AI-Driven Mechanistic Modeling of Biological Processes for Drought-Resilient Crop Design

AI-Driven Mechanistic Modeling of Biological Processes for Drought-Resilient Crop Design | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article AI-Driven Mechanistic Modeling of Biological Processes for Drought-Resilient Crop Design Francisco Calderon, Edgar S Correa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9040376/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Living systems respond to environmental constraints through multiscale interactions between genetic potential and physiological processes. Mechanistic modelling encodes this causality, and its strengths in calibration and prediction can be extended by workflows that interrogate model structure to support transfer across environments. This study contributes an integrated diagnosis–design framework that unifies: (i) satellite-driven characterization of environmental structure (terrain–soil–climate), (ii) mechanistic–data-driven benchmarking to quantify residual error after calibration, and (iii) bounded inverse engineering that explores phenotype space under physiological constraints and projects computational optima onto field-characterized cultivars. Using rainfed aerobic rice, 3D PCA–Gaussian mixture clustering identifies twelve environments from soil–climate attributes. Despite optimized calibration, CERES-Rice plateaus ( r ² = 0.049–0.200 ) and predicts water-use efficiency (WUE) magnitudes ( max ~0.24 kg ha⁻¹ mm⁻¹ ) inconsistent with drought-adapted aerobic ranges. An ARX/NARX benchmark reaches r ² = 0.99 for grain yield and r ² = 0.80 for biomass/grain number, recovering WUE in the aerobic range ( ~2–15 kg ha⁻¹ mm⁻¹ ) while remaining mechanistically opaque. This motivates probing mechanistic behaviour across the constraint space. Inverse engineering quantifies a 22–30% gap to optima and maps them onto a panel of 21 field-validated cultivars, highlighting WAB56-50 and DKAP2 as closest candidates. Agricultural Engineering AI-driven modeling Biological dynamics Crop adaptation Earth observation Mechanistic modeling Multiscale systems Phenotyping Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Mechanistic modelling encodes this causality, and its strengths in calibration and prediction can be extended by workflows that interrogate model structure to support transfer across environments. This study contributes an integrated diagnosis–design framework that unifies: (i) satellite-driven characterization of environmental structure (terrain–soil–climate), (ii) mechanistic–data-driven benchmarking to quantify residual error after calibration, and (iii) bounded inverse engineering that explores phenotype space under physiological constraints and projects computational optima onto field-characterized cultivars.\u003c/p\u003e\n\u003cp\u003eUsing rainfed aerobic rice, 3D PCA–Gaussian mixture clustering identifies twelve environments from soil–climate attributes. Despite optimized calibration, CERES-Rice plateaus (\u003cem\u003er\u003c/em\u003e\u003cstrong\u003e² = 0.049–0.200\u003c/strong\u003e) and predicts water-use efficiency (WUE) magnitudes (\u003cstrong\u003emax ~0.24 kg ha⁻¹ mm⁻¹\u003c/strong\u003e) inconsistent with drought-adapted aerobic ranges. An ARX/NARX benchmark reaches \u003cem\u003er\u003c/em\u003e\u003cstrong\u003e² = 0.99\u003c/strong\u003e for grain yield and \u003cem\u003er\u003c/em\u003e\u003cstrong\u003e² = 0.80\u003c/strong\u003e for biomass/grain number, recovering WUE in the aerobic range (\u003cstrong\u003e~2–15 kg ha⁻¹ mm⁻¹\u003c/strong\u003e) while remaining mechanistically opaque. This motivates probing mechanistic behaviour across the constraint space. 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