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Analyzing the Impact of Greenhouse Planting Strategy and Plant Architecture on Tomato Plant Physiology and Estimated Dry Matter

. 2022 ; 13 () : 828252. [epub] 20220215

Status PubMed-not-MEDLINE Language English Country Switzerland Media electronic-ecollection

Document type Journal Article

Determine the level of significance of planting strategy and plant architecture and how they affect plant physiology and dry matter accumulation within greenhouses is essential to actual greenhouse plant management and breeding. We thus analyzed four planting strategies (plant spacing, furrow distance, row orientation, planting pattern) and eight different plant architectural traits (internode length, leaf azimuth angle, leaf elevation angle, leaf length, leaflet curve, leaflet elevation, leaflet number/area ratio, leaflet length/width ratio) with the same plant leaf area using a formerly developed functional-structural model for a Chinese Liaoshen-solar greenhouse and tomato plant, which used to simulate the plant physiology of light interception, temperature, stomatal conductance, photosynthesis, and dry matter. Our study led to the conclusion that the planting strategies have a more significant impact overall on plant radiation, temperature, photosynthesis, and dry matter compared to plant architecture changes. According to our findings, increasing the plant spacing will have the most significant impact to increase light interception. E-W orientation has better total light interception but yet weaker light uniformity. Changes in planting patterns have limited influence on the overall canopy physiology. Increasing the plant leaflet area by leaflet N/A ratio from what we could observe for a rose the total dry matter by 6.6%, which is significantly better than all the other plant architecture traits. An ideal tomato plant architecture which combined all the above optimal architectural traits was also designed to provide guidance on phenotypic traits selection of breeding process. The combined analysis approach described herein established the causal relationship between investigated traits, which could directly apply to provide management and breeding insights on other plant species with different solar greenhouse structures.

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Albasha R., Fournier C., Pradal C., Chelle M., Prieto J. A., Louarn G., et al. (2019). HydroShoot: a functional-structural plant model for simulating hydraulic structure, gas and energy exchange dynamics of complex plant canopies under water deficit - Application to grapevine (Vitis vinifera). In Silico Plants 1 1–26. 10.1093/insilicoplants/diz007 DOI

Atherton J., Rudich J. (2012). The Tomato Crop: A Scientific Basis for Improvement. Berlin: Springer Science & Business Media.

Auzmendi I., Hanan J. S. (2020). Investigating tree and fruit growth through functional-structural modelling: implications of carbon autonomy at different scales. Ann. Bot. 126 775–788. 10.1093/aob/mcaa098 PubMed DOI PMC

Barberán A., Ramirez K. S., Leff J. W., Bradford M. A., Wall D. H., Fierer N. (2014). Why are some microbes more ubiquitous than others? Predicting the habitat breadth of soil bacteria. Ecol. Lett. 17 794–802. 10.1111/ele.12282 PubMed DOI

Buck-Sorlin G. H., Cornilleau X., Rahme S., Truffault V., Brajeul E. (2020). A functional-structural plant model of greenhouse-grown cucumber under LED lighting. Acta Hortic. 381–388. 10.17660/ActaHortic.2020.1296.49 PubMed DOI

Buck-Sorlin G. H., de Visser P. H. B., Henke M., Sarlikioti V., van der Heijden G. W. A. M., Marcelis L. F. M., et al. (2011). Towards a functionalstructural plant model of cut-rose: simulation of light environment, light absorption, photosynthesis and interference with the plant structure. Ann. Bot. 108 1121–1134. 10.1093/aob/mcr190 PubMed DOI PMC

Burgess A. J., Retkute R., Herman T., Murchie E. H. (2017). Exploring relationships between canopy architecture, light distribution, and photosynthesis in contrasting rice genotypes using 3D canopy reconstruction. Front. Plant Sci. 8:734. 10.3389/fpls.2017.00734 PubMed DOI PMC

Campos I., Neale C. M. U., Calera A. (2017). Is row orientation a determinant factor for radiation interception in row vineyards? Aust. J. Grape Wine Res. 23 77–86. 10.1111/ajgw.12246 DOI

Chelle M. (2005). Phylloclimate or the climate perceived by individual plant organs: what is it? How to model it? What for? New Phytol. 166 781–790. 10.1111/j.1469-8137.2005.01350.x PubMed DOI

Chen T. W., Henke M., de Visser P. H. B., Buck-Sorlin G. H., Wiechers D., Kahlen K., et al. (2014). What is the most prominent factor limiting photosynthesis in different layers of a greenhouse cucumber canopy? Ann. Bot. 114 677–688. 10.1093/aob/mcu100 PubMed DOI PMC

Choab N., Allouhi A., El Maakoul A., Kousksou T., Saadeddine S., Jamil A. (2019). Review on greenhouse microclimate and application: design parameters, thermal modeling and simulation, climate controlling technologies. Sol. Energy 191 109–137. 10.1016/j.solener.2019.08.042 DOI

