Bekele Shiferaw 1, Asfaw Negassa, Jawoo Koo, Stanley Wood, Kai Sonder, Hans Joachim Braun, and Thomas Payne

Transcription

1 Future of Wheat Production in Sub-Saharan Africa: Analyses of the Expanding Gap between Supply and Demand and Economic Profitability of Domestic Production Bekele Shiferaw 1, 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) This conference paper has not been peer reviewed. Any opinions stated herein are those of the author(s) and are not necessarily endorsed by or representative of IFPRI or of the cosponsoring or supporting organizations.

2 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 , 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, 2

3 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 3

4 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 ratios 2 have been declining since the 2 The self-sufficiency ratio is given as the ratio of production to the sum of production plus net import. 4

5 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 openeconomy 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, 5

6 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 6

7 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 T 0 ), 50% (denoted as T 1 ) and 100% (denoted as T 2 ) 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 7

8 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 rainfed 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: The average FOB, freight, and insurance costs was estimated at 270 US$/ton, 51 US$/ton, and 3 US$/ton, 8

9 respectively for the period from April 2010 to May 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). 9

10 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 T 2 and 5% less for the T 0 as compared to T 1. Fertilizer cost was based onfarm 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. 10

11 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 nonnormality 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 R 2 was greater than 30% indicating that fertilizer use, agro-ecological zones and the interaction between agroecological 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. 11

12 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 nonparametric 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 T 2 gave the highest wheat yield followed by T 1 and the lowest wheat yield was observed for T 0. 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 T 0, the average wheat yield varied from 742 kg/ha for Zimbabwe to 3022 kg/ha for Rwanda. The average wheat yield for T 0 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 T 1 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 T 2, the average wheat yield varied from 1838 kg/ha for Mozambique to 4915 kg/ha for Rwanda. Under T 2, the 12

13 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 T 0, 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 T 1. For T 2, 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 T 0, 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 T 1, 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 T 1. The results for T 2 were similar to those for T 1. 13

14 In general, profitability of wheat production increased with the level of fertilizer used for most SSA countries 3. 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 T 1 and T 2 are to the right of T 0 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 T 2 gave the highest NER among the three fertilizer regimes and T 1 also gave higher NER than the T 0 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. 14

15 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 15

16 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 Bekele, W Stochastic Dominance Analysis of Soil and Water Conservation in Subsistence Crop Production in the Eastern Ethiopian Highlands: The Case of the Hunde-Lafto Area, Environmental & Resource Economics, 32: CIMMYT, From Agronomic Data to Farmer Recommendations: An Economics Training Manual. Completely revised edition. Mexico, DF. de Janvry, A. and Sadoulet, E Quantitative Development Policy Analysis. Baltimore and London, The John Hopkins University Press Gittinger, P J 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 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 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 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 The DSSAT cropping system model. European Journal of Agronomy 18: DOI: Pii S (02) Langyintuo, A., E.K. Yiridoe, W. Dogbe, and J. Lowenberg-Deboer Yield and income risk-efficiency analysis of alternative systems for rice production in the Guinea Savannah of Northern Ghana. Agricultural Economics 32:

17 Morris, M.L., and Byerlee, D Narrowing the Wheat Gap in Sub-Saharan Africa: A Review of Consumption and Production. Economic Development and Cultural Change, 41(4): Negassa, A. and Myers, R.J Estimating policy effects on spatial market efficiency: an extension of the parity bounds model. American Journal of Agricultural Economics 89(2): Rashid, S. and N. Minot Are staple food markets in Africa efficient? Prepared for the Comesa policy seminar on Food price variability: Causes, consequence, and policy options, Maputo, Mozambique, January, under the African Agricultural Marketing Project (AAMP). Shively, Gerald E Risks and Returns from Soil Conservation: Evidence from Low-Income Farms in the Philippines, Agricultural Economics, 21: Tsakok, I Agricultural Price Policy: a Practitioner's Guide to Partial Equilibrium Analysis. Ithaca, New York, Cornell University Press. 17

18 Table 1 Annual total and per capita wheat production trends in selected Sub-Saharan African countries and regions, to Country/region Total wheat production Per capita wheat production (thousand tons/year) Kg/year Annual growth rate (%) 1990 s 2000 s 1990 s 2000 s 1990 s 2000 s Angola (21.2) 0.3(5.7) Burundi (11.7) 1.1(5.6) DRC (9.8) 0.1(1.7) Ethiopia (7.3) 29.2(11.5) Kenya (13.9) 8.7(21.9) Madagascar (38.8) 0.6(10.3) Mozambique (22.6) 0.1(11.7) Rwanda (39.7) 2.9(28.4) Tanzania (25.8) 2.2(14.8) Uganda (7.5) 0.6(4.4) Zambia (16.1) 9.8(24.3) Zimbabwe (37.8) 12.8(37.8) Eastern Africa (6.7) 10.2(10.5) Middle Africa (10.1) 0.2(14.5) West Africa (29.8) 0.3(12.6) North Africa (21.8) 83.1(15.5) South Africa (18.4) 37.4(15.9) Africa (18.3) 3.2(12.2) World (3.5) 95.0(4.1) Source: Based on FAOSTAT. 18

