- Open Access
Genotypic variation in spatiotemporal distribution of canopy light interception in relation to yield formation in cotton
© The Author(s) 2018
- Received: 24 May 2018
- Accepted: 21 September 2018
- Published: 7 November 2018
Within-canopy interception of photosynthetically active radiation (PAR) impacts yield and other agronomic traits in cotton (Gossypium hirsutum L.). Field experiments were conducted to investigate the influence of 6 cotton varieties (they belong to 3 different plant types) on yield, yield distribution, light interception (LI), LI distribution and the relationship between yield formation and LI in Anyang, Henan, in 2014 and 2015.
The results showed that cotton cultivars with long branches (loose-type) intercepted more LI than did cultivars with short branches (compact-type), due to increased LI in the middle and upper canopy. Although loose-type varieties had greater LI, they did not yield significantly higher than compact-type varieties, due to decreased harvest index. Therefore, improving the harvest index by adjusting the source-to-sink relationship may further increase cotton yield for loose-type cotton. In addition, there was a positive relationship between reproductive organ biomass accumulation and canopy-accumulated LI, indicating that enhancing LI is important for yield improvement for each cultivar. Furthermore, yield distribution within the canopy was significantly linearly related to vertical LI distribution.
Therefore, optimizing canopy structure of different plant type and subsequently optimizing LI distribution within the cotton canopy can effectively enhance the yield.
- Cotton cultivars
- Light interception
- Plant type structure
- Boll distribution
The capacity of the crop canopy to intercept and efficiently use solar radiation can greatly influence crop growth and development. Photosynthetically active radiation (PAR), consisting of wavelengths that can be absorbed by green plants and used for photosynthesis (Madakadze et al. 1998; Maddonni and Otegui 1996; McIntyre et al. 1996), is the major driver of plant photosynthetic processes(Meir et al. 2002). Spatial canopy interception determines how much PAR reaches the elements of the canopy and helps to determine the optimal canopy form for a certain crop. Light interception (LI) by the canopy is an important environmental factor, in addition to genetic factors (Bai et al. 2016), that determines the plant architecture type (Xue et al. 2015).
Knowing the factors that affect and determine yield and the ability to predict the yield of crops through in-season measurements are of paramount importance. Crop yield is highly correlated with canopy light interception, leaf area index (LAI) and above ground biomass in vegetables, soybeans, maize, sorghum, cotton and rice (Mo et al. 2005; Zarate-Valdez et al. 2012). The amount of light intercepted by the canopy and Radiation Use Efficiency (RUE), which is the efficiency of converting the captured radiation into biomass, are crucial elements for crop production and development (Louarn et al. 2008). LI is determined by canopy configuration (Chen et al. 1997; Dauzat et al. 2008), which increases not only land productivity but also resource use efficiency. Some studies have indicated that leaf area components have the greatest effect on light intensities (Baldissera et al. 2014). Among the characteristics that increase economic yield, the canopy microclimate is considered very important. PAR interception can be altered by canopy construction (Barthélémy and Caraglio 2007) and leaf area (Vargas et al. 2002). Therefore, in-depth exploration of the precise characteristics of light distribution in a crop canopy is necessary for improving crop productivity.
In cotton (Gossypium hirsutum L), adequate LI is essential for growth. Understanding the effects of plant architecture on radiation interception by cotton can be very useful for optimizing canopy architecture to intercept more light. The goal of cotton agronomy is to improve cotton yields through optimal management, but this accomplishment is made more difficult by the perennial growth habit of cotton (Kaggwa-Asiimwe et al. 2013). In addition, there is limited knowledge on how sink-source relationships and biomass production are affected by light interception(Wang et al. 2016) and how these relationships may be manipulated by different cultivars. The optimal spatial distribution of light and the specific boll spatial distribution are important for the efficient utilization of light. Therefore, identifying and selecting optimal cotton cultivars with a high efficiency for intercepting and converting solar radiation, as well as better understanding the characteristics of radiation interception and dry matter accumulation in more detail, are crucial.
