Determining Optimal Catch in Age-Structured Multispecies Fisheries

This study investigates optimal catch of Barents Sea stocks, namely Northeast Arctic Cod and Capelin in multispecies ecosystem. We solve a multispecies age structured bioeconomic model for predator-prey interaction. Barents Sea stock data from ICES are employed for model application. Among others, we also include sustainability constraint in the model that contributes towards ecosystem based management of fishery. Our preliminary result suggests that a conservative harvest is optimal for capelin compared to the single species model and a higher harvest is possible in cod in multispecies model. Furthermore, we found that a pulse fishing yields higher value in cod (predator) compared to the uniform (current) fishing policy

Analyzing Risk of Stock Collapse in a Fishery under Stochastic Growth Model

Acknowledging that there is stochasticity in the dynamics of a fish stock, one has a situation where the fish stock can collapse even without any fishing pressure. To derive the probability of collapse, we suggest a Monte Carlo approach because it is relatively simple model and can capture complex stock dynamics. We use an economic model with downward sloping demand for fish and stock dependent costs. Then, we calculate the optimal harvest profile as a feedback control rule. We analyze effects of different level stochasticity. We observe that the stochastic solution is more conservative compared to deterministic solution apart from a very small stock level, however, the effect from increased stochasticity is small at high stock levels. We simulate the system forward in time with the optimal solution. In simulated paths some stock collapsed, while other recovered. The paths are easily identified and we group the paths and estimate the probability of collapse for a given stock level. The precision of the estimate only depends on the number of paths. We have also looked at the time required for stock to reach stable level. The system needs more time to stabilize if the initial stock level is small and with stochasticity. Finally, the stochastic stock stabilizes on a level which is lower than the deterministic level. In this study, we demonstrate an approach to quantify stochasticity in fish stock dynamics, derive the optimal stochastic harvest profile, and demonstrate a method to assess the risk of collapse

Fisheries Management under Irreversible Investment: Does Stochasticity Matter?

We present a continuous, nonlinear, stochastic and dynamic model for capital investment in the exploitation of a renewable resource. Both the resource stock and capital stock are treated as state variables. The resource owner controls fishing effort and the investment rate in an optimal way. Biological stock growth and capital depreciation rate are stochastic in the model. We find that the stochastic resource should be managed conservatively. The capital utilization rate is found to be a non-increasing function of stochasticity. Investment could be either higher or lower depending on the interaction between the capital and the resource stocks. In general a stochastic capital depreciation rate has only weak influence on optimal management. In the long run, the steady state harvest for a stochastic resource becomes lower than the deterministic level.Physical capital; irreversible investment; stochastic growth; long-term sustainable optimal

SAS2

SAS reports are made available in order to provide timely access to the information by interested researchers. This report has been subject to an internal review process to ensure accuracy and quality.From forest clearing to landslides, then private claims to ownership, and with diversion of streams causing new landslides, a progression of environmental crises is tracked over time. The paper provides a timeline of 23 major events affecting the health of Rupa Lake and its wetlands (1952-2005). By 1986 government efforts were launched to control flooding and landslides, building check dams and planting trees. A Community Forestry Program to support local ownership and control of forests was begun. At the same time, new settlements were built in the watershed, large forest areas were cleared, thus creating conditions for a pattern of disasters

SAS2

SAS reports are made available in order to provide timely access to the information by interested researchers. This report has been subject to an internal review process to ensure accuracy and quality

On Farm Conservation of Crop Genetic Resource: Declining De Facto Diversity and Optimal Funding Strategy

Crop genetic resources (CGRs) are crucial natural resource which ensure food or livelihood security of billions of people today as well as ensure future agricultural innovations. However, the CGR diversity remaining in in situ, particularly in subsistence farming is becoming extinct due to change in economic and technological development over time. An optimal funding strategy is required for conservation of these CGRs. In this paper, I have discussed an economic perspective on why and how the de facto crop genetic resources (CGRs) diversity declines with changing economic and environmental context. The model maximizes the net revenue from the farmers land allocation strategy to different CGRs under economic and technical constraints with linear demand and cost functions. Furthermore, the model suggests how to minimize the cost of on farm conservation of these crop genetic resources in situ (or ex situ) without forfeiting farmer's well-being in a changing perspective of economics and technology. The theoretical model developed in this study is employed to demonstrate the applicability for on farm conservation of rice genetic diversity in Nepal. The study suggests an optimal fund allocation strategy that minimizes the cost of conservation by (i) identifying particular CGRs (rice landraces) that are prone to extinct from the community and (ii) categorizing the farmers in the community having minimum cost of conservation for those particular landraces. As the model maximizes the farmers' revenues, it could ensure better livelihood of individuals in the community while minimizing the cost of in situ conservation of biodiversity on farm

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

Optimal Harvest in a Multispecies Age Structured Fishery Model at Different Level of Density Dependency

This study suggests an optimal harvest policy for Barents Sea species, namely Northeast Arctic Cod (Gadus morhua) and Capelin (Mallotus villosus) in the multispecies ecosystem. We have solved a multispecies age structured bioeconomic model for predator-prey interaction. Barents Sea stock data from ICES (International Council for the Exploration of the Seas) are employed for the application of the model. Basically, this paper is an application of theoretical model by Steinshamn (2011), which highlights that stock density dependency plays greater role in the optimal management of fishery. We study several scenarios of stock density for predator-prey ecosystem to investigate the optimal harvest policy in multispecies environment. Of the several general biological and economic constraints, we also include the sustainability constraint in the model that contributes towards the ecosystem based management of fishery. Our preliminary findings are that smooth but lower harvest is optimal for capelin fishery compared to the single species model. While pulse fishing yields higher value in cod (predator) compared to the current fishing policy because of the lower cost of harvesting due to density dependency.Keywords: Modeling and Economic Theory, Modelling and Management, Fisheries Economic

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

Copyright © 2015 Diwakar Poudel et al.This is an open access article distributed underthe Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.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

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