Commercial horticultural variety trials and crop models: a data suitability assessment

Commercial horticultural variety trials and crop models: a data suitability assessment | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 9 February 2026 V1 Latest version Share on Commercial horticultural variety trials and crop models: a data suitability assessment Authors : Steven Doyle 0000-0003-4662-679X and Ankita Raturi 0000-0003-0637-8541 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177067529.92243845/v1 93 views 58 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Horticultural production systems are complex, driven by a combination of genetic, environmental, and management factors. Model-driven decision support tools may reduce this complexity, if suitable data to train and calibrate these crop models can be identified. Commercial variety trials may be a suitable data source. In this study, a systematic data suitability assessment of commercial pepper (Capsicum annuum L.), tomato (Solanum lycopersicum L.), and watermelon (Citrullus lanatus L.) variety trials was conducted for use with five crop models (AquaCrop, DSSAT, STICS, reduced order TOMGRO, reduced order de Koning) and a hypothetical machine learning yield prediction model. Using a novel data suitability assessment methodology, variables from a corpus of variety trial reports and raw data were evaluated against model-variable requirements using three suitability criteria: variable availability in the corpus, spatiotemporal resolution compatibility, and variable criticality for model functioning. Variety trial data were found suitable for only the machine learning yield prediction model. Unsuitability was driven by the low availability of temporally dynamic structural characteristic variables, which often required destructive sampling. However, many potentially measurable variables were cumulatively laborious and beyond trial scope. It is therefore recommended that variety trial data be used only with machine learning yield prediction models until practitioners of theory-informed modeling can collaborate with variety trial operators to develop crop models with reasonable variable requirements. It is also recommended that variety trial operators supply raw data when possible and make use of reporting systems that minimize the burden of data entry because raw data contains crucial modeling variables. Supplementary Material File (manuscript_doyle_and_raturi.docx) Download 4.50 MB Information & Authors Information Version history V1 Version 1 09 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords agricultural informatics crop models data assimilation data suitability vegetable crops Authors Affiliations Steven Doyle 0000-0003-4662-679X Purdue University View all articles by this author Ankita Raturi 0000-0003-0637-8541 [email protected] Purdue University View all articles by this author Metrics & Citations Metrics Article Usage 93 views 58 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Steven Doyle, Ankita Raturi. Commercial horticultural variety trials and crop models: a data suitability assessment. Authorea . 09 February 2026. DOI: https://doi.org/10.22541/au.177067529.92243845/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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