Predicting future plantation forest development in response to policy initiatives: A case study of the Warren River Catchment in Western Australia. Issue 92 (February 2019)

• Record Type: Journal Article
• Authors: Nery, Thayse; Sadler, Rohan; White, Ben; Polyakov, Maksym
• Is Part Of: Environmental science & policy. Issue 92(2019)
• Page Start: 299
• Page End: 310
• Publication Date: 2019-02
• Subjects: Land-use policy -- Plantation forest -- Multilayer perceptron -- Cellular automata -- Western Australia; Environmental policy -- Periodicals; Environmental sciences -- Periodicals; Environnement -- Politique gouvernementale -- Périodiques; Sciences de l'environnement -- Périodiques; Environmental policy; Environmental sciences; Periodicals; Electronic journals; 363.70561
• DOI: 10.1016/j.envsci.2018.11.021
• Languages: English
• ISSNs: 1462-9011

Abstract

Highlights: No major clearing of native forest since the amendment of the clearing control. The conversion of agriculture to plantation is centred on the north-east of the catchment. Areas of plantations are predicted to be the smallest since the launch of tax incentives. The decline of plantation forest areas may jeopardize efforts to reduce dryland salinity. Periodic changes in policy might create serious problems for the forestry sector. Abstract: The Warren River Catchment in the south-west of Western Australia exemplifies how inconsistent forestry investment policies can have adverse environmental effects especially related to dryland salinity. The catchment experienced a rapid expansion of plantation forest in the late 1990s due to tax benefits for forestry Managed Investment Schemes. This paper assesses the landscape effect of incentive based policies by measuring the land use and land cover (LULC) response in the catchment, and forecasts LULC change. The paper applies a spatial modelling procedure that integrates Markov transition probabilities, multilayer perceptron neural network and cellular automata to provide an accurate forecast of LULC change over 35 years from 1979 to 2014. In the first stage, geospatial analysis determines the spatial drivers of LULC conversions. Second, Markov transition probability matrices are estimated, and the multilayer perceptron was trained to determine a model for every transition based on spatial drivers. Finally, a cellular automata model was applied to forecast spatially explicit changes in LULC to 2025 under the current policy regime. The predictive power of the model was validated with Kappa statistics, and vis-à-vis a null model. The simulation forecasts an increase in the agricultural areas by 2025 compared to 2014; whereas harvested native forest areas were predicted to decrease, contributing to a slight increase of the native forest areas. Despite government efforts to increase the areas of plantations, the model predicts a decrease in this land use due to a progressive reduction in tax incentives provided to Managed Investment Schemes. These results will assist decision-makers in improving policy, by working through the long-term implications of policy incentives for forestry in terms of broader landscape objectives related to salinity and conservation.