Improving The Utility of Precision Agriculture Through Machine Learning and Climate-Smart Practices

Climate Smart Practices are management strategies that focus on increasing soil and crop productivity, utilize site-specific strategies to increase resiliency against the effects of climate change, and mitigate these negative effects by reducing greenhouse gas (GHG) emissions. Decision Support Systems (DSSs) using machine learning (ML) can adjust models based on new information and help farmers make climate smart decisions within their operation. The 4R nutrient management model of right source, rate, location, and time also demonstrates a framework that may be considered climate smart by improving soil and crop productivity. However, when initially conceptualized, the 4R model did not consider GHG emissions. Additionally, the long-term adoption of DSSs has been low in agriculture, reducing the ability of farmers to collect and analyze farm data to the fullest. Therefore, the objective of the first chapter is to examine applications of, and barriers to, DSSs in precision agriculture (PA). The objective of the second chapter evaluates the 4R model to determine the impact of GHG emissions when utilizing near continuous chambers over a two-year period. The GHG emissions were measured by analyzing nitrous oxide and carbon dioxide emissions from a 50/50 split application of 157 kg N/ha that was applied to corn (Zea mays) at pre-emergence and V6 compared to a single application at pre-emergence 157 kg N/ha in a two-year replicated study. Results from the first chapter identify the barriers preventing farmers from using DSSs as well as suggesting solutions to these challenges. Results from the second chapter indicate that the split application can reduce carbon dioxide and carbon equivalent emissions and therefore, may be a useful framework for DSSs to follow in achieving Climate Smart Practices

Improving Decision Support Systems with Machine Learning: Identifying Barriers to Adoption

Precision agriculture (PA) has been defined as a “management strategy that gathers, processes and analyzes temporal, spatial and individual data and combines it with other information to support management decisions according to estimated variability for improved resource use efficiency, productivity, quality, profitability and sustainability of agricultural production.” This definition suggests that because PA should simultaneously increase food production and reduce the environmental footprint, the barriers to adoption of PA should be explored. These barriers include: 1) the financial constraints associated with adopting DSS, 2) the hesitancy of farmers to change from their trusted advisor to a computer program often behaves as a black box, 3) questions about data ownership and privacy, and 4) the lack of a trained workforce to provide the necessary training to implement DSSs on individual farms. This paper also discusses the lessons learned from successful and unsuccessful efforts to implement DSSs, the importance of communication with end-users during DSS development, and potential career opportunities that DSSs are creating in PA

Rethinking ‘responsibility’ in precision agriculture innovation: lessons from an interdisciplinary research team

We examine the interactions, decisions, and evaluations of an interdisciplinary team of researchers tasked with developing an artificial intelligence-based agricultural decision support system that can provide farmers site-specific information about managing nutrients on their land. We answer the following research questions: (1) How does a relational perspective help an interdisciplinary team conceptualize ‘responsibility’ in a project that develops precision agriculture (PA)? and (2) What are some lessons for a research team embarking on a similar interdisciplinary technology development project? We show that how RI is materialized in practice within an interdisciplinary research team can produce different understandings of responsibility, notions of measurement of ‘matter,’ and metrics of success. Future interdisciplinary projects should (1) create mechanisms for project members to see how power and privilege are exercised in the design of new technology and (2) harness social sciences as a bridge between natural sciences and engineering for organic and equitable collaborations

Rethinking ‘responsibility’ in precision agriculture innovation: lessons from an interdisciplinary research team

ABSTRACTWe examine the interactions, decisions, and evaluations of an interdisciplinary team of researchers tasked with developing an artificial intelligence-based agricultural decision support system that can provide farmers site-specific information about managing nutrients on their land. We answer the following research questions: (1) How does a relational perspective help an interdisciplinary team conceptualize ‘responsibility' in a project that develops precision agriculture (PA)? and (2) What are some lessons for a research team embarking on a similar interdisciplinary technology development project? We show that how RI is materialized in practice within an interdisciplinary research team can produce different understandings of responsibility, notions of measurement of ‘matter,’ and metrics of success. Future interdisciplinary projects should (1) create mechanisms for project members to see how power and privilege are exercised in the design of new technology and (2) harness social sciences as a bridge between natural sciences and engineering for organic and equitable collaborations