Economics of Land Degradation and Improvement

1. Economics of Land Degradation
and Improvement
Alisher Mirzabaev
ZEF Interdisciplinary Course 2014

2. Why soil health matters
• Healthy soil ecosystems - which are well-
functioning to ensure their services - are essential
to sustainable development, including food
security and improved livelihoods.
– Private goods: food, feed, fuel, fibres, grazing
grounds, urban and industrial locations, an investment
asset
– Public goods: ecosystems functions (e.g. water
cleaning, carbon storage, biodiversity)
2

3. Soil/land ecosystem services
• Supporting services maintain the conditions of life on Earth
- soil development (conservation/formation), primary
production, nutrient cycling.
• Regulating services: benefits obtained from the regulation
of ecosystem processes - water regulation, climate
regulation (local and global)
• Provisioning services: goods provided, such as food, fiber,
forage, fuelwood, biochemicals, fresh water.
• Cultural services: non-material benefits obtained from the
ecosystems, such as recreation, landscapes, heritage.
3

4. Soil vs land degradation
• Land and soil are not the same thing.
 Land is the part of the world not covered by
oceans.
 Soil is the layer of material that covers the land
and supports the growth of plants.
• Land degradation is a broader term than soil
degradation, as it includes soil erosion, salinization,
waterlogging, overgrazing, etc
4

5. Relevant definitions
• Ecosystem services are the benefits people
derive from ecosystems (MA 2005) and can be
classified into
– Provisioning (goods such as food, fiber, water,
fuelwood…)
– Regulating (such as climate and water regulating)
– Cultural (nonmaterial: heritage, recreational, …)
– Supporting (maintaining basic preconditions of
life: soil conservation/formation, nutrients cycling,
primary production…)
5

6. Relevant definitions
• Ecosystem disservices are understood as
functions of ecosystems that are perceived as
negative for human well-being (Lyytimäki,
Sipilä 2009). No generally accepted definition
exists, however. Examples are
– Plants causing intoxication
– Animals eating crops or transmitting diseases
– Forests in areas prone to (wild) fires
6

7. Relevant definitons
• United Nations Convention to Combat Desertification
(UNCCD) (1996) defines land as a terrestrial
ecosystem consisting of flora, fauna, hydrological
processes and other ecological services beneficial to
human beings.
7

8. Relevant definitions
• Land degradation is the persistent reduction of land’s
biological and/or economic production capacity, or is
the long-term loss of land ecosystem functions and
services (Safriel 2007, Vogt et al. 2011).
• The Millennium Ecosystem Assessment (MA 2005)
defines land degradation as long-term loss of on-site
and off-site terrestrial ecosystem goods and services,
which humans derive from them.
8

9. Relevant definitions
• Sustainable land management (SLM), according to
TerrAfrica (2006), is “adoption of land use systems
that, through appropriate management practices,
enables land users to maximize the economic and
social benefits from the land, while maintaining or
enhancing the ecological support functions of the
land resources.”
9

10. Land degradation mapping
• Generalized Map of the Status of Desertification in
Arid Lands by UNEP (1977) – 35% of land is affected
by desertification
• Desertification of Arid Lands and Global
Desertification Dimensions and Costs by Texas Tech
University (1992) – 48% land area degraded
• …
• Bai et al. (2008) under GLADA (24%)
• Le et al. (2014) under the ZEF ELD project (29%)

11. Global land degradation hotspots
11Source: Le et al. (2014). Baseline – 1982-86. Endline: 2004-06. Based on biomass productivity.
Accounts for the masking effects of rainfall and atmospheric fertilization. Groundtruthed
through community focus group discussions and, in some cases, soil samples collection.

12. Global land improvement bright spots
12Source: Le et al. (2014). Baseline – 1982-86. Endline: 2004-06. Based on biomass productivity.

13. 13
Author(s) Annual cost
reported
(US$ billion)
Equiv.
annual cost
in 2007
US$ billion
Comments
UNEP (1992) 42 62.06 Review of literature
FAO (2007) 40 40.00 Methods not reported
UNCCD (2013) 490 685.40 Review of literature
Trivedi et al (2008) 43-65 41.4-62.6 Loss of carbon sink due to deforestation of tropical
rainforests
Dregne & Chou
(1998)
43 54.69 Loss of productivity of cropland and grassland
Basson (2010) 18.5 17.58 Off-site cost of soil erosion: (i) reduced water storage
structures, with the replacement costs of silted-up
reservoirs (ii) loss of hydroelectric power (HEP) & damage
to HEP infrastructure (iii) reduction of irrigation reservoir
Myers (2000) 300 361.15 Cost of protection of biodiversity loss
Costanza et al
(2014)
9400 9400.00 Benefit transfer approach to estimate the Total economic
(TEV) of ecosystem services. Cost of terrestrial land
degradation computed as net loss/gain of value of
ecosystem services of terrestrial biomes.
Trustcost (2014) 6900 6900.00 Literature review & government studies and stylized
environmental evaluation methods of environmental
pollution
Dodd et al (2013) 900 800.73 Anthropogenic degradation of freshwater ecosystem
services
Chiabai et al (2007) 261a 277.07 Simulation using IMAGE 2.4 model of net present value of
forest ecosystem services, 2000-2050.

