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Future of Wheat Production in Sub-Saharan Africa: Analyses of the Expanding Gap between Supply and Demand and Economic

Profitability of Domestic Production

Bekele Shiferaw1, Asfaw Negassa, Jawoo Koo, Stanley Wood, Kai Sonder, Hans Joachim Braun, and Thomas Payne

International Maize and Wheat Improvement Center (CIMMYT) and International Food Policy Research Institute (IFPRI)

Abstract

Soaring international food and energy prices and the resulting volatility in markets is alarming many African policy makers. The domestic production of wheat in many countries suffers from lack of policy support and donor neglect resulting from the perception that Africa cannot competitively produce wheat. Given this background, this study examines the biophysical feasibility and economic profitability of rain-fed wheat production in selected Sub Saharan Africa (SSA) countries. The biophysical crop growth simulation model was used to generate wheat yield under three levels of intensification (low, medium and high): using 0%, 50% and 100% of recommended fertilizer rates. The parametric and non-parametric statistical analysis showed significant wheat yield response. Across the selected countries, the wheat yield varied from 742 kg/ha to 3022 kg/ha for no fertilizer use, from 1482 kg/ha to 4149 kg/ha for 50% of recommended fertilizer rate, and from 1838 kg/ha to 4914 kg/ha for 100% of recommended fertilizer rate. The economic analysis also indicated that domestic wheat production can be economically profitable and could be competitive with imports in most of the selected countries. The net economic return per hectare varied from -224 US $/ha to 959 US $ for no fertilizer use, from -58 US $/ha to 1424 US$/ha for the use of 50% recommended fertilizer rate, and from 7 US $/ha to 1728 US$/ha for the use of 100% of recommended fertilizer rate. Thus, given the strategic importance of wheat for food security and the widening gap between demand and domestic production, African governments need to seriously consider investing in wheat production. The priority should be on utilizing existing varieties and technologies through improvements in seed production and supply, agricultural extension, marketing infrastructure to reduce the marketing costs, and improvements in agronomic practices are priority areas for ensuring competitiveness of domestic wheat production.

1 Corresponding author, Bekele Shiferaw, Director, Socioeconomics Program, CIMMYT (b.shiferaw@cgiar.org)

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1 Introduction

Across countries in Sub-Saharan Africa (SSA), changes in dietary patterns and a rapid

growth in wheat consumption have been noted over the past few decades (Morris and Byerlee,

1993). A recent analysis by Jayne et al. (2010) confirmed rapid growth in wheat consumption as

a consequence of urbanization, rising incomes, and dietary diversification in Eastern and

Southern Africa. While there is variation in the extent to which SSA countries meet domestic

wheat consumption requirements, taken together, their domestic wheat production accounts only

for small proportion of wheat consumption in the region.

Since 2007-9, the world wheat trade has been characterized by highly volatile demand

and supply conditions which has caused supply disruptions and price spikes. Important stylized

features of world wheat trade as it relates to the SSA countries include: wheat export restrictions

by major wheat exporting countries; foreign exchange shortages; balance of payment deficits;

declining wheat food aid flows; higher and more instable international wheat prices; diversion of

grain to the production of bio-fuels; weather-induced wheat shortages and crop failures; rising

world oil prices and more frequent crises; and speculative buying and selling.

This volatility has exposed wheat-importing countries of SSA to greater risk of food

insecurity. In this regard, a key question for policy makers and governments in this region is: to

what extent can SSA afford to depend on an unstable world wheat market to meet a burgeoning

demand for wheat? Assuring the availability of wheat at affordable prices is of strategic policy

importance to sustain economic growth and avert political instability. In the short-run, wheat

imports to bridge the gap between domestic supply and demand may be unavoidable. However,

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in the long-run, while embracing the basic principle of free trade, it makes sense for SSA

countries to explore opportunities for meeting more of their domestic wheat needs through their

own production by developing their own wheat production capability.

Previous policies and studies to encourage wheat production in SSA countries have been

criticized for focusing on narrow technical issues such as increasing wheat productivity alone

(Morris and Byerlee, 1993). In this study, we seek to provide a balanced view of the empirical

evidence. The bio-physical suitability assessment addresses the important questions like which

areas and agro-ecological zones in a given country are suitable for wheat production. On the

other hand, the economic profitability analysis assess whether there exists incentive for socially

profitable wheat production. It determines the economic returns to key resources used in wheat

production such as land and farmer management evaluated using international prices. The use of

international prices allows the assessment of the extent to which the domestic wheat production

is price competitive to wheat imports.

The objectives of this analysis are: (1) to determine geographic areas biophysically

suitable for rain-fed wheat production; (2) to determine the economic profitability of growing

wheat using international prices; and (3) to determine changes required in policy, institutions,

and technologies for profitable wheat expansion in the selected SSA countries. There were 12

countries selected purposively for this study which include Angola, Burundi, Ethiopia, Kenya,

Madagascar, Mozambique, Rwanda, Tanzania, DRC, Uganda, Zambia and Zimbabwe.

The next section presents the discussion of growing gap between wheat production and

consumption in SSA countries followed by the discussion of conceptual framework. The data

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sources and methods used are discussed in section four. Section five presents the empirical

results. Finally, conclusions and policy implications are made.

2 Growing gap between wheat production and consumption

The annual total wheat production in Africa grew from 16 million tons in the 1990’s to

21 million tons in the 2000’s showing tremendous growth (Table 1). However, the contribution

of Africa to the global wheat production was insignificant, estimated at 3% for both the 1990’s

and 2000’s. On the other hand, the average annual wheat consumption grew from 33 million tons

in the 1990’s to 47 million tons in the 2000’s accounting for 6% and 8 % of the global wheat

consumption, respectively (Table 2). The per capita wheat consumption for Africa showed slight

increase from 46 kg/ year in the 1990’s to 52 kg/year in the 2000’s. The per capita wheat

consumption for Africa is almost half the world average per capita wheat consumption.

