big data, machine learning algorithms, AI) that are ethical, equitable and effective for farming and non-farming communities. We contend that open and inclusive deliberations on the unintended and undesired implications of DSSs involving social actors across the food system value chain can inform guiding principles for designing and governing new DSSs and associated technologies (e.g. farmers, farm advisors, researchers in the industry and universities, policy makers, regulators – each of whom is positioned differently in the PA value chain, giving them unique experiences and affectations with the technologies (Gutiérrez et al. The design, development, use, and regulation of DSSs follow a complex and dynamic trajectory involving many different actors and organizations within the food system – e.g.
While some food system actors are excited about being empowered by big data technologies, others are equally concerned about the ownership, privacy and use of their data by corporations or regulators (Finn and Donovan 2016 Jakku et al. Innovation in DSSs depends on several activities, such as the collection and processing of big data, development of new cyber infrastructures, data sharing platforms, and machine learning algorithms, all of which have their own social and technical challenges. Yet the prevailing narrative about PA that presents DSS as a techno-scientific fix to the challenges of agricultural decision-making must be treated with caution (Rose and Chilvers 2018). Proponents of PA claim that widespread use of data-driven DSSs will streamline the decision-making process in agriculture as these systems are based on empirical data and not guesswork. Farmers in India and Colombia are adopting DSSs, such as Microsoft’s Cortana Intelligence Suite, to assist in determining optimal planting dates for crops (López and Corrales 2018) and unmanned drones are translating agronomic information into maps that are visualized on DSSs to locate and remove weeds from fields (Lottes et al. 2017 Bongiovanni and Lowenberg-DeBoer 2004). By providing targeted farming recommendations to farmers, DSSs enable a ‘farming by the foot’ approach that can facilitate an increase in farm productivity, reduce greenhouse gas emissions (GHGs), decrease farm management costs, and advance coordination across the food system value chain (El Bilali and Allahyari 2018 Balafoutis et al. More recently, DSSs are integrated with current technologies like the Internet of Things (IoT), big data, artificial intelligence (AI), cloud computing and remote sensing to transform environmental and agronomic farm data into ‘precise’ and ‘accurate’ farming recommendations (Kamilaris, Kartakoullis, and Prenafeta-Boldú 2017 Rose et al. Since the 1970s, DSSs have emerged from the rapid development of computing and electronics, which have allowed agricultural machines to perform operations efficiently (McCown, Hochman, and Carberry 2002 Zhai et al. However, DSSs are not new to agriculture. Under the broader ambit of PA, agricultural decision support systems (DSSs) are becoming increasingly popular for translating agronomic and climate sciences to crop, livestock, and dairy farmers. Precision agriculture (PA) employs data-based agricultural technologies and practices with localized farm data to generate site-specific farm recommendations (Banerjee, Bandyopadhyay, and Acharya 2013 Bongiovanni and Lowenberg-DeBoer 2004 Rossel and Bouma 2016 Smith 2018). Inclusive processes of open deliberation are modalities of responsible innovation, tasked with mitigating frictions within socio-technical systems.įood and agriculture systems in the US are undergoing a technological and sustainability revolution (Rose and Chilvers 2018). We suggest that agritech developers implement inclusive and deliberative processes when redesigning DSSs to engender ethical, equitable and sustainable improvements to food production systems. We find that DSSs transform agricultural knowledge production, reconfigure labor arrangements and unevenly distribute benefits and burdens among farmers. Utilizing a mixed-methods approach that consisted of focus group discussions and a follow-up survey questionnaire, we highlight the experiences and affectations of heterogeneous food system actors from Vermont and South Dakota. Despite the promise of DSSs to address many challenges of the farm economy, there are social and ethical concerns that need to be addressed.
#Big farm mobile harvest cowshed yield software
Agricultural decision support systems (DSSs) are hardware and software tools that utilize big data collected from satellites and drones, ground-based sensors, and analyzed with machine learning algorithms to provide site-specific farming recommendations.
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