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首页> 外文期刊>Indian Journal of Ecology >Meteorological Model for Rice Yield Forecasting in Ludhiana Region
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Meteorological Model for Rice Yield Forecasting in Ludhiana Region

机译：卢迪亚纳地区水稻产量预报气象模型

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摘要

An attempt was made to predict rice (Oryza sativa L.) yield by regression models. Three statistical models were developed for forecasting the yield of the rice in Ludhiana district by using the rice yield as well as meteorological data (1972-2012). The weekly weather variables used forthis study were maximum and minimum temperature, relative humidity (morning and evening), sunshine hours, rainfall and number of rainy days. In the first basic model, sensitive period for rice yield with respect to weather parameters was identified for different weather parameters for selected windows and correlations were developed. The selected sensitive weekly parameters were taken for further regression analysis. In the second model (modified model), time trend wastaken as one of the extra variable in multiple regressions. In the third model, multiple regression analysis was done using Statistical Package for the Social Sciences (SPSS) software. Regression equations were developed separately for all the three models and were used to predict the rice yield. The data for a period of (1972-2009) was used to develop the forecast model. The three year meteorological data (2010-2012) was used to validate the models. Among all the three models, basic model explained upto 64 per cent variation, modified model explained 67 per cent and SPSS model predicted highest i.e. 88 per cent variation in rice yield due to weather parameters. The results revealed that SPSS accurately predicted the rice yield as compared to other models and it had strong relation with rice crop yield.
机译：尝试通过回归模型预测水稻（Oryza sativa L.）的产量。利用水稻产量和气象数据（1972-2012年），开发了三种统计模型来预测卢迪亚纳地区的水稻产量。用于这项研究的每周天气变量是最高和最低温度，相对湿度（早晨和晚上），日照时间，降雨和雨天数。在第一个基本模型中，针对选定窗口的不同天气参数，确定了水稻产量对天气参数的敏感期，并建立了相关性。选择的敏感每周参数用于进一步的回归分析。在第二个模型（修改的模型）中，时间趋势被视为多元回归中的额外变量之一。在第三个模型中，使用社会科学统计软件包（SPSS）软件进行了多元回归分析。为这三个模型分别建立了回归方程，并用于预测水稻产量。使用1972-2009年期间的数据来开发预测模型。三年的气象数据（2010-2012年）用于验证模型。在这三个模型中，基本模型可解释高达64％的变化，改良模型可解释67％的变化，SPSS模型可预测最高，即由于天气参数而导致水稻产量变化88％。结果表明，与其他模型相比，SPSS能够准确预测水稻产量，并且与水稻产量有很强的关系。

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