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Note from Land Portal:
Taylor & Francis Online contains many publications related to land issues, though mostly at the charge of a fee.
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Displaying 131 - 135 of 661The Role of Markets, Technology, and Policy in Generating Palm-Oil Demand in Indonesia
Indonesia produces more palm oil and consumes more palm oil per capita than any country in the world. This article examines the processes through which Indonesia has promoted palm-oil consumption and some of the consequences of that promotion. Partial equilibrium modelling shows that Indonesia's remarkable increase in palm-oil consumption since 1985 is not largely attributable to population and income growth. Instead, much of this consumption growth has resulted from substitution away from coconut oil, facilitated by government policies on technology, pricing, distribution, and trade.
Cambodia’s Unofficial Regime of Extraction: Illicit Logging in the Shadow of Transnational Governance and Investment
Cambodia has recently demonstrated one of the highest rates of deforestation in the world. While scholars have long explored the drivers of tropical forest loss, the case of Cambodia offers particular insights into the role of the state where transnational governance and regional integration are increasingly the norm. Given the significant role logging rents play in Cambodia’s post-conflict state formation, this article explores the contemporary regime and its ongoing codependent relationship with forested land.
Balanced nutrient management for crop intensification and livelihood improvement: A case study from watershed in Andhra Pradesh, India
Soil health assessment of farmers’ fields in watershed villages in Medak district, Andhra Pradesh, India showed widespread deficiencies of sulfur (S), boron (B), and zinc (Zn) in addition to organic carbon and phosphorus (P). Participatory on-farm trials on soil test-based application of deficient Zn, B, and S along with nitrogen (N) and P during 2009 to 2012 significantly increased crop yields over farmers’ practice (FP)—by 31% to 45% in chickpea, 15% to 16% in cotton, 12% to 15% in paddy, and 8% to 9% in sugarcane.
new locally-adaptive classification method LAGMA for large-scale land cover mapping using remote-sensing data
A new locally-adaptive image classification method LAGMA (Locally-Adaptive Global Mapping Algorithm) has been developed to meet requirements of land cover mapping over large areas using remote-sensing data. The LAGMA involves the grid-based supervised image classification using classes’ features estimated locally in classified pixels’ surrounding from spatially distributed reference data.
High-resolution landcover classification using Random Forest
Potential data sets for landcover classification, such as Landsat (or pre-processed data such as the National Land Cover Dataset (NLCD)), are often too coarse for fine-scale research needs or are cost-prohibitive (Quickbird, Ikonos and Geoeye). Repeated attempts at classifying high spatial resolution data, National Agricultural Imagery Program (NAIP) imagery, based on traditional techniques, such as a maximum likelihood supervised classification, have failed to produce a product with sufficient accuracy.