metadata: authors: 0: author: Seo, Niggol
1: author: Mendelsohn, Robert
2: author: Kurukulasuriya, Pradeep
3: author: Dinar, Ariel
4: author: Hassan, Rashid
count: Africa
docna: 0: docna: Differential adaptation strategies to
            climate change in African cropland by agro-ecological zones
docty: Policy Research Working Paper
subtopic: Adaptation to Climate Change,Climate Change Impacts,Climate Change and Agriculture,Science of Climate Change,Climate Change and Health,Social Aspects of Climate Change,Climate Change and Environment,Crops and Crop Management Systems,Food Security
teratopic: Agriculture,Energy,Industry,Poverty Reduction,Environment
abstracts: cdata!: This paper quantifies how African
            farmers have adapted their crop and irrigation decisions to
            their farm's current agro-ecological zone.  The results
            indicate that farmers carefully consider the climate and
            other conditions of their farm when making these choices.
            These results are then used to forecast how farmers might
            change their irrigation and crop choice decisions if climate
            changes. The model predicts African farmers would adopt
            irrigation more often under a very hot and dry climate
            scenario but less often with a mild and wet scenario.
            However, farms in the deserts, lowland humid forest, or mid
            elevation humid forest would reduce irrigation even in the
            very hot and dry climate scenario. Area under fruits and
            vegetables would increase Africa-wide with the very hot and
            dry climate scenario, except in the lowland semi-arid
            agro-ecological zone. Millet would increase overall under
            the mild and wet scenario, but decline substantially in the
            lowland dry savannah and lowland semi-arid agro-ecological
            zones. Maize would be chosen less often across all the
            agro-ecological zones under both climate scenarios. Wheat
            would decrease across Africa. The authors recommend that
            care must be taken to match adaptations to local conditions
            because the optimal adaptation would depend on the
            agro-ecological zone and the climate scenario.
repnme: repnme: Differential adaptation strategies to
            climate change in African cropland by agro-ecological zones
display_title: Differential adaptation strategies
            to climate change in African cropland by agro-ecological zones
listing_relative_url: /research/2008/04/9365682/differential-adaptation-strategies-climate-change-african-cropland-agro-ecological-zones
topicv3: Climate Change Impacts m1326159 1773,Agriculture and Food Security m1326205 435,Crop Production m1326627 174,Climate Change m1326829 567,Climate Change Adaptation m1327767 574
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Agriculture,Energy,Industry,Poverty Reduction,Environment
no_of_pages: 42
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: NA
3: author_key: NA
4: author_key: NA
model_extractions: WPS4600 Policy ReseaRch WoRking PaPeR 4600 Differential Adaptation Strategies to Climate Change in African Cropland by Agro-Ecological Zones _Niggol Seo Robert Mendelsohn Pradeep Kurukulasuriya Ariel Dinar Rashid Hassan_ The World Bank Development Research Group Sustainable Rural and Urban Development Team April 2008
Policy ReseaRch WoRking PaPeR 4600

# **Abstract**

This paper quantifies how African farmers have adapted their crop and irrigation decisions to their farm’s current agro-ecological zone. The results indicate that farmers carefully consider the climate and other conditions of their farm when making these choices. These results are then used to forecast how farmers might change their irrigation and crop choice decisions if climate changes. The model predicts African farmers would adopt irrigation more often under a very hot and dry climate scenario but less often with a mild and wet scenario. However, farms in the deserts, lowland humid forest, or mid elevation humid forest would reduce irrigation even in the very hot and dry climate scenario. Area under fruits and vegetables would increase Africa-wide with the very hot and dry climate scenario, except in the lowland semi-arid agro-ecological zone. Millet would increase overall under the mild and wet scenario, but decline substantially in the lowland dry savannah and lowland semi-arid agro-ecological zones. Maize would be chosen less often across all the agro-ecological zones under both climate scenarios. Wheat would decrease across Africa. The authors recommend that care must be taken to match adaptations to local conditions because the optimal adaptation would depend on the agro-ecological zone and the climate scenario.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to mainstream research on climate change. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Niggol.seo@yale.edu, Robert.mendelsohn@ yale.edu, Adinar@worldbank.org, Rashid.hassan@up.ac.za, and Pradeep.kurukulasuriya@undp.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **DIFFERENTIAL ADAPTATION STRATEGIES TO CLIMATE CHANGE IN AFRICAN CROPLAND BY AGRO-ECOLOGICAL ZONES**<sup>1</sup>

Niggol Seo<sup>2</sup> , Robert Mendelsohn<sup>3</sup> , Pradeep Kurukulasuriya<sup>4</sup> , Ariel Dinar<sup>5</sup> , and Rashid Hassan<sup>6</sup>

> 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 Trust Fund and conducted in DECRG at the World Bank.. 2 School of Forestry and Environmental Studies, Yale University and consultant to the World Bank; 230 Prospect St. (Room 203), New Haven, CT 06511, USA, and a Consultant to the World Bank,; email- Niggol.seo@yale.edu

> 3 School of Forestry and Environmental Studies, Yale University and consultant to the World Bank; 230 Prospect St. (Room 207), New Haven, CT 06511, USA, and a Consultant to the World Bank,; email- <u>Robert.Mendelsohn@yale.edu</u>

> 4 Energy and Environment Group, Bureau of Development Policy, United Nations Development Programme, New York; phone 212-217 2512; email: <u>pradeep.kurukulasuriya@undp.org</u>

> 5 Development Research Group, World Bank, 1818 H St. NW, Washington DC 20433; phone 202-473-0434; email <u>adinar@worldbank.org.</u>

> 6 Department of Agricultural Economics, University of Pretoria, and Center for Environmental Economics for Africa; email Rashid.hassan@up.ac.za.
# **1. Introduction**

Awareness of global warming has increased rapidly among scientists, policy makers, and the general public over the past decade (Nordhaus 1992, 2007, IPCC 1996, 2001, 2007). There is an increasing consensus that greenhouse gases should be curbed by international cooperation. However, the very high cost of completely eliminating greenhouse gas emissions suggests that mitigation policy should only slow climate change, not completely halt it at least in the near term (Nordhaus 1992, 2007). Consequently, even with an efficient international mitigation policy, global warming is likely to continue for several decades if not the rest of the century. Communities around the world should consequently be prepared to adapt to climate change. This is especially urgent for farmers who so clearly depend on the climate for their livelihood. Adaptation is especially urgent for low latitude developing country farmers who are expected to bear the brunt of climate change impacts (Mendelsohn et al., 2006). Agriculture in developing countries is one of the most vulnerable sectors of the global economy to climate change (Rosenzweig and Parry 1994, Kurukulasuriya et al 2006; Seo and Mendelsohn 2008c). Farmers will be especially hard hit if they do not adjust at all to new climates (Mendelsohn et al. 1994, Rosenzweig and Hillel 1998; Reilly et al. 1996).

Recent empirical studies indicate that farmers have already adapted to the existing climates that they face by choosing crops or livestock or irrigation (Kurukulasuriya and Mendelsohn 2007, 2008; Nhemachena and Hassan 2007; Seo and Mendelsohn 2008a, 2008b) ideal for their current climate. Farmers currently choose their crops or livestock or some mix of them to match their climate. It therefore follows that farmers are likely to select new crops and livestock as climate changes, at least in the long run. By studying adaptation, researchers can help farmers and policy makers identify efficient adaptations, adaptations that will maximize future income in new climate conditions.

Existing adaptation studies suggest that farmers should take different adaptation measures depending on their initial climate conditions. For example, a farmer in a wet location would choose vegetables more often than a farmer in a dry location would do. Farmers may also choose not to irrigate given that sufficient rainfall is available to support cultivation. However, these studies focused on the possibility of farmers to adapt to climate change but did not provide differential adaptation strategies specific to a certain zone. This information is crucial to the 2
farmers and policy makers who are interested in making adjustments in anticipation of future climate changes because continental scale adaptation measures would be misleading due to a wide variety of agro-economic conditions across the continent. The purpose of this paper is to provide differential adaptation measures suitable for each location across the landscape. We make use of the Food and Agriculture Organization’s (FAO) typology of Agro-Ecological Zones (AEZs) of Africa. Specifically, we focus on the choice of crops and irrigation in African cropland by 16 AEZs. The results of this analysis are then extrapolated from the sample of farms explored in this study to all of Africa using the AEZ classification of farms.

We begin by analyzing the choice of crops and irrigation as a function of climate and other control variables using a sample of over 9000 farmers from 11 countries in Africa who grow crops. We then use the FAO classification of African cropland into 16 AEZs to examine AEZ specific adaptation strategies. We use these zone specific adaptation strategies to see how adaptations would be applied across Africa.

The next section develops a simple theoretical model of crop and irrigation choice. We use a logit to explain irrigation choice and a multinomial logit to examine crop choice. In the following section, we describe the data used in this paper which is based on GEF/World Bank project in Africa and the FAO classification of Agro-Ecological Zones. In the rest of the paper, we present empirical results and simulation results of the impacts of climate change on these decisions based on two climate models. We conclude the paper with a summary of key results and a discussion of relevant policy insights.

# **2. Economic Theory**

Farmers are observed to make many management decisions on their farms. We assume that they make these choices to maximize profit. Through generations of learning by doing, most farmers know what choices work best on their farms. With changing conditions, of course, farmers must determine how to adapt, how to change these choices. Farmers are commonly observed adjusting to changes in government policy, market prices, availability of new varieties, and changes in access as these changes occur. This paper does not address the short term problems farmers face keeping up with rapidly changing conditions. Rather, we focus on long-term adaptations that farmers make after they have had time to learn about the new conditions and adjust to them.

