<p>Forest management
involves numerous stochastic elements. To sustainably manage forest
resources, it is crucial to acknowledge
these sources as uncertainty or risk, and incorporate them in adaptive
decision-making. Here, I developed several stochastic programming models in the
form of passive or active adaptive management for natural mixed-species
hardwood forests in Indiana. I demonstrated how to use these tools to deal with
time-invariant and time-variant natural disturbances in optimal planning of
harvests.</p>
<p> Markov decision process (MDP)
models were first constructed based upon stochastic simulations of an empirical
forest growth model for the forest type of interest. Then, they were optimized
to seek the optimal or near-optimal harvesting decisions while considering risk
and uncertainty in natural disturbances. In particular, a classic
expected-criterion infinite-horizon MDP model was first used as a passive
adaptive management tool to determine the optimal action for a specific forest
state when the probabilities of forest transition remained constant over time.
Next, a two-stage non-stationary MDP model combined with a rolling-horizon
heuristic was developed, which allowed information
update and then adjustments of decisions accordingly. It was used to determine
active adaptive harvesting decisions for a three-decade planning horizon during
which natural disturbance probabilities may be altered by climate change.</p>
<p> The empirical results can be used
to make some useful quantitative management recommendations, and shed light on
the impacts of decision-making on the forests and timber yield when some
stochastic elements in forest management changed. In general, the increase in
the likelihood of damages by natural disturbance to forests would cause more
aggressive decisions if timber production was the management objective. When
windthrow did not pose a threat to mixed hardwood forests, the average optimal
yield of sawtimber was estimated to be 1,376 ft<sup>3</sup>/ac/acre, while the
residual basal area was 88 ft<sup>2</sup>/ac. Assuming a 10 percent per decade probability
of windthrow that would reduce the stand basal area considerably, the optimal sawtimber yield per decade would
decline by 17%, but the residual basal area would be lowered only by 5%. Assuming
that the frequency of windthrow increased in the magnitude of 5% every decade
under climate change, the average sawtimber yield would be reduced by 31%, with
an average residual basal area slightly around 76 ft<sup>2</sup>/ac. For
validation purpose, I compared the total sawtimber yield in three decades
obtained from the heuristic approach to that of a three-decade MDP model making
<i>ex post</i> decisions. The heuristic
approach was proved to provide a satisfactory result which was only about 18%
lower than the actual optimum.</p>
These findings highlight the need for landowners, both private and
public, to monitor forests frequently and use flexible planning approaches in
order to anticipate for climate change impacts. They also suggest that climate
change may considerably lower sawtimber yield, causing a concerning decline in
the timber supply in Indiana. Future improvements of the approaches used here are
recommended, including addressing the changing stumpage market condition and
developing a more flexible rolling-horizon heuristic approach.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12723293 |
| Date | 28 July 2020 |
| Creators | Vamsi K Vipparla (9174710) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/ADAPTIVE_MANAGEMENT_OF_MIXED-SPECIES_HARDWOOD_FORESTS_UNDER_RISK_AND_UNCERTAINTY/12723293 |
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