| Literature DB >> 32566470 |
Guillaume Soullier1,2, Matty Demont3, Aminou Arouna4, Frédéric Lançon1,2, Patricio Mendez Del Villar5,6.
Abstract
Following the food price crisis in 2008, African governments implemented policies aiming at crowding in investment in rice value chain upgrading to help domestic rice compete with imports. We assess the state of rice value chain upgrading in West Africa by reviewing evidence on rice millers' investment in semi-industrial and industrial milling technologies, contract farming and vertical integration during the post-crisis period 2009-2019. We find that upgrading is more dynamic in countries with high rice production and import bills and limited comparative advantage in demand. However, scaling of upgrading faces several challenges in terms of vertical coordination, technology, finance and policies. Our assessment can help value chain actors and policy makers refine upgrading strategies and policies to increase food security in West Africa.Entities:
Keywords: Africa; Contract farming; Milling; Rice; Upgrading; Value chain
Year: 2020 PMID: 32566470 PMCID: PMC7299077 DOI: 10.1016/j.gfs.2020.100365
Source DB: PubMed Journal: Glob Food Sec
State of rice value chain upgrading in 15 countries in West Africa, 2009–2019.
| Country | Number of investments that were operational in 2019 | Aggregate upgraded milling capacity (tons per hour) | Origin of investments | Vertical coordination | Exposure to imports | Average annual milled rice production, 2009–2019 (103 tons) | Source | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Contract farming (number of farmers) | Share of contracted farmers (%) | Vertical integration (hectares) | Share of area under vertical integration (%) | Import barriersa | 2008 import bill (106 US$)b | ||||||
| Nigeria | 24 industrial mills | 177 | FDI, DPI | 3000 | 0.61 | 20,400 | 0.69 | None | 772 | 3736 | c,d,e |
| Senegal | 15 industrial and semi-industrial mills | 60 | FDI, DPI | 3500 | 1.85 | 3590 | 1.86 | None | 645 | 508 | c,f |
| Ghana | 1 industrial mill, 3 semi-industrial mills | 26 | FDI, DPI | 4000 | 9.09 | 750 | 0.34 | None | 216 | 359 | c,g,h,i |
| Mali | 4 industrial mills | 20 | FDI, DPI | – | – | 3200 | 0.44 | Physical, cultural | 66 | 1442 | j,k |
| Côte d’Ivoire | 2 industrial mills, 1 semi-industrial mill | 15 | PI, DPI | 10 (experimental) | 0.00 | – | – | Cultural | 472 | 1111 | l |
| Burkina Faso | 1 industrial mill, 1 semi-industrial mill | 7 | DPI | 140 | 0.08 | – | – | Physical | 56 | 204 | m,n,o,p |
| Liberia | 2 semi-industrial mills | 4 | DPI, PI | – | – | – | – | None | 75 | 172 | c,s |
| Niger | 2 semi-industrial mills | 4 | PI | – | – | – | – | Physical | 126 | 61 | p,q,r |
| Sierra Leone | 1 semi-industrial mill | 2 | DPI | – | – | 1300 | 0.21 | Cultural | 85 | 702 | c |
| Benin | 17 ESOP | – | DPI | 140 | 0.18 | – | – | None | 185 | 142 | c,t,u,v |
| Togo | 15 ESOP | – | DPI | 100 | 0.24 | – | – | None | 9.3 | 90 | c;t,v |
| Guinea | – | – | – | – | – | – | – | Cultural | 153 | 1300 | c |
| Mauritania | – | – | – | – | – | – | – | None | 77 | 129 | c |
| The Gambia | – | – | – | – | – | – | – | Cultural | 28 | 35 | c |
| Guinea-Bissau | – | – | – | – | – | – | – | Cultural | 10 | 108 | c |
| West Africa | 57 upgraded units | 315 | 10,890 | 0.26 | 29,240 | 0.39 | 2975.3 | 10,098 | |||
Notes: The evidence of investments in semi-industrial and industrial technologies and implementation of vertical coordination needs to be interpreted as being relative to the baseline, which consists of traditional millers purchasing paddy from traders on spot markets or interlinked transactions. Only operating units and installed over the last decade are reported; unfinished investments or units that had terminated their operations were not considered as evidence of upgrading. Semi-industrial mills can theoretically process between two and three tons of paddy per hour and perform at least four quality upgrading functions. Industrial mills can theoretically process between three and five tons of paddy per hour and perform at least six quality upgrading functions. Traditional mills process under two tons of paddy per hour. Entreprises de Services et Organizations de Producteurs (ESOPs) are traditional processing units in which farmers have the opportunity to gradually become shareholders. FDI: foreign direct investment; DPI: domestic private investment; PI: public investment. Dashes indicate absence of evidence of operating upgraded technologies or vertical coordination.