3
In this paper, we focus on two important decisions by crop farmers: whether to irrigate or not, and which crops to grow. Let the profit associated with irrigation in a specific AEZ ( _w_ ) be written in the following form: where _Z_ is a vector of exogenous characteristics of the farm and characteristics of the farmer _._ The subscript j=1 refers to irrigated farms and j=0 to rainfed farms. The subscript w refers to the AEZs. The farmer will choose to have irrigation if: Assuming that the cumulative distribution of the error term is a logistic function, the choice of whether or not to establish irrigation system can be estimated with a standard logit model.

Modeling the choice of crops is slightly more involved technically since the choice set includes more than two alternatives. Additionally, some farmers can choose a combination of different crops whereas other farmers select only one crop. To include all combinations of crops as a discrete choice is not feasible since African farmers report more than 50 individual crops. In this study, we examine all the combination of crops that appear in significantly large numbers of farms in the sample (Seo and Mendelsohn 2008a, Kurukulasuriya and Mendelsohn 2007). The majority of farms have a single crop or a combination of two crops in our sample.

Let the profit from raising a specific crop or a combination of crops for a farm in AEZ _w_ be written in the following form: where _Z_ is a vector of all the independent variables that are appropriate for the explanation of farm profits _._ For example, Z could include climate, soils, water availability, access variables, electricity provision, household size, education of the farmer, and crop prices. The subscript n refers to the n-th farm in the sample, j refers to a crop or a combination of crops, and w refers to Agro-Ecological Zones at which the farm is located. Note that the farmer chooses crop j, but he does not choose AEZ w. The profit function in equation 3 is composed of two components: the observable component _V_ and an error term ε. The error term is not known to the researcher but may be known to the farmers. The error term is known up to its cumulative distribution.

4
The decision of a farmer who is located in AEZ _w_ is to choose one crop from the many alternative crops that is most profitable to him given the external conditions, which can be written succinctly as follows: arg max _j_ Suppressing subscript n and _w_ for convenience of the discussion for the moment, the farmer will choose crop j over all other crops if:

> The probability<sup>_P_</sup> _j_<sup>for crop j to be chosen is then</sup>

The probability for the n-th farm is calculated by integrating the appropriate indicator function as follows: where _I_ is the indicator function and _f_ is the probability density function of the error term. If the density _f_ follows an identical and independent Type I Extreme Value distribution and the profit can be written linearly in the parameters, then the probability can be calculated by successive integration of the above density function as which gives the probability of crop j to be chosen among J crops (McFadden 1981). For each AEZ _w_ , the marginal effect of climate change on the probability can be obtained by differentiating Equation (8): 5
The coefficients of the choice model γ and α are not dependent on the AEZ. However, the marginal impact of climate on the probability of selecting a crop depends on the climate conditions in each AEZ and so will vary by AEZ.

# **3. Description of Data**

A typology of AEZs was developed by the FAO as a mechanism to classify the growing potential of land using the length of the growing season (FAO 1978). The growing season is defined as the period where precipitation and stored soil moisture is greater than half of the evapotranspiration. 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).

Figure 1 maps AEZs across Africa. AEZs are classified by climate, soils, and altitude. They are divided into five zones depending upon the length of the growing period: semi-arid, dry savannah, moist savannah, sub-humid, and humid forest. Each of these five zones is again divided into three zones depending upon elevation: lowland, mid-elevation, and high elevation. The remaining AEZ is desert. The Sahara desert occupies a vast amount of area in the north. There is also a desert in the south-western edge of the continent. South of the Sahara desert is semi-arid zones followed by dryland savannah, moist savannah, and humid forest. In central Africa around Cameroon, it is mostly humid forest in high elevation with high rainfall. This high-elevation humid forest turns into mid-elevation and then into dry savannah as it stretches east toward Kenya. South of the humid forest is moist savannah followed by dry savannah. The AEZs of South Africa are mostly moist savannah in the east, dry savannah in the center, and desert in the west.

The economic data for this study were collected by national teams as part of the GEF/World Bank project on climate change in Africa (Dinar et al 2008). The survey asked detailed questions on crops and livestock operations during the agricultural period of July 2002 to June 2003. The data were collected for each plot within a household and household level data were constructed from plot level data. In each country, districts were chosen to get a wide representation of farms across climate conditions in that country. In each chosen district, a survey was conducted of randomly selected farms. The sampling was clustered in villages to reduce sampling cost. A total 6
of 9,597 surveys were administered across the 11 countries in the study.

Data on climate were gathered from two sources (Mendelsohn et al. 2007). We relied on temperature data from satellites operated by the Department of Defense of the United States (Basist et al. 2001). The precipitation data came from the Africa Rainfall and Temperature Evaluation System (ARTES) (World Bank 2003). This dataset, created by the National Oceanic and Atmospheric Association’s Climate Prediction Center, is based on ground station measurements of precipitation.

Soil data were obtained from FAO (2003). The FAO data provide information about the major and minor soils in each location as well as slope and texture. Data concerning the hydrology were obtained from the results of an analysis of climate change impacts on African hydrology (Strzepek & McCluskey, 2006). Using a hydrological model for Africa, the authors calculated flow and runoff for each district in the surveyed countries. Data on elevation at the centroid of each district were obtained from the United States Geological Survey (USGS 2004). The USGS data is derived from a global digital elevation model with a horizontal grid spacing of 30 arc seconds (approximately one kilometer).

# **4. Empirical Results**

Tables 1, 2, and 3 summarize the data on irrigation and crop choice in Africa. About 25% of the farms in our sample irrigated their land. The irrigation clearly depends on where the farm is located. Farms in dry places such as dry savannah, semi-arid, and deserts are highly likely to irrigate their land whereas farms in sub-humid and humid forest, especially in the lowland, do not.

Table 2 summarizes eight crops or combinations of crops that are chosen most often by African farmers. For Africa as a whole, maize (32%), millet (5%), wheat (7%), and fruits and vegetables (10%) are chosen widely as a single crop to manage. Most farms choose a mix of some crops: fruit/vegetables and maize (17%), maize and ground nuts (14%), millet and ground nuts (11%), millet and sorghum (6%). The crop shares reflect the percent of farms that select this particular crop or crop combination.

The distribution of crops chosen differs widely across different AEZs, which is shown in Table 3. Maize is chosen very frequently in mid elevation dry or moist savannah, but by fewer farms in 7
the deserts, high elevation dry savannah, or high elevation semi-arid farms choose maize. Fruits and vegetables with maize or without maize are chosen very often by the farms in the humid forests regardless of the elevation of the farms. Wheat is the choice for many farms in high elevation or dry places including deserts. Millet is the choice of crop when the farm is located in dry places such as high elevation dry savannah, lowland dry savannah, or mid elevation semiarid AEZs. Ground nut and maize combination is chosen most often in mid elevation moist savannah. Ground nut and millet combination is chosen most often in lowland dry savannah.

Tables 1, 2, and 3 clearly suggest that both irrigation and crop choice vary with AEZs<sup>7</sup> . To test whether there is a statistical relationship between these choices and climate, we run in Table 4 a binary choice model of whether to choose irrigation or not over climate variables and controls. Control variables in Table 3 include a set of soils, water availability, and household characteristics. The choice of irrigation clearly depends on dominant soil types. When soil Arenasols is dominant in the district, farmers tend to choose rainfed agriculture. On the other hand, when the soil is Cambisols or Planasols, they irrigate more often. Large farms are more likely to irrigate, so are farms with electricity. Irrigation requires a substantial capital investment in many cases and electricity as well. Farms in high elevation tend to irrigate less often. The amount of water flowing into the districts does not affect the choice. The variables of most concern to us are climate variables. The model is specified as a quadratic function of summer and winter temperature and precipitation. All four seasons were not relevant for modeling irrigation choice. Many climate variables are significant, though weakly, indicating that irrigation decision depends on the climate where the farm is located<sup>8</sup> . The Likelihood Ratio test indicates the overall model is very significant.

To understand what might happen to irrigation adoption when the current climate is disturbed, we calculate marginal effects of climate change on the probability to choose irrigation at the mean climate of the corresponding AEZs in Table 5. As temperature increases, farmers tend to irrigate more frequently. Irrigation is clearly an adaptation strategy to warming. When precipitation increases, they tend to irrigate less often and resort to natural rainfall more often. However, these regional results do not apply to all AEZs. Farms in the deserts reduce irrigation when temperature increases. Similarly, when precipitation increases, farms close to the deserts

> 7 Note that we do not use AEZs as independent variables in which case climate variables are correlated with.

> 8 This implies climate change will shift the current AEZ and irrigation decisions.

8
# increase irrigation.

