农作物产量的数学模型

A. Likhatsevich
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引用次数: 2

摘要

农业科学研究成果呈现的理论基础是数理统计和概率论,运用实验数据推广的经验形式。为了改进利用数字技术规划野外实验和处理实验数据的方法,我们建议优先采用基于封闭物理系统中因果相互作用平衡的物理原理的数学建模。在分析环境因素对作物产量的影响时,最初规定的作物产量的数学建模是基于与作物特性和自然条件无关的,因此,模型选项在应用中是通用的,并且对任何农业作物都有效,无论种植区域如何。为保证数字信息的统计准确性,在建立数学模型的基础上,旨在确定作物产量对产量形成因素的依赖关系的田间试验布局应至少包括4个营养水平(NPK)选项,研究时间至少为4年。为了验证所建立的作物产量模型的准确性,采用了N.N. Semenenko教授对大麦和冬季小黑麦进行的独立田间试验数据。据确定,在白俄罗斯,由于影响粮食产量,形成产量的因素按以下降序排列:施用氮磷钾的总剂量º生长季节活跃阶段的降水量→同一时期的气温。计算表明,数学模型中考虑的产量形成因素从3个(粮食、水分和热量)减少到2个(粮食和水分),对粮食作物产量计算的准确性降低不显著。
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Mathematical model of agricultural crop yield
Theoretical basis for presenting research results in agricultural science is mathematical statistics and probability theory using empirical forms of generalization of experimental data. To improve the methods of planning field experiment and processing its data using digital technologies, we proposed to use mathematical modeling based on physical principle of balance of cause-and-effect interactions in a closed physical system as a priority option. When analyzing impact of environmental factors on crop yields, the initial provisions, the mathematical modeling of the crop yield is based, on are not associated with characteristics of crops and natural conditions, therefore, the model options are universal in application and are valid for any agricultural crop, regardless of the region of cultivation. To ensure statistically correct digital information, based on the established forms of mathematical model, the field experiment layout aimed at establishing the dependence of the crop yield on yield-forming factors should include at least 4 options for nutritional levels (NPK) with a research duration of at least 4 years. To check the accuracy of the developed crop yield model, the data of independent field experiments of Professor N.N. Semenenko with barley and winter triticale has been used. It has been determined that, in Belarus, yield-forming factors, as a result of their impact on the grain yield, are arranged in the following decreasing sequence: total dose of applied NPK º the amount of precipitation during the active phases of growing season → air temperature for the same period. Calculations have shown that decrease in the number of yield-forming factors taken into account in the mathematical model from three (food, moisture and heat) to two (food and moisture) reduces the accuracy of calculating the grain crop yield insignificantly.
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