Biology subjects

Rotundo, J.

Publications and source records attributed to Rotundo, J..

3 recordsLinked to original sources

bioRxiv2026

Ectopic expression of ARGOS8 mediates silk elongation rate response to water deficit in maize (Zea mays L.)

Water deficit is ubiquitous in maize (Zea mays L.) cropping systems worldwide. Ethylene insensitivity in maize has been implicated in improving kernel set and yield under drought, and ARGOS genes modulate ethylene signal transduction by reducing ethylene sensitivity. Given the strong water sensitivity of silk elongation, ARGOS8 overexpression is expected to alter silk growth responses to drought. Experiments were conducted under controlled and field conditions to test the effect of ARGOS8 gene overexpression on silk growth under water deficit. Silk lengths and water use were continuously monitored, and silk elongation rate (SER) response to the fraction of transpirable soil water (FTSW) was evaluated. Silk emergence dynamics were measured in the field by daily counting the silks under contrasting water regimes. ARGOS8 transgenics maintained SER at lower FTSW than controls; however, responses varied among hybrids. Higher SER under water stress translated into a faster silk exertion rate in ARGOS8 transgenics than controls (45 vs. 25 silks d-1), leading to a greater number of exerted silks at three days post-silking (424 vs. 377, [~]90% vs. 84% of total silks, p < 0.01). Together, these results help clarify the mechanism underlying the ectopically expressed ARGOS8 effect on maize yield improvement under water stress. HighlightARGOS8 transgenic expression sustains silk elongation rates and increases silk emergence under water deficit, improving the reproductive performance of maize in water-limited environments.

plant biology↗

bioRxiv2020

Two decades of creating drought tolerant maize and underpinning prediction technologies in the US corn-belt: Review and perspectives on the future of crop design

Over the last decade, society witnessed the largest expansion of agricultural land planted with drought tolerant (DT) maize (Zea mays L.) Dedicated efforts to drought breeding led to development of DT maize. Here we show that after two decades of sustained breeding efforts the rate of crop improvement under drought is in the range 1.0-1.6% yr-1, which is higher than rates (0.7% yr-1) reported prior to drought breeding. Prediction technologies that leverage biological understanding and statistical learning to improve upon the quantitative genetics framework will further accelerate genetic gain. A review of published and unpublished analyses conducted on data including 138 breeding populations and 93 environments between 2009 and 2019 demonstrated an average prediction skill (r) improvement around 0.2. These methods applied to pre-commercial stages showed accuracies higher that current statistical approaches (0.85 vs. 0.70). Improvement in hybrid and management choice can increase water productivity. Digital gap analyses are applicable at field scale suggesting the possibility of transition from evaluating hybrids to designing genotype x management (GxM) technologies for target cropping systems in drought prone areas. Due to the biocomplexity of drought, research and development efforts should be sustained to advance knowledge and iteratively improve models. HighlightCrop improvement rate in maize increased after implementation of drought breeding efforts. Harnessing crop, quantitative genetics and gap models will enable the transition from genetic evaluation to crop design.

plant biology↗

bioRxiv2020

Intercontinental prediction of soybean phenology via hybrid ensemble of knowledge-based and data-driven models

The timing of crop development has significant impacts on management decisions and subsequent yield formation. A large intercontinental dataset recording the timing of soybean developmental stages was used to establish ensembling approaches that leverage both discrete-time dynamical system models of soybean phenology and data-driven, machine-learned models to achieve accurate and interpretable predictions. We demonstrate that the knowledge-based, dynamical models can improve machine learning by generating expert-engineered features. Combining the predictions of the diverse component models via super learning resulted in a mean absolute error of 4.12 and 4.55 days to flowering (R1) and physiological maturity (R7), providing an improvement relative to the best benchmark model error of 6.90 and 15.47 days, respectively. The hybrid intercontinental model applies to a much wider range of management and temperature conditions than previous mechanistic models, enabling improved decision support as alternative cropping systems arise, farm sizes increase, and changes in the global climate continue to accelerate.

plant biology↗