Cieslak M., Seleznyova A. N., Hanan J. (2011). A functionalstructural kiwifruit vine model integrating architecture, carbon dynamics and effects of the environment. Ann. Bot. 107 747–764. 10.1093/aob/mcq180 PubMed DOI PMC

de Visser P. H. B., Buck-Sorlin G. H., van der Heijden G. W. A. M. (2014). Optimizing illumination in the greenhouse using a 3D model of tomato and a ray tracer. Front. Plant Sci. 5:48. 10.3389/fpls.2014.00048 PubMed DOI PMC

Evers J. B., Vos J., Yin X., Romero P., van Der Putten P. E. L., Struik P. C. (2010). Simulation of wheat growth and development based on organ-level photosynthesis and assimilate allocation. J. Exp. Bot. 61 2203–2216. 10.1093/jxb/erq025 PubMed DOI

Falster D. S., Westoby M. (2003). Leaf size and angle vary widely across species: what consequences for light interception? New Phytol. 158 509–525. 10.1046/j.1469-8137.2003.00765.x PubMed DOI

Feng L., Raza M. A., Chen Y., Bin Khalid M. H., Meraj T. A., Ahsan F., et al. (2019). Narrow-wide row planting pattern improves the light environment and seed yields of intercrop species in relay intercropping system. PLoS One 14:e0212885. 10.1371/journal.pone.0212885 PubMed DOI PMC

Granier C., Vile D. (2014). Phenotyping and beyond: modelling the relationships between traits. Curr. Opin. Plant Biol. 18 96–102. 10.1016/j.pbi.2014.02.009 PubMed DOI

Hemmerling R., Kniemeyer O., Lanwert D., Kurth W., Buck-Sorlin G. (2008). The rule-based language XL and the modelling environment GroIMP illustrated with simulated tree competition. Funct. Plant Biol. 35 739–750. 10.1071/FP08052 PubMed DOI

Henke M., Kurth W., Buck-Sorlin G. H. (2016). FSPM-P: towards a general functional-structural plant model for robust and comprehensive model development. Front. Comput. Sci. 10:1103–1117. 10.1007/s11704-015-4472-8 DOI

Ichihashi Y., Aguilar-Martínez J. A., Farhi M., Chitwood D. H., Kumar R., Millon L. V., et al. (2014). Evolutionary developmental transcriptomics reveals a gene network module regulating interspecific diversity in plant leaf shape. Proc. Natl. Acad. Sci. U.S.A. 111 E2616–E2621. 10.1073/pnas.1402835111 PubMed DOI PMC

Jung D. H., Lee J. W., Kang W. H., Hwang I. H., Son J. E. (2018). Estimation of whole plant photosynthetic rate of irwin mango under artificial and natural lights using a three-dimensional plant model and ray-tracing. Int. J. Mol. Sci. 19 1–14. 10.3390/ijms19010152 PubMed DOI PMC

Kim D., Kang W. H., Hwang I., Kim J., Kim J. H., Park K. S., et al. (2020). Use of structurally-accurate 3D plant models for estimating light interception and photosynthesis of sweet pepper (Capsicum annuum) plants. Comput. Electron. Agric. 177:105689. 10.1016/j.compag.2020.105689 DOI

Kniemeyer O. (2008). Design and Implementation of a Graph Grammar Based Language for Functional-Structural Plant Modelling. PhD Thesis. Cottbus: Technische Universität Cottbus, 432.

Louarn G., Song Y. (2020). Two decades of functional–structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology. Ann. Bot. 126 501–509. 10.1093/aob/mcaa143 PubMed DOI PMC

Maddonni G. A., Chelle M., Drouet J. L., Andrieu B. (2001). Light interception of contrasting azimuth canopies under square and rectangular plant spatial distributions: simulations and crop measurements. F. Crop. Res. 70 1–13. 10.1016/S0378-4290(00)00144-1 DOI

Moin-E-Ddin Rezvani S. R., Shamshiri R., Hameed I. A., Zare Abyane H., Godarzi M., Momeni D., et al. (2021). “Greenhouse crop simulation models and microclimate control systems, a review,” in Next-Generation Greenhouses for Food Security, ed. Shamshiri R. R. (London: IntechOpen; ), 13. 10.5772/intechopen.97361 DOI

Niinemets Ü., Fleck S. (2002). Petiole mechanics, leaf inclination, morphology, and investment in support in relation to light availability in the canopy of Liriodendron tulipifera. Oecologia 132 21–33. 10.1007/s00442-002-0902-z PubMed DOI

Ohashi Y., Torii T., Ishigami Y., Goto E. (2020). Estimation of the light interception of a cultivated tomato crop canopy under different furrow distances in a greenhouse using the ray tracing. J. Agric. Meteorol. 76 188–193. 10.2480/agrmet.D-20-00030 PubMed DOI

Pradal C., Dufour-Kowalski S., Boudon F., Fournier C., Godin C. (2008). OpenAlea: a visual programming and component-based software platform for plant modelling. Funct. Plant Biol. 35 751–760. 10.1071/FP08084 PubMed DOI

Ringle C. M., Wende S., Becker J.-M. (2015). SmartPLS 3. Boenningstedt SmartPLS GmbH.

