Fig 1.
Conceptual framework of biological process-based crop modeling: genotype-by-environment interactions unfold as environmental inputs drive physiological processes governed by genetic-based coefficients.
Source: Original diagram created by the author using AI-assisted tools.
Fig 2.
Genetic coefficients in CERES-Rice organized by physiological function: phenological development (P1, P2O, P2R, P5), source-sink partitioning (PHINT, G1, G2, G3), and thermal stress thresholds (THOT, TCLDP, TCLDF).
Parameter interdependencies create a high-dimensional optimization landscape where sensitivity analysis guides parameter selection for genetic algorithm exploration. Source: Original diagram created by the author using AI-assisted tools. Microscopy images used for illustration are reprinted from Fan et al. (2023) [31], Liu et al. (2016) [32], and Yang et al. (2025) [33] under a CC BY license, with permission from the respective publishers, original copyright 2023, 2016, and 2025. All files are freely available online for use, distribution, and reproduction with proper attribution.
Table 1.
Environmental inputs and phenotypic outputs for process-based modeling. Soil, climate, and crop parameters used in the crop growth model.
Fig 3.
Environmental categories in the Casamance and Eastern Senegal region.
Base map layers (coastlines and administrative boundaries) were derived from publicly available datasets distributed with MATLAB Mapping Toolbox and are compatible with CC BY 4.0 licensing. Environmental classification and data layers were adapted from Correa et al. (2025), published under a CC BY license [25]. Map generated using MATLAB R2024b with Mapping Toolbox and Image Processing Toolbox.
Fig 4.
Morris method sampling strategy for CERES-Rice sensitivity analysis.
a) Trajectory generation in parameter space (illustrated for dimensions); consecutive points differ by one parameter, enabling elementary effect computation. b) Step size () and bounds for each of the 11 physiological genetic-based coefficients, defined from ranges reported for rice cultivars.
Fig 5.
Genetic crop growth coefficients for 21 rice cultivars characterized through rainfed field experiments.
Cultivars are classified by genetic group: indica (green), japonica (blue), and hybrids (orange). These field-validated parameters serve as the reference panel for similarity analysis against computationally optimized ideotypes.
Fig 6.
Sensitivity analysis reveals hierarchical parameter control over crop performance.
Phenological parameters (P1, PHINT) dominate developmental timing and biomass accumulation, while reproductive parameters (G3, G2) govern yield component formation. Thermal stress parameters (TCLDP, TCLDF) exhibit negligible sensitivity under the studied conditions. Relative Sensitivity Index (RSI) for CERES-Rice model outputs: a) Biomass, b) Grain Yield, c) Number of Grains, d) Number of Tillers, e) Anthesis, f) Maturity. Values represent means across 20 independent replications; complete statistics with 95% CI in S1 Table.
Fig 7.
Phenotypic landscape explored by genetic algorithm optimization across four contrasting environments.
Each point represents a virtual cultivar evaluated through CERES-Rice simulation (n = 5,364). The HI-WUE index demonstrates strong correlation with grain yield (R² = 0.78–0.86, p 0.001), harvest index (R² = 0.88–0.97, p 0.001), and water use efficiency (R² = 0.86–0.93, p 0.001). Biomass, root architecture, and phenological timing exhibit negligible correlation (R² 0.08), revealing that efficiency optimization operates through assimilate partitioning rather than source accumulation. Complete correlation statistics in S2 Table (Supplementary Material).
Fig 8.
Optimized ideotype performance across four environments characterized by the intersection of soil water retention (high to low) and precipitation regime (815 mm in the south vs. 540 mm in the north).
The optimization identified two distinct adaptive strategies: extended growth exploiting favorable conditions (Env 1) and drought-escape phenology under water limitation (Env 2–4). Reproductive parameters remained stable across environments while phenological coefficients required environment-specific tuning—consistent with the hierarchical parameter control identified through sensitivity analysis. Complete genetic coefficients are reported in S3 Table (Supplementary Material). Base map layers (coastlines and administrative boundaries) were derived from publicly available datasets distributed with MATLAB Mapping Toolbox and are compatible with CC BY 4.0 licensing. Environmental classification and data layers were adapted from Correa et al. (2025), published under a CC BY license [25]. Map generated using MATLAB R2024b with Mapping Toolbox and Image Processing Toolbox.
Fig 9.
Genetic similarity between optimized ideotypes and field-validated cultivars.
(a) PCA of eight genetic coefficients (85.8% variance explained). Dotted lines connect ideotypes (ID1–ID4) to their nearest cultivars. Colors indicate genetic groups: indica (green), japonica (blue), hybrids (orange), and ideotypes (red). (b) Top cultivar matches ranked by similarity (solid bars) and frequency across metrics (striped bars). Bar color shows predominance across ideotypes (4: purple to 1: white). WAB56−50 and DKAP2 are the most promising breeding candidates.
Fig 10.
Similarity heatmap between optimized ideotypes and field-validated cultivars.
Each cell represents the average similarity (%) computed from Euclidean, Manhattan, and Cosine distance metrics. Values above 50% are displayed in black text, below 50% in white. Red boxes indicate the top five cultivars with highest global similarity: WAB56−50 (70.7%), DKAP2 (67.2%), NERICA17 (66.6%), WABC165 (64.1%), and RD 23 (63.4%). Bold cultivar names denote high-affinity candidates.
Fig 11.
Genetic crop growth parameters of optimized ideotypes and highest-affinity cultivars.
Comparison of the eight genetic coefficients (P1, P5, P2R, PHINT, P2O, G1, G2, G3) between the four optimized ideotypes (ID1–ID4) and the top field-validated cultivars identified through similarity analysis: WAB56-50, DKAP2, NERICA17, RD 23, and WABC165. Parameter values are normalized for visual comparison.