Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Multiple Regression01:25

Multiple Regression

3.0K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.0K
Regression Analysis01:11

Regression Analysis

5.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.7K
Light Acquisition02:16

Light Acquisition

8.4K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.4K
Response Surface Methodology01:16

Response Surface Methodology

119
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
119
Precipitation Titration Curve: Analysis01:21

Precipitation Titration Curve: Analysis

1.1K
The precipitation titration curve demonstrates the change in concentration of one reactant with the volume of titrant added. During the titration of chloride ions with silver nitrate, the precipitation titration curve is divided into three regions: before, at, and after the equivalence point. Before the equivalence point, low redissolution of the sparingly soluble silver chloride precipitate gives a low silver ion concentration. However, in the second region, representing the equivalence point,...
1.1K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.8K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
1.8K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Practices in the Management of Incidental Gallbladder Cancer.

South Asian journal of cancer·2024
Same author

The projected impact of the inflation reduction act's climate provisions on cardiovascular and respiratory outcomes.

American journal of preventive cardiology·2024
Same author

Current Pediatric Endoscopy Training Situation in the Asia-Pacific Region: A Collaborative Survey by the Asian Pan-Pacific Society for Pediatric Gastroenterology, Hepatology and Nutrition Endoscopy Scientific Subcommittee.

Pediatric gastroenterology, hepatology & nutrition·2024
Same author

Method validation, residue behaviour and dietary risk assessment of insecticides (cyantraniliprole, acetamiprid, flubendiamide and its metabolite, des-iodo flubendiamide) in or on broccoli using LC-MS/MS.

Biomedical chromatography : BMC·2024
Same author

Dissipation kinetics and the evaluation of dietary risks associated with deltamethrin, ethion, fenazaquin, and fenpropathrin on bell pepper (Solanum annuum L.).

Environmental geochemistry and health·2024
Same author

Oxidative Coupling and Self-Assembly of Polyphenols for the Development of Novel Biomaterials.

ACS omega·2024

相关实验视频

Updated: Jun 24, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

11.5K

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
PubMed
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.

关键词:
估计 估计 估计多变量分析多变量分析.在RMSE和MAPE中.大米的产量大米的产量.天气参数 天气参数

更多相关视频

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.3K

相关实验视频

Last Updated: Jun 24, 2025

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions
15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

11.5K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.4K
Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
11:49

Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images

Published on: February 2, 2019

9.3K

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.