Adaptive Management on Sustainability of Cork Oak Woodlands

The cork oak woodland production systems result from the integration of conflicting activities in the same space creating the need of constant search of equilibrium between its components in order to achieve sustainability. In a climate change environment, associated with recent modifications in rural societies, adaptive management concepts are needed so as to maintain cork oak woodland systems sustainable. Nowadays/Currently cork oak woodlands are facing disturbances that are affecting the production system sustainability both by intensification of the activities undercover- that leads to a lack of regeneration and consequent disappearing of the crown cover, loss of cork production and site degradation mainly by soil loss-, or by the abandonment that conducts to an invasion of shrubs and other oaks increasing the competition (reducing cork production) and the risk of forest fire. Only adaptive management techniques associated with growth models and decision support systems, constructed in knowledge based monitoring system, are able to prevent cork wood land decline with the adoption of management practices focused in long term objectives. For the present study it was selected a set of permanent plots according with site quality and stand age and structure. Simulation studies results indicates that cork oak woodland system sustainability (both economical and ecological) is supported in regeneration events associated with the shrub control techniques without soil mobilization with strong dependency of cork prices and valuation of carbon sequestration, especially in the less productive soils. Without modification of actual funding policies and the valuation of carbon sequestration, the system faces increased risks of decline due to the maintenance of actual non sustainable management practices by the stake holders driven by their financial needs. This study is particularly relevant regarding that woodlands dominate the landscape of the south-western Iberian Peninsula, occupying approximately 3.1 million hectares in Spain and 1.2 million hectares in Portugal.

GI-Technology

To deal with the complexity of land use allocation in a spatio-temporally variable context, a generic framework for automated support to multi-objective land use planning is proposed. The framework is rooted in the discipline of land evaluation which is considered a go-between between land resources survey and land use planning. It draws on own experiences and on lessons learnt from literature. It consists of five integrated and interoperable components. The core three ones, the spatio-temporal database, the engine for data query, transformation and analysis and the user interface are adopted from geographical information systems (GIS). A ‘knowledge and model base’ component adds capability for assessing land performance over time. Finally, a multicriteria decision analysis component allows for identifying optimal land units and optimal land use options. The framework’s applicability and the limitations of geographical information technology (GI-Technology) to generate spatio-temporal decision support systems (stDSS) are illustrated with two cases: one in data rich and one in data poor conditions.

MicroLEIS DSS

The main focus of this chapter is that using soil type information in decision making is at the heart for sustainable use and management of agricultural land. The MicroLEIS decision support system (DSS) is based on the multifunctional evaluation of biophysical soil quality, using basically input data collected in standard soil inventories, and with particular reference to the peculiarities of the Mediterranean region. Its design philosophy is a toolkit approach, integrating many software instruments: databases, statistic models, expert systems, neural networks, Web and GIS applications, and other information technologies. As a case study applying MicroLEIS DSS to Cordoba Province (Spain), soil specific strategies to maximize land productivity and to prevent land degradation are predicted within two major topics: i) strategies related to land use planning at a regional scale, and ii) strategies related to soil management planning at a farm level. This DSS has proved to be an appropriate methodology for converting knowledge on land use and management systems, as estimated by research scientists, into information that is readily comprehensible to policy makers and farmers.

A Decision Support System for Scheduling Partial Harvesting in Aquaculture

The implementation of partial harvesting for intensive aquaculture is a difficult undertaking for the aqua-farmers, due to the complex nature of tracking the effects of reducing density on growth, survival and eventually on productivity and profitability. In this chapter, we describe the partial harvesting decision support system (PHDSS) developed by Kam et al. (2008). The PHDSS is designed to assist aqua-farmers in determining the best harvesting strategy for a production cycle. Potential harvesting strategies include both partial harvest and single-batch harvest. The chapter navigates the readers through the system, using shrimp culture as a case study.

Assessing the Efficiency and Distribution of Agricultural Activities Using DSS

This work describes the sequential use of different Information Systems and Decision Support Systems (DSS) to measure the efficiency of a set of agricultural activities, and subsequently to propose alternative reallocations of these activities within a geographical region. The region selected as a case study was Ribatejo e Oeste (RO), an important agrarian region in mainland Portugal. The DEA (Data Envelopment Analysis) methodology was used to assess the efficiency of the most important agricultural activities in RO, using the Frontier Analyst DSS to study alternative modelling options. In a second phase, plans for redistributing the evaluated activities were studied, aiming at promoting the most efficient activities (according to DEA) but without creating at the same time drastic changes in current land uses. Several plans constituting different compromises between these two objectives were found using a multiobjective linear programming DSS. A Geographical Information System was used to constrain the areas that were adequate for each type of crop and to graphically illustrate some proposed plans.

