1a. Objectives (from AD-416):
To develop a global geospatial data aimed to satisfy the needs for globally consistent information on agriculture and resource use by creating a sustainable interoperable cyber-infrastructure that coordinates the work of the most prominent data in the world.
1b. Approach (from AD-416):
Establish a pilot in which a group of active players in the generation of global datasets combine their strengths to collaborate in the production of global data focusing on land use, irrigation and climate. The pilot consists of six different nodes, three global and three regional focusing on Asia, Africa, and Latin America. The Geoshare data base resulting from the pilot product will be presented in a capstone conference and donors’ meeting. The initiative will be hosted by Purdue University, through the HubZero technology originally developed with NSF funding to support collaborative research in the field of Nano-technology. HubZero is a collaborative environment with web interface that allows sharing data analysis and modeling tools. This initiative has great potential to overcome some of the most serious constraints to effective utilization of geo-spatial information in developing countries. Efforts will be made to coordinate this project with AgMIP PI's (Jerry Hatfield, Cynthia Rosenzweig, John Antle, Jim Jones) to harmonize approaches.
3. Progress Report:
The PI spent a month at the Rockefeller Foundation’s Bellagio Center collaborating on geospatial methods and analysis in support of economic and social development. This allowed the refinement of the vision for the HUBzero architecture, which now has a more prominent place the project. Purdue’s IT department is providing two years of HUB support. Project support has been reallocated to allow moving ahead with the HUB. Support was obtained from ILSI to develop two critical workflows on the HUB. These workflows are designed to start with 500 layers of geospatial source data, and utilize the SPAM software to generate consistent, gridded data, usable by biophysical and economic models, which are also intended to run on the HUB. In this way, contributors of new data sets, such as the new land cover data set for South Asia, can upload their new source data, replace the old data set in this workflow, run it through SPAM, and analyze the consequences for issues ranging from climate impacts on India to the impact of future water scarcity on production and food security. Funding from DEFRA, UK was finalized, which supports work at the two global nodes: McGill University (land cover) and the University of Bonn (irrigation). With this funding in place, work can proceed on these fronts. South Asia Case Study: The South Asia case study is being led IRRI. Despite rapid growth in the overall economy, rural poverty remains stubbornly high in India. One hypothesis is that this lack of progress with poverty reduction is due to low rates of agricultural productivity growth – particularly in the poorest districts where poverty rates sometimes exceed 50 percent. This project seeks to better understand the linkage between agricultural productivity and rural poverty by taking advantage of a unique, district-level data set which has been developed for India. Cooperators will measure the rate of agricultural productivity growth at the district level in India, and assess the extent to which it has influenced rural poverty reduction. This will enable policy makers to better direct rural development assistance to boost incomes and reduce rural poverty in the poorest districts of India. Sub-Sahara Africa Data Base and Case Study: Feedback received from stakeholders in East Africa led to shifting the emphasis focus on rice production in Tanzania. This case study also complements, and expands on, the GEOSHARE data base effort currently underway in the region. Rice is one of the most important sources of employment, income, and food security in Tanzania. In East and Central Africa, Tanzania is a leader in rice production, second to Madagascar. Suitability for the smart expansion will measured with the help of GEOSHARE-facilitated geospatial datasets using such indicators as land use, soil properties, population density, poverty levels, rainfall variability, climate, proximity to watersheds and protected areas1, market accessibility, and yield response to nitrogen and phosphorus fertilizer applications.