The non-uniform growth and development of crops within Chinese Solar Greenhouses (CSG) is directly related to the micro-light climate within canopy. In practice, reflective films are used to improve micro-light climate within plant canopy by homogenizing light distribution and so increasing total plant light interception. However, as to our knowledge, the contributions to light distribution within canopy have not been investigated for passive reflector like reflective films. Field experiments dealing with light conditions and growth behavior over time, are complicated to carry out, time-consuming and hard to control, while however, accurate measurements of how reflective films influence the micro-light climate of canopy are an essential step to improve the growth conditions for any crop. Here, we propose a supplementary light strategy using reflective films to improve light distribution within plant canopy. Based on the example of CSG, a 3D greenhouse model including a detailed 3D tomato canopy structure was constructed to simulate the influence of supplementary reflective films to improve micro-light climate. Comparison of measured solar radiation intensity with predicted model data demonstrated that the model could precisely predict light radiation intensity over time with different time points and positions in the greenhouse. A series of reflective film configurations were investigated based on features analysis of light distribution in the tomato canopy on sunny days using the proposed model. The reflective film configuration scheme with the highest impact significantly improved the evenness of horizontal and vertical light distribution in tomato canopy. The strategy provided here can be used to configure reflective films that will enhance light conditions in CSG, which can be applied and extended in different scenarios.

College of Engineering Shenyang Agricultural University Shenyang China

College of Horticulture Shenyang Agricultural University Shenyang China

Key Laboratory of Protected Horticulture Ministry of Education Shenyang China

Leibniz Institute of Plant Genetics and Crop Plant Research Stadt Seeland Germany

National and Local Joint Engineering Research Center of Northern Horticultural Facilities Design and Application Technology Shenyang China

Plant Sciences Core Facility CEITEC Central European Institute of Technology Masaryk University Brno Czechia

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Agricultural Mechanization Management Department of China (2018). National greenhouse area data. Available at: http://data.sheshiyuanyi.com/AreaData/ (Accessed July 31, 2022).

Boo H., Kim H., Lee H. (2010). Changes in sugar content and sucrose synthase enzymes during fruit growth in eggplant (Solanum melongena L.) grown on different polyethylene mulches. HortScience 45, 775–777. doi: 10.21273/hortsci.45.5.775 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. doi: 10.1093/aob/mcr190, PMID: PubMed DOI PMC

Cabrera F. J., Baille A., López J. C., González-Real M. M., Pérez-Parra J. (2009). Effects of cover diffusive properties on the components of greenhouse solar radiation. Biosyst. Eng. 103, 344–356. doi: 10.1016/j.biosystemseng.2009.03.008 DOI

Chen T.-W., Henke M., de Visser P. H. B., Buck-Sorlin G., 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. doi: 10.1093/aob/mcu100, PMID: PubMed DOI PMC

Chen C., Li Y., Li N., Wei S., Yang F., Ling H., et al. (2018). A computational model to determine the optimal orientation for solar greenhouses located at different latitudes in China. Sol. Energy 165, 19–26. doi: 10.1016/j.solener.2018.02.022 DOI

Chen C., Yu N., Yang F., Mahkamov K., Han F., Li Y., et al. (2019). Theoretical and experimental study on selection of physical dimensions of passive solar greenhouses for enhanced energy performance. Sol. Energy 191, 46–56. doi: 10.1016/j.solener.2019.07.089 DOI

Cossu M., Cossu A., Deligios P. A., Ledda L., Li Z., Fatnassi H., et al. (2018). Assessment and comparison of the solar radiation distribution inside the main commercial photovoltaic greenhouse types in Europe. Renew. Sust. Energ. Rev. 94, 822–834. doi: 10.1016/j.rser.2018.06.001 DOI

Coussement J., Henke M., Lootens P., Roldán-Ruiz I., Steppe K., de Swaef T. (2018). Modelling leaf spectral properties in a soybean functional-structural plant model by integrating the prospect radiative transfer model. Ann. Bot. 122, 669–676. doi: 10.1093/aob/mcy105, PMID: PubMed DOI PMC

Cui Q., Cao C., Hu X., Yi L. (2005). Practice and theoretic analysis of supplementing illumination in the solar greenhouse with reflecting film. Chin. J. Eco-Agric. 13, 82–84.

