IDEAS home Printed from https://ideas.repec.org/a/caa/jnlpse/v63y2017i11id417-2017-pse.html
   My bibliography  Save this article

Using QUEFTS model for estimating nutrient requirements of maize in the Northeast China

Author

Listed:
  • Wenting JIANG

    (College of Land and Environment, Shenyang Agriculture University, Shenyang, Liaoning, P.R. China)

  • Xiaohu LIU
  • Wen QI

    (College of Land and Environment, Shenyang Agriculture University, Shenyang, Liaoning, P.R. China)

  • Xiaonan XU

    (College of Land and Environment, Shenyang Agriculture University, Shenyang, Liaoning, P.R. China)

  • Yucui ZHU

    (College of Land and Environment, Shenyang Agriculture University, Shenyang, Liaoning, P.R. China)

Abstract

Accurate estimating of the balanced nutrition for maize is necessary for optimizing fertilizer management to prevent nutrient supply surplus or deficiency. Data from 300 field experiments in the Northeast China conducted between 2006 and 2011 were gathered to study the characteristics of maize yield, and using the QUEFTS model to estimate the balanced nutrition at different yield potential. The average grain yield was 10 427 kg/ha, and average internal efficiencies were 54.3, 251.5 and 78.2 kg grain per kg plant nitrogen (N), phosphorus (P) and potassium (K), respectively. With the harvest index values < 0.40 as outliers were excluded, the model simulated a linear-parabolic-plateau curve for the balanced N, P and K uptake when the initial yield target increased to the yield potential levels of 10 000 to 14 000 kg/ha. When the yield target reached approximately 60-70% of the yield potential, 16.7 kg N, 3.8 kg P, and 11.4 kg K were required to produce 1000 kg grain. The corresponding internal efficiencies were 60.0, 265.7 and 88.0 kg grain per kg plant N, P and K, respectively. These results contributed to improving nutrient use efficiency, and to demonstrate that the QUEFTS model could be a promising approach for estimating the balanced nutrition.

Suggested Citation

  • Wenting JIANG & Xiaohu LIU & Wen QI & Xiaonan XU & Yucui ZHU, 2017. "Using QUEFTS model for estimating nutrient requirements of maize in the Northeast China," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 63(11), pages 498-504.
    • Handle: RePEc:caa:jnlpse:v:63:y:2017:i:11:id:417-2017-pse
    DOI: 10.17221/417/2017-PSE
    as

