This paper examines the impact of weather derivatives on the value of an agricultural corporation in Japan. The paper uses a case of Hob, a listed Japanese agricultural corporation focusing on strawberry. The paper finds out the impacts of adverse weather conditions, temperature and hours of sunshine, on Hob's enterprise value are significant. The paper employs Wang Transforms for the valuation analysis in order to address the incompleteness of the weather derivatives markets and incorporate the managers' risk averseness. Weather derivatives contribute to the firm value significantly as long as the safety loading of weather derivatives is lower than or equal to 0.2 and managers of Hob are moderately risk averse (λ in Wang Transforms is 0.4 or higher). However, if the safety loading is higher than or equal to 0.6 implied by weather derivatives sold by Japanese insurance companies, the weather derivatives are not demanded by the managers even if they are extremely risk averse (λ is 1.0).

River environmental management contains controlling physical and biological processes. Both processes are different phenomena with each other but are in common continuous-time. In general, the physical processes related to river hydraulics can be measured frequently and the continuous-time nature is captured. On the other hand, the biological processes like growth of population and organisms cannot be observed so frequently. Their observation is thus actually discrete-time. Many models for environmental management assume observation of the biological processes to be continuous-time, which is inconsistent with the reality. The objective of this paper is to develop a new model that can harmonize the continuous and discrete natures in management of the biological processes. This is achieved through employing a recent stochastic process formalism. Focusing on algae population management in river environment, we derive the optimality equation to determine the most cost-effective harvesting policy (observation timing and workload) based on discrete observations. A computational example with practical implications is presented focusing on a problem of benthic algae population management in Hii River, Japan.