Author:
Rozenstein Offer,Cohen Yafit,Alchanatis Victor,Behrendt Karl,Bonfil David J.,Eshel Gil,Harari Ally,Harris W. Edwin,Klapp Iftach,Laor Yael,Linker Raphael,Paz-Kagan Tarin,Peets Sven,Rutter S. Mark,Salzer Yael,Lowenberg-DeBoer James
Abstract
AbstractSustainability in our food and fiber agriculture systems is inherently knowledge intensive. It is more likely to be achieved by using all the knowledge, technology, and resources available, including data-driven agricultural technology and precision agriculture methods, than by relying entirely on human powers of observation, analysis, and memory following practical experience. Data collected by sensors and digested by artificial intelligence (AI) can help farmers learn about synergies between the domains of natural systems that are key to simultaneously achieve sustainability and food security. In the quest for agricultural sustainability, some high-payoff research areas are suggested to resolve critical legal and technical barriers as well as economic and social constraints. These include: the development of holistic decision-making systems, automated animal intake measurement, low-cost environmental sensors, robot obstacle avoidance, integrating remote sensing with crop and pasture models, extension methods for data-driven agriculture, methods for exploiting naturally occurring Genotype x Environment x Management experiments, innovation in business models for data sharing and data regulation reinforcing trust. Public funding for research is needed in several critical areas identified in this paper to enable sustainable agriculture and innovation.
Publisher
Springer Science and Business Media LLC
Subject
General Agricultural and Biological Sciences
Reference29 articles.
1. Agricultural Research Organization (ARO). (2018). A model farm for studying, demonstrating and implementing sustainable agricultural practices. Newe Ya’ar Volcani Institute, Israel. Retrieved July 1, 2023, from https://www.modelfarm-aro.org/
2. Atzberger, C. (2013). Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2), 949–981.
3. Bacco, M., Barsocchi, P., Ferro, E., Gotta, A., & Ruggeri, M. (2019). The digitisation of agriculture: A survey of research activities on smart farming. Array, 3, 100009.
4. Behrendt, K., Malcolm, B., & Jackson, T. (2014). Beef business management. In L. Kahn & D. Cottle (Eds.), Beef cattle production and trade (pp. 493–513). CSIRO Publishing.
5. Bronson, K., & Knezevic, I. (2016). Big data in food and agriculture. Big Data & Society, 3(1), 2053951716648174.
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献