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Estimating the upper and lower limits of kernel weight under different water regimes in hybrid maize seed production

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  • Wang, Jintao
  • Kang, Shaozhong
  • Du, Taisheng
  • Tong, Ling
  • Ding, Risheng
  • Li, Sien

Abstract

In hybrid maize (Zea mays L.) seed production, both the kernel quality and the germination rate, which are positively related to kernel weight (KW), are very important. Water deficit can change the source–sink ratio (SSR) and thus affects KW. To create a water-saving irrigation program that facilitates the production of high-quality seed, it is necessary to properly model the KW–water relationship. Irrigation experiments were conducted in 2014 and 2015 in an arid region of Northwest China to investigate the effects of deficit irrigation on maize plant biomass and yield; and pollination experiments were conducted in 2016 to obtain a wide range of SSR and KW data. Analysis of the results showed that water deficit at the vegetative or flowering stages reduced post-flowering biomass gain (PBG) and kernel number (KN), thus significantly affecting SSR. At the grain-filling stage it reduced PBG but had no significant effect on KN, thus reducing SSR. Only the treatment of no irrigation in the grain-filling stage in 2015 significantly reduced KW. The Jensen model can accurately simulate the relationship between PBG and relative evapotranspiration at each growth stage. The water sensitivity index of PBG in the vegetative, flowering, grain-filling and ripening stages were respectively 0.48, 0.48, 0.97, and 0.16. Based on the experimental data of 2016, the hyperbolic upper (UpKW) and lower (LowKW) limit equations were created for KW as a function of SSR using boundary analysis. UpKW and LowKW increased as SSR increased, but the difference between UpKW and LowKW first increased and then decreased as SSR increased. When SSR was 0, UpKW was 178.39 mg and LowKW was 155.56 mg. When SSR is not less than 867.23 mg kernel−1, UpKW and LowKW are both 326.97 mg, which is the potential KW. Combined with the KN–water model, the models developed in this study can be used to develop a water-saving and irrigation program that produces high-quality seed.

Suggested Citation

  • Wang, Jintao & Kang, Shaozhong & Du, Taisheng & Tong, Ling & Ding, Risheng & Li, Sien, 2019. "Estimating the upper and lower limits of kernel weight under different water regimes in hybrid maize seed production," Agricultural Water Management, Elsevier, vol. 213(C), pages 128-134.
    • Handle: RePEc:eee:agiwat:v:213:y:2019:i:c:p:128-134
    DOI: 10.1016/j.agwat.2018.09.014
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    References listed on IDEAS

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    1. Yang, J.M. & Yang, J.Y. & Liu, S. & Hoogenboom, G., 2014. "An evaluation of the statistical methods for testing the performance of crop models with observed data," Agricultural Systems, Elsevier, vol. 127(C), pages 81-89.
    2. Wang, Jintao & Kang, Shaozhong & Zhang, Xiaotao & Du, Taisheng & Tong, Ling & Ding, Risheng & Li, Sien, 2018. "Simulating kernel number under different water regimes using the Water-Flowering Model in hybrid maize seed production," Agricultural Water Management, Elsevier, vol. 209(C), pages 188-196.
    3. Chen, Jinliang & Kang, Shaozhong & Du, Taisheng & Guo, Ping & Qiu, Rangjian & Chen, Renqiang & Gu, Feng, 2014. "Modeling relations of tomato yield and fruit quality with water deficit at different growth stages under greenhouse condition," Agricultural Water Management, Elsevier, vol. 146(C), pages 131-148.
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    Cited by:

    1. Shi, Rongchao & Wang, Jintao & Tong, Ling & Du, Taisheng & Shukla, Manoj Kumar & Jiang, Xuelian & Li, Donghao & Qin, Yonghui & He, Liuyue & Bai, Xiaorui & Guo, Xiaoxu, 2022. "Optimizing planting density and irrigation depth of hybrid maize seed production under limited water availability," Agricultural Water Management, Elsevier, vol. 271(C).
    2. Kang, Jian & Hao, Xinmei & Zhou, Huiping & Ding, Risheng, 2021. "An integrated strategy for improving water use efficiency by understanding physiological mechanisms of crops responding to water deficit: Present and prospect," Agricultural Water Management, Elsevier, vol. 255(C).
    3. Shi, Rongchao & Tong, Ling & Ding, Risheng & Du, Taisheng & Shukla, Manoj Kumar, 2021. "Modeling kernel weight of hybrid maize seed production with different water regimes," Agricultural Water Management, Elsevier, vol. 250(C).
    4. Wang, Jintao & Dong, Xinliang & Qiu, Rangjian & Lou, Boyuan & Tian, Liu & Chen, Pei & Zhang, Xuejia & Liu, Xiaojing & Sun, Hongyong, 2023. "Optimization of sowing date and irrigation schedule of maize in different cropping systems by APSIM for realizing grain mechanical harvesting in the North China Plain," Agricultural Water Management, Elsevier, vol. 276(C).

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