Rötter R. P., Tao F., Höhn J. G., Palosuo T. (2015). Use of crop simulation modelling to aid ideotype design of future cereal cultivars. J. Exp. Bot. 66 3463–3476. 10.1093/jxb/erv098 PubMed DOI

Rowland S. D., Zumstein K., Nakayama H., Cheng Z., Flores A. M., Chitwood D. H., et al. (2020). Leaf shape is a predictor of fruit quality and cultivar performance in tomato. New Phytol. 226 851–865. 10.1111/nph.16403 PubMed DOI PMC

Sarlikioti V., de Visser P. H. B., Buck-Sorlin G. H., Marcelis L. F. M. (2011a). How plant architecture affects light absorption and photosynthesis in tomato: towards an ideotype for plant architecture using a functionalstructural plant model. Ann. Bot. 108 1065–1073. 10.1093/aob/mcr221 PubMed DOI PMC

Sarlikioti V., de Visser P. H. B., Marcelis L. F. M. (2011b). Exploring the spatial distribution of light interception and photosynthesis of canopies by means of a functionalstructural plant model. Ann. Bot. 107 875–883. 10.1093/aob/mcr006 PubMed DOI PMC

Tang L., Yin D., Chen C., Yu D., Han W. (2019). Optimal design of plant canopy based on light interception: a case study with loquat. Front. Plant Sci. 10:364. 10.3389/fpls.2019.00364 PubMed DOI PMC

Thompson D. W. (1992). in On Growth and Form, ed. Bonner J. T. (Cambridge: Cambridge University Press; ), 10.1017/CBO9781107325852 DOI

Tong G., Christopher D. M., Li T., Wang T. (2013). Passive solar energy utilization: a review of cross-section building parameter selection for Chinese solar greenhouses. Renew. Sustain. Energy Rev. 26 540–548. 10.1016/j.rser.2013.06.026 DOI

Trentacoste E. R., Connor D. J., Gómez-del-Campo M. (2015). Row orientation: applications to productivity and design of hedgerows in horticultural and olive orchards. Sci. Hortic. 187 15–29. 10.1016/j.scienta.2015.02.032 DOI

Utama D. N. (2015). The Optimization of the 3-d structure of plants, using functional-structural plant models. case study of Rice (Oryza sativa L.) in Indonesia. Environ. Informatics Georg. Univ. Sch. Sci. 1–185. Available at: https://ediss.uni-goettingen.de/handle/11858/00-1735-0000-0028-8659-5

Valladares F., Pearcy R. W. (1998). The functional ecology of shoot architecture in sun and shade plants of Heteromeles arbutifolia M. Roem., a Californian chaparral shrub. Oecologia 114 1–10. 10.1007/s004420050413 PubMed DOI

van der Meer M., de Visser P. H. B., Heuvelink E., Marcelis L. F. M. (2021). Row orientation affects the uniformity of light absorption, but hardly affects crop photosynthesis in hedgerow tomato crops. Silico Plants 3 1–35. 10.1093/insilicoplants/diab025 DOI

Vermeiren J., Villers S. L. Y., Wittemans L., Vanlommel W., Van Roy J., Marien H., et al. (2020). Quantifying the importance of a realistic tomato (Solanum lycopersicum) leaflet shape for 3-D light modelling. Ann. Bot. 126 661–670. 10.1093/aob/mcz205 PubMed DOI PMC

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. J. Exp. Bot. 61 2101–2115. 10.1093/jxb/erp345 PubMed DOI

Yan H. P., Meng Z. K., De Reffye P., Dingkuhn M. (2004). A dynamic, architectural plant model simulating resource-dependent growth. Ann. Bot. 93 591–602. 10.1093/aob/mch078 PubMed DOI PMC

Zhang S., Guo Y., Zhao H., Wang Y., Chow D., Fang Y. (2020). Methodologies of control strategies for improving energy efficiency in agricultural greenhouses. J. Clean. Prod. 274:122695. 10.1016/j.jclepro.2020.122695 DOI

Zhang Y., Henke M., Buck-Sorlin G. H., Li Y., Xu H., Liu X., et al. (2021). Estimating canopy leaf physiology of tomato plants grown in a solar greenhouse: evidence from simulations of light and thermal microclimate using a Functional-Structural Plant Model. Agric. For. Meteorol. 307:108494. 10.1016/j.agrformet.2021.108494 DOI

Zhang Y., Henke M., Li Y., Yue X., Xu D., Liu X., et al. (2020). High resolution 3D simulation of light climate and thermal performance of a solar greenhouse model under tomato canopy structure. Renew. Energy. 160 730–745. 10.1016/j.renene.2020.06.144 DOI

Zhu J., Dai Z., Vivin P., Gambetta G. A., Henke M., Peccoux A., et al. (2018). A 3-D functional-structural grapevine model that couples the dynamics of water transport with leaf gas exchange. Ann. Bot. 121 833–848. 10.1093/aob/mcx141 PubMed DOI PMC

Zhu J., van der Werf W., Anten N. P. R., Vos J., Evers J. B. (2015). The contribution of phenotypic plasticity to complementary light capture in plant mixtures. New Phytol. 207 1213–1222. 10.1111/nph.13416 PubMed DOI

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