  of climate variables along with other control variables. By grouping the various variables, the reduced form of the net income becomes

[pi] = X[beta] + Zr + W[phi] + H[lambda] + L[eta] + u (2)

where X is a vector of climate variables and their squared values, Z is a vector of soil variables, W is a vector of water flow variables, H is a vector of household characteristics, L is a set of country dummies, and u is an error term which is identically and independently Normal distributed. The OLS OLS Ordinary Least Squares
OLS Online Library System
OLS Ottawa Linux Symposium
OLS Operation Lifeline Sudan
OLS Operational Linescan System
OLS Online Service
OLS Organizational Leadership and Supervision
OLS On Line Support
OLS Online System
 version of this model does not include the country dummies and the fixed effects version does include them.

We expect that the maximum profit varies by Agro-Ecological Zones. Certainly, desert areas are less suitable for farming except near oases or irrigation infrastructure. Lowland semi-arid areas may also not be a good place for crops (Kurukulasuriya et al. 2006). Low land moist moist

having a moderate moisture content, slightly wet to the touch.


moist dermatitis
see moist dermatitis of rabbits.

moist grain storage
grain stored at about 30% moisture in airtight silos.
 forests may not serve as a good place for animal husbandry animal husbandry, aspect of agriculture concerned with the care and breeding of domestic animals such as cattle, goats, sheep, hogs, and horses. Domestication of wild animal species was a crucial achievement in the prehistoric transition of human civilization from  (Seo et al. 2006). These underlying productivity differences will lead to varying profits across climate, soil, and altitude. Because these variables are different from one AEZ to another, productivity and profits will also vary by Agro-Ecological Zones. Hence, calculation of marginal effects from the estimated parameters should use the appropriate temperature and precipitation for each AEZ. For example, the marginal effect of temperature in lowland moist savannah (AEZ2) should be calculated as follows:

[d[pi]/dT].sub.AEZ2] = d[pi]/dT (T = [[bar.T].sub.AEZ2]3)

In order to measure the change in welfare (AW) of a change from one climate ([C.sub.A]) to another climate ([C.sub.B]), we subtract A relational DBMS operation that generates a third file from all the records in one file that are not in a second file.  the net revenue before the change from the net revenue after the change for each farm household. The welfare change is the difference between the two. If the value is negative (positive), net revenue declines (increases), and the climate change causes damages (benefits):

[DELTA]W = [pi]([C.sub.B]) - [pi]([C.sub.A]4)

Note that this welfare measure does not take into account changes in prices (Cline cline, in biology, any gradual change in a particular characteristic of a population of organisms from one end of the geographical range of the population to the other.  1996). Because of trade, price changes are more likely to depend on global production than local production. Unless temperatures warm well above 4C, climate change is not expected to change global production and therefore global agricultural prices noticeably no·tice·a·ble  
adj.
1. Evident; observable: noticeable changes in temperature; a noticeable lack of friendliness.

2. Worthy of notice; significant.
 (Reilly et al. 1996). The omission omission n. 1) failure to perform an act agreed to, where there is a duty to an individual or the public to act (including omitting to take care) or is required by law. Such an omission may give rise to a lawsuit in the same way as a negligent or improper act.  of prices is therefore likely to be of second order importance. However, if local prices were to change because of local conditions, the welfare estimate from the Ricardian model will overestimate o·ver·es·ti·mate  
tr.v. o·ver·es·ti·mat·ed, o·ver·es·ti·mat·ing, o·ver·es·ti·mates
1. To estimate too highly.

2. To esteem too greatly.
 the size of the revenue change. For example, if production falls, prices will rise, and so the true revenue will fall less than what the Ricardian model predicts.

3. Description of Data

The FAO has developed a typology typology /ty·pol·o·gy/ (ti-pol´ah-je) the study of types; the science of classifying, as bacteria according to type.

typology

the study of types; the science of classifying, as bacteria according to type.
 of AEZs as a mechanism to classify clas·si·fy  
tr.v. clas·si·fied, clas·si·fy·ing, clas·si·fies
1. To arrange or organize according to class or category.

2. To designate (a document, for example) as confidential, secret, or top secret.
 the growing potential of land (FAO 1978). The AEZs are defined using the length of the growing season growing season, period during which plant growth takes place. In temperate climates the growing season is limited by seasonal changes in temperature and is defined as the period between the last killing frost of spring and the first killing frost of autumn, at which . The growing season, in turn, is defined as the period where precipitation and stored soil moisture is greater than half of the evapotranspiration evapotranspiration

Loss of water from the soil both by evaporation from the soil surface and by transpiration from the leaves of the plants growing on it. Factors that affect the rate of evapotranspiration include the amount of solar radiation, atmospheric vapor pressure,
. The longer the growing season, the more crops can be planted (or in multiple seasons) and the higher are the yields (Fischer and van Velthuizen 1996, Vortman et al. 1999). FAO has classified land throughout Africa using this AEZ concept. Our study will use these FAO defined AEZ classifications.

The economic data for this study were collected by national teams (Dinar et al 2008). The data were collected for each plot within a household and household level data was constructed from the plot level data. In each country, districts were chosen to get a wide representation of farms across climate conditions in that country. The districts were not representative of the distribution of farms in each country as there are more farms in more productive locations. In each chosen district, a survey was conducted of randomly selected farms. The sampling was clustered in villages to reduce sampling cost. All economic data were collected in national currency and converted to USD USD

In currencies, this is the abbreviation for the U.S. Dollar.

Notes:
The currency market, also known as the Foreign Exchange market, is the largest financial market in the world, with a daily average volume of over US $1 trillion.
 using official exchange rates.

A total of 9597 surveys were administered across the 11 countries in the study. The number of surveys varied from country to country. Not all the surveys could be used. Some surveys contained incorrect information about the size of the farm, cropping area or some of the farm operating costs operating costs nplgastos mpl operacionales . Implausible im·plau·si·ble  
adj.
Difficult to believe; not plausible.



im·plausi·bil
 values were treated as missing values In statistics, missing values are a common occurrence. Several statistical methods have been developed to deal with this problem. Missing values mean that no data value is stored for the variable in the current observation. . It is not clear what the sources of these errors were but field and measurement errors are most likely. They may reflect a misunderstanding of the units of measurement Units of measurement

Values, quantities, or magnitudes in terms of which other such are expressed. Units are grouped into systems, suitable for use in the measurement of physical quantities and in the convenient statement of laws relating physical quantities.
, they may reflect a language barrier, or they may be intentional in·ten·tion·al  
adj.
1. Done deliberately; intended: an intentional slight. See Synonyms at voluntary.

