AFRICA AGRICULTURE STATUS REPORT 2016

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chains to make such a shift possible, including investments in electricity, storage facilities, transport sectors, and road and rail infrastructure. Changing farm land dynamics Parts of SSA are experiencing major changes in farm size distribution as part of broader economic transformations the region is now experiencing. Land rental markets are increasing in importance (Deininger, Xia, & Savastano, 2015). Perhaps the most salient change in farmland ownership patterns is the rise of medium-scale farms. A recent study by Jayne et al. (2016) assesses changes over the past decade in farm size distribution in Ghana, Kenya, Tanzania and Zambia and finds that among all farms below 100 hectares, the share of land on small-scale holdings smaller than 5 hectares has declined except in Kenya (Table 1.4). Medium-scale farms (defined as farm holdings between 5 and 100 hectares) account for a rising share of total farmland, especially in the 10 to 100 hectare range where the growth in the number of these farms is especially rapid. Medium-scale farms control roughly 20 percent of total farmland (including large farms) in Kenya, 32 percent in Ghana, 39 percent in Tanzania, and over 50 percent in Zambia (Figure 1.8). This trend in most cases reflects increased interest in land by urban-based professionals or influential rural people. About half of these farmers obtained their land later in life, financed by non-farm income. The rise of medium-scale farms is affecting the region in diverse ways that are difficult to generalize. Many such farms are a source of dynamism, technical change and commercialization of African agriculture. In densely populated areas, however, the growth of investor farms may be exacerbating problems of land scarcity within rural communities. Investor farmers tend to dominate farm lobby groups and influence agricultural policies and public expenditures to agriculture in their favor. Nationally representative Demographic and Health Survey (DHS) data from six countries (Ghana, Kenya, Malawi, Rwanda, Tanzania, and Zambia) show that urban households own 10 to 35 percent of total agricultural land and that this share is rising in all countries where DHS surveys were repeated (Jayne et al., 2016). This suggests a new and hitherto unrecognized channel by which medium-scale farmers may be shifting the strength and location of agricultural growth and employment multipliers between rural and urban areas. Under de facto land policies, medium-scale farms are likely to soon account for most farmland under production in many African countries. Figure 1.8. Area owned/controlled by small-scale (0 - 5 ha), medium-scale (5 - 100 ha) and large-scale (>100 ha) farm holdings in 2015 All figures are in millions Ghana Kenya Tanzania Zambia 0.5ha 5-100ha >100ha 0.84 0.65 1.29 0.74 2.19 2.21 2.94 3.32 4.21 6.12 5.86 8.59 Sources: Jayne et al. (2016); large-scale areas owned/ controlled are the sum of area under large-scale holdings according to data sources reported in Section 3, plus estimates of large-scale acquisitions between 2005 and 2015 from the Land Matrix (http://landmatrix.org/en/); data on area owned/ controlled by small-scale and medium-scale farms come from the sources reported in Section 3 AFRICA AGRICULTURE STATUS REPORT 2016 15
