Literature DB >> 26382017

Within-season yield prediction with different nitrogen inputs under rain-fed condition using CERES-Wheat model in the northwest of China.

Zhengpeng Li1,2, Mingdan Song1,2, Hao Feng1,2,3, Ying Zhao2,4.   

Abstract

BACKGROUND: Yield prediction within season is of great use to improve agricultural risk management and decision making. The objectives of this study were to access the yield forecast performance with increasing nitrogen inputs and to determine when the acceptable predicted yield can be achieved using the CERES-Wheat model.
RESULTS: the calibrated model simulated wheat yield very well under various water and nitrogen conditions. Long-term simulation demonstrated that nitrogen input enlarged the annual variability of wheat yield generally. Within-season yield prediction showed that, regardless of nitrogen inputs, yield forecasts in the later growing season improved the accuracy and reduced the uncertainty of yield prediction. In a low-yielding year (2011-2012) and a high-yielding year (1991-1992), the date of acceptable predicted yield was achieved 62 and 65 days prior to wheat maturity, respectively. In a normal-yielding year (1983-1984), inadequate precipitation after the jointing stage in most historical years led to the underestimation of wheat yield and the date of accurate yield prediction was delayed to 235-250 days after simulation (7-22 days prior to maturity) for different N inputs.
CONCLUSION: Yield prediction was highly influenced by the distribution of meteorological elements during the growing season and may show great improvement if future weather can be reliably forecast early.
© 2015 Society of Chemical Industry. © 2015 Society of Chemical Industry.

Entities:  

Keywords:  CERES-Wheat model; forecast accuracy; nitrogen; precipitation distribution; yield forecast

Mesh:

Substances:

Year:  2015        PMID: 26382017     DOI: 10.1002/jsfa.7467

Source DB:  PubMed          Journal:  J Sci Food Agric        ISSN: 0022-5142            Impact factor:   3.638


  1 in total

1.  Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt.

Authors:  Mohsen Shahhosseini; Guiping Hu; Isaiah Huber; Sotirios V Archontoulis
Journal:  Sci Rep       Date:  2021-01-15       Impact factor: 4.379

  1 in total

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