Simulation of Maize Lethal Necrosis (MLN) Damage Using the CERES-Maize Model

被引:7
|
作者
Batchelor, William D. [1 ]
Suresh, L. M. [2 ]
Zhen, Xiaoxing [3 ]
Beyene, Yoseph [2 ]
Wilson, Mwaura [2 ]
Kruseman, Gideon [4 ]
Prasanna, Boddupalli [2 ]
机构
[1] Auburn Univ, Biosyst Engn Dept, Auburn, AL 36849 USA
[2] World Agroforestry Ctr ICRAF, Int Maize & Wheat Improvement Ctr CIMMYT, United Nat Ave,Gigiri POB 1041-00621, Nairobi, Kenya
[3] Auburn Univ, Crop Soil & Environm Sci Dept, Auburn, AL 36849 USA
[4] CIMMYT, Km 45 Mexico Veracruz, El Batan 56237, Texcoco, Mexico
来源
AGRONOMY-BASEL | 2020年 / 10卷 / 05期
基金
比尔及梅琳达.盖茨基金会; 美国食品与农业研究所;
关键词
Crop modeling; disease simulation; DSSAT; DISEASE; IMPACTS;
D O I
10.3390/agronomy10050710
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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 R-2 of 0.97 for simulated vs. observed yield for the calibration dataset and an R-2 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.
引用
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页数:14
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