Special Issue:
园艺-分子生物合辑Horticulture — Genetics · Breeding
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What are the differences in yield formation among two cucumber (Cucumis sativus L.) cultivars and their F1 hybrid? |
WANG Xiu-juan1, 2, KANG Meng-zhen1, 3, FAN Xing-rong4, YANG Li-li5, ZHANG Bao-gui6, HUANG San-wen7, Philippe DE REFFYE8, WANG Fei-yue1, 9 |
1 The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P.R.China
2 Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, P.R.China
3 Innovation Center for Parallel Agriculture, Qingdao Academy of Intelligent Industries, Qingdao 266109, P.R.China
4 School of Computer Science and Information Engineering, Chongqing Technology and Business University, Chongqing 400067, P.R.China
5 College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, P.R.China
6 College of Land Science and Technology, China Agricultural University, Beijing 100193, P.R.China
7 Agricultural Genomes Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, P.R.China
8 AMAP, University Montpellier, CIRAD, CNRS, INRA, IRD, Montpellier 34000, France
9 The School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100049, P.R.China |
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Abstract To elucidate the mechanisms underlying the differences in yield formation among two parents (P1 and P2) and their F1 hybrid of cucumber, biomass production and whole source–sink dynamics were analyzed using a functional–structural plant model (FSPM) that simulates both the number and size of individual organs. Observations of plant development and organ biomass were recorded throughout the growth periods of the plants. The GreenLab Model was used to analyze the differences in fruit setting, organ expansion, biomass production and biomass allocation. The source–sink parameters were estimated from the experimental measurements. Moreover, a particle swarm optimization algorithm (PSO) was applied to analyze whether the fruit setting is related to the source–sink ratio. The results showed that the internal source–sink ratio increased in the vegetative stage and reached a peak until the first fruit setting. The high yield of hybrid F1 is the compound result of both fruit setting and the internal source–sink ratio. The optimization results also revealed that the incremental changes in fruit weight result from the increases in sink strength and proportion of plant biomass allocation for fruits. The model-aided analysis revealed that heterosis is a result of a delicate compromise between fruit setting and fruit sink strength. The organ-level model may provide a computational approach to define the target of breeding by combination with a genetic model.
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Received: 30 January 2019
Accepted:
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Fund: This work was supported by the National Natural Science Foundation of China (31700315 and 61533019), the Natural Science Foundation of Chongqing, China (cstc2018jcyjAX0587) and the Chinese Academy of Science (CAS)–Thailand National Science and Technology Development Agency (NSTDA) Joint Research Program (GJHZ2076). |
Corresponding Authors:
Correspondence KANG Meng-zhen, Tel: +86-10-82544776, Fax: +86-10-82544799, E-mail: mengzhen.kang@ia.ac.cn
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Cite this article:
WANG Xiu-juan, KANG Meng-zhen, FAN Xing-rong, YANG Li-li, ZHANG Bao-gui, HUANG San-wen, Philippe DE REFFYE, WANG Fei-yue.
2020.
What are the differences in yield formation among two cucumber (Cucumis sativus L.) cultivars and their F1 hybrid?. Journal of Integrative Agriculture, 19(7): 1789-1801.
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Allen R G, Pereira L S, Raes D, Smith M. 1998. Crop Evapotranspiration - Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper No. 56. Food and Agriculture Organization, Rome.
Birchler J A. 2015. Heterosis: The genetic basis of hybrid vigour. Nature Plants, 1, 15020.
Chew Y H, Seaton D D, Millar A J. 2017. Multi-scale modelling to synergise plant systems biology and crop science. Field Crops Research, 202, 77–83.
Christophe A, Letort V, Hummel I, Cournède P H, de Reffye P, Lecoeur J. 2008. A model-based analysis of the dynamics of carbon balance at the whole-plant level in Arabidopsis thaliana. Functional Plant Biology, 35, 1147–1162.
