adaptation and climate change impacts_ a selection model of irrigation and farm income in africa

7/27/2019 Adaptation and Climate Change Impacts_ a Selection Model of Irrigation and Farm Income in Africa

1/35

F o r P e e r R

e v i e w

Adaptation and Climate Change Impacts: A Selection Model

of Irrigation and Farm Income in Africa

Journal: Journal of African Economies

Manuscript ID: JAE-2009-013

Manuscript Type: Article

Keyword: climate change, agriculture, irrigation, household, adaptation,cross-sectional

http://mc.manuscriptcentral.com/jafeco

Manuscripts submitted to Journal of African Economies


2/35

F o r P e e r R e v i e w

Adaptation and Climate Change Impacts:

A Selection Model of Irrigation and Farm Income in Africa

Abstract

Although there is now an extensive literature on the economic impacts of climate change,

there are surprisingly few studies that have examined adaptation. This paper examines

whether irrigation can be an effective adaptation strategy against climate change in Africa.

The paper develops a selection model of irrigation choice and conditional income. Using

data from farmers across eleven African countries, the paper demonstrates that the choice of

irrigation is sensitive to both temperature and precipitation. Rainfed and irrigated farm

income both respond to climate but not in a similar fashion. We demonstrate that it is

important to anticipate that irrigation will change in some climate scenarios. Even with the

endogenous irrigation model, however, African agriculture is very sensitive to climate change

scenarios. The results suggest that irrigation is an important adaptation strategy in climate

scenarios if there is sufficient water.

e 1 of 34 Manuscripts submitted to Journal of African Economies


3/35


1. Introduction

Many researchers have now developed methods to measure the economic impacts of climate

change on especially agriculture. Agroeconomic models have made predictions of climate

impacts in the United States based on the results of crop simulation models (Adams et al

1999). The Ricardian model estimates the relationship between farmland values and climate

(Mendelsohn et al. 1994) and has been applied to measure climate sensitivity in the United

States (Mendelsohn et al. 1994; Mendelsohn and Dinar 2003), Sri Lanka (Seo et al. 2005;

Kurukulasuriya and Ajwad 2007), Israel (Fleischer et al. 2008), Africa (Kurukulasuriya et al

2006), and Latin America (Seo and Mendelsohn 2008). A consistent criticism that has been

leveled against Ricardian studies, however, concerns whether or not the studies properly take

into account irrigation (Cline 1996; Darwin 1999; Schlenker et al. 2005). One suggestedcorrection is to estimate a separate response function for rainfed farms alone (Schlenker at al.

2005) or to estimate separate Ricardian regressions for rainfed and irrigated farms

(Kurukulasuriya and Mendelsohn 2007; Seo and Mendelsohn 2008). A final approach to

measuring climate change impacts on farms is to use panel data and examine how farm net

revenues fluctuate with weather using fixed effects to control for cross sectional variation

(Greenstone and Deschenes 2006).

The agroeconomic models have an advantage by building from the ground up so that they

contain enormous farm detail. However, it is difficult in practice to observe and capture the

adaptations that farmers are actually making to climate. The current models do a good job of

capturing crop switching but they have no good way of capturing changes farmers are making

to raise specific crops. The traditional Ricardian approach does do a good job of capturing

long run adaptation, but it treats adaptation as a black box. It is not at all clear what changes

farmers have made to adapt to climate using this model. The Schlenker et al. model identifies

irrigation as being important but it treats the choice to irrigate as though it is exogenous even

though it is sensitive to climate. The panel fixed effects model does not include adaptation at

all and treats climate change as a continual surprise.

In this paper, we try to explicitly model irrigation in order to begin to understand what

specific adaptations will help farmers adapt to climate change. We build on the extensive

irrigation literature that recognizes irrigation is a choice (see Caswel and Zilberman 1986;

Page 2 Manuscripts submitted to Journal of African Economies


4/35


Dinar and Yaron 1990; Negri and Brooks 1990; Dinar and Zilberman 1991; Dinar, Campbell,

and Zilberman 1992) but, in this paper, we develop the role that climate plays. We build an

endogenous irrigation model that recognizes the potential of sample selection bias (Heckman

1979). In the first stage, we estimate the probability of irrigation including climate, district

flows, and other exogenous variables. In the second stage, we estimate the conditional

income from rainfed and irrigated farming including a sample selection correction term.

We test this model using a sample of over 10,000 plots across 11 African countries. Studying

the impacts of climate change on Africa is very important. There is a growing body of

evidence that low latitude countries and especially Africa will bear the brunt of climate

change damages (Pearce et al. 1996; Mendelsohn and Williams 2004; Mendelsohn, Dinar,and Williams 2006; Tol 2002; Kurukulasuriya et al, 2006). Low latitude countries are more

vulnerable than mid to high latitude countries because they are hotter, have a larger fraction

of their economy in agriculture, and have less wealth and technology for adaptation.

The empirical results reveal that the choice of irrigation is endogenous. As long as there is a

sufficient flow of water, irrigation is an important adaptation strategy to climate. The

estimation of the net revenue functions, however, does not reveal any evidence of sample

selection bias. The coefficient on the inverse Mills ratio is not significant and there are no

significant changes in any remaining coefficients.

Section 2 develops a formal theoretical model. Section 3 presents the data used in this study

and the empirical cross-sectional results. Section 4 displays the cross sectional results of the

empirical modeling. Section 5 utilizes the empirical model to simulate how irrigation andexpected net revenues might be affected by both a mild and a severe climate scenario. The

predictions of the endogenous irrigation model are compared with predictions from models

that assume irrigation is exogenous. With the mild wet scenario, the exogenous predictions

are biased because they fail to account for the large increase in irrigation. With the more

severe and dry scenarios, however, there is very little change in irrigation and so the bias is

small. The paper concludes by summarizing the results and discussing some policy

implications.



5/35


2. Model

The underlying theoretical structure of this model assumes that each farm maximizes profits:

WX E X Q P i = ),(max* (1)

where is profit, P i is output prices, Q* is output, X are chosen inputs, E is environmental

factors such as climate and soils, and W is the price of inputs. In this paper, we assume that

the amount of cropland is fixed, in order to focus on the issue of irrigation. 1 Profit is defined

broadly to include not only sold goods but also goods consumed by the household.

We develop a sample selection model (Heckman 1979). However, there is an important

difference between this case and the labor selection model. In the labor example, people who

did not work had no observed income. In this model, farmers who choose not to irrigate, still

have observed income from rainfed farming.

We assume that a farmer irrigates if irrigation is more profitable than rainfed farming.

Clearly the cost of irrigation lies largely in expensive capital. The farmer must weigh

whether the present value of the additional annual returns from irrigation is worth the cost.

