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Community Organizations Land Journal
Land Journal
Land Journal
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Land (ISSN 2073-445X) is an international, scholarly, open access journal of land use and land management published quarterly online by MDPI. 

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Displaying 1091 - 1095 of 2258

Effects of Land Cover Changes on Net Primary Productivity in the Terrestrial Ecosystems of China from 2001 to 2012

Peer-reviewed publication
Diciembre, 2020
China
Norway
Russia
United States of America

The 2001–2012 MODIS MCD12Q1 land cover data and MOD17A3 NPP data were used to calculate changes in land cover in China and annual changes in net primary productivity (NPP) during a 12-year period and to quantitatively analyze the effects of land cover change on the NPP of China’s terrestrial ecosystems. The results revealed that during the study period, no changes in land cover type occurred in 7447.31 thousand km2 of China, while the area of vegetation cover increased by 160.97 thousand km2 in the rest of the country.

Identifying Land Use Change Trajectories in Brazil’s Agricultural Frontier

Peer-reviewed publication
Diciembre, 2020
Brazil
United States of America

Many of the world’s agricultural frontiers are located in the tropics. Crop and cattle expansion in these regions has a strong environmental impact. This paper examines land use and land cover transformations in Brazil, where large swaths of natural vegetation are being removed to make way for agricultural production. In Brazil, the land use dynamics are of great interest regarding the country’s sustainable development and climate mitigation actions, leading to the formulation and implantation of public policies and supply chain interventions to reduce deforestation.

Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil

Peer-reviewed publication
Diciembre, 2020
Global

Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC.

Drivers of Fire Anomalies in the Brazilian Amazon: Lessons Learned from the 2019 Fire Crisis

Peer-reviewed publication
Diciembre, 2020
Israel

The 2019 fire crisis in Amazonia dominated global news and triggered fundamental questions about the possible causes behind it. Here we performed an in-depth investigation of the drivers of active fire anomalies in the Brazilian Amazon biome. We assessed a 2003–2019 time-series of active fires, deforestation, and water deficit and evaluated potential drivers of active fire occurrence in 2019, at the biome-scale, state level, and local level. Our results revealed abnormally high monthly fire counts in 2019 for the states of Acre, Amazonas, and Roraima.

Understanding the Relationships between Extensive Livestock Systems, Land-Cover Changes, and CAP Support in Less-Favored Mediterranean Areas

Peer-reviewed publication
Diciembre, 2020
Global

Farm abandonment and over-extensification trends in less-favored livestock breeding areas in the Mediterranean have led to socio-environmental issues that are difficult to assess and address, due to the characteristics of these areas (e.g., poor data availability and reliability). In a study case that presents many of the characteristics common to these areas, we combine qualitative and quantitative approaches to assess (i) the relationship between livestock production and land-cover change and (ii) the drivers of farmer decisions, concerning the types of livestock they breed.