14. What are the implications ?
• About 25-30% of global land area is degraded (Lal et al.
2012, Le et al. 2014).
• The poorest are affected the most. Nachtergaele et al.
(2010) estimate that 42% of the poor around the world
derive their livelihoods from degraded and marginal areas.
• This may pose a daunting challenge to achieving national
and international development goals (von Braun et al.
2012)
14

15. Supply-side pressures
• Water scarcity, climate change, increasing
weather volatility
• Slower growth rates in crop yields
Demand-side pressures
• Population growth, income growth
• Rising demand for bioenergy
Other accompanying pressures
15

16. Meanwhile, total arable land is limited
1220
1240
1260
1280
1300
1320
1340
1360
1380
1400
1420
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
Global Arable Land (Mio ha)
Source: FAOSTAT 16

17. 0
1
2
3
4
5
6
1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003
Averageannualgrowthrate(%)
maize
rice
wheat
Source: World Development Report 2008. The data is global.
Decreasing productivity growth
17

18. Food price rises
Source: http://www.worldbank.org/en/results/2013/04/11/global-food-crisis-response-program-results-profile 18

19. Farmland Price Rises
Even as such, market prices are capturing only marketed values, ignoring numerous essential
ecosystem services of land.
Source: von Braun et al. (2013)
19

20. Problem definiton
• So, clearly, land degradation is a global problem.
However, the efforts to address it have not been
sufficient.
• We need a policy action framework, driven by
evidence-based science.
• Economics of Land Degradation (ELD) research
strives to contribute to building up this evidence
base for policy actions.
20

21. Why ELD is needed
• Action against land degradation most likely to
be taken if actors know the costs & benefits of
action vs inaction (see examples of climate
change and biodiversity)
21

22. Causes of land degradation
• Proximate causes:
o Biophysical causes (slopes, rainfall, wind, ...)
o Unsustainable land management practices
(deforestation, soil nutrient mining, cultivating
slopes, ...)
• Underlying causes:
o Population density, poverty, land tenure, policy
incentives, infrastructures, market access, ...
22

23. The conceptual framework
Source: Nkonya et al . (2011)
23

24. • Changes in ecosystem services
• On- and off-site costs (at plot level, within the watershed or globally),
e.g.:
– Productivity losses
– Costs due to sedimentation
• Direct and indirect costs (consequences of land degradtion), e.g.:
– Agricultural production losses, higher production costs
– Price, market effects
– Food security
– Multiplier effects (economy-wide costs)
– Poverty
– Socio-economic impacts
24
Global assessment of costs of land degradation

25. • Based on:
– Assessments of where and to what extent land degradation
takes place
– Impacts of land degradation:
• On the environment
• On the economy
• On humans:
– On land users and their dependents
– On the society
– Time-dependence and site-dependence
– Institutional and policy arrangements
• Identification of hotspots for immediate policy action using
GIS/Mapping
25
Global assessment of costs of land degradation

26. Time dimension and discounting
• Higher discount rate means you value present
more than future.
• The poorer usually have higher discount rates
than the richer.
• The choice of the discount rate in economic
analysis is value-based.
26

27. Empirical Research
• Costs of land degradation
• Causes of land degradation
• Impacts of land degradation
27
Cost of action
Drivers of SLM
Actions to address land degradation

28. Global costs of land degradation
1. Land degradation due to land use/cover change (LUCC)
2. Land degradation due to use of land degrading
management practices on a static cropland
28

29. 29
Based on
MODIS
LUCC
Data
Baseline: 2001
Endline: 2009

30. 30
Based on
MODIS
LUCC
Data
Baseline: 2001
Endline: 2009

31. TEEB studies on ecosystem valuation
31
Source: Nkonya et al. (forthcoming)
which provide with the TEV values of LUCC

32. Cost of action
• Re-establishment of higher value biome
• Maintenance costs
• Opportunity cost of lower value biome
• Cost of SLM for static land
32

