一个贝叶斯框架来建模谷物产量对植物密度的响应差异
Nicolas Giordano1, Dustin Hayes2, Trevor J Hefley2
1Department of Agronomy, Kansas State University, 2004 Throckmorton Center, 1712 Claflin Rd., Manhattan, KS, 66506, USA. ngiordano@ksu.edu.
Plant methods
|March 18, 2025
概括
这项研究引入了贝叶斯框架来建模冬季小麦产量变异,确定最佳的植物密度以获得最大产量和最小风险. 最低风险植物密度 (minRPD) 确保在各种条件下稳定的产量.
科学领域:
- 农业科学 农业科学
- 农业学是一种农业学.
- 统计建模 统计建模
背景情况:
- 冬季小麦产量对植物密度的反应通常遵循着回报率下降的函数.
- 之前的研究假设恒定差异,忽略了产量优化的风险.
- 在量化最佳植物密度以最大限度地以最小的风险实现产量方面存在差距.
研究的目的:
- 提出和演示贝叶斯的等级框架,用于模拟谷物产量变异,以应对植物密度.
- 确定植物密度,最大限度地提高预期产量并最大限度地降低产量差异.
- 要量化冬季小麦的最低危险植物密度 (minRPD).
主要方法:
- 利用了加拿大大草原和堪萨斯州实地实验的数据.
- 采用贝叶斯的层次结构框架来建模与植物密度相关的产量差异.
- 分析了种子清洁,种子大小,播种率和化学处理的因数组合.
主要成果:
- 拟议的模型成功地描述了预期产量和产量差异,涉及植物密度.
- 最低风险植物密度 (minRPD) 始终高于农学最佳植物密度 (AOPD).
- 特定的种子处理和清洗方法在降低低产环境中的产量差异方面显示出好处.
结论:
- 贝叶斯框架是有效的模拟异种产量响应数据.
- minRPD提供了一种优化植物密度的策略,以最大限度地提高产量并最大限度地降低风险.
- 未来的研究应该探索基因型与环境的相互作用,并将框架扩展到其他作物管理决策.
相关概念视频
Distributions to Estimate Population Parameter
4.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.0K
Multiple Regression
2.9K
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...
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...
2.9K
Estimating Population Standard Deviation
3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
54
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
54
Estimating Population Mean with Unknown Standard Deviation
7.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
7.6K
Estimating Population Mean with Known Standard Deviation
8.2K
To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
8.2K