The second analysis of cropland farm adaptations is crop switching. Table 6 shows seven sets of regressions, setting wheat as base case, from multinomial logit model of crop choice. The choice set includes fruits and vegetables, maize, millet, and wheat as a single crop, and a combination of fruits/vegetables and maize, a combination of maize and ground nuts, a combination of millet and ground nuts, and a combination of millet and sorghum. The choice of one crop from the eight available crops was run against climate variables, soils, water availability, household characteristics, and crop prices. Soils are significant factors to the decision of crops to plant. When soil is Nitosols, farmers tend to choose the seven crops more often in contrast to wheat. When soil is Gleysols, it reduces the chance of millet being chosen. Large farms tend to avoid millet. Farms in high elevation tend to choose millet for a single crop or in combination with sorghum or ground nuts. Family size does not matter in the choice of crops. Farms with electricity tend to choose fruits and vegetables or millet less often. The amount of water flowing in the district affects the crop choice significantly. When summer flow is high, it reduces the choice of the seven crops while when fall flow is high it increases the choice of these crops in contrast to wheat. Crop choices depend on crop prices of maize, millet, and wheat. The prices of ground nuts or sorghum are not significant. When maize or millet price is higher, farmers tend to reduce the planting of these seven crops while when wheat price is higher, they increase the planting of these crops<sup>9</sup> .

The regressions confirm that the choices of all the crops are sensitive to climate. In contrast to irrigation choice, most of the seasonal climate parameters are significant. All four seasons are relevant in modeling crop choice in part because it involves many alternatives in the choice set than the binary choice of irrigation. Most of the quadratic terms are also significant indicating second order relationship of the choice of each crop to the corresponding climate variables.

However, due to its complex specification in Table 6, it is difficult to interpret these results in terms of climate vulnerability. In Table 7, we calculate marginal effects of an annual increase in temperature and an annual increase in precipitation evaluated at the mean climate for the sample of farms that choose each crop combination. If temperature increases slightly, farmers tend to

> 9 The current model is only concerned with supply side. But it is likely that demand conditions such as preference changes affect the future crop choice.

9
move away from wheat, maize, millet-ground nuts, or millet-sorghum. Instead, they choose fruits-vegetables, fruits-vegetables-maize, millet, or ground nuts-maize. If precipitation increases, farmers move away from groundnut-millet and maize towards fruits-vegetables-maize, maizegroundnut, and millet.

These behavioral changes at the African level, however, do not hold for all Agro-Ecological Zones. Although the choice of fruits-vegetables is expected to increase when rainfall increases, it is reduced in dry zones such as deserts, lowland dry savannah, and lowland semi-arid zone. Similarly, millet will decrease even though temperature increases if the farm is located in lowland moist savannah or lowland sub-humid AEZs. Maize will decrease with higher temperature, but the exceptions are deserts and high elevation dry savannah.

# **5. Forecasting Climate Change Impacts on Irrigation and Crop Choices**

As climate change unfolds over the coming century, farmers are likely to adapt to it by switching crops or irrigating their land. In this section, we use the results from the previous section to predict how farmers might adapt in the future. We explore how adaptations might be different depending on the climate scenario. We also explore how they might be different depending on the AEZ of each farm. In practice, future farm practices will also depend on economic development, technological changes, and price changes. We do not examine these other important influences but rather focus simply on the role of climate change. We are predicting how climate influences farm choice, not each farmer’s actual future choices. We assume all other factors remain unchanged and examine the effects of climate change alone.

We examine a set of climate change scenarios that are consistent with the range of likely outcomes predicted in the most recent Intergovernmental Panel on Climate Change report (IPCC 2007). Specifically, we use the A1 scenarios from the following two models: CCC (Canadian Climate Centre) (Boer et al. 2000) and PCM (Parallel Climate Model) (Washington et al. 2000).

Table 8 presents the mean temperature and rainfall predicted by the two models for the years 2020 and 2100. In Africa in 2100, PCM predicts a 2°C increase and CCC a 6°C increase in temperature. Rainfall predictions vary. PCM predicts a 10% increase in rainfall in Africa and CCC a 10% decrease by 2100. Even though the mean rainfall in Africa is predicted to increase/decrease depending on the scenario, there is also substantial variation in rainfall across countries. Examining the path of climate change over time reveals that temperatures are 10
predicted to increase over time for all two models. Precipitation predictions, however, vary across time for Africa: CCC predicts declining precipitation whereas PCM predicts a slight increase. However, it should be noted that predicted changes vary slightly for individual countries and regions.

# **_5.1 Analysis for Africa_**

We first present the predicted changes in the probabilities to choose irrigation for Africa as a whole in Table 9. In 2020, under the relatively hot CCC scenarios, more farmers are expected to irrigate their land. On the other hand, if precipitation increases as in PCM, farmers tend to irrigate less often and rely on natural rainfall. By 2100, farmers increase irrigation by 15% under the CCC scenario, but reduce it by 2% under the PCM scenario.

Table 10a describes simulation results of crop choice by 2020 for Africa. Under CCC, farmers choose fruits/-maize, millet, or millet- sorghum more often while they choose the other crops less often. The results from PCM are quite different. They increase fruits/ vegetables, millet, or millet-sorghum while they reduce the other crops. By the end of this century, as shown in Table 10b, farmers increase fruits/vegetables with maize or without maize substantially as well as millet and sorghum. On the other hand they reduce maize substantially under CCC. Under PCM, they choose fruits/vegetables or millet at the sacrifice of maize.

# **_5.2 Analysis by Agro-Ecological Zones_**

Which crops to grow or whether to irrigate is certainly dependent on the current AEZ of the farm. Farmers cannot simply follow the advice which is deemed appropriate for Africa as a whole. They must determine what is the most appropriate response to climate change in their AEZ.

As we can see in Table 9, African farmers are better off by adopting more irrigation under the CCC 2020 scenario. However, farms in the deserts, lowland humid forest, or mid elevation humid forest are better off by reducing irrigation in the same scenario. Under PCM 2100, farmers tend to irrigate less often due to higher precipitation, but the farms in the deserts are still better off by irrigating more often.

In Figure 2, we extrapolate our results to all of Africa using AEZ information. The figure maps changes in the probability to irrigate the land. Under the CCC scenario (Left), farmers will increase irrigation substantially due to higher temperature expected in this scenario. The 11
expected increase is lower in the deserts while it is higher in West Africa, Central Africa, North of the Sahara desert, and East Africa. Under the PCM scenario (Right), on the other hand, farmers reduce irrigation overall except for the desert areas. The reduction is largest in the lowland and wet zones.

Crop choice would also vary widely across the AEZs. Table 10a describes which crops would likely be chosen in each AEZ. Although the combination of fruits/ vegetables and maize is expected to increase in Africa under CCC 2020, many AEZs would see the decline of this combination, especially in the high elevation semi-arid AEZ. Similarly, maize is likely to decline Africa wide under CCC 2020, but it will increase in some AEZs such as high elevation dry savannah.

By 2100, as shown in Table 10b, these differential responses continue to magnify. Fruits and vegetables increase Africa wide under the CCC scenario, but they are expected to decline substantially in the lowland semi-arid AEZ. Under the PCM scenario, millet increases overall, but it declines substantially in the lowland dry savannah and lowland semi-arid AEZs. Not all the crops exhibit differential responses across the AEZs. For example, maize is chosen less often across all AEZs under both climate scenarios.

In Figures 3 and 4, we extrapolate the results for two crops, maize and fruits/vegetables, to all of Africa using the AEZ information. Maize will be reduced across all of Africa due to higher temperature, but farms in the lowland wet zones are hit the hardest under CCC. Under PCM, however, farms in the high elevation are the most affected. In the case of fruits and vegetables, the variation across the AEZs is much larger. Under CCC, it will increase substantially in high elevations, lowland savannahs, and in Southern Africa. Farms in the deserts or lowland wet zones will lose this type of crop. The probability distribution under PCM will be similar to that under CCC with some areas seeing larger increases while other areas seeing larger losses.

# **6. Conclusion and Policy Implications**

This paper quantifies differential farm adaptations taken by cropland farmers in Africa in 16 Agro-Ecological Zones. We rely on the economic data from the recently completed GEF/World Bank project and the FAO classification of the AEZs of Africa. We focus on two important farm adaptation decisions in cropland: irrigation and crop switching. Simple logit and multinomial logit models are used to examine the sensitivities of these decisions to climate.

12
We find that farmers make irrigation decisions to match the current AEZ in which the farm is located. Comparing choices across climates, at the African continental level, farmers tend to irrigate more frequently in warmer climates. In wetter climates, they tend to irrigate less often and resort to natural rainfall more often. However, these regional results do not apply to all AEZs. Farms in the deserts reduce irrigation when temperature increases. Similarly, when precipitation increases, farms close to the deserts and dry areas increase irrigation.

Crop choices also depend on the current AEZs. At the African continental level, in warmer places, farmers tend to choose wheat, maize, millet-ground nuts, or millet-sorghum less often and they instead choose fruits/vegetables, fruits/vegetables-maize, millet, or ground nuts-maize more often. In wetter places, farmers choose fruits/vegetables or millet more often at the expense of wheat or maize. However, the responses differ substantially across the AEZs. Although fruits/vegetables are expected to increase in wetter places, they decline in dry zones such as deserts, lowland dry savannah, and lowland semi-arid zone. Similarly, millet will decrease even though temperature increases if the farm is located in lowland moist savannah or lowland subhumid AEZs. Maize will decrease with higher temperature, but not in deserts and high elevation dry savannah. However, farmers across Africa are less likely to choose wheat in warmer places. .

Based on the estimated parameters from the sample, we simulated how these farm choices might change as climate changes. The results indicate that by 2100, African farmers will adopt more irrigation under the very hot and dry CCC scenario but less irrigation under the mild and moist PCM scenario. However, farms in the deserts, lowland humid forest, or mid elevation humid forest reduce irrigation even under CCC. Similarly, farms in the deserts irrigate more even under PCM.