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. doi: 10.3389/fpls.2014.00048, PMID: PubMed DOI PMC

Esmaeli H., Roshandel R. (2020). Optimal design for solar greenhouses based on climate conditions. Renew. Energy 145, 1255–1265. doi: 10.1016/j.renene.2019.06.090 DOI

Glenn D. M., Puterka G. J. (2007). The use of plastic films and sprayable reflective particle films to increase light penetration in apple canopy and improve apple color and weight. HortScience 42, 91–96. doi: 10.21273/hortsci.42.1.91 DOI

Gomez C., Morrow R. C., Bourget C. M., Massa G. D., Mitchell G. A. (2013). Comparison of intracanopy light-emitting diode towers and overhead high-pressure sodium lamps for supplemental lighting of greenhouse-grown tomatoes. Horttechnology 23, 93–98. doi: 10.21273/HORTTECH.23.1.93, PMID: DOI

Guo S., Sun J., Shu S., Lu X., Tian J., Wang J. (2012). Analysis of general situation, characteristics, existing problems and development trend of protected horticulture in China. China Veget. 18, 1–14.

Gupta R., Tiwari G. N. (2005). Modeling of energy distribution inside greenhouse using concept of solar fraction with and without reflecting surface on north wall. Build. Environ. 40, 63–71. doi: 10.1016/j.buildenv.2004.03.014 DOI

Ham J. M., Kluitenberg G. J. (1994). Modeling the effect of mulch optical properties and mulch-soil contact resistance on soil heating under plastic mulch culture. Agric. For. Meteorol. 71, 403–424. doi: 10.1016/0168-1923(94)90022-1 DOI

Hemmerling R., Evers J. B., Smoleňová K., Buck-Sorlin G., Kurth W. (2013). Extension of the GroIMP modelling platform to allow easy specification of differential equations describing biological processes within plant models. Comput. Electron. Agric. 92, 1–8. doi: 10.1016/j.compag.2012.12.007 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. doi: 10.1071/FP08052, PMID: PubMed DOI

Hemming S., Bakker J. C., Campen J. B., Kempkes F. L. K. (2019). “Sustainable use of energy in greenhouses,” in Achieving Sustainable Greenhouse Cultivation. eds. Marcelis L., Heuvelink E. (Cambridge, UK: Burleigh Dodds Science Publishing Limited; ), 445–492.

Henke M., Buck-Sorlin G. H. (2017). Using a full spectral raytracer for calculating light microclimate in functional-structural plant modelling. Comput. Inform. 36, 1492–1522. doi: 10.4149/cai_2017_6_1492 DOI

Jiang N., Jin L. F., Teixeira da Silva J. A., Islam M. Z., Gao H. W., Liu Y. Z., et al. (2014). Activities of enzymes directly related with sucrose and citric acid metabolism in citrus fruit in response to soil plastic film mulch. Sci. Hortic. 168, 73–80. doi: 10.1016/j.scienta.2014.01.021 DOI

Jones H., Black T. A., Jassal R. S., Nesic Z., Johnson M. S., Smukler S. (2021). Characterization of shortwave and longwave properties of several plastic film mulches and their impact on the surface energy balance and soil temperature. Sol. Energy 214, 457–470. doi: 10.1016/j.solener.2020.11.058 DOI

Katzin D., Marcelis L. F. M., van Mourik S. (2021). Energy savings in greenhouses by transition from high-pressure sodium to LED lighting. Appl. Energy 281:116019. doi: 10.1016/j.apenergy.2020.116019 DOI

Kim R., Kim J., Lee I., Yeo U., Lee S., Decano-Valentin C. (2021). Development of three-dimensional visualisation technology of the aerodynamic environment in a greenhouse using CFD and VR technology, part 2: development of an educational VR simulator. Biosyst. Eng. 207, 12–32. doi: 10.1016/j.biosystemseng.2021.02.018 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.

