A Spatial Analysis Of Ethiopia'S 2015 Drought: New Directions In Predicting High Damage, Low Frequency Cereal Crop Loss

Domaine:

agricultureclimategeospatial

Type de record:

modelpaper

Créateur:

ManWar

Éditeur:

Hôte:

Without critical analysis, many policymakers perceive that droughts affect countries in a relatively random fashion. In other words, the vagaries of rainfall deficits impact several parts of the country without any discernible pattern. In this study, we provide various empirical evidence of localized crop damage clusters in the 2015 growing season. Three principal results include, using a time series data fusion model, predicating the existence of high crop damage neighborhoods, early crop season productivityforecasts (“Now Casting”), and a binary machine learning model that seeks to explain crop specific, large damage (>25%), localized events.

Predicting High-Magnitude, Low-Frequency Crop Losses Using Machine Learning: An application to cereal crops in Ethiopia

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