The distribution of crops across Africa in the future will be different depending upon which climate scenario occurs. Fruits and vegetables will increase Africa wide under CCC, but decline substantially in the lowland semi-arid AEZ. Under PCM, millet will increase overall, but it will decline substantially in the lowland dry savannah and lowland semi-arid AEZs. However, not all the crops exhibit differential responses across the AEZs. For example, maize is chosen less often across all AEZs under all the climate scenarios. Wheat will also decline across Africa as climate warms.

Policy makers should take note of the spatial variation of desired adaptations across the AEZs.

13
First, AEZ-specific policies could be designed for each AEZ. These polices could be the same across many African countries. Second, adaptations can be designed to match climate change over time as well. Policy makers can prepare the needed infrastructure, institutions, and budgets both across space and across time.

Although there is a need for an African-wide policy, it probably should not be a blanket policy that treats every location alike. Rather, the ideal policy would be a quilt like arrangement designed around AEZs. Because AEZs do not recognize political boundaries, they become transboundary in nature. As such, experience gained by one country in a ‘shared’ AEZ can be used by a neighboring country as well. This fact calls for cooperation among countries that share similar AEZs. Institutions can be developed that transfer technologies, experience, and data. All these may work to the benefit of farmers residing and working in Africa who share a common AEZ.

14
# **References**

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- Boer, G., G. Flato, and D. Ramsden, 2000. “A transient climate change simulation with greenhouse gas and aerosol forcing: projected climate for the 21<sup>st</sup> century”, _Climate Dynamics_ **16** , 427-450.

- Dinar, A., R. Hassan, R. Mendelsohn, and J. Benhin, 2008. Climate Change and Agriculture in Africa: Impact Assessment and Adaptation Strategies, London: EarthScan (forthcoming).

- Food and Agriculture Organization (FAO), 1978. “Report on Agro-Ecological Zones; Volume 1: Methodology and Results for Africa” Rome.

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- IPCC, 2000. Special Report on Emissions Scenarios. Intergovernmental Panel on Climate Change. Cambridge University Press: Cambridge, UK.

- IPCC, 2007. State of the Science Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change,.Cambridge University Press, Cambridge, UK

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- Kurukulasuriya, P. and R. Mendelsohn, 2007. “Modeling Endogenous Irrigation: The Impact Of Climate Change On Farmers In Africa”. World Bank Policy Research Working Paper 4278.

- Kurukulasuriya, P. and R. Mendelsohn. 2008. “Crop Switching as an Adaptation Strategy to Climate Change” African Journal Agriculture and Resource Economics (forthcoming).

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McFadden, D. 1981. “Econometric Models of Probabilistic Choice”, in D. McFadden, _Structural Analysis of Discrete Data and Econometric Applications_ , Cambridge, MIT Press.

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- Mendelsohn, Robert, Pradeep Kurukulasuriya, Alan Basist, Felix Kogan, and Claude Williams, 2007. “Measuring Climate Change Impacts with Satellite versus Weather Station Data.”,

15
_Climatic Change_ 81: 71-83.

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- Nhemachena, C. and R Hassan, 2007., “Determinants of climate adaptation strategies of African farmers: Multinomial choice analysis, Draft Report, CEEPA, University of Pretoria

- Nordhaus, W. D., 1992. “An Optimal Transition Path for Controlling Greenhouse Gases”, _Science_ 258:1315-1319.

- Nordhaus, W., 2007. “To Tax or Not to Tax: Alternative Approaches to Slow Global Warming”, _Review of Environmental Economics and Policy_ 1 (1):22-44.

- Reilly, J., et al., 1996. “Agriculture in a Changing Climate: Impacts and Adaptations” in IPCC (Intergovernmental Panel on Climate Change), Watson, R., M. Zinyowera, R. Moss, and D. Dokken (eds.) _Climate Change 1995: Impacts, Adaptations, and Mitigation of Climate Change: Scientific-Technical Analyses,_ Cambridge University Press: Cambridge p427468.

- Rosenzweig, C., and D. Hillel, 1998. Climate change and the global harvest: potential impacts of the greenhouse effect on agriculture. Oxford University Press, New York, USA.

- Schenkler, W., Hanemann, M., and Fisher, A., 2005. “Will US Agriculture Really Benefit From Global Warming?  Accounting for Irrigation in the Hedonic Approach” _American Economic Review_ **95:** 395-406

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- Seo, S. N. and R. Mendelsohn, 2008b. “An Analysis of Crop Choice: Adapting to Climate Change in South American Farms”, _Ecological Economics_ (forthcoming).

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USGS National Mapping Division, EROS Data Centre.

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16
# Table 1: Percentage of Irrigated Farms by AEZs

|AEZ|Description|Number|Percentage|
|---|---|---|---|
|Africa||2147|23.59|
|AEZ 1|Desert|821|85.61|
|AEZ 2|High elevation dry savanna|93|79.49|
|AEZ 3|High elevation humid forest|317|33.69|
|AEZ 4|High elevation moist savannah|240|63.66|
|AEZ 5|High elevation semi-arid|67|91.78|
|AEZ 6|High elevation sub-humid|491|61.38|
|AEZ 7|Lowland dry savannah|509|15.42|
|AEZ 8|Lowland humid forest|103|8.29|
|AEZ 9|Lowland moist Savannah|417|17.5|
|AEZ 10|Lowland semi-arid|288|41.2|
|AEZ 11|Lowland sub-humid|159|12.36|
|AEZ 12|Mid-elevation dry savannah|391|29.09|
|AEZ 13|Mid-elevation humid forest|226|22.97|
|AEZ 14|Mid-elevation moist savannah|636|24.32|
|AEZ 15|Mid-elevation semi-arid|97|72.39|
|AEZ 16|Mid-elevation sub-humid|498|47.93|

17
Table 2: Percentage of Farms Adopting Each Crop Choice in Africa

|Crops|Percentage|Crops|Percentage|
|---|---|---|---|
|Fruits/Vegetables||Maize and ground nut||
|and Maize|16.62||13.55|
|Fruits/Vegetables|9.94|Millet|4.79|
|Ground nut and||Millet and Sorghum||
|Millet|10.86||6.11|
|Maize|31.67|Wheat|6.45|

18
Table 3: Percentage of Farms Adopting Each Crop Choice by AEZs

|AEZ|Percentage|AEZ|Percentage|
|---|---|---|---|
|||AEZ 9 Lowland moist||
|AEZ 1 Desert||Savannah||
|Fruits/Vegetables and Maize|10.13|Fruits/Vegetables and Maize|<br>15.89|
|Fruits/Vegetables|26.58|Fruits/Vegetables|5.66|
|Ground nut and Millet||Ground nut and Millet|11.43|
|Maize|14.87|Maize|28.07|
|Maize and ground nut||Maize and ground nut|16.1|
|Millet||Millet|5.01|
|Millet and Sorghum|0.95|Millet and Sorghum|11.53|
|Wheat|47.47|Wheat|6.31|
|AEZ 2 High elevation dry<br>savanna||AEZ 10 Lowland semi-arid||
|Fruits/Vegetables and Maize||Fruits/Vegetables and Maize|<br>12.24|
|Fruits/Vegetables|12.5|Fruits/Vegetables|27|
|Ground nut and Millet||Ground nut and Millet|3.8|
|Maize|2.08|Maize|11.39|
|Maize and ground nut||Maize and ground nut|0.42|
|Millet|14.58|Millet|5.91|
|Millet and Sorghum|22.92|Millet and Sorghum|5.06|
|Wheat|47.92|Wheat|34.18|
|AEZ 3 High elevation humid<br>forest||AEZ 11 Lowland sub-humid||
|Fruits/Vegetables and Maize|41.93|Fruits/Vegetables and Maize|<br>24.5|
|Fruits/Vegetables|7.59|Fruits/Vegetables|14.18|
|Ground nut and Millet||Ground nut and Millet|4.44|
|Maize|29.91|Maize|35.53|
|Maize and ground nut|1.9|Maize and ground nut|17.05|
|Millet|0.16|Millet|1.72|
|Millet and Sorghum|5.85|Millet and Sorghum|2.15|
|Wheat|12.66|Wheat|0.43|
|AEZ 4 High elevation moist<br>savannah||AEZ 12 Mid-elevation dry<br>savannah||
|Fruits/Vegetables and Maize|24.34|Fruits/Vegetables and Maize|<br>6.46|
|Fruits/Vegetables|2.65|Fruits/Vegetables|3.17|
|Ground nut and Millet|1.33|Ground nut and Millet|0.35|
|Maize|26.11|Maize|46.01|
|Maize and ground nut|3.1|Maize and ground nut|22.3|
|Millet|5.75|Millet|1.29|
|Millet and Sorghum|12.39|Millet and Sorghum|9.51|
|Wheat|24.34|Wheat|10.92|

