North Carolina State University is working on some super promising research that could be a game-changer for agriculture in Bangladesh. By combining satellite images with machine learning, this study aims to optimize rice production in a country where this grain is downright vital for the economy and for filling plates. A project that matters, not only for Bangladesh, but also in the global fight against the effects of climate change on agriculture.
Why is rice so crucial in Bangladesh?
Bangladesh is the third largest rice producer in the world. For them, rice is not just a food that 90% of Bangladeshis eat every day. It is also a big part of their economy: it weighs around a sixth of the national GDP. So, if rice production takes a hit, it has a direct impact on food and economic security.
But now, the country faces enormous challenges because of climate change. Considered the sixth most vulnerable country in the face of these climatic upheavals, Bangladesh sees its agricultural production threatened by extreme weather phenomena which disrupt usual crop cycles.
When old methods show their limits
Until now, monitoring how rice production is doing has relied on data collected directly on site. But according to Varun Tiwari (the main author of the study), this method is “time-consuming and requires a lot of hands”. In addition, when it comes to extending this data to the entire country, it is not always precise. These old methods prevent decision-makers from acting quickly on issues of exports, imports or even crop prices.
To get around these problems, the team mixed satellite data and information collected on site. This hybrid model makes it possible to precisely evaluate crop productivity between 2002 and 2021. The first results are rather encouraging: with a precision between 90% and 92% and a margin of error reduced to 2%, this model offers a tool strong to best manage agricultural resources.
What if we thought bigger?
This method developed by the team can prove itself elsewhere than in Bangladesh. Tiwari explains that if we can obtain similar datasets in other agricultural regions of the world, this model could be used there to improve resilience to climate change.
-With this increased precision in production estimates, decision-makers could act more effectively by allocating more resources or introducing varieties adapted to new climatic conditions. The major floods which hit Bangladesh in 2024 (causing a lot of damage to crops) clearly show that there is an urgent need to adopt this type of innovation.
Ultimately, this research highlights how important it is to have accurate estimates where every decision can have major consequences for global food security. As the world continues its battle against the effects of climate change on agriculture, this study paves the way for better management of essential natural resources.
The possibility of adapting this model to different regions offers a positive prospect for strengthening agricultural resilience around the world. By combining advanced technology and local know-how, we can not only boost agricultural productivity but also guarantee sustainable food security for our children and grandchildren.
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