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Development of growth model for Ber powdery mildew in relation to weather parameters

机译:关于天气参数的BER粉末状霉菌生长模型的发展

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An experiment was conducted at the orchard of Chaudhary Charan Singh, Haryana, Agricultural University, Hisar (75°46′E and 29°10′N) and 215.2 a.m.s.l. The Ber powdery mildew index was recorded during the peak period (44–49 standard meteorological weeks) at different time intervals for the years 2015, 2016 and 2017 on Ber cultivar Umran. The study was carried out to find out the best fit growth models and significant weather variables in the disease progress curve. Five growth models viz. Exponential, Gompertz, Logistic, Log Logistic and Weibull were analysed for summarizing and comparing different plant disease epidemics. The statistical package R was used to illustrate models and disease progress over time. The best fit growth model was found by Akaike Information Criterion and Bayesian Information Criterion. The R code was developed and the regression analysis was performed to find out the best weather variables in the disease progress curve. It was observed that Exponential model was best fit growth model for the disease progress over time during 2015, whereas the Gompertz model was best fit during 2016 and 2017. The maximum and minimum temperature showed significant correlation with powdery mildew during 2015, whereas during 2017 the powdery mildew has significantly and negatively correlation with minimum temperature (??0.74) and evening relative humidity (??0.72). The coefficient of determination (R_(2)) during 2015 and 2017 was found 78 and 68%, respectively.
机译:在Chaudharary Charan Singh,Haryana,农业大学,哈里亚纳,自杀(75°46'e和29°10'n)和215.2上午215.2时,进行了一个实验。在2015年的不同时间间隔的高峰期(44-49个标准气象周)在2015年,2016年和2017年在BER品种乌兰的不同时间间隔期间记录了BER粉末状霉菌指数。进行了该研究,以了解疾病进展曲线中最合适的增长模型和重要的天气变量。五个增长模型viz。分析了指数,Gompertz,逻辑,对数逻辑和威布尔,总结和比较不同植物疾病流行病。统计包r用于显示模型和疾病随时间的进展。 Akaike信息标准和贝叶斯信息标准发现了最佳拟合增长模型。 R代码开发,进行了回归分析,以找出疾病进展曲线中最佳的天气变量。据观察,指数模型在2015年期间,疾病的进展是最合适的增长模型,而在2016年和2017年期间,Gompertz模型最适合。2015年最高和最低温度与粉状霉变有显着相关性,而2017年期间粉末状霉菌与最小温度(?? 0.74)和晚间相对湿度(约0.72)具有显着和负面相关性。 2015年和2017期间的测定系数(R_(2))分别发现了78%和68%。

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