A Participatory Approach for Analyzing and Modeling Decision Processes

Most decision analytic research does not focus on initial steps of modeling and mostly concentrates on selecting preexisting algorithms. In this chapter we present how we can formalize decision intensive business processes based on a case study on a Decision Support System (DSS) for cultivation planning. Decisions in this problem area depend notably on expertise and experience acquired by the farmer. As a first step the decision process of the agriculturist needs to be explored, analyzed and documented. Afterwards all information and data, which leads up to a decision, will be collected, systemized and grouped. We will apply user participative techniques that integrate the farmer as a cooperative partner into the modeling process. The outcome of this modeling leads to a formalized model later on. On account of this approach the DSS will represent the real decision process of the farmer and increases trust in the decisions suggested by the system.

Decision Support System for Greenhouse Tomato Yield Prediction using Artificial Intelligence Techniques

This chapter introduces a decision support system which is capable of predicting the weekly yields of tomatoes in a greenhouse. The development of this system involves a set of Artificial Intelligence based techniques, namely Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), and Grey System Theory (GST). The prediction was performed by an ANN using a set of optimised input variables, chosen from all available environmental and measured yield parameters. The reduction and optimisation of the inputs was done using either GAs or GST and compared in terms of the ANN’s performance. It was shown that the use of artificial intelligence based methods can offer a promising approach to yield prediction and compared favourably with traditional methods.

The Role of Decision Support Systems (DSS) in Planning For Improved Water Quality in Coastal Lakes

Coastal lakes are ecosystems which provide significant environmental, social and economic values. They are a key habitat for many aquatic species, particularly for juvenile fish and aquatic invertebrates. They are a focus for human activity, including recreation, tourism, and many forms of industry and production such as oyster and commercial fisheries. More and more the foreshore areas of lakes are seen as a desirable place to live, with urban development a key pressure on lake systems. However current development, use and management of these systems mean that these values are already under threat. Environmental managers, urban planners and other decision makers need to make complex decisions about patterns of current and future use of these systems which allow for the trade-offs associated with various activities to be effectively taken into account. Decision support systems (DSS) are seen to have a role to play in supporting these activities.When developed properly, DSS can support decision making processes by providing users with a tool that shows the relationships between drivers of a system and outcomes. Environmental outcomes (e.g. estuary health) are controlled by often complex biophysical, ecological, economic and/or social drivers and processes. In this context a DSS should address uncertainty in data, knowledge and predictions, and allow users to explore the sensitivity of outcomes to controllable drivers (e.g. management actions), uncontrollable drivers (e.g. climate variability) and uncertainty. The DSS development and adoption process also needs to be flexible to a changing decision making environment. Ultimately the success of any DSS will depend not only on its technical capacity, including the robustness of any science underlying it, or the ease of use of any interface but also on the circumstances into which it arrives: the time and money allowed for training, capacity building, incorporation of stakeholder comments and development of trust between DSS developers, scientists and the community; the way in which the DSS is embedded in the decision making process; and the ‘politics’ and constantly changing face of the decision making environment.This chapter will discuss issues regarding the development of a DSS under typical planning timeframes where there are limited resources (time and budgetary) and where current and future management issues may not be certain and/or may change over the planning timeframe. The chapter largely draws on experiences gained during the development and application of the CAPER DSS in the Great Lakes, NSW Australia.

A Knowledge-Based SDSS for Medfly Area-Wide Control

The aim of this chapter is to describe the motivation to develop a knowledge-based spatial decision support system (KB-SDSS) for medfly control in citrus in Israel, its development approach and procedure, its validation, and the steps towards its assimilation among the zone-managers. Development of the KB-SDSS for medfly control in Israel, also known as MedCila involved four main phases: 1. acquisition of expert and domain knowledge related to the control decision process; 2. identification of the relevant criteria and modeling each criterion and the overall decision making procedure; 3. its integration into a GIS environment; and 4. its performance evaluation by an expert-panel considering four aspects: verification, validation, acceptance and effectiveness. Comparison with data-mining approach exemplifies the effectiveness of the KB approach in this case. Our results show that the MedCila may reduce spraying actions by at least 8%, which is estimated to save ca. 13.3 ton/year of chemicals. Ceratitis capitata; Medfly control, knowledge-based spatial decision support system, Stanford certain factor algebra, expert panel, verification, validation, acceptance, effectiveness.

A Web-based Spatial DSS for estimating biomass-to-energy supply in Thessaly

Biomass-to-energy projects have become attractive these days because of recent European policy measures that attempt to address acute environmental, agricultural and energy challenges accumulated during the last 30 years. Bio-energy issues constitute spatially dependent problems by definition due to the state-of-the-art technology and the bulky nature of biomass. Moreover, biomass profitability is linked to the structure and perspectives of the arable cropping systems since these are able to supply considerable quantities in the short and medium term required to fulfil the ambitious targets aimed at by policy makers. Therefore, appropriate tools are necessary to enable a comprehensive analysis and support decisions of policy makers, industry, researchers and farmers. Spatial Decision Support Systems that have been developed to support bio-energy decisions are used as a basis enhanced by a web-based interface, in this exercise resulting in a Web-SDSS. This tool is implemented in Thessaly, the most significant arable cropping region in Greece, in order to evaluate selected energy crop supply. The methodology and architecture of this tool are detailed in this paper, followed by an illustrative description of its operational version implemented in ex-tobacco producing areas.