基于气象因子日效应的稻米品质评价方法

    Evaluation method for rice quality based on the daily effect of meteorological factors

    • 摘要: 为明确环境气象条件对稻米品质形成的影响,该研究利用安徽省2008—2021年中籼和中粳水稻区域性试验稻米品质资料和对应逐日气象资料,采用相关分析、计算机数值模拟、回归分析等方法,明确了中籼和中粳稻米品质形成关键期具体时间,构建了基于气象因子日效应的中籼和中粳稻米品质评价模型,并对模型进行了回代检验和验证。结果表明:中籼和中粳稻米品质形成关键期分别为齐穗至齐穗后31和35 d。中籼稻米品质形成的最适日平均气温为24.5 °C,最适日辐射和气温日较差分别为>15.2 MJ/m2和>12.1 °C。中粳稻米品质形成的最适日平均气温为22.7 °C,最适日辐射和气温日较差分别为>14.7 MJ/m2和>10.9 °C。中籼稻米优质一等、二等、三等和普通等级对应的综合气象指数( y_c1 )分别为: y_c1 ≥28.35、26.87≤ y_c1 <28.35、25.12≤ y_c1 <26.87和 y_c1 <25.12。中粳稻米优质一等、二等、三等和普通等级对应的综合气象指数( y_c2 )分别为: y_c2 ≥30.31、28.88≤ y_c2 <30.31、26.89≤ y_c2 <28.88和 y_c2 <26.89。经验证,该研究构建的中籼和中粳稻米品质评价模型回代准确率分别为78.6%和72.7%,模拟检验准确率分别为72.2%和73.3%,总体上可用于定量化评价气象条件对稻米品质的影响。该研究可为合理利用区域气候资源提升稻米品质提供理论依据。

       

      Abstract: Rice is one of the important food crops in China.With the enhancement of living standards, the cultivation of high-quality rice has emerged as a pivotal direction for the advancement of China's rice industry. Environmental meteorological conditions represent a significant factor in the formation of rice quality. Consequently, it is essential to elucidate the suitable meteorological conditions for the formation of rice quality and to construct a quantitative evaluation model of rice quality based on meteorological factors. This is considered to be a fundamental prerequisite for utilizing regional climatic resources to enhance the quality of rice.This study utilized rice quality data and the corresponding daily meteorological data from the regional trials of mid-season indica and mid-season japonica rice in Anhui Province from 2008 to 2021, in which the rice quality data included head rice yield, chalkiness, transparency, alkali spreading value, gel consistency, as well as amylose content,and the meteorological data primarily included average daily air temperature (°C), maximum air temperature (°C), minimum air temperature (°C), and radiation (MJ/m2).To elucidate the critical period for the formation of indica and japonica rice quality and construct meteorological index that comprehensively influence rice quality, as well as a model for evaluating the quality of indica and japonica rice based on the daily impact of meteorological factors,a multifaceted approach was employed, encompassing correlation analysis, computer numerical simulation, regression analysis, and other methodologies. And the model's validation was facilitated by data from the staged sowing trials conducted in Lujiang County in 2018, Chizhou City in 2023, and Hefei City in 2023. The results showed that: the critical period for the formation of rice quality is 31d and 35d after full-heading date for mid-season indica and mid-season japonica rice, respectively. The optimum daily mean temperature for the formation of mid-season indica rice quality was 24.5 °C, and the optimum daily radiation and daily range in temperature were more than 15.2 MJ/m2 and 12.2 °C higher, respectively. And for the formation of the quality of mid-season japonica rice, the optimum daily mean temperature was 22.7 °C, and the optimum daily radiation and daily range in temperature were more than 14.7 MJ/m2 and 10.9 °C higher, respectively. Suitable temperature, sufficient radiation and relatively large daily range in temperature difference are important meteorological conditions for the formation of rice quality. The composite meteorological index was used to determine the rice quality grading criteria, and the composite meteorological indices ( y_c1 ) corresponding to the mid-season indica rice quality grades are: y_c1 ≥28.35、26.87≤ y_c1 <28.35、25.12≤ y_c1 <26.87 and y_c1 <25.12 from first-class to the normal grades. The composite meteorological indices ( y_c1 ) corresponding to the mid-season japonica rice quality grades are: y_c2 ≥30.31、28.88≤ y_c2 <30.31、26.89≤ y_c2 <28.88 and y_c2 <26.89 from first-class to the normal grades.It was verified that the back substitution accuracy of the mid-season indica and mid-japonica rice quality evaluation models constructed in this study were 77.9% and 72.5%, respectively, and the simulation accuracy were 72.2% and 73.3%, respectively. The model has been demonstrated to possess the capacity to facilitate quantitative assessments of the impact that meteorological conditions exert on the quality of rice,and have the potential to offer a theoretical foundation for the rational utilization of regional climate resources, with the objective of enhancing the quality of rice.

       

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