land use, land use change and forestry activities for climate change mitigation faculty of...

Land Use, Land Use Change and Forestry activities for Climate Change Mitigation

Faculty of Bio-science Engineering

Antonio Trabucco - PhD Defense

Supervisors:

Prof. dr. ir. Bart Muys

Prof. dr. ir. Jos Van Orshoven

Introduction

GHG and Climate Change

• Energy absorption of Greenhouses Gasses (GHG) is converted in atmospheric warming and then scattered partly downward to earth’s surface ("greenhouse effect“).

• CO2 is the most relevant GHG for global warming potential.

• Anthropogenic activities have released in the atmosphere large carbon stocks stored as fossil deposits or in ecosystems:

- Fossil fuel use → ~ 75% of extra CO2 now found in our atmosphere- Land use changes → remaining 25%

Introduction

Land Use, Land-Use Change and Forestry (LULUCF)

"A GHG inventory sector that covers emissions and removals of GHG resulting from direct human-induced land use, land-use change and forestry activities.” (UN Climate Change Secretariat)

Two LULUCF schemes:

Carbon sequestration by plant growth to increase the biomass in terrestrial ecosystems.

Production of renewable biofuels to diminish fossil fuel use.

Introduction

LULUCF – Carbon sequestration activities

CDM Afforestation and Reforestation

• The Clean Development Mechanism (CDM) finances reduction of GHG emission for projects in developing countries.

• Carbon sequestered into semi-permanent ‘sinks’, primarily by growing trees through afforestation and reforestation (CDM-AR).

• Sustainable development in developing countries (poverty reduction, environmental benefits and cost-effective emissions reductions).

• Impact of reforestation on water availability.

Introduction

LULUCF – Biofuel production

• Biofuels are mainly processed from food crops (Food vs Energy)

• Additional production of biofuel crops will require land use changes, often engaged to alternative ecosystem services.

• Benefits to climate change mitigation questioned by Life Cycle Assessment.

Jatropha

• Jatropha produce non-food biofuel crops in marginal and degraded areas where trade-offs with ecosystem services should be limited.

• Large investments in Jatropha plantations, following optimistic claims rather than science knowledge → many financial failures

Introduction

Objectives

• Two main atmospheric carbon reduction schemes through LULUFC activities are evaluated at global scale:

CDM Affo/Reforestation to increase ecosystem carbon stocks

Production of Jatropha biofuel to replace fossil fuel use

• Evaluate:

If and how these LULUCF activities can be modeled to define opportunity potentials and trade-offs with ecosystem services

If these LULUCF activities can achieve their targets, and impact on other ecosystem services

Introduction - LULUCF Activities and Climate Change

1 Land Suitability for Afforestation and Reforestation

2 Hydrologic impacts of Afforestation and Reforestation

3 Jatropha yield modeling

4 Jatropha: energy, food security and climate change mitigation

Conclusions and perspectives







1. Land Suitability for Afforestation and Reforestation

Where is the land available for reforestation projects and what are the socio-ecological characteristics of these areas?

Bio-physical suitability:

– Climate, water availability, tree line and land use classes

Reforestation Guidelines (CDM-AR):

– Not currently forested (defined by tree canopy cover)– Was not forested on 1990 (No incentive of recent deforestation)– No negative (direct) impact on food security


Spatial global land suitability (~1km or 30”) - Criteria:

– Not currently forested, Tree Canopy Cover < 30% (MODIS Vegetation Continuous Field, 2001)

– Exclusion of water bodies, tundra, urban areas, Forest and intensive agriculture (USGS Global Land Cover Characterization,1993)

– Exclusion of protected areas (World Database on Protected Areas, 2004)

– Areas below timberline, Average Temp. Growing Season > 6.5 C (WorldClim, 2000)

– Water availability for vegetation demand, Aridity Index (PET/Prec) > 0.65 (WorldClim & CSI-PET, 2000)



Several PET calculations were tested, and compared with FAO Penman-Monteith measurements (FAOCLIM).

Hargreaves method has the lower deviance (20 mm/month).

PET calculated according to 5 methods for Africa in July

Aridity Index =



Global suitable land for reforestation (750 Mha, dark green) within developing countries, according to climate constraints (e.g. aridity, timberline) and specific land use opportunities in compliance with CDM guidelines.


Rainfed cropland constitutes half of the suitable land.