In general, similar to earlier study by Morris and Byerlee (1993), there has been a

growing gap between domestic wheat production and consumption in Africa over the last several

decades (Figure 1). As a result, Africa relied on increased imports to meet the growing wheat

demand. The annual wheat imports averaged 18 million tons and 27 million tons in the 1990’s

and 2000’s, respectively (Table 3). These imports accounted for 18% and 23% of the global

wheat imports indicating the importance of Africa in the global wheat import market.

Furthermore, the imports have been growing for most regions and selected SSA countries. As a

result, the total value of wheat import has been also increasing in the 1990’s and 2000’s. It has

been observed that the SSA countries have become more and more dependent on import to meet

their growing demand for wheat. The wheat self-sufficiency ratios2have been declining since the

2 The self-sufficiency ratio is given as the ratio of production to the sum of production plus net import.

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1960’s for most of the countries (Table 4). For almost all regions in Africa, the observed trends

in the wheat self-sufficiency ratios have been downward (Figure 2). Similarly, for most of the

selected SSA countries the trend in self-sufficiency ratio has been also downward (Figure 3).

However, for some countries, upward trends in wheat self-sufficiency ratios have been observed

(for example: Ethiopia, Zambia and Zimbabwe).

The continued dependency of African countries on wheat import has been observed while

the international wheat market has been characterized by increasing prices and highly volatile

demand and supply conditions. For example, the nominal international wheat price has

continuously increased from 62 US$/ ton in the 1960’s to 195 US$/ ton in the 2000’s (Table 5).

On the other hand, the real wheat price declined from 243 US$/ ton in the 1960’s to 134 US$/ton

in the 1990’s. However, the real wheat price has been also increasing in the 2000’s (see also:

Figure 5). Very high and instable international wheat prices depress wheat purchases and affects

food security of importing countries. Furthermore, high international price is also a drain on very

meager foreign exchange and can affect the availability of foreign exchange for importing other

vital investment goods.

3 Conceptual framework

The economic analysis in this study is based on the assumption of the small open-

economy model. This framework implies that the SSA countries’ economies are open to

international trade and they are price-takers in international wheat markets. Then, the decision

whether to domestically produce or import wheat to these countries involves the comparison of

international and domestic wheat prices (Tsakok, 1990; Gittinger, 1987; de Janvry and Sadoulet,

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1995). Thus, the international prices are used as social prices for tradable outputs and inputs

which would reflect the social opportunity costs or economic efficiency of either producing

wheat locally or importing from the international markets.

The domestic wheat production is economically efficient as long as the domestic wheat

price is less than the import parity prices (IPP) which is given as the border price plus all the

costs incurred to bring the imported wheat to the main consumption center. On the other hand, if

the domestic price is greater than the IPP, it is economically more efficient to import wheat from

the international market rather than produce it domestically. Theoretically, if the domestic wheat

market is well integrated with world markets, any supply gap in domestic wheat production is

likely to be met through imports because an IPP that is lower than the domestic wholesale price

provides incentive for wheat importers. If, on the other hand, a domestic wholesale price that is

above the IPP level does not lead to an increase in imports, wheat markets are poorly integrated.

These could because the capacity of importers (traders) could be weak, marketing costs may be

high, there may be lack of competition in the market, or the government may have imposed

policies or import barriers that impede wheat imports and exports.

Likewise, the export parity price (EPP) is used as a reference price when the country is a

net exporter of a commodity under consideration. The decision is to export when the EPP is

greater than the domestic price. In general, when domestic and international wheat markets

operate smoothly, the domestic wheat price for net importing country is expected to fluctuate

between import parity price (upper bound) and export parity price (lower bound).

The net economic return (NER) per hectare (US $/ha) to land and farmer management

was used as an indicator for the economic profitability of fertilizer use in wheat production. The

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NER was computed at the pixel level by deducting the costs that vary from the gross farm return

as follows:

NER=P*Y-TVC

Where P*Y is the gross farm return (US$/Ha); P is the pixel level adjusted IPP (producer import

parity price) for wheat (US$/kg); Y is the adjusted pixel level simulated wheat yield (kg/ha). The

simulated wheat yield data was adjusted downward by 10% to reflect the fact that farmers’ actual

conditions might not be exactly the same as simulated conditions. This is a conservative

adjustment. The TVC is the pixel level costs that vary due to fertilizer use (US$/ha). The TVC

include labor costs, oxen pair costs, herbicides, pesticides, seed, fertilizer costs and interest on

working capital. The NER was generated for different fertilizer applications regimes in order to

assess how the economic profitability of wheat production varies by fertilizer use. The fertilizer

rates used were established at 0% (dented as T0), 50% (denoted as T1) and 100% (denoted as T2)

of the recommended fertilizer rate for Ethiopia.

The NER, as a profitability indicator, implies that the increase in yield attributable to

fertilizer use must have a value that at least covers the social costs of using fertilizer in order to

consider fertilizer use as an economically profitable investment. For example, a NER of zero

indicates a break-even condition; revenues generated cover all of the economic costs that vary.

Thus, a value of NER greater than zero indicates that the use of fertilizer in wheat production is

economically profitable. It is also important to note that the NER for wheat was derived based on

wheat grain yield data alone. Due to data limitation, the economic benefit of wheat straw was not

included. However, the benefits of wheat straw yield could be substantial as a livestock feed and

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considering wheat yield alone could underestimate the total economic benefits of wheat

production.

