Field trial guidelines for evaluating enhanced efficiency fertilizers

领域(数学) 现场试验 环境科学 农业工程 数学 农学 工程类 生物 纯数学
作者
Sarah E. Lyons,D. Brian Arnall,Dana Ashford‐Kornburger,Sylvie M. Brouder,E. R. Christian,Achim Dobermann,Stephan M. Haefele,Jason W. Haegele,Matthew J. Helmers,Virginia L. Jin,Andrew J. Margenot,Joshua M. McGrath,Kelly T. Morgan,T. S. Murrell,Deanna L. Osmond,David E. Pelster,Nathan A. Slaton,Peter A. Vadas,Rodney T. Venterea,Jeffrey J. Volenec,Claudia Wagner‐Riddle
出处
期刊:Soil Science Society of America Journal [Wiley]
卷期号:89 (1)
标识
DOI:10.1002/saj2.20787
摘要

Abstract There are many fertilizer additives and alternatives that aim to increase plant nutrient use efficiency and reduce nutrient losses to the environment, here referred to collectively as enhanced efficiency fertilizers (EEFs). However, there is often insufficient published scientific field trial results across a variety of locations, climates, soils, cropping systems, and management scenarios to prove their efficacy and conditions for use. Guidelines for common minimum datasets and data stewardship in evaluating the agronomic performance and environmental impact of EEFs are needed for researchers to follow. Such guidelines will improve hypothesis testing centered on product efficacy and provide producers with guidance on how these technologies function and perform when integrated with other management practices within the 4R Nutrient Stewardship Framework. A scientific committee was formed to develop a set of protocol guidelines for evaluating EEFs in replicated, plot‐based field trials on an international scale. The guidelines are composed of experimental design and core metadata, crop and soil analyses, environmental loss measurements, and data stewardship, and include both recommended and required components to allow for flexibility and adaptability depending on the trial location, objectives, infrastructure capacity, product type, and depth of understanding of the potential EEF efficacy. This approach will ensure consistency and compatibility in experimental design and data collection to support data integration, analysis, and reuse leading to large‐scale impact and end‐user confidence.

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