ICT, farming and law

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

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