Few studies have compared spatial LI and biomass production in different cultivars, and LI in different vertical and horizontal zones of the canopy has not yet been determined. In this study, the primary objective was to determine how different cotton cultivars alter the canopy LI in different vertical and horizontal zones of the canopy, the boll spatial distribution and the seed cotton yield. A secondary objective was to evaluate how the boll spatial distribution, biomass, LAI and seed cotton yield were altered by LI variation.
Yield and yield components of different cultivars
Varietal effects on cotton yield and yield components in 2014 and 2015
Seed cotton yield /(kg·hm−2)
Boll number per plant
Boll weight /g
4 616 a§
4 494 ab
4 164 c
4 129 d
4 308 c
4 353 abc
4 350 bc
4 208 cd
4 543 ab
4 616 a
4 672 a
4 427 ab
The spatial yield distribution within canopy of different cultivars
Spatial yield distribution within the cotton canopies of different cultivars in 2014 and 2015
Horizontally, C915 had more yield (99.1%) distributed in the inner part of the plant than did other varieties, except for L28 (95.2%) in 2014, and C113 had the least yield in the inner part of the plant (86.0%) (Table 2). In 2015, C60 had the least yield (87.7%) distributed in inner part of the plant, but other varieties did not show any significant difference in the yield distribution of the inner part of the plant. Therefore, cotton varieties with compact plant type showed relatively more yield distribution in the inner part of the plant only in 2014.
LI spatial distribution in canopies of different cultivars
LAI and accumulated biomass
Effect of cotton cultivar on cotton biomass accumulation and biomass partitioning in 2014 and 2015
Relationship between LI and yield formation in different cultivars
Cotton yield and yield distribution of different cultivars and plant types
Cotton yield was significantly affected by cotton cultivar (Girma et al. 2007). In this study, the cotton yields of six different cultivars ranged from 4164 to 4672 kg·hm− 2 and 4129 to 4616 kg·hm− 2 in 2014 and 2015, respectively. The optimal cotton yield was achieved by J958 and L28 in 2014 and by J228 in 2015, indicating the different performance of cotton cultivars in different environments. Yield was found to be a function of both boll number and boll weight, with the relative effects of each influenced by cultivar (Wang et al. 2009). In this study, J228 and J958 attained a higher cotton yield mainly due to the greater average boll weight, while L28 did so mainly due to a greater boll number per plant. Cotton yield varied significantly within plants (Bednarz et al. 2000), and yield difference can be traced to different positions within a cotton plant (Bednarz et al. 2006). Cotton boll distribution is very cultivar-dependent (Snowden et al. 2013). Our study showed that both vertical and horizontal yield distribution were affected by cotton variety and plant type. In general, loose-type cultivars had more bolls in the middle of the canopy, while compact-type cultivars had more bolls at the top and bottom of the plant, suggesting that compact-type varieties distributed yield more than did loose-type varieties. Cotton bolls located in the middle canopy weighed more than those in the lower and upper canopy (CRI 2013). Therefore, a higher boll distribution in the middle canopy might be another reason for the greater boll weight of loose-type cotton varieties (J958 and J228), in addition to genetic difference. Cotton varieties with a compact plant type showed relatively more yield distribution in the inner part of the plant only in 2014, but this did not occur in 2015. During the cotton growth period, with more rainfall in 2014, fruiting branches grew faster, especially in loose-type varieties, resulting in relatively greater outer yield distribution for loose-type than for compact-type cotton varieties.
