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With research staff from more than 70 countries, and offices across the globe, IFPRI provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries.

researcher spotlight

Lilia Bliznashka

Lily Bliznashka is a Research Fellow in the Nutrition, Diets, and Health Unit. Her research focuses on assessing the effectiveness of multi-input nutrition-sensitive and nutrition-specific interventions and the mechanisms through which they work to improve maternal and child health and nutrition globally. She has worked in Burkina Faso, Burundi, Tanzania, and Uganda.

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Since 1975, IFPRI’s research has been informing policies and development programs to improve food security, nutrition, and livelihoods around the world.

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IFPRI currently has more than 480 employees working in over 70 countries with a wide range of local, national, and international partners.

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George Mason University is Virginia’s largest public research university. The College of Health and Human Services (CHHS) supports their intensive research mission and advancing the field of health. The faculty of CHHS works collaboratively answer health questions from a multidisciplinary perspective.

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Publications

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Conference Paper

Climate risk profiles and agricultural vulnerability in Africa: A multi-dimensional analysis

2024Guo, Zhe; Yang, Ruixin
Details

Climate risk profiles and agricultural vulnerability in Africa: A multi-dimensional analysis

Climate risk profile studies play a vital role in Africa, considering the continent’s unique vulnerability to climate change. With diverse ecosystems, economies, and social contexts, Africa faces a multitude of climate-related risks, including extreme weather events, water scarcity, agricultural disruptions. Understanding and assessing these risks through climate risk profile studies is crucial for effective adaptation and mitigation planning. Our study aimed to construct seasonal crop risk profiles utilizing a composite index methodology, analyzing historical climate data spanning the past three decades. This composite index integrates spatially explicit evaluations of heat stress, drought stress, and flood hazards. Specifically focused on agricultural risks, our study further refines these hazards concerning crops during the growing seasons. Furthermore, our analysis will assess changes and trends in the frequency and severity of these hazards over time. These investigations offer invaluable insights into the precise climate threats confronting African nations and regions, empowering policymakers and communities to make well-informed decisions regarding resource allocation, infrastructure development, and socioeconomic planning.

Year published

2024

Authors

Guo, Zhe; Yang, Ruixin

Citation

Guo, Zhe; and Yang, Ruixin. 2024. Climate risk profiles and agricultural vulnerability in Africa: A multi-dimensional analysis. 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/Agro-Geoinformatics262780.2024.10660821

Keywords

Africa; Climate Change; Extreme Weather Events; Water Scarcity; Agriculture; Climate Change Adaptation; Resource Allocation; Development

Language

English

Access/Licence

Limited Access

Links

https://doi.org/10.1109/agro-geoinformatics262780.2024.10660821 Source record

Project

Climate Resilience

Record type

Conference Paper

Resource thumbnail

Conference Paper

In-season crop-type mapping in Kenya using Sentinel-2 imagery

2024Li, Hui; Guo, Zhe; Di, Liping; Guo, Liying; Zhang, Chen; Lin, Li
Details

In-season crop-type mapping in Kenya using Sentinel-2 imagery

In-season crop type mapping is essential to agriculture management applications, including yield estimates, crop planting acreage statistics, food market predictions, and land use change analysis that support relevant decision-making, pushing economic development in certain agricultural export nations like Keny. This study employed a supervised machine learning method to produce three Kenya counties’ in-season crop-type maps in September 2023. We used surveyed growing crop ground truth data at the end of August 2023 and European Space Agency (ESA) WorldCover data serving as training labels, including nine crop types (Maize, Coffee, Grassland, Tea, Sugarcane, Exotic tree, Legumes, Vegetable, Native tree). The 15-day composite Sentinel-2 time series data was generated, incorporating training labels to assemble into training samples. They engaged in training a random forest classifier, conducting crop-type classifying in Nandi, Vihiga, and Kisumu Counties of Kenya. Moreover, the majority filter served to refine the classification. The validation results confirmed that grassland, sugarcane, tree, and tea possess high classification accuracy (0.80−0.91), and coffee and maize showcase low accuracy (0.67 0.73) due to the massive mix pixels. This study attempted to produce in-season crop-type maps in an African nation with fragmented crop fields.

Year published

2024

Authors

Li, Hui; Guo, Zhe; Di, Liping; Guo, Liying; Zhang, Chen; Lin, Li

Citation

Li, Hui; Guo, Zhe; Di, Liping; Guo, Liying; Zhang, Chen; and Lin, Li. 2024. In-season crop-type mapping in Kenya using Sentinel-2 imagery. 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/Agro-Geoinformatics262780.2024.10660971

Country/Region

Kenya

Keywords

Eastern Africa; Crops; Modelling; Agriculture; Markets; Machine Learning; Yields

Language

English

Access/Licence

Limited Access

Links

https://doi.org/10.1109/agro-geoinformatics262780.2024.10660971 Source record

Project

Nature-Positive Solutions

Record type

Conference Paper

Resource thumbnail

Conference Paper

Mapping above ground carbon storage and sequestration using the InVEST model: A case study of three countries in Kenya

2024Guo, Zhe
Details

Mapping above ground carbon storage and sequestration using the InVEST model: A case study of three countries in Kenya

Numerous studies have been undertaken to understand shifts in ecological indicators over time, employing various valuation methods and comprehensive ecological health monitoring efforts. This study presents a cost-effective approach to evaluating carbon storage and sequestration in Kenya, utilizing crop type maps derived from remote sensing data and the InVEST (Integrated Valuation of Environmental Services and Tradeoffs) carbon model. By harnessing publicly available satellite data and open-source ecosystem models, our spatially explicit analysis addresses concerns regarding data scarcity, providing essential insights into carbon stock dynamics across diverse land use types. We employ a proximity-based scenario generator to project future scenarios, including the expansion of select crop types, offering valuable tools for assessing land use changes and their impacts on carbon stocks. Spatial-temporal assessments uncover the dynamic nature of carbon stocks in response to land use changes over time.

Year published

2024

Authors

Guo, Zhe

Citation

Guo, Zhe. 2024. Mapping above ground carbon storage and sequestration using the InVEST model: A case study of three countries in Kenya. 12th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). Institute of Electrical and Electronics Engineers. https://ieeexplore.ieee.org/document/10661076

Country/Region

Kenya

Keywords

Eastern Africa; Ecological Indicators; Carbon Sequestration; Crops; Remote Sensing; Modelling; Land-use Change

Language

English

Access/Licence

Limited Access

Links

https://doi.org/10.1109/agro-geoinformatics262780.2024.10661076 Source record

Project

Climate Resilience

Record type

Conference Paper