Agro-meteorological risks to maize production in Tanzania: sensitivity of an adapted water requirements satisfaction index (WRSI) model to rainfall

Tarnavsky, E. ORCID: https://orcid.org/0000-0003-3403-0411, Chavez, E. and Boogaard, H. (2018) Agro-meteorological risks to maize production in Tanzania: sensitivity of an adapted water requirements satisfaction index (WRSI) model to rainfall. International Journal of Applied Earth Observation and Geoinformation, 73. pp. 77-87. ISSN 0303-2434 doi: 10.1016/j.jag.2018.04.008

Abstract/Summary

The water requirements satisfaction index (WRSI) – a simplified crop water stress model – is widely used in drought and famine early warning systems, as well as in agro-meteorological risk management instruments such as crop insurance. We developed an adapted WRSI model, as introduced here, to characterise the impact of using different rainfall input datasets, ARC2, CHIRPS, and TAMSAT, on key WRSI model parameters and outputs. Results from our analyses indicate that CHIRPS best captures seasonal rainfall characteristics such as season onset and duration, which are critical for the WRSI model. Additionally, we consider planting scenarios for short-, medium-, and long-growing cycle maize and compare simulated WRSI and model outputs against reported yield at the national level for maize-growing areas in Tan- zania. We find that over half of the variability in yield is explained by water stress when the CHIRPS dataset is used in the WRSI model (R2 = 0.52- 0.61 for maize varieties of 120-160 days growing length). Overall, CHIRPS and TAMSAT show highest skill (R2 = 0.46-0.55 and 0.44-0.58, respectively) in capturing country-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agro-meteorological risk applications.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/77771
Item Type	Article
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Walker Institute Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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