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Comparison of hydrological and vegetation remote sensing datasets as proxies for rainfed maize yield in Malawi

Comparison of hydrological and vegetation remote sensing datasets as proxies for rainfed maize yield in Malawi
Comparison of hydrological and vegetation remote sensing datasets as proxies for rainfed maize yield in Malawi

Weather Index-based Insurances (WIIs) have emerged as a promising risk coping mechanism to compensate for weather-induced damage to rainfed agriculture. Remote sensing may provide cost-effective information capable of discriminating the weather spatial variability thus reducing the spatial basis risk, i.e., the mismatch between the weather-based index triggering the insurance payout and the actual damage experienced by the farmers, which is often one of the causes hindering the wide implementation of WIIs. In this work we assess which indices based on remote sensing datasets are the best proxy indicators for rainfed maize yield in Malawi. We analyse the spatial (district scale) and temporal (monthly) correlations of historical maize yield data and several remote sensing datasets including the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset, the ESA CCI Soil Moisture combined dataset (version 4.2), the Evaporative Stress Index (ESI) from the Atmosphere-Land Exchange Inversion model (ALEXI), the MOD13Q1 Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). With respect to the previous literature, this work exploits a historical crop yield dataset at the sub-national level which allows us to analyse the correlation of the hydro-meteorological and vegetation variables at a higher spatial resolution than what is commonly done (i.e., at the national level using FAO national yield statistics) and ultimately explore the issues related to WII spatial basis risk. Results show that the correlations between crop yield and satellite datasets show high spatial and temporal variability, making it difficult to identify a unique WII index that is at the same time simple and effective for the entire country. Precipitation, particularly the standardized March precipitation anomaly, has the highest correlations with maize yield (with Pearson correlation values higher than 0.55), in Central and South Malawi. Soil moisture and NDVI do not add much value to precipitation in anticipating historical maize yield at the district scale. From a methodological perspective, our work shows that WII indexes are best identified by: i) considering datasets with fine spatial resolution, whenever possible; ii) accounting for the vulnerability of the different crop growing stages to water-stress; iii) distinguishing between water scarce and water abundant events.

Data scarce regions, Drought risk, Precipitation, Spatial basis risk, Sub-Saharan Africa, Weather Index-based Insurance
0378-3774
Anghileri, Daniela
611ecf6c-55d5-4e63-b051-53e2324a7698
Bozzini, Veronica
d59741cc-054c-4763-966d-a34272f22ce5
Molnar, Peter
99f2d15c-5348-4c80-bb35-fb54c133862d
Jamali, Andrew A.J.
3af2db7b-deb4-48ec-b997-11b946dc8b32
Sheffield, Justin
9588e0e5-9a06-4efd-be50-74d56711bca8
Anghileri, Daniela
611ecf6c-55d5-4e63-b051-53e2324a7698
Bozzini, Veronica
d59741cc-054c-4763-966d-a34272f22ce5
Molnar, Peter
99f2d15c-5348-4c80-bb35-fb54c133862d
Jamali, Andrew A.J.
3af2db7b-deb4-48ec-b997-11b946dc8b32
Sheffield, Justin
9588e0e5-9a06-4efd-be50-74d56711bca8

Anghileri, Daniela, Bozzini, Veronica, Molnar, Peter, Jamali, Andrew A.J. and Sheffield, Justin (2022) Comparison of hydrological and vegetation remote sensing datasets as proxies for rainfed maize yield in Malawi. Agricultural Water Management, 262, [107375]. (doi:10.1016/j.agwat.2021.107375).

Record type: Article

Abstract

Weather Index-based Insurances (WIIs) have emerged as a promising risk coping mechanism to compensate for weather-induced damage to rainfed agriculture. Remote sensing may provide cost-effective information capable of discriminating the weather spatial variability thus reducing the spatial basis risk, i.e., the mismatch between the weather-based index triggering the insurance payout and the actual damage experienced by the farmers, which is often one of the causes hindering the wide implementation of WIIs. In this work we assess which indices based on remote sensing datasets are the best proxy indicators for rainfed maize yield in Malawi. We analyse the spatial (district scale) and temporal (monthly) correlations of historical maize yield data and several remote sensing datasets including the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset, the ESA CCI Soil Moisture combined dataset (version 4.2), the Evaporative Stress Index (ESI) from the Atmosphere-Land Exchange Inversion model (ALEXI), the MOD13Q1 Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). With respect to the previous literature, this work exploits a historical crop yield dataset at the sub-national level which allows us to analyse the correlation of the hydro-meteorological and vegetation variables at a higher spatial resolution than what is commonly done (i.e., at the national level using FAO national yield statistics) and ultimately explore the issues related to WII spatial basis risk. Results show that the correlations between crop yield and satellite datasets show high spatial and temporal variability, making it difficult to identify a unique WII index that is at the same time simple and effective for the entire country. Precipitation, particularly the standardized March precipitation anomaly, has the highest correlations with maize yield (with Pearson correlation values higher than 0.55), in Central and South Malawi. Soil moisture and NDVI do not add much value to precipitation in anticipating historical maize yield at the district scale. From a methodological perspective, our work shows that WII indexes are best identified by: i) considering datasets with fine spatial resolution, whenever possible; ii) accounting for the vulnerability of the different crop growing stages to water-stress; iii) distinguishing between water scarce and water abundant events.

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Published date: 31 March 2022
Additional Information: Funding Information: This work is part of the ‘Building REsearch Capacity for sustainable water and food security In drylands of sub-saharan Africa’ (BRECcIA) which is supported by UK Research and Innovation as part of the Global Challenges Research Fund, grant number NE/P021093/1. We thank Andrew A. Jamali for providing the yield data and supporting us during the analysis. We thank Henry Hunga for his insights on the Malawian food and agricultural policies. Publisher Copyright: © 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Keywords: Data scarce regions, Drought risk, Precipitation, Spatial basis risk, Sub-Saharan Africa, Weather Index-based Insurance

Identifiers

Local EPrints ID: 455940
URI: http://eprints.soton.ac.uk/id/eprint/455940
ISSN: 0378-3774
PURE UUID: 9d38da2d-5479-42d9-9fc2-818e94548140
ORCID for Daniela Anghileri: ORCID iD orcid.org/0000-0001-6220-8593

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Date deposited: 08 Apr 2022 17:56
Last modified: 23 Jul 2022 02:22

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Contributors

Author: Veronica Bozzini
Author: Peter Molnar
Author: Andrew A.J. Jamali
Author: Justin Sheffield

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