A multi-level system for planning compensatory habitats as a new tool to prevent biodiversity loss in protected areas due to development plans

Background In Slovenia, compensatory habitats (CH) are currently determined on the basis of a subjective expert judgement and without using any clearly defined methodology, due to which the success rates of CH implementation are low. The aim of this research is to fill in a methodological gap and propose a new multi-level system for planning CH. The system assures a transparent and more objective determination of the size of a CH in the processes of appropriate assessments (AA).  Materials and methods: The system was developed by using a multi criteria decision analysis, a multi-attribute decision support model and the DEXi modelling tool. It was tested on a study case the Škofljica bypass road with its impact to a Whinchat (Saxicola rubetra) at Natura 2000 site Ljubljansko barje.  Results: The system with three modules and a possibility of what-if analysis was developed to assess the species endangerment and the size of the CH. The system identified that the case study has significant impacts to the Whinchat, therefore the CH of a slightly larger size than the habitat lost was proposed. In addition, the system indicated that only one of the three potential locations of the CH is suitable for implementing the CH.  Conclusions: The system allows a transparent and more objective assessment of the spatial plan. It is a new, easy-to-use, adjustable, cost- and time-efficient method that can be used to make reliable and transparent decisions during the assessment processes. &nbs

Veza poznavanja nogometa i geografskog znanja maturanata Gimnazije Antuna Gustava Matoša u Samoboru

Geografija i nogomet dva su usko povezana pojma i učenjem o jednome često naučimo nešto i o drugome. Prema tome, osobe koje prate nogomet raspolažu s više geografskog, pretežno činjeničnog znanja od osoba koje nogomet ne prate. Hipoteza ovog rada je da osoba koja prati nogomet, samim poznavanjem nogometnih klubova i liga u kojim se ti klubovi natječu, posjeduje razinu geografskog znanja kakvu osobe koje ne prate nogomet ne posjeduju. Anketnim upitnikom dokazano je postojanje srednje jake pozitivne korelacije između geografskog i nogometnog znanja, čime je potvrđena hipoteza ovog rada

Veza poznavanja nogometa i geografskog znanja maturanata Gimnazije Antuna Gustava Matoša u Samoboru

Geografija i nogomet dva su usko povezana pojma i učenjem o jednome često naučimo nešto i o drugome. Prema tome, osobe koje prate nogomet raspolažu s više geografskog, pretežno činjeničnog znanja od osoba koje nogomet ne prate. Hipoteza ovog rada je da osoba koja prati nogomet, samim poznavanjem nogometnih klubova i liga u kojim se ti klubovi natječu, posjeduje razinu geografskog znanja kakvu osobe koje ne prate nogomet ne posjeduju. Anketnim upitnikom dokazano je postojanje srednje jake pozitivne korelacije između geografskog i nogometnog znanja, čime je potvrđena hipoteza ovog rada

Development of an Agricultural Primary Productivity Decision Support Model: A Case Study in France

Agricultural soils provide society with several functions, one of which is primary productivity. This function is defined as the capacity of a soil to supply nutrients and water and to produce plant biomass for human use, providing food, feed, fiber, and fuel. For farmers, the productivity function delivers an economic basis and is a prerequisite for agricultural sustainability. Our study was designed to develop an agricultural primary productivity decision support model. To obtain a highly accurate decision support model that helps farmers and advisors to assess and manage the provision of the primary productivity soil function on their agricultural fields, we addressed the following specific objectives: (i) to construct a qualitative decision support model to assess the primary productivity soil function at the agricultural field level; (ii) to carry out verification, calibration, and sensitivity analysis of this model; and (iii) to validate the model based on empirical data. The result is a hierarchical qualitative model consisting of 25 input attributes describing soil properties, environmental conditions, cropping specifications, and management practices on each respective field. An extensive dataset from France containing data from 399 sites was used to calibrate and validate the model. The large amount of data enabled data mining to support model calibration. The accuracy of the decision support model prior to calibration supported by data mining was ~40%. The data mining approach improved the accuracy to 77%. The proposed methodology of combining decision modeling and data mining proved to be an important step forward. This iterative approach yielded an accurate, reliable, and useful decision support model for the assessment of the primary productivity soil function at the field level. This can assist farmers and advisors in selecting the most appropriate crop management practices. Embedding this decision support model in a set of complementary models for four adjacent soil functions, as endeavored in the H2020 LANDMARK project, will help take the integrated sustainability of arable cropping systems to a new level

For the love of wise agronomy: transition paths to legume supported food- and feed-systems in Europe

info:eu-repo/semantics/publishedVersio