19
|||AEZ 13 Mid-elevation humid||
|---|---|---|---|
|AEZ 5 High elevation semi-arid||forest||
|Fruits/Vegetables and Maize||Fruits/Vegetables and Maize|36.71|
|Fruits/Vegetables|38.89|Fruits/Vegetables|13.72|
|Ground nut and Millet||Ground nut and Millet||
|Maize||Maize|32.25|
|Maize and ground nut||Maize and ground nut|6.86|
|Millet||Millet|0.17|
|Millet and Sorghum|55.56|Millet and Sorghum|2.06|
|Wheat|5.56|Wheat|8.23|
|AEZ 6 High elevation sub-<br>humid||AEZ 14 Mid-elevation moist<br>savannah||
|Fruits/Vegetables and Maize|26.08|Fruits/Vegetables and Maize|14.82|
|Fruits/Vegetables|7.11|Fruits/Vegetables|3.33|
|Ground nut and Millet||Ground nut and Millet|0.52|
|Maize|29.53|Maize|44.8|
|Maize and ground nut|0.43|Maize and ground nut|22|
|Millet|1.08|Millet|1.49|
|Millet and Sorghum|16.16|Millet and Sorghum|7.58|
|Wheat|19.61|Wheat|5.46|
|||AEZ 15 Mid-elevation semi-||
|AEZ 7 Lowland dry savannah||arid||
|Fruits/Vegetables and Maize|6.17|Fruits/Vegetables and Maize|3.33|
|Fruits/Vegetables|6.1|Fruits/Vegetables|23.33|
|Ground nut and Millet|25.88|Ground nut and Millet||
|Maize|27.57|Maize|6.67|
|Maize and ground nut|11.25|Maize and ground nut||
|Millet|10.64|Millet|23.33|
|Millet and Sorghum|6.91|Millet and Sorghum|36.67|
|Wheat|5.49|Wheat|6.67|
|||AEZ 16 Mid-elevation sub-||
|AEZ 8 Lowland humid forest||humid||
|Fruits/Vegetables and Maize|35.08|Fruits/Vegetables and Maize|35.9|
|Fruits/Vegetables|23.43|Fruits/Vegetables|8.06|
|Ground nut and Millet||Ground nut and Millet|0.35|
|Maize|33.42|Maize|23.29|
|Maize and ground nut|8.07|Maize and ground nut|3.15|
|Millet||Millet|1.93|
|Millet and Sorghum||Millet and Sorghum|10.51|
|Wheat||Wheat|16.81|

20
# Table 4: Logit Model of Irrigation choice

|Variable|Estimate<br>Chi|sq Statistic<br>P value|
|---|---|---|
|Intercept|0.9447|1.52<br>0.2169|
|Summer Temperature<br>|-0.0847|2.73<br>0.0983|
|Summer Temperature<sup>2</sup>|-0.00015|0.02<br>0.8866|
|Summer Precipitation<br>|-0.00481|9.38<br>0.0022|
|Summer Precipitation<sup>2</sup>|5.48E-06|0.95<br>0.3287|
|Winter Temperature<br>|-0.0572|1.12<br>0.2907|
|Winter Temperature<sup>2</sup>|0.00489|10.69<br>0.0011|
|Winter Precipitation<br>|-0.00423|3.14<br>0.0762|
|Winter Precipitation<sup>2</sup>|-3.77E-06|0.05<br>0.8175|
|Flow summer|-0.0373|0.25<br>0.6203|
|Flow winter|-0.4994|0.71<br>0.3978|
|Flow spring|0.5799|1.94<br>0.1634|
|Flow fall|0.0752|0.46<br>0.4991|
|Log farm land|0.1195|30.64<br><.0001|
|Log elev|-0.0474|4.71<br>0.03|
|Log household size|0.5127|112.3<br><.0001|
|Electricity|0.0317|44.3<br><.0001|
|Soil Cambisols|1.1152|16.33<br><.0001|
|Soil Lithosols|0.1568|0.58<br>0.4452|
|Soil Arenasols|-1.2377|108.38<br><.0001|
|Soil Planasols|0.9985|5.98<br>0.0145|
|N=9102|||
|LR=1231.5 (P<0.0001)|||

21
Table 5: Marginal Climate Effects on Probability of Irrigation (%)

||Africa|AEZ1|AEZ2|AEZ3|AEZ4|AEZ5|
|---|---|---|---|---|---|---|
|Base<br>|0.499|0.561|0.488|0.484|0.467|0.498|
|T (<sup>◦</sup>C)|0.011|-0.005|0.011|0.007|0.007|0.007|
|P (mm/mo)|-0.001|0.000|-0.001|-0.002|-0.001|-0.001|
||AEZ6|AEZ7|AEZ8|AEZ9|AEZ10|AEZ11|
|Base|0.475|0.476|0.517|0.513|0.500|0.558|
|T (<sup>◦</sup>C)|0.006|0.017|0.012|0.015|0.018|0.014|
|P (mm/mo)|-0.001|-0.001|-0.002|-0.001|-0.001|-0.001|
||AEZ12|AEZ13|AEZ14|AEZ15|AEZ16||
|Base|0.428|0.466|0.419|0.488|0.489||
|T (<sup>◦</sup>C)|0.007|0.008|0.006|0.006|0.008||
|P (mm/mo)|-0.001|-0.002|-0.001|-0.001|-0.001||

22
# Table 6: Multinomial Logit Model of Crop Choice

||Fruits/Vegetables<br>Maize|and|Fruits/Vegetables||
|---|---|---|---|---|
||Coefficient|Chi-sq|Coefficient|Chi-sq|
|Intercept|-5.6471|0.38|-3.6519|0.15|
|Summer Temperature<br>|-9.4634|33.88|-8.6524|28.7|
|Summer Temperature<sup>2</sup>|0.1398|16.34|0.1258|13.54|
|Summer Precipitation|-0.0411|6.56|-0.0345|3.68|
|Summer Precipitation<sup>2</sup>|0.000081|2.52|0.000021|0.12|
|Winter Temperature<br>|-4.1439|24.31|-3.2764|16.38|
|Winter Temperature<sup>2</sup>|0.1097|19.04|0.0823|11.27|
|Winter Precipitation<br>|-0.3908|40.78|-0.3795|38.63|
|Winter Precipitation<sup>2</sup>|0.00395|19.83|0.00392|19.56|
|Spring Temperature|5.1551|33.77|3.2715|15.5|
|Spring Temperature<sup>2</sup>|-0.1183|27.52|-0.0735|11.65|
|Spring Precipitation<br>|0.1082|33.05|0.0914|19.52|
|Spring Precipitation<sup>2</sup>|-0.00011|0.00|-0.00005|0.88|
|Fall Temperature<br>|8.7806|21.63|8.9192|22.08|
|Fall Temperature<sup>2</sup>|-0.1217|8.04|-0.1262|8.65|
|Fall Precipitation|0.0439|13.03|0.0451|9.39|
|<br>Fall Precipitation<sup>2</sup>|-0.00005|0.00|-0.00002|0.3|
|Flow summer|-5.8743|12.88|-8.7044|23.7|
|Flow winter|-8.5132|2.03|-16.9561|7.86|
|Flow spring|-1.2433|0.07|4.4689|0.85|
|Flow fall|6.6689|14.79|9.4867|29.27|
|Log farm land|0.0048|0.00|0.0115|0.01|
|<br>Log elev|0.3048|3.02|0.2013|1.48|
|Log household size|0.0317|0.01|-0.0978|0.13|
|Electricity|-0.4545|3.61|-0.7801|10.37|
|Soil Gleysols|0.6272|0.15|-0.1618|0.01|
|Soil Nitosols|6.454|34.63|4.6676|19.13|
|Maize price|-4.1686|3.76|-2.3409|1.3|
|<br>Ground nuts price|0.421|0.13|-1.5366|1.6|
|Millet price|-11.2749|8.19|-11.277|7.93|
|Wheat price|12.1774|21.39|10.3714|14.2|
|Sorghum price|1.1661|0.28|3.3064|2.25|

Note: N=4882, LR test= P<0.001 23
# Table 6: Continued.

||Ground nuts and|millet<br>M|aize||
|---|---|---|---|---|
||Coefficient|Chi-sq|Coefficient|Chi-sq|
|Intercept|-66.4997|10.89|-5.9205|0.44|
|Summer Temperature<br>|-5.9788|6.18|-9.126|33.31|
|Summer Temperature<sup>2</sup>|0.1164|6.27|0.1418|17.86|
|Summer Precipitation<br>|-0.3102|62.81|-0.00013|0|
|Summer Precipitation<sup>2</sup>|0.000895|41.34|-0.0001|3.73|
|Winter Temperature<br>|-11.8131|21.09|-2.7359|11.63|
|Winter Temperature<sup>2</sup>|0.2822|19.58|0.0577|5.71|
|Winter Precipitation|-0.3872|21.13|-0.34|31.14|
|Winter Precipitation<sup>2</sup>|0.00357|14.11|0.00367|17.13|
|Spring Temperature<br>|15.911|17.04|4.0045|23.12|
|Spring Temperature<sup>2</sup>|-0.3033|16.17|-0.0798|13.96|
|Spring Precipitation|0.0562|1.16|0.0693|13.16|
|<br>Spring Precipitation<sup>2</sup>|0.00018|0.22|0.000054|1.83|
|Fall Temperature<br>|4.919|3.08|8.7403|22.41|
|Fall Temperature<sup>2</sup>|-0.114|3.84|-0.1354|10.47|
|Fall Precipitation<br>|0.3561|32.33|0.00945|0.61|
|Fall Precipitation<sup>2</sup>|-0.00125|28.42|0.000066|0.00|
|Flow summer|-6.1847|7.41|-6.6475|16.8|
|Flow winter|7.7337|0.51|-10.0751|2.91|
|Flow spring|-15.7417|1.53|-0.7136|0.02|
|Flow fall|4.9392|5.03|7.5618|19.47|
|Log farm land|0.1058|0.29|0.0289|0.07|
|Log elev|0.8685|10.52|0.2768|2.59|
|Log household size|0.0824|0.06|-0.2302|0.77|
|Electricity|-0.1665|0.31|-0.4764|4.08|
|Soil Gleysols|0.1712|0.01|1.5694|1|
|Soil Nitosols|4.7433|15.88|5.6247|27.14|
|Maize price|-19.787|23.41|-6.6727|10.03|
|Ground nuts price|-0.2425|0.02|-0.2859|0.07|
|Millet price|-7.2536|2.54|-13.1645|11.32|
|Wheat price|19.3352|18.05|13.6417|28.65|
|Sorghum price|-1.534|0.37|1.7167|0.65|