Sources: aDemont (2013), Demont and Ndour (2015) and Demont et al. (2017); bIRRI (2019); cFFI and GAIN (2017); dAwotide et al. (2015); eHathie (2016); fSoullier and Moustier (2019); gAyeduvor (2018); hBidzakin et al. (2018); iBannor et al. (2017); jCoulibaly and Havard (2015); kCoulibaly and Soullier (2020); l(Soullier et al., 2019); mBila (2015); nTapsoba (2016); o(Sirdey et al., 2018); pVECO (2014); q (Fall 2016); rRiceHub (2016); sOxfam (2018); tETD (2016); uMaertens and Vande Velde (2017); vAdabe et al. (2019).
Main indicators for food security and rice sector in West Africa.
| Country/Region | Food Security | Rice sector | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Production | Imports | Consumption | Import dependency | ||||||||||||
| Average annual number of people undernourished, 2009–2018 (million) | Average annual growth of the number of undernourished people, 2009–2018 (%) | Average annual rice consumption, 2009–2013 (kg/capita/year) | Average annual growth of rice consumption, 2009–2013 (%) | Average daily energy consumption through rice, 2009–2013 (kilocalories/capita/day) | Average annual production (milled equivalent), 2009–2019 (103 ton) | Average annual growth of production, 2009–2019 (%) | Average annual area, 2009–2019 (103 ha) | Average annual number of rice growers, 2009–2019 | Average annual imports, 2009–2019 (103 ton) | Average annual growth of imports, 2009–2019 (%) | Average annual consumption, 2009–2019 (103 ton) | Average annual growth of consumption, 2009–2019 (%) | Average annual import dependency, 2009–2019 | Average annual growth of import dependency, 2009–2019 (%) | |
| World | 821.6 | −0.4 | 54 | 0.0 | 543 | 478,319 | 1.2 | 160,996 | 39,243 | 4.3 | 467,996 | 1.2 | 0.08 | 4.32 | |
| Sub-Saharan Africa | 199.0 | 3.0 | 23 | 1.4 | 232 | 15,402 | 6.8 | 11,514 | 8,053,309 | 12,409 | 7.8 | 27,249 | 6.8 | 0.46 | 1.26 |
| West Africa | 39.1 | 7.1 | 39 | 1.0 | 381 | 10,098 | 10.1 | 7565 | 4,131,103 | 8205 | 7.0 | 17,986 | 8.1 | 0.46 | −1.09 |
| Benin | 1.1 | 0.0 | 55 | −0.7 | 208 | 142 | 13.5 | 67 | 78,063 | 411 | 37.1 | 542 | 22.2 | 0.76 | 1.22 |
| Burkina | 3.5 | 1.7 | 21 | −0.7 | 208 | 204 | 6.2 | 144 | 177,653 | 395 | 15.4 | 590 | 12.4 | 0.67 | 1.40 |
| Cote d'Ivoire | 4.6 | 0.8 | 64 | −0.3 | 578 | 1111 | 19.4 | 731 | 838,962 | 1184 | 7.1 | 2180 | 9.4 | 0.54 | 4.30 |
| Gambia, The | 0.2 | 0.0 | 66 | −1.8 | 649 | 35 | −5.9 | 67 | 32,056 | 168 | 15.8 | 200 | 12.9 | 0.84 | 1.84 |
| Ghana | 1.6 | 2.6 | 32 | 1.8 | 301 | 359 | 9.1 | 220 | 43,985 | 655 | 13.6 | 986 | 10.6 | 0.66 | 2.48 |
| Guinea | 1.9 | 1.2 | 97 | −0.4 | 983 | 1300 | 5.5 | 1342 | 965,658 | 566 | 16.5 | 1752 | 8.3 | 0.32 | 1.08 |
| Guinea-Bissau | 0.4 | 7.4 | 95 | 1.5 | 948 | 108 | −0.3 | 104 | 87,870 | 135 | 4.1 | 241 | 1.9 | 0.56 | −3.89 |
| Liberia | 1.6 | 3.2 | 91 | 1.2 | 914 | 172 | −1.3 | 233 | 182,088 | 293 | 7.4 | 455 | 3.9 | 0.64 | 0.13 |
| Mali | 1.0 | 1.0 | 57 | −0.4 | 567 | 1442 | 16.3 | 730 | 405,228 | 188 | 15.5 | 1627 | 12.9 | 0.12 | −4.01 |