19 Table 2 Annual total and per capita wheat consumption trends in selected Sub-Saharan African countries and regions, to 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 (41.5) 2.5(54.3) Burundi (10.8) 1.4(15.1) DRC (13.9) 4.9(31.8) Ethiopia (16.0) 40.0(11.7) Kenya (12.5) 25.5(8.4) Madagascar (33.2) 3.7(28.0) Mozambique (33.6) 17.2(41.9) Rwanda (22.1) 2.6(23.7) Tanzania (28.7) 14.6(17.9) Uganda (57.1) 9.1(28.9) Zambia (18.2) 15.0(24.6) Zimbabwe (39.8) 18.5(45.6) Eastern Africa (8.1) 21.3(9.0) Middle Africa (13.3) 7.3(14.6) West Africa (15.6) 17.4(41.1) North Africa (9.4) 170.3(4.5) South Africa (19.1) 56.5(9.9) Africa (8.1) 52.1(2.9) World (4.0) 93.8(3.9) Source: Based on FAOSTAT. 19

20 Table 3 Annual total quantity and value of wheat import trends in selected Sub-Saharan African countries and regions, to 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) (57.7) 5.8(68.7) Burundi 1.7(54.8) 2.0(58.9) (56.3) 0.8(68.6) DRC 120.2(15.9) 273.1(31.1) (31.4) 49.5(30.8) Ethiopia 560.5(50.4) 869.3(41.1) (41.8) 227.8(47.0) Kenya 392.9(23.1) 567.6(16.1) (26.2) 114.7(22.7) Madagascar 48.5(38.3) 55.7(35.1) (49.8) 15.2(45.9) Mozambique 155.4(34.5) 353.3(41.2) (39.1) 66.1(25.4) Rwanda 3.6(52.5) 2.6(77.0) (42.6) 1.2(71.6) Tanzania 106.7(49.2) 532.7(22.1) (43.0) 119.2(23.1) Uganda 24.5(90.0) 244.9(30.5) (73.6) 81.2(25.1) Zambia 33.8(41.7) 66.6(41.6) (37.7) 19.7(32.7) Zimbabwe 97.0(72.7) 57.6(110.0) (74.3) 19.9(98.4) Eastern Africa 1734(17.4) (10.3) (18.1) 764.1(15.7) Middle Africa 344.8(14.0) 792.6(14.4) (12.3) 172.9(11.5) West Africa (15.7) 4583(44.1) (13.7) 1045(33.9) North Africa (7.5) (13.2) (15.5) (32.9) South Africa 916.8(27.6) (22.5) (35.3) 218.4(27.7) Africa (6.3) (8.0) (13.4) (21.3) World (7.2) (4.3) (10.9) (20.8) Source: Based on FAOSTAT. 20

21 Table 4 Trends in wheat self-sufficiency ratio in selected Sub-Saharan African countries and the World, to Country/region Wheat self-sufficiency ratio (%) 1960 s 1970 s 1980 s 1990 s 2000 s Angola Burundi DRC Ethiopia Kenya Madagascar Mozambique Rwanda Tanzania Uganda Zambia Zimbabwe Eastern Africa Middle Africa West Africa North Africa South Africa Africa World Source: Based on FAOSTAT. 21

22 Table 5 Trends in nominal and real international wheat prices, US HRW FOB Gulf of Mexico, to Period Nominal price Real price US $/kg Annual Growth rate (%) US $/kg Annual Growth rate (%) 1960 s 61.6(6.2) (7.5) s 129.7(23.9) (26.9) s 145.9(13.2) (12.9) s 146.6(19.1) (15.1) s 195.1(17.4) (15.9) 5.5 Source: Based on FAOSTAT. 22

