Applications of [open] data science for agriculture | datos.gob.es

In many areas, there is a false belief that the primary sector is a technologically backward sector that is not involved in digitization. Perhaps because throughout history, as economic development indicators progressed, their weight in GDP and employment rates has been decreasing in favor of other sectors.

However, the agricultural sector is once again gaining prominence, as it currently faces a set of challenges that are closely related to the future of humanity. Climate change, food security and population growth are just some of the most challenging. The European Commission itself, through the Horizon 2020 program, is allocating important research resources to the social challenge of food security, sustainable agriculture, marine and maritime research, and bioeconomy.

As in other industries, digital transformation is helping to change the operating bases of the agricultural sector, giving rise to what is already known as smart agriculture or precision agriculture, which is also a very important part of the solution to the mentioned challenges.

The agricultural sector is increasingly combining technologies, such as geolocation, soil and environmental conditions monitoring, artificial intelligence, cloud computing and Internet of Things (IoT), to accurately measure the variations of numerous variables in the crop fields and thus improve the quantity and quality of agricultural products.

As in many other industries, the base of digital transformation is data: open, private or enriched with each other, with data science in a leading role. Data science helps farmers see and understand what is happening in their fields with unprecedented precision, allowing them to make much more sustainable decisions to get an efficient production.

From seeds genetics to environmental conditions -and not only weather, but also any issue that affects agricultural production- can be measured and analyzed in order to make sustainable decisions.

Since we know that small variations in the quantities of materials selectively used at specific times and places can generate huge differences in crop yields, farmers can use data science to decide the right amount of seeds that should be planted in a field or the amount of water, fertilizers or pesticides needed to maximize the seeds yield and minimize environmental damage.

To be real, these applications need to combine open data with internal data coming from the sensorization of the crop fields or aerial images from drones or satellites.

Given the importance of the topic, the Open Data Charter, through the Global Open Data for Agriculture and Nutrition (GODAN) initiative, and as part of the Agriculture Open Data Package (AgPack), has classified the key datasets that public administrations handle and whose opening would have an important impact for the agricultural sector into 14 categories, which are explained in the following figure.

In the figure we find from evident data sets, such as those that contain meteorological, hydrological or geographical data, to others that are useful for multiple sectors besides agriculture, such as imports, exports, prices or quality standards of agricultural products. And of course, data on allowed pesticides, inspections or legislative texts related to one of the most regulated sectors of our economy. GODAN has produced important informative material like this or this webinar about Agpack.