and who gets a slice?

Transcription

1 how big is the pie and who gets a slice? Reassessing the potential for agricultural expansion in sub- Saharan Africa Jordan Chamberlin T S Jayne Liang You

2 Surplus land in SSA Land is widely perceived to be an abundant and underutilized resource in SSA Great local variability is acknowledged Renewed interest in quantifying this question spurred by rise in global food prices and global interest in unutilized arable land Recent assessments have been widely commented on Deininger & Byerlee / Fischer & Shah (2010)

3 How much land is there really? Assertions of over-optimistic estimates are frequently rooted in non-systematic field observations and gut feelings of observers e.g. Young 1999 Rapid advances in new (and arguably more accurate) data We are increasingly able to refine our approaches to addressing this question Unutilized versus unallocated land

4 History of inquiry: how much land? Early efforts often failed to control for land quality Critique: Pingali & Binswanger 1984 (labor-land) 2 nd generation: more refined estimates of production potential and other qualities FAO, IIASA (GAEZ) 3 rd generation: Addressing the economics of expansion DB/FS 2010: market access threshold You et al. 2011: irrigation expansion, pot. net rev.

5 Our approach Builds on earlier work Incorporates newer data, higher resolution Rural population distributions Cultivated area, forest cover information Argues for a more nuanced approach to viewing the economics of expansion Agronomic potential potential yields Economic remoteness spatially varying prices Not the definitive answer! Incremental advance in how best to address the question net potential revenue

6 Methods 1. Criteria for determining expansion envelope: PD<25, non-forest, uncultivated, suitable+ Spatial database: 5 grid cells 2. Criteria for determining economic viability: For each grid cell: economic returns to expansion calculated for 20 crops (rainfed, low inputs) Returns calculated for most profitable crop mix, given by grid cell maximization of potential annual net revenue from expansion alternatives

7 Potential Net Revenue from expansion Net Revenue = (AAAA j YYYYY j ) (AAAA j YYYYY j ) j P j C j AAAA j = area expansion in crop j YYYYY j = attainable yield in crop j AAAA j = currently cultivated area in crop j YYYYY j = current yield in crop j P j C j = net unit price for crop j

8 Improved treatment of prices & costs Output prices decline in remoteness Production costs may also vary over space Costs of land conversion depend upon cover type

9 5 spatial database for SSA Land cover Cultivated area Forest cover Population Potential yields Accessibility Prices

10 Rural population estimates Spatially disaggregated estimates of population Input data vary by year & reporting unit Disaggregation approaches differ GRUMP: 1km, ruralurban distinction AfriPop: 100m, probabalistic model (LC)

11 What is the extent of currently cultivated land? Depends on the data source

12 Sequence of estimates Baseline Var. 1 Var. 2 Var. 3 Var. 4 Var. 5 LC1 LC2 LC3 LC3+6hrs LC3+PNR>500 LC3+PNR>500 Land cover Cultivated area GAEZ SPAM GlobCover GlobCover GlobCover GlobCover Forested area GAEZ GlobCover GlobCover GlobCover GlobCover GlobCover Population Rural density GAEZ AfriPop AfriPop AfriPop AfriPop AfriPop Potential Suitability & potential yields GAEZ suitability classes (S & VS) GAEZ suitability classes (S & VS) GAEZ suitability classes (S & VS) GAEZ suitability classes (S & VS) Returns Crop prices (none) (none) (none) (>6 hrs to market) Attainable share of potential yield (reducing in remoteness) Attainable share of potential yield (reducing in remoteness) Farm gate Farm gate prices reducing prices reducing in remoteness in remoteness Production costs (none) (none) (none) ( ) Proportional Proportional Conversion Conversion costs (none) (none) (none) (none) Based on land cover Based on land cover Uncertainty IRR (none) (none) (none) (none) (none) Discount rate incorporates uncertainty