Table 1.4: Changes in farm structure in Ghana (1992–2005), Tanzania (2008–2012), Zambia (2001–2012) and Kenya (1994V2006) based on official national survey data Farm size category (hectares) Number of farms (% of total) % growth in number of farms between initial and latest year % of total operated land on farms between 0–100 ha Ghana 1992 2012 1992 2005 0–5 2,037,430 (92.1) 2,792,201 (84.5) 37.1 60.7 48.9 5–10 116,800 (5.3) 304,182 (9.2) 160.4 17.2 19.5 10–-20 38,690 (1.7) 130,746 (4.0) 238.0 11.0 16.0 20–100 18,980 (0.9) 78,520 (2.4) 313.7 11.1 15.6 Total 2,211,900 3,305,649 49.5 100.0 100.0 Tanzania 2008 2012 2008 2012 0–5 5,454,961 (92.8) 6,151,035 (91.4) 12.8 62.4 56.3 5–10 300,511 (5.1) 406,947 (6.0) 35.4 15.9 18.0 10–20 77,668 (1.3) 109,960 (1.6) 41.6 7.9 9.7 20–100 45,700 (0.7) 64,588 (0.9) 41.3 13.8 16.0 Total 5,878,840 6,732,530 14.5 100.0 100.0 Zambia 2008 2014 2008 2014 0–5 984,976 (88.8) 1,142,041 (78.7) 15.9 54.1 38.8 5–10 87,719 (7.9) 211,862 (14.5) 141.5 19.6 25.6 10–20 29,197 (2.6) 74,959 (5.2) 156.7 13.3 18.1 20–100 7,471 (0.7) 22,584 (1.6) 202.3 13.0 17.5 Total 1,109,362 1,451,446 227.2 100 100 Kenya 1994 2006 1994 2006 0–5 2,217,706 (92.2) 2,972,031 (98.8) 34.0 61.5 72.0 5–10 93,871 (3.9) 17,451 (0.6) -81.4 21.4 2.3 >10 92,498 (3.8) 19,493 (0.6) -78.9*** 17.1 22.7 Total 2,404,075 3,008,975 25.2 100.0 100.0 Sources: Ghana Living Standards Surveys 1992/3 and 2005/2006. Tanzania National Panel Surveys, 2008 and 2012; Zambia Ministry of Agriculture Crop Forecast Surveys, 2008, 2014; Kenya Central Bureau of Statistics, Welfare Monitoring Survey II, 1994: Basic Report (Kenya: Central Bureau of Statistics, Office of the Vice-President and Ministry of Planning and National Development, 1996); Kenya National Bureau of Statistics, Kenya Integrated Household Budget Survey 2005–2006 (Nairobi, Kenya: Kenya National Bureau of Statistics, Ministry of Planning and National Development, 2006) In short, there appears to be great dynamism in farmland ownership and farm size distribution patterns. We do not yet know how generalizable these trends are across the region. However, it is probably safe to say that existing population-based data collection platforms are systematically under-reporting a very dynamic segment of African agriculture: the mediumscale farms. While this omission is understandable, it has profound implications. Under the status quo, African governments cannot monitor, much less understand, how farm structure is changing over time. Similarly, policy makers cannot adequately address such routine questions as the magnitude and location of farm production and marketed agricultural surplus. These questions are certainly important for guiding strategic policy decisions aimed at stimulating agricultural growth, reducing rural poverty, and managing strategic food reserves and trade policies. 16 AFRICA AGRICULTURE STATUS REPORT 2016
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Page 5 and 6: FOREWORD Over the last decade, mill
Page 7 and 8: ACKNOWLEDGEMENTS The Africa Agricul
Page 9 and 10: ACRONYMS Africa Lead AAS AASR AATIF
Page 11 and 12: FMARD FSN FTF GACSA GAFSP GAIN GART
Page 13 and 14: SAKSS SBCC SCARDA SCM SDG SDI SI SM
Page 15 and 16: Introduction For decades, observers
Page 17 and 18: and changing food diets associated
Page 19 and 20: Table 1.2: Population growth of sel
Page 21 and 22: Figure 1.4: Changes in annual agric
Page 23 and 24: already occurring (Headey & Jayne,
Page 25 and 26: lease of customary land without the
Page 27: Hence, the pattern of trade illustr
Page 31 and 32: of the pathway to food and nutritio
Page 33 and 34: Jayne, T. S., Chamberlin, J., Traub
