Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
Crop yield potential is intrinsically related to canopy photosynthesis; therefore, improving canopy photosynthetic efficiency is a major focus of current efforts to enhance crop yield. Canopy photosynthesis rate (Ac) is influenced by several factors, including plant architecture, leaf chlorophyll content, and leaf photosynthetic properties, which interact with each other. Identifying factors that restrict canopy photosynthesis and target adjustments to improve canopy photosynthesis in a specific crop cultivar pose an important challenge for the breeding community. To address this challenge, we developed a novel pipeline that utilizes factorial analysis, canopy photosynthesis modeling, and phenomics data collected using a 64-camera multi-view stereo system, enabling the dissection of the contributions of different factors to differences in canopy photosynthesis between maize cultivars. We applied this method to 2 maize varieties, W64A and A619, and found that leaf photosynthetic efficiency is the primary determinant (17.5% to 29.2%) of the difference in Ac between 2 maize varieties at all stages, and plant architecture at early stages also contribute to the difference in Ac (5.3% to 6.7%). Additionally, the contributions of each leaf photosynthetic parameter and plant architectural trait were dissected. We also found that the leaf photosynthetic parameters were linearly correlated with Ac and plant architecture traits were non-linearly related to Ac. This study developed a novel pipeline that provides a method for dissecting the relationship among individual phenotypes controlling the complex trait of canopy photosynthesis.
Long SP, Zhu X-G, Naidu SL, Ort DR. Can improvement in photosynthesis increase crop yields? Plant Cell Environ. 2006;29:315–330.
Puckridge DW. Photosynthesis of wheat under field conditions Ⅲ. Seasonal trends in carbon dioxide uptake of crop communities. Aust J Agr Res. 1971;22(1):1–9.
Vietor DM, Musgrave RB. Photosynthetic selection of Zea mays L. Ⅱ. The relationship between CO2 exchange and dry matter accumulation of canopies of two hybrids. Crop Sci. 1979;19:70–75.
Wells R, Meredith WR, Williford JR. Canopy photosynthesis and its relationship to plant productivity in near-isogenic cotton lines differing in leaf morphology. Plant Physiol. 1986;82:635–640.
Wells R, Schulze LL, Ashley DA, Boerma HR, Brown RH. Cultivar differences in canopy apparent photosynthesis and their relationship to seed yield in soybeans. Crop Sci. 1982;22:886–890.
Zelitch I. The close relationship between net photosynthesis and crop yield. Bioscience. 1982;32:796–802.
Song Q, Chu C, Parry MAJ, Zhu X-G. Genetics-based dynamic systems model of canopy photosynthesis: The key to improve light and resource use efficiencies for crops. Food Energy Secur. 2016;5:18–25.
Chang S, Chang T, Song Q, Zhu X-G, Deng Q. Photosynthetic and agronomic traits of an elite hybrid rice Y-Liang-you 900 with a record-high yield. Field Crop Res. 2016;187:49–57.
Peng S, Khush GS, Virk P, Tang Q, Zou Y. Progress in ideotype breeding to increase rice yield potential. Field Crop Res. 2008;108:32–38.
Sharma-Natu P, Ghildiyal MC. Potential targets for improving photosynthesis and crop yield. Curr Sci. 2005;88(1):1918–1928.
Long SP, Ainsworth E, Leakey ADB, Nösberger J, Ort DR. Food for thought: Lower-than-expected crop yield stimulation with rising CO2 concentrations. Science. 2006;312(5782):1918–1921.
Sun J, Yang L, Wang Y, Ort DR. FACE-ing the global change: Opportunities for improvement in photosynthetic radiation use efficiency and crop yield. Plant Sci. 2009;177:511–522.
Evans LT, Dunstone RL. Some physiological aspects of evolution in wheat. Australian J Biol Sci. 1970;23(4):725–742.
Peng S, Krieg DR, Girma FS. Leaf photosynthetic rate is correlated with biomass and grain production in grain sorghum lines. Photosynth Res. 1991;28:1–7.
Watanabe N, Evans JR, Chow WS. Changes in the photosynthetic properties of Australian wheat cultivars over the last century. Aust J Plant Physiol. 1994;21(2):169–183.
Niinemets Ü. A review of light interception in plant stands from leaf to canopy in different plant functional types and in species with varying shade tolerance. Ecol Res. 2010;25:693–714.
