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Taylor & Francis Group publishes books for all levels of academic study and professional development, across a wide range of subjects and disciplines.


Taylor & Francis Group publishes quality peer-reviewed journals under the Routledge and Taylor & Francis imprints. The newest part of the group, Cogent OA, offers a purely open access program.


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Taylor & Francis Online contains many publications related to land issues, though mostly at the charge of a fee.

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Displaying 36 - 40 of 662

Critical success factors for governing farmer-managed public goods in rural areas in the Netherlands

Journal Articles & Books
December, 2016
Netherlands
Europe

Multifunctional land use has become a widely supported pathway for Europe's countryside. Brussels and the national governments stimulate farmers to integrate primary production with non-agricultural practices from which they can also benefit. In favour of this development different stakeholders are encouraged to collaborate to produce the so-called farmer-managed public goods. This paper explores critical success factors for the production and maintenance of these public goods.

GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models

Journal Articles & Books
December, 2016
Iran

The aim of this research was to produce forest fire susceptibility maps (FFSM) based on evidential belief function (EBF) and binary logistic regression (BLR) models in the Minudasht Forests, Golestan Province, Iran. At first, 151 forest fire locations were identified from Moderate-Resolution Imaging Spectero Radiometer data, extensive field surveys, and some reports (collected in year 2010). Out of these locations, 106 (70%) were randomly selected as training data and the remaining 45 (30%) cases were used for the validation goals.

Assessment of SAR speckle filters in the context of object-based image analysis

Journal Articles & Books
December, 2016

The initial step in most object-based classification methodologies is the application of a segmentation algorithm to define objects. In the context of synthetic aperture radar (SAR) image analysis, the presence of speckle noise might hamper the segmentation quality. The aim of this study is to assess the segmentation performance of SAR images when no filter or different filters are applied before segmentation.

Predicting ESP and SAR by artificial neural network and regression models using soil pH and EC data (Miankangi Region, Sistan and Baluchestan Province, Iran)

Journal Articles & Books
December, 2016
Iran

Monitoring exchangeable sodium percentage (ESP) and sodium adsorption ratio (SAR) variability in soils is both time-consuming and expensive. However, in order to estimate the amounts of amendments and land management, it is essential to know ESP and SAR variations and values in sodic or saline and sodic soils. Thus, presenting a method which uses easily obtained indices to estimate ESP and SAR indirectly is more optimal and economical. Input data of the current research were 189 soil samples collected based on a regular networking approach from Miankangi region, Sistan plain, Iran.

Development of a land suitability model for saffron (Crocus sativus L.) cultivation in Khost Province of Afghanistan using GIS and AHP techniques

Journal Articles & Books
December, 2016
Afghanistan

In this study, we have attempted to develop a land suitability model for saffron, an agronomic crop, which is economically viable, environmentally bearable and socially equitable at Khost Province of Afghanistan. The objective was to determine different land suitability classes for saffron cultivation using Analytical Hierarchy Process (AHP) and Geographic Information System (GIS). A decision tree was developed encompassing the physical, economic and social criteria.