Effects of nutrient level and planting density on population relationship in soybean and wheat intercropping populations

Autoři: Jialing Huang aff001;  Yihang Li aff001;  Yu Shi aff001;  Lihong Wang aff001;  Qing Zhou aff001;  Xiaohua Huang aff003
Působiště autorů: State Key Laboratory of Food Science and Technology, School of Environment and Civil Engineering, Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi, China aff001;  Jiangsu Cooperative Innovation Center of Water Treatment Technology and Materials, Suzhou University of Science and Technology, Suzhou, China aff002;  Jiangsu Collaborative Innovation Center of Biomedical Functional Materials, Jiangsu Key Laboratory of Biomedical Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing, China aff003
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225810


A positive interaction between plant populations is a type of population relationship formed during long-term evolution. This interaction can alleviate population competition, improve resource utilization in populations, and promote population harmony and community stability. However, cultivated plant populations may have insufficient time to establish a positive interaction, thereby hindering the formation of the positive interaction. As current studies have not fully addressed these issues, our study established soybean/wheat intercropping populations beneficial for growth and explored the effects of nutrient level and planting density on the positive interaction between the two crops. Changes across population modules in both sole cropping and intercropping populations of soybean and wheat were analyzed. Results using nutrient levels of ½- or ¼-strength Hoagland solution indicated that soybean/wheat intercropping population modules significantly increased at low planting densities (D20 and D26) and significantly decreased at high planting densities (D32 and D60). Therefore, as planting density increased, the modules of both intercropping populations initially increased before decreasing. Similarly, positive interaction initially strengthened before weakening. Moreover, at an intermediate planting density, the population modules reached their maxima, and the positive interaction was the strongest. Under the same planting density, ¼-strength Hoagland solution recorded better growth for the soybean/wheat intercropping population modules compared to results using the ½-strength Hoagland solution. These findings indicated that low nutrient level can increase the positive interaction of intercropping populations at a given planting density, and that environmental nutrient level and population planting densities constrain the positive interaction between soybean and wheat populations in the intercropping system. This study highlights issues that need to be addressed when constructing intercropping populations.

Klíčová slova:

Cereal crops – Density – Leaves – Planting – Population density – Soybean – Wheat – Intercropping


1. Ellstrand NC. Plant population ecology. Ecology. 1983; 64: 1321–1322.

2. Lortie CJ, Filazzola A, Welham C, and Turkington R. A cost–benefit model for plant–plant interactions: a density-series tool to detect facilitation. Plant Ecol. 2016; 217: 1–15.

3. Haugo RD, and Halpern CB. Tree age and tree species shape positive and negative interactions in a montane meadow. Botanique. 2010; 88: 488–499.

4. Olofsson J, Antarctic A, and Research A. Positive and negative plant–plant interactions in two contrasting arctic-alpine plant communities. Arct Antarct Alp Res. 2004; 36: 464–467.

5. Callaway RM. Positive interactions among plants. Bot Rev. 1995; 61: 306–349.

6. Callaway RM, Brooker RW, and Choler P. Positive interactions among alpine plants increase with stress. Nature. 2002; 417: 844–848. doi: 10.1038/nature00812 12075350

7. Harper JL. Population biology of plants. Popul Biol Plants. 1977.

8. Bakker LM, Mommer L, Ruijven, Jasper, and Van. Can root trait diversity explain complementarity effects in a grassland biodiversity experiment? J Plant Ecol. 2018; 11: 73–84.

9. Hu HQ, Wang LH, Zhou Q, and Huang XH. Combined effects of simulated acid rain and lanthanum chloride on chloroplast structure and functional elements in rice. Environ Sci Pollut Res Int. 2016; 23: 8902–8916. doi: 10.1007/s11356-015-5962-9 26815371

10. Liu HE, Shi ZW, Li JF, Zhao P, Qin SY, and Nie ZJ. The impact of phosphorus supply on selenium uptake during hydroponics experiment of winter wheat (Triticum aestivum) in China. Front Plant Sci. 2018; 9: 1–9. doi: 10.3389/fpls.2018.00001

11. Xia BX, Wang LH, Nie LJ, Zhou Q, and Huang XH. A pathway of bisphenol A affecting mineral element contents in plant roots at different growth stages. Ecotoxicol Environ Saf. 2017; 135: 115–122. doi: 10.1016/j.ecoenv.2016.09.028 27723463

12. Granzow S, Kaiser K, Wemheuer B, Pfeiffer B, Daniel R, and Vidal S et al. The effects of cropping regimes on fungal and bacterial communities of wheat and faba bean in a greenhouse pot experiment differ between plant species and compartment. Front Microbiol. 2017; 8: 1–22. doi: 10.3389/fmicb.2017.00001

13. Zhang F, Li YH, Shi Y, Wang LH, Zhou Q, and Huang XH. A novel evaluation of the effect of lanthanum exposure on plant populations. Chemosphere. 2018; 202: 377–386.