2. Having to do with intention.
 incorrect answers.

Data on climate was gathered from two sources (Dinar et al. 2008). We relied on temperature data from satellites operated by the Department of Defense (Basist et al. 2001). The Defense Department uses a set of polar orbiting satellites that pass above each location on earth between 6am and 6pm every day. These satellites are equipped with sensors
  • Thermocouple
  • RTD - Resistance Temperature Detector or Resistance thermometer or Pt100
  • Microphone
  • Hydrophones
  • Seismometers
  • Photoresistor
  • Phototransistor
  • Infrared thermometer
  • Multi-User Multimodal Tabletop Interaction
  • Cationic Sensor
 that measure surface temperature by detecting microwaves that pass through clouds. The precipitation data comes from the Africa Rainfall and Temperature Evaluation System (ARTES ARTES Automated Remote Time Entry System ) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric atmospheric /at·mos·pher·ic/ (at?mos-fer´ik) of or pertaining to the atmosphere.

atmospheric

of or pertaining to the atmosphere.
 Association's Climate Prediction Climate prediction refers to :
  • Global warming
  • Climateprediction.net
 Center, is based on ground station measurements of precipitation.

It is not self-evident how to represent monthly temperatures and precipitation data in a Ricardian regression model. The correlation between adjacent months is too high to include every month. Kurukulasuriya et al. (2007) explored several ways of defining three-month average seasons. Comparing the results, the authors found that defining winter in the northern hemisphere as the average of November, December and January provided the most robust results for Africa. This assumption in turn implies that the next three months, February, March and April would be spring, May, June and July would be summer, and August, September and October would be fall (in the north). The seasons in the southern hemisphere are six months apart, i.e. winter in the southern hemisphere is defined as the average of May, June and July. These seasonal definitions were chosen because they provided the best fit with the data and reflected the mid-point for key rainy rain·y  
adj. rain·i·er, rain·i·est
Characterized by, full of, or bringing rain.



raini·ness n.

Adj.
 seasons in the sample. The authors adjusted for the fact that seasons in the southern and northern hemispheres occur at exactly the opposite months of the year. The authors also explored defining seasons by the coldest month, the month with highest rainfall, and solar position, but found these definitions did a poorer job of explaining current agricultural performance.

Soil data were obtained from FAO (2004). The FAO data provides information about the major and minor soils in each location as well as slope and texture. Data concerning the hydrology hydrology, study of water and its properties, including its distribution and movement in and through the land areas of the earth. The hydrologic cycle consists of the passage of water from the oceans into the atmosphere by evaporation and transpiration (or  was obtained from the results of an analysis of climate change impacts on African hydrology (Strzepek and McCluskey 2006). Using a hydrological hy·drol·o·gy  
n.
The scientific study of the properties, distribution, and effects of water on the earth's surface, in the soil and underlying rocks, and in the atmosphere.
 model for Africa, the authors calculated flow and runoff Runoff

The procedure of printing the end-of-day prices for every stock on an exchange onto ticker tape.

Notes:
If the "tape is late" then it can take a long time to print off all the closing prices.
 for each district in the surveyed countries. Data on elevation elevation, vertical distance from a datum plane, usually mean sea level to a point above the earth. Often used synonymously with altitude, elevation is the height on the earth's surface and altitude, the height in space above the surface.  at the centroid centroid

In geometry, the centre of mass of a two-dimensional figure or three-dimensional solid. Thus the centroid of a two-dimensional figure represents the point at which it could be balanced if it were cut out of, for example, sheet metal.
 of each district was obtained from the United States Geological Survey The United States Geological Survey (USGS) is a scientific agency of the United States government. The scientists of the USGS study the landscape of the United States, its natural resources, and the natural hazards that threaten it.  (USGS USGS United States Geological Survey (US Department of the Interior)  2004). The USGS data are derived from a global digital elevation model A digital map of the elevation of an area on the earth. The data are either collected by a private party or purchased from an organization such as the U.S. Geological Survey (USGS) that has already undertaken the exploration of the area.  with a horizontal grid spacing of 30 arc seconds (approximately one kilometer kilometer

one thousand (103) meters; 3280.83 feet; five-eighths of a mile; abbreviated km.
).

4. Empirical Results

FAO has identified 16 Agro-Ecological Zones in Africa. Table 1 shows the classification of AEZs and several descriptive statistics descriptive statistics

see statistics.
 by AEZs. The AEZs are classified into dry savannah, humid hu·mid  
adj.
Containing or characterized by a high amount of water or water vapor: humid air; a humid evening. See Synonyms at wet.
 forest, moist savannah, semi-arid, and sub-humid by the length of the growing season. Within each AEZ, they are further broken down by elevation into high, mid, and low elevation. The other remaining zone is desert. Table 1 also shows the average profit per hectare hectare (hĕk`târ, –tär), abbr. ha, unit of area in the metric system, equal to 10,000 sq m, or about 2.47 acres.  of land in USD for each AEZ in the survey period. Farmers earn higher profits in high elevation moist savannah and sub humid zones and mid elevation dry savannah and sub humid zones. Farmers earn lower profits in high elevation dry savannah, humid forest, and semi arid ar·id  
adj.
1. Lacking moisture, especially having insufficient rainfall to support trees or woody plants: an arid climate.

2.
 zones, the lowland semi-arid zone, and in the desert zone.

Figure 1 shows the distribution of the 16 agro-ecological zones across the continent. The Sahara desert occupies a vast land area in the north. There are also desert zones in the eastern and southern edge of the continent. Just beneath the Sahara in West Africa West Africa

A region of western Africa between the Sahara Desert and the Gulf of Guinea. It was largely controlled by colonial powers until the 20th century.



West African adj. & n.
 is a lowland semi-arid zone, followed by lowland dry savannah, lowland moist savannah, and lowland sub-humid zone. The lowland humid forest then stretches from Cameroon across Central Africa. Eastern Africa is composed of some desert, lowland dry savannah, and some high elevation humid forest and high elevation dry savannah which are located around Mount Kilimanjaro and part of Kenya. Southern Africa consists of lowland or mid elevation moist savannah, and lowland or mid elevation dry savannah.

Farms in different agro-ecological zones clearly face different conditions for farming. Hence, we expect that farms in favorable fa·vor·a·ble  
adj.
1. Advantageous; helpful: favorable winds.

2. Encouraging; propitious: a favorable diagnosis.

3.
 ecological ecological

emanating from or pertaining to ecology.


ecological biome
see biome.

ecological climax
the state of balance in an ecosystem when its inhabitants have established their permanent relationships with each
 zones for agriculture earn higher profits while farms in unfavorable zones earn much less per hectare. In order to examine the climate sensitivity of farms in each AEZ, we examine the variation of farm profits across different climate zones.

In Table 2, we show four different specifications of the Ricardian model of net revenue per hectare of land. For all the regressions, the dependent variable is net revenue from both crops and livestock divided by the hectares of cropland for each farm (7). As many farms in Africa consume their own produce, in this study we valued own consumption at the market values of each product (Kurukulasuriya et al. 2006, Seo et al. 2006). In addition, farmers use their own family labor which is not paid for the work. It was therefore empirically difficult to find a proper wage rate for household labor and so it is not included as a cost. As a result, household farms that rely mainly on their own labor may appear to have higher net revenues per hectare in comparison to commercial farms that rely on hired labor.