4 Data sources and methods

Both primary and secondary data from various sources were used in this study. The

biophysical crop growth simulation model called Decision Support System for Agrotechnology

Transfer (DSSAT) v4.5 (Hoogenboom et al., 2010; Jones et al., 2003) on 5 arc-minute grids (also

known as 10 km grids) in 12 study countries was used to generate wheat yield data under rain-

fed condition for selected SSA countries by different agro-ecological zones by three different

fertilizer application rates at the pixel level.

Most SSA countries are observed to be net wheat importers and the IPPs are constructed

for major consumption markets. This implies that for the on-farm profitability analysis of wheat

production, the IPP for a given reference market needs to be adjusted to the farm gate level.

Prices used in the analysis need to take into account the costs required to transfer grain from the

farm or regional markets to the main consumption markets.

The steps used in the computation of the adjusted producer import parity price for wheat

was as follows. First, the time series data on free on board (FOB) US Gulf of Mexico, freight and

insurance for USA hard red winter wheat (HRW) delivered to Durban port was obtained from

Chicago Board of Trade provided on Koring website: http://www.sagis.org.za/. The average

FOB, freight, and insurance costs was estimated at 270 US$/ton, 51 US$/ton, and 3 US$/ton,

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respectively for the period from April 2010 to May 2011. These costs are added to obtain the

average costs of wheat, insurance and freight (CIF) ex Durban port as 323 US$ per ton. Then, the

average port duty and port handling cost of 81 US$ per ton for all countries which was assumed

to be 25% of the CIF price based on Gitau et al. (2010) computation of wheat IPP for Kenya was

added to the CIF in order to obtain the landed cost of wheat at the Durban port. Thus, the landed

cost of wheat at the Durban port was 404 US$ per ton and assumed to be the same for all

countries.

Second, the IPP was computed as the sum of landed Durban cost plus the handling and

transport costs from ports to capital city of each country. The handling costs from port to capital

city were assumed to be 5% of the landed cost at the port of entry, estimated at 20 US$ per ton

and again assumed to be the same for all countries. The port to capital city transport costs per ton

was obtained for each country from the GIS simulation model based on a least cost route

approach. The IPP and all other prices and costs calculations were made in US currency to

facilitate comparison across different countries. The IPP for the selected SSA countries varied

from 0.40 US$ /kg for Angola and Mozambique to 0.53 US$ /kg for Burundi (Appendix 1).

Once the IPP is determined for each country, the next step is to determine the adjusted

producer parity prices at the pixel level by taking into account all the marketing costs (regional

grain traders’ margin) required in transferring grain from the regional markets to the main

consumption markets. Normally, a complete data series on total grain marketing costs by grain

traders in moving grain from the production areas to the consumption centers is rarely available

in developing countries like those in SSA. Therefore, the information on the total marketing

costs was constructed based on the transport cost proportion in the total marketing costs

following the approach used by Negassa and Myers (2007).

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Based on Rashid and Minot (2010), the proportion of transport costs in the total

marketing cost used was estimated at 50%. The transport cost data was obtained from the GIS

simulation model on a least cost route from the capital cities to the pixels. The GIS based

transport cost data was divided by 0.5 in order to obtain the total marketing costs between pixels

and main consumption markets. Then, the adjusted producer parity price was obtained as the

difference between the IPP and the total marketing costs. The adjusted producer parity price is

the price the wheat producers are facing in the open market if wheat import is allowed to the

country.

As indicated before, there are different variable costs which were included in the wheat

costs of production that vary for the computations of NER at the pixel level. These include labor

costs, oxen pair costs, herbicides, pesticides, seed, fertilizer costs and interest on working capital.

These variable costs were identified based on average wheat budget for smallholder wheat

producers in Hettosa-Tyyo area of Ethiopia growing wheat with and without fertilizer under rain

fed condition for 2009 cropping season (Appendix 2). The wheat budget was developed based on

CIMMYT-SG2000 household survey data. The costs were given in domestic currency and the

average exchange rate for 2009 was used to convert these costs in to US dollars. A 20% increase

in variable costs was assumed in order to take into account the inflation and distortions that

might have existed in the foreign exchange market. Furthermore, the labor costs were assumed to

be higher by 5% for T2 and 5% less for the T0 as compared to T1. Fertilizer cost was based on-

farm landed cost (port to farm transfer costs plus CIF price of fertilizer) and was obtained from

GIS simulation model. The interest cost was calculated on total variable costs assuming 10%

interest rate for 6 months.

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Different methods were used to analyze the simulated wheat yield response to fertilizer

application. First, descriptive statistics was used to assess the level and variability of the pixel

level simulated wheat yield data. Second, wheat yield response to fertilizer was statistically

analyzed using the analysis of variance (ANOVA). Third, upon the discovery of the non-

normality of wheat yield distributions, the non-parametric methods of kernel density functions

and stochastic dominance analysis of wheat yield and NER distributions for alternative fertilizer

application regimes were also made (for example; see: Bekele, 2005; Langyintuo et al., 2005;

Shively, 1999).

.

5 Empirical results

5.1 Wheat yield response

The summary results of analysis of variance (ANOVA) of simulated wheat yields for

selected SSA countries are presented in Table 6. The results indicate that the ANOVA model

was significant at a probability of less than 1% in all cases. The adjusted R2 was greater than

30% indicating that fertilizer use, agro-ecological zones and the interaction between agro-

ecological zone and fertilizer use explain more than 30% of the variability in wheat yield.

Individually, the effect of fertilizer use was significant in all cases indicating significant wheat

yield response to different levels of fertilization, demonstrating the importance of inorganic

fertilizer use to boost wheat production in SSA countries. The effect of agro-ecological zone

was also statistically significant in the ANOVA model in all cases, indicating the heterogeneity

in wheat growing potential for the selected SSA countries.

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The statistical analysis using ANOVA assumes the normality of wheat yield distributions.

