Journal of Integrative Agriculture ›› 2020, Vol. 19 ›› Issue (3): 668-679.DOI: 10.1016/S2095-3119(19)62661-4

所属专题: 玉米遗传育种合辑Maize Genetics · Breeding · Germplasm Resources 玉米耕作栽培合辑Maize Physiology · Biochemistry · Cultivation · Tillage

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  • 收稿日期:2018-10-12 出版日期:2020-03-01 发布日期:2020-03-04

Quantitative design of yield components to simulate yield formation for maize in China

HOU Hai-peng1*, MA Wei1*, Mehmood Ali NOOR1, TANG Li-yuan2, LI Cong-feng1, DING Zai-song1, ZHAO Ming1 
  

  1. 1 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture and Rural Affairs, Beijing 100081, P.R.China 
    2 Institute of Cotton, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050051, P.R.China
  • Received:2018-10-12 Online:2020-03-01 Published:2020-03-04
  • Contact: Correspondence MA Wei, E-mail: mawei02@caas.cn; ZHAO Ming, Tel/Fax: +86-10-82108752, E-mail: zhaoming@caas.cn
  • About author: * These authors contributed equally to this study
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2018YFD020060 and 2017YFD0301307), the National Natural Science Foundation of China (31971851), and the earmarked fund for China Agriculture Research System (CARS-02-12).

Abstract:

Maize (Zea mays L.) stands prominently as one of the major cereal crops in China as well as in the rest of the world.  Therefore, predicting the growth and yield of maize for large areas through yield components under high-yielding environments will help in understanding the process of yield formation and yield potential under different environmental conditions.  This accurate early assessment of yield requires accuracy in the formation process of yield components as well.  In order to formulate the quantitative design for high yields of maize in China, yield performance parameters of quantitative design for high grain yields were evaluated in this study, by utilizing the yield performance equation with normalization of planting density.  Planting density was evaluated by parameters including the maximum leaf area index and the maximum leaf area per plant.  Results showed that the variation of the maximum leaf area per plant with varying plant density conformed to the Reciprocal Model, which proved to have excellent prediction with root mean square error (RMSE) value of 5.95%.  Yield model estimation depicted that the best optimal maximum leaf area per plant was 0.63 times the potential maximum leaf area per plant of hybrids.  Yield performance parameters for different yield levels were quantitatively designed based on the yield performance equation.  Through validation of the yield performance model by simulating high yields of spring maize in the Inner Mongolia Autonomous Region and Jilin Province, China, and summer maize in Shandong Province, the yield performance equation showed excellent prediction with the satisfactory mean RMSE value (7.72%) of all the parameters.  The present study provides theoretical support for the formulation of quantitative design for sustainable high yield of maize in China, through consideration of planting density normalization in the yield prediction process, providing there is no water and nutrient limitation.

Key words: maize ,  yield performance parameters ,  high yield ,  yield prediction process ,  quantitative design