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Predicting Rainy Season Onset in the Ethiopian Highlands for Agricultural Planning

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  • 1 Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, Wisconsin
  • | 2 Faculty of Civil and Water Resources Engineering, Bahir Dar Institute of Technology, Bahir Dar, Ethiopia
  • | 3 Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison, Wisconsin
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ABSTRACT

The Kiremt rainy season is the foundation of agriculture in the Ethiopian Highlands and a key driver of economic development as well as the instigator of famines that have plagued the country’s history. Despite the importance of these rains, relatively little research exists on predicting the season’s onset; even less research evaluates statistical modeling approaches, in spite of their demonstrated utility for decision-making at local scales. To explore these methods, predictions are generated conditioned on three definitions of onset, at three lead times, using partial least squares (PLS) regression and random forest classification. Results illustrate moderate prediction skill and an ability to avoid false onsets, which may guide planting decisions; however, they are highly sensitive to how onset is defined, suggesting that future prediction approaches should additionally consider local agricultural definitions of onset.

Corresponding author: Jonathan Lala, jonalala@hotmail.com

ABSTRACT

The Kiremt rainy season is the foundation of agriculture in the Ethiopian Highlands and a key driver of economic development as well as the instigator of famines that have plagued the country’s history. Despite the importance of these rains, relatively little research exists on predicting the season’s onset; even less research evaluates statistical modeling approaches, in spite of their demonstrated utility for decision-making at local scales. To explore these methods, predictions are generated conditioned on three definitions of onset, at three lead times, using partial least squares (PLS) regression and random forest classification. Results illustrate moderate prediction skill and an ability to avoid false onsets, which may guide planting decisions; however, they are highly sensitive to how onset is defined, suggesting that future prediction approaches should additionally consider local agricultural definitions of onset.

Corresponding author: Jonathan Lala, jonalala@hotmail.com
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