Li D, Wang L, Wang M, Xu Y-Y, Luo W, Liu Y-J, Xu ZH, Li J, Chong K. Engineering OsBAK1 gene as a molecular tool to improve rice architecture for high yield. Plant Biotechnol J. 2009;7:791–806.
Sakamoto T, Morinaka Y, Ohnishi T, Sunohara H, Fujioka S, Ueguchi-Tanaka M, Mizutani M, Sakata K, Takatsuto S, Yoshida S, et al. Erect leaves caused by brassinosteroid deficiency increase biomass production and grain yield in rice. Nat Biotechnol. 2006;24:105–109.
Biswal AK, Pattanayak GK, Pandey SS, Leelavathi S, Reddy VS, Govindjee, Tripathy BC. Light intensity-dependent modulation of chlorophyll b biosynthesis and photosynthesis by overexpression of chlorophyllide a oxygenase (CAO) in tobacco. Plant Physiol. 2012;159(1):433–449.
Long SP, Marshall-Colon A, Zhu X-G. Meeting the global food demand of the future by engineering crop photosynthesis and yield potential. Cell. 2015;161:56–66.
Zhu X-G, Long SP, Ort DR. Improving photosynthetic efficiency for greater yield. Annu Rev Plant Biol. 2010;61:235–261.
de Carvalho RC, Cunha A, da Silva JM. Photosynthesis by six Portuguese maize cultivars during drought stress and recovery. Acta Physiol Plant. 2011;33(2):359–374.
Driever SM, Lawson T, Andralojc PJ, Raines CA, Parry MAJ. Natural variation in photosynthetic capacity, growth, and yield in 64 field-grown wheat genotypes. J Exp Bot. 2014;65(17):4959–4973.
Qu M, Zheng G, Hamdani S, Essemine J, Song Q, Wang H, Chu C, Sirault X, Zhu XG. Leaf photosynthetic parameters related to biomass accumulation in a global rice diversity survey. Plant Physiol. 2017;175:248–258.
Quan M, Liu X, Du Q, Xiao L, Lu W, Fang Y, Li P, Ji L, Zhang D. Genome-wide association studies reveal the coordinated regulatory networks underlying photosynthesis and wood formation in Populus. J Exp Bot. 2021;72(15):5372–5389.
Drouet J-L, Bonhomme R. Effect of 3D nitrogen, dry mass per area and local irradiance on canopy photosynthesis within leaves of contrasted heterogeneous maize crops. Ann Bot. 2004;93(6):699–710.
Wang X, Guo Y, Li B, Wang X, Ma Y. Evaluating a three dimensional model of diffuse photosynthetically active radiation in maize canopies. Int J Biometeorol. 2006;50:349–357.
Burgess AJ, Retkute R, Pound MP, Foulkes J, Preston SP, Jensen OE, Pridmore TP, Murchie EH. High-resolution three-dimensional structural data quantify the impact of photoinhibition on long-term carbon gain in wheat canopies in the field. Plant Physiol. 2015;169(2):1192–1204.
Chang T-G, Shi Z, Zhao H, Song Q, He Z, Van Rie J, Boer BD, Galle A, Zhu X-G. 3dCAP-wheat: An open-source comprehensive computational framework precisely quantifies wheat foliar, nonfoliar, and canopy photosynthesis. Plant Phenomics. 2022;2022: Article 9758148.
Burgess AJ, Retkute R, Herman T, Murchie EH. Exploring relationships between canopy architecture, light distribution, and photosynthesis in contrasting rice genotypes using 3D canopy reconstruction. Front Plant Sci. 2017;8: Article 734.
Chang T-G, Zhao H, Wang N, Song Q, Xiao Y, Qu M, Zhu XG. A three-dimensional canopy photosynthesis model in rice with a complete description of the canopy architecture, leaf physiology, and mechanical properties. J Exp Bot. 2019;70(9):2479–2490.
Song Q, Zhang G, Zhu X-G. Optimal crop canopy architecture to maximise canopy photosynthetic CO2 uptake under elevated CO2—A theoretical study using a mechanistic model of canopy photosynthesis. Funct Plant Biol. 2013;40(2):109–124.
Zheng BY, Shi LJ, Ma YT, Deng QY, Li BG, Guo Y. Comparison of architecture among different cultivars of hybrid rice using a spatial light model based on 3-D digitising. Funct Plant Biol. 2008;35:900–910.
Song Q, Srinivasan V, Long S, Zhu X-G. Decomposition analysis on soybean productivity increase under elevated CO2 using 3-D canopy model reveals synergistic effects of CO2 and light in photosynthesis. Ann Bot. 2020;126:601–614.