24
# Table 6: Continued.

||Maize and Gr|ound|||||
|---|---|---|---|---|---|---|
||Nut||Millet||Millet and So|rghum|
||Coefficient|Chisq|Coefficient|Chi-sq|Coefficient|Chi-sq|
|Intercept<br>|-23.0095|4.18|-43.5027|6.7|-15.515|2.41|
|Summer Temperature<br>|-10.6886|39.09|-5.0479|4.78|-10.1016|35.93|
|Summer Temperature<sup>2</sup>|0.1739|23.34|0.1184|7.06|0.1804|25.61|
|Summer Precipitation<br>|-0.0151|0.61|-0.2817|55.14|-0.0426|3.69|
|Summer Precipitation<sup>2</sup>|-8.98E-06|0.02|0.000836|42.87|0.0001|1.69|
|Winter Temperature<br>|-2.9099|9.47|-5.1111|5.21|-1.4776|2|
|Winter Temperature<sup>2</sup>|0.0401|2.09|0.1365|5.56|0.0196|0.42|
|Winter Precipitation<br>|-0.3084|24.74|-0.3066|14.99|-0.4306|46.11|
|Winter Precipitation<sup>2</sup>|0.00363|16.65|0.00282|8.8|0.00417|21.78|
|Spring Temperature<br>|2.8425|7.96|7.6047|5.76|3.125|7.44|
|Spring Temperature<sup>2</sup>|-0.0371|2.22|-0.1777|7.69|-0.0589|4.66|
|Spring Precipitation<br>|0.058|7.15|0.0626|2.43|0.1449|33.94|
|Spring Precipitation<sup>2</sup>|0.000088|2.34|0.000542|7.88|-0.00032|11.11|
|Fall Temperature<br>|12.761|34.27|4.1057|2.89|9.8931|24.92|
|Fall Temperature<sup>2</sup>|-0.2238|20.3|-0.0878|2.85|-0.1792|15.75|
|Fall Precipitation<br>|0.0426|7.48|0.3982|49.43|0.043|3.63|
|Fall Precipitation<sup>2</sup>|-0.00003|0.79|-0.00123|34.78|-0.00011|2.13|
|Flow summer|-6.0605|13.43|-4.5023|6.92|-7.4169|20.22|
|Flow winter|0.8522|0.02|2.6275|0.12|-17.6754|8.13|
|Flow spring|-10.7095|4.64|-18.7256|6.85|4.9707|1.06|
|Flow fall|6.0499|11.6|5.1426|7.43|9.0126|26.27|
|Log farm land|0.1071|0.89|-0.444|5.2|0.2179|2.93|
|Log elev|0.0945|0.24|1.4395|27.09|0.5663|6.61|
|Log household size|0.3825|1.89|-0.3592|1.13|0.2855|0.96|
|Electricity|-0.319|1.69|-0.0578|0.04|0.4211|2.15|
|Soil Gleysols|-0.1882|0.01|-6.4971|6.95|-0.22|0.02|
|Soil Nitosols|4.4139|16.29|5.2091|18.88|5.5647|24.2|
|Maize price|-7.1471|10.57|-24.3805|35.91|-4.8962|4.61|
|<br>Ground nuts price|-0.9233|0.64|-0.8272|0.29|-0.0757|0|
|Millet price|-7.8208|3.77|-2.4492|0.3|-7.7912|3.68|
|Wheat price|16.2668|38.32|21.8431|30.63|11.3867|18.28|
|Sorghum price|-0.0693|0|-4.5841|2.57|-0.4599|0.04|

25
Table 7: Marginal Climate Effects on Probability of Crop Choice by AEZs (%)

|Fruits/Vegetables|Africa<br>|AEZ1<br>|AEZ2<br>|AEZ3<br>|AEZ4<br>|AEZ5<br>|
|---|---|---|---|---|---|---|
|and Maize|0.1805|0.0729|0.0894|0.4491|0.2369|0.1505|
|T (<sup>◦</sup>C)|0.0172|0.0268|0.0219|0.0443|0.0255|0.0299|
|P (mm/mo)|0.0010|-0.0003|0.0005|0.0032|0.0019|0.0005|
|Fruits/Vegetables|0.0793|0.1366|0.0706|0.0812|0.0598|0.0802|
|T (<sup>◦</sup>C)|0.0115|0.0112|0.0223|0.0097|0.0098|0.0165|
|P (mm/mo)|0.0002|-0.0008|0.0003|0.0002|0.0002|0.0003|
|Ground nuts and<br>millet|0.1136|0.0001|0.0241|0.0001|0.0149|0.0001|
|T (<sup>◦</sup>C)|-0.0066|0.0000|0.0174|0.0000|0.0043|0.0002|
|P (mm/mo)|-0.0020|0.0000|-0.0014|0.0000|-0.0007|0.0000|
|Maize|0.3376|0.1015|0.3145|0.3470|0.4046|0.4878|
|T (<sup>◦</sup>C)|-0.0280|0.0164|0.0098|-0.0391|-0.0176|-0.0078|
|P (mm/mo)|-0.0028|-0.0007|-0.0019|-0.0032|-0.0030|-0.0016|
|Maize and ground<br>nuts|0.1412|0.0058|0.0137|0.0270|0.0412|0.0108|
|T (<sup>◦</sup>C)|0.0153|-0.0005|0.0053|-0.0009|0.0074|0.0017|
|P (mm/mo)|0.0025|0.0000|0.0002|0.0005|0.0008|0.0001|
|Millet|0.0414|0.0083|0.0756|0.0043|0.0357|0.0136|
|T (<sup>◦</sup>C)|0.0004|0.0010|0.0173|0.0006|0.0059|0.0066|
|P (mm/mo)|0.0011|0.0003|0.0019|0.0003|0.0011|0.0006|
|Millet and|||||||
|Sorghum|0.0447|0.0642|0.0794|0.0418|0.0687|0.1459|
|T (<sup>◦</sup>C)|-0.0005|0.0026|-0.0019|-0.0031|0.0058|-0.0083|
|P (mm/mo)|0.0000|-0.0004|-0.0001|-0.0001|0.0000|0.0000|
|Wheat|0.0616|0.6106|0.3328|0.0496|0.1382|0.1111|
|T (<sup>◦</sup>C)|-0.0093|-0.0576|-0.0920|-0.0114|-0.0410|-0.0387|
|P (mm/mo)|-0.0001|0.0019|0.0005|-0.0008|-0.0003|0.0001|

26
# Table 7 continued.

|Fruits/Vegetables|AEZ6<br>|AEZ7<br>|AEZ8<br>|AEZ9<br>|AEZ10<br>|AEZ11<br>|
|---|---|---|---|---|---|---|
|and Maize|0.2922|0.0775|0.3461|0.1678|0.1296|0.2344|
|T (<sup>◦</sup>C)|0.0221|0.0073|0.0365|0.0138|0.0166|0.0258|
|P (mm/mo)|0.0029|0.0003|0.0012|0.0012|0.0004|0.0017|
|Fruits/Vegetables|0.0849|0.0607|0.1601|0.0520|0.2424|0.1048|
|T (<sup>◦</sup>C)|0.0087|0.0098|0.0223|0.0095|0.0196|0.0237|
|P (mm/mo)|0.0003|-0.0001|0.0005|0.0005|-0.0015|0.0006|
|Ground nuts and<br>millet|0.0010|0.2901|0.0013|0.1121|0.0418|0.0371|
|T (<sup>◦</sup>C)|0.0011|-0.0091|0.0016|-0.0193|-0.0022|-0.0044|
|P (mm/mo)|0.0000|-0.0026|-0.0003|-0.0050|-0.0004|-0.0028|
|Maize|0.3777|0.2849|0.3479|0.3070|0.0910|0.3874|
|T (<sup>◦</sup>C)|-0.0143|-0.0198|-0.0517|-0.0259|-0.0153|-0.0478|
|P (mm/mo)|-0.0034|-0.0027|-0.0023|-0.0026|-0.0009|-0.0020|
|Maize and ground|||||||
|nuts|0.0311|0.1310|0.1164|0.2040|0.0013|0.1715|
|T (<sup>◦</sup>C)|0.0036|0.0144|-0.0030|0.0320|-0.0004|0.0148|
|P (mm/mo)|0.0002|0.0022|0.0014|0.0049|0.0000|0.0028|
|Millet|0.0146|0.0894|0.0028|0.0625|0.0877|0.0167|
|T (<sup>◦</sup>C)|0.0033|0.0042|0.0005|-0.0025|0.0002|-0.0025|
|P (mm/mo)|0.0008|0.0029|-0.0004|0.0006|0.0025|0.0000|
|Millet and|||||||
|Sorghum|0.0947|0.0357|0.0185|0.0546|0.0354|0.0398|
|T (<sup>◦</sup>C)|0.0047|-0.0001|-0.0050|0.0006|-0.0083|-0.0074|
|P (mm/mo)|0.0003|0.0000|0.0000|0.0004|-0.0003|-0.0001|
|Wheat|0.1038|0.0306|0.0070|0.0399|0.3708|0.0083|
|T (<sup>◦</sup>C)|-0.0293|-0.0067|-0.0013|-0.0081|-0.0102|-0.0021|
|P (mm/mo)|-0.0011|0.0000|-0.0002|0.0000|0.0002|-0.0002|