Canopy LI and LI distribution of different varieties and plant types
As the primary source of energy, light plays an important role in plant growth. LI by the canopy is an important factor determining biomass production and crop development (Chenu et al. 2005; Escobar-Gutiérrez et al. 2009). The interception of light by the crop canopy is complicated and is affected by the canopy architecture (Mariscal et al. 2000). The canopy structure of a crop is determined largely by the plant type. In this study, the loose-type cultivars intercepted more LI than did compact-type cultivars in both years, mainly due to the greater LI in full-flowering and boll-setting stages. The peak LI occurred later in 2015 relative to 2014 due to the lower temperature and less rainfall in 2015 resulting in slow plant growth and development. The greater LI of loose-type cotton varieties could be explained by higher peak LAI, which was a determinant factor of LI by the cotton canopy (Reynolds et al. 2000). Plant type can also affect canopy light distribution(Arduini et al. 2006). LI distribution within canopy was significantly different among cotton cultivars and plant types both vertically and horizontally. The loose-type cultivars intercepted more light than did the compact-type cultivars in H2, H3 and V2 in both years, indicating that the difference in LI of the whole canopy among different cotton cultivars mainly resulted from the LI of the middle and upper canopy and of the outer canopy. Due to the longer fruiting branches and higher LAI of loose-type cotton cultivars, the light transmittance to the lower canopy is relatively less compared with that to the upper and middle canopy.
The relationship between yield and LI in different cultivars and plant types
Optimum canopy structure is the basis of improving photosynthetic efficiency and achieving high crop yields(Da Silva et al. 2014; Zhang et al. 2008). In this study, a significant linear relationship was observed between total biomass accumulation and accumulated LI, which was consistent with previous studies (Xue et al. 2015; Zarate-Valdez et al. 2012; Monteith 1977). However, no obvious relationship was observed between cotton yield and the total canopy-accumulated LI, which was also found by Zarate-Valdez et al. (2012) and Xue et al. (2015). A possible explanation for this result might be the different transport efficiency of LI to reproductive organs as indicated by different harvest index values. However, for each cultivar, cotton reproductive organs biomass was positively related to LI, indicating that increasing LI can effectively improve cotton yield for each cultivar. In addition, there was a linear relationship between vertical cotton yield distribution and LI distribution within the canopy, indicating that yield distribution within the canopy is in accordance with LI distribution. Therefore, proper canopy architecture can optimize yield distribution and subsequently improve cotton yield and fiber quality by optimizing LI distribution.
Weather information for the cotton growth season in 2014 and 2015
Accumulated temperature ≥10 °C / °C
Sunshine time /h
In both years, the land was plowed and irrigated in early spring before planting. The cotton was fertilized with 225 kg·hm− 2 N, 150 kg·hm− 2 P2O5 and 225 kg·hm− 2 K2O before sowing. Supplemental irrigation was provided at a total volume of approximately 45 mm by flooding the furrows during the flowering stage. Other field management activities were conducted according to local agronomic practices.
PAR interception and transmission in the canopy
We measured transmission PAR (TPAR) and reflection PAR (RPAR) in different canopy layers every ten days using a portable 1.0 m light quantum sensor (LI-191SA, LI-COR, Lincoln, NE,USA) and a data logger (LI-1400, LI-COR, Lincoln, NE, USA). The measurement by the spatial grid sampled method in the same sample area of each plot during the crop season between the square stage and maturity in each year and were performed 1 h before solar noon under clear skies. The sample row was divided 5 measuring position between two rows in the horizontal distance. Similarly, in the vertical direction, we divide canopy into layers of every 20 cm. In addition, the incident PAR (IPAR) above the canopy was automatically monitored and recorded at every 5 s intervals.
Estimation of PAR distribution in the canopy
where r(xi, xj) is the measured value of the variation function; φ is the Lagrangian, r(xi, x0) is the measured and calculated PAR, and x0 is the estimated value of the calculated point as computed by the unbiased estimate.
Calculation of accumulated TR within the whole canopy
where the coefficient vector is [1, 2, 2, 2, …, 2, 2, 2,1]; Δx is the vertical distance of the grid, Δy is the horizontal distance; h(i,j) is the grid node value in row i and column j.