Falster D S, Westoby M. 2003. Leaf size and angle vary widely across species: What consequences for light interception? New Phytologist, 158, 509–525.
Fan X R, Kang M Z, Heuvelink E, de Reffye P, Hu B G. 2015. A knowledge-and-data-driven modeling approach for simulating plant growth: A case study on tomato growth. Ecological Modelling, 312, 363–373.
Ghaderi A, Lower R L. 1978. Heterosis and phenotypic stablility of F1 hybrids in cucumber under controlled environment. Journal of the American Society for Horticultural Science, 103, 275–278.
Guo Y, Ma Y T, Zhan Z G, Li B G. 2006. Parameter optimization and field validation of the functional-structural model GREENLAB for maize. Annals of Botany, 97, 217–230.
Hammer G L, Chapman S, van Oosterom E, Podlich D W. 2005. Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems. Australian Journal of Agricultural Research, 56, 947–960.
Hayes H K, Jones D F. 1916. First generation crosses in cucumbers. In: Report of the Connecticut Agricultural Experiment Station. Connecticut Agricultural Experiment Station, United States. pp. 319–322.
Heuvelink E, Marcelis L F M, Bakker M J, van der Ploeg A. 2007. Use of crop growth models to evaluate physiological traits in genotypes of horticultural crops. In: Spiertz J H J, Struik P C, Van Laar H H, eds., Scale and Complexity in Plant Systems Research: Gene-Plant-Crop Relations. Dordrecht, Dordrecht. pp. 223–233.
Kang M Z, Heuvelink E, Carvalho S M P, de Reffye P. 2012. A virtual plant that responds to the environment like a real one: The case for chrysanthemum. New Phytologist, 195, 384–395.
Kang M Z, Yang L L, Zhang B G, de Reffye P. 2011. Correlation between dynamic tomato fruit-set and source–sink ratio: A common relationship for different plant densities and seasons? Annals of Botany, 107, 805–815.
Letort V, Mahe P, Cournede P H, de Reffye P, Courtois B. 2008. Quantitative genetics and functional–structural plant growth models: Simulation of quantitative trait loci detection for model parameters and application to potential yield optimization. Annals of Botany, 101, 1243–1254.
Ma Y T, Wen M P, Guo Y, Li B G, Cournède P H, de Reffye P. 2008. Parameter optimization and field validation of the functional–structural model GREENLAB for maize at different population densities. Annals of Botany, 101, 1185–1194.
Ma Y T, Wubs A M, Mathieu A, Heuvelink E, Zhu J Y, Hu B G, Cournède P H, de Reffye P. 2011. Simulation of fruit-set and trophic competition and optimization of yield advantages in six Capsicum cultivars using functional–structural plant modelling. Annals of Botany, 107, 793–803.
Marcelis L F M. 1992. The dynamics of growth and dry matter distribution in cucumber. Annals of Botany, 69, 487–492.
Marcelis L F M. 1994. A simulation model for dry matter partitioning in cucumber. Annals of Botany, 74, 43–52.
Marcelis L F M, Heuvelink E, Baan Hofman-Eijer L R, Den Bakker J, Xue L B. 2004. Flower and fruit abortion in sweet pepper in relation to source and sink strength. Journal of Experimental Botany, 55, 2261–2268.
Mathieu A, Cournède P H, Barthélémy D, de Reffye P. 2008. Rhythms and alternating patterns in plants as emergent properties of a model of interaction between development and functioning. Annals of Botany, 101, 1233–1242.
Mathieu A, Zhang B, Heuvelink E, Liu S J, Cournède P H, de Reffye P. 2007. Calibration of fruit cyclic patterns in cucumber plants as a function of source–sink ratio with the GreenLab model. In: The 5th International Workshop on Functional–Structural Plant Models. Napier, New Zealand. pp. 31–34.
Qi R, Ma Y T, Hu B G, de Reffye P, Cournède, P H. 2010. Optimization of source–sink dynamics in plant growth for ideotype breeding: A case study on maize. Computer and Electronics in Agriculture, 71, 96–105.
