UNCCD 2nd Scientific Conference

1. PREDICTING FUTURE LAND
DEGRADATION AND ITS ECONOMIC
EFFECTS
-Evidence From an Econometric Approach-
Bonn, April 10, 2013
UNCCD 2nd Scientific Conference
Alex De Pinto
Akiko Haruna
Tingju Zhu
International Food Policy Research Institute

2. Purpose of the study
“Land degradation is silent emerging
process that increases the risk for the
livelihood of millions”
“Prevention is less costly than restoration”
Create a tool that allows to “reasonably”
predict land degradation and to prioritize
action.
Establish a link between LD, climate change
and food security.

3. Major drivers of land degradation
What the literature says:
Physical drivers Socioeconomic drivers
Climatic factors Domestic Policy +
-Rainfalls + Institutional capacity +
-Rainfall intensity - Land tenure/property rights +/-
-Wind - Technology +/-
Slope - Information +
Vegetation cover + Population density -/+
Soil type Economy
Fire - -Market access +/-
-Livelihood diversification +/-
Selected source: Huber et al. 2011; Begue et al. 2011; Zhao et al. 2010; Safriel
and Adeel 2005; Ravi et al. 2010; Le et al. 2012; Sonneveld and Keyzer 2000; -Poverty -/+
Vogt et al., 2011; Pardini et al 2004; Eswaran et al. 2001; Young, 2001; Mitchell
2004; Kassam et al. 2009; Jansen et al. 2006; K.J. Wessels et al. 2007; Tesfey,
2006; Geist and Lambin 2004; Pender, Place and Ehui, 2006, Hagos and
-Economic growth +
Holden, 2006; Mulvaney, Khan, and Ellsworth 2009; Nkonya et al. 2004; Boyd
and Slaymaker 2000, Pretty et al. 2011; Benin et al 2007; Bai et al. 2008; Vlek
et al. 2010; Nachtergaele et al. 2010; Moti Jaleta, Menale Kassie, 2012;
+ : beneficial for prevention of LD - : drive LD
Zimmerman et al., 2003; Li and Reuveney, 2006 + / - : ambiguous

4. NDVI: Proxy for LD and Land Carrying
Capacity
• Advantage of NDVI
• Global coverage
• Single index with readily available dataset
• Excellent temporal and spatial extensions
• Weakness of NDVI
• Coarse resolution (for non-global analyses)
• Accuracy of observations
• Differentiation from land cover and land use and other human
interventions
• Truly representative of land degradation?

5. Approach
•

6. Model and data details
• Dataset: global level
• Covariates: Climatic, Geophysical and socioeconomic
variables
• Control on other ecological factors: 6 AEZ-LPG (length of
plant growth) dummies
• Control for irrigations: Irrigated vs. cultivated land ratio
using IFPRI’s Spatial Production Allocation Model (SPAM)
• Control for potential spatial correlation: regular sampling
method (3x3 grids)

7. Variables for model estimation
Variable Resolution Period Data source
GIMMS-AVHRR
0.083o x
Max. NDVI 2002–2006 dataset (Global Land
0.083o
Cover Facility)
Climate Research Unit
Avg. Precipitation 0.54o x 0.54o 2002–2006 (CRU), University of
East Anglia
Climate Research Unit
Rainfall intensity (# of
0.54o x 0.54o 2002–2006 (CRU), University of
events 1 S.D. above mean)
East Anglia
Climate Research Unit
Avg. Temperature 0.54o x 0.54o 2002–2006 (CRU), University of
East Anglia
0.008o x
Slope GMTED2010, USGS
0.008o
Soil Organic Carbon 0.5o x 0.5o Hiederer et al (2012)
Population density 0.5o x 0.5o 2000 CIESIN
0.008o x Uchida and Nelson
Access to market 2000
0.008o (2009)
UNSTAT constant
Avg. GDP growth rate Country level 2002-2006
2005 prices
The World
Avg. input usage Country level 2002-2006 Development
Indicators
Worldwide
Avg. Rules of Law Country level 2002–2006
Governance Indicators
Infant Mortality Rate Regional level 2000 CIESIN