Determination of the agronomic traits of cotton
Three plants were randomly uprooted from the center of each test plot and then divided into roots, stems, leaves and reproductive organs. For each plot, ten plants were randomly sampled for plant mapping to count boll numbers. The leaf area was determined using a scanner (Phantom 9800xl; Microtek, Shanghai, China) and Image-Pro plus 7.0 (Media Cybernetics, Rockville, MD, USA). The dry matter of the cotton plants was determined by drying at 80 °C to a constant weight. Cotton seed yield was manually harvested three times in 2014 and 2015, respectively.
Yield and yield component determination
In the beginning of October, cotton bolls from an area of 8 m2 (0.8 m× 10 m) in the central two rows of each plot were hand-harvested for cotton yield estimation. Plant density, boll number per plant and average seed cotton weight per boll were recorded to calculate the cotton yield.
Determination of spatial yield distribution
Before harvest, final plant mapping measurements were made on 30 consecutive undamaged plants in the two middle rows of each plot. Measurements included plant height, total nodes, and bolls present by fruiting site on each individual plant. Bolls located from the first to the fourth fruiting branches were designated as the lower bolls, those in the fifth to the eighth fruiting branches were middle bolls and those in the ninth and higher fruiting branches were upper bolls. In addition, bolls in the first and second fruit positions were referred to as inner bolls and those in the third fruit position and beyond were outer bolls. Yield spatial distribution was then determined by the boll number in each fruiting site divided by the total number of bolls per plant.
The experimental data were analyzed with SPSS 11.0. Differences between treatment means were tested for significance using least significant difference (LSD) after analysis of variance, which indicated a significant treatment effect by F-test at the probability level of 0.05.
This study demonstrated the effects of cotton cultivar and plant type on cotton yield, yield distribution, LI, LI distribution, and the relationship between yield and LI. In this study, loose-type cotton cultivars intercepted more accumulated LI than did compact-type cotton cultivars due to there being more LI in the middle and upper canopy. Although loose-type varieties had greater LI, they did not have distinct advantages in cotton yield comparing with compact-type cultivars, no significant relationship was observed between cotton yield, and canopy-accumulated LI resulted from different harvest index values of different cotton cultivars, therefore, for loose-type cotton cultivars with high LI in this study, improving the harvest index by adjusting the source-to-sink relationship is a way to further increase cotton yield. In addition, there was a positive relationship between reproductive organ biomass accumulation and canopy-accumulated LI, indicating that enhancing LI is important for yield improvement for each cultivar. Furthermore, the yield distribution within canopy was significantly linearly related to vertical LI distribution. Therefore, optimizing the canopy structure and subsequently optimizing the LI distribution within the cotton canopy can effectively manipulate the yield distribution, which can further influence cotton yield and fiber quality.
The authors are grateful for the work of the technicians at the experimental station of the Institute of Cotton Research of Chinese Academy of Agricultural Sciences.
This work was funded by the National Natural Science Foundation of China (31371561).