Since it is not clear at first which specification of Equation 2 in the theory section fits the model best, we test the following four specifications in Table 2. The first regression uses two seasons (winter and summer) along with soils and the other control variables as independent variables. In the second regression, we test whether climate interaction terms between temperatures and precipitations should be included. The third regression tests whether country fixed effects are important (8). In a continental study like this, there can be substantial country specific effects not captured by the variations in climate and other control variables. For example, agricultural policies, trade policies, and stages of economic development all vary across countries. Finally, the fourth regression tests whether all four seasons in a year are important in determining net revenues in Africa. Although all four seasons are significant in temperate temperate /tem·per·ate/ (tem´per-at) restrained; characterized by moderation; as a temperate bacteriophage, which infects but does not lyse its host.

tem·per·ate
adj.
 climates, they may not be as effective in tropical climates where the seasons are more alike all year long.

The estimated coefficients of the four regressions show that the climate coefficients are mostly significant except for the model with four seasons. The net revenue responses to summer temperature are all concave Concave

Property that a curve is below a straight line connecting two end points. If the curve falls above the straight line, it is called convex.
 while the responses to winter temperature are all convex Convex

Curved, as in the shape of the outside of a circle. Usually referring to the price/required yield relationship for option-free bonds.
. Responses to summer and winter precipitation depend upon whether or not country fixed effects are included in the model. Without country fixed effects, precipitation is convex and with country fixed effects, precipitation is concave with respect to net revenue. Summer climate interaction terms are generally negative and significant whereas winter climate interaction effects are positive but insignificant. The inclusion of country fixed dummies affects the significance of the other control variables. Water flow and electricity coefficients are positive and strongly significant when country fixed effects are not included, but become insignificant when country fixed effects are introduced. Most of the significant soil coefficients are negative. When included, country dummies are positive and significant for Egypt and Cameroon. West African West Africa

A region of western Africa between the Sahara Desert and the Gulf of Guinea. It was largely controlled by colonial powers until the 20th century.



West African adj. & n.
 countries such as Niger, Burkina Faso Burkina Faso (burkē`nə fä`sō), republic (2005 est. pop. 13,925,000), 105,869 sq mi (274,200 sq km), W Africa. It borders on Mali in the west and north, on Niger in the northeast, on Benin in the southeast, and on Togo, Ghana, and , and Senegal had negative coefficients.

The second model is superior to model 1 in that it captures climate interaction effects that are significant. The third model might be superior to model 2 because it controls for country fixed effects which can capture agricultural policies, development, language, and trade differences between countries. However, the country fixed effects also remove a great deal of the variation in climate across Africa. So, it is not clear which of these two models is the best one to use for assessing policy interventions. The fourth model, however, is clearly not an improvement over the third model because it does not increase the significance of the coefficients. When all four seasons are included, the climate coefficients mostly become insignificant.

Because climate is introduced in a quadratic form In mathematics, a quadratic form is a homogeneous polynomial of degree two in a number of variables. The term quadratic form is also often used to refer to a quadratic space, which is a pair (V,q) where V is a vector space over a field k , it is difficult to interpret the impact of climate directly from the climate coefficients. Table 3 calculates the marginal change in net revenue from a marginal change in temperatures and precipitations for the four models in Table 2. These marginal effects are calculated at the mean climate of each Agro-Ecological Zone. One result that remains the same across all the impact specifications is that higher temperatures are harmful. Net revenues fall as temperatures rise in every AEZ.

However, although Africa is generally dry, it is not dry in every AEZ. Consequently, the marginal effect of increased rainfall is not always beneficial. For example, more rain will benefit some regions in West Africa close to the Sahara desert where it is very dry, but more rain will harm farms in Cameroon where it is very wet. The first two specifications imply more rain is generally beneficial, but the last two specifications imply that rainfall is generally harmful. With the third specification, rainfall is predicted to be harmful for Africa as a whole but the marginal effects vary across AEZs. The marginal damage is largest in high elevation dry savanna savanna or savannah (both: səvăn`ə), tropical or subtropical grassland lying on the margin of the trade wind belts. , lowland humid forest, and lowland sub-humid AEZs. These AEZs do not receive the benefits from increased rainfall due to high elevation and/or already humid conditions which make more rainfall harmful. In many of the remaining AEZs, however, increased rainfall is beneficial even in the third specification.

What these results suggest is that climate change impacts will vary substantially across different agro-ecological zones. In the third regression, even though aggregate estimate indicates damage from increased rainfall, farms in most AEZs will get benefits from more rainfall. It is the harmful effects of increased rainfall on several distinct AEZs that turn the overall aggregate negative.

5. Predictions

In this section, we simulate simulate - simulation  the impact of future climate change scenarios on African agriculture using the results from the estimated coefficients in the previous section. Note that in these simulations only climate changes, all other factors remain the same. Clearly, this will not be the case over time. Technology, capital, consumption, and access will all change over time and these factors will have an enormous impact on future farm net revenues. The purpose of this exercise is not to predict the future but simply to see what role climate may play in that future.

In order to examine a wide range of climate outcomes, we rely on two Atmospheric-Oceanic Global Circulations Models (AOGMC's): CCC CCC

A very speculative grade assigned to a debt obligation by a rating agency. Such a rating indicates default or considerable doubt that interest will be paid or principal repaid. Also called Caa.
 (Canadian Climate Centre) (Boer et al. 2000) and PCM (1) See phase change memory.

(2) (Plug Compatible Manufacturer) An organization that makes a computer or electronic device that is compatible with an existing machine.
 (Parallel Climate Model) (Washington et al. 2000). We use the A2 emission scenario from the SRES SRES Seniors Real Estate Specialist
SRES Special Report on Emission Scenarios (Intergovernmental Panel on Climate Change)
SRES Senate Resolution
SRES Signed Response
SRES Surgically Remediable Epilepsy Syndromes
SRES System Resource
 report (IPCC 2000). Given these emission trajectories, each of these models generates a future climate scenario. These scenarios were chosen because they bracket In programming, brackets (the [ and ] characters) are used to enclose numbers and subscripts. For example, in the C statement int menustart [4] = ; the [4] indicates the number of elements in the array, and the contents are enclosed in curly braces.  the range of outcomes predicted in the most recent IPCC (Intergovernmental Panel on Climate Change) report (IPCC 2007). In each of these scenarios, climate changes at the grid cell level were summed with population weights to predict climate changes by country. We then examined the consequences of these country level climate change scenarios for 2020, 2060, and 2100.

