New algorithm for pearl millet modelling in APSIM allowing a mechanistic simulation of tillers
Garin, V.; Van Oosterom, E.; McLean, G.; Hammer, G.; Murugesan, T.; Kaliamoorthy, S.; Diancumba, M.; Hajjarpoor, A.; Kholova, J.
Show abstract
We present a new algorithm for pearl millet simulation in APSIM. Compared to the actual released model, this new model increases the ability to simulate dynamic tillers by integrating recent progresses about biological understanding of the tillering mechanism. The new algorithm also offers the possibility to have an increased genetic control over key functions like canopy development and tillering through additional genotype related parameters. Next to model description, we also present the parametrization of 9444 and HHB 67-2, two genotypes broadly used in India. Overall, we could show that the new algorithm is able to reconstruct the main plant function like biomass accumulation and tillering. Some margin of improvement remains concerning the simulation of tiller cessation.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- An integrative process-based model for biomass and yield estimation of hardneck garlic (Allium sativum) 98%
- Comparative analysis of machine learning and evolutionary optimization algorithms for precision tissue culture of Cannabis sativa: Prediction and validation of in vitro shoot growth and development based on the optimization of light and carbohydrate sources 96%
- Identification of QTL hotspots affecting agronomic traits and high-throughput vegetation indices in rainfed wheat 94%
Similar papers in this journal
- Development of a model estimating root length density from root impacts on a soil profile in pearl millet (Pennisetum glaucum (L.) R. Br). Application to measure root system response to water stress in field conditions 96%
- Single and Multi-trait Genomic Prediction for agronomic traits in Euterpe edulis 96%
- Transpiration efficiency variations in the pearl millet reference collection PMiGAP 96%
Similar papers in this journal
- Incorporating A Dynamic Gene-Based Process Module Into A Crop Simulation Model 96%
- CRONOSOJA: a daily time-step hierarchical model predicting soybean development across maturity groups in the Southern Cone 95%
- AraRoot - A Comprehensive Genome-Scale Metabolic Model for the Arabidopsis Root System. 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.