Comparison of statistical models for analyzing wheat yield time series.

Area of Science:

• Agricultural Science
• Statistical Modeling
• Food Security Analysis

Background:

• Global population growth necessitates enhanced agricultural output to ensure food security.
• Current food security projections often rely on crop yield time series analysis.
• A detailed evaluation of statistical model performance for yield prediction is lacking.

Purpose of the Study:

• To compare the predictive performance of eight statistical models for wheat yield.
• To identify the most accurate models for national and regional wheat yield forecasting.
• To analyze wheat yield trends and uncertainty using validated statistical methods.

Main Methods:

• Analysis of wheat yield time series data from the Food and Agriculture Organization of the United Nations and the French Ministry of Agriculture.
• Implementation and comparison of eight distinct statistical models, including Holt-Winters and dynamic linear models.
• Retrospective analysis of past yield trends and estimation of prediction uncertainty.

Main Results:

• Holt-Winters and dynamic linear models demonstrated the highest predictive accuracy for wheat yield.
• Dynamic linear models enable retrospective trend reconstruction and uncertainty analysis, outperforming Holt-Winters models in these aspects.
• Wheat yield stagnation was observed in many countries, though positive growth rates persist in several regions.
• Significant uncertainty in yield increase estimates was noted for major wheat-producing nations.
• Substantial variation in yield increase rates across French regions highlights the need for localized analysis.

Conclusions:

• Dynamic linear models are recommended for wheat yield time series analysis due to their predictive accuracy and analytical capabilities.
• Addressing wheat yield stagnation requires a focus on subnational factors and regional specificities.
• Accurate yield forecasting and trend analysis are essential for informed agricultural policy and global food security planning.