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Alessandro DE PINTO "Toward an analytical framework to assess the value of action and inaction against land degradation: new insights, and policy challenges"
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Alessandro DE PINTO "Toward an analytical framework to assess the value of action and inaction against land degradation: new insights, and policy challenges"

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UNCCD 2nd Scientific Conference

UNCCD 2nd Scientific Conference

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  • Precursor studies used NDVI as an LD index (Bai et al 2008, Safriel 2007, Vlek et al 2008)NDVI-derived indexes being developed: RUE adjusted NDVI (Bai et al 2008, Nachtergaele et al 2010), RESTREND (Safriel 2007, Wessels et al 2007)
  • To qualify as a hotspot, a region must meet two strict criteria: it must contain at least 1,500 species of vascular plants (> 0.5 percent of the world’s total) as endemics, and it has to have lost at least 70 percent of its original habitat.
  • To qualify as a hotspot, a region must meet two strict criteria: it must contain at least 1,500 species of vascular plants (> 0.5 percent of the world’s total) as endemics, and it has to have lost at least 70 percent of its original habitat.

Alessandro DE PINTO "Toward an analytical framework to assess the value of action and inaction against land degradation: new insights, and policy challenges" Alessandro DE PINTO "Toward an analytical framework to assess the value of action and inaction against land degradation: new insights, and policy challenges" Presentation Transcript

  • PREDICTING FUTURE LANDDEGRADATION AND ITS ECONOMICEFFECTS-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
  • 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 prioritizeaction.Establish a link between LD, climate changeand food security.
  • 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; Safrieland 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; Mitchell2004; 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; Boydand Slaymaker 2000, Pretty et al. 2011; Benin et al 2007; Bai et al. 2008; Vleket al. 2010; Nachtergaele et al. 2010; Moti Jaleta, Menale Kassie, 2012; + : beneficial for prevention of LD - : drive LDZimmerman et al., 2003; Li and Reuveney, 2006 + / - : ambiguous
  • NDVI: Proxy for LD and Land CarryingCapacity• 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?
  • Approach•
  • 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)
  • Variables for model estimationVariable Resolution Period Data source GIMMS-AVHRR 0.083o xMax. NDVI 2002–2006 dataset (Global Land 0.083o Cover Facility) Climate Research UnitAvg. Precipitation 0.54o x 0.54o 2002–2006 (CRU), University of East Anglia Climate Research UnitRainfall intensity (# of 0.54o x 0.54o 2002–2006 (CRU), University ofevents 1 S.D. above mean) East Anglia Climate Research UnitAvg. Temperature 0.54o x 0.54o 2002–2006 (CRU), University of East Anglia 0.008o xSlope GMTED2010, USGS 0.008oSoil 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 NelsonAccess to market 2000 0.008o (2009) UNSTAT constantAvg. GDP growth rate Country level 2002-2006 2005 prices The WorldAvg. input usage Country level 2002-2006 Development Indicators WorldwideAvg. Rules of Law Country level 2002–2006 Governance IndicatorsInfant Mortality Rate Regional level 2000 CIESIN
  • OLS regression coefficientsDependent 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
  • Future scenariosVariable Resolution Period Data source Jones et al. (2009), DownscaledPrecipitation 0.54o x 0.54o 2050 IPCC-AR4 GCMsRainfall intensity (1 S.D. above mean Jones et al. (2009), Downscaled 0.54o x 0.54o 2050precipitation) IPCC-AR4 GCMs Jones et al. (2009), DownscaledTemperature 0.54o x 0.54o 2050 IPCC-AR4 GCMsPopulation density 0.5o x 0.5o 2050 UN World Population ProspectsGDP growth rate Country level 2050 IMPACT pessimistic scenarioInput usage (fertilizers) Country level 2050 (Wood et al, 2004) Derived from IMPACTMortality rate Regional level 2050 malnutrition estimateSlope, SOC, Access to market, Rule of Constant from thelaw 2000’s
  • Areas with declines in NDVI2000 – 2050 Socioeconomic variables only
  • Climatic variables have an impactMIROC pessimistic scenario
  • Climatic variables have an impactCSIRO pessimistic scenario
  • 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
  • Food security implication: MIROCIdentified areas with below mean of negative NDVI changes and areaswith above mean of calorie production MIROC pessimistic scenario: 116 million ha of above-average production cropland affected (current output: 65 billion USD)
  • Food security implication: CSIROIdentified areas with below mean of negative NDVI changes and areaswith above mean of calorie production CSIRO pessimistic scenario: 105 million ha of above-average production cropland affected (current output: 54 billion USD)
  • Food security implication:Dark Red: Areas with below mean of negative NDVI changes and areas withabove mean of calorie production MIROC pessimistic scenario: 116 million ha of above-average production cropland affected (current output: 65 billion USD)
  • Food security implication:Dark Red: Areas with below mean of negative NDVI changes and areas withabove mean of calorie production CSIRO pessimistic scenario: 105 million ha of above-average production cropland affected (current output: 54 billion USD)
  • Food security implication:Dark Red: Areas with negative changes in NDVI greater than 10% and areaswith above mean of calorie production CSIRO pessimistic scenario: 13 million ha of above-average production cropland affected (current output: 11 billion USD)
  • Food security implication:Dark Red: Areas with negative changes in NDVI greater than 10% and areaswith above mean of calorie production MIROC pessimistic scenario: 15 million ha of above-average production cropland affected (current output: 13 billion USD)
  • Other possible important implications:Biodiversity Hotspots: at least 1,500 species of vascular plants asendemics, and it has to have lost at least 70 percent of its original habitat Source: Conservation International
  • Other possible important implications:Predicted LD hotspots (MIROC) and biodiversity hotspots
  • 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