TAMSAT-ALERT v1: a new framework for agricultural decision support

Asfaw, Dagmawi; Black, Emily; Brown, Matthew; Nicklin, Kathryn Jane; Otu-Larbi, Frederick; Pinnington, Ewan; Challinor, Andrew; Maidment, Ross; Quaife, Tristan

doi:https://doi.org/10.5194/gmd-11-2353-2018

Articles | Volume 11, issue 6

https://doi.org/10.5194/gmd-11-2353-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-11-2353-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 6

Model description paper

|

19 Jun 2018

Model description paper |

| 19 Jun 2018

TAMSAT-ALERT v1: a new framework for agricultural decision support

Dagmawi Asfaw, Emily Black, Matthew Brown, Kathryn Jane Nicklin, Frederick Otu-Larbi, Ewan Pinnington, Andrew Challinor, Ross Maidment, and Tristan Quaife

Download

Final revised paper (published on 19 Jun 2018)
Supplement to the final revised paper
Preprint (discussion started on 06 Feb 2018)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

- Printer-friendly version

- Supplement

RC1: 'Review of TAMSAT ALERT v1', Anonymous Referee #1, 14 May 2018
- AC1: 'Author Comment', Dagmawi Teklu Asfaw, 25 May 2018
RC2: 'General and Specific Comments to TAMSAT-ALERT-v1:A New Framework for agricultural decision support', Anonymous Referee #2, 16 May 2018
- AC2: 'Author Comment', Dagmawi Teklu Asfaw, 25 May 2018

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Dagmawi Teklu Asfaw on behalf of the Authors (28 May 2018) Manuscript

ED: Publish as is (08 Jun 2018) by Carlos Sierra

AR by Dagmawi Teklu Asfaw on behalf of the Authors (10 Jun 2018)

Short summary

TAMSAT-ALERT is a framework for combining observational and forecast information into continually updated assessments of the likelihood of user-defined adverse events like low cumulative rainfall or lower than average crop yield. It is easy to use and flexible to accommodate any impact model that uses meteorological data. The results show that it can be used to monitor the meteorological impact on yield within a growing season and to test the value of routinely issued seasonal forecasts.