13 Results: viable underutilized land (1000s ha) Baseline Var. 1 Var. 2 Var. 3 Var. 4 country LC1 LC2 LC3 LC3+6hrs LC3+PNR>500 Angola 26,521 23,860 6, Burkina Faso 1,661 1, CAR 28,975 6,475 5, Chad 9,028 12,112 4,915 2,327 2,893 Congo-Brazz. 4,243 8,525 4,866 1, Ethiopia 8,764 6,406 2, Ghana 1, ,339 1,773 1,274 Guinea 4,676 3,749 2,341 2,109 1,776 Ivory Coast 636 1,281 1, Kenya 1, Madagascar 18,588 14,972 24,211 13,604 8,142 Malawi Mali 7,764 9,430 2, ,945 Mozambique 14,451 7,555 1, Niger 1,580 1,580 1, ,580 Nigeria Sudan 37,872 28,755 18,070 7,493 4,764 Tanzania 6,585 9,856 2, DRC 2,952 15,396 5,027 1, Zambia 10,654 7,201 3,486 1,275 15% 927 Total 198, ,845 94,355 39,113 28,489

14 Concentrated surplus under all scenarios % of total baseline lc2 lc3 lc3+6hrs lc3+pnr>500 20% 1 1 <1 <1 <1 40% % % Rank 1st Sudan Sudan Madagascar Madagascar Madagascar 2nd CAR Angola Sudan Sudan Sudan 3rd Angola DRC Angola Chad Chad 4th Madagascar Madagascar CAR Guinea Mali 5th Mozambique Chad DRC Ghana Guinea 6th Zambia Tanzania Chad Zambia Niger 7th Chad Mali Congo-Brazz. DRC Ghana 8th Ethiopia Congo-Brazz. Zambia Congo-Brazz. 9th Mozambique Guinea 10th Zambia Ghana 11th CAR

15 Constraints to expansion Limited production potential Marginal lands Low profitability (mainly due to remoteness) Increasing input/output price ratios Institutional constraints Can t just walk in Unutilized unallocated/unclaimed Formal mechanisms are less accessible to small farmers Informal mechanisms may also be unequally accessible Security constraints

16 Insecurity Many of the areas of apparent abundance are characterized by chronic insecurity South Sudan DRC Source: ACLED (Armed Conflict Location and Event Dataset)

17 Conclusions DB/FS estimate appears very optimistic Sensitive to data input Sensitive to assumptions about prices & costs With few, very conservative additional assumptions about economic feasibility, the estimate of suitable surplus land declines by 50 80% Note that we are almost certainly underestimating true costs of expansion

18 Where to next? Extensions/refinements to our modeling work Improve price data (more local prices) Refine cost structure assumptions Link more explicitly with household information Evaluate more input/management levels Evaluate irrigation and/or other water mgt Allow for interaction across grid cells (ABM)

19 Uncertainty Incorporate measure(s) of uncertainty, e.g. probability of a 10-day failure of rainfall within the growing season Lots of new data coming on-line

20 Knowledge gaps Underutilized versus unallocated land We do not observe allocations Rural mobility Interlinked labor and land markets Who is moving to the frontier? Mechanisms of land transfer at small scales Who is buying? Who is selling? Importance of vernacular & clandestine markets

21 *

22 Baseline Var. 4 (LC3+PNR>500) Estimated land resources under Var.4 as % of Baseline

23 Convergence Rural population densities are growing, but do not appear to be redistributing in a way that suggests convergence Source: Kenya Bureau of Statistics

24 Convergence Rural population densities are growing, but do not appear to be redistributing in a way that suggests convergence Source: Zambia Bureau of Statistics

25 Kenya Kenya pop. density district share of rural pop. Zambia Zambia pop. density district share of rural pop.

26 Sensitivity analysis

27 Enhanced treatment of prices & costs Output prices decline in remoteness Attainable share of potential yield is declining in remoteness Costs of land conversion depend upon cover type

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