Journal of Arid Land

, Volume 10, Issue 2, pp 292–303 | Cite as

Planting density affected biomass and grain yield of maize for seed production in an arid region of Northwest China

  • Xuelian Jiang
  • Ling Tong
  • Shaozhong Kang
  • Fusheng Li
  • Donghao Li
  • Yonghui Qin
  • Rongchao Shi
  • Jianbing Li


Field experiments were conducted from 2012 to 2015 in an arid region of Northwest China to investigate the effects of planting density on plant growth, yield, and water use efficiency (WUE) of maize for seed production. Five planting densities of 6.75, 8.25, 9.75, 11.25 and 12.75 plants/m2 were conducted in 2012, and a planting density of 14.25 plants/m2 was added from 2013 to 2015. Through comparison with the AquaCrop yield model, a modified model was developed to estimate the biomass accumulation and yield under different planting densities using adjustment coefficient for normalized biomass water productivity and harvest index. It was found that the modified yield model had a better performance and could generate results with higher determination coefficient and lower error. The results indicated that higher planting density increased the leaf area index and biomass accumulation, but decreased the biomass accumulation per plant. The total yield increased rapidly as planting density increased to 11.25 plants/m2, but only a slight increase was observed when the density was greater than 11.25 plants/m2. The WUE also reached the maximum when planting density was 11.25 plants/m2, which was the recommended planting density of maize for seed production in Northwest China.


planting density yield model biomass accumulation grain yield water use efficiency Northwest China 


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We are grateful for the research grants from the National Natural Science Foundation of China (51379208, 91425302, 51621061), the Government Public Research Funds for Projects of the Ministry of Agriculture (201503125) and the Discipline Innovative Engineering Plan (111 Program, B14002).


