Dynamical climatic model for time to flowering in Vigna radiata.
Climatic factors
GWAS
Model
Mungbean
Vigna
Journal
BMC plant biology
ISSN: 1471-2229
Titre abrégé: BMC Plant Biol
Pays: England
ID NLM: 100967807
Informations de publication
Date de publication:
14 Oct 2020
14 Oct 2020
Historique:
received:
30
03
2020
accepted:
27
04
2020
entrez:
14
10
2020
pubmed:
15
10
2020
medline:
30
3
2021
Statut:
epublish
Résumé
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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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.
Identifiants
pubmed: 33050872
doi: 10.1186/s12870-020-02408-1
pii: 10.1186/s12870-020-02408-1
pmc: PMC7556928
doi:
Types de publication
Journal Article
Validation Study
Langues
eng
Sous-ensembles de citation
IM
Pagination
202Commentaires et corrections
Type : ErratumIn
Références
Dharajiya DT, Ravindrababu Y, Pagi NK. Screening of Mungbean [Vigna radiata (L,) Wilczek] Genotypes for Resistance against Mungbean Yellow Mosaic Virus (MYMV) under Field Condition. Intl J Curr Microbiol Appl Sci. 2018; 7(05):3472–83.
doi: 10.20546/ijcmas.2018.705.401
Vishnyakova MA, Burlyaeva MO, Samsonova MG. Green gram and black gram: prospects of cultivation and breeding in Russian Federation. Vavilov J Genet Breed. 2018; 22(8):957–66.
doi: 10.18699/VJ18.438
Burlyaeva M, Vishnyakova M, Gurkina M, Kozlov K, Lee C-R, Ting C-T, Schafleitner R, Nuzhdin S, Samsonova M, von Wettberg E. Collections of Mungbean [Vigna radiata) (L,) R. Wilczek] and urdbean [V. mungo (L.) Hepper] in Vavilov Institute (VIR): Traits diversity and trends in the breeding process over the last 100 years. Genet Resour Crop Evol. 2019.
Schafleitner R, Nair RM, Rathore A, Wang Y-w, Lin C-y, Chu S-h, Lin P-y, Chang J-C, Ebert AW. The AVRDC – The World Vegetable Center mungbean (Vigna radiata) core and mini core collections. BMC Genomics. 2015; 16(1):344.
doi: 10.1186/s12864-015-1556-7
Mondal MMA, Puteh AB, Malek MA, Ismail MR, Rafii MY, Latif MA. Seed Yield of Mungbean (Vignaradiata (L,) Wilczek) in relation to Growth and Developmental Aspects. Sci World J. 2012; 2012:1–7.
doi: 10.1100/2012/425168
Imrie BC, Drake DW, Delacy IH, Byth DE. Analysis of genotypic and environmental variation in international mungbean trials. Euphytica. 1981; 30(2):301–11.
doi: 10.1007/BF00033991
Swindell R, Poehlman JM. INHERITANCE OF PHOTOPERIOD RESPONSE (VIGNA RADIATA [L,] WILCZEK)~. Euphytica. 1978; 27:325–33.
doi: 10.1007/BF00039150
Ellis RH, Lawn RJ, Summerfield RJ, Qi A, Roberts EH, Chay PM, Brouwer JB, Rose JL, Yeates SJ, Sandover S. Towards the Reliable Prediction of Time to Flowering in Six Annual Crops, IV. Cultivated and Wild Mung Bean. Exp Agric. 1994; 30(1):31–43.
doi: 10.1017/S0014479700023826
Nath D, Dasgupta T. Genotype x Environment Interaction and Stability Analysis in Mungbean. IOSR J Agric Vet Sci. 2013; 5(1):62–70.
doi: 10.9790/2380-0516270
Godwin ID, Rutkoski J, Varshney RK, Hickey LT. Technological perspectives for plant breeding. Theor Appl Genet. 2019; 132(3):555–7.
doi: 10.1007/s00122-019-03321-4
Singh V, Yadav NR, Singh J. Role of Genomic tools for Mungbean [Vigna radiata (L,) Wilczek] improvement. Legum Res Int J. 2017.
