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<p>Late blight, caused by <i>Phytophthora infestans</i>, is a major threat to potato production in Rwanda, severely impacting yields and farmer livelihoods. In response, we adapted and piloted the integration of the potato late blight forecasting model into the existing Meteo Rwanda weather and climate services platforms, inspired by the CARAH model successfully used in Belgium and some 15 provinces all over China. Three automatic weather stations were installed, and disease forecasting was validated on pilot plots in three sites namely Burera, Musanze, and Nyabihu Districts. The yield and the quality of the produced potatoes were analysed. The results from this study revealed that, the application of the model significantly contributed to reduced pesticide use by 37.5%, increased yields by 54%, and reduced disease severity. This study demonstrates that a climate-smart, data-driven approach can sustainably manage late blight in Rwanda and increase potato yields in small-scale farmers’ fields.</p>

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Integration of the CARAH model for potato late blight management into the Meteo Rwanda platform

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月1日

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  • Marc Antoine Ndisanze,
  • Daniel Niyikiza,
  • Dieudonne Ngabitsinze,
  • Francois Serneels,
  • Maxime Bonnave,
  • Cyrille Andre Vryghem,
  • Claude Maniragaba,
  • Joselyne Byukusenge,
  • Sandrine Bazizane,
  • Enatha Mutimukeye,
  • Valentin Uwishema,
  • Claude Jean Habimana

摘要

Late blight, caused by Phytophthora infestans, is a major threat to potato production in Rwanda, severely impacting yields and farmer livelihoods. In response, we adapted and piloted the integration of the potato late blight forecasting model into the existing Meteo Rwanda weather and climate services platforms, inspired by the CARAH model successfully used in Belgium and some 15 provinces all over China. Three automatic weather stations were installed, and disease forecasting was validated on pilot plots in three sites namely Burera, Musanze, and Nyabihu Districts. The yield and the quality of the produced potatoes were analysed. The results from this study revealed that, the application of the model significantly contributed to reduced pesticide use by 37.5%, increased yields by 54%, and reduced disease severity. This study demonstrates that a climate-smart, data-driven approach can sustainably manage late blight in Rwanda and increase potato yields in small-scale farmers’ fields.