To obtain district level climate predictions for each scenario, we added the predicted change in temperature from the climate model to the baseline temperature for each season in each district. For precipitation, we multiplied the predicted percentage change in precipitation from the climate models by the baseline precipitation for each season in each district. Table 4 presents the African mean temperature and rainfall predicted by the two models for each season for the years 2020, 2060 and 2100. In Africa in 2100, PCM predicts a 2[degrees]C increase and CCC a 6.5[degrees]C increase in annual mean temperature. Although temperature predictions vary in its magnitude of change by the models, rainfall predictions vary also in its direction of change by the models. PCM predicts a 10% increase in annual mean rainfall in Africa and CCC a 15% decrease. Even though the annual mean rainfall in Africa is predicted to increase/decrease depending on the scenario, there is substantial variation in rainfall across countries. However, all models predict summer rainfall to decrease while winter rainfall to increase.

Looking at the trajectories of temperature and precipitation for the coming century, we find that temperatures are predicted to increase steadily until 2100 for both models. Precipitation predictions, however, vary across time for Africa: CCC predicts a declining trend and PCM predicts an initial increase, and then decrease, and increase again.

We predict net revenues based on the estimated parameters in Table 2 and future climates in Table 4. Climate change impacts are measured as the net revenues in the future at 2020, 2060, and 2100 minus the net revenues in the base year. Impact estimates for each AEZ are calculated at the mean of a climate variable at that AEZ. In predicting impacts, we assume that it is only the corresponding climate variable that changes in the future.

We present impact estimates from Model 3 with country fixed effects and Model 2 without country fixed effects in Tables 5a and 5b. Table 5a presents the results from model 3, country fixed effects model, in Table 2. Impacts are presented in both absolute magnitude absolute magnitude: see magnitude.  and percentage change for both Africa as a whole and by each AEZ. African farmers earn $630 per year for a hectare of land based on the agricultural activities during July 2002 to June 2003. With the parameter estimates from Model 3, they are expected to lose 10% of their income under CCC, but gain 24% more income under PCM by 2020. Over time the estimates do not change much. This result indicates that African farmers are more resilient See resiliency.  to climate change than earlier studies predicted (Rosenzweig and Parry 1994; Kurukulasuriya and Mendelsohn 2008). These results differ from past findings because farm income includes both crop and livestock income. Reductions in crop income are being partially offset by increases in livestock income. By not only adjusting their methods of growing crops but also switching back and forth between crops and livestock, farmers can adapt to future changes in climate. Farmers are therefore predicted to tolerate tol·er·ate
v.
1. To allow without prohibiting or opposing; permit.

2. To put up with; endure.

3. To have tolerance for a substance or pathogen.
 and even take advantage of climate change unless a large increase in temperature materializes along with a substantial drying. Table 5a shows how climate change affects farm net revenues in each AEZ. Except for the mid elevation savannahs under the CCC scenario, all the AEZs are predicted to get benefits from global warming.

However, the estimates from Model 2 without country fixed effects tell a slightly different story. Under the CCC scenario, farmers are increasingly vulnerable to climate change. Damage estimates increase from 16% in 2020 to 27% in 2100. On the other hand, African agriculture will benefit if climate change turns out to be mild with a small increase in temperature and an increase in precipitation.

Looking across different agro-ecological zones, farms in moist savannah and dry savannah are the most vulnerable to higher temperature and reduced precipitation regardless of the elevation of these farms. On the other hand, the farms in sub-humid or humid forest gain even from this severe climate change. These results indicate that major agricultural areas in Africa will shift in the future. Farmers will reduce farming in the currently productive moist savannah and dry savannah to the sub-humid AEZ which is currently less populated pop·u·late  
tr.v. pop·u·lat·ed, pop·u·lat·ing, pop·u·lates
1. To supply with inhabitants, as by colonization; people.

2.
 by farmers.

Current climate already limits the incomes of African farmers. The results suggest that unless warming is severe, farmer incomes will not fall much further. Farmer incomes will even rise with the PCM scenario. These results should be understood in terms of what farmers can do in the case of climate change. Previous studies revealed that farmers can change livestock species, crop varieties, adopt irrigation, and change farm types to adapt to climate change. These adaptations will reduce the damage from climate change substantially (Seo and Mendelsohn 2008a, 2008b, Mendelsohn and Seo 2007). The results also indicate that farmers will even change locations in the case of a severe climate change.

In Figures 2 and 3, we examine the spatial distribution of impacts from the two climate scenarios based on Model 3 with country fixed effects. The maps show the percentage loss of agricultural profits across Africa for each AEZ. Under the CCC scenario, lowland AEZs in general gain from climate change. However, desert areas, mid elevation AEZs and high elevation AEZs are predicted to lose a large percentage of net revenue. Predictions from the PCM scenario are quite different. All places would gain except for the deserts. However, the largest benefits from climate change would fall on the mid elevation AEZs and highlands. Thus even in scenarios where the continental average income may not fall, farmers in selected region may be damaged by climate change.

6. Conclusion and Policy Implications

This paper examines the impact of climate change on different Agro-Ecological Zones in Africa. Agro-ecological zone data were obtained from FAO and combined with the economic surveys collected from the previous studies. The paper shows how different AEZs would be affected by future climate change. Based on the AEZ classification, we were able to extrapolate impact estimates to the whole Africa. The paper also combines crop and livestock income into a single net revenue measure in contrast to earlier studies that primarily focused on crop income alone.

The paper examines four different specifications of the Ricardian regression of farm net revenues on climates: a two season model, a temperature and precipitation interaction model, a country fixed effects model, and a four season model. The results indicate that climate variables are important determinants of farm net revenues in Africa. Summer and winter temperature and precipitation are all significant. A small increase in temperature would harm agricultural net revenues in Africa across all the models. A small increase in precipitation would harm farmers according to according to
prep.
1. As stated or indicated by; on the authority of: according to historians.

2. In keeping with: according to instructions.

3.
 the country fixed effects model but help them according to the OLS model.

The estimated coefficients from the models with and without country fixed effects were then used to predict climate change impacts for the coming century for Africa as well as for each AEZ. Two AOGCM AOGCM Atmosphere-Ocean General Circulation Model  scenarios were used to reflect a range of climate predictions. With country fixed effects included in the model, farms are expected to lose 10% of their income under CCC scenario, but gain 24% under PCM by 2020. Over time, the impacts become slightly more harmful. Without country fixed effects, farmers are increasingly vulnerable over time to climate change under the CCC scenario. Damage estimates increase from 16% in 2020 to 27% in 2100. With the mild PCM scenario, African agriculture is predicted to benefit on average.

The predicted outcomes are surprising in contrast to earlier studies. This study is suggesting that farm incomes will be threatened only if the harshest climate scenarios come to pass. Farmers will be able to tolerate and even take advantage of climate change. The reason for this new result is that the study takes into account both crop and livestock income whereas earlier research focused primarily on just crop income. Warming is likely to increase livestock income which will offset losses in crop income.

The study also suggests that impacts will vary across Africa. Farms in some AEZs will benefit while farms in other AEZs lose. For example, farms in moist savannah and dry savannah are the most vulnerable to higher temperature and reduced precipitation. On the other hand, the farms in sub-humid or humid forest gain even from a severe climate change. This indicates that the impacts of climate change will not be evenly distributed across Africa.

