Preprints
https://doi.org/10.5194/gc-2023-1
https://doi.org/10.5194/gc-2023-1
06 Jun 2023
 | 06 Jun 2023
Status: this preprint is currently under review for the journal GC.

Planning a geostatistical survey to map soil and crop properties: eliciting sampling densities

Christopher Chagumaira, Joseph G. Chimungu, Patson C. Nalivata, Martin R. Broadley, Alice E. Milne, and R. Murray Lark

Abstract. The communication of uncertainty is not only a challenge when soil information has been produced but also in the planning stage. When planning a survey of soil properties it is necessary to make decisions about the sampling density. Sampling density determines both the quality of predictions and the cost of fieldwork. In this study, we considered four ways in which the relationship between sample density and the uncertainty of predictions can be related, based on prior information about the variability of the target quantity. These were offset correlation, prediction intervals, conditional probabilities of the interpretation errors and implicit loss functions. Offset correlation is a measure of the consistency of kriging predictions made from sample grids with the same spacing but different origins. Prediction intervals and conditional probabilities are based on the prediction distribution of the variable of interest. All four of these methods were investigated using the information on soil pH and Se concentration in grain in Malawi. They were presented to a group of stakeholders, who were asked to use them in turn to select a sampling density. Their responses were evaluated and they were then asked to rank the methods based on their effectiveness, in their experience, and in terms of finding a level of uncertainty that they were able to tolerate when deciding about a sampling grid spacing. Our results show that the approach that stakeholders favoured was offset correlation, and some approaches were not well understood (conditional probability and implicit loss function). During feedback sessions, the stakeholders highlighted that they were more familiar with the concept of correlation, with a closed interval of [0,1] and this explains the more consistent responses under this method. The offset correlation will likely be more useful to stakeholders, with little or no statistical background, who are unable to express their requirements of information quality based on other measures of uncertainty.

Christopher Chagumaira, Joseph G. Chimungu, Patson C. Nalivata, Martin R. Broadley, Alice E. Milne, and R. Murray Lark

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gc-2023-1', Anonymous Referee #1, 28 Aug 2023
  • RC2: 'Comment on gc-2023-1', Anonymous Referee #2, 03 Feb 2024
Christopher Chagumaira, Joseph G. Chimungu, Patson C. Nalivata, Martin R. Broadley, Alice E. Milne, and R. Murray Lark
Christopher Chagumaira, Joseph G. Chimungu, Patson C. Nalivata, Martin R. Broadley, Alice E. Milne, and R. Murray Lark

Viewed

Total article views: 354 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
263 69 22 354 37 10 13
  • HTML: 263
  • PDF: 69
  • XML: 22
  • Total: 354
  • Supplement: 37
  • BibTeX: 10
  • EndNote: 13
Views and downloads (calculated since 06 Jun 2023)
Cumulative views and downloads (calculated since 06 Jun 2023)

Viewed (geographical distribution)

Total article views: 336 (including HTML, PDF, and XML) Thereof 336 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 14 Feb 2024
Download
Short summary
Our study is concerned with how uncertainty in spatial information about environmental variables can be communicated to stakeholders to make decisions about sampling whilst considering the trade-off between sample effort and reducing uncertainty. We tested four approaches that relate sampling density and uncertainty by eliciting the opinions of end-users. End-users preferred the method not direct link to decision-making. More work is needed to develop and elucidate decision-specific approaches.
Altmetrics