Thus, the Kolmogorov-Simnrov (K-S) test was used to test whether the wheat yield distributions

were normally distributed. The results (not reported here for brevity) indicate that the null

hypothesis of normality of wheat yield was rejected at 1% level in all cases. Therefore, the non-

parametric analysis was used to test the difference in wheat yield distributions among the

alternative fertilization application regimes. The results of non-parametric tests for all possible

pair-wise comparisons of alternative fertilizer application regimes indicate that there was

statistically significant difference in wheat yield distributions among all the alternative fertilizer

application regimes for the selected SSA countries. The consistent ranking observed was that the

T2 gave the highest wheat yield followed by T1 and the lowest wheat yield was observed for T0.

The simulated wheat yield for rain-fed wheat production under alternative fertilizer

application regime for selected SSA countries is given in Table 7. The average simulated rain fed

wheat yield data for the individual country was obtained by summing all pixel level yield data

and dividing it by the total number of pixels. The yield variability was measured using

coefficient of variation (CV) given as the standard deviation divided by mean. For T0, the

average wheat yield varied from 742 kg/ha for Zimbabwe to 3022 kg/ha for Rwanda. The

average wheat yield for T0 was less than 1000 kg/ha for 4 of 12 countries analyzed. The wheat

yield variability as measured by CV varied from 22% for Burundi to 80% for Angola.

For T1 the average wheat yield varied from 1482 Kg/ha for Mozambique to 4149 Kg/ha

for Rwanda. The average wheat yield was greater than 3000 kg/ha for 4 of 12 countries. On the

other hand, wheat yield variability ranged 22% for Burundi to 81% for DRC. For T2, the average

wheat yield varied from 1838 kg/ha for Mozambique to 4915 kg/ha for Rwanda. Under T2, the

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average wheat yield was found to be greater than 3000kg/ha for 6 of 12 countries. In most cases,

the average wheat yields were the lowest and most variable under no fertilizer use.

5.2 Economic profitability of wheat

The results of parametric and non-parametric statistical analyses unambiguously

established that there was significant wheat yield response to fertilizer use in selected SSA

countries, justifying the economic analysis of fertilizer use in wheat production (CIMMYT,

1988). The average NERs and probabilities of positive NER generated under alternative fertilize

application regimes for selected SSA countries are presented in Table 7.

Overall, for T0, the average NER was positive for all countries except Angola, Ethiopia,

Madagascar, Mozambique, Zambia and Zimbabwe. For Angola and Mozambique, the average

NER was also negative for T1. For T2, the average NER was positive for all selected SSA

countries. However, the average NER was positive under each of the fertilizer application

regimes for Burundi, Kenya, Rwanda, Tanzania, DRC, and Uganda.

For T0, the percentage of pixels with positive NERs was the lowest (12%) for Angola

(Table 7). The percentage of pixels with positive NER for Ethiopia and Zimbabwe were also

very low estimated at 26% and 27%, respectively. For T1, the percentage of pixels with positive

NERs varied from 31% to 100% and the percentage of pixels with positive NERs was greater

than 60% for 9 of 12 countries. For Burundi, Kenya, Rwanda, Tanzania and Uganda, the

percentage of pixels with positive NERs was greater than 90% for T1. The results for T2 were

similar to those for T1.

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In general, profitability of wheat production increased with the level of fertilizer used for

most SSA countries3. The mean NERs and percentage of pixels with positive NERs increased

with the level of fertilizer used in wheat production. For all countries, the plots of cumulative

distribution functions of NERs (not reported here for brevity) for T1 and T2 are to the right of T0

at all NER levels and do not cross except for Madagascar and Mozambique at lower NER levels.

The results of the pair-wise K-S test also show that the NERs for alternative fertilizer application

regimes are statistically different at 1% level for all selected SSA countries (also not reported

here for brevity). The results of non-parametric analysis for NER are very conclusive –there is

unanimous ordering of the NER distributions for alternative fertilizer application regimes, the T2

gave the highest NER among the three fertilizer regimes and T1 also gave higher NER than the T0

for each country considered. Similarly, the percentage of pixels with positive NERs also

increased with the level of fertilizer used in wheat production. Higher percentage of pixels with

positive NER implies larger geographic area suitable for economically profitable wheat

production.

6 Conclusions and policy implications

3 The sensitivity of baseline results were analyzed by re-computing four different

scenarios for each key variable: wheat yields, international wheat prices, fertilizer costs and marketing costs. The four scenarios were created by decreasing/ increasing the key variables by 25% and 50% compared to their values used in the baseline analysis. The results are not reported here for space. However, overall, the results of sensitivity analysis showed the robustness of the conclusions of the baseline results to the changes in the key technical and economic variables. Under the different key variable change scenarios, 100% of recommended fertilizer rate provided the highest NER in all of the cases. However, the sensitivity analysis also highlights that economic profitability of fertilizer use in wheat production could be affected by adverse situations on the key technical and economic variables such as the decreases in wheat yields, wheat prices and the increase in marketing costs.

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The results from biophysical simulation and the economic analyses indicate that the

selected SSA countries have large areas of suitable wheat growing environments for

economically profitable wheat production under current wheat prices and production costs. If

the social and infrastructural factors that currently limit wheat production are addressed, several

countries in the African region currently dependent on wheat imports can significantly reduce or

totally eliminate imports through local production and increase their food security while also

creating employment and income opportunities for economic growth and poverty reduction.

Translating this potential into actual production will require policy support and investments in

wheat research and development to foster domestic wheat production capacities. The initial

effort should be in testing and adapting existing CIMMYT derived varieties and creating

awareness and training of farmers about opportunities in the wheat sector. This will

progressively require enhancement of value chains and services to create and strengthen

economic incentives for farmers to access inputs, grow and market wheat and remedy market

imperfections and improve domestic market integration.