Kim SK, Nair RM, Lee J, Lee S-H. Genomic resources in mungbean for future breeding programs. Front Plant Sci. 2015; 6.
Kang YJ, Kim SK, Kim MY, Lestari P, Kim KH, Ha B-K, Jun TH, Hwang WJ, Lee T, Lee J, Shim S, Yoon MY, Jang YE, Han KS, Taeprayoon P, Yoon N, Somta P, Tanya P, Kim KS, Gwag J-G, Moon J-K, Lee Y-H, Park B-S, Bombarely A, Doyle JJ, Jackson SA, Schafleitner R, Srinives P, Varshney RK, Lee S-H. Genome sequence of mungbean and insights into evolution within Vigna species. Nat Commun. 2014; 5(1):5443.
doi: 10.1038/ncomms6443
Boote KJ, Jones J, Pickering N. Potential Uses and Limitations of Crop Models. Agron J. 1996; 88:704–16.
doi: 10.2134/agronj1996.00021962008800050005x
Mabhaudhi T, Chibarabada TP, Chimonyo VGP, Modi AT. Modelling climate change impact: A case of bambara groundnut (Vigna subterranea). Phys Chemist Earth Parts A/B/C. 2018; 105:25–31.
doi: 10.1016/j.pce.2018.01.003
Chapman SC, Cooper M, Hammer GL, Butler DG. Genotype by environment interactions affecting grain sorghum. II, Frequencies of different seasonal patterns of drought stress are related to location effects on hybrid yields. Aust J Agric Res. 2000; 51(2):209.
doi: 10.1071/AR99021
Chauhan YS, Douglas C, Rachaputi RCN, Agius P, Martin W, Skerman A. Physiology of mungbean and development of the mungbean crop model. In: Proceedings. Gold Coast: 2010. p. 11.
Hwang C, Correll MJ, Gezan SA, Zhang L, Bhakta MS, Vallejos CE, Boote KJ, Clavijo-Michelangeli JA, Jones JW. Next generation crop models: A modular approach to model early vegetative and reproductive development of the common bean (Phaseolusvulgaris L). Agric Syst. 2017; 155:225–39.
doi: 10.1016/j.agsy.2016.10.010
Hatfield J, Walthall C. Meeting Global Food Needs: Realizing the Potential via Genetics x Environment x Management Interactions. Agron J. 2015; 107:1215–26.
doi: 10.2134/agronj15.0076
Tardieu F, Tuberosa R. Dissection and modelling of abiotic stress tolerance in plants. Curr Opinion Plant Biol. 2010; 13:206–12.
doi: 10.1016/j.pbi.2009.12.012
Kawecki TJ, Ebert D. Conceptual issues in local adaptation. Ecol Lett. 2004; 7(12):1225–41.
doi: 10.1111/j.1461-0248.2004.00684.x
Lasky JR, Upadhyaya HD, Ramu P, Deshpande S, Hash CT, Bonnette J, Juenger TE, Hyma K, Acharya C, Mitchell SE, Buckler ES, Brenton Z, Kresovich S, Morris GP. Genome-environment associations in sorghum landraces predict adaptive traits. Sci Adv. 2015; 1(6). http://arxiv.org/abs/http://advances.sciencemag.org/content/1/6/e1400218.full.pdf.
Lippert C, Listgarten J, Liu Y, Kadie CM, Davidson RI, Heckerman D. FaST linear mixed models for genome-wide association studies. Nat Methods. 2011; 8(10):833–5.
doi: 10.1038/nmeth.1681
Storey JD. The positive false discovery rate: A Bayesian interpretation and the q-value. Annals Stat. 2003; 31(6):2013–35.
doi: 10.1214/aos/1074290335
Stackhouse PW, Perez R, Sengupta M, Knapp K, Mikovitz JC, Schlemmer J, Scarino B, Zhang T, Cox SJ. An Assessment of New Satellite Data Products for the Development of a Long-term Global Solar Resource At 10-100 km. In: Proceedings of the Solar 2016 Conference. San Francisco: International Solar Energy Society: 2016. p. 1–6.