Savanna is relevant for reforestation in Sub-Saharan Africa (68%) and South America (40%),accounting for a total global extent of 296 Mha.

Mixed/shrubland and grassland types add 63 Mha, while contribution of barren or sparsely vegetated land is very limited (about 2 Mha).


Lands suitable for CDM-AR generally fall into low to moderate productivity categories.

Globally, 88% of available land has a NPP below 10 tC ha-1yr-1.

Higher productivity lands are mainly intensive and irrigated cropping and forested areas.

Sub-Sahara Africa has the most productive lands, Asia the lowest.







2. Hydrologic impacts of Afforestation and Reforestation

Hydrological impact of reforestation:

– increase in transpiration and in evaporation from interception losses– increase in the volume of the rooting zone, from which water is extracted– decrease in streamflow

On-site impact mainly positive (reduced runoff, erosion and sediment loads, increased control over nutrient fluxes and water quality).

Off-site effects may be mainly negative (lower base flow), but also positive (flow regulation and decrease of flood risk).

Conflicts with other ecosystem services for available water (especially in semi-arid areas).


SWC is the soil water content

Eff_Prec is the effective precipitation

AET is the Actual EvapoTranspiration

R is the runoff component (surface

runoff, subsurface lateral flows and deep percolation)

A monthly Thornthwaite-Mather soil-water budget is implemented to estimate changes in water use following reforestation.

At global scale and high resolution (1 km)

Differences in hydrological dimensions between existing land use in 1993(GLCC 1993) and land use, if reforested.

ΔSWC = Eff_Prec – AET - R


The fraction of precipitation intercepted by vegetation is proportional to interception coefficients (K_int).

Actual Evapotranspiration is calculated as:

AET = PET * Kveg * Ksoil

- Kveg and K_int are coefficients that are modeled for different land use types using literature values (FAO Paper 56, Agrimet, Wucols)

- Ksoil is a measure of soil stress (0-1) and is a linear function of Soil Water Content over maximum SWC (SWCmax)

The water exceeding SWCmax is converted into excess water (or R)


Hydrologic impact model uses the following global geodatasets:

- Precipitation - WorldClim (Hijmans et al. 2005)

- PET – Hargreaves Method

- SWCMAX is calculated as the product of Maximum Available Soil Moisture (FAO 1995) & Global Map of Ecosystem Rooting Depth (Schenk et al 2005)

- Kveg and K_int coefficients are modeled upon the Global Land Cover Characterization (USGS, 1993)


Only 20% (144 Mha) of suitable land for reforestation shows moderate impact on runoff (0-40% decrease in runoff).

High hydrological impacts, i.e. (80–100% decrease in runoff) are predicted to occur on over 27% (200 Mha) of all suitable land.








3. Jatropha yield modeling


Jatropha

• Non-food crop, unlike most biofuels.

• Adapted to semiarid conditions, only few agro-climatic properties clear

• Large investments based on overoptimistic expectations for productivity and suitability in arid conditions

• Traditional methods to evaluate land suitability and crop productivity not feasible for Jatropha because insufficient knowledge available

MaxEnt – distribution of species occurrence

• SDM machine learning method based on maximum entropy.

• Uses occurrence locations and environmental geodatasets to reconstruct species abundance or fitness response to environment.

Ecological relations - fitness and seed productivity

• Linear relationship between yield and probability occurrence, according to recruitment patterns observed in seed mass addition experiments.

• Jatropha yield scaled between 0 and 5 tons of seeds ha-1 yr-1.

Validation with available Jatropha field data


Data Inputs

Training locations:• Herbarium specimen (World Biodiversity Information Network) - 325 locations.

Explanatory variables- Global environmental geodatasets:• Bioclimate (WorldClim) – Monthly averages (1 km) for current climate

• Soil properties – ISRIC-WISE and FAO-HWSD (~10 km).

• Slope (1 km) derived from CSI-SRTM Global DEM

Data reduction (to avoid collinearity and significant responses):• Soil and slope factors were not considered (limited training gain)

• 8 bioclimate factors were retained (high training gain and low correlation)


Responses to environmental factors: Temperature


Responses to environmental factors: Precipitation


• Jatropha follows largely a pan-tropical distribution, favoring areas with a dry season.

• In warm temperate climates with enough rainfall, a drought period and no frost risk.