27
# Table 7 continued.

||AEZ12<br>A|EZ13<br>A|EZ14<br>A|EZ15<br>AEZ16|
|---|---|---|---|---|
|Fruits/Vegetables|||||
|and Maize|0.1094|0.4095|0.1617|0.1369<br>0.3651|
|T (<sup>◦</sup>C)|0.0088|0.0336|0.0082|0.0287<br>0.0278|
|P (mm/mo)|0.0005|0.0018|0.0013|0.0006<br>0.0027|
|Fruits/Vegetables|0.0518|0.1241|0.0501|0.1162<br>0.0886|
|T (<sup>◦</sup>C)|0.0066|0.0131|0.0041|0.0280<br>0.0126|
|P (mm/mo)|0.0001|0.0002|0.0002|0.0003<br>0.0003|
|Ground nuts and<br>millet|0.0060|0.0000|0.0048|0.0015<br>0.0020|
|T (<sup>◦</sup>C)|0.0010|0.0000|0.0000|0.0010<br>0.0011|
|P (mm/mo)|-0.0003|0.0000|-0.0002|-0.0001<br>-0.0001|
|Maize|0.4791|0.3453|0.4752|0.4022<br>0.3157|
|T (<sup>◦</sup>C)|-0.0304|-0.0404|-0.0309|-0.0134<br>-0.0274|
|P (mm/mo)|-0.0043|-0.0020|-0.0054|-0.0023<br>-0.0033|
|Maize and ground<br>nuts|0.2190|0.0699|0.2123|0.0189<br>0.0577|
|T (<sup>◦</sup>C)|0.0253|0.0019|0.0267|0.0021<br>0.0068|
|P (mm/mo)|0.0034|0.0006|0.0033|0.0002<br>0.0002|
|Millet|0.0148|0.0023|0.0149|0.0568<br>0.0196|
|T (<sup>◦</sup>C)|0.0020|0.0001|0.0002|0.0116<br>0.0008|
|P (mm/mo)|0.0007|0.0001|0.0008|0.0014<br>0.0010|
|Millet and|||||
|Sorghum|0.0587|0.0266|0.0470|0.1151<br>0.0708|
|T (<sup>◦</sup>C)|0.0019|-0.0043|0.0015|-0.0034<br>-0.0002|
|P (mm/mo)|0.0002|-0.0003|0.0002|-0.0002<br>0.0001|
|Wheat|0.0611|0.0224|0.0339|0.1523<br>0.0805|
|T (<sup>◦</sup>C)|-0.0151|-0.0040|-0.0097|-0.0545<br>-0.0214|
|P (mm/mo)|-0.0002|-0.0004|-0.0002|0.0001<br>-0.0010|

28
# Table 8: AOGCM Scenarios

|Curr|ent<br>2020<br>210|0|
|---|---|---|
|Summer Temperature (°C )|||
|CCC|25.7<br>1.4|6.0|
|PCM|25.7<br>0.7|2.2|
|Winter Temperature (°C )|||
|CCC|22.4<br>2.2|7.3|
|PCM|22.4<br>1.1|3.1|
|Summer Rainfall (mm/month)|||
|CCC|149.8<br>-4.6|-33.7|
|PCM|149.8<br>-4.7|-4.7|
|Winter Rainfall (mm/month)|||
|CCC|12.8<br>1.1|3.5|
|PCM|12.8<br>18.8|21.6|

29
Table 9: Climate Change Impacts on Probability of Irrigation Choice by AEZs (%)

||Africa<br>A|EZ1|AEZ2|AEZ3|AEZ4|AEZ5|
|---|---|---|---|---|---|---|
|Baseline|0.501|0.564|0.488|0.488|0.477|0.510|
|2020|||||||
|CCC|0.038|0.010|0.036|0.026|0.030|0.029|
|PCM|-0.025|0.099|-0.055|-0.186|-0.090|-0.076|
|2100|||||||
|CCC|0.155|0.036|0.144|0.091|0.116|0.120|
|PCM|-0.019|0.107|-0.023|-0.168|-0.064|-0.046|
||AEZ6<br>A|EZ7|AEZ8|AEZ9|AEZ10|AEZ11|
|Baseline|0.480|0.479|0.517|0.515|0.513|0.559|
|2020|||||||
|CCC|0.026|0.056|0.026|0.050|0.061|0.033|
|PCM|-0.147|-0.012|-0.076|-0.027|-0.020|-0.072|
|2100|||||||
|CCC|0.107|0.207|0.150|0.193|0.208|0.153|
|PCM|-0.123|-0.025|-0.071|-0.008|-0.059|-0.058|
||AEZ12|AEZ13|AEZ14|<br>AEZ|15<br>A|EZ16|
|Baseline|0.432|0|.470|0.421|0.498|0.492|
|2020|||||||
|CCC|0.030|0|.026|0.027|0.028|0.030|
|PCM|-0.028|-0|.160<br>|-0.039|-0.051|-0.167|
|2100|||||||
|CCC|0.132|0|.119|0.123|0.114|0.126|
|PCM|-0.008|-0|.147<br>|-0.019|-0.025|-0.142|

30
Table 10a: Climate Change Impacts on Probability of Crop Choice by AEZs by 2020 (%)

|Fruits&|Africa<br>A|EZ1<br>A|EZ2<br>|AEZ3<br>A|EZ4<br>|AEZ5|
|---|---|---|---|---|---|---|
|vegetables|||||||
|&maize|0.181|0.073|0.089|0.449|0.237|0.151|
|CCC|0.052|-0.073|0.104|-0.013|0.053|-0.104|
|PCM|-0.006|-0.073|0.128|-0.138|0.111|-0.088|
|Fruits &|||||||
|vegetables|0.079|0.137|0.071|0.081|0.060|0.080|
|CCC|-0.032|-0.136|-0.032|0.019|-0.004|-0.066|
|PCM|0.043|-0.134|0.057|0.145|0.225|-0.007|
|Groundnut|||||||
|&Millet|0.114|0.000|0.024|0.000|0.015|0.000|
|CCC|-0.063|0.134|-0.024|0.000|0.027|0.000|
|PCM|-0.089|0.002|-0.019|0.000|-0.012|0.015|
|Maize|0.338|0.102|0.314|0.347|0.405|0.488|
|CCC|-0.162|-0.099|0.160|-0.046|-0.006|-0.095|
|PCM|-0.123|-0.101|0.074|-0.291|-0.172|-0.230|
|Maize&Gro|||||||
|undnuts|0.141|0.006|0.014|0.027|0.041|0.011|
|CCC|-0.101|-0.005|-0.013|-0.019|-0.040|-0.010|
|PCM|-0.069|0.061|0.045|-0.021|-0.003|0.065|
|Millet|0.041|0.008|0.076|0.004|0.036|0.014|
|CCC|0.034|-0.008|-0.076|0.089|0.000|-0.014|
|PCM|0.054|-0.008|0.106|0.223|0.013|0.471|
|Millet&Sor|||||||
|ghum|0.045|0.064|0.079|0.042|0.069|0.146|
|CCC|0.176|0.021|0.141|-0.018|0.056|0.188|
|PCM|0.088|-0.030|-0.058|0.131|-0.051|-0.114|
|Wheat|0.062|0.611|0.333|0.050|0.138|0.111|
|CCC|-0.096|0.167|-0.261|-0.012|-0.086|0.100|
|PCM|-0.027|0.283|-0.333|-0.050|-0.112|-0.111|

31
# Table 10a continued.

|Fruits&|AEZ6<br>A|EZ7<br>A|EZ8<br>A|EZ9<br>A|EZ10<br>A|EZ11|
|---|---|---|---|---|---|---|
|vegetables|||||||
|&maize|0.292|0.078|0.346|0.078|0.346|0.168|
|CCC|0.007|-0.012|0.163|-0.012|0.163|0.035|
|PCM|-0.005|-0.013|-0.001|-0.013|-0.001|0.009|
|Fruits &|||||||
|vegetables|0.085|0.061|0.160|0.061|0.160|0.052|
|CCC|-0.032|-0.045|-0.082|-0.045|-0.082|0.012|
|PCM|0.222|-0.006|-0.009|-0.006|-0.009|0.099|
|Groundnut|||||||
|&Millet|0.001|0.290|0.001|0.290|0.001|0.112|
|CCC|0.000|-0.269|-0.001|-0.269|-0.001|-0.019|
|PCM|0.040|-0.266|-0.001|-0.266|-0.001|-0.104|
|Maize|0.378|0.285|0.348|0.285|0.348|0.307|
|CCC|0.003|-0.170|-0.218|-0.170|-0.218|-0.170|
|PCM|-0.311|-0.017|-0.153|-0.017|-0.153|-0.054|
|Maize&Gro|||||||
|undnuts|0.031|0.131|0.116|0.131|0.116|0.204|
|CCC|-0.030|-0.021|-0.111|-0.021|-0.111|-0.184|
|PCM|-0.023|-0.004|-0.066|-0.004|-0.066|-0.112|
|Millet|0.015|0.089|0.003|0.089|0.003|0.062|
|CCC|0.050|-0.083|0.225|-0.083|0.225|0.004|
|PCM|0.100|-0.056|0.227|-0.056|0.227|-0.056|
|Millet&Sor|||||||
|ghum|0.095|0.036|0.018|0.036|0.018|0.055|
|CCC|-0.040|0.429|-0.017|0.429|-0.017|0.253|
|PCM|0.000|0.151|-0.009|0.151|-0.009|0.184|
|Wheat|0.104|0.031|0.007|0.031|0.007|0.040|
|CCC|0.043|0.172|0.041|0.172|0.041|0.070|
|PCM|-0.023|0.211|0.013|0.211|0.013|0.034|