O’Neill M, Ryan C. Grammatical evolution. IEEE Trans Evol Comput. 2001; 5(4):349–58.
doi: 10.1109/4235.942529
Noorian F, de Silva AM, Leong PHW. gramEvol: Grammatical Evolution in R. J Stat Softw. 2016; 71(1):1–26.
doi: 10.18637/jss.v071.i01
Kozlov K, Samsonov A. DEEP – Differential Evolution Entirely Parallel Method for Gene Regulatory Networks. J Supercomput. 2011; 57:172–8.
doi: 10.1007/s11227-010-0390-6
Kozlov K, Samsonov AM, Samsonova M. A software for parameter optimization with Differential Evolution Entirely Parallel method. PeerJ Comput Sci. 2016; 2:74.
doi: 10.7717/peerj-cs.74
Kozlov KN, Novikova LY, Seferova IV, Samsonova MG. Mathematical model of soybean development dependence on climatic factors. Biofizika. 2018; 63(1):175–6.
Storn R, Price K. Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Technical Report Technical Report TR-95-012, ICSI. 1995.
Zaharie D. Parameter Adaptation in Differential Evolution by Controlling the Population Diversity In: Petcu D, editor. Proc. of 4th InternationalWorkshop on Symbolic and Numeric Algorithms for Scientific Computing, Seria Matematica-Informatica, vol. XL. Timisoara: Analele Universitatii Timisoara: 2002. p. 385–97.
Srinivasa Rao M, Swathi P, Rama Rao CA, Rao KV, Raju BMK, Srinivas K, Manimanjari D, Maheswari M. Model and Scenario Variations in Predicted Number of Generations of Spodoptera litura Fab, on Peanut during Future Climate Change Scenario. PLoS ONE. 2015; 10(2):0116762.
doi: 10.1371/journal.pone.0116762
Jones PG, Thornton PK. Spatial and temporal variability of rainfall related to a third-order Markov model. Agric Forest Meteorol. 1997; 86(1-2):127–38.
doi: 10.1016/S0168-1923(96)02399-4
Jones PG, Thornton PK. Fitting a third-order Markov rainfall model to interpolated climate surfaces. Agric Forest Meteorol. 1999; 97(3):213–31.
doi: 10.1016/S0168-1923(99)00067-2
Jones PG, Thornton PK. MarkSim: Software to Generate Daily Weather Data for Latin America and Africa. Agron J. 2000; 92:9.
doi: 10.2134/agronj2000.923445x
Jones PG, Jones AL, Centro Internacional de Agricultura Tropical. MarkSim: A Computer Tool That Generates Simulated Weather Data for Crop Modeling and Risk Assessment. CIAT. OCLC: 54981281. 2002.
van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK. The representative concentration pathways: An overview. Clim Chang. 2011; 109(1-2):5–31.
doi: 10.1007/s10584-011-0148-z
Abebe TD, Naz AA, Léon J. Landscape genomics reveal signatures of local adaptation in barley (Hordeum vulgare L.)Front Plant Sci. 2015; 6.
De Kort H, Vandepitte K, Bruun HH, Closset-Kopp D, Honnay O, Mergeay J. Landscape genomics and a common garden trial reveal adaptive differentiation to temperature across Europe in the tree species Alnusglutinosa. Mol Ecol. 2014; 23(19):4709–21.
doi: 10.1111/mec.12813
Dell’Acqua M, Zuccolo A, Tuna M, Gianfranceschi L, Pè M. Targeting environmental adaptation in the monocot model Brachypodium distachyon: A multi-faceted approach. BMC Genomics. 2014; 15(1):801.
doi: 10.1186/1471-2164-15-801
Westengen OT, Berg PR, Kent MP, Brysting AK. Spatial Structure and Climatic Adaptation in African Maize Revealed by Surveying SNP Diversity in Relation to Global Breeding and Landrace Panels. PLoS ONE. 2012; 7(10):47832.
doi: 10.1371/journal.pone.0047832