A detailed land use plan is necessary to conserve farmland in disadvantaged areas. Although many studies have been done on land classification for farmland preservation, little has been done on the economic measure based method as applied to detailed land use planning. This paper presents an economic land classification method for farmland preservation.
We chose 83 plots of farmland in the mountainous area of Ohda City, Shimane Prefecture as a case study. Serious farm abandonment and subsequent problems including no insect management and wild boar damage to rice crops have occurred because of the aging and depopulation of farm households and stagnant price of rice products.
First, land productivity of each plot was estimated by a logistic regression model with surveyed farm data including farmland yield, area, damage by wild boars, rice lodging and solar radiation. The survey was conducted from August to September in 2002. Second, an estimation function for labor productivity was determined by substituting harvest time, farmland area, length of the short side of a farm field and probability of rice lodging in the model. The estimation successfully fit the harvesting time stated by each farmer. Subsequently, a mathematical programming model was constructed to estimate the expected income under the given conditions. Negative externality by neighboring abandoned farmlands, irrigation facilitymanagement and movement frequency between fields were considered in the model.
As a result, optimal land use plans were developed under the given labor input. In the simulated case where scattered abandonment occurred, the effect of negative externality was larger than that for the optimized case. We also simulated the effects of particular farmland conservation on environmental and other concerns.
The advantage of this method is that we can determine which land plots should be conserved by economic measures in definite consideration of political effects and special characteristics such as negative externality or movement frequency. For improvement of the model, data reliability of the externality effect needs to be extended. Year round labor input and multiple subjects should also be considered in the model.