33. Action vs inaction
Regions Annual cost of
land degradation,
bln USD
Cost of action
(30 yrs)
Cost of inaction
(30 yrs)
Ratio
SSA 543 797 3343 4
LAC 473 754 2977 4
NAM 238 751 4545 6
East Asia 164 508 2594 5
Oceania 125 407 2442 6
South Asia 87 210 646 3
SE Asia 52 135 400 3
East Europe 52 777 4813 6
West Europe 47 181 926 5
Central Asia 216 53 277 5
NENA 94 80 504 6
Total 2091 4653 23465 5
33Source: Nkonya et al. (forthcoming)

34. Cost of action against land degradation,
30-year planning horizon
34
Source: Nkonya et al. (forthcoming)

35. Causes of land degradation
• The causes of land degradation are numerous and
complex.
• Quite often, the same causal factor could lead to
diverging consequences in different contexts.
• Not much debate about proximate causes, but strong
debate about the role of underlying causes.
35

36. 36
Drivers Type Examples of causality References
Population
Density
underlying No definite answer.
Population density leads to land improvement
Population density leads to land degradation
Bai et al. (2008); Tiffen et
al. (1994), Boserup (1965)
Grepperud (1996)
Market access
underlying No definite answer.
Land users in areas with good market access have
more incentives to invest in good land management.
High market access raises opportunity cost of labor,
making households less likely to adopt labor-
intensive sustainable land management practices.
Pender et al. (2006)
Scherr and Hazell (1994)
Land tenure
underlying No definite answer.
Insecure land tenure can lead to the adoption of
unsustainable land management practices.
Insecure land rights do not deter farmers from
making investments in sustainable land management.
Kabubo-Mariara (2007)
Besley (1995),
Brasselle et al. (2002)
Poverty
underlying No definite answer.
There is a vicious cycle between poverty and land
degradation. Poverty leads to land degradation
and land degradation leads to poverty.
The poor heavily depend on the land, and thus, have
a strong incentive to invest their limited capital into
preventing or mitigating land degradation.
Way (2006);
Cleaver and Schreiber
(1994);
Scherr (2000)
de Janvry et al. (1991)
Nkonya et al. (2008)
Source: von Braun et al. (2013)

37. Causes of land degradation
Global analysis. Datasets:
• Land degradation hotspots by Le et al. (2014)
• Precipitation, AEZ, distance to markets, population
density, infant mortality rate, GDP per capita, night
time lighting intensity, land tenure security, rule of
law, land use/cover, regional and country dummies
37

38. Causes of land degradation
Methodological contributions:
1. Improved land degradation indicator
2. Improved land degradation categories
3. Sub-global regressions for robustness
4. Adressing endogeneity and omitted variables
38

39. Raw NDVI vs LD hotspots by Le et al. (2014)
Land degradation categories
Nkonya and Weston (2014)
degraded not degraded
Le et al. (2014)
degraded 8% 11%
not degraded 25% 55%
39
The coefficient of correlation: 0.1

40. Causes of land degradation
40
Variables
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Full
No endline
variables
No country
dummies
No squared
terms
No interaction
terms
No change
variables
Precipitation 0.000139*** 0.00024*** 0.000216*** 0.000151*** 0.000142*** 0.000144***
Population density -0.000376*** -0.00057*** -0.000488*** -0.000508*** -0.000339*** -0.000346***
Population density, squared 1.24e-09 3.27e-09 6.03e-09* 1.40e-08*** 9.84e-10
Distance to market 0.000379*** 0.00045*** 0.000428*** 0.000197*** 0.000377*** 0.000379***
Distance to market, sq -3.55e-08*** -4.51e-08*** -4.48e-08*** -3.52e-08*** -3.57e-08***
Night time lighting intensity -0.0247*** -0.0308*** -0.0335*** -0.0134*** -0.0283*** -0.0248***
Night time lighting intensity, sq 0.000308*** 0.00041*** 0.000477*** 0.000470*** 0.000307***
GDP per capita 1.38e-07 .0000236 9.51e-05*** 9.30e-06 2.77e-06 1.24e-05
GDP per capita, sq 2.40e-10 -2.64e-10 -2.43e-09*** 1.81e-10 -0
Rule of Law 0.0960* .0481726 -0.0747*** 0.0870 0.0950* 0.0699
Change in Rule of Law 0.118 0.328*** 0.115 0.106
Infant mortality rate 0.00325*** 0.0050*** 0.00444*** 0.00345*** 0.00358*** 0.00430***
Change in infant mortality rate 0.00257*** 0.00403*** 0.00240*** 0.00234***
Distance to market with cropland 0.0446*** 0.0207*** 0.0665*** 0.0413***
Night time lighting intensity with population
density 7.78e-06*** .000011*** 8.40e-06*** 1.18e-05*** 7.38e-06***
Distance to market with population density -2.93e-06*** -2.98e-06*** -4.91e-06*** -2.43e-06*** -3.05e-06***
Land tenure security, Base - good
Moderate concern 0.729*** -0.0320*** 0.718*** 0.723*** 0.853***
Severe concern -0.850*** -0.0444*** -0.903*** -0.920*** -0.966***
Extremely severe concern -0.166 0.0783*** -0.218** -0.176* -0.256***
Missing values for land tenure -0.404** 0.175*** -0.397** -0.540*** -0.290*
Observations 1,572,534 1,585,096 1,572,534 1,572,534 1,572,594 1,585,096
Source: Mirzabaev et al. (forthcoming). Ongoing research, presented for discussion purposes only, not for quoting.