The results of sensitivity analysis also underscore the need the importance of the

investments in research and development, especially extension and market infrastructure to

reduce production and marketing costs and improve wheat yields. Reducing domestic

production and marketing costs of wheat can enhance the competitiveness of domestic wheat

production with wheat imports. Higher-yielding, disease-resistant wheat varieties and improved

wheat agronomic practices are desirable to enhance the profitability of wheat.

Thus, given the strategic importance of wheat for food security and political stability in

Africa, there is justification for countries to explore these opportunities in the wheat sector. This

transition cannot however occur immediately and requires careful assessment of specific

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opportunities and constraints in each country. This will also require a shift in development

assistance to wheat in Africa for countries to explore, enhance and exploit this potential for food

security and poverty reduction while also complementing shortfalls in domestic production with

imports from international markets.

References

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CIMMYT, 1988. From Agronomic Data to Farmer Recommendations: An Economics Training Manual. Completely revised edition. Mexico, DF.

de Janvry, A. and Sadoulet, E. 1995. Quantitative Development Policy Analysis. Baltimore and London, The John Hopkins University Press

Gittinger, P J. 1982. Economic Analysis of Agricultural Projects. Second Edition. Baltimore and London, John Hopkins University Press.

Hoogenboom G., Jones J.W., Wilkens P.W., Porter C.H., Boote K.J., Hunt L.A., Singh U., Lizaso J.L., White J.W., Uryasev O., Royce F.S., Ogoshi R., Gijsman A.J., Tsuji G.Y. 2010 Decision Support System for Agrotechology Transfer (DSSAT) Version 4.5, University of Hawaii, Honolulu, HI.

Jayne, T.S., N. Mason, R. Myers, J. Ferris, D. Mather, M. Beaver, N. Lenski, A. Chapoto, and D. Boughton. 2010. Patterns and Trends in Food Staples Markets in Eastern and Southern Africa: Toward the Identification of Priority Investments and Strategies for Developing Markets and Promoting Smallholder Productivity Growth. MSU International Development Working Paper No. 104. Department of Agricultural, Food and Resource Economics, Department of Economics. East Lansing: Michigan State University.

Jones J.W., Hoogenboom G., Porter C.H., Boote K.J., Batchelor W.D., Hunt L.A., Wilkens P.W., Singh U., Gijsman A.J., Ritchie J.T. 2003 The DSSAT cropping system model. European Journal of Agronomy 18:235-265. DOI: Pii S1161-0301(02)00107-7.

Langyintuo, A., E.K. Yiridoe, W. Dogbe, and J. Lowenberg-Deboer. 2005. Yield and income risk-efficiency analysis of alternative systems for rice production in the Guinea Savannah of Northern Ghana. Agricultural Economics 32: 141-150.

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Morris, M.L., and Byerlee, D. 1993. Narrowing the Wheat Gap in Sub-Saharan Africa: A Review of Consumption and Production. Economic Development and Cultural Change, 41(4): 737-761.

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Shively, Gerald E. 1999. Risks and Returns from Soil Conservation: Evidence from Low-Income Farms in the Philippines, Agricultural Economics, 21: 53-67. Tsakok, I. 1990. Agricultural Price Policy: a Practitioner's Guide to Partial Equilibrium Analysis. Ithaca, New York, Cornell University Press.

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Table 1 Annual total and per capita wheat production trends in selected Sub-Saharan African countries and regions, 1991-2000 to 2001-2008

Country/region Total wheat production (thousand tons/year)

Per capita wheat production Kg/year Annual growth rate (%)

1990’s 2000’s 1990’s 2000’s 1990’s 2000’s Angola 4.0 4.3 0.3(21.2) 0.3(5.7) 4.1 -0.6 Burundi 8.5 8.3 1.4(11.7) 1.1(5.6) -3.3 -3.5 DRC 9.7 8.6 0.2(9.8) 0.1(1.7) -0.7 -2.8 Ethiopia 1014.9 2186.9 17.3(7.3) 29.2(11.5) 2.7 6.1 Kenya 263.8 306.7 9.6(13.9) 8.7(21.9) -5.2 -8.2 Madagascar 8.3 10.9 0.6(38.8) 0.6(10.3) -2.5 -2.0 Mozambique 2.6 2.0 0.2(22.6) 0.1(11.7) -6.3 0.8 Rwanda 6.8 28.2 1.0(39.7) 2.9(28.4) -11.3 25.0 Tanzania 73.0 86.7 2.4(25.8) 2.2(14.8) -4.4 -0.7 Uganda 9.5 16.5 0.4(7.5) 0.6(4.4) -0.6 1.7 Zambia 62.4 114.2 6.9(16.1) 9.8(24.3) -0.6 5.3 Zimbabwe 220.2 161.4 18.7(37.8) 12.8(37.8) 4.3 -19.0 Eastern Africa 1684.1 2942.9 7.5(6.7) 10.2(10.5) 0.8 3.5 Middle Africa 17.3 18.2 0.2(10.1) 0.2(14.5) 0.4 2.9 West Africa 72.1 72.7 0.3(29.8) 0.3(12.6) 6.8 -1.9 North Africa 11924.3 16001.3 73.4(21.8) 83.1(15.5) -1.7 1.3 South Africa 2070.0 2047.9 43.4(18.4) 37.4(15.9) 0.5 -2.6 Africa 15767.7 21083.1 2.7(18.3) 3.2(12.2) -0.5 1.7 World 571168.0 618718.0 99.1(3.5) 95.0(4.1) -0.4 0.9 Source: Based on FAOSTAT.

