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March 10, 20260 citationsOpen Access

Time-Series Forecasting for Yield Improvement in Industrial Machinery Fleets

Time-Series Forecasting Model Evaluation for Yield Improvement in Industrial Machinery Fleets of Uganda: A Methodological Approach

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Authors

SMSamson Kasozi Mukasa

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Overview

Evaluative analysis shows significant yield improvement in agricultural machinery fleets, highlighting implications for productivity management.

Key Points

  • The aim is to evaluate a time-series forecasting model to improve yield outcomes in industrial machinery fleets in Uganda.
  • Conducted time-series analysis using historical machinery usage, weather, and crop yield data.
  • Utilized ARIMA methodology with robust standard errors for model development.
  • Employed statistical software for model validation and accuracy assessment.
  • Found a significant positive correlation between machinery usage and yield outcomes.
  • Achieved an average forecast accuracy rate of 78% over the tested periods.
  • Demonstrated the potential of time-series forecasting to enhance farm productivity management.

Cite This Study

Samson Kasozi Mukasa (2010) studied this question.

synapsesocial.com/papers/69af95cf70916d39fea4dd23https://doi.org/10.5281/zenodo.18906238
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