| Mauritania | 0.4 | 7.4 | 43 | 4.0 | 423 | 129 | 42.0 | 42 | 10,934 | 111 | 2.7 | 234 | 11.6 | 0.47 | −1.05 |
| Niger | 2.3 | 9.9 | 12 | 0.4 | 111 | 61 | 3.1 | 19 | 7121 | 295 | 4.2 | 357 | 4.0 | 0.83 | 19.23 |
| Nigeria | 15.7 | 18.8 | 28 | 2.3 | 284 | 3736 | 10.8 | 2967 | 487,573 | 2359 | −0.9 | 6032 | 5.5 | 0.39 | 5.94 |
| Senegal | 1.8 | 0.7 | 71 | 0.9 | 698 | 508 | 18.8 | 198 | 186,631 | 981 | 6.8 | 1527 | 8.9 | 0.64 | −0.59 |
| Sierra Leone | 1.7 | 0.6 | 99 | 2.0 | 929 | 702 | 3.9 | 625 | 559,304 | 295 | 45.5 | 1006 | 8.4 | 0.29 | 0.57 |
| Togo | 1.3 | −0.8 | 23 | 3.7 | 233 | 90 | 1.5 | 75 | 41,063 | 168 | 25.0 | 256 | 11.5 | 0.66 | 2.75 |
Notes: Import dependency is calculated as the share of consumption covered by imports.
Sources: Data on food security retrieved from FAO (2019); data about the rice sector retrieved from USDA (2019), except for the number of rice growers that was calculated by applying the rural populations' growth rates estimated by FAO and AfDB (2015) to the 2009 estimate of Diagne et al. (2013).
Fig. 1Average global rice price (US$/t f.o.b.).
Note: Prices are free on board (f.o.b.), weighted by their share of the total rice internationally traded.
Source: OSIRIZ/InfoArroz (2018).
Determinants of aggregate upgraded milling capacity in 15 countries in West Africa (linear regression).
| Variable | Coefficient | SE | P-value |
|---|---|---|---|
| 2008 import bill (106 USD) | 0.058 | 0.030 | 0.077* |
| Average annual milled rice production (2009–2019, 103 tons) | 0.032 | 0.007 | 0.001*** |
| Cultural import barriers | −24.660 | 9.613 | 0.028** |
| Physical import barriers | −2.769 | 11.251 | 0.811 |
| Constant | −1.780 | 8.169 | 0.832 |
Notes: Sample size = 15; R2 = 0.911; Adjusted R2 = 0.875; SE: standard error. Cultural and physical import barriers are captured through dummies. Variance inflation factors (VIF) are in the range of 1.15–2.61 with a mean VIF of 1.84. A Breusch-Pagan/Cook-Weisberg test for heteroscedasticity generates a P-value of 0.788. Significance levels: *p < 0.1; **p < 0.05; ***p < 0.01.
Source: Data compiled in Table 2.
Determinants of aggregate upgraded milling capacity in 15 countries in West Africa (stepwise linear regression).
| Variable | Coefficient | SE | P-value |
|---|---|---|---|
| 2008 import bill (106 USD) | 0.061 | 0.026 | 0.042** |
| Average annual milled rice production (2009–2019, 103 tons) | 0.032 | 0.006 | 0.000*** |
| Cultural import barriers (dummy) | −24.168 | 8.992 | 0.021** |
| Constant | −2.773 | 6.792 | 0.691 |
Notes: Sample size = 15; R2 = 0.910; Adjusted R2 = 0.886; SE: standard error. Cultural and physical import barriers are captured through dummies. Variance inflation factors (VIF) are in the range of 1.20–2.29 with a mean VIF of 1.90. A Breusch-Pagan/Cook-Weisberg test for heteroscedasticity generates a P-value of 0.774. Significance levels: *p < 0.1; **p < 0.05; ***p < 0.01.
Source: Data compiled in Table 2.