Sarlikioti V, de Visser P H B, Buck-Sorlin G H, Marcelis L F M. 2011. How plant architecture affects light absorption and photosynthesis in tomato: Towards an ideotype for plant architecture using a functional–structural plant model. Annals of Botany, 108, 1065–1073.
Seidel S J, Palosuo T, Thorburn P, Wallach D. 2018. Towards improved calibration of crop models - Where are we now and where should we go? European Journal of Agronomy, 94, 25–35.
Vavitsara M E, Sabatier S, Kang M Z, Ranarijaona H L T, Reffye P D. 2017. Yield analysis as a function of stochastic plant architecture: Case of Spilanthes acmella in the wet and dry season. Computers & Electronics in Agriculture, 138, 105–116.
Vile D, Garnier E, Shipley B, Laurent G, Navas M L, Roumet C, Lavorel S, DÍAz S, Hodgson J G, Lloret F, Midgley G F, Poorter H, Rutherford M C, Wilson P J, Wright L J. 2005. Specific leaf area and dry matter content estimate thickness in laminar leaves. Annals of Botany, 96, 1129–1136.
Vos J, Evers J B, Buck-Sorlin G H, Andrieu B, Chelle M, de Visser P H B. 2010. Functional–structural plant modelling: a new versatile tool in crop science. Journal of Experimental Botany, 61, 2101–2115.
Vos J, Marcelis L F M, Evers J B. 2007. Functional–structural plant modelling in crop production: Adding a dimension. In: Vos J, Marcelis L F M, de Visser P H B, Struik P C, Evers J B, eds., Functional–Structural Plant Modelling in Crop Production. Springer, Dordrecht. pp. 1–12.
Wright I J, Westoby M. 2002. Leaves at low versus high rainfall: Coordination of structure, lifespan and physiology. New Phytologist, 155, 403–416.
Wu L, Le Dimet F X, de Reffye P, Hu B G, Cournede P H, Kang M Z. 2012. An optimal control methodology for plant growth-case study of a water supply problem of sunflower. Mathematics and Computers in Simulation, 82, 909–923.
Wubs A M, Ma Y, Heuvelink E, Marcelis L F M. 2009. Genetic differences in fruit-set patterns are determined by differences in fruit sink strength and a source: Sink threshold for fruit set. Annals of Botany, 104, 957–964.
Xie F L, Zhang X C, Li F, Chen W Z. 2009. Research on Heredity trend of morphological charactersof tomato germplasm resources. Chinese Agricultural Science Bulletin, 25, 259–266.
Xu L F, Henke M, Zhu J, Kurth W, Buck-Sorlin G. 2011. A functional–structural model of rice linking quantitative genetic information with morphological development and physiological processes. Annals of Botany, 107, 817–828.
Yan H P, Barczi J F, de Reffye P, Hu B G, Jaeger M, Roux J L. 2002. Fast algorithms of plant computation based on substructure instances. In: International Conferences in Central Europe on Computer Graphics. Schloss Dagstuhl, Germany.
Yan H P, Kang M Z, de Reffye P, Dingkuhn M. 2004. A dynamic, architectural plant model simulating resource-dependent growth. Annals of Botany, 93, 591–602.
Yin X, Struik P C, Gu J, Wang H. 2016. Modelling QTL-trait-crop relationships: Past experiences and future prospects. In: Yin X, Struik P C, eds., Crop Systems Biology: Narrowing the Gaps Between Crop Modeling and Genetics. Springer International Publishing, Cham. pp. 193–218.
Zhan Z G, de Reffye P, Houllier F, Hu B G. 2003. Fitting a functional–structural growth model with plant architectural data. In: Proceedings PMA03: The First International Symposium on Plant Growth Modeling, Simulation, Visualization and their Applications. Tsinghua University Press, Springer, Beijing, China.
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