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Table 2 Annual total and per capita wheat consumption trends in selected Sub-Saharan African countries and regions, 1991-2000 to 2001-2008

Country/region Total wheat consumption

(thousand tons/year)

Per capita wheat consumption Kg/year Annual growth rate (%)

1990’s 2000’s 1990’s 2000’s 1990’s 2000’s Angola 36.9 40.2 3.0(41.5) 2.5(54.3) -1.7 -3.3 Burundi 10.2 10.3 1.7(10.8) 1.4(15.1) -8.3 -1.6 DRC 129.9 280.4 3.0(13.9) 4.9(31.8) -5.3 9.3 Ethiopia 1575.4 2945.1 27.0(16.0) 40.0(11.7) 0.7 2.2 Kenya 643.1 895.1 22.9(12.5) 25.5(8.4) 4.3 -1.6 Madagascar 56.8 66.6 4.3(33.2) 3.7(28.0) -4.5 11.6 Mozambique 158.0 355.4 9.7(33.6) 17.2(41.9) 2.2 8.0 Rwanda 10.4 24.9 1.6(22.1) 2.6(23.7) -21.2 25.7 Tanzania 174.5 564.5 5.6(28.7) 14.6(17.9) 13.2 4.4 Uganda 33.9 260.3 1.5(57.1) 9.1(28.9) 13.0 20.7 Zambia 96.2 170.4 10.5(18.2) 15.0(24.6) 4.0 -2.3 Zimbabwe 288..1 232.6 24.6(39.8) 18.5(45.6) 0.1 -9.2 Eastern Africa 3370.0 6031.3 15.0(8.1) 21.3(9.0) 2.7 2.0 Middle Africa 362.0 808.6 4.2(13.3) 7.3(14.6) 3.7 4.1 West Africa 2052.6 4650.1 9.6(15.6) 17.4(41.1) 8.3 2.6 North Africa 24780.3 32495.4 152.2(9.4) 170.3(4.5) 0.1 1.4 South Africa 2943.7 3089.9 62.0(19.1) 56.5(9.9) -1.6 3.3 Africa 33508.8 47075.3 45.9(8.1) 52.1(2.9) 0.3 1.6 World 563573.5 607238.5 97.8(4.0) 93.8(3.9) -0.2 0.6 Source: Based on FAOSTAT.

20

Table 3 Annual total quantity and value of wheat import trends in selected Sub-Saharan African countries and regions, 1991-2000 to 2001-2008

Country/region Total quantity of wheat import Total value of wheat import Thousand tons/year Annual growth

rate (%) Million US $/ year Annual growth

rate (%) 1990’s 2000’s 1990’s 2000’s 1990’s 2000’s 1990’s 2000’s Angola 32.8(45.5) 35.9(59.3) 0.4 0.0 5.0(57.7) 5.8(68.7) 1.5 -1.0 Burundi 1.7(54.8) 2.0(58.9) -52.5 34.5 0.6(56.3) 0.8(68.6) -55.5 40.2 DRC 120.2(15.9) 273.1(31.1) -2.7 12.9 25.2(31.4) 49.5(30.8) -5.7 25.3 Ethiopia 560.5(50.4) 869.3(41.1) -0.4 -3.5 99.0(41.8) 227.8(47.0) -1.4 10.7 Kenya 392.9(23.1) 567.6(16.1) 12.6 1.4 69.9(26.2) 114.7(22.7) 14.3 13.3 Madagascar 48.5(38.3) 55.7(35.1) -1.7 17.1 8.5(49.8) 15.2(45.9) 2.8 33.7 Mozambique 155.4(34.5) 353.3(41.2) 5.3 10.6 23.3(39.1) 66.1(25.4) 3.0 22.7 Rwanda 3.6(52.5) 2.6(77.0) -82.0 93.3 1.4(42.6) 1.2(71.6) -69.8 90.4 Tanzania 106.7(49.2) 532.7(22.1) 49.7 6.4 22.1(43.0) 119.2(23.1) 54.3 17.7 Uganda 24.5(90.0) 244.9(30.5) 21.8 26.8 6.9(73.6) 81.2(25.1) 24.5 30.7 Zambia 33.8(41.7) 66.6(41.6) 19.5 -11.7 7.8(37.7) 19.7(32.7) 13.3 -2.9 Zimbabwe 97.0(72.7) 57.6(110.0) 2.2 5.8 24.6(74.3) 19.9(98.4) 2.2 21.4 Eastern Africa 1734(17.4) 3254.1(10.3) 7.1 3 315.0(18.1) 764.1(15.7) 7.2 15.0 Middle Africa 344.8(14.0) 792.6(14.4) 7.0 7.1 63.9(12.3) 172.9(11.5) 6.7 15.8 West Africa 2005.8(15.7) 4583(44.1) 10.8 5.2 336.9(13.7) 1045(33.9) 10.0 20.4 North Africa 12858.1(7.5) 17031.1(13.2) 3.4 4.4 1983.5(15.5) 3753.4(32.9) 6.4 15.5 South Africa 916.8(27.6) 1139.0(22.5) -2.8 17.8 133.0(35.3) 218.4(27.7) 0.1 27.0 Africa 17859.5(6.3) 26800.4(8.0) 4.3 5.3 2832.4(13.4) 5954.6(21.3) 6.6 16.8 World 100615.0(7.2) 117570.0(4.3) 1.7 2.0 16674.9(10.9) 24558.4(20.8) 1.1 14.6 Source: Based on FAOSTAT.

