Natural Sciences

Improving food security through earth science data – World

Written by Mamie M. Arndt

News Highlights: Improving food security through earth science data – World

When most of the agriculture in the world is rural, getting crucial geoscience information to farmers is a technical challenge that a few organizations are just starting to grasp.

Food security is defined as all people who have physical, social and economic access at all times to sufficient, safe and nutritious food for an active and healthy life. Food insecurity exists when any of these factors are hindered. Chronic food insecurity is usually the result of persistent poverty. Acute food insecurity, on the other hand, is related to human-induced or natural shocks in the food system – such as a drought or flood – that reduce short-term availability or access to food, especially for those already experiencing chronic food insecurity.

Earth science observations have been used for decades to evaluate food production in countries with food insecurity, but they are only used sporadically. Weather and climate variations have profound implications for crop production and ultimately food security, although such observations are still not regularly used to understand agriculture and support decision-making about food security. The challenge for scientists, modelers, and policymakers is connecting real-time geospatial earth science data to those serving the food system, who are largely rural and lack the technological expertise to respond.

Over multi-year timeframes, climatic extremes and global environmental changes affect food production and the welfare of agricultural communities in complex ways that are difficult to assess with any degree of certainty [Vermeulen et al., 2012]. Meanwhile, weather during the growing season affects how well crops grow in a given year and thus the income of farmers, traders, wage workers and others in the agricultural sector, which in turn affects entire economies on a local, regional, and possibly national and global scale. . Acute food insecurity from repeated weather can eventually lead to chronic malnutrition, with significant economic and physical consequences for affected communities [Brown et al., 2020].

Earth science data and observations can quantify weather and climate effects on a region and can be integrated into predictive models to inform adaptation plans. But developing rural economies to enable the application of such data requires the introduction of new agricultural technologies and investments in rural livelihoods that can strengthen the wider food system.

Create comprehensive datasets for farmers

Rural livelihood regions are dominated by small-scale agriculture, such as those in sub-Saharan Africa, where most of the owners of the approximately 33 million small farms are also often food insecure and live below the official poverty line. [Gassner et al., 2019]. Although rural economies differ, most smallholders today have stagnant incomes and declining yields, even in places where the rural population is growing. The effects of climate change emphasize these areas even more.

There are very few data sets to generate this information for small-scale farming, especially subsistence farms, which are often isolated from global market forces.

If these farmers want to increase production, they need reliable information about the local market prices for their crops, as well as the costs and availability of agricultural inputs (eg, seeds, fertilizers, machines and labor) and an understanding of best management practices. to reduce the impact of pests and weather variability on production. However, there are very few data sets to generate this information for small-scale farming, especially subsistence farms, which are often isolated from global market forces. Sufficiently localized and up-to-date data on subsistence farming is very difficult to obtain due to the diversity of crops, cultivation methods and ecologies of agricultural regions around the world.

Some organizations are starting to address this challenge. The World Bank Measurement of living standards: study-integrated surveys on agriculture is a family-level data collection project in eight countries in sub-Saharan Africa. An important goal of the project is to stimulate innovation in sustainability methods, specifically for small farmers. But this and other efforts like the sequel 50 × 2030 Initiative, are still quite limited in size and do not integrate much near-real-time earth science data.

Using earth science data to improve food security

A variety of institutions concerned with food security in low- and middle-income countries partner with the earth science community. Given the huge variety of small farms around the world – from shifting cultivation from the rainforest of Colombia to millet farmers in northern Niger to paddy farmers in Cambodia – it is unrealistic to constantly gather information about these communities on the spot. However, satellite observations already being collected by geoscientists can help ease the burden of data collection.

In Uganda, for example, anomalous vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA’s Terra satellite is the main trigger for the government’s Disaster Risk Financing Fund (DRF). In 2017, satellite data showed that drought would affect crop yields. The early warning allowed the government to use the DRF fund provide assistance to approximately 150,000 Ugandans who would have been affected by the food shortage. Initiatives such as this one, which work to integrate earth science information to improve food security decision-making, can be transformative.

In 2011, the G20 launched a plan to promote international coordination and transparency regarding food production. The platform for this effort is it Agricultural Market Information Systemor AMIS, which assesses the global food supply with the aim of preventing or preparing for market shocks and uncertainty. AMIS relies on agricultural experts who provide regional market and policy information. It also relies, crucially, on geoscience data. These data are provided by GEOGLAM (Group on Earth Observations Global Agricultural Monitoring Initiative), the other half of the G20 initiative. GEOGLAM is operated by the University of Maryland and monitors crop health through satellite observations.

By working together to build a food production information system based on high-quality earth science and local expertise, farmers and everyone else in the food supply chain will have access to information that can help them avoid acute food insecurity before it hits.

For example, Brazil’s national agricultural agency, Conab, became one GEOGLAM partner in 2018 and is now including these earth observations in the national harvest assessments provided to farmers. In Zambia and Zimbabwe, GEOGLAM is working with the national agriculture ministries to create high resolution cropland masks by combining Sentinel-2 observations with information from the local population. By working together to build a food production information system based on high-quality earth science and local expertise, farmers and everyone else in the food supply chain will have access to information that can help them avoid acute food insecurity before it hits.

Field level information products containing remote sensing data are being developed by Kenya Pula. The company provides affordable insurance to small farmers in Africa and the Middle East. The insurance protects livelihoods when drought, flooding, locust swarms, or other events damage crops or prevent germination. Locals use smartphone apps to report information on crop production, which the company combines with geospatial observations. Pula then uses those apps to provide targeted agronomic advice, as well as warnings about weather, pests and diseases. Pula reports that the number of insured farmers in Kenya has grown from 1,000 to 10,000 in about 3 years. The company has paid about $ 766,000 for crop losses so far, and about 80% of households say they use some of those insurance funds to buy food.

Earth observations are of course only useful if they can provide meaningful information. Radiant Earth Foundation develops machine learning methods analyze and classify observations from the Sentinel-2 satellite. The organization is building an open library of spatially specific field data through collaborations, such as a dataset of 319 agricultural plots in Kenya that combines earth observations with measurements on the ground.

Coordination and involvement between these efforts is critical to improve our understanding of the Earth system and to bring real benefits to smallholders and the vulnerable communities they serve. Scientists engaged in these and other efforts to link Earth systems science with food security outcomes should encourage further discussion between funding agencies, governments, universities and scientists and work to bring in additional partners who can help deliver timely and reliable information, insights and technology. The drive to provide more and better information about food security is literally a life or death proposition.

Via: reliefweb.int

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About the author

Mamie M. Arndt