DOI: 10.5281/zenodo.17096240
title: Thermal-time NDVI reveals stay-green dynamics and yield determinants under contrasting nitrogen regimes in rice

description: 10.5281/zenodo.17096240

What this deposit contains--------------------------This package reproduces the analyses and figures reported in the manuscript. It includes R and Pythonscripts, curated input data, and a complete environment snapshot.

Folder layout-------------- Script_Software/  - Script_Rstudio/: NDVI_Anova_Descriptive_RLRT.R (α-lattice MET), NDVI_PCA_Corr_cluster.R, NDVI_regression.R, NDVI_blup.R  - Script_python/: NDVI_publication.ipynb, ndvi_pipeline_publication.py, requirements.txt, environment.yml, README.md- Dataset_Input_Output/  - dataset_Rstudio/: NDVI_anova_input_publication.csv, BLUP_ByENV_AdjustedMeans_SE.csv, BLUP_NDVI.csv  - dataset_python/: NDVI_ADD_phenology.xlsx, NDVI_Dates_DM.xlsx, NDVI_results_Tb10_*.xlsx- Reproducibility/: sessionInfo_R_complete.txt, project_packages_versions.csv, installed_packages.csv- LICENSE (MIT for code), LICENSE_DATA.txt (CC BY 4.0 for data/figures/tables)- checksums.txt, compute_checksums.R

How to rebuild (R)------------------1) Restore package versions:options(repos=c(CRAN="https://cran.rstudio.com"))if(!requireNamespace("remotes", quietly=TRUE)) install.packages("remotes")man <- read.csv("Reproducibility/project_packages_versions.csv", stringsAsFactors=FALSE)for(i in seq_len(nrow(man))){  remotes::install_version(man$Package[i], version=as.character(man$Version[i]),                           repos="https://cran.rstudio.com", upgrade="never")}2) From the Zenodo root, run:source("Script_Software/Script_Rstudio/NDVI_Anova_Descriptive_RLRT.R")source("Script_Software/Script_Rstudio/NDVI_PCA_Corr_cluster.R")source("Script_Software/Script_Rstudio/NDVI_regression.R")# Optional:source("Script_Software/Script_Rstudio/NDVI_blup.R")

How to rebuild (Python)-----------------------pip install -r Script_Software/Script_python/requirements.txt# orconda env create -f Script_Software/Script_python/environment.yml

Provenance & environment------------------------- R: see Reproducibility/sessionInfo_R_complete.txt (R 4.5.1, Windows 11, tz Asia/Calcutta, seed 123).- Traits/labels: SR (NDVI per °C·day), Stg (NDVI), AUC (NDVI·°C·day), Chl (µmol m⁻²; also referred to as CCI),  Yield (t ha⁻¹), DTH/DTA/DTM (days).

Integrity (checksums)---------------------R:  source("compute_checksums.R")       # regenerate checksums.txt  verify_checksums("checksums.txt")   # verifyPowerShell:  .\compute_checksums.ps1  .\compute_checksums.ps1 -Verify

ContactLukram Shantikumar

lukeshanti@gmail.comDepartment of Basic Sciences and Humanities,College of Horticulture and Forestry, CAU (Imphal),Pasighat, Arunachal Pradesh, 791102, India
description: Nitrogen (N) deficiency constrains rice productivity by accelerating canopy senescence and shortening grain-filling. We evaluated 234 Oryza sativa accessions across two wet seasons under control N (CN; 120 kg N ha⁻¹) and low N (LN; no added N). Weekly normalized difference vegetation index (NDVI) readings from heading to maturity were regressed on accumulated degree days (ADD; base 10 °C) to estimate the senescence rate (SR), NDVI at physiological maturity (Stg), and area under the NDVI–thermal time curve (AUC). Nitrogen and year significantly influenced senescence traits and grain yield (GY), while phenology was dominated by year effects. LN triggered an earlier NDVI decline, reduced Stg and GY, and shortened the regression window. SR appeared smaller under LN, not due to slower senescence, but because of an earlier NDVI decline. AUC remained comparatively stable across treatments. Yield showed positive correlations with Stg (r ≈ 0.12–0.33) and AUC (r ≈ 0.27–0.36), and negative with SR (r ≈ –0.24 to –0.31). Heritability was high (H² = 0.76–0.99), particularly for AUC (≥0.98). Stepwise models explained 31–36% of GY variance with cross-validated RMSEs of 0.81–1.15 t ha⁻¹, consistently selecting Stg/AUC while penalizing longer developmental durations. These NDVI–thermal time metrics capture meaningful stay-green variation without confounding crop duration and support ideotyping for low-input systems that combine early flowering with sustained greenness (high Stg, AUC). These scalable traits offer promise for high-throughput phenotyping and genomic selection to improve nitrogen use efficiency and yield stability under N-limited conditions.