Related Experiment Video
Updated: Aug 3, 2026
11:49
Cereal Crop Ear Counting in Field Conditions Using Zenithal RGB Images
Published on: February 2, 2019
Variability and uncertainty in spatial, temporal and spatiotemporal crop-yield and related data
A B McBratney1, B M Whelan, T M Shatar
1Department of Agricultural Chemistry & Soil Science, University of Sydney, Australia.
Ciba Foundation Symposium
|January 1, 1997
Summary
Precision agriculture faces uncertainty from spatial and temporal variations in farming. Research suggests uniform management may be optimal due to large annual yield variations over field scales.
Area of Science:
- Agricultural Science
- Agronomy
- Soil Science
Background:
- Precision agriculture application in broad-acre farming requires managing information uncertainty.
- Uncertainty arises from spatial and temporal variations affecting yield and environmental losses.
Purpose of the Study:
- To analyze yield and related variables using spatial and temporal variance.
- To determine appropriate management scales based on variation magnitudes.
- To investigate the implications of temporal variation for precision agriculture strategies.
Main Methods:
- Analysis of spatial and temporal variance in yield and related variables.
- Review of theoretical frameworks for process-based variation analysis.
- Case studies from Rothamsted experiments, Australian yield-monitoring, Danish barley trials, and soil moisture networks.
Main Results:
- Annual temporal variation in yield significantly exceeds spatial variation within fields.
- Simultaneous reporting on spatial and temporal variation is uncommon, with theory still developing.
- Data suggests temporal fluctuations are a dominant factor in yield variability.
Conclusions:
- Large annual temporal variations relative to field scale suggest uniform management as a potential optimal strategy.
- Precision agriculture requires a robust scientific basis, considering dominant temporal effects.
- Further research is needed to test the null hypothesis of uniform management being optimal.
Related Concept Videos
01:16What is Climate?
Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
03:37Uncertainty in Measurement: Accuracy and Precision
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
01:25Multiple Regression
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...
01:04Random Error
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
01:11Variability: Analysis
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...