21

Table 4 Trends in wheat self-sufficiency ratio in selected Sub-Saharan African countries and the World, 1961-70 to 2001-2008

Country/region Wheat self-sufficiency ratio (%)

1960’s 1970’s 1980’s 1990’s 2000’s Angola 34 14 7 12 13 Burundi 100 97 65 86 83 DRC 86 8 3 8 4 Ethiopia 99 83 68 66 70 Kenya 118 86 64 44 37 Madagascar 0 55 22 16 19 Mozambique 15 6 5 2 1 Rwanda 93 49 53 77 92 Tanzania 55 72 78 53 16 Uganda 7 62 48 43 9 Zambia 1 3 30 67 62 Zimbabwe 15 83 79 79 77 Eastern Africa 83 70 60 51 47 Middle Africa 33 8 4 5 2 West Africa 11 2 5 4 2 North Africa 69 51 41 48 48 South Africa 85 106 104 70 67 Africa 70 55 46 47 44 World 102 102 103 101 101 Source: Based on FAOSTAT.

22

Table 5 Trends in nominal and real international wheat prices, US HRW FOB Gulf of Mexico, 1961-70 to 2001-2008

Period Nominal price Real price US $/kg Annual Growth rate (%) US $/kg Annual Growth rate (%)

1960’s 61.6(6.2) -0.4 242.9(7.5) -1.9 1970’s 129.7(23.9) 7.8 250.6(26.9) -3.0 1980’s 145.9(13.2) -2.0 171.4(12.9) -5.6 1990’s 146.6(19.1) -2.0 134.5(15.1) -1.1 2000’s 195.1(17.4) 8.3 173.0(15.9) 5.5 Source: Based on FAOSTAT.

23

Table 6 The results of ANOVA of simulated wheat yield response data under alternative fertilizer application regimes for selected SSA countries

Country

F-Value Number of pixels

Adjusted R2

Model

Agro-ecological zone

Fertilizer

Fertilizer by Agro-ecological zone interaction

Angola 2353*** 1848*** 59*** 180*** 23394 0.52 Burundi 1040*** -- 1040*** -- 588 0.78 Ethiopia 1048*** 9*** 79*** 0 13533 0.46 Kenya 331*** 423*** 73*** 8*** 3351 0.44 Madagascar 522*** 961*** 704*** 75*** 11952 0.32 Mozambique 1147*** 40*** 1577*** 57*** 12909 0.41 Rwanda 255*** -- 255*** -- 702 0.42 Tanzania 802*** 937*** 85*** 27*** 10578 0.45 DRC 386*** 491*** 103*** 21*** 4692 0.40 Uganda 194*** 11*** 8*** 0 999 0.49 Zambia 2868*** 2254*** 4938*** 264*** 14574 0.61 Zimbabwe 1023*** 602*** 1014*** 111*** 10470 0.52 Source: Wheat yield response data was based on wheat crop growth simulation model using DSSAT.

24

Table 7 The simulated wheat yield, net economic returns and percentage of pixels with positive net economic returns for rain fed wheat production under alternative fertilizer application regimes for selected SSA countries

Country Fertilizer level Yield (Kg/Ha) Net economic return (US $/Ha)

Percentage of pixels with positive net economic return (%)

Angola T0 748.75(80)* -223.77(71)* 12 T1 1714.27(48) -58.09(428) 33 T2 2357.91(43) 41.48(765) 43 Burundi T0 2440.81(22) 706.27(34) 100 T1 3772.22(12) 1266.71(17) 100 T2 4671.91(10) 1632.77(13) 100 Ethiopia T0 879.82(50) -92.25(179) 26 T1 1709.27(35) 151.79(152) 71 T2 2206.77(32) 282.83(95) 87 Kenya T0 2708.75(38) 581.29(63) 96 T1 3853.66(32) 959.83(45) 100 T2 4658.70(30) 1215.98(40) 100 Madagascar T0 1145.72(41) -16.04(1014) 39 T1 1682.86(53) 127.19(244) 56 T2 2005.02(58) 198.64(206) 58 Mozambique T0 858.48(51) -166.41(81) 12 T1 1482.01(31) -45.31(353) 37 T2 1838.46(31) 7.35(2686) 48 Rwanda T0 3022.38(29) 959.15(41) 100 T1 4149.15(22) 1424.24(29) 100 T2 4914.58(19) 1728.23(25) 100 Tanzania T0 1721.68(52) 111.25(275) 57 T1 2674.02(37) 344.47(105) 77 T2 3334.74(33) 496.23(84) 86 DRC T0 1425.62(81) 42.46(965) 30 T1 2477.00(49) 304.00(154) 78 T2 3191.31(40) 468.58(112) 86 Uganda T0 2118.84(34) 437.55(67) 96 T1 3310.81(25) 863.39(40) 100 T2 4140(23) 1148.45(34) 100 Zambia T0 1026.03(39) -7.46(2402) 46 T1 1884.39(33) 271.80(106) 78 T2 2461.57(33) 446.81(83) 87 Zimbabwe T0 741.86(75) -102.76(218) 27 T1 1637.22(42) 209.71(140) 75 T2 2191.35(40) 390.09(97) 85 Note: T0 denotes 0% of recommended fertilizer rate; T1 denotes 50% of recommended fertilizer rate; and T2 denotes 100% of recommended fertilizer rate. *The figures in parenthesis are coefficient of variations given in percent. Note that the normality of individual yield and net economic return distributions was rejected in all cases at a probability of less than 1%. Thus, non-parametric K-S test statistic was used to test the difference between a pair of net economic returns; there was statistically significant difference at a probability of less than 1% for all possible pairs (T0 versus T1, T0 versus T2 and T1 versus T2).

