Country profiles present national-level data of large-scale land acquisitions and transactions including who the investors are, what the aim of the investment is, who the former owner was and what the land was previously used for, and what the potential benefits and impacts of the land deals are.
By making this information available, the Land Matrix hopes to enhance broad engagement and data exchange, facilitating the continuous improvement of the data. Find out how to get involved here .
Of the total land area of Kenya, only around 10% is classified as arable land, with the 14 recorded concluded deals in the Land Matrix database equalling just 0.46% of the total land area. Although this is a relatively small land footprint, these deals may still have significant implications for …
Since large-scale land acquisitions (LSLAs) cover less than 9% of Ghana’s 4.7 million hectares of arable land, it may not seem like they would have a significant impact on the country’s agricultural landscape. However, their impact in fact goes far beyond the land footprint only, through their use of water …
This detailed country profile presents the Land Matrix data for large-scale land acquisitions in Zambia.
Download it here.
Author: Josh Gabbatiss
Published: 12 March 2024
Source: Carbon Brief
Logging companies have “acquired” roughly 1m hectares of Indigenous peoples’ territory in the Democratic Republic of the Congo since 2000, according to a new study.
This is part of a wider trend in which companies and governments take advantage of …
Authors: Jorge A. Rincón Barajas, Christoph Kubitza and Jann Lay
Published: 2024
Source: Land Use Policy
This research by Land Matrix partner GIGA conceptualises and empirically assesses the socioeconomic and environmental risks of large-scale land acquisitions (LSLAs) for communal lands in the Global South. These risks include the displacement of …
Author: Konrad Hentze
Published: 2023
Source: Remote Sensing Research Group (University of Bonn)
This storyline gives an overview of current land grabbing databases, their lack of spatial information, and how remote sensing datasets can overcome this lack when being used to detect large scale agricultural production schemes.
View the story …