Financing agricultural drought risk through ex-ante cash transfers

Gabriela Guimarães Nobre, Frank Davenport, Konstantinos Bischiniotis, Ted Veldkamp, Brenden Jongman, Christopher C. Funk, Gregory Husak, Philip J. Ward, Jeroen C.J.H. Aerts

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Despite advances in drought early warning systems, forecast information is rarely used for triggering and financing early actions, such as cash transfer. Scaling up cash transfer pay-outs, and overcoming the barriers to actions based on forecasts, requires an understanding of costs resulting from False Alarms, and the potential benefits associated with appropriate early interventions. On this study, we evaluate the potential cost-effectiveness of cash transfer responses, comparing the relative costs of ex-ante cash transfers during the maize growing season to ex-post cash transfers after harvesting in Kenya. For that, we developed a forecast model using Fast-and Frugal Trees that unravels early warning relationships between climate variability, vegetation coverage, and maize yields at multiple lead times. Results indicate that our models correctly forecast low maize yield events 85% of the time across the districts studied, some already six months before harvesting. The models' performance improves towards the end of the growing season driven by a decrease of 29% in the probability of False Alarms. Overall, we show that timely cash transfers ex-ante to a disaster can often be more cost-effective than investing in ex-post expenditures. Our findings suggest that early response can yield significant cost savings, and can potentially increase the effectiveness of existing cash transfer systems.
LanguageEnglish
Pages523-535
Number of pages13
JournalScience of the Total Environment
Volume653
DOIs
StatePublished - 25 Feb 2019

Fingerprint

Drought
drought
cost
maize
Costs
growing season
Alarm systems
Cost effectiveness
early warning system
Disasters
expenditure
savings
disaster
financing
forecast
vegetation
climate
alarm

Keywords

  • Drought
  • Cash transfer
  • Food security
  • Forecasting
  • Disaster risk financing
  • Machine learning

Cite this

@article{5a55812c793747a0a6563c626867bab2,
title = "Financing agricultural drought risk through ex-ante cash transfers",
abstract = "Despite advances in drought early warning systems, forecast information is rarely used for triggering and financing early actions, such as cash transfer. Scaling up cash transfer pay-outs, and overcoming the barriers to actions based on forecasts, requires an understanding of costs resulting from False Alarms, and the potential benefits associated with appropriate early interventions. On this study, we evaluate the potential cost-effectiveness of cash transfer responses, comparing the relative costs of ex-ante cash transfers during the maize growing season to ex-post cash transfers after harvesting in Kenya. For that, we developed a forecast model using Fast-and Frugal Trees that unravels early warning relationships between climate variability, vegetation coverage, and maize yields at multiple lead times. Results indicate that our models correctly forecast low maize yield events 85{\%} of the time across the districts studied, some already six months before harvesting. The models' performance improves towards the end of the growing season driven by a decrease of 29{\%} in the probability of False Alarms. Overall, we show that timely cash transfers ex-ante to a disaster can often be more cost-effective than investing in ex-post expenditures. Our findings suggest that early response can yield significant cost savings, and can potentially increase the effectiveness of existing cash transfer systems.",
keywords = "Drought, Cash transfer, Food security, Forecasting, Disaster risk financing, Machine learning",
author = "{Guimar{\~a}es Nobre}, Gabriela and Frank Davenport and Konstantinos Bischiniotis and Ted Veldkamp and Brenden Jongman and Funk, {Christopher C.} and Gregory Husak and Ward, {Philip J.} and Aerts, {Jeroen C.J.H.}",
year = "2019",
month = "2",
day = "25",
doi = "10.1016/j.scitotenv.2018.10.406",
language = "English",
volume = "653",
pages = "523--535",
journal = "Science of the Total Environment",
issn = "0048-9697",
publisher = "Elsevier",

}

Financing agricultural drought risk through ex-ante cash transfers. / Guimarães Nobre, Gabriela; Davenport, Frank; Bischiniotis, Konstantinos; Veldkamp, Ted; Jongman, Brenden; Funk, Christopher C.; Husak, Gregory; Ward, Philip J.; Aerts, Jeroen C.J.H.

In: Science of the Total Environment, Vol. 653, 25.02.2019, p. 523-535.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - Financing agricultural drought risk through ex-ante cash transfers

AU - Guimarães Nobre,Gabriela

AU - Davenport,Frank

AU - Bischiniotis,Konstantinos

AU - Veldkamp,Ted

AU - Jongman,Brenden

AU - Funk,Christopher C.

AU - Husak,Gregory

AU - Ward,Philip J.

AU - Aerts,Jeroen C.J.H.

PY - 2019/2/25

Y1 - 2019/2/25

N2 - Despite advances in drought early warning systems, forecast information is rarely used for triggering and financing early actions, such as cash transfer. Scaling up cash transfer pay-outs, and overcoming the barriers to actions based on forecasts, requires an understanding of costs resulting from False Alarms, and the potential benefits associated with appropriate early interventions. On this study, we evaluate the potential cost-effectiveness of cash transfer responses, comparing the relative costs of ex-ante cash transfers during the maize growing season to ex-post cash transfers after harvesting in Kenya. For that, we developed a forecast model using Fast-and Frugal Trees that unravels early warning relationships between climate variability, vegetation coverage, and maize yields at multiple lead times. Results indicate that our models correctly forecast low maize yield events 85% of the time across the districts studied, some already six months before harvesting. The models' performance improves towards the end of the growing season driven by a decrease of 29% in the probability of False Alarms. Overall, we show that timely cash transfers ex-ante to a disaster can often be more cost-effective than investing in ex-post expenditures. Our findings suggest that early response can yield significant cost savings, and can potentially increase the effectiveness of existing cash transfer systems.

AB - Despite advances in drought early warning systems, forecast information is rarely used for triggering and financing early actions, such as cash transfer. Scaling up cash transfer pay-outs, and overcoming the barriers to actions based on forecasts, requires an understanding of costs resulting from False Alarms, and the potential benefits associated with appropriate early interventions. On this study, we evaluate the potential cost-effectiveness of cash transfer responses, comparing the relative costs of ex-ante cash transfers during the maize growing season to ex-post cash transfers after harvesting in Kenya. For that, we developed a forecast model using Fast-and Frugal Trees that unravels early warning relationships between climate variability, vegetation coverage, and maize yields at multiple lead times. Results indicate that our models correctly forecast low maize yield events 85% of the time across the districts studied, some already six months before harvesting. The models' performance improves towards the end of the growing season driven by a decrease of 29% in the probability of False Alarms. Overall, we show that timely cash transfers ex-ante to a disaster can often be more cost-effective than investing in ex-post expenditures. Our findings suggest that early response can yield significant cost savings, and can potentially increase the effectiveness of existing cash transfer systems.

KW - Drought

KW - Cash transfer

KW - Food security

KW - Forecasting

KW - Disaster risk financing

KW - Machine learning

U2 - 10.1016/j.scitotenv.2018.10.406

DO - 10.1016/j.scitotenv.2018.10.406

M3 - Article

VL - 653

SP - 523

EP - 535

JO - Science of the Total Environment

T2 - Science of the Total Environment

JF - Science of the Total Environment

SN - 0048-9697

ER -