The higher the additional net revenue each year, the more attractive irrigation becomes. In

the first stage, we estimate a dichotomous choice model of irrigation, Y , where Y=1 is

irrigation (1) and Y=0 is rainfed farming:

11 += X Y i (2)

We identify the choice equation with altitude, district surface water flow, and a dummy for

access to capital. It is easier to irrigate at high altitudes probably because there is more

potential slope to the land allowing farmers to direct water at low cost. Once one controls for

climate, altitude has little effect on conditional net revenues. Higher water flows also make it

easier to irrigate. Water flows are not expected to affect conditional earnings because

farmers with access to water generally use as much as they want (quantities are not

restricted). We introduce a dummy variable for countries with well developed capital

markets (Egypt and South Africa). Note that this dummy variable could just have easily

1 Land uses themselves are influenced by climate and other variables (Mendelsohn et al. 1996). However, thistopic is beyond the scope of this paper.



6/35


measured good governance. In the second stage, we estimate a conditional profit function for

each type of farming based on the available exogenous variables, Z :

1Yif 211 =+= Z i (3)

0Yif 3 =+= D D

D Z (4)

where Y 1 is a latent variable explaining the choice of irrigation, I is the net profit of farms

that have chosen irrigation, and D is the net profit of farms that have chosen rainfed

farming, X is a k -vector of regressors, Z I is an m-vector of regressors for irrigation, Z D is an

n-vector of regressors for rainfed, and the error terms 1 and 2 and 1 and 3 are jointly

normally distributed, independently of X and Z , with zero expectations.

1 ~ N(0,1)

2 ~ N(0, 2)

3 ~ N(0, 3)

corr( 1 , 2 ) = 2

corr( 1 , 3 ) = 3

Irrigation is observed only if it is more profitable than rainfed farming. Thus, the observed

dependent variable Y is:

Y=1 if I > D

Y=0 if D > I

When 2 = 0 , an Ordinary Least Squares (OLS) regression could provide unbiased estimates

of the coefficients for the conditional irrigation equation, but when 0 the OLS estimates are biased. A parallel result holds for 3 and the rainfed regression coefficients. We

consequently employ the inverse Mills ratio from the selection model in both the irrigated

and rainfed conditional regressions in order to control for selection (Heckman 1979). We

expect the signs on the coefficient of the inverse Mills ratio to be the opposite in each

regression.



7/35


The availability of water is often not just a farmers decision. In the United States, there are

examples of extensive canal systems that bring water long distances to farmers. The choice

to irrigate, in this case, is a combination of what the farmer chooses and what the irrigation

district provides. In Africa, such extensive canal systems are not so common. Irrigation is

often just the choice of a farmer. However, even when the choice involves both a farmer and

irrigation district, the question is still the same. Is it worthwhile to irrigate given the cost?

The decision, even if it is a joint one, will still be sensitive to cost benefit criteria. Even if the

decision is a joint one, it is sensitive to climate. In this paper, we use the water flows in a

political district to identify the availability of water. This is an exogenous measure of natural

flows, not an endogenous choice.

3. Data

The empirical analysis is based on a household survey of farms conducted in 11 countries

across Africa: Burkina Faso, Cameroon, Egypt, Ethiopia, Kenya, Ghana, Niger, Senegal,

South Africa, Zambia and Zimbabwe. 2 The sample was chosen to select farms across a wide

range of climates within each country. The sample across the 11 countries is has

approximately the same mean characteristics as farms in the continent have.

As many African countries do not have formal land markets, collecting land values is

difficult. Instead, we rely on measurements of net revenue per hectare. Net revenues are

appropriate measurements of the annual net productivity of the land. However, compared to

land values, net revenues are a more volatile measure since they reflect factors that change

year by year. Net revenue is defined as gross revenue minus the cost of transport, packaging

and marketing, storage, post-harvest losses, hired labor (valued at the median market wage

rate), light farm tools (such as files, axes, machetes, etc.), rental on heavy machinery

(tractors, ploughs, threshers and others), fertilizer and pesticide. Median district prices from

the survey were used in estimating the values of both input and crop prices. Household labor

costs are not included as a cost in net revenues because it was not clear what value to assign

to wages. We controlled for household labor by using household size as a proxy.

2

We are deeply grateful to the country teams from each of these countries for designing, collecting, andcleaning this data and making this project a success. For more information about the entire study, see Dinar etal 2006.



8/35


In each country, districts were chosen in order to sample farms across a wide range of agro-

climatic conditions in each country. In each chosen district, a random but clustered sample of

farms was selected. After data cleaning, removing farms that did not grow crops, and surveyswith field errors and missing information, the final number of useable surveys was 8463. We

conducted the analysis at the plot level of each farm as the dataset was sufficiently detailed to

extract and utilize information about whether or not a particular plot (from a set of three) was

irrigated or not. There are 10,915 plots in the data set. Each farm provided plot specific data

on whether or not irrigation was used, crop production (including crop type, amount

harvested, quantity sold, quantity consumed and amount of sales receipt) and crop costs

(fertilizer, pesticide and seed data). Using this data, prices per crop and yields per hectare of farmland and cropland were estimated, as well as plot specific crop revenues and farm level

gross and net revenues. The estimated prices and yields were validated based on official

records of district and national level prices and yields per hectare. Net revenue estimates are

at the farm level because the input data, including labor (both hired and household) and

machinery, were available only at that unit of measurement. It was not possible to allocate

most inputs to specific plots as much of it was applied to several plots at a time. The dataset

we used contains 1750 irrigated plots and 9183 rainfed plots. The distribution of surveys

irrigated and rainfed plots by country is shown in Table 1. The farm plots reflect a

representative sample of African agro-ecological zones.

Because the analysis collects net revenue data for only one year but we are interested in the

impact of climate, the survey inquired whether the weather was average or atypical in the

year of the survey. The large majority of the farmers reported the weather was typical.

Because the size of the weather aberrations is small in this particular survey, it is notexpected that they will bias the results. The fact that there is only one year of revenue data

for each site, however, does make this data unsuitable for studying climate variance.

This study relies on climate normals (mean long term weather) of both precipitation and

temperature for each district. The monthly temperature data comes from US Department of

Defense satellite measurements between 1988 and 2003 (Basist et al. 2001). This set of polar

orbiting satellites takes measurements at every location on earth at 6am and 6pm every day.



9/35


The satellites are equipped with sensors that measure surface temperature by detecting

microwaves that pass through clouds (Weng & Grody 1998). The monthly precipitation data

comes from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank

2003). This dataset, created by the National Oceanic and Atmospheric Associations Climate

Prediction Center, is interpolated from ground station measurements of precipitation over the

period 19482001. This combination of using temperature measurements from satellites and

precipitation data from ground stations provides the best available climate measures for

agricultural analysis (Mendelsohn et al 2006). The average temperatures and precipitation

for each country in the sample are shown in Appendix A. Note that there is a wide range of

climates across the 11 countries in the sample.

It is not possible to use every month of climate in a Ricardian regression because of the high

correlation between one month and the next. Consequently, we clustered the monthly data

into three month seasons. We explored several alternatives but finally selected November,

December, and January as winter, February through April as spring, May through July as

summer, and August through October as fall. These seasonal definitions provide the best

fit with the data. We adjusted for the fact that seasons in the southern and northern

hemispheres occur at exactly the opposite months of the year. Note that although Egyptianand South African climates resemble mid latitude seasonal climates, that the distribution of

temperatures in countries near the equator is quite different with very warm springs and

summers. Rainfall depended on monsoons which tended to come in fall and winter.

