Shared and Divergent Histories Drive Future Dynamics of the Mongolian Plateau
1. Shared & Divergent Histories
Drive Future Dynamics of the
Mongolian Plateau
GINGER R.H. ALLINGTON, WEI LI, JIQUAN
CHEN, RANJEET JOHN, DANIEL G. BROWN
5. System dynamics models
• Provide insight into a
system's structure
• Because of feedback loops,
it’s often difficult to infer the
behavior of a system from its
causal structure.
• Can be used to simulate
alternative scenarios of
system under different initial
or boundary conditions.
NPP
Livestock Biomas
s
-
+
+
+
PROCESS NOT
PRODUCT
11. SCENARIOS
XILINGOL SUHKBAATAR
Baserun: Continuation of current
conditions.
Scenario 1: Increased precipitation.
Scenario 2: No grassland protection
policies.
Scenario 3: No restrictions to crop
expansion.
Scenario 4: “Worst Case”
Baserun: Continuation of current conditions.
Scenario 1: Increased
urbanization/industrialization
Scenario 2: Enhanced mobility/ communal
cooperation /rural infrastructure development
Scenario 3: Increased privatization of resources &
services
12. Model outputs and predictions-
Xilingol
Baserun: Continuation of
current conditions.
1: Increased precipitation.
2: No grassland protection
policies.
3: No restrictions to crop
expansion.
4: “Worst Case”
13. Model outputs and predictions-
Xilingol
Baserun: Continuation of
current conditions.
1: Increased precipitation.
2: No grassland protection
policies.
3: No restrictions to crop
expansion.
4: “Worst Case”
14. Model outputs and predictions-
Suhkbaatar
Baserun: Continuation of current
conditions.
1: Increased
urbanization/industrialization
2: Enhanced mobility/ communal
cooperation /rural infrastructure
development
3: Increased privatization of resources &
services
15. Model outputs and predictions-
Suhkbaatar
Baserun: Continuation of current
conditions.
1: Increased urbanization/industrialization
2: Enhanced mobility/ communal
cooperation /rural infrastructure
development
3: Increased privatization of resources &
services
But
REALLY??
16. Key factors influencing dynamics of the
two systems
1. Urbanization
2. Policies promoting protection and restoration
of grasslands (IMAR)
3. Policies limiting cropland expansion (IMAR)
4. Policies promoting rural infrastructure that
supports community cooperation and mobility
(MG)
17. KEY AREAS OF
UNCERTAINTY:
-Urbanization trends into
the future; rural/urban
flows
-Impacts of CBRMs
-Market Access
-Absentee herders
-Herd
structure/composition
-Categorical grassland
classifications
Notas do Editor
The Mongolian plateau is one of the worlds largest contiguous arid rangeland systems in the world. Like other arid rangeland systems of the world, the climate on the plateau is inherently variable in space and time and the pastoralists here have, for thousands of years, relied on mobility to respond to this stochastic environment, following seasonal migration patterns to track areas of suitable forage.
Plateau covers the northen part of China, the Inner Mongolia AR and Mongolia, and while there is a shared ecological and cultural history in this region that extends back thousands of years, there have also been more recent political and social changes that have taken place wihtin these two countries that underly some of the differences in the dynamics driving the systems in these two countries, which I’ll be focussing on today.
Y-axis: Long-term policies v. short-term policies
X-axis: Low precipitation to high precipitation
Y-axis: Green policy v. private market
X-axis: Industry and mining v. Agriculture and livestock sector [investments& policy by govt]
And when we run those scenarios through the model we can start to see how some of these changes in policy or climate might affect the dynamics, for instance, here in the top left model predictions for Xilingol the model predicts a decline in livestock population under all scenarios except #4, in which the population fluctuates around current levels. Similarly, if we look at biomass remaining at the end of the growing season, which we are using as a proxy of grazing pressure, I have removed scenarios 2&3 bc they overlapped with the top two and it was confusing to read but again all predict long-term increases in remaining biomass, with the exception of Scenario 4. And if we examine the model behavior in more detail and the sensitivities, we found that this trend is really driven strongly by the declining rural population, and the loss of herders as people move to urban areas.
Urbanization is also driving the dyanmics controlling grassland area as well, where the dynamics stay relatively stable when population is urbanizaing, however in the absence of the strong policies controlling grazing pressue we see steep declines in grass area and increasing conversion of grassland to agriculture.
In Suhkbaatar the model predicts the highest amount of livestock population growth under scenario that supports rural infrastructure development and enhanced mobility, but interestingly that trend also corresponds with predicted decreases in remaining biomass over time, due to increasing grazing pressure.
The Scenario 3, which includes increased provatizatino of resources ends with the highest livestock populations and lowest levels of remaining biomass.
And when we look at grassland area, things get interesting. Grassland area is predicted to decline if current coniditons conitnue into the future, but all other scenarios predicted stable or slight increases in grassland area. If we look at the sensitivities controlling this trend it is clear, again, that the mian factor here in the trend toward urbanization, which is linked to declining livestock populations due to decreases in rural populations or herders. HOWEVER. Livestock numbers are continuing to increase in Mongolia despite huge rates of urbanization, and that is due to a phenomemnon known as absentee herders, where more affluent individuals in the cities still maintain herds by hiring others to herd the livestock, and those herd sizes are often quite large, so we need to go back to this model and find ways that we can incorporate that decision-making by absentee herders which may continue to drive up livestock populations into the future.
Based on these preliminary assesments we can draw a few conclusions about drivers in these two systems, namely
Urbanizatin trends will have a large impact on future dyanmics
There are also Differences in the particularls of these two areas in terms of the specific policies governing their dynamics and the impacts those will have in the future.
