Precision agriculture is becoming more and more data-driven. But adoption on small and medium-sized farms is slower than expected. How can we turn this around and harvest the economic and societal benefits of full data-driven precision farming?
It is my believe that it is very likely that farming around the world will become more and more data-driven more in the coming years. A public-private cooperation in The Netherlands addresses the prerequisite: a mature Farm Data Space. A code of Conduct on agricultural data use will play a key role in getting there.
At the same time, there are challenges in getting high quality data and advisory models in the Farm Data Space tailored to medium and small-sized farms. Most software tools and services for digital farming have been developed for large farms (> 1000 ha). The challenges of small and medium-sized farms cannot not be met by simply copying the precision farming technology used on large farms.
As an example, on large-scaled arable farms in North America and some other regions in the world, where optimization at the scale of 1-10 ha already pays off, accuracy of geo-referencing of satellite images is not too important. However, on farms in The Netherlands, where optimization starts to pay of only at below 10-100 m2, accuracy of geo-referencing is much more important (a geo referencing error of 1 meter is already unacceptable). And because yields are already high, the quality of the advisory models also has to be high to give added value.
Looking at ongoing R&D, it is likely that requirements for medium and small-sized farms will be met in the future with a development path of its own. In addition, more sensor systems that detect diseases, plant stress, biodiversity will come to market, next to yield mapping systems for root crops . And this will be done with autonomous navigation platforms/field robots more and more.
Adoption of this will benefit from a more farm-centered approach and with an eye for the needs of small and medium-sized farms.