Jatropha Yield Map


Significant accuracy evaluation from seed yield measurements (r2 = 0.62)

Yields aggregated for Köppen climate zones

High productivity in tropical climate either monsoonal or with distinct dry season. Outside the tropics, suitable growing opportunities for Jatropha are found in warm temperate climates with low frost risk, characterized by dry seasons or fully humid.


4. Jatropha: energy, food security and climate change mitigation







A framework to evaluate sustainability of LULUCF for biofuels: Jatropha

Biofuel Production

Energy Security

Climate Change Mitigation

Food Security

Biofuels imply LULUCF activities for renewable energy production

However, they also induce: - positive or negative benefits for climate change mitigation - displacement of food crop production


Jatropha Yield in area idoneous to its farming based on:• Excluding protected areas• Sub-humid and semi-arid conditions (annual precipitation < 1500 mm)• Financial suitability and positive Carbon emission saving (Seed

productivity > 850 kg yr-1 ha-1)


Jatropha biofuel production aggregated for land use types (GLC2000)

*Estimated global oil consumption is 31,056 million barrels per year in 2007


Carbon Emission Savings:• Biofuel use > Fossil fuel use

• Continuous emission savings

• Emission savings evaluated by LCA

Ecosystem Carbon Stock Changes:• Biofuel plantation > existing Land Use

• Initial ecosystem carbon debt (or gain)

• Carbon Debt (or gain) evaluated with Carbon stocks assessments

𝐄𝐂𝐏𝐓=𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦𝐂𝐚𝐫𝐛𝐨𝐧𝐃𝐞𝐛𝐭𝐂𝐚𝐫𝐛𝐨𝐧𝐄𝐦𝐢𝐬𝐬𝐢𝐨𝐧𝐒𝐚𝐯𝐢𝐧𝐠𝐬

The Ecosystem Carbon Payback Time (ECPT) evaluate the period when Carbon Emission Savings offset eventual Ecosystem Carbon Debt


Carbon Emission Savings:LCA savings rates increase

with higher Jatropha yields, since

most of emissions are related to

agronomic practices.

Ecosystem Carbon Stock Changes:• - Carbon biomass applied to GLC2000 LU classification (Ruesch et al 2008), using

IPCC Tier-1 methodology, continental, forest frontier and eco-floristic regions

• - Carbon biomass in Jatropha plantation estimated 11.6 tons of C per ha

𝐄𝐂𝐏𝐓=𝐄𝐜𝐨𝐬𝐲𝐬𝐭𝐞𝐦𝐂𝐚𝐫𝐛𝐨𝐧𝐃𝐞𝐛𝐭𝐂𝐚𝐫𝐛𝐨𝐧𝐄𝐦𝐢𝐬𝐬𝐢𝐨𝐧𝐒𝐚𝐯𝐢𝐧𝐠𝐬


The spatial variation of ECPT of Jatropha biofuel systems


The effective production of Jatropha biofuel systems by land uses is grouped according to ECPT classes. Negative ECPT indicates an Ecosystem Carbon Gain in addition to carbon emission savings.

No ecosystemCarbon debt


• The framework quantify trade-off between nutritional and energy production

• Food security was defined at 1996 WHO summit as existing “when all people at all times have access to sufficient, safe, nutritious food to maintain a healthy and active life”

• Similarly, energy security is related to fulfillment of human energy requirements.

Mean Adequacy Ratio (MAR):How many persons are satisfied for their nutritional intake requirements by one ha of food crop

Energy Adequacy Ratio (EAR): How many persons are satisfied for their energetic requirements by one ha of biofuel


Recommended Nutrient Intake (RNI) can be defined for calories and proteins, as well as for other nutrients

In developing countries (body weight across age groups of 60 kg for male and 50 kg for female) RNI is equal to:

- 2,380 kcals per day per capita- 40 g of proteins per day per capita


Population size(age and sex

groups)

RNI (calories)

RNI (proteins) RNI (oth. nutrients)



groups)

RNI (calories)


Food Crop Production

Calories Production

Proteins Production

Oth. Nutrients production



groups)

RNI (calories)



Nutrient Adequacy Ratio - NAR (calories)

Nutrient Adequacy Ratio - NAR (proteins)

Nutrient Adequacy Ratio - NAR (oth.

nutrients)

Calories Production

Proteins Production



43


groups)

RNI (calories)



