Knowledge-based decision support strategies: Linking spatial and temporal components within site-specific weed management

Gregg A Johnson, David R. Huggins

Research output: Contribution to journalReview article

4 Scopus citations

Abstract

Research and application of site-specific farming has focused on technological advancements that enable site-specific field operations. The current emphasis on technological tools of site-specific farming, rather than the development of new crop and pest management systems that integrate and optimize the use of these new tools, limits our abilities to realize the full potential and promise of site-specific management. The objective of this paper is to introduce a conceptual framework that integrates spatial and temporal data, information, knowledge, and wisdom into a knowledge-based decision support strategy (KBDSS). Our goal is to stimulate researchers, engineers, farmers, and other agri-business personnel to refocus their approach to site-specific resource management. Moreover, this knowledge-based decision support strategy must embrace questions of economic, environmental, and social sustainability as well as create an opportunity for people to share information and experiences in addressing emerging issues in crop production. Current strategies emphasize data collection and information management followed by an immediate action. We argue that implementation of decisions derived using only data and information circumvents knowledge and wisdom, often leading to inappropriate resource management. A successful KBDSS will incorporate a better understanding of interdependency among factors affecting or affected by a site-specific decision. We believe that the KBDSS outlined in this paper is a first step in improving integration of weed biology, enhancing the utility of bioeconomic models, shaping farmer/advisor relationships, and recognizing the importance of long-term learning and experience-building within the landscape.

Original languageEnglish (US)
Pages (from-to)225-238
Number of pages14
JournalJournal of Crop Production
Volume2
Issue number1
DOIs
StatePublished - Jan 1 1999

Keywords

  • Modelling
  • Weed biology

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