IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i12p579-d450554.html
   My bibliography  Save this article

Maize Yield Components as Affected by Plant Population, Planting Date and Soil Coverings in Brazil

Author

Listed:
  • Gustavo Castilho Beruski

    (Department of Biosystems Engineering, ESALQ/University of São Paulo, 11 Pádua Dias Ave., Mail Box 9, Piracicaba, SP 13635-900, Brazil)

  • Luis Miguel Schiebelbein

    (Department of Soil Science and Agricultural Engineering, State University of Ponta Grossa, 4748 Carlos Cavalcanti Ave., Uvaranas, Ponta Grossa, PR 84030-900, Brazil)

  • André Belmont Pereira

    (Department of Soil Science and Agricultural Engineering, State University of Ponta Grossa, 4748 Carlos Cavalcanti Ave., Uvaranas, Ponta Grossa, PR 84030-900, Brazil)

Abstract

The potential yield of annual crops is affected by management practices and water and energy availabilities throughout the crop season. The current work aimed to assess the effects of plant population, planting dates and soil covering on yield components of maize. Field experiments were carried out during the 2014–2015 and 2015–2016 growing seasons at areas grown with oat straw, voluntary plants and bare soil, considering five plant populations (40,000, 60,000, 80,000, 100,000 and 120,000 plants ha −1 ) and three sowing dates (15 September, 30 October and 15 December) for the hybrid P30F53YH in Ponta Grossa, State of Paraná, Brazil. Non-impacts of soil covering or plant population on plant height at the flowering phenological stage were observed. Significant effects of soil covering on yield components and final yield responses throughout the 2014–2015 season were detected. An influence of plant populations on yield components was evidenced, suggesting that, from 80,000 plants ha −1 , the P30F53YH hybrid performs a compensatory effect among assessed yield components in such a way as to not compromise productivity insofar as the plant population increases up to 120,000 plants ha −1 . It was noticed, a positive trend of yield components and crop final yield as a function of plant density increments.

