Land suitability analysis for emerging fruit crops in central Portugal using GIS

Fruit production is an important component of agricultural production in Portugal, and it has a positive impact on the economy, especially in rural areas. In recent years, there has been increased investment in so-called ‘emerging crops’. It is agreed that using the crops that are best suited to the soil and climate conditions as well as the socio-economic environment promotes sustainable use in rural areas. The objective of this study is to determine the suitability of different emerging fruit crops for cultivation in the Beira Baixa region based on analysis of soil and climate factors. The pistachio tree (Pistacia vera L.), strawberry tree (Arbutus unedo L.), almond tree (Prunus dulcis (Mill.) DA Webb) and walnut tree (Juglans regia L.) were checked against the biophysical criteria for cultivation. The results were processed using a geographic information system. Analysis was performed using the analytical hierarchy process (AHP). Thus, after dividing the problem into hierarchical levels of decision-making, a pairwise comparison of criteria was performed to evaluate the weights of these criteria based on a scale of importance. Then, the consistency of these operations was validated. The AHP was adequate for evaluation of fruit tree species’ suitability since it enabled integration of several criteria, decision-making and problem resolution. It is essential to be aware of the suitability and resilience of new crops in order to meet the need to adapt to climate change.info:eu-repo/semantics/publishedVersio

Techniques for optimal crop selection in a controlled ecological life support system

A Controlled Ecological Life Support System (CELSS) utilizes a plant's natural ability to regenerate air and water while being grown as a food source in a closed life support system. Current plant research is directed toward obtaining quantitative empirical data on the regenerative ability of each species of plant and the system volume and power requirements. Two techniques were adapted to optimize crop species selection while at the same time minimizing the system volume and power requirements. Each allows the level of life support supplied by the plants to be selected, as well as other system parameters. The first technique uses decision analysis in the form of a spreadsheet. The second method, which is used as a comparison with and validation of the first, utilizes standard design optimization techniques. Simple models of plant processes are used in the development of these methods

Tree species selection for land rehabilitation in Ethiopia: from fragmented knowledge to an integrated multi-criteria decision approach

Dryland regions worldwide are increasingly suffering from losses of soil and biodiversity as a consequence of land degradation. Integrated conservation, rehabilitation and community-based management of natural resources are therefore of vital importance. Local planting efforts should focus on species performing a wide range of functions. Too often however, unsuitable tree species are planted when both ecological suitability for the targeted area or preferences of local stakeholders are not properly taken into account during selection. To develop a decision support tool for multi-purpose species selection, first information needs to be pooled on species-specific ranges, characteristics and functions for a set of potentially valuable species. In this study such database has been developed for the highly degraded northern Ethiopian highlands, using a unique combination of information sources, and with particular attention for local ecological knowledge and preferences. A set of candidate tree species and potentially relevant criteria, a flexible input database with species performance scores upon these criteria, and a ready-to-use multi-criteria decision support tool are presented. Two examples of species selection under different scenarios have been worked out in detail, with highest scores obtained for Cordia africana and Dodonaea angustifolia, as well as Eucalyptus spp., Acacia abyssinica, Acacia saligna, Olea europaea and Faidherbia albida. Sensitivity to criteria weights, and reliability and lack of knowledge on particular species attributes remain constraints towards applicability, particularly when many species are jointly evaluated. Nonetheless, the amount and diversity of the knowledge pooled in the presented database is high, covering 91 species and 45 attributes

A novel improved model for building energy consumption prediction based on model integration

Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Constantly improving the performance of prediction models is the key to ensuring the efficient operation of energy systems. Moreover, accuracy is no longer the only factor in revealing model performance, it is more important to evaluate the model from multiple perspectives, considering the characteristics of engineering applications. Based on the idea of model integration, this paper proposes a novel improved integration model (stacking model) that can be used to forecast building energy consumption. The stacking model combines advantages of various base prediction algorithms and forms them into “meta-features” to ensure that the final model can observe datasets from different spatial and structural angles. Two cases are used to demonstrate practical engineering applications of the stacking model. A comparative analysis is performed to evaluate the prediction performance of the stacking model in contrast with existing well-known prediction models including Random Forest, Gradient Boosted Decision Tree, Extreme Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor. The results indicate that the stacking method achieves better performance than other models, regarding accuracy (improvement of 9.5%–31.6% for Case A and 16.2%–49.4% for Case B), generalization (improvement of 6.7%–29.5% for Case A and 7.1%-34.6% for Case B), and robustness (improvement of 1.5%–34.1% for Case A and 1.8%–19.3% for Case B). The proposed model enriches the diversity of algorithm libraries of empirical models

An integrated MCDA software application for forest planning : a case study in southwestern Sweden

Forest planning in Sweden today translates not only into planning of timber production, but also for the provision of other functions and services. Multi-criteria decision analysis (MCDA) methods provide a way to take also non-monetary values into account in planning. The purpose of this study was to gain experience on how to use a forest decision support system combined with an MCDA tool in practical forestry. We used a new forest planning tool, PlanWise, which includes an integrated MCDA module, PlanEval. Using the software, the decision maker can compare different forest plans and evaluate them against his/her objectives in a structured and analytical manner. The analysis thus provides a ranking of the alternatives based on the individual preferences of the decision maker. PlanEval and the MCDA planning process are described in a case study, where the manager of a forest estate in southwestern Sweden used the program to compare different forest plans made for the estate. In the paper, we analyze possibilities and challenges of this approach and identify problems such as the adherence to formal requirements of MCDA techniques and the difficulty of comparing maps. Possibilities to expedite an MCDA planning process further are also discussed. The findings confirm that integration of an MCDA tool with a forest decision support system is valuable, but requires expert assistance to be successful

Simulating California reservoir operation using the classification and regression-tree algorithm combined with a shuffled cross-validation scheme

The controlled outflows from a reservoir or dam are highly dependent on the decisions made by the reservoir operators, instead of a natural hydrological process. Difference exists between the natural upstream inflows to reservoirs and the controlled outflows from reservoirs that supply the downstream users. With the decision maker's awareness of changing climate, reservoir management requires adaptable means to incorporate more information into decision making, such as water delivery requirement, environmental constraints, dry/wet conditions, etc. In this paper, a robust reservoir outflow simulation model is presented, which incorporates one of the well-developed data-mining models (Classification and Regression Tree) to predict the complicated human-controlled reservoir outflows and extract the reservoir operation patterns. A shuffled cross-validation approach is further implemented to improve CART's predictive performance. An application study of nine major reservoirs in California is carried out. Results produced by the enhanced CART, original CART, and random forest are compared with observation. The statistical measurements show that the enhanced CART and random forest overperform the CART control run in general, and the enhanced CART algorithm gives a better predictive performance over random forest in simulating the peak flows. The results also show that the proposed model is able to consistently and reasonably predict the expert release decisions. Experiments indicate that the release operation in the Oroville Lake is significantly dominated by SWP allocation amount and reservoirs with low elevation are more sensitive to inflow amount than others