使用基于气象参数的多变量分析技术估计大米产量
Ajay Sharma1, Joginder Kumar1, Mandeep Redhu2
1Department of Mathematics and Statistics, CCS, Haryana Agricultural University, Hisar, Haryana, India.
Scientific reports
|June 1, 2024
概括
区分函数分析使用历史数据和天气信息准确地预测大米产量. 在收获前一个月的预测为农业规划和决策提供了宝贵的见解.
科学领域:
- 农业科学 农业科学
- 统计建模 统计建模
- 预测作物 预测作物
背景情况:
- 准确的产预测对于粮食安全和经济稳定至关重要.
- 传统的方法往往缺乏有效农业规划所需的精度.
- 将气象数据与统计模型相结合,可以提高产量预测的准确性.
研究的目的:
- 开发和比较大米产量的多变量预测模型.
- 确定预测哈里亚纳州作物产量的最有效的统计技术.
- 确定在收获之前预测大米产量的最佳时间.
主要方法:
- 逐步多重回归,差分函数分析和后勤回归的应用.
- 使用产量时间序列数据 (1980-2021) 和每两周的气象数据.
- 将作物产量数据分为两类和三类进行分析.
主要成果:
- 与物流回归相比,区分函数分析在预测水产量方面表现出更高的准确性.
- 评估指标包括根平均平方误差,预测误差平方和,平均绝对偏差和平均绝对百分比误差.
- 该研究确定收获前一个月是最佳预测窗口.
结论:
- 差别函数分析是准确预测大米产量的高效工具.
- 整合天气数据和统计建模可以提高农业规划能力.
- 及时准确的产量预测支持农业的知情决策.
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