As policy makers seek to address the vulnerability of developing countries to climate change, they may be tempted to apply interventions across the board, applying the same policy interventions to an entire society facing climate risks. However, climate change is likely to have very different effects on different farmers in various locations. Further, their economic and institutional ability to implement adaptation measures may also vary. It is possible that farmers facing similar climate situations may be affected differently, depending on other physical and economic/institutional conditions they face. Both physical and economic/institutional conditions may affect the type of adaptation relevant for each location and the ability of the farmers residing in each location to adapt. Therefore, policy makers should consider tools that tailor assistance as needed as needed prn. See prn order. . Policy makers should look carefully at impact assessments to identify the most attractive adaptation options. They should apply policies across the landscape using a 'quilt' rather than a 'blanket' approach. The proposed quilt policy approach will allow much more flexibility and will likely lead to much more effective and locally beneficial outcomes.

Several points can help in prioritizing, sequencing, and packaging interventions. First, even across the AEZs, policies that are designed in different countries should take into account the existing institutions and infrastructure in the country. While this advice may seem obvious, experience in replicating 'best practices' across countries and regions suggest that such considerations are not always taken into account.

The results in Table 1 and Figure 2 show that there is lot of variation between the AEZs in terms of the population living in them, the income volatility, and the magnitude of impacts. Policy makers may want to sequence their interventions so that they address the most vulnerable AEZs first. This analysis does not lead to specific policy recommendations concerning what interventions are needed. However, it does show that targeting particular AEZs rather than using a blanket approach across the entire landscape makes sense.

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S. Niggol Seo (2), Robert Mendelsohn Robert Mendelsohn may refer to:
  • Robert O. Mendelsohn, American economist
  • Robert S. Mendelsohn, American pediatrician
 (3), Ariel Dinar (4), Rashid Hassan (5).and Pradeep Kurukulasuriya (6)

(1) This paper is one of the product of a study "Measuring the Impact of and Adaptation to Climate Change Using Agroecological Zones in Africa" funded by the KCP KCP Party of Communists of Kyrgyzstan
KCP Khmer Citizen Party (Cambodia)
KCP Kingston Computer Planet
KCP knurling cup point (fasteners)
KCP Kernel Control Path
 Trust Fund and conducted in DECRG at the World Bank. We benefited from comments by Richard Adams, Brian Hurd, and Robert Evenson on an earlier draft.

(2) School of Forestry and Environmental Studies, Yale University Yale University, at New Haven, Conn.; coeducational. Chartered as a collegiate school for men in 1701 largely as a result of the efforts of James Pierpont, it opened at Killingworth (now Clinton) in 1702, moved (1707) to Saybrook (now Old Saybrook), and in 1716 was , and consultant to the World Bank; 230 Prospect St. , New Haven New Haven, city (1990 pop. 130,474), New Haven co., S Conn., a port of entry where the Quinnipiac and other small rivers enter Long Island Sound; inc. 1784. Firearms and ammunition, clocks and watches, tools, rubber and paper products, and textiles are among the many , CT06511; phone 203-432-9771; email Niggol.seo@yale.edu.

(3) School of Forestry and Environmental Studies, Yale University; 230 Prospect St, New Haven, CT06511 and a consultant to the World Bank; phone 203-432-5128; email Robert.mendelsohn@yale.edu.

(4) Development Research Group, World Bank, 1818 H St. NW, Washington DC 20433; phone 202-473-0434; email adinar@worldbank.org.

(5) Department of Agricultural Economics, University of Pretoria, and Center for Environmental Economics for Africa; email Rashid.hassan@up.ac.za.

(6) Energy and Environment Group, Bureau of Development Policy, United Nations Development Programme, New York New York, state, United States
New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of
; phone 212-217 2512; email: pradeep.kurukulasuriya@undp.org.

(7) In Africa, it was difficult to get the amount of pasture pasture, land used for grazing livestock. Land unsuited for cultivation, e.g., hilly or stony land, may be used as pasture. Tilled land and meadow may be pastured after the crops are removed.  that each farm owns for livestock since most of them rely on public land to raise livestock. We divided net revenue per farm by the amount of cropland.

(8) The regression leaves out Kenya as the base.
Table 1: Descriptive Statistics by Agro-Ecological Zones

AEZ    Description      Observations      Annual mean       Profit
                                          net revenue       Std Dev
                                          (USD/ha)

1      Desert                    879            2211            4277

2      High elevation            115             392             749
       dry savanna

3      High elevation            928             442             661
       humid forest

4      High elevation            353            8247          128987
       moist savannah

5      High elevation             70             542             947
       semi-arid

6      High elevation            781            3753           86680
       sub-humid

7      Lowland dry              2745            1427           46525
       savannah

8      Lowland humid            1215             794             919
       forest

9      Lowland moist            2085            1766           53210
       Savannah

10     Lowland                   674             635            2735
       semi-arid

11     Lowland                  1273             773            5668
       sub-humid

12     Mid-elevation             874            4030           82910
       dry savannah

13     Mid-elevation             971             741            1479
       humid forest

14     Mid-elevation            1958            2312           55620
       moist savannah

15     Mid-elevation             107            1612            9075
       semi-arid

16     Mid-elevation            1016            3910           76580
       sub-humid

AEZ    Description      Annual          Annual mean
                        mean            precipitation
                        temperature     (mm/month)
                        (C)

1      Desert                   18.8            11.7

2      High elevation           20.4            61.0
       dry savanna

3      High elevation           18.0            91.6
       humid forest

4      High elevation           18.7            74.2
       moist savannah

5      High elevation           20.0            48.5
       semi-arid

6      High elevation           18.0            85.5
       sub-humid

7      Lowland dry              25.9            48.5
       savannah

8      Lowland humid            20.4           113.3
       forest

9      Lowland moist            24.1            68.6
       Savannah

10     Lowland                  26.7            34.2
       semi-arid

11     Lowland                  22.3            89.9
       sub-humid

12     Mid-elevation            20.4            63.9
       dry savannah

13     Mid-elevation            18.2           117.0
       humid forest

14     Mid-elevation            19.7            73.6
       moist savannah

15     Mid-elevation            20.3            50.2
       semi-arid

16     Mid-elevation            19.0            94.4
       sub-humid

Table 2: Ricardian Regressions on Net Revenue (USD per Hectare)

                 Model 1: Two            Model 2: Climate
                 Seasons                 Interactions

