Diwakar Poudel

An Analysis of Social Seed Network and Its Contribution to On-Farm Conservation of Crop Genetic Diversity in Nepal

Social seed systems are important for the maintenance of crop genetic diversity on farm. This is governed by local and informal system in the community through a farmers’ network. This paper analyses these local seed systems through application of social network analysis tools and mappings and examines the network member and its stability over space and time in a small rice farming community in Nepal. NetDraw software is used for data analysis and network mapping. We found that the dynamic network structure had key role in provisioning of traditional varieties and maintaining of crop genetic diversity on farm. We identify and ascertain the key network members, constituted either as nodal or bridging (connector) farmers, occupying central position in the network who promote seed flow of local crop diversity, thus strengthening crop genetic resource diversity on farm.

Do Species Interactions and Stochasticity Matter to Optimal Management of Multispecies Fisheries

The multispecies fisheries management looks at a bigger picture in addressing the long-term consequences of present decisions. This implies an ecosystem management that includes a number of species and their physical, biological and economic interactions. These interactions make the growth of resources stochastic and increase complexity in understanding stock dynamics and optimal catch for such a stochastic and multiple stocks´ system. To address the issue of identifying optimal catch of stochastically growing multi stocks, we have formulated and applied a time-continuous stochastic model. The model contributes to multispecies bioeconomic management of marine ecosystems. An application of model in a predator-prey relationship in Barent Sea revealed that the optimal catch for stochastically growing stocks in a multispecies interaction model is different from the deterministic model.

Analyzing Risk of Stock Collapse in a Fishery Under Stochastic Profit Maximization

In commercial fisheries, stock collapse is an intrinsic problem caused by overexploitation or due to pure stochasticity. To analyze the risk of stock collapse, we apply a relatively simple Monte Carlo approach which can capture complex stock dynamics. We use an economic model with downward sloping demand and stock dependent costs. First, we derive an optimal exploitation policy as a feedback control rule and analyze the effects of stochasticity. We observe that the stochastic solution is more conservative compared to the deterministic solution at low level of stochasticity. For moderate level of stochasticity, a more myopic exploitation is optimal at small stock and conservative at large stock level. For relatively high stochasticity, one should be myopic in exploitation. Then, we simulate the system forward in time with the optimal solution. In simulated paths, some stock recovered while others collapsed. From the simulation approach, we estimate the probability of stock collapse and characterize the long term stable region.

Stochastically Induced Critical Depensation and Risk of Stock Collapse

ABSTRACT This article investigates the risk of stock collapse due to stochastically induced critical depensation in managed fisheries. We use a continuous-time surplus production model and an economic model with downward-sloping demand and stock-dependent costs. First, we derive an optimal exploitation policy as a feedback control rule by applying the Hamilton-Jacobi-Bellman approach and analyze the effects of stochasticity on the optimal policy. Then, we characterize the long-term sustainable optimal state and estimate the risk of stock collapse due to stochastically induced critical depensation. We find that the optimal harvest policy in the stochastic setting is conservative at low stochasticity and approaches the myopic solution at high stochasticity. The risk of stock collapse is increasing with the stochasticity and decreasing with stock sizes. JEL Codes: C61, Q22, Q57.

Stochastic Optimization for Multispecies Fisheries in the Barents Sea

This study presents a multispecies stochastic model. The model suggests optimal fishing policy for two species in a three species predator prey ecosystem in the Barents Sea. We have employed stochastic dynamic programing to solve a three dimensional model, where catch is optimized by a multispecies feedback strategy. Application of the model in cod, capelin and herring ecosystem in the Barents Sea shows that the optimal catch for stochastic interaction model is more conservative compared to deterministic policy. Furthermore, we found that stochasticity has strong effect on optimal exploitation policy in the prey (capelin) compared to the predator (cod) species.