32
# Table 10a continued.

|AE<br>Fruits&|Z12<br>AE|Z13<br>AE|Z14<br>AE|Z15<br>AE|Z16|
|---|---|---|---|---|---|
|vegetable||||||
|s &maize|0.130|0.234|0.109|0.410|0.162|
|CCC|0.001|0.339|-0.026|-0.070|-0.039|
|PCM|-0.072|0.135|-0.008|-0.239|-0.022|
|Fruits &||||||
|vegetable||||||
|s|0.242|0.105|0.052|0.124|0.050|
|CCC|-0.221|-0.018|-0.031|-0.056|-0.011|
|PCM|-0.096|0.104|0.064|0.047|0.072|
|Groundnu||||||
|t&Millet|0.042|0.037|0.006|0.000|0.005|
|CCC|0.060|-0.037|0.045|0.000|0.100|
|PCM|-0.039|-0.037|0.042|0.000|0.059|
|Maize|0.091|0.387|0.479|0.345|0.475|
|CCC|-0.050|-0.199|-0.252|-0.114|-0.234|
|PCM|-0.089|-0.169|-0.094|-0.298|-0.114|
|Maize&G||||||
|roundnuts|0.001|0.172|0.219|0.070|0.212|
|CCC|0.075|-0.167|-0.214|-0.062|-0.207|
|PCM|0.230|-0.115|-0.172|-0.049|-0.169|
|Millet|0.088|0.017|0.015|0.002|0.015|
|CCC|-0.088|0.063|0.031|0.314|0.051|
|PCM|-0.088|0.067|0.056|0.481|0.039|
|Millet&S||||||
|orghum|0.035|0.040|0.059|0.027|0.047|
|CCC|0.286|-0.028|0.306|-0.012|0.189|
|PCM|0.103|0.000|0.124|0.080|0.127|
|Wheat|0.371|0.008|0.061|0.022|0.034|
|CCC|-0.065|0.048|0.141|0.001|0.153|
|PCM|0.050|0.015|-0.012|-0.022|0.008|

33
Table 10b: Climate Change Impacts on Probability of Crop Choice by AEZs by 2100 (%)

|Fruits&|Africa<br>A|EZ1<br>A|EZ2<br>A|EZ3<br>A|EZ4<br>|AEZ5|
|---|---|---|---|---|---|---|
|vegetables|||||||
|&maize|0.181|0.073|0.089|0.449|0.237|0.151|
|CCC|0.228|-0.073|0.450|0.329|0.304|0.097|
|PCM|0.012|-0.073|0.168|-0.219|0.027|-0.098|
|Fruits &|||||||
|vegetables|0.079|0.137|0.071|0.081|0.060|0.080|
|CCC|0.018|-0.133|0.087|0.048|0.063|0.018|
|PCM|0.051|-0.135|0.078|0.176|0.182|-0.017|
|Groundnut|||||||
|&Millet|0.114|0.000|0.024|0.000|0.015|0.000|
|CCC|-0.067|0.001|-0.024|0.000|0.058|0.000|
|PCM|-0.072|0.002|-0.012|0.000|0.055|0.035|
|Maize|0.338|0.102|0.314|0.347|0.405|0.488|
|CCC|-0.245|-0.097|-0.094|-0.263|-0.239|-0.060|
|PCM|-0.188|-0.101|-0.005|-0.315|-0.227|-0.300|
|Maize&Gro|||||||
|undnuts|0.141|0.006|0.014|0.027|0.041|0.011|
|CCC|-0.078|-0.001|-0.013|-0.025|-0.038|-0.008|
|PCM|-0.065|0.079|0.026|-0.022|-0.029|0.041|
|Millet|0.041|0.008|0.076|0.004|0.036|0.014|
|CCC|-0.024|-0.008|-0.076|-0.003|-0.025|-0.014|
|PCM|0.098|-0.008|0.142|0.387|0.162|0.576|
|Millet&Sor|||||||
|ghum|0.045|0.064|0.079|0.042|0.069|0.146|
|CCC|0.129|0.037|-0.029|-0.040|-0.018|-0.012|
|PCM|0.086|-0.043|-0.065|0.043|-0.054|-0.126|
|Wheat|0.062|0.611|0.333|0.050|0.138|0.111|
|CCC|-0.019|0.275|-0.302|-0.047|-0.105|-0.022|
|PCM|-0.027|0.280|-0.333|-0.050|-0.118|-0.111|

34
# Table 10b continued.

|<br>Fruits&|AEZ6<br>A|EZ7<br>A|EZ8<br>A|EZ9<br>A|EZ10<br>A|EZ11|
|---|---|---|---|---|---|---|
|vegetables|||||||
|&maize|0.292|0.078|0.346|0.168|0.130|0.234|
|CCC|0.320|0.030|0.551|0.166|0.035|0.640|
|PCM|-0.095|-0.013|0.111|0.027|-0.055|0.246|
|Fruits &|||||||
|vegetables|0.085|0.061|0.160|0.052|0.242|0.105|
|CCC|0.036|0.103|-0.109|0.047|-0.203|-0.044|
|PCM|0.233|0.012|-0.004|0.090|-0.099|0.129|
|Groundnut|||||||
|&Millet|0.001|0.290|0.001|0.112|0.042|0.037|
|CCC|0.003|-0.276|-0.001|0.020|-0.034|-0.037|
|PCM|0.047|-0.236|-0.001|-0.062|-0.038|-0.037|
|Maize|0.378|0.285|0.348|0.307|0.091|0.387|
|CCC|-0.234|-0.157|-0.345|-0.233|-0.078|-0.378|
|PCM|-0.322|-0.089|-0.244|-0.110|-0.089|-0.257|
|Maize&Gro|||||||
|undnuts|0.031|0.131|0.116|0.204|0.001|0.172|
|CCC|-0.031|0.044|-0.116|-0.157|0.151|-0.171|
|PCM|-0.027|0.020|-0.089|-0.079|0.260|-0.147|
|Millet|0.015|0.089|0.003|0.062|0.088|0.017|
|CCC|-0.015|-0.086|-0.001|-0.031|-0.088|-0.017|
|PCM|0.256|-0.027|0.233|-0.040|-0.088|0.078|
|Millet&Sor|||||||
|ghum|0.095|0.036|0.018|0.055|0.035|0.040|
|CCC|-0.086|0.316|-0.011|0.194|0.193|-0.031|
|PCM|-0.053|0.163|-0.012|0.159|0.069|-0.020|
|Wheat|0.104|0.031|0.007|0.040|0.371|0.008|
|CCC|0.006|0.026|0.031|-0.006|0.024|0.037|
|PCM|-0.040|0.170|0.007|0.015|0.039|0.008|

35
# Table 10b continued.

|A|EZ12<br>AE|Z13<br>A|EZ14<br>A|EZ15<br>A|EZ16|
|---|---|---|---|---|---|
|Fruits&||||||
|vegetables||||||
|&maize|0.109|0.410|0.162|0.137|0.365|
|CCC|0.043|0.448|0.035|0.187|0.391|
|PCM|-0.012|-0.156|-0.066|-0.033|-0.105|
|Fruits &||||||
|vegetables|0.052|0.124|0.050|0.116|0.089|
|CCC|-0.002|-0.042|0.012|0.040|0.030|
|PCM|0.040|0.073|0.051|0.000|0.207|
|Groundnut&Mill||||||
|et|0.006|0.000|0.005|0.001|0.002|
|CCC|0.067|0.000|0.115|-0.001|0.001|
|PCM|0.068|0.000|0.101|0.021|0.037|
|Maize|0.479|0.345|0.475|0.402|0.316|
|CCC|-0.272|-0.291|-0.274|-0.098|-0.228|
|PCM|-0.177|-0.314|-0.198|-0.160|-0.294|
|Maize&Groundn||||||
|uts|0.219|0.070|0.212|0.019|0.058|
|CCC|-0.199|-0.068|-0.189|-0.017|-0.057|
|PCM|-0.162|-0.052|-0.170|0.035|-0.039|
|Millet|0.015|0.002|0.015|0.057|0.020|
|CCC|0.015|0.001|0.029|-0.057|-0.020|
|PCM|0.077|0.470|0.119|0.312|0.278|
|Millet&Sorghum|0.059|0.027|0.047|0.115|0.071|
|CCC|0.340|-0.026|0.235|-0.029|-0.068|
|PCM|0.194|0.002|0.168|-0.095|-0.022|
|Wheat|0.061|0.022|0.034|0.152|0.080|
|CCC|0.008|-0.022|0.038|-0.025|-0.049|
|PCM|-0.028|-0.022|-0.006|-0.079|-0.061|

36
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