Presentation by Dolapo Enahoro and Karl M. Rich at the Southern Africa Towards Inclusive Economic Development (SA-TIED) Programme – A Scoping Workshop on Climate Change Pretoria, South Africa, 4 February 2019

1. Regional Livestock Modeling for Climate Change
Adaptation and Mitigation in Southern Africa
Dolapo Enahoro and Karl M. Rich
Southern Africa Towards Inclusive Economic Development (SA-TIED)
Programme – A Scoping Workshop on Climate Change
Pretoria, South Africa, 04 February 2019

2. Outline
Background
Modeling the livestock sector using
global models
Knowledge gaps
Options for SA-TIED Climate &
Energy research
Environmental impact assessments
Pasture-climate change impacts
Potential outputs

3. Background
Incomes and the demand for
animal-source foods are generally
growing in low- and middle-
income countries (LMICs)
There’s a need to understand
emerging opportunities for food
security, poverty reduction,
economic development, etc.
Much also unknown about the
implications for vulnerable
landscapes and the world’s
poorest populations.

4. The IMPACT model
Source: Rosegrant et al., 2014
The IMPACT Model System
•Model traditionally
better suited to the
crop sector
•Work is ongoing to
improve model’s
capacity for livestock
sector assessments
•IMPACT is a system of linked models of global agriculture simulating
multi-country multi-commodity markets, water and crop models

5. Using IMPACT for livestock modeling
• Key applications & advantages :
 Simulate economic change, climate change effects and
adaptation strategies relevant to livestock sector
 Project demand, production, trade of livestock-derived
foods (LDF), and their welfare impacts
 Assess systems and regions for growth potential,
response to shocks, competitiveness, trade-offs, etc.
 Assess countries/regions’ technology, investment and
policy options in the context of megatrends.

6. Recent results from global livestock
modeling
• LDF to be higher in selected LMICs in 2050; Much of new
demand (>=40%) to be met through imports. This has
implications for household incomes and nutrition (Enahoro
et al., 2018)
• Investments to increase livestock productivity in South
Asia, sub-Saharan Africa to improve food security and
producer incomes while limiting GHG emissions,
agricultural resource use;
• Market-focused interventions lead to increased needs to
manage environmental impacts (Enahoro et al., 2019).
• Production systems matter! Climate change mitigation
policies will be most efficient when they are targeted
directly to the source of emissions (Havlik et al., 2014).

7. What else do we want to know?
Production(millionsoftonnes)
LMICs
Year
HICs
•Where will ASF and
feed demand growth
occur the most?
•Where and how will
the needed ASF and
feeds be produced?
•How will climate
change affect
outcomes?
•What policy
considerations are
needed to promote
sustainability?

8. Exploring intervention options
0
1
2
3
4
5
6
7
8
0 1000 2000 3000 4000 5000 6000
methane (CO2eq)/kg milk
Milk yield (kg/lactation)
Largest
improvements
in low
producing
animals
FAO 2013, Herrero et al 2013
• Exploiting yield gaps may be key to achieving environmental
benefits in ruminant-based systems
• It may also play an important role in achieving food security.

9. Livestock and climate change in Southern Africa:
the Issues
• 8.1 million poor people in Southern Africa derive
livelihoods from livestock (Robinson et al., 2011;
FAOStats, 2010 est.); the poorest likely eat the least ASF,
but are most vulnerable to CC impacts on agriculture;
PLK in
country’s
population
Share of
PLK in
region
Share of
cattle &
shoat
stocks
Share of
chicken
and pig
heads
Share of
emissions
(CO2 equiv.)
Botswana 14% 4% 6% <1% 9%
Lesotho 27% 7% 5% 4% 4%
Namibia 31% 8% 12% 5% 14%
South
Africa
12% 77% 76% 88% 71%
Eswatini 29% 4% 2% 2% 3%
Table 1: Selected statistics

10. Research Options
• Linking IMPACT’s climate change simulations to results
from the G-Range model (Boone et al., 2017)
Food security and resource use changes associated with
pastoral and agro-pastoral livestock production
• Environmental impact assessments and resource use
analysis in IMPACT + CLEANED (Pfeiffer et al., 2016)
Land use and environmental impact outcomes of future CC-
Income interactions in Southern Africa

