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Simulation of maize lethal necrosis (MLN) damage using the CERES-Maize model

Creator: Batchelor, W.D.
Creator: Mahabaleswara, S.L.
Creator: Xiaoxing Zhen
Creator: Beyene, Y.
Creator: Wilson, M.
Creator: Kruseman, G.
Creator: Prasanna, B.M.
Year: 2020
Format: PDF
Language: English
Publisher: MDPI
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: Basel (Switzerland)
Issue: 5
Volume: 10
DOI: 10.3390/agronomy10050710
Keywords: Disease Simulation
Keywords: DSSAT
Description: Maize lethal necrosis (MLN), maize streak virus (MSV), grey leaf spot (GLS) and turcicum leaf blight (TLB) are among the major diseases affecting maize grain yields in sub-Saharan Africa. Crop models allow researchers to estimate the impact of pest damage on yield under different management and environments. The CERES-Maize model distributed with DSSAT v4.7 has the capability to simulate the impact of major diseases on maize crop growth and yield. The purpose of this study was to develop and test a method to simulate the impact of MLN on maize growth and yield. A field experiment consisting of 17 maize hybrids with different levels of MLN tolerance was planted under MLN virus-inoculated and non-inoculated conditions in 2016 and 2018 at the MLN Screening Facility in Naivasha, Kenya. Time series disease progress scores were recorded and translated into daily damage, including leaf necrosis and death, as inputs in the crop model. The model genetic coefficients were calibrated for each hybrid using the 2016 non-inoculated treatment and evaluated using the 2016 and 2018 inoculated treatments. Overall, the model performed well in simulating the impact of MLN damage on maize grain yield. The model gave an R2 of 0.97 for simulated vs. observed yield for the calibration dataset and an R2 of 0.92 for the evaluation dataset. The simulation techniques developed in this study can be potentially used for other major diseases of maize. The key to simulating other diseases is to develop the appropriate relationship between disease severity scores, percent leaf chlorosis and dead leaf area.
Agrovoc: MAIZE
ISSN: 2073-4395
Journal: Agronomy

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This item appears in the following Collection(s)

  • Maize
    Maize breeding, phytopathology, entomology, physiology, quality, and biotech
  • Socioeconomics
    Including topics such as farming systems, markets, impact & targeting, innovations, and GIS

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