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Robustly forecasting maize yields in Tanzania based on climatic predictors.

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This study introduces a new statistical model for forecasting maize yield in Tanzania, crucial for food security. The model accurately predicts yields weeks before harvest using climate data, even with limited historical information.

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Area of Science:

  • Agricultural Science
  • Climate Science
  • Data Science

Background:

  • Seasonal yield forecasts are vital for agricultural development and food security in developing nations.
  • Tanzania currently lacks an operational sub-national forecasting system for crop yields.
  • Existing forecasting methods may be limited by data availability and quality in many regions.

Purpose of the Study:

  • To develop and validate a statistical model for sub-national maize yield forecasting in Tanzania.
  • To provide actionable yield predictions approximately six weeks before harvest.
  • To assess the model's applicability in data-scarce environments.

Main Methods:

  • Utilized regional maize yield statistics and climatic predictors from 2009-2019 for model development.
  • Employed statistical forecasting techniques to predict both yield anomalies and absolute yields.
  • Conducted out-of-sample cross-validation and variable selection for robust assessment.
  • Used global climate data, suitable for regions with limited local weather data.

Main Results:

  • Achieved a median Nash-Sutcliffe efficiency coefficient of 0.72 for yield anomalies and 0.79 for absolute yields in cross-validation.
  • Obtained a median root mean squared error of 0.13 t/ha for absolute yield predictions.
  • Successfully generated independent forecasts for the 2019 harvest year.
  • Demonstrated the model's effectiveness with a short time series of yield data.

Conclusions:

  • The developed statistical model offers a reliable method for sub-national maize yield forecasting in Tanzania.
  • The approach is adaptable to other countries facing similar data limitations.
  • This system can significantly support agricultural planning and enhance food security.