Wang Y, Song Q, Jaiswal D, de Souza A, Long S, Zhu XG. Development of a three-dimensional ray-tracing model of sugarcane canopy photosynthesis and its application in assessing impacts of varied row spacing. BioEnergy Res. 2017;10:626–634.
Kim JH, Lee JW, Ahn TI, Shin JH, Park KS, Son JE. Sweet pepper (Capsicum annuum L.) canopy photosynthesis modeling using 3D plant architecture and light ray-tracing. Front Plant Sci. 2016;7:1321.
Shi Z, Chang T-G, Chen G, Song Q, Wang Y, Zhou Z, Wang M, Qu M, Wang B, Zhu XG. Dissection of mechanisms for high yield in two elite rice cultivars. Field Crop Res. 2019;241: Article 107563.
Christian Rose J, Paulus S, Kuhlmann H. Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level. Sensors. 2015;15(5):9651–9665.
Pound MP, French AP, Murchie EH, Pridmore TP. Automated recovery of three-dimensional models of plant shoots from multiple color images. Plant Physiol. 2014;166:1688–1698.
Wang Y, Wen W, Wu S, Wang C, Yu Z, Guo X, Zhao C. Maize plant phenotyping: Comparing 3D laser scanning, multi-view stereo reconstruction, and 3D digitizing estimates. Remote Sens. 2019;11(1):63.
Wu S, Wen W, Wang Y, Fan J, Wang C, Gou W, Guo X. MVS-Pheno: A portable and low-cost phenotyping platform for maize shoots using multiview stereo 3D reconstruction. Plant Phenomics. 2020;2020: Article 1848437.
Liu F, Song Q, Zhao J, Mao L, Bu H, Hu Y, Zhu XG. Canopy occupation volume as an indicator of canopy photosynthetic capacity. New Phytol. 2021;232:941–956.
Liu F, Hu P, Zheng B, Duan T, Zhu B, Guo Y. A field-based high-throughput method for acquiring canopy architecture using unmanned aerial vehicle images. Agric For Meteorol. 2021;296: Article 108231.
Dornbusch T, Watt J, Baccar R, Fournier C, Andrieu B. A comparative analysis of leaf shape of wheat, barley and maize using an empirical shape model. Ann Bot. 2011;107(5):865–873.
Uddling J, Gelang-Alfredsson J, Piikki K, Pleijel H. Evaluating the relationship between leaf chlorophyll concentration and SPAD-502 chlorophyll meter readings. Photosynth Res. 2007;91:37–46.
Song Q, Wang Y, Qu M, Ort DR, Zhu X. The impact of modifying photosystem antenna size on canopy photosynthetic efficiency—Development of a new canopy photosynthesis model scaling from metabolism to canopy level processes. Plant Cell Environ. 2017;40(12):2946–2957.
Thornley JHM. Instantaneous canopy photosynthesis: Analytical expressions for sun and shade leaves based on exponential light decay down the canopy and an acclimated non-rectangular hyperbola for leaf photosynthesis. Ann Bot. 2002;89:451–458.
Simkin AJ, López-Calcagno PE, Raines CA. Feeding the world: Improving photosynthetic efficiency for sustainable crop production. J Exp Bot. 2019;70:1119–1140.
Evans JR. Photosynthesis and nitrogen relationships in leaves of C3 plants. Oecologia. 1989;78(1):9–19.
Feng Y-L, Lei Y-B, Wang R-F, Callaway RM, Valiente-Banuet A, Inderjit, Li Y-P, Zheng Y-L. Evolutionary tradeoffs for nitrogen allocation to photosynthesis versus cell walls in an invasive plant. Proc Natl Acad Sci. 2008;106(6):1853–1856.
Zhong C, Jian SF, Huang J, Jin QY, Cao XC. Trade-off of within-leaf nitrogen allocation between photosynthetic nitrogen-use efficiency and water deficit stress acclimation in rice (Oryza sativa L.). Plant Physiol. Biochem. 2019;135:41–50.
Mao L, Song Q, Li M, Liu X, Shi Z, Chen F, Chen GY, Zheng H, Zhu XG. Decreasing photosystem antenna size by inhibiting chlorophyll synthesis: A double-edged sword for photosynthetic efficiency. Crop Environ. 2023;2:46–58.
Zhou Z, Struik PC, Gu J, van der Putten PEL, Wang Z, Yin X, Yang J. Enhancing leaf photosynthesis from altered chlorophyll content requires optimal partitioning of nitrogen. Crop Environ. 2023;2:24–36.