41. Causes of land degradation
Variables SSA LAC
North
America NENA Asia Europe
Precipitation 0.000592*** 0.000125*** 0.000347*** 0.000497*** -8.21e-05*** 0.000312***
Population density -1.79e-05 -0.000517*** -0.000208 -0.000828*** -0.000658*** -8.06e-05
Population density, squared 1.13e-08 -2.70e-08*** 7.80e-08*** 3.07e-08 7.87e-09** 4.30e-10
Distance to market 0.00107*** 0.00121*** 0.000885*** 0.000724*** 0.000231*** 0.00100***
Distance to market, squared -2.86e-07*** -2.33e-07*** -1.12e-07*** -1.09e-07*** -8.74e-09*** -1.46e-07***
Night time lighting intensity -0.0611*** -0.0562*** -0.0439*** -0.0160*** 0.00617*** -0.0108***
Night time lighting intensity, squared 0.000998*** 0.000831*** 0.000624*** 0.000156* -0.000225*** 0.000260***
GDP per capita 0.000327** -0.000926 -3.18e-05 -0.000482 0.000220 3.17e-05
GDP per capita, squared -3.06e-08** 1.21e-07 1.01e-07 -5.26e-09 5.52e-11
Rule of Law 0.391*** 0.449 -0.00900 2.31e-05 -0.402***
Change in Rule of Law 0.316** -1.239 0.302 -0.0970 -0.623*
Infant mortality rate 0.00400* 0.00678 0.00324 0.00468*** 0.00345
Change in infant mortality rate -0.00231*** 0.0141*** 0.0942*** -0.00470*** 0.00576*** 0.00572***
Distance to market with cropland 0.115*** -0.0187 0.127*** 0.192*** 0.0939*** -0.240***
Night time lighting intensity with population
density 4.95e-06** 1.63e-05*** 1.62e-06 1.28e-05*** 9.20e-06*** 1.73e-06*
Distance to market with population density -9.61e-06*** -9.99e-06*** -5.79e-06*** -5.27e-06*** -2.17e-06*** -1.50e-06***
Land tenure security, Base - good
Moderate concern 0.836*** -0.259 -1.820**
Severe concern 0.555*** 2.013** -0.487*
Extremely severe concern 0.512*** 0.158 0.0569 0.130 -0.679
Missing values for land tenure 1.560*** 0.164 -0.339
Observations 175,919 33,631 354,118 173,015 299,334 461,666
41Source: Mirzabaev et al. (forthcoming)Source: Mirzabaev et al. (forthcoming). Ongoing research, presented for discussion purposes only, not for quoting.