Availability of data and materials
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
Li YB, Han YC, Wang GP designed the study. Xing FF, Feng L and Zhi XY wrote the main manuscript text and prepared figures 1-8. Xiong SW, Wang ZB, Fan ZY, Du WL carried out the experimental work and Yang BF, Xing FF, Lei YP, Feng L, Zhi XY analyzed data. All authors reviewed the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Arduini I, Masoni A, Ercoli L, Mariotti M. Grain yield, and dry matter and nitrogen accumulation and remobilization in durum wheat as affected by variety and seeding rate. Eur J Agron. 2006;25:309–18. https://doi.org/10.1016/j.eja.2006.06.009.View ArticleGoogle Scholar
- Bai ZG, Mao SC, Han YC, et al. Study on light interception and biomass production of different cotton cultivars. PLoS One. 2016;11(5):e0156335.https://doi.org/10.1371/journal.pone.0156335.View ArticleGoogle Scholar
- Baldissera TC, Frak E, Carvalho PCD, Louarn G. Plant development controls leaf area expansion in alfalfa plants competing for light. Ann Bot. 2014;113:145–57. https://doi.org/10.1093/aob/mct251.View ArticleGoogle Scholar
- Barthélémy D, Caraglio Y. Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. Ann Bot. 2007;99:375–407. https://doi.org/10.1093/aob/mcl260.View ArticleGoogle Scholar
- Bednarz CW, Bridges DC, Brown SM. Analysis of cotton yield stability across population densities. Agron J. 2000;92:128–35. https://doi.org/10.2134/agronj2000.921128x.View ArticleGoogle Scholar
- Bednarz CW, Nichols RL, Brown SM. Plant density modifications of cotton within-boll yield components. Crop Sci. 2006;46(5):2076–80. https://doi.org/10.2135/cropsci2005.12.0493.View ArticleGoogle Scholar
- Chen JM, Blanken PD, Black TA, et al. Radiation regime and canopy architecture in a boreal aspen forest. Agric For Meteorol. 1997;86:107–25. https://doi.org/10.1016/S0168-1923(96)02402-1.View ArticleGoogle Scholar
- Chenu K, Franck N, Dauzat J, et al. Integrated responses of rosette organogenesis, morphogenesis and architecture to reduced incident light in Arabidopsis thaliana results in higher efficiency of light interception. Functional Plant Biol. 2005;32:1123–34. https://doi.org/10.1071/FP05091.View ArticleGoogle Scholar
- CRI (Cotton Research Institute, Chinese Academy of Agricultural Sciences). Cultivation of cotton in China. Shanghai: Shanghai Science and Technology Press; 2013. (in Chinese).Google Scholar
- Da Silva D, Han L, Costes E. Light interception efficiency of apple trees: a multiscale computational study based on MAppleT. Ecol Model. 2014;290:45–53. https://doi.org/10.1016/j.ecolmodel.2013.12.001.View ArticleGoogle Scholar
- Dauzat J, Clouvel P, Luquet D, Martin P. Using virtual plants to analyse the light-foraging efficiency of a low-density cotton crop. Ann Bot. 2008;101:1153–66. https://doi.org/10.1093/aob/mcm316.View ArticleGoogle Scholar
- Escobar-Gutiérrez A, Combes D, Rakocevic M, et al. Functional relationships to estimate morphogenetically active radiation (MAR) from PAR and solar broadband irradiance measurements: the case of a sorghum crop. Agric Meteorologica. 2009;149:1244–53.https://doi.org/10.1016/j.agrformet.2009.02.011.View ArticleGoogle Scholar
- Girma K, Teal RK, Freeman KW, et al. Cotton lint yield and quality as affected by applications of N, P, and K fertilizers. J Cotton Sci. 2007;11:12–9.Google Scholar
- Kaggwa-Asiimwe R, Andrade-Sanchez P, Wang GY. Plant architecture influences growth and yield response of upland cotton to population density. Field Crops Res. 2013;145:52–9. https://doi.org/10.1016/j.fcr.2013.02.005.View ArticleGoogle Scholar
- Louarn G, Lecoeur J, Lebon E. A three-dimensional statistical reconstruction model of grapevine (Vitis vinifera) simulating canopy structure variability within and between cultivar/training system pairs. Ann Bot. 2008;101:1167–84. https://doi.org/10.1093/aob/mcm170.View ArticleGoogle Scholar