Page 35 and 36: KEY MESSAGES ONE CAADP is an unpara
Page 37 and 38: Figure 2.1.Overview of the CAADP Im
Page 39 and 40: level. The success of ReSAKSS is be
Page 41 and 42: agricultural potential, alternative
Page 43 and 44: Table 2.1: Trends in selected CAADP
Page 45 and 46: poverty fell faster during this per
Page 47 and 48: expenditure (research, extension, i
Page 49 and 50: Table 2.4. Average annual change in
Page 51 and 52: captures the individual pathways of
Page 53 and 54: MA also has to be underpinned by lo
Page 55 and 56: References African Union. (2014). M
Page 57 and 58: Meenakshi, J.V., Johnson, N.L., Man
Page 59 and 60: KEY MESSAGES ONE TWO THREE FOUR Afr
Page 61 and 62: GDP declined by 1.4 percent in 2009
Page 63 and 64: from political violence, terrorism,
Page 65 and 66: interpretation is implied, this obs
Page 67 and 68: Differences in the African pattern
Page 69 and 70: Table 3.5: Trends in types of emplo
Page 71 and 72: that would likely affect the contri
Page 73 and 74: c. Concentration of farm structure
Page 75 and 76: a. Invest in education to upgrade t
Page 77 and 78: These could include policy measures
Page 79 and 80:
References African Center for Econo
Page 81 and 82:
Giller, K. E., Rowe, E. C., de Ridd
Page 83 and 84:
Ndung’u, N. S. (2016). Viewpoint:
Page 85 and 86:
World Bank. (2015). World developme
Page 87 and 88:
KEY MESSAGES ONE Although sustainab
Page 89 and 90:
BOX 4.1: Components of Sustainable
Page 91 and 92:
of the world. These authors also re
Page 93 and 94:
Further, Locatelli et al. (2008) ha
Page 95 and 96:
Climate and Weather Variability The
Page 97 and 98:
Resilience of livelihoods is determ
Page 99 and 100:
While acknowledging successes of SI
Page 101 and 102:
Entry Points For Sustainable Intens
Page 103 and 104:
BOX 4.4: The CSA Compendium: A scie
Page 105 and 106:
Successful interventions include fo
Page 107 and 108:
Open data combined with agricultura
Page 109 and 110:
According to Tshibaka (2014), evide
Page 111 and 112:
References Abdulai, A., & Delgado,
Page 113 and 114:
Enfors, E. (2013). Social-ecologica
Page 115 and 116:
Lipper, L., Thornton, P., Campbell,
Page 117 and 118:
Toulmin, C., Leonard, R., Brock, K.
Page 119 and 120:
KEY MESSAGES ONE In SSA, sustainabl
Page 121 and 122:
Figure 5.1: Poverty traps framework
Page 123 and 124:
Cereal Yield/Kilogram Per Hectare F
Page 125 and 126:
Table 5.2: Correlation between fact
Page 127 and 128:
Table 5.3: Farmer use of improved i
Page 129 and 130:
Figure 5.9: Agricultural research s
Page 131 and 132:
association is found for cereal out
Page 133 and 134:
AGRA’s Experience The observation
Page 135 and 136:
Increase access to affordable crop
Page 137 and 138:
Sheahan, M., & Barrett, C. B. (2014
Page 139 and 140:
KEY MESSAGES ONE Smallholder farmer
Page 141 and 142:
transformation in Africa. The achie
Page 143 and 144:
from agribusiness actors to smallho
Page 145 and 146:
season, maize prices could be 270 p
Page 147 and 148:
critical challenges and the solutio
Page 149 and 150:
Limited access and high cost of acc
Page 151 and 152:
of onion production in northern Gha
Page 153 and 154:
Conclusions This chapter presents t
Page 155 and 156:
Collins, D., Morduch, ,. J., Ruther
Page 157 and 158:
Minot, N. (2014). Food price volati
Page 159 and 160:
Weatherspoon, D. D., & Reardon, T.
Page 161 and 162:
KEY MESSAGES ONE Access to finance
Page 163 and 164:
esources into agriculture. This in
Page 165 and 166:
Table 7.1: Indicative investments t
Page 167 and 168:
technical knowledge about products.