Soil data from FAO (2003) is included in this analysis. The FAO data provides information

about the major soil, soil texture, and slope in each location. Data concerning the hydrology

is based on the predicted output from a hydrological model for Africa developed for this

study (IWMI & University of Colorado 2003). The model calculated the water flow through

each district in the surveyed countries in each season. Data on elevation at the centroid of

each district is from the United States Geological Survey (USGS, 2004). The USGS data

derives from a global digital elevation model with a horizontal grid spacing of 30 arc seconds

(approximately one kilometer).



10/35


During pre-testing of the survey instrument 3, it was clear that some farmers cultivated at least

two plots of land. Subsequently, the survey data collected crop data, including production

quantities, amount sold, and sale receipts from crops for the largest single plot of cultivated

land (referred to hereafter as the main plot) and all others (referred to as the secondary plot).

The following analysis is based on this plot data.

We tested whether clustering affected the significance of the reported results. Clustering is

not expected to bias the coefficients but it is expected to reduce the significance of the

coefficients. We find that a comparison of the marginal climate effects when clustering is

controlled with the analysis presented in this paper suggests that the results remain significant

and robust 4. The predictive ability of the model is not compromised by clustering.

4. Empirical results

Table 2 presents the first stage of the analysis, a probit model of whether a plot is irrigated or

not. There are 10915 plots with complete information for the regression. The explanatory

variables in the first stage include seasonal climate variables, farm characteristics, soils, and

seasonal water flow. Both linear and quadratic climate and flow variables are introduced inthe probit to capture nonlinearities in climate responses. The quadratic temperature,

precipitation, and flow variables are significant. The reported standard errors in the paper are

based on the Huber-White estimator of variance which is robust against many types of

misspecification of the model (Heltberg & Tarp 2002).

The seasonal district surface water flow variables and altitude identify choice. The

coefficients on most of these variables are significant. Higher altitudes imply rougher terrain

that makes trapping water easier. Higher water flows make irrigation a more attractive

(possible) alternative for each season except winter. Note that this is not the water available

to a specific farmer but rather the exogenous water flow in a district.

3 Available upon request from the authors.4 The results of the marginal analysis with clustering can be obtained from the authors.



11/35


In the selection model, we also control for soils and other farm characteristics. The soil

variables reflect the proportion of a district with a particular soil type. Soils can increase or

decrease the probability of irrigation depending on whether they are hilly or undulating

(positive) or steep (negative). Often, fine soils are negatively associated with irrigation and

medium is positively associated. The effects of types of soils vary depending on slope and

soil texture. Electricity is positively associated with irrigation. This may reflect the role of

electricity in pumping or just access to markets. Plot size is not related to irrigation choice.

Larger households are more likely to irrigate which suggests that irrigation is labor intensive

on a per hectare basis. Other household variables such as education, age, and experience

were not significant.

The climate and flow coefficients are highly significant. However, with the quadratic

functional form, they are hard to interpret. Using the coefficients in Table 2, we present the

mean marginal impact of temperature, precipitation, and flow in Table 4. The probability of

adopting irrigation increases with higher temperatures in each season except in spring. The

annual effect of higher temperatures reduces the probability of adopting irrigation. Irrigation

allows crops to withstand higher temperatures and the combination of irrigation and higher

temperatures allows for multiple seasons. The probability of adopting irrigation falls withmore precipitation in every season except summer. With more rain, farmers can grow crops

without irrigation, making the cost of irrigation unnecessary. The probability of adopting

irrigation falls if there is a uniform annual increase in flow across all seasons. However, this

is because flow during the winter season is very harmful, probably causing damage to

irrigated systems. Flow during the spring and fall seasons substantially increase the

probability of irrigation. In general, farmers favor irrigation in warmer and drier African

climates with good flow in the spring and fall.

The second stage model of net revenue conditional on irrigation choice in shown in Table 3.

The dependent variable is annual net income per hectare and the independent variables

include climate, soils, and other control variables. We present two sets of regressions.

Columns (b) and (d) are estimated with OLS. Following the standard selection model

(Heckman 1979), we include the inverse Mills ratio in columns (a) and (c) to control for self-

selection bias in the second stage OLS model. In both cases, there is one regression for

Page 10 oManuscripts submitted to Journal of African Economies


12/35


rainfed plots and one for irrigated plots. The coefficient on the estimated Mills ratio is not

significant but it has the negative sign expected in the rainfed regression. Comparing the

regression coefficients in the OLS and corrected models reveals that they are not significantly

different. There is little evidence of selection bias.

Farm size is significant and negative for both irrigated and rainfed plots. Larger plots have

lower net revenue per hectare. This may partially be due to our omission of household labor

as a cost in net revenue (a measurement bias). Household labor per hectare will tend to be

greater in smaller plots. The result may also be due to higher management intensity on

smaller plots (a real effect). We also include a dummy variable that denotes whether or not a

farm has electricity. Electrified farms outperform farms that do not have electricity in boththe irrigated and rainfed models. Electrification might directly enhance productivity and

earnings or it may simply be a proxy for farms that are closer to markets or more modern.

Farms with larger households have higher net revenue in both samples but the coefficient is

significant in only the irrigated sample.

The second stage regressions also give important insights into the climate sensitivity of

farms. The results show that rainfed and irrigated farms are both sensitive to climate but

have different climate responses. In order to interpret the climate coefficients, the mean

marginal impact is presented in Table 4. Annual warmer temperatures have no effect on

irrigated farm income as seasonal effects are offsetting. Annual precipitation does not have a

significant effect in irrigated farm income either, though wetter summers are beneficial and

wetter falls are harmful. Warmer annual temperatures reduce the income from rainfed plots

with harmful effects from warmer springs and falls but offsetting beneficial effects from

warmer winters and summers. Although these seasonal results are quite different from

temperate climate findings (Mendelsohn et al 1994; Mendelsohn and Dinar 2003), one must

remember that spring is often the hottest season in Africa. More annual precipitation

increases rainfed plot income. Precipitation is especially beneficial in the spring and harmful

only in the winter. The standard deviations in Table 4 were calculated using bootstrapping.

Although the analysis above makes a strong attempt to adjust for some unwanted variation by

introducing available control measures, there are many variables affecting farm income that



13/35


cannot be measured. In particular, there may be a number of variables that vary at the

national level including agricultural policy, taxes, credit availability, trade, and technology.

In the next analysis, we control for these effects by using country fixed effects. Of course,

using country fixed effects is not a perfect solution as it removes a lot of desired variation in

climate as well. However, by comparing the fixed effects results with the uncontrolled

results, the reader can get a sense of the potential importance of national scale effects. Egypt

and South Africa are omitted in the fixed effect model.

The country fixed effect results of both the probit and the conditional income regressions are

presented in Table 5. The functional form and independent variables are identical to the

earlier regression except that country dummy variables have been added to both regressions.The temperature coefficients remain significant but several of the precipitation and flow

coefficients are less significant with the country fixed effects. With the fixed effects model,

the identifying variables in the probit equation are generally less significant than in Table 2.

