Supplementary Code and Data for: Carbon Footprint Uncertainty and Antioxidant–Carbon Efficiency of Soilless and Non-Chemical Kale Production Systems

This repository contains the analysis code and extracted data supporting the manuscript "Carbon Footprint Uncertainty and Antioxidant–Carbon Efficiency of Soilless and Non-Chemical Kale Production Systems," submitted to Agriculture & Food Security. This is a secondary re-analysis of a previously published dataset comparing kale (Brassica oleracea L.) grown under a plant factory with artificial lighting (PFAL) system and a non-chemical farming (N-CF) system, originally reported in: Hatongkham W, Sranacharoenpong K, Suwanmanee U. Comparing of nutritional and environmental aspects of soilless and nonchemical farming food production systems. J Sustain Agric Environ. 2025;4:e70060. https://doi.org/10.1002/sae2.70060 Contents:- monte_carlo_cf_acer.py — Python script implementing the Monte Carlo uncertainty analysis of the carbon footprint, the pedigree-matrix sensitivity analysis, the convergence check, and the Antioxidant–Carbon Efficiency Ratio (ACER) calculations reported in the manuscript.- data/ — the raw life-cycle inventory workbook (Sum_CFncpfal.xlsx) and nutritional/antioxidant replicate data (kale_nutrition.xlsx) extracted from the original study's records.- output/ — all tables and figures generated by the script, corresponding directly to the manuscript's Tables 1–5, Supplementary Table S1, and Figures 1–2, plus full per-system and per-parameter sensitivity results and convergence-check results referenced in the manuscript as "Additional file 1."- README.md — full documentation of the analysis pipeline, reproducibility notes (random seeds, package versions), and a known-discrepancy note regarding the N-CF carbon-footprint reconstruction. Reproducibility: all Monte Carlo simulations use fixed random seeds (Python 3, NumPy 2.4.4) and are fully reproducible by running monte_carlo_cf_acer.py against the provided data files. See README.md for full instructions. License: Code and data are made available under CC-BY 4.0. Please cite both this repository and the original dataset (Hatongkham et al., 2025, https://doi.org/10.1002/sae2.70060) when reusing.