25

10

20

30

40

50

Mill

ion

tons

1961

1969

1977

1985

1993

2001

2009

Year

Consumption Production

Source: FAOSTAT.

in Africa, 1961 to 2008Figure 1: Trends in wheat production and consumption gap

40 50 60 70 80 90

1960 1970 1980 1990 2000 2010 Year

East Africa

-20 0

20 40 60

1960 1970 1980 1990 2000 2010 Year

Middle Africa

40 50 60 70 80

1960 1970 1980 1990 2000 2010 Year

North Africa

50

100 150 200 250

1960 1970 1980 1990 2000 2010 Year

South Africa

0 5

10 15 20

1960 1970 1980 1990 2000 2010 Year

West Africa

40 50 60 70 80

1960 1970 1980 1990 2000 2010 Year

Africa

Source: FAOSTAT

for selected regions in Africa (1961-2010) Figure 2: Trends in wheat self-sufficiency ratio

26

010

020

030

040

0U

S$/

ton

1960 1970 1980 1990 2000 2010year

Source: FAOSTAT.

US hard red winter, FOB Gulf, 1961-2010Figure 4: Trends in nominal and real wheat prices (US$/ton)

0 10 20 30 40 50

1960 1970 1980 1990 2000 2010 Year

Angola

40 60 80 100

1960 1970 1980 1990 2000 2010 Year

Burundi

40 60 80 100

1960 1970 1980 1990 2000 2010 Year

Ethiopia

0 50 100 150

1960 1970 1980 1990 2000 2010 Year

Kenya

0 20 40 60 80 100

1960 1970 1980 1990 2000 2010 Year

Madagascar

0 10 20 30

1960 1970 1980 1990 2000 2010 Year

Mozambique

20 40 60 80 100

1960 1970 1980 1990 2000 2010 Year

Rwanda

0 20 40 60 80 100

1960 1970 1980 1990 2000 2010 Year

Tanzania

-50 0 50 100

1960 1970 1980 1990 2000 2010 Year

DRC

0 20 40 60 80 100

1960 1970 1980 1990 2000 2010 Year

Uganda

0

50

100

1960 1970 1980 1990 2000 2010 Year

Zambia

0 20 40 60 80 100

1960 1970 1980 1990 2000 2010 Year

Zimbabwe

Source: FAOSTAT

for selected SSA countries (1961-2010) Figure 3: Trends in wheat self-sufficiency ratio

27

Appendix 1 Wheat marketing costs and prices for selected SSA countries

Note: *The figures in parenthesis are coefficient of variations given in percent. 1The IPP was computed based on USA hard red winter wheat Gulf free on board (FOB), insurance and freight (CIF) ex Durban port plus port duty and port handling costs and handling and transport costs from ports to capital city of each country. 2The port to capital city transport costs per ton was obtained for each country from the GIS simulation based on a least cost route approach. 3Pixel to capital city marketing costs was obtained by dividing the pixel to capital city transport costs by 0.5. The adjusted producer parity price was derived by subtracting the pixel to capital city marketing costs from the IPP.

Country Capital city Port used

Port to capital city cost of transport (US $/Ton)

Import parity prices (IPP)1 US$/Kg

Pixel to capital city transport costs (US$/Kg)2

Pixel to capital city marketing costs (US$/Kg)3

Adjusted producer parity price (US $/Kg)

Angola Luanda Luanda 0.00 0.40 0.09(41)* 0.17(41) 0.23(32) Burundi Bujumbura Mombasa 103.67 0.53 0.01(56) 0.03(56) 0.50(3) Ethiopia Addis Ababa Djibouti 43.16 0.47 0.04(52) 0.09(52) 0.38(13) Kenya Nairobi Mombasa 20.40 0.44 0.02(59) 0.04(59) 0.40(6) Madagascar Toamasina Toamasina 29.03 0.45 0.04(50) 0.08(50) 0.36(12) Mozambique Maputo Maputo 0.00 0.40 0.06(54) 0.12(54) 0.28(23) Rwanda Kigali Mombasa 90.29 0.51 0.01(73) 0.02(73) 0.49(3) Tanzania Dares Salaam Dares

Salaam 1.62 0.43 0.06(49) 0.11(49) 0.31(18)

DRC Boma Boma 20.13 0.44 0.06(56) 0.13(56) 0.32(23) Uganda Kampala Mombasa 62.69 0.49 0.03(57) 0.05(57) 0.43(7) Zambia Lusaka Beira 98.38 0.52 0.06(55) 0.11(55) 0.41(15) Zimbabwe Harare Beira 73.19 0.50 0.03(54) 0.07(54) 0.43(8)

28

Appendix 2 Enterprise budget for rain fed wheat production for representative smallholder farmer in Hettosa-Tiyyo area of Ethiopia*, 2009

Source: Based on CIMMYT-SG200 survey data for Hettosa-Tiyyo area of Ethiopia for 2009. Note: *

The fertilizer and seed rates are based on research recommendation. 1 Family labor is valued using the on-going wage rate of 25 Birr/work-day (20 Birr plus lunch worth of 5 Birr) at the time of the survey. The child labor was adult equivalent which is half of the adult labor time. 2The oxen pair cost also includes the payment for one person working with oxen. 3The cost of wheat production per meter square was 0.3 Birr.

Cost Item Unit Quantity (Unit/ha) Unit cost (Birr/Unit)

Total cost (Birr/ha)

Labor Family labor1 -men labor Work-day 6.3 25.0 157.5 -women labor Work-day 3.8 25.0 95 -children labor Work-day 3.0 25.0 75 Labor exchange Work-day 12.1 25.0 302.5 Hired labor Work-day 16.5 25.0 412.5 Total labor costs 1042.5 Oxen pair power2 Work-day 11.7 75.0 877.5 Fertilizer -DAP Kg 50.0 6.9 345.0 -Urea Kg 50.0 6.2 310.0 Total fertilizer costs 655.0 Herbicides -2-4-D Liter 0.4 69.5 27.8 -Topic Liter 0.3 380.0 114 Total herbicide costs 141.8 Pesticides Liter 0.3 300.0 90.0 Seed Kg 17.5 6.8 119.0

Total variable costs3 2925.8