The remaining coefficients are generally quite similar. Examining the country coefficients,

we find that farmers in Cameroon and Kenya are more likely to irrigate, controlling for the

rest of the independent variables.

The country fixed effect conditional income equations are also presented in Table 5. The

fixed effect precipitation and temperature coefficients for the irrigated regression are similar

to those in Table 3. However, the climate coefficients for the fixed effect rainfed regression

are quite different. Household, farm control, and soil variables are very similar in Tables 3

and 5. Controlling for other factors, irrigated farmers in all the included countries earn less

than Egyptian and South African irrigated farmers. This is because of the high level of

capital and technology applied to irrigated farms in those two countries. Rainfed farmers in

most of the remaining countries earn less than rainfed farmers in South Africa (there are no

rainfed farms in Egypt), but the difference is significant only in Kenya and Zambia.

Rainfed farmers in Cameroon earn the most income of every country, controlling for other

variables. The coefficients of the inverse Mills ratios are insignificant in Table 5 suggesting

again that there is not a serious sample selection bias problem.



14/35


Table 6 presents the climate marginals for the fixed effect model. Most of the results are

similar to Table 4. Higher temperatures continue to encourage farmers to adopt irrigation.

The annual temperature and precipitation continue to have an insignificant effect on

conditional income in the irrigated equation and annual precipitation continues to be positive

and significant in the rainfed equation. Higher annual flow continues to reduce the

probability of irrigation but again this is because of a large negative impact from winter flow.

However, some of the results have changed. With fixed effects, annual precipitation no

longer has a significant effect on the choice of irrigation and annual temperature no longer

has a significant impact on the income from rainfed plots. It is difficult to determine whether

the choice of irrigation is a country effect (e.g. Egypt) or an annual precipitation effect. The

impact of annual temperature on rainfed income may also be caused by country level

variables. For example, most of the countries with temperate climates also have more

productive and modern agriculture (Egypt, South Africa, and highland Kenya).

5. Climate change simulation

In this section, we calculate the welfare effect of a changing climate. In the previous

analyses, the comparisons were cross sectional in nature, reflecting the performance of one

farm in one climate against another farm in a different climate. In the analysis in this section,

we use these empirical cross sectional results to project impacts over time. It must be

understood that this exercise is trying to measure long term impacts and adaptations as

farmers fully adapt to a new climate. The projections are not intended to trace dynamic

adjustments from year to year.

We examine how alternative future climate scenarios may affect the choice of irrigation and

net revenue per hectare. We rely on three climate models to provide a range of plausible

predictions: the Parallel Climate Model (PCM) (Washington et al. 2001), the Center for

Climate System Research (CCSR) model (Emori et al. 1999) and the Canadian Climate

Centre model (CCC) (Boer et al. 2000). We look at predicted climate changes in each

African country in 2100 5. On average, PCM predicts a relatively small increase in

temperature (2.3C), CCSR is between (4.5C), and the CCC model predicts a very large

5 The choice of 2100 as a scenario is for exposition purposes. The analysis can easily projectimpacts for other scenarios.



15/35


increase (6.5C) in temperature for Africa. The PCM predicts a slight increase in

precipitation, especially in winter, whereas the CCC and CCSR predict slight reductions in

precipitation.

For all the comparisons, we assume that African agriculture remains otherwise unchanged.

That is, we examine the impact of a future climate change scenario on current farms. In

practice, farms will change over time. They are likely to have more variable inputs, more

capital, new technology, and better access to markets. All of these changes would likely have

a large influence on future outcomes. In their absence, it is important to recognize that the

predictions in this paper are not good forecasts of future outcomes. The predictions are

intended simply to provide a sense of the role that climate might play.

Nonmarginal changes in climate may induce other changes, for example, in prices. Exactly

how prices will change is hard to predict because prices will likely depend on global

production and demand. Although it is likely that African crop production will be reduced by

warming, it is not at all clear that global production will be affected (Gitay et al. 2001). If

market access in 100 years is good, the local price will be equal to the global price and there

may be no price effects. If prices increase (decrease), farmers will gain (lose) and consumers

will lose (gain). In this paper, we assume that there will be no price effects so we might

overestimate the impacts to African farmers.

Using the probit coefficients for irrigation, we first examine what happens to the probability

of selecting irrigation in the two scenarios. In the PCM scenario, the seasonal temperature

effects are largely offsetting. However, the large increase in winter precipitation encouragesmany farms to switch to irrigation. Ignoring the effects on flow, the probability of irrigation

in the sample rises dramatically to 56% (see Table 7). However, the big increase in winter

flow actually has a negative effect on irrigation. When the change in flow is taken into

account, the increase in irrigation in the PCM scenario is smaller (44%). Note that new water

storage facilities which could hold back winter flows and make them available in the spring

and summer might convert harmful winter flows into beneficial spring and summer flows.

We do not take different water management techniques into account in this analysis but they



16/35


are potentially very promising. With the CCC and CCSR scenario, the seasonal temperature,

precipitation, and flow effects are offsetting and the probability of irrigation falls slightly.

Multiplying through the probability of selecting irrigation times the conditional income from

irrigation and the probability of selecting rainfed agriculture times the conditional income of

rainfed farming yields an expected income for each farm. The welfare effect per hectare is

the average impact across the sample. Repeating this process in each climate scenario

provides an estimate of the expected income in each scenario. The change in expected

income is an estimate of the annual welfare effects of each scenario.

We compare three estimates of welfare effects for each climate scenario in Table 7. The first

column presents the welfare effects assuming that rainfed and irrigated farms stay as they are

now. That is, the probability of irrigation does not change and there is no sample selection

bias. The second model again assumes that the probability of irrigation does not change but it

uses the corrected regression for sample selection bias. The third estimate allows the

probability of irrigation to adjust and it uses the corrected regression estimates. Standard

deviations were computed using bootstrapping (350 repetitions).

The first two measures of welfare are virtually identical. There is no evidence of sample

selection bias in the data. However, the exogenous estimates grossly underestimate the

benefits of the PCM scenario because they do not take into account the large increase in

irrigation permitted by PCM. The PCM scenario predicts a huge increase in irrigation along

with the wetter and mildly warmer climate. The exogenous models consequently predict that

PCM would lead to only a small benefit of 9% whereas the endogenous model predicts a benefit of 35%. Adjusting for the harmful effect of winter flows, the endogenous model still

predicts that the PCM scenario would lead to a welfare benefit of 24%.

Comparing the welfare results with the two dry climate scenarios, reveals that they are all

quite similar. There is not a large change in irrigation, so the exogenous welfare estimates

are quite similar to the endogenous estimates. It is interesting to note that although the PCM

scenario actually predicts gains for African farmers, the other two climate scenarios predict



17/35


large losses. Without additional water, irrigation will not help farmers escape the very high

temperatures of these scenarios.

6. Conclusions

This paper provides a modeling framework to explicitly capture irrigation in the Ricardian

model. We control for the endogeneity of irrigation by building a two stage selection model.