Var              Est          T          Est          T

Intercept          1181.4       1.71        570.9       0.55
T summer          215.1 *       4.37      256.8 *       3.31
T summer2         -3.32 *      -3.36      -3.55 *      -2.47
T winter         -266.6 *      -4.63     -282.8 *      -4.74
T winter 2         4.26 *       2.69       4.22 *       2.50
P summer          -6.19 *      -4.11         1.83       0.40
P summer2          0.03 *       5.20       0.02 *       3.01
P winter             2.15       0.84        -9.78      -1.54
P winter 2           0.00      -0.25         0.00      -0.20
T spring
T spring2
T fall
T fall 2
P spring
P spring2
P fall
P fall 2
T sum * P sum                               -0.27      -1.75
T win * P win                              0.66 *       1.99
Water flow        24.06 *       4.20      23.70 *       4.11
Head farm          -197.4      -1.59       -177.9      -1.43
Soil type1          445.8       0.27        539.6       0.32
Soil type2        -1462 *      -3.64      -1505 *      -3.74
Soil type3        -5157 *      -2.07      -5506 *      -2.21
Soil type4        -3672 *      -2.56      -3680 *      -2.56
Soil type5        -2278 *      -3.07      -2409 *      -3.24
Electricity       510.9 *       7.92        492 *       7.61
Burkinafaso
Egypt
Ethiopia
Ghana
Niger
Senegal
S Africa
Zambia
Cameroon
R sq                            0.10                    0.10
N                               8509                    8509

                 Model 3: Country        Model 4: Four
                 Fixed Effects           Seasons

Var              Est          T          Est          T

Intercept          -904.0      -0.76      -1242.7      -0.87
T summer          264.3 *       2.55        325.8       1.36
T summer2           -3.17      -1.73        -3.84      -0.92
T winter         -228.9 *      -3.05     -344.3 *      -1.98
T winter 2         3.82 *       2.09         7.87       1.75
P summer          17.05 *       3.44      22.67 *       2.95
P summer2         -0.02 *      -2.21      -0.04 *      -2.24
P winter            -1.49      -0.22        -4.71      -0.54
P winter 2           0.03       1.58       0.06 *       2.18
T spring                                    119.9       0.60
T spring2                                   -3.45      -0.80
T fall                                      -64.4      -0.25
T fall 2                                     0.78       0.15
P spring                                     5.46       1.08
P spring2                                   -0.02      -0.64
P fall                                      -4.39      -1.06
P fall 2                                     0.02       1.23
T sum * P sum     -0.60 *      -3.34      -0.62 *      -2.92
T win * P win       -0.01      -0.02        -0.24      -0.59
Water flow           9.15       1.50         8.57       1.40
Head farm           -87.7      -0.70        -86.9      -0.69
Soil type1         1217.1       0.72       1175.8       0.70
Soil type2         -244.9      -0.57       -215.4      -0.49
Soil type3        -3876.5      -1.55      -4331.7      -1.71
Soil type4        -3160 *      -2.18      -3290 *      -2.26
Soil type5        -1714 *      -2.28      -1926 *      -2.47
Electricity         76.95       0.99        74.91       0.96
Burkinafaso       -180.59      -0.91       -180.2      -0.72
Egypt            1296.8 *       3.47     1479.6 *       3.29
Ethiopia           -136.0      -1.02       -171.8      -0.81
Ghana                51.6       0.35         23.2       0.13
Niger            -551.5 *      -2.36       -511.0      -1.89
Senegal          -507.4 *      -2.33       -353.5      -1.19
S Africa           -116.6      -0.35       -170.6      -0.51
Zambia           -540.8 *      -3.15     -423.3 *      -2.01
Cameroon          948.6 *       6.12      801.0 *       3.73
R sq                            0.12                    0.12
N                               8509                    8509

Note: a) Dependent variable is net revenue per hectare which
includes both crop net revenue and livestock net revenue.
b) * denotes significance at 5% level.

Table 3: Marginal Effects and Elasticities by AEZ (USD per ha)

(1) Model 1: Two Seasons

AEZ                                   Marginal Effects

                                      T              P
                                      (USD/C)        (USD/mm/mo)

Africa                                     -44.29           0.92
Desert                                    -106.02          -3.54
High elevation dry savanna                 -31.01           1.89
High elevation humid forest                -17.05           2.33
High elevation moist savannah              -25.02           1.98
High elevation semi-arid                   -36.56           0.32
High elevation sub-humid                   -26.05           3.24
Lowland dry savannah                       -47.46          -0.43
Lowland humid forest                       -18.00           3.92
Lowland moist Savannah                     -37.91           1.14
Lowland semi-arid                          -56.34          -1.38
Lowland sub-humid                          -22.25           3.12
Mid-elevation dry savannah                 -39.61           0.54
Mid-elevation humid forest                 -17.52           4.10
Mid-elevation moist savannah               -38.23           1.27
Mid-elevation semi-arid                    -47.57           0.48
Mid-elevation sub-humid                    -20.73           3.32

AEZ                                   Elasticities

                                      T              P
                                      (USD/C)        (USD/mm/mo)

Africa                                      -0.07          0.004
Desert                                      -0.09         -0.002
High elevation dry savanna                  -1.85          0.339
High elevation humid forest                 -0.15          0.102
High elevation moist savannah               -0.08          0.024
High elevation semi-arid                    -1.55          0.033
High elevation sub-humid                    -0.13          0.075
Lowland dry savannah                        -0.38         -0.006
Lowland humid forest                        -0.29          0.354
Lowland moist Savannah                      -0.25          0.021
Lowland semi-arid                           -0.12         -0.004
Lowland sub-humid                           -0.44          0.252
Mid-elevation dry savannah                  -0.08          0.004
Mid-elevation humid forest                  -0.11          0.172
Mid-elevation moist savannah                -0.18          0.023
Mid-elevation semi-arid                     -0.02          0.001
Mid-elevation sub-humid                     -0.11          0.085

(2) Model 2: Climate Interactions

AEZ                                   Marginal Effects

                                      T              P
                                      (USD/C)        (USD/mm/mo)

Africa                                     -39.20           2.02
Desert                                     -87.57          -5.87
High elevation dry savanna                 -40.28           2.95
High elevation humid forest                  0.58           3.36
High elevation moist savannah              -20.32           2.84
High elevation semi-arid                   -37.48           1.41
High elevation sub-humid                   -29.22           3.46
Lowland dry savannah                       -47.08           2.10
Lowland humid forest                       -11.73           5.09
Lowland moist Savannah                     -33.62           3.08
Lowland semi-arid                          -53.19           0.85
Lowland sub-humid                          -24.95           4.77
Mid-elevation dry savannah                 -25.90           1.36
Mid-elevation humid forest                  -6.29           4.61
Mid-elevation moist savannah               -19.24           1.69
Mid-elevation semi-arid                    -49.27           0.89
Mid-elevation sub-humid                    -17.66           4.10

AEZ                                   Elasticities

                                      T              P
                                      (USD/C)        (USD/mm/mo)

