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HPE Sandbox to boost food production
- September 30, 2020
- William Payne
Hewlett Packard Enterprise is collaborating with digital agriculture research body CGIAR to improve agricultural production methods. HPE will apply its Memory-Driven Computing Sandbox to CGIAR’s big data sets to model food and agricultural production systems.
The aim of the collaboration is to address the challenge of developing sustainable global food production. Nearly 800 million people are chronically undernourished and 2 billion are micronutrient deficient, while the number of smaller farms, globally, is on the decline because profitability is so difficult. These problems are forecast to worsen as the UN predicts world population will grow to 8.5 billion by 2030, and the World Economic Forum predicts a population of 9.8 billion by 2050, requiring 70 percent more food than is consumed today.
The problem has worsened in light of the global COVID-19 pandemic. The crisis is affecting food systems and supply chains worldwide, but it is unfolding differently around the world, which means the problems cannot be solved with one universal solution.
CGIAR is a global research partnership of 14 non-profit agricultural research institutes working in over 100 countries on research into virtually every aspect of food security. In its 11 genebanks around the world, CGIAR preserves and regenerates 760,000 varieties of food crops that represent important genetic diversity available for building resilience in the global food supply.
CGIAR needs to generate a timely, high-frequency picture of what is happening in “food basket” locations – or areas of significant food production – around the world. A complete picture often requires data from multiple sources including crop performance, weather records, economic activity, and surveys.
Insights from this data could help researchers answer questions such as: how is economic activity and food movement happening in food baskets on a weekly basis; how can these analytics guide the agriculture sector and its most vulnerable participants in a period of increasing climate variability and extreme weather events; how can public, private, and non-profit actors meaningfully share all of this data to enable better outcomes for all; how can stakeholders track and measure progress toward the UN’s Sustainable Development Goals for zero hunger by 2030?
“Being able to create a picture in 200 cities or settlements in a short amount of time is dramatically different from what we can do with our existing compute resources,” said Brian King, coordinator of CGIAR’s Platform for Big Data in Agriculture. “Since the impacts of COVID-19 are unfolding differently by country, our ability to look at the situation both at the aggregate level and from an on-the-ground, local view is incredibly valuable. That capability enables a different way for us to operate as a research organisation. But generating high-frequency insights across multiple distinct contexts at once demands compute power to support it and more compute capacity than we had. The Memory-Driven Computing Sandbox appeared at just the right time.”
With access to the Sandbox, named due to the controlled environment it offers to experiment with advanced compute resources, CGIAR is building cross-cutting, high-frequency views of food systems linking crop modelling, including weather records and how crops performed and what the yield was, by year and location – survey data, and overall economic activity, such as movement of goods and people.
CGIAR is monitoring emissions from up to 1,000 points across India and East Africa using public satellite data from space agencies. Changes in emissions indicate changes in economic activity that give researchers important context for understanding how food security challenges are unfolding by location. Equipped with this dynamic, unfolding picture, CGIAR is able to compare with crop and survey data to monitor how that individual crop will impact the broader food supply.
Insights from this data will enable CGIAR to see and increasingly predict how food security challenges are unfolding from the COVID-19 crisis to inform policy makers, food relief actors, and other stakeholders. Using CGIAR’s existing technology, emissions analysis on one point on the Earth could take four to five hours to run. Today, CGIAR can run multiple analyses over multiple points with sufficient frequency to inform timely action on food security.
“At HPE, our purpose is to advance the way people live and work, and we are committed to applying technology to help address some of society’s toughest challenges,” said Janice Zdankus, VP, Innovation for Social Impact, HPE. “One of our focus areas is world hunger, inspired by results from Purdue University’s 1,400-acre research farm and its application of precision agriculture to increase crop yields while drastically conserving resources. With CGIAR, we saw the opportunity to apply innovative technologies, like HPE’s Memory-Driven Computing Sandbox to drive faster insights and help address this incredibly complex challenge.”
While the pandemic has caused immediate and near-term issues that must be addressed, the future of food security must be evaluated on a longer horizon as well. Mapping and predicting climate risk, vulnerability, and adaptation options are top priorities for CGIAR.
CGIAR is using models to predict the kinds of climate hazards such as disaster events like cyclones, floods, and droughts, that will likely threaten small-holder farmers in developing economies. CGIAR then helps policy makers and the at-risk farmers themselves prepare for these types of climatic shocks so that the impact to the food system is minimised.
“Building monitoring and modelling capabilities is critical,” said King. “The whole world was caught flat footed during the pandemic because we found that there were huge gaps in timely, good quality data to monitor and respond quickly to the myriad – and very different – food system disruptions unfolding as a result of the COVID crisis. The global community of food security actors has begun to build up capabilities for more timely, localised diagnosis and response to food security challenges, and complement these with mapping and predicting risks and vulnerabilities of potential climate shocks to food security on a longer time horizon. We will be able to highlight the best options for vulnerable farmers in those areas to adapt to changing conditions and help equip them for that before the next crisis arises.”