Our results indicate that there is little evidence of sample selection bias between African

rainfed and irrigated farms. However, it is important to treat irrigation as though it is

endogenous because the choice is sensitive to at least some climate scenarios. In particular,

with a dry continent that relies on rainfed agriculture, irrigation can increase substantially

with a wetter climate. Similarly, it is important to model irrigation in places that currently

rely on irrigation but which might become dry in the future. Impact studies will be biased if

they fail to take into account the substantial changes in irrigation that climate change might

cause. In Africa, models that did not account for the endogeneity of irrigation seriously

underestimated the benefits of the PCM wetter scenario. However, with the dry CCSR and

CCC scenarios, irrigation did not change very much. All the models predicted similar

damages from these climate scenarios.

The analysis reveals that rainfed and irrigated plots in Africa do not have similar responses to

temperature. Net revenues from rainfed plots tend to fall with higher temperatures whereas

net revenues on irrigated plots are less affected. However, both rainfed and irrigated plots

appear to have similar positive responses to higher precipitation levels except in places with

high rainfall. These results suggest one must be careful not to extrapolate from results on

just rainfed plots or results using just rainfed crops to agriculture as a whole.

The results indicate that current African agriculture is sensitive to climate change. A mild

increase in temperature with an increase in precipitation may be beneficial to African

farmers, but a severe increase in temperature without any increase in precipitation will be

very harmful. Warming and reductions in precipitation will be especially deleterious to

rainfed farmers, generally the poorest segment of the agriculture community. In contrast,

many of the current farms in temperate places or who practice irrigation may actually benefitfrom climate change.



18/35


The projected impacts of future climate scenarios for African agriculture in this paper are

merely suggestive. The paper assumes that African farms remain as they are now. In

practice, future farms are likely to be quite different from what is there today. These changesmay have a large impact on climate sensitivity. Further research exploring how farms in

Africa might evolve and how this might affect future climate sensitivity is needed.

Finally, the paper hints that water management is likely to be an important issue for Africa.

The results suggest that flows of rivers in the winter are actually harmful to farms, probably

because they are associated with flooding. If these flows could be delayed into spring and

summer, the model suggests they would turn from being harmful into being beneficial.Systems of dams that would store water for a season or two could both reduce the harms of

floods and increase the value of irrigation water. This is an immediate benefit that could be

enjoyed today. However, in a future warmer world, water will be even more scarce and such

adaptations even more important.



19/35


REFERENCES

Basist, A., C. Williams, N. Grody, T. Ross, S. Shen, A. Chang, R. Ferraro, and M. Menne.

2001. Using the Special Sensor Microwave Imager to monitor surface wetness. Journal of

Hydrometeorology 2: 297308.

Boer, G., G. Flato, and D. Ramsden. 2000. A transient climate change simulation with

greenhouse gas and aerosol forcing: projected climate for the 21 st century, Climate

Dynamics 16 , 427-450.

Caswell, M. and D. Zilberman. 1986. The Effect of Well Depth and Land Quality On the

Choice of Irrigation Technology. American J. of Agricultural Economics 68 : 798-11.

Cline, W. 1996. The impact of global warming on agriculture: Comment. American

Economic Review 86 : 13091312.

Darwin, R. 1999. The impacts of global warming on agriculture: A Ricardian analysis:

Comment. American Economic Review 89 : 10491052.

Dinar, A. and D. Yaron. 1990. Influence of Quality and Scarcity of Inputs on the Adoption

of Modern Irrigation Technologies. Western J. of Agricultural Economics 15 (2): 224-33.

Dinar, A. and D. Zilberman. 1991. The Economics of Resource-Conservation, Pollution-

Reduction Technology Selection, The Case of Irrigation Water. Resources and Energy 13 :

323-48.

Dinar, A., M. B. Campbell, and D. Zilberman. 1992. Adoption of Improved Irrigation and

Drainage Reduction Technologies Under Limiting Environmental Conditions.

Environmental & Resource Economics 2: 373-98.

Emori, S. T. Nozawa, A. Abe-Ouchi, A. Namaguti, and M. Kimoto, (1999). Coupled ocean-

atmospheric model experiments of future climate change with an explicit representation of

sulfate aerosol scattering. Journal of Meteorological Society, 77, 12991307, Japan.



20/35


FAO (Food And Agriculture Organization). 1997. Irrigation Potential in Africa: A basin

approach . FAO Land and Water Bulletin, 4, FAO Land and Water Development Division,

Rome.

FAO (Food And Agriculture Organization). 2003. The Digital Soil Map of the World :

Version 3.6 (January), Rome, Italy.

Gitay, H., S. Brown, W. Easterling, B. Jallow. 2001. in McCarthy, J., O. Canziani, N. Leary,

D. Dokken, and K. White (eds.) Climate Change 2001: Impacts, Adaptation, and

Vulnerability , Intergovernmental Panel on Climate Change Cambridge University Press:

Cambridge, pp 235-342.

Heckman, J. 1979. Sample selection bias as a specification error. Econometrica 47: 153

161.

Heltberg, R and F. Tarp. 2002. Agricultural supply response and poverty in Mozambique.

Food Policy 27: 103124.

IMWI (International Water Management Institute) and The University of Colorado. 2003.

Hydroclimatic Data. GEF/CEEPA/World Bank Project on Climate, water and agriculture:

Impacts and adaptation of agro-ecological systems in Africa.

Kurukulasuriya, P., R. Mendelsohn, R. Hassan, J. Benhin, M. Diop, H. M. Eid, K.Y. Fosu, G.

Gbetibouo, S. Jain, , A. Mahamadou, S. El-Marsafawy, S. Ouda, M. Ouedraogo, I. Sne, N.

Seo, D. Maddison and A. Dinar, 2006. Will African Agriculture Survive Climate Change?

World Bank Economic Review 20: 367-388 .

Kurukulasuriya, P.,and M. I. Ajwad. 2007. Application of the Ricardian Technique to

Estimate the Impact of Climate Change on Smallholder Farming in Sri Lanka Climatic

Change 81 : 39-59.

Mendelsohn R., W. Nordhaus, and D. Shaw. 1994. The impact of global warming onagriculture: A Ricardian analysis. American Economic Review 84 : 753771.

http://www.ceepa.co.za/Climate_Change/climate-about.htmlhttp://www.ceepa.co.za/Climate_Change/climate-about.htmlhttp://www.ceepa.co.za/Climate_Change/climate-about.htmlhttp://www.ceepa.co.za/Climate_Change/climate-about.htmlhttp://www.ceepa.co.za/Climate_Change/climate-about.html


21/35


Mendelsohn, R. and A. Dinar, 2003. Climate, water, and agriculture. Land Economics 79 :

328341.

Mendelsohn, R. and L. Williams. 2004. Comparing Forecasts of the Global Impacts of

Climate Change Mitigation and Adaptation Strategies for Global Change 9: 315-333.

Mendelsohn, R., A. Dinar, and L. Williams. 2006. The Distributional Impact of Climate

Change On Rich and Poor Countries Environment and Development Economics 11 : 1-20.

Mendelsohn, R., A. Basist, A. Dinar, F. Kogan, P. Kurukulasuriya, and C. Williams. 2006.

Climate Analysis with Satellites Versus Weather Station Data Climatic Change 81: 71-83.

.

Negri, D. H., and D. H. Brooks. 1990. Determinants of Irrigation Technology Choice.