Nutrient Adequacy Ratio - NAR (calories)

Nutrient Adequacy Ratio - NAR (proteins)

Nutrient Adequacy Ratio - NAR (oth.

nutrients)

Calories Production

Proteins Production


Mean Adequacy Ratio - MAR (average of

several NAR)


- The SPAM dataset (You, Crespo et al. 2010) spatially redistribute (Cross-entropy approach) crop production statistics for main crops

- Food crop production converted and aggregated into nutritional production

- Related to fulfillment of human nutritional requirements (NAR and MAR)


Thousands of Kcalories equivalent from ha of foodcrop (in parenthesis, persons whose calorie requirements are satisfied from ha of foodcrops)

• MAR = persons * nutritional requirements satisfied from one ha of cropland

• EAR = persons * energy requirements satisfied from one ha of Jatropha crop

• Energy requirements in rural areas in developing countries (0.38 Tons of Oil Equivalent, 500 litres of biodiesel per capita)

• MAR / EAR on average 10, but high variability (in Africa as low as 2)



Synthesis

- Relative contribution of barren/sparsely vegetated (marginal) land is very limited for reforestation (0.2%) and Jatropha (2%) activities.

- Relevant for reforestation and Jatropha activities is the relative contribution of grassland, shrubland and savanna, holding ecosystem carbon stocks and/or food crop production.

- Financeable demand for CDM-AR fulfilled by only 4-8 Mha (1-2% of the grassland /shrubland /savanna globally suitable for reforestation). Carbon sequestration may often have local high water footprint.

- Jatropha biofuels production from all marginal land matches very limited oil demand in the global market → high risks of expansion on land with ecosystem services.


Synthesis

- Jatropha replacing forest, tree mosaics and shrubland causes negative impact on climate change mitigation. Ecosystem Carbon Gains on grassland and cropland.

- Jatropha on cropland acceptable for agriculture intensification in Africa (low trade-offs between energy and food security) where agricultural productivity is low (lack of agronomic inputs).

- Existing policies largely promoting biofuels sometimes in contrast with sustainable opportunity potentials (Jatropha).

- Sustainable biofuel opportunities require integration/intensification of land uses rather than full land use changes.


Limitations of data, methodology and results

- Several relevant environmental factors were not implemented (Peatland, Soil Organic Carbon, Grazing).

- Relevance of soil as explanatory factors was not easy to implement (low resolution of soil datasets).

- Other relevant trade-offs are underemphasized (e.g. biodiversity).

- Indirect land use change integrating human-environment interactions (GLUE, IMAGE, INVEST).


Perspectives and direction for further research

- Impact of land use change on Soil Organic Carbon (SOC), which can have magnitudes similar and exceeding Biomass Organic carbon (BOC) for various ecosystems.

- Feedback of reforestation on climate change is the albedo effect that follows land use change activities.

- Impact on biodiversity should also be considered and evaluated.

- Nutrient and energy requirement are based on subsistence (food and energy security). Actual consumptions are defined dynamically by socio-economic interactions.

Thank You

Potential EvapoTranspiration (PET or ET0) is the atmospheric potential to remove water through evapotranspiration, assuming no water shortage.

Four methods were tested using global high resolution climate dataset (WorldClim), and compared with FAO Penman-Monteith measurements (FAOCLIM):

• Thornthwaite (1948)• Mod Thornthwaite (Holand, 1978)• Hargreaves (1994)• Mod. Hargreaves (Droodgers and Allen)

Comparison of PET method performances shows that the Hargreaves method has the lower deviance (20 mm/month) from PET measurements.



Globally, more than 50% of the land has low population densities (< 25 people km-2), and more than 35% has densities < 5 people km-2.

South America shows the lowest population levels (95% of suitable area < 100 people km-2).

Sub-Sahara Africa has less empty land (85% of suitable area < 100 people km-2).

Land suitable in Asia shows higher population densities.

Model calibration and validation: locations


Traditional methods to evaluate land suitability and crop productivity not feasible for Jatropha because insufficient knowledge available.

Jatropha yield modeling two-steps approach

• In the first step, using a species distribution model (MaxEnt), we predicted relations between environmental factors and measures of Jatropha abundance, i.e. fitness

• In the second step, we relied on population biology principles supported by seed mass addition experiments to relate fitness to reproductive potential, hence seed yield

• Validation with available Jatropha field data


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