Supporting the vulnerable: Increasing the adaptive capacity of agro-pastoralists to climatic change in West and Southern Africa using a

transdisciplinary research approach

BMZ CC projects meeting, May 2010

ILRI• Boni Moyo (Moza office), Augustine Ayantunde (Mali office), An

Notenbaert, Philip Thornton and Mario Herrero

• PIK• Christoph Muller, Hermann Lotze-Campen, Alex Popp

• DITSL• Brigitte Kauffmann

•IER, Mali: Lassine Diarra

•IIAM, Mozambique: Xoares Xerinda

• AU-IBAR: Simplice Nouala

Who are we?

Estimationof climate change

impacts

Identifying and documenting local

adaptive responses

Revitalising the policy environment to

increase access to adaptation options

Promoting active learning

by communities

Dissemination andcapacity building

Adaptation to

Climate change

5

Estimation and documentation of the effects of climate variability and change on primary productivity of crops, rangelands and livestock, and associated livelihoods impacts under different scenarios (PIK, ILRI)

Activities

• Assemble climate change scenarios

• Define a systems classification framework incorporating crops and livestock and intensification (market access, agricultural potential, …)

• Identify key impacts on primary productivity of crops and livestock under different scenarios by system

• Identify and characterise areas of reduced primary productivity (crops and rangelands), overlaid with information on poverty levels and livelihoods to identify sites where productivity reductions may have serious repercussions on smallholder wellbeing

• Sensitivity analysis on uncertainties related to climate variability on selected hotspots

Assemble climate change scenarios

Key impacts on primary productivity

Identify sites where productivity reductions may have serious repercussions on smallholder wellbeing

Multiple models – multiple scenarios - GLOBAL

AOGCMs used in the downscaling work

Randall et al. (2007)

Scheme of the down-scaling analysis

MarkSim stochastic weather

generator

Observed climate grid at

resolutionof choice

Generate daily data characteristic of a

chosen “year” (time-slice) from 2000-

2099

Applications

WorldClimCRU etcWeather typing

Jones et al 2009

Applications

Daily data that are characteristic (to some extent) of the climatology of future time slices:

• Rainfall• Maximum temp• Minimum temp

With these, can derive or estimate other variables:

• Daily: Solar radiation (a function of Tmax, Tmin, lat, long)

• Seasonal: Length of growing period, season start date, duration, ending date (simple water balance, soil data)

Drive vegetation, crop, livestock models …

IPCC (2007) AR4 data: Mozambique

Historical

• Mean annual temperature up by 0.6°C since 1960 (0.13°C per decade)

• Increasing frequency of ‘hot’ (10%) days and nights in all seasons

• Mean annual rainfall down at a rate of 2.5mm per month (3.1%) per decade since 1960 (mostly due to decreases in DJF rainfall)

IPCC (2007) AR4 data: Mozambique

Future

• Mean annual temp up by 1.0-2.8°C by 2060s, 1.4-4.6°C by 2090s – more rapid warming in interior

• Few substantial changes in annual rainfall projected (model range is large, straddles negative and positive). Decreases in dry season (JJA and SON), offset partially by increases in wet season (DJF)

• Potential changes in intensity and tracks of tropical cyclones very uncertain. No agreement in projected changes in the amplitude of future El Niño events

• Sea-level rises to 2090s: 0.13 - 0.43m (B1); 0.16 - 0.53m (A1B); 0.18 - 0.56m (A2)

Average annual temperature changes (°C*10) from 2000 to 2050, A2

CNR CSI

ECH MIR

Annual rainfall changes (mm) from 2000 to 2050, A2

CNR CSI

ECH MIR

Length of growing period change (days per year) from 2000 to 2050, A2

CNR CSI

ECH MIR

Integrating livestock in global models• Model of agricultural productivity: LPJmL

• Grassland management systems• Grassland productivity• Carbon & water dynamics

• Model of land-use dynamics: MAgPIE• Feed mixes & energy requirements• Production costs• Intensification vs. expansion• Resource usage• Environmental impacts (GHG emissions)

Grassland productivityFrom cows to grass

Grassland that sustains livestock for milk and/or meat • can be managed intensive or extensive• can be temporal or permanent• can be irrigated or rain-fed• can be ... differs in yield

Question: How much grassland is needed to feed livestock?

Approach:

Combination ofCA: cattle abundances per livestock system EC: energy demand of dairy and beef cattleEG: energy content of temperate and tropical grassesGA: grassland areas

grassland yield = CA*EC/EG/GA

Rolinsky et al., in prep.

