Dynamical climatic model for time to flowering in Vigna radiata

Abstract

Background Phenology data collected recently for about 300 accessions of Vigna radiata (mungbean) is an invaluable resource for investigation of impacts of climatic factors on plant development. Results We developed a new mathematical model that describes the dynamic control of time to flowering by daily values of maximal and minimal temperature, precipitation, day length and solar radiation. We obtained model parameters by adaptation to the available experimental data. The models were validated by cross-validation and used to demonstrate that the phenology of adaptive traits, like flowering time, is strongly predicted not only by local environmental factors but also by plant geographic origin and genotype. Conclusions Of local environmental factors maximal temperature appeared to be the most critical factor determining how faithfully the model describes the data. The models were applied to forecast time to flowering of accessions grown in Taiwan in future years 2020-2030.

Modeling of Flowering Time in Vigna radiata with Approximate Bayesian Computation

“…The advantages of our model are the automatically constructed analytic form of functional dependence and the presence of the genotype-to-climatic factors interactions. In comparison to dynamical models developed earlier [43], the formulae ( 6) is more complex. That is because the data on accessions with long time to flowering was included into the dataset.…”

“…It fits into crop rotations due to a short duration cycle. One setting where mungbean fits well is in rotation with winter wheat, which is often harvested late in spring, leaving only a short summer season for mungbean; short duration is particularly critical to set seed before cool weather begins in this role [43]. Development of new varieties adapted to different conditions is necessary to meet the needs of growing global demand.…”

“…In this work, we built a new dynamical model for time to flowering in 1596 accessions of Vigna radiata phenotyped during six field trials in different years and locations. Developing on our previous studies for chickpea [40][41][42] and mungbean [43], a new approach was proposed that uses Approximate Bayesian Computation with Differential Evolution [44,45] to construct a pool of models for time to flowering. The models in the pool use daily values of climatic factors and GWAS results for genetic information and genotype-by-environment interactions to model a daily progression rate from sowing to flowering.…”

Solution of Mixed-Integer Optimization Problems in Bioinformatics with Differential Evolution Method

“…The control parameters G max = 300, NP = 200, Ξ = 5, Ψ = 2 and Θ = 24 were chosen to be the same as those used in the successful previous applications of the method [32,35]. The objective function Q used in the evaluation step in Algorithm 1 measures the deviation of the model solution from provided experimental data.…”

“…In this work, we propose a modification of the differential evolution entirely parallel (DEEP) method introduced recently [29,30]. DEEP was successfully applied in mixedinteger optimization problems, such as the identification of the biogeographic origins of highly mixed individuals implemented in the online ancestry prediction tool reAdmix [31], regression and dynamical models of flowering time in chickpea [32][33][34] and mungbean [35]. The DEEP method operates on real-valued parameters that can be converted to integers, using either rounding operation or array index transformation in which the resulting integers are the indices in the sorted array of real parameters.…”