11. OPTION ONE: Linking IMPACT to G-Range
LinkageCould we link IMPACT simulations of climate change
impacts to G-Range results to assess for Southern Africa:
• Livestock demand, production and trade
• Feed demand and pasture availability
• Food security
• Producer and consumer incomes
These linkages will however not include feedbacks

12. The G-RANGE Model
The G-Range model:
• Moderate complexity spatial ecosystem model
quantifying global changes expected in rangelands
under future climates
Key results (Boone et al., 2017):
• Baseline and mean changes in ensemble results using 7
GCMs are presented for 13 global rangeland ecosystem
responses under RCP 4.5 and 8.5 Climate change to
have substantial impacts on forage production; shifting
distribution of livestock production
• Populations already food insecure may become
increasingly so.

13. Sample Spatial Assessments
Figure: Regional percent changes in selected attributes from ensemble simulation results in
2050 (Boone et al., 2017)

14. OPTION TWO: Linking IMPACT-CLEANED
The CLEANED tool:
Spatially explicit simulation tool that computes land use and
environmental impacts based on parameters of livestock
production defined by the user (Pfeifer et al., 2016).
resampled open access
data
IMPACT defined livestock
categories (dairy cattle, meat
cattle, shoat, pigs and chicken)
GIS pre-processing code
Production and land
allocation module
Water impact
Greenhouse gas
Bio-diversity
Soil nitrogen balance
Land use change
module
Pfeifer et al., 2016

15. Preliminary Results from IMPACT-CLEANED
simulations in East and West Africa
In Tanzania, land did not pose a constraint to livestock sector
transformation under key macro scenarios
Outcome depends on substantial crop productivity gains and
shifts of arable land into feed grain and fodder production
Alternatively, about twice as much land area needed for crop
monocrop to support livestock production in 2050.
Analysis for Burkina Faso showed LDF production implied by the
model projections require higher productivity gains than
producers currently attain or aspire to.

16. Proposed activities
Options Key steps/outputs
1. IMPACT-CLEANED
(environmental
impact assessment)
Parameterize CLEANED for relevant Southern
Africa countries
Identify (through stakeholders) macro-
scenarios of relevance to the region
Assess impacts of identified scenarios on land
use, feed use and food security
2. IMPACT-G-
RANGE
(rangeland
modeling)
Collate key results of climate scenarios
previously quantified in IMPACT and G-Range
Analyze important complementarities and
trade-offs associated with rangeland systems
production
3. Combination or
other
???
Table 2: Potential outputs

17. This presentation is licensed for use under the Creative Commons Attribution 4.0 International Licence.
better lives through livestock
ilri.org

18. Our work at ILRI on livestock sector foresight analysis has
received financial support from
CGIAR Research Programs:
- Policies, Institutions and Markets
- Livestock
- Climate Change, Agriculture and Food Security
Bi-lateral donors:
- The Bill & Melinda Gates Foundation
- US Agency for International Development
(USAID)
Acknowledgements

19. Key References
• IMPACT: Robinson, S., Mason-D’Croz, D., Islam, S., Sulser, T.
B., Robertson, R. D., Zhu, T., … Rosegrant, M. W. (2015). The
International Model for Policy Analysis of Agricultural
Commodities and Trade (IMPACT): Model description for
version 3. IFPRI Discussion Paper 01483 (IFPRI Discussion
Paper No. 1483). Washington, DC.
• CLEANED: Pfeifer, C., Morris, J., & Lannerstad, M. 2016.
(2016). The CLEANED R simulation tool to assess the
environmental impacts of livestock production (Livestock and
Fish Brief No. 18). Nairobi, KENYA.
• G-RANGE: Boone, R. B., Conant, R. T., Sircely, J., Thornton, P.
K., & Herrero, M. (2018). Climate change impacts on
selected global rangeland ecosystem services. Global Change
Biology, 24(3), 1382–1393.
https://doi.org/10.1111/gcb.13995