  1. Akmal M, Asim M, Gilbert M. 2014. Influence of seasonal variation on radiation use efficiency and crop growth of maize planted at various densities and nitrogen rates. Pakistan Journal of Agricultural Sciences, 51(4): 835–846.Google Scholar
  2. Allen R G, Pereira L S, Raes D, et al. 1998. Crop evaporation: guidelines for computing crop water requirements. In: FAO Irrigation and Drainage Paper No. 56. Rome: FAO, 300.Google Scholar
  3. Coetto E, Di Candillo M, Castelli F, et al. 2013. Comparing solar radiation interception and use efficiency for the energy crops giant reed (Arundo donax L.) and sweet sorghum (Sorghum bicolor L. Moench). Field Crops Research, 149: 159–166.CrossRefGoogle Scholar
  4. Cox W J, Otis D J. 1993. Grain and silage yield responses of commercial corn hybrids to plant densities. In: Agronomy Abstracts. Madison, Wisconsin: American Society of Agronomy, 132.Google Scholar
  5. Dai J L, Li W J, Tang W, et al. 2015. Manipulation of dry matter accumulation and partitioning with plant density in relation to yield stability of cotton under intensive management. Field Crops Research, 180: 207–215.CrossRefGoogle Scholar
  6. DeLougherty R L, Crookston R K. 1979. Harvest index of corn affected by population density, maturity rating, and environment. Agronomy Journal, 71(4): 577–580.CrossRefGoogle Scholar
  7. Duncan W G. 1986. Planting patterns and soybean yields. Crop Science, 26(3): 584–588.CrossRefGoogle Scholar
  8. El-Hendawy S E, El-Lattief E A A, Ahmed M S, et al. 2008. Irrigation rate and plant density effects on yield and water use efficiency of drip-irrigated corn. Agricultural Water Management, 95(7): 836–844.CrossRefGoogle Scholar
  9. Fulton J M. 1970. Relationships among soil moisture stress, plant populations, row spacing and yield of corn. Canadian Journal of Plant Science, 50(1): 31–38.CrossRefGoogle Scholar
  10. Griesh M H, Yakout G M. 2001. Effect of plant population density and nitrogen fertilization on yield and yield components of some white and yellow maize hybrids under drip irrigation system in sandy soil. In: Horst W J, Schenk M K, Bürkert A, et al. Plant Nutrition. Dordrecht: Springer, 810–811.CrossRefGoogle Scholar
  11. Harper L A, Pallas J E Jr, Bruce R R, et al. 1979. Greenhouse microclimate for tomatoes in the southeast USA. American Society for Horticultural Science, 104(5): 659–663.Google Scholar
  12. Hashemi-Dezfouli A, Herbert S J. 1992. Intensifying plant density response of corn with artificial shade. Agronomy Journal, 84(4): 547–551.CrossRefGoogle Scholar
  13. Holt D F, Timmons D R. 1968. Influence of precipitation, soil water, and plant population interactions on corn grain yields. Agronomy Journal, 60(4): 379–381.CrossRefGoogle Scholar
  14. Jiang X L, Kang S Z, Tong L, et al. 2014. Crop coefficient and evapotranspiration of grain maize modified by planting density in an arid region of northwest China. Agricultural Water Management, 142: 135–143.CrossRefGoogle Scholar
  15. Johnsonb R R, Green D E, Jordan C W. 1982. What is the best soybean row width? A U.S. perspective. Crops and Soils Magazine, 43(4): 10–13.Google Scholar
  16. Kamel M S, El-Raouf M S, Mahmoud E A, et al. 1983. Response of two maize varieties to different plant densities in relation to weed control treatments. Annals of Agricultural Sciences, 19: 79–93.Google Scholar
  17. Karlen D L, Camp C R. 1985. Row spacing, plant population, and water management effects on corn in the Atlantic Coastal Plain. Agronomy Journal, 77(3): 393–398.CrossRefGoogle Scholar
  18. Lang A L, Pendleton J W, Dungan G H. 1956. Influence of population and nitrogen levels on yield and protein and oil contents of nine corn hybrids. Agronomy Journal, 48(7): 284–289.CrossRefGoogle Scholar
  19. Legates D R, McCabe G J Jr. 1999. Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1): 233–241.CrossRefGoogle Scholar
  20. Lemcoff J H, Loomis R S. 1986. Nitrogen influences on yield determination in maize. Crop Science, 26(5): 1017–1022.CrossRefGoogle Scholar
  21. Li S E, Kang S Z, Li F S, et al. 2008. Evapotranspiration and crop coefficient of spring maize with plastic mulch using eddy covariance in northwest China. Agricultural Water Management, 95(11): 1214–1222.CrossRefGoogle Scholar
  22. Loomis R S, Connor D J. 1992. Crop Ecology: Productivity and Management in Agricultural Systems. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  23. Mohamed M M A. 1999. Effect of some agronomic practices on corn production (Zea mays L.) under drip irrigation system. PhD Dissertation. Ismailia, Egypt: Faculty of Agriculture, Suez Canal University, 107.Google Scholar
  24. Olson R A, Sander D H. 1988. Corn production. In: Sprague G F, Dudley J W. Corn and Corn Improvement (3rd ed.). Madison, WI: American Society of Agronomy, 639–686.Google Scholar
  25. Papadopoulos A P, Pararajasingham S. 1997. The influence of plant spacing on light interception and use in greenhouse tomato (Lycopersicon esculentum Mill.): a review. Scientia Horticulturae, 69(1–2): 1–29.CrossRefGoogle Scholar
  26. Qiu R J, Song J J, Du T S, et al. 2013. Response of evapotranspiration and yield to planting density of solar greenhouse grown tomato in northwest China. Agricultural Water Management, 130: 44–51.CrossRefGoogle Scholar
  27. Raes D, Steduto P, HsiaoT C, et al. 2009. AquaCrop–The FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agronomy Journal, 101(3): 438–477.Google Scholar
  28. Raes D, Steduto P, Hsiao C, et al. 2011. AquaCrop Version 3.1Plus Reference Manual. Rome, Italy: FAO.Google Scholar
  29. Rahmati H. 2009. Effect of plant density and nitrogen rates on yield and nitrogen efficiency of grain corn. World Applied Science Journal, 7(8): 958–961.Google Scholar
  30. Rana G, Katerji N. 2000. Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: a review. European Journal of Agronomy, 13(2–3): 125–153.CrossRefGoogle Scholar
  31. Sangoi L, Gracietti M A, Rampazzo C, et al. 2002. Response of Brazilian maize hybrids from different eras to changes in plant density. Field Crops Research, 79(1): 39–51.CrossRefGoogle Scholar
  32. Soliman F H, Goda A S, Ragheb M M, et al. 1995. Response of maize (Zea mays L.) hybrids to plant populations density under different environmental conditions. Hip International, 23(6):124–124.Google Scholar
  33. Steduto P, Hsiao T C, Raes D, et al. 2009. AquaCrop-the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101(3): 426–437.Google Scholar
  34. Tetio-Kagho F, Gardner F P. 1998. Responses of maize to plant population density. II. Reproductive development, yield, and yield adjustments. Agronomy Journal, 80(6): 935–940.Google Scholar
  35. Thimmappa V, Reddy M S, Reddy U, et al. 2014. Effect of nitrogen levels and plant densities on growth parameters, yield attributes and yield of kharif maize (Zea mays L.). Crop Research, 47(1–3): 29–32.Google Scholar
  36. Westgate M E, Forcella F, Reicosky D C, et al. 1997. Rapid canopy closure for maize production in the northern US corn belt: Radiation-use efficiency and grain yield. Field Crops Research, 49(2–3): 249–258.CrossRefGoogle Scholar

Copyright information

© Xinjiang Institute of Ecology and Geography, the Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xuelian Jiang
    • 1
    • 2
  • Ling Tong
    • 1
  • Shaozhong Kang
    • 1
  • Fusheng Li
    • 3
  • Donghao Li
    • 1
  • Yonghui Qin
    • 1
  • Rongchao Shi
    • 1
  • Jianbing Li
    • 4
  1. 1.Center for Agricultural Water Research in ChinaChina Agricultural UniversityBeijingChina
  2. 2.Key Laboratory of Biochemistry and Molecular Biology in Universities of ShandongWeifang UniversityWeifangChina
  3. 3.College of AgricultureGuangxi UniversityNanningChina
  4. 4.Environmental Science and Engineering ProgramUniversity of Northern British ColumbiaPrince GeorgeCanada

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