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| Relationships Between the Leaf Respiration of Soybean and Vegetation
Indexes and Leaf Characteristics |
| WANG Jin1, 2, CHEN Shu-tao1, 2*, DING Si-cheng1, 2, YAO Xue-wen1, 2, ZHANG Miao-miao1, 2, HU Zheng-hua2 |
1. Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
2. School of Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Abstract A field experiment was performed to investigate the relationships between the leaf respiration of soybean and vegetation indexes and leaf characteristics. The respiration and respiration coefficient of the first, second and third leaves from the top at the different growth stages were measured. The normalized difference vegetation index (NDVI), difference vegetation index (DVI), ratio index (RVI), enhanced vegetation index (EVI), photochemical reflectance index (PRI) and red edge chlorophyll indexes (RECI) were obtained from the hyperspectral method as well as the leaf characteristics of chlorophyll SPAD value, fresh mass, dried mass, water content, leaf area, specific leaf area and nitrogen content were also determined. The results showed that the respiration of a single leaf and respiration coefficient had obvious seasonal patterns. The seasonal mean respiration of the single first, second or third leaf from the top was (0.157±0.019), (0.162±0.014) and (0.142±0.010) mg·d-1, respectively. The seasonal mean respiration coefficient of the first, second or third leaf from the top was (0.638±0.072)，(0.678±0.082)，(0.642±0.076) mg·g-1·d-1, respectively. There were no significant (p>0.05) differences in the seasonal mean leaf respiration and respiration coefficient between the first, second or third leaf from the top. There were significant (p<0.05) differences in the seasonal patterns between the different vegetation indexes. The relatively high RVI, EVI, PRI and RECI appeared mid-growth stages. The seasonal patterns of RVI, EVI, PRI and RECI showed a single unimodal curve. The SPAD value, fresh mass, dried mass and leaf area decreased with the decrease in leaf position except for at the beginning growth stages. The leaf water content decreased with the growth of leaf growth. The respiration of a single leaf was highly significantly (p<0.01) correlated with the RECI and nitrogen content. The respiration of a single leaf was significantly (p<0.05) correlated with the air temperature and PRI. A model based on these four factors explained 60.4% of the variation in the respiration of a single leaf. The respiration coefficient was highly significantly (p<0.01) correlated with thedried mass and specific leaf area. The respiration coefficient was significantly (p<0.01) correlated with the air temperature and SPAD. A model based on these four factors explained 72.4% of the variation in the respiration coefficient. The present study showed that the leaf respiration of soybean could be linked with the hyperspectral vegetation indexes and the leaf characteristics. The seasonal variations in the leaf respiration and leaf respiration coefficient in the different positions could be effectively modeled with the hyperspectral vegetation indexes.
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Received: 2021-04-18
Accepted: 2021-06-10
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Corresponding Authors:
CHEN Shu-tao
E-mail: chenstyf@aliyun.com
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[1] Canadell J G, Le Quéré C, Raupach M R, et al. Proceedings of the National Acadamy of Sciences of the USA, 2007, 104(47): 18866.
[2] Jung M, Reichstein M, Schwalm C R, et al. Nature, 2017, 541(7638)：516.
[3] Melillo J M, McGuire A D, Kicklighter D W, et al. Nature, 1993, 363: 234.
[4] Atkin O K, Bloomfield K J, Reich P B, et al. New Phytologist, 2015, 206(2): 614.
[5] Bouma T J, de Visser R, van Leeuwen P H, et al. Journal of Experimental Botany, 1995, 46(9): 1185.
[6] Tjoelker M G, Oleksyn J, Reich P B. Global Change Biology, 2001, 7(2): 223.
[7] Clark D B, Mercado L M, Sitch S, et al. Geoscientific Model Development, 2011, 4(3): 701.
[8] Mitchell K A, Bolstad P V, Vose J M. Tree Physiology, 1999, 19: 861.
[9] Ryan M G. Plant, Cell and Environment, 1995, 18: 765.
[10] Tjoelker M G, Reich P B, Oleksyn J. Plant, Cell and Environment, 1999, 22: 767.
[11] Broge N H, Leblanc E. Remote Sensing of Environment, 2001, 76(2): 156.
[12] Wu C Y, Niu Z, Tang Q, et al. Agricultural and Forest Meteorology, 2008, 148(8-9): 1230.
[13] Justice C, Belward A, Morisette J, et al. International Journal of Remote Sensing, 2000, 21(17): 3383.
[14] Huang N, Niu Z, Zhan Y, et al. Agricultural and Forest Meteorology, 2012, 160(4): 80.
[15] BAO Shi-dan(鲍士旦). Soil Agrochemical Analysis(土壤农化分析). 3rd edition(第3版). Beijing, Chinese Agricultural Press(北京: 中国农业出版社), 2019.
[16] Gamon J A, Field C B, Goulden M L, et al. Ecological Applications, 1995, 5(1): 28.
[17] Sims D A, Gamon J A. Remote Sensing of Environment, 2002, 81(2): 337.
[18] Huete A, Didan K, Miura T, et al. Remote Sensing of Environment, 2002, 83(1-2): 195.
[19] Posada D, Buckley T R. Systematic Biology, 2004, 53(5): 793.
[20] Wu F, Wu L, Xu F. Field Crops Research, 1998, 56(3): 309.
[21] WU Gai-hong, FENG Mei-chen, YANG Wu-de, et al(武改红, 冯美臣, 杨武德, 等). Chinese Journal of Ecology(生态学杂志), 2018, 37(5): 1589.
[22] Weerasinghe L K, Creek D, Crous K Y, et al. Tree Physiology, 2014, 34(6): 564.
[23] Kosugi Y, Takanashi S, Yokoyama N, et al. Journal of Plant Research, 2012, 125: 1.
[24] Jia M, Wang K, Zhou C, et al. Computers and Electronics in Agriculture, 2019, 165: 104942.
[25] Zhu X, He H, Ma M, et al. Sustainability, 2020, 12：2099.
[26] Meng B, Gao J, Liang T, et al. Remote Sensing, 2018, 10(2): 320.
[27] Reich P B, Walters M B, Tjoelker M G, et al. Functional Ecology, 1998, 12(3): 395.
[28] Reich P B, Walters M B, Ellsworth D S, et al. Oecologia, 1998b, 114(4): 471.
[29] SUN Wen-juan, HUANG Yao, CHEN Shu-tao, et al(孙文娟, 黄 耀, 陈书涛, 等). Environmental Science(环境科学), 2004, 25(3): 1.
[30] SUN Wen-juan, HUANG Yao, CHEN Shu-tao, et al(孙文娟, 黄 耀, 陈书涛, 等). Acta Ecoligica Sinica (生态学报), 2005, 5(5): 1152.
[31] Ryan M G. Ecological Applications, 1991, 1(2): 157.
[32] Sun W, Huang Y, Chen S, et al. Advances in Atmospheric Sciences, 2007, 24(1): 55.
[33] Lambers H, Poorter H. Advances in Ecological Research, 1992, 23: 187.
[34] Ali A M, Darvishzadeh R, Skidmore A K, et al. Agricultural and Forest Meteorology, 2017, 236: 162.
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