Suggested Citation

  • Gustavo Castilho Beruski & Luis Miguel Schiebelbein & André Belmont Pereira, 2020. "Maize Yield Components as Affected by Plant Population, Planting Date and Soil Coverings in Brazil," Agriculture, MDPI, vol. 10(12), pages 1-20, November.
  • Handle: RePEc:gam:jagris:v:10:y:2020:i:12:p:579-:d:450554
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/12/579/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/10/12/579/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anapalli, Saseendran S. & Green, Timothy R. & Reddy, Krishna N. & Gowda, Prasanna H. & Sui, Ruixiu & Fisher, Daniel K. & Moorhead, Jerry E. & Marek, Gary W., 2018. "Application of an energy balance method for estimating evapotranspiration in cropping systems," Agricultural Water Management, Elsevier, vol. 204(C), pages 107-117.
    2. Unkovich, Murray & Baldock, Jeff & Farquharson, Ryan, 2018. "Field measurements of bare soil evaporation and crop transpiration, and transpiration efficiency, for rainfed grain crops in Australia – A review," Agricultural Water Management, Elsevier, vol. 205(C), pages 72-80.
    3. Irmak, Suat & Kukal, Meetpal S. & Mohammed, Ali T. & Djaman, Koffi, 2019. "Disk-till vs. no-till maize evapotranspiration, microclimate, grain yield, production functions and water productivity," Agricultural Water Management, Elsevier, vol. 216(C), pages 177-195.
    4. J. P. Royston, 1982. "The W Test for Normality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 31(2), pages 176-180, June.
    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. Minguez, Ana & Javier Sese, F., 2022. "Why do you want a relationship, anyway? Consent to receive marketing communications and donors’ willingness to engage with nonprofits," Journal of Business Research, Elsevier, vol. 148(C), pages 356-367.
    2. Lada, Emily K. & Wilson, James R., 2006. "A wavelet-based spectral procedure for steady-state simulation analysis," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1769-1801, November.
    3. Ali Derakhshan Asl & Kuan Yew Wong & Manoj Kumar Tiwari, 2016. "Unequal-area stochastic facility layout problems: solutions using improved covariance matrix adaptation evolution strategy, particle swarm optimisation, and genetic algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 54(3), pages 799-823, February.
    4. repec:ers:journl:v:volumexxi:y:2018:i:issue4:p:622-636 is not listed on IDEAS
    5. Lauren Bin Dong & David E. A. Giles, 2004. "An Empirical Likelihood Ratio Test for Normality," Econometrics Working Papers 0401, Department of Economics, University of Victoria.
    6. Anthony Medford & James W. Vaupel, 2020. "Extremes are not normal: a reminder to demographers," Journal of Population Research, Springer, vol. 37(1), pages 91-106, March.
    7. Wangli Xu & Yanwen Li & Dawo Song, 2013. "Testing normality in mixed models using a transformation method," Statistical Papers, Springer, vol. 54(1), pages 71-84, February.
    8. Wang, Tianxin & Melton, Forrest S. & Pôças, Isabel & Johnson, Lee F. & Thao, Touyee & Post, Kirk & Cassel-Sharma, Florence, 2021. "Evaluation of crop coefficient and evapotranspiration data for sugar beets from landsat surface reflectances using micrometeorological measurements and weighing lysimetry," Agricultural Water Management, Elsevier, vol. 244(C).
    9. Vexler, Albert & Gurevich, Gregory, 2010. "Empirical likelihood ratios applied to goodness-of-fit tests based on sample entropy," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 531-545, February.
    10. Shalit, Haim, 2012. "Using OLS to test for normality," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 2050-2058.
    11. Kukal, M.S. & Irmak, S., 2020. "Impact of irrigation on interannual variability in United States agricultural productivity," Agricultural Water Management, Elsevier, vol. 234(C).
    12. Shah, Chandra, 1997. "Model selection in univariate time series forecasting using discriminant analysis," International Journal of Forecasting, Elsevier, vol. 13(4), pages 489-500, December.
    13. Yan, Zhenxing & Zhang, Wenying & Liu, Xiuwei & Wang, Qingsuo & Liu, Binhui & Mei, Xurong, 2024. "Grain yield and water productivity of winter wheat controlled by irrigation regime and manure substitution in the North China Plain," Agricultural Water Management, Elsevier, vol. 295(C).
    14. Predrag Petrović & Goran Nikolić, 2018. "Schumpeterian Growth Theory: Empirical Testing Of Barriers To Competition-Proximity To Frontier Algorithm," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 63(217), pages 7-38, April – J.
    15. Seongmin Kang & Jeahyung Cha & Changsang Cho & Ki-Hyun Kim & Eui-Chan Jeon, 2020. "Estimation of appropriate CO2 concentration sampling cycle for MSW incinerators," Energy & Environment, , vol. 31(3), pages 535-544, May.
    16. Lizhen Bai & Xiangying Kong & Hui Li & Huibin Zhu & Chengwu Wang & Shiao Ma, 2022. "Effects of Conservation Tillage on Soil Properties and Maize Yield in Karst Regions, Southwest China," Agriculture, MDPI, vol. 12(9), pages 1-10, September.
    17. Tomasz Górecki & Lajos Horváth & Piotr Kokoszka, 2020. "Tests of Normality of Functional Data," International Statistical Review, International Statistical Institute, vol. 88(3), pages 677-697, December.
    18. Yang, J. & Greenwood, D. J. & Rowell, D. L. & Wadsworth, G. A. & Burns, I. G., 2000. "Statistical methods for evaluating a crop nitrogen simulation model, N_ABLE," Agricultural Systems, Elsevier, vol. 64(1), pages 37-53, April.
    19. repec:dau:papers:123456789/2714 is not listed on IDEAS
    20. Irmak, Suat & Kukal, Meetpal S., 2019. "Disk-till vs. no-till maize grass- and alfalfa-reference single (average) and basal (dual) crop coefficients," Agricultural Water Management, Elsevier, vol. 226(C).
    21. Amir Abolhassani & Gale Boyd & Majid Jaridi & Bhaskaran Gopalakrishnan & James Harner, 2023. "“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants," Energies, MDPI, vol. 16(4), pages 1-35, February.
    22. Lai Zhi Yong & Siti Khairunniza-Bejo & Mahirah Jahari & Farrah Melissa Muharam, 2022. "Automatic Disease Detection of Basal Stem Rot Using Deep Learning and Hyperspectral Imaging," Agriculture, MDPI, vol. 13(1), pages 1-16, December.

    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:gam:jagris:v:10:y:2020:i:12:p:579-:d:450554. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.