Peter the Great St. Petersburg Polytechnic University, 29 Polytechnicheskaya, St. Petersburg, 195251, Russia.
Program Molecular and Computation Biology, University of California, University Park, Los-Angeles, 24105, CA, USA.
BMC Plant Biol. 2019 Mar 19;19(Suppl 2):94. doi: 10.1186/s12870-019-1685-2.
Accurate prediction of crop flowering time is required for reaching maximal farm efficiency. Several models developed to accomplish this goal are based on deep knowledge of plant phenology, requiring large investment for every individual crop or new variety. Mathematical modeling can be used to make better use of more shallow data and to extract information from it with higher efficiency. Cultivars of chickpea, Cicer arietanum, are currently being improved by introgressing wild C. reticulatum biodiversity with very different flowering time requirements. More understanding is required for how flowering time will depend on environmental conditions in these cultivars developed by introgression of wild alleles.
We built a novel model for flowering time of wild chickpeas collected at 21 different sites in Turkey and grown in 4 distinct environmental conditions over several different years and seasons. We propose a general approach, in which the analytic forms of dependence of flowering time on climatic parameters, their regression coefficients, and a set of predictors are inferred automatically by stochastic minimization of the deviation of the model output from data. By using a combination of Grammatical Evolution and Differential Evolution Entirely Parallel method, we have identified a model that reflects the influence of effects of day length, temperature, humidity and precipitation and has a coefficient of determination of R=0.97.
We used our model to test two important hypotheses. We propose that chickpea phenology may be strongly predicted by accession geographic origin, as well as local environmental conditions at the site of growth. Indeed, the site of origin-by-growth environment interaction accounts for about 14.7% of variation in time period from sowing to flowering. Secondly, as the adaptation to specific environments is blueprinted in genomes, the effects of genes on flowering time may be conditioned on environmental factors. Genotype-by-environment interaction accounts for about 17.2% of overall variation in flowering time. We also identified several genomic markers associated with different reactions to climatic factor changes. Our methodology is general and can be further applied to extend existing crop models, especially when phenological information is limited.
为了达到最高的农场效率，需要准确预测作物的开花时间。为了实现这一目标，已经开发了几种基于植物物候学的深入知识的模型，这需要对每个作物或新品种进行大量投资。数学建模可以更好地利用更浅层的数据，并以更高的效率从中提取信息。鹰嘴豆，Cicer arietanum 的栽培品种正在通过与具有非常不同开花时间要求的野生 C. reticulatum 生物多样性杂交来改良。需要更多地了解这些通过野生等位基因导入杂交而开发的品种的开花时间将如何取决于环境条件。
我们为在土耳其的 21 个不同地点收集的野生鹰嘴豆建立了一个新的开花时间模型，这些野生鹰嘴豆在 4 个不同的环境条件下，经过多年和多个季节的生长。我们提出了一种通用方法，通过随机最小化模型输出与数据的偏差，自动推断开花时间对气候参数的依赖的解析形式、其回归系数和一组预测因子。通过使用语法进化和差分进化完全并行方法的组合，我们确定了一个反映日照长度、温度、湿度和降水影响的模型，其决定系数为 R=0.97。
我们使用我们的模型来检验两个重要的假设。我们提出，鹰嘴豆物候学可以通过入口的地理起源以及生长地点的当地环境条件来进行强烈预测。事实上，起源地点-生长环境的相互作用占播种到开花时间的 14.7%左右。其次，由于对特定环境的适应在基因组中被描绘出来，基因对开花时间的影响可能取决于环境因素。基因型-环境的相互作用占开花时间总变化的 17.2%左右。我们还确定了与对气候因子变化的不同反应相关的几个基因组标记。我们的方法是通用的，可以进一步应用于扩展现有的作物模型，特别是在物候学信息有限的情况下。