- Madakadze IC, Stewart K, Peterson PR, et al. Light interception, use-efficiency and energy yield of switchgrass (Panicum virgatum L.) grown in a short season area. Biomass Bioenergy. 1998;15:475–82. https://doi.org/10.1016/S0961-9534(98)00060-9.View ArticleGoogle Scholar
- Maddonni GA, Otegui ME. Leaf area, light interception, and crop development in maize. Field Crops Res. 1996;48:81–7. https://doi.org/10.1016/0378-4290(96)00035-4.View ArticleGoogle Scholar
- Mariscal M, Martens S, Ustin S, et al. Modelling and measurement of radiation interception by olive canopies. Agric For Meteorol. 2000;100:183–97. https://doi.org/10.1016/S0168-1923(99)00137-9.View ArticleGoogle Scholar
- McIntyre BD, Riha SJ, Ong CK. Light interception and evapotranspiration in hedgerow agroforestry systems. Agric For Meteorol. 1996;81:31–40. https://doi.org/10.1016/0168-1923(95)02303-8.View ArticleGoogle Scholar
- Meir P, Kruijt B, Broadmeadow M, et al. Acclimation of photosynthetic capacity to irradiance in tree canopies in relation to leaf nitrogen concentration and leaf mass per unit area. Plant Cell Environ. 2002;25:343–57. https://doi.org/10.1046/j.0016-8025.2001.00811.x.View ArticleGoogle Scholar
- Mo XG, Liu SX, Lin ZH, et al. Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China plain. Ecol Model. 2005;183:301–22. https://doi.org/10.1016/j.ecolmodel.2004.07.032.View ArticleGoogle Scholar
- Monteith JL. Climate and the efficiency of crop production in Britain. Philos Trans R Soc Lond B Biol Sci. 1977;281:277–94. https://doi.org/10.1098/rstb.1977.0140.View ArticleGoogle Scholar
- Reynolds MP, Delgado MI, Gutiérrez-Rodríguez M, Larqué-Saavedra A. Photosynthesis of wheat in a warm, irrigated environment: I: Genetic diversity and crop productivity. Field Crops Res. 2000;66:37–50. https://doi.org/10.1016/S0378-4290(99)00077-5.View ArticleGoogle Scholar
- Snowden C, Ritchie GL, Cave J, et al. Multiple irrigation levels affect boll distribution, yield, and fiber micronaire in cotton. Agron J. 2013;105:1536–44. https://doi.org/10.2134/agronj2013.0084.View ArticleGoogle Scholar
- Vargas LA, Andersen MN, Jensen CR, Jørgensen U. Estimation of leaf area index, light interception and biomass accumulation of Miscanthus sinensis ‘Goliath’ from radiation measurements. Biomass Bioenergy. 2002;22:1–14.View ArticleGoogle Scholar
- Wang Q, Han S, Zhang LZ, et al. Boll size affects the insecticidal protein content in Bacillus thuringiensis (Bt) cotton. Field Crops Res. 2009;110:106–10.https://doi.org/10.1016/j.fcr.2008.07.008.View ArticleGoogle Scholar
- Wang Q, Han S, Zhang LZ, et al. Density responses and spatial distribution of cotton yield and yield components in jujube (Zizyphus jujube)/cotton (Gossypium hirsutum) agroforestry. Eur J Agron. 2016;79:58–65. https://doi.org/10.1016/j.eja.2016.05.009.View ArticleGoogle Scholar
- Xue HY, Han YC, Li YB, et al. Spatial distribution of light interception by different plant population densities and its relationship with yield. Field Crops Res. 2015;184:17–27. https://doi.org/10.1016/j.fcr.2015.09.004.View ArticleGoogle Scholar
- Zarate-Valdez JL, Whiting ML, Lampinen BD, et al. Prediction of leaf area index in almonds by vegetation indexes. Comput Electron Agr. 2012;85:24–32. https://doi.org/10.1016/j.compag.2012.03.009.View ArticleGoogle Scholar
- Zhang L, Van der Werf W, Bastiaans L, et al. Light interception and utilization in relay intercrops of wheat and cotton. Field Crops Res. 2008;107:29–42. https://doi.org/10.1016/j.fcr.2007.12.014.View ArticleGoogle Scholar
- Zhi XY, Han YC, Mao SC, et al. Light spatial distribution in the canopy and crop development in cotton. PLoS One. 2014;9:1–10. https://doi.org/10.1371/journal.pone.0113409.View ArticleGoogle Scholar