High resolution mapping of crop distribution at global scale: updating past/current information and predicting the future
The Alliance of Bioversity and CIAT, CIAT Regional Office for Africa| Duduville Campus, off Kasarani Road. P.O. Box 823-00621, Nairobi, Kenya | Phone: +254 20 8632800 | +254 721 574967
(1) Aniruddha Ghosh, Email: firstname.lastname@example.org
(2) Jawoo Koo, Email: email@example.com | International Food Policy Research Institute (IFPRI), 1201 Eye St., NW, Washington, DC 20005-3915 USA
3. Project Description:
Spatially-explicit data on crop distribution and production are increasingly becoming important to guide agricultural research and development practitioners to properly target, prioritize, and plan for investments. To find the best location-specific adoption and mitigation strategies under climate change, we need accurate and updated information on which crop is grown where and how the growing conditions are likely change in the future. Within the CGIAR system, both IFPRI and the Alliance have been leading the effort to generate spatially-explicit knowledge products for supporting strategic agricultural investment decisions. Building on their existing ongoing work, the current project aims to
- Update crop production statistics: IFPRI’s Spatial Production Allocation Model (SPAM) spatially disaggregates subnational crop production statistics, (yield, quantity and value of production, harvested area) for 42 priority crops, on 10 km (5 arc-minute) grid-cells at the global level. Its 2005 version, released in early 2015, has been widely used in the targeting and priority setting of agricultural research programs and development projects. The SPAM team has recently updated 2010 statistics for the Sub-Saharan African countries (SSA). As a next step, we aim to update the production information for remaining countries as well as develop a reproducible workflow to support such activities in the future.
- Quantify climate change impact on crop production areas: Climate change is expected to alter the growing conditions of the crops across the world. To better understand the potential impact of climate change on crop production, we aim to model the changes in suitability of crop-grown areas. We will model the scenarios with downscaled climate models, crop growth models, as well as various machine learning techniques.
The interns will receive initial training on required programming skills and statistical models during the project period. However, knowledge of basic programming in R, any geospatial and statistical modeling is a plus.
4. Expected Outcomes:
The project will result in:
- an updated map of crop geography for circa 2010 or later, spatially disaggregated crop production statistics data at 1 sqkm spatial resolution (the actual year will depend on the data availability)
- future and change of suitability map for 42 crops covered in the SPAM model
- At least one data paper published in open access journal such as https://www.journals.elsevier.com/data-in-brief, https://www.mdpi.com/journal/data or https://www.nature.com/sdata/
We believe these datasets will support a large number of agricultural policies, research, and development issues, as well as decision-making efforts considering farming systems, poverty, nutrition, and climate change.
Applications for this internship should be submitted via the online application system, stating clearly the title of the internship.
Deadline for applications: February 28th, 2021 at 11:59 PM Central Africa Time (CAT)