23 Table 6 The results of ANOVA of simulated wheat yield response data under alternative fertilizer application regimes for selected SSA countries Country Model Agroecological zone F-Value Fertilizer Fertilizer by Agroecological zone interaction Number of pixels Adjusted R 2 Angola 2353 *** 1848 *** 59 *** 180 *** Burundi 1040 *** *** Ethiopia 1048 *** 9 *** 79 *** Kenya 331 *** 423 *** 73 *** 8 *** Madagascar 522 *** 961 *** 704 *** 75 *** Mozambique 1147 *** 40 *** 1577 *** 57 *** Rwanda 255 *** *** Tanzania 802 *** 937 *** 85 *** 27 *** DRC 386 *** 491 *** 103 *** 21 *** Uganda 194 *** 11 *** 8 *** Zambia 2868 *** 2254 *** 4938 *** 264 *** Zimbabwe 1023 *** 602 *** 1014 *** 111 *** Source: Wheat yield response data was based on wheat crop growth simulation model using DSSAT. 23

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 T (80) * (71) * 12 T (48) (428) 33 T (43) 41.48(765) 43 Burundi T (22) (34) 100 T (12) (17) 100 T (10) (13) 100 Ethiopia T (50) (179) 26 T (35) (152) 71 T (32) (95) 87 Kenya T (38) (63) 96 T (32) (45) 100 T (30) (40) 100 Madagascar T (41) (1014) 39 T (53) (244) 56 T (58) (206) 58 Mozambique T (51) (81) 12 T (31) (353) 37 T (31) 7.35(2686) 48 Rwanda T (29) (41) 100 T (22) (29) 100 T (19) (25) 100 Tanzania T (52) (275) 57 T (37) (105) 77 T (33) (84) 86 DRC T (81) 42.46(965) 30 T (49) (154) 78 T (40) (112) 86 Uganda T (34) (67) 96 T (25) (40) 100 T (23) (34) 100 Zambia T (39) -7.46(2402) 46 T (33) (106) 78 T (33) (83) 87 Zimbabwe T (75) (218) 27 T (42) (140) 75 T (40) (97) 85 Note: T 0 denotes 0% of recommended fertilizer rate; T 1 denotes 50% of recommended fertilizer rate; and T 2 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 (T 0 versus T 1, T 0 versus T 2 and T 1 versus T 2). 24

25 50 Figure 1: Trends in wheat production and consumption gap in Africa, 1961 to Million tons Consumption Production Source: FAOSTAT. Figure 2: Trends in wheat self-sufficiency ratio for selected regions in Africa ( ) 90 East Africa 60 Middle Africa 80 North Africa South Africa Source: FAOSTAT West Africa Africa 25

26 Figure 3: Trends in wheat self-sufficiency ratio for selected SSA countries ( ) Angola Burundi Ethiopia Kenya Madagascar Mozambique Rwanda Tanzania DRC Uganda Zambia Zimbabwe Source: FAOSTAT US$/ ton Figure 4: Trends in nominal and real wheat prices (US$/ton) US hard red winter, FOB Gulf, year Source: FAOSTAT. 26

27 Appendix 1 Wheat marketing costs and prices for selected SSA countries 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 (41) * 0.17(41) 0.23(32) Burundi Bujumbura Mombasa (56) 0.03(56) 0.50(3) Ethiopia Addis Ababa Djibouti (52) 0.09(52) 0.38(13) Kenya Nairobi Mombasa (59) 0.04(59) 0.40(6) Madagascar Toamasina Toamasina (50) 0.08(50) 0.36(12) Mozambique Maputo Maputo (54) 0.12(54) 0.28(23) Rwanda Kigali Mombasa (73) 0.02(73) 0.49(3) Tanzania Dares Salaam Dares (49) 0.11(49) 0.31(18) Salaam DRC Boma Boma (56) 0.13(56) 0.32(23) Uganda Kampala Mombasa (57) 0.05(57) 0.43(7) Zambia Lusaka Beira (55) 0.11(55) 0.41(15) Zimbabwe Harare Beira (54) 0.07(54) 0.43(8) Note: * The figures in parenthesis are coefficient of variations given in percent. 1 The 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. 2 The port to capital city transport costs per ton was obtained for each country from the GIS simulation based on a least cost route approach. 3 Pixel 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. 27

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 Note: * Cost Item Unit Quantity (Unit/ha) Unit cost Total cost (Birr/ha) (Birr/Unit) Labor Family labor 1 -men labor Work-day women labor Work-day children labor Work-day Labor exchange Work-day Hired labor Work-day Total labor costs Oxen pair power 2 Work-day Fertilizer -DAP Kg Urea Kg Total fertilizer costs Herbicides -2-4-D Liter Topic Liter Total herbicide costs Pesticides Liter Seed Kg Total variable costs 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. 2 The oxen pair cost also includes the payment for one person working with oxen. 3 The cost of wheat production per meter square was 0.3 Birr. 28