Page 169 and 170:
continue to experience inadequate a
Page 171 and 172:
Box 7.1: GAFSP: Country Examples Rw
Page 173 and 174:
Private Equity Association, total p
Page 175 and 176:
drawing down assets, and 25 percent
Page 177 and 178:
equires: recognition of the critica
Page 179 and 180:
Policy Recommendations This review
Page 181 and 182:
Meyer, R. L. (2015, March). Financi
Page 183 and 184:
KEY MESSAGES ONE The African contin
Page 185 and 186:
Figure 8.1: Mobile phone based serv
Page 187 and 188:
high input costs, and a disconnecte
Page 189 and 190:
available through less high-tech de
Page 191 and 192:
Figure 8.4: Kenya leads the pack fo
Page 193 and 194:
years and higher quality seeds are
Page 195 and 196:
CASE STUDY FieldLook South Sudan in
Page 197 and 198:
Rationale behind rapid adoption •
Page 199 and 200:
CASE STUDY Eastern Africa Farmer Fe
Page 201 and 202:
many others have none or have strin
Page 203 and 204:
the strategic adoption of ICT with
Page 205 and 206:
To address constraints to improving
Page 207 and 208:
References Accenture. (2015). Digit
Page 209 and 210:
Gustafson, S. (27.1.2016). The Digi
Page 211 and 212:
Wolfenson K. D. (2013). Coping with
Page 213 and 214:
KEY MESSAGES ONE A guided evolution
Page 215 and 216:
een registered over the last decade
Page 217 and 218:
the AIS paradigm at policy and prog
Page 219 and 220:
Box 9.2: Rwanda - A Phoenix Rising
Page 221 and 222:
other supportive partners (tertiary
Page 223 and 224:
Table 9.2: SSA AR4D funding through
Page 225 and 226:
(e.g., NARIs, universities, NGOs, F
Page 227 and 228:
Table 9.3: Number of extension agen
Page 229 and 230:
the whole farm whereas the field sc
Page 231 and 232:
approach, organizational or system
Page 233 and 234:
CASE STUDY Regional Universities Fo
Page 235 and 236:
CASE STUDY African Network for Agri
Page 237 and 238:
CASE STUDY FAO—Tropical Agricultu
Page 239 and 240:
Bizimana, C. (2014). Rwanda’s Ach
Page 241 and 242:
Rwanda. Retrieved from http://www.m
Page 243 and 244:
World Bank. (2007). Project Apprais
Page 245 and 246:
KEY MESSAGES ONE The poverty rate a
Page 247 and 248:
Table 10.1: Number of undernourishe
Page 249 and 250:
Figure 10.6: Stunting in children u
Page 251 and 252:
Why single out nutrition matters? A
Page 253 and 254:
BOX 10.1: The 7 Malabo Declaration
Page 255 and 256:
Reducing post-harvest losses FAO es
Page 257 and 258:
BOX 10.4: Growing trend towards a m
Page 259 and 260:
References African Union. (2015). T
Page 261 and 262:
Schmidt, R. H., & Rodrick, G. E. (2
Page 263 and 264:
“Africa is simply tired of being
Page 265 and 266:
2025, tripling intra-African trade,
Page 267 and 268:
towards agricultural transformation
Page 269 and 270:
While agricultural research generat
Page 271 and 272:
258 AFRICA AGRICULTURE STATUS REPOR
Page 273 and 274:
Technical Notes The following conve
Page 275 and 276:
Madagascar 72.6 72.3 72.1 71.8 71.2
Page 277 and 278:
Madagascar 2.81 -15.28 6.54 2.19 1.
Page 279 and 280:
Madagascar 351.1 337.2 332.9 323.7
Page 281 and 282:
Madagascar 2,020 1,967 2,202 2,354
Page 283 and 284:
Madagascar 86.0 84.2 87.3 91.8 103.
Page 285 and 286:
Mali .. .. 52.0 15.7 17.5 31.1 22.5
Page 287 and 288:
Madagascar -0.0053 -0.0075 0.0073 -
Page 289 and 290:
Malawi 401.9 405.1 437.2 438.9 511.
Page 291 and 292:
Madagascar 0.2 0.3 0.4 0.5 0.6 0.6
Page 293 and 294:
Madagascar 0.9 1.0 1.6 1.9 2.8 5.6
Page 295 and 296:
Liberia .. .. .. .. .. .. .. .. ..
Page 297:
Liberia 2.0 1.9 1.8 1.7 1.5 1.3 1.5
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