Time Series Forecasting for Agriculture/Crop Volume & Pricing – Looking for Advice [D]

Hi everyone,

I work for a major berry company, and a large part of my role involves forecasting total industry crop volumes (weekly harvest/production forecasts) as well as future pricing.

I'm relatively new to ML-based forecasting. This is only my second professional role, and I have a bachelor's degree in Information Systems with a few machine learning courses under my belt, but I'm definitely not a forecasting expert.

For crop forecasting, I've been working with USDA and other industry datasets. I started with SARIMA models and have recently been experimenting with XGBoost and Holt-Winters methods to compare performance.

I'm looking for recommendations on:

• Libraries/frameworks that are commonly used for production-grade time series forecasting
• Models that work well for agricultural production forecasting
• Approaches for forecasting commodity/produce pricing
• Feature engineering ideas (weather, seasonality, acreage, imports, etc.)
• Any papers, blogs, or resources that would be useful

Most of the data is weekly and highly seasonal, with weather and supply conditions playing a major role.

Any suggestions, lessons learned, or pointers from people working in forecasting would be greatly appreciated.