The population of the planet is going to growth from the
current 7.7 billion to 9.7 billion in 2050 and nearly 11 billion by 2100, meaning
that on one hand, a higher pressure to the availability of land for
agriculture, and on the other need to greater agricultural production for food,
raw materials and energy. Global climate change due to human activity and
environmental degradation implies that extending the agricultural frontiers by
further depleting existing forests is not an option.
Smart farming consists of a suite of technologies rather
than a single technology, and its global market stood at nearly 5 billion US
dollars in 2016, expecting to reach 16 billion US dollars by 2025. AI can be used to process that data, forecast
production output and anomalies for better distribution, financial planning and
mitigation; smart sensors can collect vast amount of data to forecast
production outputs and anomalies; driverless machinery can perform different
tasks around the clock, with replicable precision and subject to adverse
environmental conditions; drones are being used to gather data and control both
crops and animal production; geographic information systems allow farmers to increase production
to map and project fluctuations in environmental factors; and digital
veterinary applications include telemedicine, trackers, wearable, monitoring
and identification devices, and visual and sound recording. The use of these
technologies in agricultural production refer to a range of legal issues, some
of which have currently clear definition and others that might need some
adaptations and reform.
Artificial intelligence in agriculture attracts all the
legal issues currently being pointed to artificial intelligence in general, including
contractual data issues, with some that might have specific impact on
agricultural production. The main use of systems based on algorithms is to
forecast different scenarios, using current and past data to find patterns, and,
for example, there might be legal uncertainty when management decisions lead to
severe variations in agricultural output, due to the lack of transparency and
accountability found in some AI.
The vast amount of data produced by sensors in fields and
animals lead to the need of retailoring agricultural contracts to identify the
different responsibilities and limitation of liability arising from the
negative consequences of wrong decisions based on faulty data. At the same
time, some of the data may result in the identification of the producers, which
would attract the whole set of data protection laws to data that seems to be
unrelated to it in ways not foretold by legislators. Furthermore, due to the
sensitivity of the data recollected, the security of the data needs to be a clear
legal requirement, not only at contractual level but also with some public
safety and market transparency considerations.
The use of mechatronics, drones, geographic information
systems and the whole set of digital veterinary applications brings back the
issue of privacy, liability for both malfunction and, more importantly,
undesirable production results, adding a strong need to cybersecurity and a set
of regulatory compliance, which may bring into question some fundamental rights
issues. For example, can a farmer use technology based on drones near an
airport? If not, would the airport operator or the state compensate the farmer
for the potential losses or lack of profits? What are the security requirements
for veterinary applications that have the potential to put unhealthy products
in the consumer market through third party malign interference?
These are few of the many
issues raised by ICT use in
agricultural production, all of which deserves further analysis, so, keep an
eye on Electromate