Walker BJ, Drewry DT, Slattery RA, VanLoocke A, Cho YB, Ort DR. Chlorophyll can be reduced in crop canopies with little penalty to photosynthesis. Plant Physiol. 2018;176(2):1215–1232.
Song X, Meng X, Guo H, Cheng Q, Jing Y, Chen M, Liu G, Wang B, Wang Y, Li J, et al. Targeting a gene regulatory element enhances rice grain yield by decoupling panicle number and size. Nat. Biotechnol. 2022;40:1403–1411.
Garcia A, Gaju O, Bowerman AF, Buck SA, Evans JR, Furbank RT, Gilliham M, Millar AH, Pogson BJ, Reynolds MP, et al. Enhancing crop yields through improvements in the efficiency of photosynthesis and respiration. New Phytol. 2023;237:60–77.
Zhu X-G, Song Q, Ort DR. Elements of a dynamic systems model of canopy photosynthesis. Curr Opin Plant Biol. 2012;15:237–244.
Mantilla-Perez MB, Fernandez MGS. Differential manipulation of leaf angle throughout the canopy: Current status and prospects. J Exp Bot. 2017;68(21–22):5699–5717.
Assefa Y, Carter P, Hinds M, Bhalla G, Schon R, Jeschke M, Paszkiewicz S, Smith S, Ciampitti IA. Analysis of long term study indicates both agronomic optimal plant density and increase maize yield per plant contributed to yield gain. Sci Rep. 2018;8:Article 4937.
Sher A, Khan A, Cai LJ, Irfan Ahmad M, Asharf U, Jamoro SA. Response of maize grown under high plant density; performance, issues and management—A critical review. Adv Crop Sci Technol. 2017;5:275.
Testa G, Reyneri A, Blandino M. Maize grain yield enhancement through high plant density cultivation with different inter-row and intra-row spacings. Eur J Agron. 2016;72:28–37.
Xu W, Liu C, Wang K, Xie R, Ming B, Wang Y, Zhang G, Liu G, Zhao R, Fan P, et al. Adjusting maize plant density to different climatic conditions across a large longitudinal distance in China. Field Crop Res. 2017;212:126–134.
Dzievit MJ, Li X, Yu J. Dissection of leaf angle variation in maize through genetic mapping and meta-analysis. The Plant Genome. 2019;12(1):Article 180024.
Duan T, Chapman SC, Holland E, Rebetzke GJ, Guo Y, Zheng B. Dynamic quantification of canopy structure to characterize early plant vigour in wheat genotypes. J Exp Bot. 2016;67(15):4523–4534.
Hui F, Zhu J, Hu P, Meng L, Zhu B, Guo Y, Li B, Ma Y. Image-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations. Ann Bot. 2018;121:1079–1088.
Ninomiya S. High-throughput field crop phenotyping: Current status and challenges. Breed Sci. 2022;72:3–18.
Fernandez MGS, Bao Y, Tang L, Schnable PS. A high-throughput, field-based phenotyping technology for tall biomass crops. Plant Physiol. 2017;174(4):2008–2022.
Xiao Q, Tang W, Zhang C, Zhou L, Feng L, Shen J, Yan T, Gao P, He Y, Wu N. Spectral preprocessing combined with deep transfer learning to evaluate chlorophyll content in cotton leaves. Plant Phenomics. 2022;2022:Article 9813841.
Zhi X, Massey-Reed SR, Wu A, Potgieter A, Borrell A, Hunt C, Jordan D, Zhao Y, Chapman S, Hammer G, et al. Estimating photosynthetic attributes from high-throughput canopy hyperspectral sensing in sorghum. Plant Phenomics. 2022;2022:Article 9768502.
Mishra P, Asaari MSM, Herrero-Langreo A, Lohumi S, Diezma B, Scheunders P. Close range hyperspectral imaging of plants: A review. Biosyst Eng. 2017;164:49–67.
Edwards GE, Baker NR. Can CO2 assimilation in maize leaves be predicted accurately from chlorophyll fluorescence analysis? Photosynth Res. 1993;37(2):89–102.
Meacham-Hensold K, Fu P, Wu J, Serbin S, Montes CM, Ainsworth E, Guan K, Dracup E, Pederson T, Driever S, et al. Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging. J Exp Bot. 2020;71:2312–2328.
Distributed under a Creative Commons Attribution License (CC BY 4.0).