Smart information systems (Big Data and artificial intelligence) are used in the agricultural industry to help the planting, seeding, and harvesting of crops, as well as farm management, plant and livestock illness and disease detection. I looked at how a Digital Division at a large agricultural multinational is using smart information systems (SIS), through their SISproject, to provide farmers with local weather predictions, farm efficiency and sustainability metrics, and early detection systems for weed, pests and disease. SIS being used in agriculture, types of data retrieved from the farm, how this data is analysed, and agribusinesses involved in this burgeoning field. Agricultural SIS has the potential to automate activities that are typically done by agronomists, allowing for cost reductions, quick and effective crop forecasting, and improved decision-making and efficiency for the farmer. Agricultural SIS also offers agribusinesses an additional revenue, better customer-relations, and reduced costs from hiring additional agronomists and advisors. The world’s population will exceed 9 billion by 2050, forcing the agricultural sector to increase its production levels by up to 70%. SIS are being hailed as one possible solution to help plant, seed, harvest, and manage farms better and more effectively. However, the use of agricultural SIS may create a number of ethical concerns. For example, the accuracy of data and recommendations provided by SIS may lead to lost harvests, ill livestock, and loss of earnings. There is also a tension between ensuring an agribusinesses’ intellectual property and the protection of the farmer’s data ownership. The use of SIS is relatively expensive, which may create a digital divide. Agricultural Big Data is also vulnerable to privacy and security threats because it could be used nefariously by corrupt governments, competitors, or even market traders. Sensors, robots and devices may cause harm, distress, and damage to animal welfare and the environment.To assess if these ethical issues mirror those experienced in the field, I interviewed three members of this company working on their SIS project. This project combines data retrieved from the farmer with the company’s agronomic knowledge to manage their farm more effectively. The project was designed to provide farmers with local weather predictions, plant disease in situ detection, and recommendation tools to minimise risk, crop and yield previews, farm efficiency and sustainability metrics, and early detection systems for weed, pests and disease. One of the primary motivations for using SIS technology for the company is the ability to make the farmer’s life easier, more productive, and to save costs. The aim is to improve farm management, not by increasing fertilizer use, but by more intelligent farming decisions and practices.
The ethical issues faced in the project strongly correlated with those in the literature, with the addition of employment. The general public is concerned that SIS will replace human jobs, such as the agronomist, but the team stated that their SIS is intended to complement the human expert, rather than replace them. Accuracy and availability of data proved to be an issue because not all farmers had available data and data retrieved from third-parties may not be accurate. The team ensure that their customers’ privacy is protected by having strong security measures to avoid misuse and hacking. Data ownership belongs to the farmer and they can move to a different farm management system supplier, with that data, if they choose to. The tool is free to use to avoid the issue of a digital divide. The company incorporate a strong sustainability agenda into their SIS, developing it from the European PEF (Product Environmental Footprint) and a Life-cycle assessment (LCA) framework. Overall, my report was able to evaluate how ethical issues found within the SIS literature correlate with those identified, and tackled, in practice.