16 research outputs found
Can forest management based on natural disturbances maintain ecological resilience?
Given the increasingly global stresses on forests, many ecologists argue that managers must maintain ecological resilience: the capacity of ecosystems to absorb disturbances without undergoing fundamental change. In this review we ask: Can the emerging paradigm of natural-disturbance-based management (NDBM) maintain ecological resilience in managed forests? Applying resilience theory requires careful articulation of the ecosystem state under consideration, the disturbances and stresses that affect the persistence of possible alternative states, and the spatial and temporal scales of management relevance. Implementing NDBM while maintaining resilience means recognizing that (i) biodiversity is important for long-term ecosystem persistence, (ii) natural disturbances play a critical role as a generator of structural and compositional heterogeneity at multiple scales, and (iii) traditional management tends to produce forests more homogeneous than those disturbed naturally and increases the likelihood of unexpected catastrophic change by constraining variation of key environmental processes. NDBM may maintain resilience if silvicultural strategies retain the structures and processes that perpetuate desired states while reducing those that enhance resilience of undesirable states. Such strategies require an understanding of harvesting impacts on slow ecosystem processes, such as seed-bank or nutrient dynamics, which in the long term can lead to ecological surprises by altering the forest's capacity to reorganize after disturbance
Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing
As water resources are limited and the demand for agricultural products increases, it becomes
increasingly important to use irrigation water optimally. At a farm scale, farmerâs have a particularly
strong incentive to optimize their irrigation water use when the volume of water available over a season
is production limiting. In this situation, a farmerâs goal is to maximize farm profit, by adjusting when and
where irrigation water is used. However, making the very best decisions about when and where to
irrigate is not easy, since these daily decisions require consideration of the entire remaining irrigation
season. Future rainfall uncertainty further complicates decisions on when and which crops should be
subjected to water stress. This paper presents an innovative on-farm irrigation scheduling decision
support method called the Canterbury irrigation scheduler (CIS) that is suitable when seasonal water
availability is limited. Previous optimal scheduling methods generally use stochastic dynamic
programming, which requires over-simplistic plant models, limiting their practical usefulness. The
CIS method improves on previous methods because it accommodates realistic plant models. Future farm
profit (the objective function) is calculated using a time-series simulation model of the farm. Different
irrigation management strategies are tested using the farm simulation model. The irrigation strategies
are defined by a set of decision variables, and the decision variables are optimized using simulated
annealing. The result of this optimization is an irrigation strategy that maximizes the expected future
farm profit. This process is repeated several times during the irrigation season using the CIS method, and
the optimal irrigation strategy is modified and improved using updated climate and soil moisture
information. The ability of the CIS method to produce near optimal decisions was demonstrated by a
comparison to previous stochastic dynamic programming schedulers. A second case study shows the CIS
method can incorporate more realistic farm models than is possible when using stochastic dynamic
programming. This case study used the FarmWi$e/APSIM model developed by CSIRO, Australia. Results
show that when seasonal water limit is the primary constraint on water availability, the CIS could
increase pasture yield revenue in Canterbury (New Zealand) in the order of 10%, compared with
scheduling irrigation using current state of the art scheduling practice