Biophysical modelling to support breeding

This line refers to the development and use of simulation tools to support breeding activities.

In silico ideotyping
This technology is based (i) on the use of global sensitivity analysis techniques to identify the most relevant traits (and their values) under specific environmental and management conditions (traits on which focusing on within breeding programs), and (ii) on the evaluation of the performances of the plant types resulting from the combination of those traits (ideotypes) under a variety of conditions (including different environments, management systems, climate change scenarios).

In silico phenotyping: extending the potential of genomic prediction
The rationale behind this technology is (i) using biophysical models to decompose complex traits in simple ones (corresponding - to a large extent - to model parameters), (ii) calibrating those parameters using data from phenotyping trials, (iii) deriving relationships between genomic data (SNPs) and model parameters, (iv) using biophysical models to simulate G x E x M interaction, thus to predict the phenotype of accessions (even new ones) under a variety of conditions (again, including different environments, management systems, climate change scenarios).

References:

Paleari, L., Movedi, E., Cappelli, G., Wilson, L.T., Confalonieri, R., 2017. Surfing parameter hyperspaces under climate change scenarios to design future rice ideotypes. Global Change Biology, 23, 4651-4662.

Paleari, L., Movedi, E., Confalonieri, R., 2017. Trait-based model development to support breeding programs. A case study for salt tolerance and rice. Scientific Reports, 7:4352, doi:10.1038/s41598-017-04022-y.

Paleari, L., Bregaglio, S., Cappelli, G., Movedi, E., Confalonieri, R., 2016. ISIde: a rice modelling platform for in silico ideotyping. Computers and Electronics in Agriculture, 128, 46-49.

Paleari, L., Cappelli, G., Bregaglio, S., Acutis, M., Donatelli, M., Sacchi, G.A., Lupotto, E., Boschetti, M., Manfron, G., Confalonieri, R., 2015. District specific, in silico evaluation of rice ideotypes improved for resistance/tolerance traits to biotic and abiotic stressors under climate change scenarios. Climatic Change, 132, 661-675.

Confalonieri, R., Bregaglio, S., Cappelli, G., Francone, C., Carpani, M., Acutis, M., El Aydam, M., Niemeyer, S., Balaghi, R., Dong, Q., 2013. Wheat modelling in Morocco unexpectedly reveals predominance of photosynthesis versus leaf area expansion plant traits. Agronomy for Sustainable Development, 33, 393-403.