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Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements

December, 2020
Global

Abstract. Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress.

Priority needs for improvement of activity data to support MRV in Ethiopia’s livestock sector

December, 2019
Global

To complement an ongoing CCAFS project ‘Enhancing capacities for MRV of sustainable livestock action in East Africa (Kenya and Ethiopia)’, implemented by UNIQUE forestry and land use, ACIAR is supporting CCAFS to implement a Small Research Activity (SRA) entitled ‘Building capacities for an integrated livestock MRV system in Ethiopia’. The objective of the SRA is to support improvements in methods and procedures used to produce and manage the livestock activity data required for measurement, reporting and verification (MRV) of greenhouse gases (GHG) in Ethiopia.

Scientific data for CS-MAP in Cambodia

December, 2021
Cambodia

The presentation elaborated on the various scientific data used to develop Climate-Smart Mapping and Adaptation Planning (CS-MAP) for Cambodia. These include spatial data for flood risk and drought risk, land cover/land use type, and other spatial data (i.e., administrative boundary, natural resources, satellite images, and topography).

How can diverse national food and land-use priorities be reconciled with global sustainability targets? Lessons from the FABLE initiative

December, 2022
Global

There is an urgent need for countries to transition their national food and land-use systems toward food and nutritional security, climate stability, and environmental integrity. How can countries satisfy their demands while jointly delivering the required transformative change to achieve global sustainability targets? Here, we present a collaborative approach developed with the FABLE—Food, Agriculture, Biodiversity, Land, and Energy—Consortium to reconcile both global and national elements for developing national food and land-use system pathways.

Women’s land rights in Niger. Securing women’s resource rights through gender transformative approaches

December, 2021
Niger

This series of socio-legal reviews summarizes the legal and policy documents related to women’s land tenure in seven countries: Kyrgyzstan, Uganda, The Gambia, Ethiopia, Niger, Bangladesh, and Colombia. These synthesis documents, part of the IFAD Initiative on Women’s Resource Rights, are designed for researchers and policymakers seeking to improve women’s land and resource rights in these target countries. This review covers:
• A general characterization of land and resource tenure systems at national, regional, and local levels

Spatial and temporal contrasts in the distribution of crops and pastures across Amazonia: A new agricultural land use data set from census data since 1950: Crops and pastures across Amazonia

December, 2019
Global

Amazonia holds the largest continuous area of tropical forests with intense land use change dynamics inducing water, carbon, and energy feedbacks with regional and global impacts. Much of our knowledge of land use change in Amazonia comes from studies of the Brazilian Amazon, which accounts for two thirds of the region. Amazonia outside of Brazil has received less attention because of the difficulty of acquiring consistent data across countries.

Improved remote sensing methods to detect northern wild rice (Zizania palustris L.)

December, 2019
Global

Declining populations of Zizania palustris L. (northern wildrice, or wildrice) during
the last century drives the demand for new and innovative techniques to support monitoring of
this culturally and ecologically significant crop wild relative. We trained three wildrice detection
models in R and Google Earth Engine using data from annual aquatic vegetation surveys in
northern Minnesota. Three di erent training datasets, varying in the definition of wildrice presence,