Mitigating forest biodiversity and ecosystem service losses in the era of bio-based economy

Multi-criteria decision analysis (MCDA) is a decision aid frequently used in the field of forest management planning. It includes the evaluation of multiple criteria such as the production of timber and non-timber forest products and tangible as well as intangible values of ecosystem services (ES). Hence, it is beneficial compared to those methods that take a purely financial perspective. Accordingly, MCDA methods are increasingly popular in the wide field of sustainability assessment. Hybrid approaches allow aggregating MCDA and, potentially, other decision-making techniques to make use of their individual benefits and leading to a more holistic view of the actual consequences that come with certain decisions. This review is providing a comprehensive overview of hybrid approaches that are used in forest management planning. Today, the scientific world is facing increasing challenges regarding the evaluation of ES and the trade-offs between them, for example between provisioning and regulating services. As the preferences of multiple stakeholders are essential to improve the decision process in multi-purpose forestry, participatory and hybrid approaches turn out to be of particular importance. Accordingly, hybrid methods show great potential for becoming most relevant in future decision making. Based on the review presented here, the development of models for the use in planning processes should focus on participatory modeling and the consideration of uncertainty regarding available information.

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Hybrid MCDA Methods to Integrate Multiple Ecosystem Services in Forest Management Planning: A Critical Review

The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons.

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Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

Sustainable Rural Development is essential to maintain active local communities and avoid depopulation and degradation of rural areas. Proper assessment of development in these territories is necessary to improve decision-making and to inform public policy, while ensuring biodiversity conservation and ecosystem services supply. Rural areas include high ecological value systems but the vulnerability of environmental components in development indicators has not been sufficiently pinpointed. The main objective of this work was to propose a new sustainable rural development composite indicator (nSRDI) while considering an environmental dimension indicator based on ecosystem services vulnerability and social and economic dimension indicators established using a sequentially Benefit of the Doubt-Data Envelopment Analysis (BoD-DEA) model. It aimed also to test effects of weighting methods on nSRDI. The composite indicator was applied to 10 regions (comarcas) in the Huesca province, Spain, producing a ranking of regions accordingly. The indicator was further tested through the analysis of the effect of an equal and optimum weighting method on scores and rankings of regions. Results showed substantial differences in nSRDI scores/rankings when vulnerability was added to the process, suggesting that the environmental dimension and the perspective from which it is conceived and applied matters when addressing sustainable rural development.

Assessing Sustainable Rural Development Based on Ecosystem Services Vulnerability

Selecting the best forest management alternative by aggregating ecosystem services indicators over time: A case study in central Spain

Optimal harvest scheduling in Eucalyptus plantations under a sustainability perspective

We present an approach based on several quantitative techniques to derive a ranking of sustainable Eucalyptus plantations. A list of indicators was defined and applied to a set of heterogeneous Eucalyptus globulus plantations located in the northwest of Spain. These indicators were aggregated into a synthetic index with the help of a binary goal programming model. This model has been fed with the responses of 45 stakeholders to find out the preferential weights and targets attached to each indicator. In addition, we have analyzed the causes behind the level of sustainability achieved by each plantation. This task was carried out by taking the composite sustainability indexes as endogenous variables and a tentative set of economic, environmental, and social variables as explanatory variables. The link between endogenous and exogenous variables was made with the help of a statistical analysis. The results show how some variables such as altitude or distance to pulp mills are statistically significant, where...

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Using quantitative techniques to evaluate and explain the sustainability of forest plantations

In the Philippines, smallholder farmers have become major timber producers. But the systems of timber production practiced have several limitations. In intercropping systems, the practice of severe branch and/or root pruning reduces tree-crop competition and increases annual crop yields, but is detrimental to tree growth and incompatible with commercial timber production. In even-aged woodlots, lack of regular income and poor tree growth, resulting from farmers’ reluctance to thin their plantations, are major constraints to adoption and profitable tree farming. In the municipality of Claveria, Misamis Oriental, the recent practice of planting trees on widely spaced (6–8 m) contour grass strips established for soil conservation suggests ways to improve the adoptability (i.e., profitability, feasibility and acceptability) of timber-based agroforestry systems. Assuming that financial benefits are the main objective of timber tree farmers, we develop a simple linear programming (LP) model for the optimal allocation of land to monocropping and tree intercropping that maximizes the net present value of an infinite number of rotations and satisfies farmers’ resource constraints and regular income requirements. The application of the LP model to an average farmer in Claveria showed that cumulative additions of widely spaced tree hedgerows provides higher returns to land, and reduce the risk of agroforestry adoption by spreading over the years labour and capital investment costs and the economic benefits accruing to farmers from trees. Therefore, incremental planting of widely spaced tree hedgerows can make farm forestry more adoptable and thus benefit a larger number of resource-constrained farmers in their evolution towards more diverse and productive agroforestry systems.

Improving adoptability of farm forestry in the Philippine uplands: a linear programming model

The effective hydrological neighborhood: A new concept to formulate harvest area constraints

San Juan province, located in western Argentina, presents great climate variability with arid characteristics. Mean annual rainfall averages less than 100 mm for the whole province, and snowmelt in the Andean upper basin provides the San Juan River Basin with seasonal streamflow during summer, the period of highest water demand for irrigation. Traditional streamflow forecasts for the San Juan River are based on statistical regression models that are strongly dependent on values of snowpack in winter months (July, August, and September) and streamflow values in the spring months. However, producing forecasts for San Juan River summer streamflow using the Multivariate El Nino Southern Oscillation Index (MEI) data in the preceding June of the water year as an explicative variable can improve reservoir operating system performance for irrigation. To demonstrate this, climate predictors such as the MEI were used to forecast San Juan River streamflows to provide predictability at a six-month lead time. A backpropagation neural model, based on coupled data of snowpack and a climate predictor during the winter period, proved successful in forecasting San Juan River flows during the following summer period.

Juan Carlos Giménez

Papers

Optimal harvest scheduling in Eucalyptus plantations under a sustainability perspective

Using quantitative techniques to evaluate and explain the sustainability of forest plantations

Improving adoptability of farm forestry in the Philippine uplands: a linear programming model

The effective hydrological neighborhood: A new concept to formulate harvest area constraints

Forecasting Streamflows in the San Juan River Basin in Argentina