Africa                                      -0.06           0.01
Desert                                      -0.08           0.00
High elevation dry savanna                  -2.41           0.53
High elevation humid forest                  0.00           0.14
High elevation moist savannah               -0.06           0.03
High elevation semi-arid                    -1.59           0.14
High elevation sub-humid                    -0.14           0.08
Lowland dry savannah                        -0.38           0.03
Lowland humid forest                        -0.19           0.46
Lowland moist Savannah                      -0.22           0.06
Lowland semi-arid                           -0.11           0.00
Lowland sub-humid                           -0.50           0.38
Mid-elevation dry savannah                  -0.05           0.01
Mid-elevation humid forest                  -0.04           0.19
Mid-elevation moist savannah                -0.09           0.03
Mid-elevation semi-arid                     -0.02           0.00
Mid-elevation sub-humid                     -0.09           0.10

(3) Model 3: Country Fixed Effects

AEZ                                   Marginal Effects

                                      T              P
                                      (USD/C)        (USD/mm/mo)

Africa                                     -23.96          -0.89
Desert                                     -30.00           1.10
High elevation dry savanna                 -19.45          -0.46
High elevation humid forest                -14.32           3.93
High elevation moist savannah              -16.01           2.15
High elevation semi-arid                    -7.58           0.56
High elevation sub-humid                   -29.84           1.66
Lowland dry savannah                       -13.07          -3.95
Lowland humid forest                       -33.01           1.08
Lowland moist Savannah                     -21.47          -1.96
Lowland semi-arid                          -10.65          -3.78
Lowland sub-humid                          -27.35          -0.80
Mid-elevation dry savannah                 -15.24           1.63
Mid-elevation humid forest                 -34.17           2.93
Mid-elevation moist savannah               -22.73           2.60
Mid-elevation semi-arid                    -19.60          -0.05
Mid-elevation sub-humid                    -27.38           1.77

AEZ                                   Elasticities

                                      T              P
                                      (USD/C)        (USD/mm/mo)

Africa                                      -0.04         -0.004
Desert                                      -0.03          0.001
High elevation dry savanna                  -1.16         -0.083
High elevation humid forest                 -0.12          0.172
High elevation moist savannah               -0.05          0.026
High elevation semi-arid                    -0.32          0.058
High elevation sub-humid                    -0.15          0.038
Lowland dry savannah                        -0.11         -0.054
Lowland humid forest                        -0.54          0.097
Lowland moist Savannah                      -0.14         -0.036
Lowland semi-arid                           -0.02         -0.010
Lowland sub-humid                           -0.55         -0.065
Mid-elevation dry savannah                  -0.03          0.011
Mid-elevation humid forest                  -0.22          0.123
Mid-elevation moist savannah                -0.11          0.047
Mid-elevation semi-arid                     -0.01          0.000
Mid-elevation sub-humid                     -0.14          0.045

(4) Model 4: Four Seasons

AEZ                                   Marginal Effects

                                      T              P
                                      (USD/C)        (USD/mm/mo)

Africa                                     -29.33          -0.41
Desert                                     -24.22           1.76
High elevation dry savanna                 -16.31          -3.16
High elevation humid forest                -12.91           2.40
High elevation moist savannah              -17.55           0.25
High elevation semi-arid                    -0.33          -1.65
High elevation sub-humid                   -32.20          -0.90
Lowland dry savannah                       -14.99          -6.12
Lowland humid forest                       -32.58          -0.43
Lowland moist Savannah                     -30.69          -3.59
Lowland semi-arid                           -3.82          -6.06
Lowland sub-humid                          -30.89          -3.55
Mid-elevation dry savannah                 -22.79           0.35
Mid-elevation humid forest                 -35.38           1.63
Mid-elevation moist savannah               -36.40           1.69
Mid-elevation semi-arid                    -16.69          -2.09
Mid-elevation sub-humid                    -29.33          -0.41

AEZ                                   Elasticities

                                      T              P
                                      (USD/C)        (USD/mm/mo)

Africa                                      -0.15         -0.010
Desert                                      -0.02          0.001
High elevation dry savanna                  -0.97         -0.565
High elevation humid forest                 -0.11          0.105
High elevation moist savannah               -0.05          0.003
High elevation semi-arid                    -0.01         -0.169
High elevation sub-humid                    -0.16         -0.021
Lowland dry savannah                        -0.12         -0.084
Lowland humid forest                        -0.53         -0.039
Lowland moist Savannah                      -0.20         -0.066
Lowland semi-arid                           -0.01         -0.016
Lowland sub-humid                           -0.62         -0.287
Mid-elevation dry savannah                  -0.05          0.002
Mid-elevation humid forest                  -0.23          0.069
Mid-elevation moist savannah                -0.17          0.031
Mid-elevation semi-arid                     -0.01         -0.003
Mid-elevation sub-humid                     -0.15         -0.010

Table 4: AOGCM Scenarios

                             Current      2020      2060      2100
Summer Temperature
(([degrees])C)
  CCC                           25.7       1.4       3.0       6.0
  PCM                           25.7       0.7       1.5       2.2
Winter Temperature
(([degrees])C)
  CCC                           22.4       2.2       4.0       7.3
  PCM                           22.4       1.1       2.0       3.1
Summer Rainfall (mm/month)
  CCC                          149.8      -4.6     -21.7     -33.7
  PCM                          149.8      -4.7     -11.1      -4.7
Winter Rainfall (mm/month)
  CCC                           12.8       1.1       5.0       3.5
  PCM                           12.8      18.8      17.9      21.6

Table 5a: Climate Change Impacts by AEZs With Fixed Effects

AEZ                             Scenarios    Change (USD per ha)

                                             2020     2060     2100

Africa                          BASELINE      628      628      628
                                CCC           -63      -47      -15
                                PCM           151      103      121
Desert                          BASELINE     2632     2632     2632
                                CCC          -102     -103     -161
                                PCM          -152     -120     -177
High elevation dry savanna      BASELINE      320      320      320
                                CCC           -40      -73       15
                                PCM            75       52       15
High elevation humid forest     BASELINE      378      378      378
                                CCC           -47     -109      -33
                                PCM           816      463      510
High elevation moist savannah   BASELINE      271      271      271
                                CCC           -51      -81      -41
                                PCM           301      170      150
High elevation semi-arid        BASELINE      371      371      371
                                CCC           -33      -60       11
                                PCM           104       71       40
High elevation sub-humid        BASELINE      374      374      374
                                CCC           -59     -122      -76
                                PCM           804      461      470
Lowland dry savannah            BASELINE      234      234      234
                                CCC           -36      -13       43
                                PCM           110       82       99
Lowland humid forest            BASELINE      885      885      885
                                CCC           -53      -25       58
                                PCM           209      194      327
Lowland moist Savannah          BASELINE      261      261      261
                                CCC           -66      -59        9
                                PCM           158       85       93
Lowland semi-arid               BASELINE      650      650      650
                                CCC           -33       -7       52
                                PCM           281      195      215
Lowland sub-humid               BASELINE      552      552      552
                                CCC           -82        7       50
                                PCM           258      211      206
Mid-elevation dry savannah      BASELINE      244      244      244
                                CCC           -50      -50      -39
                                PCM           371      247      269
Mid-elevation humid forest      BASELINE      669      669      669
                                CCC           -63     -159      -63
                                PCM           705      434      515
Mid-elevation moist savannah    BASELINE      225      225      225
                                CCC           -75      -75      -96
                                PCM           363      224      260
Mid-elevation semi-arid         BASELINE      357      357      357
                                CCC           -32      -58       30
                                PCM           108       74       44
Mid-elevation sub-humid         BASELINE      496      496      496
                                CCC           -55     -105      -25
                                PCM           856      507      571