Kong Y., Meng L. (2004). Effect of reflective screen on sunlight distribution and morphological characters of tomato inside solar greenhouse. Northern Hortic. 5, 10–11. doi: 10.3969/j.issn.1001-0009.2004.05.004 DOI

Kong Y., Wang S., Yao Y., Ma C. (2008). Optimization of position of reflective boards for increasing light intensity inside Chinese lean-to greenhouses. Intern. Confer. Comp. Computing Techn. Agri. 259, 1383–1389. doi: 10.1007/978-0-387-77253-0_85 DOI

Kumar K. G. S., Hao X., Khosla S., Guo X., Bennett N. (2016). Comparison of HPS lighting and hybrid lighting with top HPS and intra-canopy LED lighting for high-wire mini-cucumber production. Acta Hortic. 1134, 111–118. doi: 10.17660/ActaHortic.2016.1134.15 DOI

Lorenzo P., Sánchez-Guerrero M. C., Medrano E., Soriano T., Castilla N. (2005). Responses of cucumbers to mulching in an unheated plastic greenhouse. J. Hortic. Sci. Biotechnol. 80, 11–17. doi: 10.1080/14620316.2005.11511883 DOI

Mao L., Zhang L., Evers J. B., Henke M., van der Werf W., Liu S., et al. (2016). Identification of plant configurations maximizing radiation capture in relay strip cotton using a functional-structural plant model. Field Crop Res. 187, 1–11. doi: 10.1016/j.fcr.2015.12.005 DOI

Meteorological Information Center (2005). Special Meteorological Data Set for Analysis of Thermal Environment in China. China Construction Industry Press. Available at: https://energyplus.net/ (Accessed July 31, 2022).

Meyer G. E., Paparozzi E. T., Walter-Shea E. A., Blankenship E. E., Adams S. A. (2012). An investigation of reflective mulches for use over capillary mat systems for winter-time greenhouse strawberry production. Appl. Eng. Agric. 28, 271–279. doi: 10.13031/2013.41345 DOI

Monostori I., Heilmann M., Kocsy G., Rakszegi M., Ahres M., Altenbach S. B., et al. (2018). LED lighting–modification of growth, metabolism, yield and flour composition in wheat by spectral quality and intensity. Front. Plant Sci. 9:605. doi: 10.3389/fpls.2018.00605, PMID: PubMed DOI PMC

Mormile P., Stahl N., Malinconico M. (2017). “The world of Plasticulture,” in Soil Degradable Bioplastics for a Sustainable modern Agriculture. ed. Malinconico M. (Heidelberg, GER: Springer-Verlag GmbH; ), 1–21.

Muneer S., Kim J. H., Park J. G., Shin M. H., Cha G. H., Kim H. L., et al. (2019). Reflective plastic film mulches enhance light intensity, floral induction, and bioactive compounds in ‘O’Neal’ southern highbush blueberry. Sci. Hortic. 246, 448–452. doi: 10.1016/j.scienta.2018.10.042 DOI

Munz S., Graeff-Hönninger S., Henke M. (2019). Functional-structural plant model for testing the effect of maize architecture on hourly light distribution in strip-intercropping systems. 2018 6th international symposium on plant growth modeling, simulation. Visualiz. Applic. (PMA) 2018, 115–119. doi: 10.1109/PMA.2018.8611609 DOI

Nguyen C. V., Fripp J., Lovell D. R., Furbank R., Kuffner P., Daily H., et al. (2016). 3D scanning system for automatic high-resolution plant Phenotyping. Intern. Confer. Digital Image Comp.: Techn. Applic. 2016, 1–8. doi: 10.1109/DICTA.2016.7796984 DOI

Overbeck V., Schmitz-Eiberger M. A., Blanke M. M. (2013). Reflective mulch enhances ripening and health compounds in apple fruit. J. Sci. Food Agric. 93, 2575–2579. doi: 10.1002/jsfa.6079, PMID: PubMed DOI

Pallas B., da Silva D., Valsesia P., Yang W., Guillaume O., Lauri P. E., et al. (2016). Simulation of carbon allocation and organ growth variability in apple tree by connecting architectural and source-sink models. Ann. Bot. 118, 317–330. doi: 10.1093/aob/mcw085, PMID: PubMed DOI PMC

Pang Q. (2004). Good effect of utilization of aluminum-plated reflective sheet in the energy-saving greenhouses. Qinghai Agro-Technol. Ext. 1, 34–35. doi: 10.3969/j.issn.1008-7117.2004.01.018 DOI

Panjvani K., Dinh A. V., Wahid K. A. (2019). LiDARPheno – A low-cost LiDAR-based 3D scanning system for leaf morphological trait extraction. Front. Plant Sci. 10:147. doi: 10.3389/fpls.2019.00147, PMID: PubMed DOI PMC

Panwar N. L., Kaushik S. C., Kothari S. (2011). Solar greenhouse an option for renewable and sustainable farming. Renew. Sust. Energ. Rev. 15, 3934–3945. doi: 10.1016/j.rser.2011.07.030 DOI

Papadopoulos A. P., Hao X. (1997). Effects of greenhouse covers on seedless cucumber growth, productivity, and energy use. Sci. Hortic. 68, 113–123. doi: 10.1016/S0304-4238(96)00961-2 DOI

Raaphorst M., Benninga J., Eveleens B. A. (2019). Quantitative Information on Dutch Greenhouse Horticulture 2019. Report WPR-898. Netherlands: Wageningen University and Research.