Western Journal of Agricultural Economics 15: 213-23.

Nordhaus, W. 2006. Geography and Macroeconomics: New Data and New Findings

Proceedings of the National Academy Sciences 103: 3510-3517.

Schlenker, W., M. Hanemann, and A. Fischer. 2005. Will US Agriculture really benefit

from global warming? Accounting for irrigation in the hedonic approach. American

Economic Review , 95(1): 395406.

Sen, A. 1962. An aspect of Indian agriculture, Economics Weekly , Annual

Number, 243-66.

Seo, S. N., R. Mendelsohn, and M. Munasinghe. 2005. Climate Change Impacts on

Agriculture in Sri Lanka Environment and Development Economics 10: 581-596.

Seo, N. and R. Mendelsohn. 2008. A Ricardian Analysis of the Impact of Climate Change

on South American Farms Agricultura Technica (forthcoming).

USGS (United States Geological Survey). 2004. Global 30 Arc Second Elevation Data ,USGS National Mapping Division, EROS Data Centre.



22/35


Washington, W., J. Weatherly, G. Meehl, A.Semtner, B. Bettge, A Craig, W. Strand, J.

Arblaster, V. Wayland, R. James, and Y. Zhang. 2000. Parallel Climate Model (PCM):

Control and Transient Scenarios. Climate Dynamics , 16: 755-774.

Weng F. and Grody N. 1998. Physical retrieval of land surface temperature using the

Special Sensor Microwave Imager. Journal of Geophysical Research 103: 88398848.

World Bank. 2003. Africa Rainfall and Temperature Evaluation System , World Bank,

Washington, DC.



23/35


Tables and Figures

Table 1: Sample of farms

Country No. of plots

Irrigatedplots

Rainfedplots

Burkina Faso 1141 59 1082

Cameroon 1013 145 868

Egypt 1030 1030 0

Ethiopia 932 67 865

Ghana 1210 49 1161Kenya 862 95 767

Niger 1133 52 1081

Senegal 1362 34 1328

South Africa 283 83 200

Zambia 1009 13 996

Zimbabwe 958 123 835

Total 10933 1750 9183



24/35


Table 2: Probit model of whether to irrigate

Variable CoefficientsTemp - winter 0.45*

(3.00)Temp - winter sq -0.003(-0.67)

Temp - spring -0.95**(-6.03)

Temp - spring sq 0.01*(2.48)

Temp - summer 1.25**(9.42)

Temp - summer sq -0.02**(-9.43)

Temp - fall -0.71**(-4.25)

Temp - fall sq 0.02**(5.54)

Precip - winter -0.01(-1.89)

Precip - winter sq 0.00**(5.06)

Precip - spring -0.01*(-2.20)

Precip - spring sq 0.0000025(0.10)Precip - summer 0.02**

(6.37)Precip - summer sq -0.000067**

(-5.48)Precip - fall -0.01**

(-3.25)Precip - fall sq 0.000036**

(4.00)Plot area (HA) 0.000067

(0.59)Log(elevation) 0.26**

(8.13)Log(Household size) 0.09*

(2.03)Household withelectricity (1/0) 0.23**

(4.33)Gleyic Luvisols - Fine,Undulating -7.34*

(-1.98)Eutric Gleysols -2.54**

(-6.57)

Variable CoefficientsChromic Cambisols -

Medium, Steep-1.54*

(-2.51)Lithsols - Coarse,Medium, Fine, Steep -5.20*

(-1.99)Ferric Luvisols - Coarse,Undulating 1.09**

(8.69)Gleyic Luvisols 0.84*

(2.60)Gleyic Luvisols -

Medium, Undulating0.78*

(2.96)Chromic Luvisols -Medium,Undulating,Hilly

0.53

(1.00)Luvic Arenosols -Coarse, Undulating -4.70**

(-3.76)Lithosols and EutricGleysols - Hilly 7.25*

(2.54)Calcic Yermosols -Coarse, Medium,Undulating, Hilly

2.74**

(5.28)

Eutric Gleysols - Coarse,Undulating -2.71

(-1.51)Chromic Vertisols -Fine, Undulating 0.6

(1.06)Chromic Luvisols -Medium, Fine,Undulating

-0.27

(-0.61)Chromic Luvisols -Medium, Steep -0.52

(-0.31)

Dystric Nitosols -1.02*

(-2.06)

Lithosolus - Hilly, Steep -0.06



25/35


Variable Coefficients(-0.09)

Orthic Luvisols -Medium, Hilly -1.5

(-1.21)Flow- winter -1.67(-1.73)

Flow - winter sq -0.8(-1.17)

Flow - spring -0.05(-0.06)

Flow - spring sq 2.12*(3.20)

Flow - summer -1.24**

Variable Coefficients(-4.83)

Flow - summer sq 0.11**(4.55)

Flow - fall 1.22**

(5.58)Flow - fall sq -0.08*

(-3.28)Constant -4.18**

(-3.73)

N 10915 Log pseudolikelihood -2122.1R 2 0.56

Dependent variable is whether or not irrigation is utilized in a plot. * p


26/35


Table 3: Conditional income regressions

Model Irrigated Model Rainfed ModelVariables Corrected OLS Corrected OLSTemp - winter 97.0 142.4 -128.2* -123.4*

(0.60) (1.05) (-2.51) (-2.43)Temp - winter sq -1.69 -2.65 4.33** 4.25*(-0.44) (-0.74) (3.31) (3.25)

Temp - spring -93.2 -165.4 4.3 -4.7(-0.47) (-1.07) (0.05) (-0.05)

Temp - spring sq -0.60 0.68 -1.98 -1.84(-0.15) (0.19) (-1.09) (-1.03)

Temp - summer 1188.1** 1287.9** 214.2* 224.7**

(3.42) (4.74) (3.24) (3.51)Temp - summer

sq-18.16* -20.16** -2.99* -3.19*

(-3.02) (-4.48) (-2.36) (-2.60)Temp - fall -1580.4** -1653.8** -82.6 -92.4

(-3.63) (-4.31) (-1.47) (-1.70)Temp - fall sq 29.43** 31.28** 1.13 1.37

(3.47) (4.35) (0.95) (1.20)Precip - winter 12.03 10.47 -2.60* -2.74*

(1.80) (1.75) (-2.20) (-2.33)Precip - winter sq -0.06 -0.05 0.02* 0.02*

(-1.44) (-1.42) (2.75) (3.01)Precip - spring -10.31 -9.71 3.71** 3.78**

(-1.61) (-1.53) (3.41) (3.50)Precip - spring sq 0.09* 0.09* -0.01 -0.01

(2.30) (2.27) (-1.44) (-1.59)Precip - summer 26.25** 27.87** 4.09** 4.21**

(4.98) (6.46) (6.08) (6.27)Precip - summer sq -0.10** -0.10** -0.02** -0.02**

(-4.88) (-5.82) -(5.29) (-5.39)Precip - fall -25.35** -26.85** -1.21* -1.28*

(-5.00) (-6.25) (-2.15) (-2.32)