    Download full text from publisher

    File URL: http://pse.agriculturejournals.cz/doi/10.17221/417/2017-PSE.html
    Download Restriction: free of charge
    File URL: http://pse.agriculturejournals.cz/doi/10.17221/417/2017-PSE.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/417/2017-PSE?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Deepak K. Ray & Navin Ramankutty & Nathaniel D. Mueller & Paul C. West & Jonathan A. Foley, 2012. "Recent patterns of crop yield growth and stagnation," Nature Communications, Nature, vol. 3(1), pages 1-7, January.
    2. Patricio Grassini & Kent M. Eskridge & Kenneth G. Cassman, 2013. "Distinguishing between yield advances and yield plateaus in historical crop production trends," Nature Communications, Nature, vol. 4(1), pages 1-11, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qiu, Bingwen & Jian, Zeyu & Yang, Peng & Tang, Zhenghong & Zhu, Xiaolin & Duan, Mingjie & Yu, Qiangyi & Chen, Xuehong & Zhang, Miao & Tu, Ping & Xu, Weiming & Zhao, Zhiyuan, 2024. "Unveiling grain production patterns in China (2005–2020) towards targeted sustainable intensification," Agricultural Systems, Elsevier, vol. 216(C).
    2. Zhongen Niu & Huimin Yan & Fang Liu, 2020. "Decreasing Cropping Intensity Dominated the Negative Trend of Cropland Productivity in Southern China in 2000–2015," Sustainability, MDPI, vol. 12(23), pages 1-14, December.
    3. Zhang, Bangbang & Li, Xian & Chen, Haibin & Niu, Wenhao & Kong, Xiangbin & Yu, Qiang & Zhao, Minjuan & Xia, Xianli, 2022. "Identifying opportunities to close yield gaps in China by use of certificated cultivars to estimate potential productivity," Land Use Policy, Elsevier, vol. 117(C).
    4. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    5. Batista, Fabiana de Souza & Duku, Confidence & Hein, Lars, 2023. "Deforestation-induced changes in rainfall decrease soybean-maize yields in Brazil," Ecological Modelling, Elsevier, vol. 486(C).
    6. Zhang, He & Tao, Fulu & Zhou, Guangsheng, 2019. "Potential yields, yield gaps, and optimal agronomic management practices for rice production systems in different regions of China," Agricultural Systems, Elsevier, vol. 171(C), pages 100-112.
    7. Ortiz-Bobea, Ariel & Tack, Jesse B., 2018. "Another genetic yield revolution is needed to offset climate change effects on U.S. maize," 2018 Annual Meeting, August 5-7, Washington, D.C. 274380, Agricultural and Applied Economics Association.
    8. Yu, Qiangyi & Wu, Wenbin & You, Liangzhi & Zhu, Tingju & van Vliet, Jasper & Verburg, Peter H. & Liu, Zhenhuan & Li, Zhengguo & Yang, Peng & Zhou, Qingbo & Tang, Huajun, 2017. "Assessing the harvested area gap in China," Agricultural Systems, Elsevier, vol. 153(C), pages 212-220.
    9. Hendricks, Nathan P. & Stigler, Matthieu M., 2020. "Global Yield Distributions since 1960," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304570, Agricultural and Applied Economics Association.
    10. Gao, Yukun & Zhao, Hongfang & Zhao, Chuang & Hu, Guohua & Zhang, Han & Liu, Xue & Li, Nan & Hou, Haiyan & Li, Xia, 2022. "Spatial and temporal variations of maize and wheat yield gaps and their relationships with climate in China," Agricultural Water Management, Elsevier, vol. 270(C).
    11. Shen Yuan & Bruce A. Linquist & Lloyd T. Wilson & Kenneth G. Cassman & Alexander M. Stuart & Valerien Pede & Berta Miro & Kazuki Saito & Nurwulan Agustiani & Vina Eka Aristya & Leonardus Y. Krisnadi &, 2021. "Sustainable intensification for a larger global rice bowl," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    12. Matteo Coronese & Martina Occelli & Francesco Lamperti & Andrea Roventini, 2024. "Towards sustainable agriculture: behaviors, spatial dynamics and policy in an evolutionary agent-based model," LEM Papers Series 2024/05, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    13. Salvatore Ceccarelli, 2014. "GM Crops, Organic Agriculture and Breeding for Sustainability," Sustainability, MDPI, vol. 6(7), pages 1-14, July.
    14. repec:ags:aaea22:335783 is not listed on IDEAS
    15. Paolo Agnolucci & Vincenzo De Lipsis, 2020. "Long-run trend in agricultural yield and climatic factors in Europe," Climatic Change, Springer, vol. 159(3), pages 385-405, April.
    16. Wang, Yufeng & Kang, Shaozhong & Li, Fusheng & Zhang, Xiaotao, 2021. "Modified water-nitrogen productivity function based on response of water sensitive index to nitrogen for hybrid maize under drip fertigation," Agricultural Water Management, Elsevier, vol. 245(C).
    17. Li, Yibo & Tao, Fulu, 2022. "Interactions of genotype, environment and management on wheat traits and grain yield variations in different climate zones across China," Agricultural Systems, Elsevier, vol. 203(C).
    18. Cao, Juan & Zhang, Zhao & Tao, Fulu & Chen, Yi & Luo, Xiangzhong & Xie, Jun, 2023. "Forecasting global crop yields based on El Nino Southern Oscillation early signals," Agricultural Systems, Elsevier, vol. 205(C).
    19. Meike Weltin & Silke Hüttel, 2023. "Sustainable Intensification Farming as an Enabler for Farm Eco-Efficiency?," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(1), pages 315-342, January.
    20. Manogna R. L. & Aswini Kumar Mishra, 2022. "Agricultural production efficiency of Indian states: Evidence from data envelopment analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4244-4255, October.
    21. Rada, Nicholas E., 2013. "Agricultural Growth in India: Examining the Post-Green Revolution Transition," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149547, Agricultural and Applied Economics Association.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnlpse:v:63:y:2017:i:11:id:417-2017-pse. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.