Rhainds M., Kovach J., Dosa E. L., English-Loeb G. (2001). Impact of reflective mulch on yield of strawberry plants and incidence of damage by tarnished plant bug (Heteroptera: Miridae). J. Econ. Entomol. 94, 1477–1484. doi: 10.1603/0022-0493-94.6.1477, PMID: PubMed DOI

Rippa M., Yan C., Petti L., Mormile P. (2018). A simple method for saving water in agriculture based on the use of photo-selective mulch film. Int. J. Agr. Environ. Res. 4, 33–43.

Sethi V. P., Arora S. (2009). Improvement in greenhouse solar drying using inclined north wall reflection. Sol. Energy 83, 1472–1484. doi: 10.1016/j.solener.2009.04.001 DOI

Sirigu A., Nannini M., Meloni S., Crescenzi A. (2011). UV-reflective mulch and chemical resistance induction for the control of tomato yellow leaf curl disease in greenhouse tomato crops. Acta Hortic. 914, 373–376. doi: 10.17660/ActaHortic.2011.914.70 DOI

Tang X., Cao L., Zhang X., Li Y., Ma H. (2015). Effects of new reflective material on the berry quality and coloring of Benni Fuji grapes. J. China Agric. Univ. 20, 51–58. doi: 10.11841/j.issn.10074333.2015.03.08 DOI

Tewolde F. T., Lu N., Shiina K., Maruo T., Takagaki M., Kozai T., et al. (2016). Nighttime supplemental LED inter-lighting improves growth and yield of single-truss tomatoes by enhancing photosynthesis in both winter and summer. Front. Plant Sci. 7, 448. doi: 10.3389/fpls.2016.00448, PMID: PubMed DOI PMC

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. Sust. Energ. Rev. 26, 540–548. doi: 10.1016/j.rser.2013.06.026 DOI

Wang J., Cui Q. F., Lin M. Z. (2002). Illumination environment of different structural solar greenhouses and their supplement illumination. Trans. Chin. Soc. Agric. Eng. 18, 86–89. doi: 10.3321/j.issn:1002-6819.200 DOI

Wang Y., Wen W., Wu S., Wang C., Yu Z., Guo X., et al. (2019). Maize plant phenotyping: comparing 3D laser scanning, multi-view stereo reconstruction, and 3D digitizing estimates. Remote Sens. 11:63. doi: 10.3390/rs11010063 DOI

Weaver G. M., van Iersel M. W., Velni J. M. (2019). A photochemistry-based method for optimising greenhouse supplemental light intensity. Biosyst. Eng. 182, 123–137. doi: 10.1016/j.biosystemseng.2019.03.008 DOI

Xu D., Li Y., Zhang Y., Xu H., Li T., Liu X. (2020). Effects of orientation and structure on solar radiation interception in Chinese solar greenhouse. PLoS One 15:e0242002. doi: 10.1371/journal.pone.0242002, PMID: PubMed DOI PMC

Yamaguchi T., Ito A., Koshioka M. (1996). Effect of combination of reflective film mulching and shading treatments on the growth of carnation. JPN Agr. Res. Q. 30, 181–188.

Zhang H., Burr J., Zhao F. (2017). A comparative life cycle assessment (LCA) of lighting technologies for greenhouse crop production. J. Clean. Prod. 140, 705–713. doi: 10.1016/j.jclepro.2016.01.014 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. doi: 10.1016/j.agrformet.2021.108494 DOI

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

Zhang X., Lv J., Xie J., Yu J., Zhang J., Tang C., et al. (2020a). Solar radiation allocation and spatial distribution in Chinese solar greenhouses: model development and application. Energies 13:1108. doi: 10.3390/en13051108 DOI