Mariculture of giant clams,Tridacna crocea and T. derasa: Management for maximum profit by smallholders in Solomon Islands

Abstract

The International Center for Living Aquatic Resources Management (ICLARM) has demonstrated that coastal village communities in Solomon Islands can successfully farm giant clams. The production technology is simple and does not require a large capital investment. The main inputs are clam seed, labour and time. Labour is used for activities such as planting, cleaning, thinning and harvesting. In this paper, a bioeconomic model is used to explore optimal farm management for two species of giant clam fanned for the aquarium and seafood markets. The theoretical basis for this analysis is found in the economic theory of optimal forestry exploitation. Optimal management involves finding the combination of the decision variables and the cycle‐length that maximises a stream of discounted profits. The decision variables considered here are husbandry which relates to cleaning, and the frequency with which thinning is undertaken. The optimal cycle‐length is determined for both a single‐clam harvest and multiple harvests for various management scenarios. The labour requirements for these management scenarios are identified for the multiple‐harvest case and input substitution between optimal combinations of labour and cycle‐length is investigated. Results indicate that profits are maximised for both species when husbandry is excellent and labour usage is most intensive. Thinning is only necessary for seafood clams for which the optimal cycle‐length is longer. Village farmers may not be profit maximisers however, and labour spent on giant‐clam farming takes them away from other activities. Rather than investing more labour and harvesting the clams earlier, a village farmer with other objectives may devote less labour and harvest the clams later, and spend more time on other activities. In general, these results are consistent with extension advice provided to village farmers by ICLARM. Optimal solutions were found to be very stable when incorporated into global optimisation routines and sensitivity analysis of a wide range of parameter values.