Precision agriculture

The first obstacle relates to the initial investment required. If, in the long term, savings must be made, certain technology tools such as drones or connected tractors are still beyond the financial means of many farmers.

How to finance the deployment of tools and systems? What are the avenues for mutualization? Public subsidies?

The technologies are still emerging and can sometimes seem incompletely adapted to agricultural uses. For example, if the sensors are able to generate considerable sums of data, it can be complex to transform them into a simple management tool. DST suppliers have made major progress on this aspect, but the simplification of decision support systems remains a major area of innovation.

How to find the balance between data generation and user experience (UX) for decision support? Who will be the players able to meet the expectations of farmers?

In addition to their simplification, the DSTs still have to convince farmers. Basing farming or livestock management decisions on digital tools requires a high level of confidence on the part of farmers, for whom error is virtually forbidden in a very tense economic context. Advances in AI and the promise of Deep Learning are and will be key elements in persuading farmers of the effectiveness of changing practices.

What will be the opportunities for integrating AI into farming practices to support precision farming? How to ensure a sufficiently solid agronomic base to convince farmers?

Other barriers relate to the very possibility of using new technologies in digital deserts. Many public investments have been made to put an end to white zones, and other innovations are developing, for example to limit the bandwidth requirements of IOT equipment via 0G, but the technical possibility of using the tools is not yet guaranteed, particularly at European level.

Will the deployment of agricultural IOT or go through the maximization of network coverage or through 0G technologies?

Precision agriculture has a strong technological component, and an approach to improving conventional practices still linked to phytosanitary products. It must therefore be reconciled with more radical approaches to the transformation of agriculture such as agroforestry or conservation agriculture. The latter highlights three principles: minimal tillage, permanent soil cover and cropping associations, which should make it possible to drastically limit the use of plant protection products or even do without them. The way of approaching precision agriculture must therefore change so as not to opt for “all digital” and instead, put agronomy and agroecology back at the heart of the system.

How will precision agriculture evolve in the face of pressing social demands to limit synthetic phytosanitary products?

Finally, at a time when questions of food sovereignty are coming back to the fore, the question of the control of agricultural data is a crucial challenge. The ownership of data generated by farmers has always been a sensitive point of debate, it becomes even more so in a context where geopolitical challenges could go so far as to constrain private actors, operators of datahubs or DSTs, creating insecurity for service users.

Who will be the central players in the system controlling the data? Will the public authorities have to intervene to support regional or national champions in the face of digital multinationals?