Big data solving big problems

The world population is expected to reach 9 billion by 2050. Today, only 1/30 of our planet surface is available for agriculture. So by 2050, we need to increase our food production by 50 to 70 % in order to feed the planet. 
 
Agriculture must increase the food production yet minimize impacts on the environment, and at the same time, keep the production profitable. The triple bottom line (People-Planet-Profit) seems somehow difficult to achieve. It is easy to see how food production increase could be challenging. Alongside the food production, agriculture also needs to reduce soil exhaustion and contamination of water bodies. Big problems, right?

Looking at new technologies, Big data is still rather new and unexplored, but looking 20 years ahead, big data could be the most important component in feeding the world.

My colleague Esther van Bergen wrote in her blog how big data is used for the purpose of “see[ing] how water stress will affect operations locally and globally, and help prioritize investments that will increase water security”. Big data will make sense of large amounts of independent data, more or less with low or no value on their own; now when composed together with other information pieces, big data generates valuable information. In light of agriculture, imagine historical soil information, and information on weather, nearby industries, water bodies, pollution and contamination, rainfall, materials in product, energy, and even movements in the market, all composed together.

Big data can be used in forecasting bad weather, crop failures, analyzing and preventing water and soil contamination, as well as ensuring sustainable and profitable food production in years to come. Furthermore, big data could help us control and reduce eutrophication, improve resource management, and contribute to agricultural research and science.

With a better understanding of what is happening and linking cause and effect in soil changes, farmers decision making will be based more upon facts than intuition, for example adjustments in type and amount of fertilizers that should be applied, and with that hopefully meet the food production goals. 

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