42. Causes of land degradation
Variables Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8
Precipitation 0.000297*** 0.000132** -0.000137*** -1.12e-05 0.000440*** -4.56e-05*** 0.000570*** 0.000463***
Population density -0.000430*** -0.00893*** -0.000496*** 0.000148** -0.000835*** -0.000378*** -0.000322 0.000683***
Population density, squared 2.97e-08*** 1.19e-05*** 7.19e-09** 9.08e-09 -5.13e-09 6.74e-08*** 2.44e-07*** -1.82e-08**
Distance to market 0.000798*** 0.000650*** 0.000299*** 0.00123*** 0.000695*** 0.000763*** 0.00128*** 0.00170***
Distance to market, squared -1.88e-07*** -1.75e-07*** -1.49e-08*** -3.89e-07*** -1.00e-07*** -9.91e-08*** -1.63e-07*** -3.13e-07***
Night time lighting intensity -0.0169*** 0.131** 0.00788*** -0.0139*** -0.0145*** -0.0287*** -0.0232*** -0.0191***
Night time lighting intensity, squared 0.000253** -0.00187 -0.000232*** 0.000246*** 9.94e-05 0.000367*** 0.000547*** 0.000291***
GDP per capita 0.000184* 0.000204 0.000181** 7.98e-05 -0.00136* 0.000143 -1.67e-06 -5.84e-05
GDP per capita, squared -1.94e-08** -3.54e-09 -8.61e-09 -3.11e-09 2.67e-07* -2.39e-09 1.29e-10 1.58e-09
Rule of Law 0.166* 0.0138 0.154 -0.219* 0.318 -0.423** -0.386** -0.390
Change in Rule of Law 0.236* -0.392 -0.332 -0.0343 0.807*** -0.406 -0.739** 0.228
Infant mortality rate 0.00454** 0.00260 0.00418** -0.00130 0.00453 -0.00584* 0.0333*** -0.0343**
Change in infant mortality rate -0.00201*** -0.0145*** 0.00523*** 0.00794*** -0.00660*** 0.00940*** -0.113*** -0.00356
Distance to market with cropland 0.101*** -0.0906 0.0667*** -0.0866*** 0.183*** -0.105*** -0.0223 -0.0469***
Night time lighting intensity with population
density 5.52e-06* -0.00314*** 6.27e-06*** -3.38e-06 1.69e-05*** 3.30e-06* -1.21e-05*** -7.81e-06***
Distance to market with population density
-5.80e-06*** -8.49e-06*** -1.87e-06*** -4.52e-06*** -7.29e-06*** -4.25e-06*** -4.76e-06*** -8.28e-06***
Land tenure security, Base - good
Moderate concern 0.638*** 1.044 -0.134 -1.240*** -1.816*** -2.981***
Severe concern 0.0222 0.370 -1.447*** -0.865** -0.296 -1.718***
Extremely severe concern 0.397*** 1.408* 0.0510 -0.481*** -0.504*** 0.740***
Missing values for land tenure 0.948*** 0.977* -1.290*** -0.858*** -1.045*** -0.660** -2.032***
Observations 126,716 24,677 161,004 94,401 161,812 769,961 23,508 210,455
42Source: Mirzabaev et al. (forthcoming)Source: Mirzabaev et al. (forthcoming). Ongoing research, presented for discussion purposes only, not for quoting.

43. Causes of land degradation
• Promoting SLM would require addressing
often context-specific effects of drivers of land
degradation.
• However, we also find that the effects of these
drivers are not completely idiosyncratic for
each individual setting, but may often follow
some generalizable patterns.
43

44. Causes of land degradation
• SLM is positively associated with
contemporaneous land tenure security, especially
in middle-income and advanced economies, and
less so in lower income countries
• Shortening the time to reach major population
centers, i.e. improving the access to markets, may
have many other desirable outcomes, but not
necessarily decrease land degradation
44Source: Mirzabaev et al. (forthcoming). Ongoing research, presented for discussion purposes only, not for quoting.

45. Impacts of land degradation
• Population pressure may not lead to land
degradation if public policies provide for
increases in non-farm jobs.
• Poverty was not found to lead to land
degradation.
• Better rule of law may lead to sustainable land
management
• As this analysis is based on global-level datasets,
its results need to be verified through further
local household-level econometric studies
45Source: Mirzabaev et al. (forthcoming). Ongoing research, presented for discussion purposes only, not for quoting.

46. 46
Impacts of SLM in Central Asia

47. Land Degradation Impacts, Household Level
Source: Aw-Hassan et al. (2011) – ICARDA. Black dots are surveyed household locations.

48. Land Degradation and the Poor
0
.2
.4
.8
.6
Kazakhstan Kyrgyzstan Tajikistan Uzbekistan
1 2 3 1 2 3 1 2 3 1 2 3
Categories: 1-poor, 2-middle, 3 –rich. Source: the survey dataset

49. The Poor and SLM Use
0
.2
.4
.6
.8
1 2 3 4 5
Categories: 1-poorest, … 5-richest. Source: the survey dataset
Pearson chi2(4) = 56.8061 Pr = 0.000

50. Impacts of SLM in Central Asia
50Quantile regression on per capita food expenses, controlling for a wide range of factors.

51. Some ideas for your research
• Interdisciplinarity is not only a catch phrase, but indeed can
lead to more accurate research results:
– Example: NDVI vs LD hotspots, then use in economic
analysis. Validation through community focus group
discussions. Further economic modelling.
• Data plurality and combination of novel, innovative
databases.
– Examples: night time lighting intensity, rule of law, soil
pH, secondary and household data. Big data. Combining
qualitative and quantitative data.
• Being open to unexpected results, which may contradict
earlier research hypotheses.
51