The study introduces a hybrid modeling framework that integrates a logistic ordinary differential equation with a Long Short-Term Memory neural network to form a Physics-Informed Neural Network (PINN) for predicting wheat plant height. Using only time and temperature as inputs, the PINN outperformed other longitudinal growth models, achieving the lowest average RMSE and reduced variability across multiple random initializations. The results suggest that embedding biological growth constraints within data‑driven models can substantially improve prediction accuracy for plant traits.

A comprehensive multi‑environment trial of 437 maize testcross hybrids derived from 38 MLN‑tolerant lines and 29 testers identified additive genetic effects as the primary driver of grain yield, disease resistance, and drought tolerance. Strong general combining ability and specific combining ability patterns were uncovered, with top hybrids delivering up to 5.75 t ha⁻¹ under MLN pressure while maintaining high performance under optimum and drought conditions. The study provides a framework for selecting elite parents and exploiting both additive and non‑additive effects to develop resilient maize hybrids for sub‑Saharan Africa.

The authors compiled and standardized published data on Rubisco dark inhibition for 157 flowering plant species, categorizing them into four inhibition levels and analyzing phylogenetic trends. Their meta‑analysis reveals a complex, uneven distribution of inhibition across taxa, suggesting underlying chloroplast microenvironment drivers and providing a new resource for future photosynthesis improvement efforts.