14. Hoagland DR. Optimum nutrient solutions for plants. Science. 1920; 52: 562–564. doi: 10.1126/science.52.1354.562 17811355

15. Yang YL, Ma T, Ding F, Ma HZ, Duan XH, and Gao TP et al. Interactive zinc, iron, and copper-induced phytotoxicity in wheat roots. Environ Sci Pollut Res. 2017; 24: 395–404.

16. Javadi T, Rohollahi D, Ghaderi N, and Nazari F. Mitigating the adverse effects of drought stress on the morpho-physiological traits and anti-oxidative enzyme activities of Prunus avium through β-amino butyric acid drenching. Sci Hortic. 2017; 218: 156–163.

17. Sun ZG, Wang LH, Zhou Q, and Huang XH. Effects and mechanisms of the combined pollution of lanthanum and acid rain on the root phenotype of soybean seedlings. Chemosphere. 2013; 93: 344–352. doi: 10.1016/j.chemosphere.2013.04.089 23726884

18. Hatzig SV, Schiessl S, Stahl A, and Snowdon RJ. Characterizing root response phenotypes by neural network analysis. J Exp Bot. 2015; 66: 5617–5624. doi: 10.1093/jxb/erv235 26019255

19. Li XY, Wang LH, Wang SM, Yang Q, Zhou Q, and Huang XH. A preliminary analysis of the effects of bisphenol A on the plant root growth via changes in endogenous plant hormones. Ecotox Environ Safe. 2018; 150: 152–158.

20. Halai AD, Woollams AM, and Ralph MAL. Using principal component analysis to capture individual differences within a unified neuropsychological model of chronic post-stroke aphasia: revealing the unique neural correlates of speech fluency, phonology and semantics. Cortex. 2017; 86: 275–289. doi: 10.1016/j.cortex.2016.04.016 27216359

21. Miller AE, and Suding BKN. Plant uptake of inorganic and organic nitrogen: Neighbor identity matters. Ecology. 2007; 88: 1832–1840. doi: 10.1890/06-0946.1 17645029

22. Hong J, Ma X, Zhang X, and Wang X. Nitrogen uptake pattern of herbaceous plants: coping strategies in altered neighbor species. Biol Fert Soils. 2017; 53: 729–735.

23. Bair E, Hastie T, and Pau D. Prediction by supervised principal components. J Am Stat Assoc. 2006; 101: 119–137.

24. Wang CL, Wu C, and Wang WJ. Application of synthetic principal component analysis model to mine area farmland heavy metal pollution assessment. J Coal Sci Eng (China). 2008; 14: 109–113.

25. Cai R, Mullen JD, Bergstrom JC, and Shurley Donald W. Using a climate index to measure crop yield response. J Agr Appl Econ. 2013, 45:719–737.

26. Eskandari H, and Ghanbari A. Environmental resource consumption in wheat (Triticum aestivum) and bean (Viciafaba) intercropping: comparison of nutrient uptake and light interception. Not Sci Biol. 2010; 2: 100–103.

27. Ahmed S, and Rao MR. Performance of maize–soybean intercrop combination in the tropics: Results of a multi-location study. Field Crop Res. 1982; 5: 147–161.

28. Natarajan MM, and Willey RW. Sorghum-pigeonpea intercropping and the effects of plant population density. J Agric Sci. 1980; 95: 51–58.

29. Reddy MS, and Willey RW. Growth and resource use studies in an intercrop of pearl millet/groundnut. Field Crop Res. 1981; 4: 13–24.

30. Bo LI, Watkinson AR, and Hara T. Dynamics of competition in populations of carrot (Daucus carota). Ann Bot.1996; 78: 0–214.

31. Calviño A, and Galetto L. Variation in sexual expression in relation to plant height and local density in the andromonoecious shrub Caesalpinia gilliesii (Fabaceae). Plant Ecol. 2010; 209: 37–45.

32. Ku LX, Zhang LK, Tian ZQ, Guo SL, Su HH, and Ren ZZ et al. Dissection of the genetic architecture underlying the plant density response by mapping plant height-related traits in maize (Zea mays, L.). Mol Genet Genom. 2015; 290: 1223–1233.

33. Lahiri D, Khalid S, and Sarkar T. Pea–barley intercropping for efficient symbiotic N2-fixation, soil N acquisition and use of other nutrients in European organic cropping systems. Field Crop Res. 2009; 113: 64–71.

34. Yang W, Li Z, and Wang J. Crop yield, nitrogen acquisition and sugarcane quality as affected by interspecific competition and nitrogen application. Field Crop Res. 2013; 146: 44–50.

35. Horst WJ, Kamh M, Jibrin JM, and Chude VO. Agronomic measures for increasing P availability to crops. Plant Soil. 2001; 237: 211–23.

36. Pace J, Gardner C, and Romay C et al. Genome-wide association analysis of seedling root development in maize (Zea mays L.). BMC Genomics. 2015; 16: 1–12. doi: 10.1186/1471-2164-16-1

Článek vyšel v časopise


2019 Číslo 12