8. OLS regression coefficients
Dependent variable: max NDVI – Range [-1, 1]
Expl. VARIABLES Coefficient
Precipitation 0.0002**
Above mean and 1 S.D. of -0.008**
precipitation
Temperature -0.005**
Slope -0.007**
Soil Organic Carbon 0.001**
Population density -4.49e-05**
Access to market -2.63e-05**
Infant Mortality Rate -0.001**
GDP growth rate 0.009**
Rule of law -0.011**
Input usage -0.0002**
Irrigated area -0.045**
AEZ dummies 0.217**; 0.262**; 0.295**; 0.291**; 0.307**
Observations 211,332
R-squared 0.69
** p<0.01, * p<0.05, + p<0.1

9. Future scenarios
Variable Resolution Period Data source
Jones et al. (2009), Downscaled
Precipitation 0.54o x 0.54o 2050
IPCC-AR4 GCMs
Rainfall intensity (1 S.D. above mean Jones et al. (2009), Downscaled
0.54o x 0.54o 2050
precipitation) IPCC-AR4 GCMs
Jones et al. (2009), Downscaled
Temperature 0.54o x 0.54o 2050
IPCC-AR4 GCMs
Population density 0.5o x 0.5o 2050 UN World Population Prospects
GDP growth rate Country level 2050 IMPACT pessimistic scenario
Input usage (fertilizers) Country level 2050 (Wood et al, 2004)
Derived from IMPACT
Mortality rate Regional level 2050
malnutrition estimate
Slope, SOC, Access to market, Rule of Constant from the
law 2000’s

10. Areas with declines in NDVI
2000 – 2050 Socioeconomic variables only

11. Climatic variables have an impact
MIROC pessimistic scenario

12. Climatic variables have an impact
CSIRO pessimistic scenario

13. Implication for food security
• Estimation of global calorie production:
• 16 major food crops (wheat, rice, maize, barley, millet, sorghum,
potato, sweet potato, cassava, banana and plantain, soybean,
other beans, other pulse, sugar cane, sugar beet, ground nuts)
• Yield and harvest area: 0.083o x 0.083o spatial dataset (SPAM)
• Calorie per unit of product (USDA, FAO)
• Calorie production per pixel = ∑ {Calorie per unit of product X yield
per product per area X harvest area per product per pixel }
• Large NDVI decline with major food production area
• Areas with NDVI change below mean of all negative NDVI changes
• Areas with calorie production above mean of all calorie productions

14. Food security implication: MIROC
Identified areas with below mean of negative NDVI changes and areas
with above mean of calorie production
MIROC pessimistic scenario: 116 million ha of above-average
production cropland affected (current output: 65 billion USD)

15. Food security implication: CSIRO
Identified areas with below mean of negative NDVI changes and areas
with above mean of calorie production
CSIRO pessimistic scenario: 105 million ha of above-average
production cropland affected (current output: 54 billion USD)

16. Food security implication:
Dark Red: Areas with below mean of negative NDVI changes and areas with
above mean of calorie production
MIROC pessimistic scenario: 116 million ha of above-average
production cropland affected (current output: 65 billion USD)

17. Food security implication:
Dark Red: Areas with below mean of negative NDVI changes and areas with
above mean of calorie production
CSIRO pessimistic scenario: 105 million ha of above-average
production cropland affected (current output: 54 billion USD)

18. Food security implication:
Dark Red: Areas with negative changes in NDVI greater than 10% and areas
with above mean of calorie production
CSIRO pessimistic scenario: 13 million ha of above-average production
cropland affected (current output: 11 billion USD)

19. Food security implication:
Dark Red: Areas with negative changes in NDVI greater than 10% and areas
with above mean of calorie production
MIROC pessimistic scenario: 15 million ha of above-average production
cropland affected (current output: 13 billion USD)

20. Other possible important implications:
Biodiversity Hotspots: at least 1,500 species of vascular plants as
endemics, and it has to have lost at least 70 percent of its original habitat
Source: Conservation International

21. Other possible important implications:
Predicted LD hotspots (MIROC) and biodiversity hotspots

22. Conclusion
• One step towards a predictive tool for LD and the
inclusion of climate change effects
• Substantial food production areas potentially
affected by LD
• Climate change appears to exacerbate LD in
certain areas
• More work on linking NDVI to reality on the
ground (recent work of Bao, Vleck, and others)
• A call for a close collaboration among scientists
from different disciplines