John T. Evans’s research while affiliated with Purdue University West Lafayette and other places

This research endeavours to guide precision conservation considering the inclusions of prairie strips in strip cropping systems, where the farm was managed by autonomous machines (swarm robots). The economics of conventional and autonomous mechanization alternatives was examined.

Autonomous machines have the potential to maintain food production and agroecological farming resilience. However, autonomous complex mixed cropping is proving to be an engineering challenge because of differences in plant height and growth pattern. Strip cropping is technically the simplest mixed cropping system, but widespread use is constrained by higher labor requirements in conventional mechanized farms. Researchers have long hypothesized that autonomous machines (i.e., crop robots) might make strip cropping profitable, thereby allowing farmers to gain additional agroecological benefits. To examine this hypothesis, this study modeled ex‐ante scenarios for the Corn Belt of central Indiana, using the experience of the Hands Free Hectare‐Linear Programming (HFH‐LP) optimization model. Results show that per annum return to operator labor, management, and risk‐taking (ROLMRT) was $568/ha and$163/ha higher for the autonomous corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] strip crop farm compared to the whole field sole crop and the conventional strip crop farms, respectively, that were operated by human drivers. The conventional strip cropping practice was found challenging as this cropping system required four times more temporary hired labor than autonomous strip cropping and three times more than whole field sole cropping. Even if autonomous machines need 100% human supervision, the ROLMRT was higher compared to whole field sole cropping. Profitable autonomous strip cropping could restore and improve in‐field biodiversity and ecosystem services through a sustainable techno‐economic and environmental approach that will address the demand for healthier food and promote environmental sustainability.

Contributed presentation on the 05th Online Symposium on Agri-Tech Economics for Sustainable Futures, 19-20 September 2022 organized by Global Institute for Agri-Tech Economics (https://www.harper-adams.ac.uk/research/giate/), Food, Land and Agribusiness Management Department, Harper Adams University, Newport, Shropshire, TF108NB, United Kingdom. Contributors: A. K. M. Abdullah Al-Amin123, James Lowenberg‑DeBoer1, Bruce Erickson2, John T Evans2, Kit Franklin1, and Karl Behrendt1 1Harper Adams University, Shropshire, Newport, TF10 8NB, UK 2Purdue University, West Lafayette, Indiana, US. 3Bangladesh Agricultural University, Mymensingh 2202, Bangladesh

Digital agriculture, with the incorporation of Internet-of-Things (IoT)-devices, presents the ability to control a system at multiple levels and generate tools for improved decision making. Recent advances in IoT hardware and software make it possible to collect data and efficiently process data from diverse sources in a connected farm. By interconnecting these IoT devices, often across large swaths of farmland, it is possible to collect data from multiple farming systems, and at different time scales, including in near real-time. This data can then be used for actionable insights. Through Lattice, we present an integrated vision for IoT solutions, data processing, and actionable analytics, with economics and policy considerations for digital agriculture. Our paper starts off with the types of datasets in typical field operations, followed by the data lifecycle and storage and fast information-retrieval solutions. It then goes on to describe the most promising aspects of machine learning and cloud computing for digital agriculture. IoT devices form a rich source for data collection and subsequent analysis. We discuss what algorithms are proving to be most impactful in this space. We conclude by discussing analytics for alternative agriculture and policy challenges in the implementation of digital agriculture in the wild.

Digital agriculture has the promise to transform agricultural throughput. It can do this by applying data science and engineering for mapping input factors to crop throughput, while bounding the available resources. In addition, as the data volumes and varieties increase with the increase in sensor deployment in agricultural fields, data engineering techniques will also be instrumental in collection of distributed data as well as distributed processing of the data. These have to be done such that the latency requirements of the end users and applications are satisfied. Understanding how farm technology and big data can improve farm productivity can significantly increase the world's food production by 2050 in the face of constrained arable land and with the water levels receding. While much has been written about digital agriculture's potential, little is known about the economic costs and benefits of these emergent systems. In particular, the on-farm decision making processes, both in terms of adoption and optimal implementation, have not been adequately addressed. For example, if some algorithm needs data from multiple data owners to be pooled together, that raises the question of data ownership. This paper is the first one to bring together the important questions that will guide the end-to-end pipeline for the evolution of a new generation of digital agricultural solutions, driving the next revolution in agriculture and sustainability under one umbrella.