Strengthening farmer-led experiments through agronomic and causal inference frameworks

Strengthening farmer-led experiments through agronomic and causal inference frameworks | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 June 2025 V1 Latest version Share on Strengthening farmer-led experiments through agronomic and causal inference frameworks Authors : Louis Longchamps 0000-0002-4761-6094 [email protected] , Phillip Lanza 0000-0002-5545-1744 , Alexander Yore 0000-0001-8642-1575 , Alicia McElwee 0000-0003-2849-9985 , Marcelo Chan Fu Wei 0000-0002-8242-8435 , Bernard Panneton , Daniel Buckley , Abdelkrim Lachgar , and Matthew Thomas 0000-0002-5331-6768 Authors Info & Affiliations https://doi.org/10.22541/au.175105563.31365924/v1 Published Agronomy Journal Version of record Peer review timeline 317 views 151 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study explores how scientists can support on-farm experiments using analytical methods that align with farmers’ endogenous learning processes. Four maize farmers across 10 site-years in New York participated in this study to evaluate the effectiveness of a nitrogen-fixing inoculant (NFI) applied with a reduced side-dress nitrogen rate. Farmers designed and implemented their own experiments using a range of layouts, including side-by-side comparisons and strip trials. Two analytical approaches were compared: a quantitative yield analysis using spatial regression, and a causal pathway analysis based on mechanistic steps informed by field sampling (e.g., qPCR detection of NFI organisms, nitrogen nutrition index, and yield). While yield data suggested positive or neutral treatment effects at all sites when simply comparing yield average, the spatial regression analysis and causal pathway analysis identified positive outcomes in only 7 or 4 of 10 site-years respectively, reflecting a more conservative interpretation of efficacy. Both methods provided consistent conclusions at 4 out of 10 site-years, demonstrating the contribution of metrics other than yield in the interpretation process. Findings suggest that simple causal diagrams can structure data collection and interpretation in ways aligned with farmers’ goals. Supporting farmer experiments with digital agronomy, mechanistic reasoning, and site-specific data enhances learning outcomes and scientific rigor without requiring formal replication. This work contributes to the development of collaborative, scalable methodologies that integrate farmer knowledge and scientific analysis in OFE. Supplementary Material File (aj-ofemanuscript_formated_final.docx) Download 5.32 MB File (table 1.docx) Download 15.68 KB File (table 2.docx) Download 16.04 KB File (table 3.docx) Download 17.07 KB Information & Authors Information Version history V1 Version 1 27 June 2025 Peer review timeline Published Agronomy Journal Version of Record 16 Dec 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords experiment design experimental approaches heterogeneity maize management nitrogen production agriculture soil microbiology spatial variability statistics Authors Affiliations Louis Longchamps 0000-0002-4761-6094 [email protected] Cornell University View all articles by this author Phillip Lanza 0000-0002-5545-1744 Cornell University View all articles by this author Alexander Yore 0000-0001-8642-1575 Cornell University View all articles by this author Alicia McElwee 0000-0003-2849-9985 Cornell University View all articles by this author Marcelo Chan Fu Wei 0000-0002-8242-8435 USP ESALQ View all articles by this author Bernard Panneton Independent Data Analysis Consultant View all articles by this author Daniel Buckley Cornell University View all articles by this author Abdelkrim Lachgar Cornell University View all articles by this author Matthew Thomas 0000-0002-5331-6768 Cornell University View all articles by this author Metrics & Citations Metrics Article Usage 317 views 151 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Louis Longchamps, Phillip Lanza, Alexander Yore, et al. Strengthening farmer-led experiments through agronomic and causal inference frameworks. Authorea . 27 June 2025. DOI: https://doi.org/10.22541/au.175105563.31365924/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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