Dataset Information

Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China.

ABSTRACT: Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) models, which are automatically parameterized with observed germination data. The seeds of five P. pratensis (Kentucky bluegrass, KB) cultivars were germinated under 36 day/night temperature regimes ranging from 5/5 to 40/40 °C with 5 °C increments. Results showed that optimal germination percentages of all five tested KB cultivars were observed under a fluctuating temperature regime of 20/25 °C. Meanwhile, the constant temperature regimes (e.g., 5/5, 10/10, 15/15 °C, etc.) suppressed the germination of all five cultivars. Furthermore, the back propagation artificial neural network (BP-ANN) algorithm was integrated to optimize temperature-germination response models from these observed germination data. It was found that integrations of GA-BP-ANN (back propagation aided genetic algorithm artificial neural network) significantly reduced the Root Mean Square Error (RMSE) values from 0.21~0.23 to 0.02~0.09. In an effort to provide a more reliable prediction of optimum sowing time for the tested KB cultivars in various regions in the country, the optimized GA-BP-ANN models were applied to map spatial and temporal germination percentages of blue grass cultivars in China. Our results demonstrate that the GA-BP-ANN model is a convenient and reliable option for constructing thermal-germination response models since it automates model parameterization and has excellent prediction accuracy.

SUBMITTER: Pi E

PROVIDER: S-EPMC4496032 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China.

Pi Erxu E Qu Liqun L Tang Xi X Peng Tingting T Jiang Bo B Guo Jiangfeng J Lu Hongfei H Du Liqun L

PloS one 20150708 7

Temperature is a predominant environmental factor affecting grass germination and distribution. Various thermal-germination models for prediction of grass seed germination have been reported, in which the relationship between temperature and germination were defined with kernel functions, such as quadratic or quintic function. However, their prediction accuracies warrant further improvements. The purpose of this study is to evaluate the relative prediction accuracies of genetic algorithm (GA) mo ...[more]

PMID: 26154163

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Project description:Continuous monoculture of cool-season turfgrass causes soil degradation, and visual turf quality decline is a major concern in black soil regions of Northeast China. Turf mixtures can enhance turfgrass resistance to biotic and abiotic stresses and increase soil microbial diversity. Understanding mechanism by plant-soil interactions and changes of black soil microbial communities in turf mixture is beneficial to restoring the degradation of urbanized black soils and maintaining sustainable development of urban landscape ecology. In this study, based on the previous research of different sowing models, two schemes of turf monoculture and mixture were conducted in field plots during 2016-2018 in a black soil of Heilongjiang province of Northeast China. The mixture turf was established by mixing 50% Kentucky bluegrass "Midnight" (Poa pratensis L.) with 50% Red fescue "Frigg" (Festuca rubra L.); and the monoculture turf was established by sowing with pure Kentucky bluegrass. Turf performance, soil physiochemical properties, and microbial composition from rhizosphere were investigated. Soil microbial communities and abundance were analyzed by Illumina MiSeq sequencing and quantitative PCR methods. Results showed that turfgrass quality, turfgrass biomass, soil organic matter (SOM), urease, alkaline phosphatase, invertase, and catalase activities increased in PF mixture, but disease percentage and soil pH decreased. The microbial diversity was also significantly enhanced under turf mixture model. The microbial community compositions were significantly different between the two schemes. Turf mixtures obviously increased the abundances of Beauveria, Lysobacter, Chryseolinea, and Gemmatimonas spp., while remarkably reduced the abundances of Myrothecium and Epicoccum spp. Redundancy analysis showed that the compositions of bacteria and fungi were related to edaphic parameters, such as SOM, pH, and enzyme activities. Since the increasing of turf quality, biomass, and disease resistance were highly correlated with the changes of soil physiochemical parameters and microbial communities in turf mixture, which suggested that turf mixture with two species (i.e., Kentucky blue grass and Red fescue) changed soil microbial communities and enhanced visual turfgrass qualities through positive plant-soil interactions by soil biota.

Project description:MADS-box genes play vital roles in multiple biological processes of plants growth and development, especially inflorescence development. In the present study, a comprehensive investigation into the identification and classification of MADS-box genes in Kentucky bluegrass (Poa pratensis) has not been reported. Here, based on the transcriptome of inflorescence, we identified 44 PpMADS-box genes, and gave an overview of the physicochemical properties, phylogeny, protein structures, and potential functions of the proteins encoded by these genes through various bioinformatics software for the first time. Analysis of physicochemical properties revealed that most PpMADS-box were alkaline proteins and possessed similar conserved motifs. Additionally, it was demonstrated that 33 PpMADS-box proteins without signal peptide, leading peptide, transmembrane structure and located in the nucleus were not transported or secreted, so directly played transcriptional regulatory roles in the nucleus. Then, peptide sequences BLAST search and analysis of phylogenetic relationships with MADS-box proteins of P. pratensis, Arabidopsis thaliana, and Oryza sativa were performed. It was found that 44 PpMADS-box proteins were separated into 33 MIKC-type (3 BS, 1 AGL17, 8 AP3/P2, 3 AP1, 5 SEP, 6 SOC and 7 AG genes, respectvely) and 11 type I-type, which include 7 Mγ and 4 Mα. Furthermore, the relative expression levels of the selected 12 genes (MADS3, 15, 16, 17, 18, 20, 24, 27, 30, 36, 38 and 40) at the booting stage, pre-anthesis, anthesis, post-anthesis, and seed filling stage of inflorescences, as well as leaves and roots of the corresponding stages of inflorescences were analyzed, showing that most PpMADS-box genes were highly expressed mainly in young leaves and later inflorescences, and had complex patters in roots. Morever, except for PpMADS30 being highly expressed in the leaves, others were significantly highly expressed in inflorescence and/ or roots, demonstrating PpMADS-box genes also regulate leaves and roots development in plant. This study provides valuable insights into the MADS-box family genes in Kentucky bluegrass and its potential functional characteristics, expression pattern, and evolution in floral organogenesis and even reproduction development. @media print { .ms-editor-squiggler { display:none !important; } } .ms-editor-squiggler { all: initial; display: block !important; height: 0px !important; width: 0px !important; }.Supplementary informationThe online version contains supplementary material available at 10.1007/s12298-022-01216-1.

Dataset Information

Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China.

Publications

Application of Genetic Algorithm to Predict Optimal Sowing Region and Timing for Kentucky Bluegrass in China.

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