AEZ                             Scenarios    Percentage change

                                             2020     2060     2100

Africa                          BASELINE
                                CCC           -10       -7       -2
                                PCM            24       16       19
Desert                          BASELINE
                                CCC            -4       -4       -6
                                PCM            -6       -5       -7
High elevation dry savanna      BASELINE
                                CCC           -13      -23        5
                                PCM            23       16        5
High elevation humid forest     BASELINE
                                CCC           -12      -29       -9
                                PCM           216      122      135
High elevation moist savannah   BASELINE
                                CCC           -19      -30      -15
                                PCM           111       63       55
High elevation semi-arid        BASELINE
                                CCC            -9      -16        3
                                PCM            28       19       11
High elevation sub-humid        BASELINE
                                CCC           -16      -33      -20
                                PCM           215      123      126
Lowland dry savannah            BASELINE
                                CCC           -15       -6       18
                                PCM            47       35       42
Lowland humid forest            BASELINE
                                CCC            -6       -3        7
                                PCM            24       22       37
Lowland moist Savannah          BASELINE
                                CCC           -25      -23        3
                                PCM            61       33       36
Lowland semi-arid               BASELINE
                                CCC            -5       -1        8
                                PCM            43       30       33
Lowland sub-humid               BASELINE
                                CCC           -15        1        9
                                PCM            47       38       37
Mid-elevation dry savannah      BASELINE
                                CCC           -20      -20      -16
                                PCM           152      101      110
Mid-elevation humid forest      BASELINE
                                CCC            -9      -24       -9
                                PCM           105       65       77
Mid-elevation moist savannah    BASELINE
                                CCC           -33      -33      -43
                                PCM           161      100      116
Mid-elevation semi-arid         BASELINE
                                CCC            -9      -16        8
                                PCM            30       21       12
Mid-elevation sub-humid         BASELINE
                                CCC           -11      -21       -5
                                PCM           173      102      115

Estimates calculated from Model 3 of Table 2.

Table 5b: Climate Change Impacts by AEZs without Country
Fixed Effects

AEZ                             Scenarios    Change (USD per ha)

                                             2020     2060     2100

Africa                          BASELINE      616      616      616
                                CCC           -96      -81     -169
                                PCM            56       65       71
Desert                          BASELINE     2360     2360     2360
                                CCC          -174     -267     -500
                                PCM          -225     -235     -371
High elevation dry savanna      BASELINE      256      256      256
                                CCC           -65     -154     -128
                                PCM           197      191      180
High elevation humid forest     BASELINE      341      341      341
                                CCC           -35      -52      -32
                                PCM           188      295      421
High elevation moist savannah   BASELINE      272      272      272
                                CCC           -54     -110     -111
                                PCM           167      209      253
High elevation semi-arid        BASELINE      362      362      362
                                CCC           -54     -141     -106
                                PCM           210      211      205
High elevation sub-humid        BASELINE      371      371      371
                                CCC           -77     -136     -171
                                PCM           118      188      266
Lowland dry savannah            BASELINE      314      314      314
                                CCC           -95     -115     -184
                                PCM            73       60       53
Lowland humid forest            BASELINE      711      711      711
                                CCC           -62      143       68
                                PCM           113      123      182
Lowland moist Savannah          BASELINE      271      271      271
                                CCC           -93     -125     -169
                                PCM            64       38       56
Lowland semi-arid               BASELINE      600      600      600
                                CCC           -90     -124     -196
                                PCM           143      116      109
Lowland sub-humid               BASELINE      401      401      401
                                CCC           -77       77       26
                                PCM           112      137      165
Mid-elevation dry savannah      BASELINE      421      421      421
                                CCC           -58      -88     -164
                                PCM           312      284      332
Mid-elevation humid forest      BASELINE      533      533      533
                                CCC           -72      -14      -86
                                PCM           130      189      286

Mid-elevation moist savannah    BASELINE      478      478      478
                                CCC           -78     -101     -221
                                PCM           236      218      276
Mid-elevation semi-arid         BASELINE      324      324      324
                                CCC           -55     -142     -110
                                PCM           243      231      228
Mid-elevation sub-humid         BASELINE      432      432      432
                                CCC           -71      -88     -116
                                PCM           189      221      319

AEZ                             Scenarios    Percentage change

                                             2020     2060     2100

Africa                          BASELINE
                                CCC           -16      -13      -27
                                PCM             9       11       12
Desert                          BASELINE
                                CCC            -7      -11      -21
                                PCM           -10      -10      -16
High elevation dry savanna      BASELINE
                                CCC           -25      -60      -50
                                PCM            77       75       70
High elevation humid forest     BASELINE
                                CCC           -10      -15       -9
                                PCM            55       87      123
High elevation moist savannah   BASELINE
                                CCC           -20      -40      -41
                                PCM            61       77       93
High elevation semi-arid        BASELINE
                                CCC           -15      -39      -29
                                PCM            58       58       57
High elevation sub-humid        BASELINE
                                CCC           -21      -37      -46
                                PCM            32       51       72
Lowland dry savannah            BASELINE
                                CCC           -30      -37      -59
                                PCM            23       19       17
Lowland humid forest            BASELINE
                                CCC            -9       20       10
                                PCM            16       17       26
Lowland moist Savannah          BASELINE
                                CCC           -34      -46      -62
                                PCM            24       14       21
Lowland semi-arid               BASELINE
                                CCC           -15      -21      -33
                                PCM            24       19       18
Lowland sub-humid               BASELINE
                                CCC           -19       19        6
                                PCM            28       34       41
Mid-elevation dry savannah      BASELINE
                                CCC           -14      -21      -39
                                PCM            74       67       79
Mid-elevation humid forest      BASELINE
                                CCC           -14       -3      -16
                                PCM            24       35       54

Mid-elevation moist savannah    BASELINE
                                CCC           -16      -21      -46
                                PCM            49       46       58
Mid-elevation semi-arid         BASELINE
                                CCC           -17      -44      -34
                                PCM            75       71       70
Mid-elevation sub-humid         BASELINE
                                CCC           -16      -20      -27
                                PCM            44       51       74

Estimates calculated from Model 2 of Table 2.
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Title Annotation:Policy Research Working Paper 4599
Author:Seo, Niggol S.; Mendelsohn, Robert; Dinar, Ariel; Hassan, Rashid; Kurukulasuriya, Pradeep
Publication:A Ricardian Analysis of the Distribution of Climate Change Impacts on Agriculture Across Agro-Ecolog
Date:Apr 1, 2008
Words:9157
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