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Cattle in Livestocksystems

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mixed rainfedmixed irrigated

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Grassland AssessmentFrom cows to grass

CA: worldwide abundance of cattle in livestock systems (ILRI data)

EC: Feed requirement per cattle in unit grass DW (Calc. by Wirsenius, 2000, and Bouwman, 2005)

EG: Energy content of grass for maintenance (NEm), growth (NEg) and lactation (NEl) (Wirsenius 2000)

GA: FAO data

grassland yield = CA*EC/EG/GAT DW/ha

Assess-ment of yield and area

Rolinsky et al., in prep.

Smit et al., 2008Grassland productivity

“observations”

Requirements

LPJmL

Next steps:• Harmonization of input data: area, management system,

livestock numbers• Implementation of different grassland management systems

in LPJmL (so far, intensive mowing only)• Calibration of grassland productivity

Land-use dynamics: trade-offs in livestock• Livestock production systems differ in

• Feed mixes (raw material, compound, crop residues)• Conversion efficiencies (feed -> livestock)• Resource demand (water, land, inputs)• Production costs• Environmental impacts (resource use, GHG)

• Livestock in future food supply• Competition for resources (land, water)• Impacts on markets (food/resource prices)• “side-effects”: GHG emissions, C sequestration

Livestock in MAgPIE• Feeding technologies play a crucial role for conversion

efficiencies and environmental impacts per unit output• For ten world regions, we define the feeding technology for

five livestock subsectors as a set of the following parameters:• productivity index p (products per producing animals and per

animals in stock)• feed energy requirements per unit output [f(p)]• required nutrient density of the feed mix [f(p)]• Feed mix• GHG emissions per unit output.

• All data consistent with FAO

Livestock Module

Feed conversion efficiency (ME, Nel, Nem, Neg)

Nutrient density requirements

(Nel, NEm)

Demand for livestock products(exogenous) Total feed

demand(ME, Nel,

Nem, Neg)

FAO-FBSFeed use

Residues

Harvest index, recovery rates, assignment rates for feed

Livestock Feed Mix Feed assignment to

different livestock systems Grazed Biomass

Productivity(endogenous)

Weindl et al., in prep.

• Work in progress:• Production costs• Elasticity of production systems

Assemble climate change scenarios

Key impacts on primary productivity

Identify sites where productivity reductions may have serious repercussions on smallholder wellbeing

1.Combining Productivity impacts with “vulnerability” - example

2. Different future vulnerability models and scenarios

Net Primary Production- Maize - Managed- Rice grass- Temp.cereals- Trop.cereals

LP

JmL

Stover:DMME

Grass:DMME

Pastoral / Agro-pastoral

Mixed

+ Feed=

Ind

ices

Cattle + Small Ruminants

LivestockUnits

=

FA

O &

Imp

act

=

Feed resource impacts of CC (in terms of changes of feed availability ME/LU/day)

Changes from 2000 to 2050

Thornton et al.

VulnerabilityIndex

Vulnerability ~ Exposure - Coping

From NPP of the CFTs to Feed

Livestock Projections

FAO gridded livestockof the world

IMPACT projections

2000 2050

Combination

Changing vulnerability

1 framework1 scenario

Multitude of Frameworks & scenarios

Inventory, documentation and dissemination of past, present and possible future agro-pastoralists coping mechanisms to deal with climate variability (ILRI DITSL IAM IER)

• 2.1. Household surveys conducted and risk profiles developed (IIAM/IER/ILRI/Kassel)

• 2.2. Documentation and inventory completed of how agro-pastoralists used to cope with climate variability (Kassel/ILRI/IIAM/IER)

• 2.3. Historical analysis (Kassel/ILRI/IIAM/IER)

• 2.4. Conduct a review of adaptation options to climate variability and change that are currently being promoted and tried by institutions in the selected regions. (ILRI/Kassel/IIAM/IER)

• 2.5. Gap analysis: identification of options that smallholders would like to pursue but are not able to, and documentation of the reasons why. (ILRI/Kassel/IIAM/IER)

Household surveys conducted to identify livelihood strategies and risk profiles of agro-pastoralists in selected hotspot areas

Case study sites in The Mopti region of Mali Gaza province in Mozambique

In each country: 12 villages * 12 surveys = 144 households Criteria for selection of villages:

Distance to market: “far” from market had been defined as more than 10 km. To divide villages where production is more versus less market orientated. Distance to the river: “far” from the river had been defined as more than 5 km. To divide villages where access to fodder and water during the dry season is or is not a constraint in dry season

5 modules: Livelihoods Herd dynamics Livestock management Welfare outcomes Vulnerability

Household surveys conducted to identify livelihood strategies and risk profiles of agro-pastoralists in selected hotspot areas

Household concerns

Mali Mozambique

Vulnerability of agro-pastoralists in Mali and Mozambique

Coping by agro-pastoralists in Mali

Not enough pasture

Not enough food

Crop Failure

Animal Health

Coping by agro-pastoralists in Mozambique

Estimating household’s vulnerability

Based on the HH’s assessment of impact of the concerns they are facing:

Vulnerability = V = ∑ wi * Ii

with n = number of concerns

wi = weight of concern iIi = impact (1: worse / 0: same / -1: better)

i=1

n

Factors influencing vulnerability (Mozambique)

Amongst others:

Distance to marketGenderAgeIncome diversityNumber of animals

Example from Mali:Changes in livestock production during the past 20 years

Decrease in:• contribution of livestock to the household needs• animal numbers, total and also per household • condition of livestock

Increase in:• concentrate feeding • production costs (concentrate) • transhumance period• animal numbers (in the Delta)• contribution of livestock to the household needs (Delta)

Lack of pasture High price of concentrateLack of water Diseases

Source: Satao, M. (MSc thesis in preparation)

Major current constraints:

Reasons for changes:

(Summary of data collected in livelihood analyses and timelines in 4 villages)

• increase in population• increase in needs• reduction of pasture area • increase in cropping area • loss of interest of young people in transhumance• improvement of climatic

conditions (Delta)

Example from Mali:Changes in crop production during the past 20 years

Extension of crop production to all members of villages

Increase in:• contribution of crop production to household needs• number of fields • field size • degree of mechanization• occupation of family members

Decrease in:• in soil productivity

Insufficient and irregular rainfall

Reduction in productionPest

Insufficient manure

Source: Satao, M. (MSc thesis in preparation)

Major current constraints:

Reasons for changes:

Crop damage through livestock

(Delta)

(Summary of data collected in livelihood analyses and timelines in 4 villages)

• Increase in population size• Droughts let to drop in

livestock production• Reduction in animal

numbers and contribution of livestock

• Search for food security• Increase in rainfall and

inundation since 1992 (Delta)

Source: Werner M. (MSc thesis in preparation)

Example from Mali - Adaptation strategy:Avoid crop failure through the choice of varieties

Trait Sunnari Sannoori

Development of the plant

short-cycle

the plant is smaller

long-cycle

the whole plant is bigger

Panicle smaller and the grains are harder bigger panicles, thus higher yields

Storage and taste n.a. it is longer storable and has a better taste

Source: pairwise ranking Nérékoro, 3 participants, 06/03/2009

Source: Werner M. (MSc thesis in preparation)

Example from Mali - Adaptation strategy:Factors influencing decision of the choice of varieties

Factors influencing the decision

Sunnari Sannoori

Amount of cereal stock at begin of planting season

Low stock Sufficient amount of cereal in stock

Date of onset rainfall Late rainfall Early rainfall

Persistence of rainy season Interruptions, resowing necessary

Regular rainfalls, no/ only short interruptions

Availability of tools (plough and draft animals)

Delays are probable due to poor availability of tools

Plough available, oxen in good condition, labour at hand: no delays

Experiences in past years Short rainfall period in past years, sannoori did not attain maturity

Long rainfall period in past years, sannoori did attain maturity

Risk attitude of decision maker

Lower yield risk Higher yield risk

Source: feedback seminar Nérékoro, 18 participants, 14/05/2009

Policy entry points for supporting the implementation of priority livestock-based adaptation options in agro-pastoral systems identified and discussed with key stakeholders (AU-IBAR leading)

Activities

Stock-taking to assess country commitment and institutional capacity to implement climate change adaptation strategies

Analysis of key actors and their linkages as well as the interactions between policies and actors to identify policy, institutional and organizational entry points and evaluate institutional capacity to support CC projects

Policy and institutional analysis to identify and evaluate promising policy and institutional options that facilitate climate change adaptation strategies

Establish regional policy dialogues on adaptation to CC

Policy entry points for supporting the implementation of priority livestock-based adaptation options in agro-pastoral systems identified and discussed with key stakeholders (AU-IBAR leading)

• AU-IBAR have reviewed the NAPAs (National Adaptation Programmes of Action – see UNFCCC website) for Mali and Mozambique

• The first policy forum is planned for May 2010 ‘Ministerial rounds on cliamte change a livestock systems’

Dissemination

Activities

Dissemination of information on implementing and managing the high-priority adaptation options identified using community facilitators, mobile extension teams, and comic books

1 PhD and 1 post doc (at PIK) together with other sources of funding

5 MSc students (DITSL – Kassel and local)

1 short film on climate change and adaptation strategies for pastoralists and agro-pastoralists produced

Development of tools for training national partners in targeting analyses and community participatory research techniques in relation to risk management and adaptation

• Papers, reports, conference presentations, other dissemination activities, in an updatable list

•PNAS Special Issue on Livestock and Global Change (2 papers: 1 PIK- led, 1 ILRI-led)

•Side event at COP15

• The Climate Change film is on the way. Filming has taken place in a range of places in Africa, and the script is being finalised and edited

Dissemination

THANKS!