Precip - fall sq 0.08** 0.09** 0.01** 0.01**(4.98) (5.92) (5.53) (5.65)Plot area (HA) -0.15* -0.14* -0.29** -0.29**

(-2.39) (-2.31) (-4.54) (-4.53)Log(Householdsize) 41.68 44.28 22.46* 23.25*

(.74) (.79) (2.05) (2.12)With electricity(1/0) 387.4** 412.9** 124.1** 125.5**

(3.66) (4.36) (7.84) (8.03)Gleyic Luvisols -111.2* -103.2*

(-2.89) (-2.70)



27/35


Luvic Arenosols-CoarseUndulating -357.0** -397.0**

(-4.60) (-5.56)Eutric Gleysols-Coarse

Undulating

-1554.0* -2045.5** -405.4** -423.7**

(-2.25) (-3.81) (-4.54) (-4.75)ChromicVertisols-FineUndulating

-1910.2* -1857.5* -708.3** -711.4**

(-2.81) (-2.80) (-3.51) (-3.53)ChromicLuvisols-MediumFine Undulating

-315.1** -304.9**

(-8.79) (-8.72)ChromicLuvisols-MediumSteep

-6510.5* -6495.5*

(-2.94) (-2.92)

Dystric Nitosols 7528.3** 7410.2**

(5.39) (5.28)Lithosolus HillySteep -877.9* -922.4** -352.7** -369.5**

(-3.29) (-3.51) (-8.40) (-8.96)

Orthic Luvisols

Medium Hilly-1885.7** -1907.0**

(-3.81) (-3.86)Inverse MillsRatio -102.2 -7.8

(-0.80) (-1.35)

Constant 4361.7* 4141.1* -295.6 -276.1

(2.72) (2.48) (-0.66) (-0.62)

N 1787 1787 9128 9128 R-squared 0.25 0.25 0.16 0.16F-stat 68.47 53.6 49.41 51.11

Note: Dependent variable is net revenue per hectare. * p


28/35


Table 4: Marginal Climate Impacts

Marginal effects calculated from coefficients in Table 2.

Conditional Income Conditional IncomeIrrigated Farms Rainfed Farms

TemperatureC

Precipitation

mm/mo

TemperatureC

Precipitation

mm/mo

Winter 45

(128)

8

(9)

55

(14)

-2

(1)

Spring -108

(140)

-6.0

(6)

-97

(16)

3

(1)

Summer 314

(130)

17

(5)

68

(14)

1

(0.3)

Fall -249

(130)

-18

(5)

-33

(15)

1

(0.4)

Annual 1

(25)

1

(10)

-7

(4)

3

(0.6)

Marginal effects calculated from corrected coefficients in Table 3 columns (a) and (c).Marginal effects estimated using the climate of each observation. The mean and standarddeviations calculated using bootstrapping (350 repetitions).

Selection Model (Irrigation Choice)

TemperatureC

Precipitationmm/mo

Flowmillion m 3/mo

Winter 0.34(0.062)

-0.002(0.005)

-2.49

(0.83)

Spring -0.52(0.068)

-0.01

(0.004)

1.47

(0.85)

Summer 0.08

(0.58)

0.01

(0.002)

-0.9

(0.28)

Fall 0.15

(0.059) -0.002(0.002)

0.91

(0.20)

Annual 0.06

(0.016)

-0.01

(0.004)

-1.06

(0.58)



29/35


Table 5: Country Fixed Effects Regressions

ChoiceModel

CorrectedIrrigated

Farms

CorrectedRainfed

FarmsTemp - winter 0.18 2.8 -56.4(0.98) (0.01) (-0.90)

Temp - winter sq 0.0015 8.68 2.21(0.31) (1.55) (1.48)

Temp - spring -0.89** 68.0 -73.8(-4.35) (0.29) (-0.72)

Temp - spring sq 0.01* -8.26 0.47(2.03) (-1.57) (0.23)

Temp - summer 1.10** 946.1* 99.1(5.80 (2.34) (1.38)

Temp - summer sq -0.02** -11.79 -2(-5.94) (-1.73) (-1.47)

Temp - fall -0.57* -1528.7* -59.8(-2.59) (-3.19) (-1.01)

Temp - fall sq 0.02** 25.07* 1.59(3.93) (2.76) (1.31)

Precip - winter -0.01 -1.42 1.34(-1.28) (-0.14) (0.97)

Precip - winter sq 0.00012** 0.01 -.004(3.92) (0.27) (-0.68)

Precip - spring -0.01* -8.55 0.43(-2.23) (-0.89) (0.35)

Precip - spring sq -0.000005 0.08 -.0009(-0.17) (1.62) (-0.17)

Precip - summer 0.01* 23.12* 2.31*(2.33) (2.62) (2.75)

Precip - summer sq -.00002 -0.09* -0.01*(-1.57) (-3.08) (-2.09)

Precip - fall -0.00002 -22.88* 0.17(0.42) (-2.58) -(0.22)

Precip - fall sq -0.000007 0.08* .0006(-0.65) (3.11) (0.25)

Plot area (HA) 0.00008 -0.16* -0.21**-(0.73) (-2.53) (-3.39)

Log(elevation) 0.13*(2.86)

Log(Householdsize) 0.10* 79.6 28.24*

(2.23) (1.35) (2.55)Household withelectricity (1/0) 0.10 244.5* 33.1*

(1.62) (2.46) (2.04)

Gleyic Luvisols -Fine, Undulating -7.08



30/35


(-1.76)Eutric Gleysols -2.04**

(-4.35)Chromic Cambisols- Medium, Steep -1.35*

(-2.31)Lithsols - Coarse,Medium, Fine, Steep -5.63*

(-2.11)Ferric Luvisols -Coarse, Undulating 1.09**

(8.64)Gleyic Luvisols 1.23** -86.70*

(3.57) (-2.01)Gleyic Luvisols -Medium, Undulating 1.17**

(4.15)Chromic Luvisols -Medium,Undulating,Hilly

1.58*

(2.52)Luvic Arenosols -Coarse, Undulating -4.76** -359.0**

(-3.83) (-3.64)Lithosols and EutricGleysols - Hilly 7.46*

(2.44)Calcic Yermosols -Coarse, Medium,Undulating, Hilly

2.70**

(5.22)Eutric Gleysols -Coarse, Undulating -2.22 273.5 95.9

(-1.25) -(0.30) -(1.15)Chromic Vertisols -Fine, Undulating 0.88 -2648.0* -634.0*

(1.41) (-3.27) (-2.99)

Chromic Luvisols -Medium, Fine,Undulating

0.51 -35.4

(1.17) (-0.93)Chromic Luvisols -Medium, Steep -0.03 -8109.0**

(-0.02) (-3.64)Dystric Nitosols -1.27* 7383.8**

(-2.07) -(5.1)Lithosolus - Hilly,Steep 0.91 -244.7 -58.1

(1.23) (-0.68) (-1.08)



31/35


Orthic Luvisols -Medium, Hilly -1.97 -1995.6**

(-1.72) (-3.85)Flow- winter -1.97

(-1.71)

Flow - winter sq -0.97

(-1.21)Flow - spring -0.21

(-0.19)Flow - spring sq 2.07*

(2.61)Flow - summer -0.76

(-1.73)Flow - summer sq 0.04

(1.32)

Flow - fall 0.83*(2.98)

Flow - fall sq -0.02(-0.68)

Inverse Mills Ratio 100.98 8.26(0.85) (0.65)

Constant -1.65 6754.5** 1202.8*(-1.30) (3.59) (2.41)

Burkina Faso 0.02 -1608.8* -9.6(0.05) (-3.24) (-0.06)

Ghana 0.37 -1599.7* -55.7(0.98) (-3.08) (-0.37)

Cameroon 1.77*** -1469.0* 345.8*(4.70) (-2.85) -(2.36)

Ethiopia 0.62 -1776.2** -265.3(1.61) (-3.75) (-1.78)

Kenya 1.03* -1431.6* -318.0*(2.69) (-2.96) (-2.34)

Niger -0.11 -2398.6** -183.8(-0.25) (-4.62) (-1.23)

Senegal -0.36 -1840.3* -64.3(-0.84) (-3.26) (-0.43)

Zimbabwe 0.46 -881.0* -25.3(1.58) (-2.77) (-0.21)

Zambia -0.17 -1071.4* -293.4*(-0.45) (-2.68) (-2.37)

R-squared 0.58 0.26 0.20 N 10915 1787 9128 F 41.93 46.59 Wald chi2(55) 1512.13Log pseudolikelihood -2053.96

Notes Pseudo R2



32/35


Table 6: Marginal Climate Impacts With Fixed Effects

Selection Model (Irrigation Choice)

TemperatureC

Precipitation

mm/mo

Flow

million m 3/mo

Winter 0.26

(0.09)

-0.003

(0.01)

-3.09

(1.1)

Spring -0.46

(0.09)

-0.01

(0.005)

1.4

(1.29)

Summer 0.08

(0.08)

0.01

(0.002)

-0.6

(0.51)

Fall 0.17

(0.09)

-0.00003

(0.002)

0.77

(0.29)

Annual 0.06

(0.02)

-0.01

(0.01)

-1.54

(0.66)

ConditionalIncome ConditionalIncome ConditionalIncome ConditionalIncome

Irrigated Farms Irrigated Farms Rainfed Farms Rainfed Farms

TemperatureC

Precipitation

mm/mo

TemperatureC

Precipitation

mm/mo

Winter 274

(177)

-6

(15)

39

(19)

1

(1.4)

Spring -233

(166)

-2

(12)

-53

(19)

0.3

(1.1)Summer 408

(158)

14

(8)

-1

(16)

0.7

(0.4)

Fall -430

(181)

-16

(9)

10

(19)

0.3

(0.5)

Annual 28

(27)

-9

(11)

-3

(6)

2.4

(0.7)

Note: Marginal effects calculated from coefficients in Table 5. Marginal effects estimated ateach observations climate. Means and standard deviations calculated using bootstrapping(350 repetitions).



33/35


Table 7: Irrigation and Welfare Results Across Three Climate Change Scenarios

IrrigationExogenousOLS

ExogenousCorrected

Endogenous(with Flowconstant)

Endogenous(with flow

adjusting toclimate)Baseline Income 483 483 483 483

PCM ScenarioProbability of Irrigation 16% 16% 56% 44%

in expectedwelfare ($/ha)*

65(100)

44(119)

169(314)

115.5(299)

in expectedwelfare (%) +13% +9% +35% +24%

CCSR ScenarioProbability of Irrigation 16% 16% 13% 13%


-196(53)

-206(64)

-211(68)

-216(63)

in expectedwelfare (%) -41% -43% -44% -45%

CCC Scenario

Probability of Irrigation 16% 16% 14% 14%


-263(70)

-276(75)

-278(75)

-288(68)

in expectedwelfare (%) -54% -57% -58% -60%

Standard deviation in parenthesis calculated from bootstrapping. Exogenous calculation usescurrent irrigation probabilities and OLS or corrected conditional results. Endogenouscalculation uses predicted future irrigation probabilities and corrected conditional results.



34/35


Appendix A: Mean temperature and Precipitation by Countries in SampleTable A1: Temperature (C) Normals (Sample Means)Seasonal climates have been adjusted so that they are consistent regardless of hemisphere. Country Winter Spring Summer FallBurkina Faso 23.6 28.3 28.9 24.5

Cameroon 19.4 21.4 20.0 18.9Egypt 11.7 13.2 24.1 23.4Ethiopia 18.6 21.5 19.7 18.1Ghana 21.8 24.8 22.6 21.2Kenya 18.8 19.7 18.4 19.1Niger 26.3 30.8 33.9 29.2Senegal 24.5 29.1 31.5 26.7South Africa 11.5 15.5 20.7 19.4Zambia 16.7 21.7 21.1 19.6Zimbabwe 16.6 21.3 22.5 20.6Total 19.8 23.4 24.5 22.2

Table A2: Precipitation (mm/mo) Normals (Sample Mean)Seasonal climates have been adjusted so that they are consistent regardless of hemisphere.

Country Winter Spring Summer FallBurkina Faso 2.6 15.8 113.8 133.1Cameroon 60.3 101.9 185.1 228.6Egypt 12.8 7.0 2.3 3.5Ethiopia 19.4 49.2 123.7 117.5Ghana 30.9 59.7 112.4 111.7Kenya 88.4 103.0 84.3 60.0Niger 0.8 3.2 64.1 70.6Senegal 2.2 1.1 47.9 112.7South Africa 1.8 55.0 86.4 68.8Zambia 48.3 57.7 108.6 100.7Zimbabwe 7.5 15.4 138.8 90.0Total 25.9 39.8 96.1 102.4

Table A3: Flow (million mm 3/mo) Normals (Sample Mean)Country Winter Spring Summer FallBurkina Faso 0.03 0.01 0.04 0.11Cameroon 0.32 0.23 0.67 1.21Egypt 3.08 2.66 7.60 11.17Ethiopia 0.11 0.11 0.45 0.63Ghana 0.23 0.13 0.47 0.95Kenya 0.12 0.16 0.21 0.16Niger 0.20 0.07 0.47 1.23Senegal 0.07 0.01 0.15 0.51South Africa 0.02 0.02 0.06 0.06Zambia 0.41 0.16 2.40 2.92Zimbabwe 0.12 0.09 0.52 0.61Total 0.43 0.33 1.18 1.78



35/35

F o r P e e r R

e v i e w

Figure 1: Temperature response functions of irrigated and rainfed farms

0

500

1000

1500

P r e

d i c t e d n e

t r e v e n u e o

f d r y

l a n

d f a r m s

16 18 20 22 24 26Temperature (Celcius)

Irrigated Farms Dryland Farms

Temperature Response Functions

Figure 2: Precipitation response functions of irrigated and rainfed farms

0

250

500

750

1000

1250

1500

1750

2000

2250

P r e

d i c t e d n e

t r e v e n u e o

f d r y

l a n

d f a r m

s

80 130 180 230Precipitation (mm)

Irrigated Farms Dryland Farms

Precipitation Response Functions


adaptation and climate change impacts_ a selection model of irrigation and farm income in africa

Documents