Time Series Forecasting for Agriculture/Crop Volume & Pricing – Looking for Advice [D]
Our take
The recent Reddit post from a data professional at a major berry company seeking advice on time series forecasting for crop volume and pricing highlights a critical intersection: the growing application of machine learning to traditionally data-rich but often intuitively-managed industries like agriculture. This individual's journey – moving from SARIMA models to experimenting with XGBoost and Holt-Winters – mirrors a broader trend of leveraging more sophisticated techniques to extract predictive power from complex datasets. It’s encouraging to see someone with a solid foundation in Information Systems actively exploring these avenues, recognizing the limitations of legacy approaches. The challenges they articulate—highly seasonal data, weather dependencies, and fluctuating supply conditions—are common hurdles in agricultural forecasting, requiring a nuanced understanding of both statistical modeling and domain-specific knowledge. The need for production-grade frameworks also underscores the move beyond proof-of-concept models to reliable, scalable solutions. For those seeking a curated resource for navigating the vast landscape of AI/ML papers, we recently published [I Built Paper Deck: A Better Way to Discover AI/ML Papers [P]](/post/i-built-paper-deck-a-better-way-to-discover-ai-ml-papers-p-cmqa6n7en0105tqtwx761hgxp), which might prove invaluable in streamlining their research.
The questions posed – regarding libraries, suitable models, pricing approaches, and feature engineering – are fundamental to any successful forecasting project. While SARIMA models provide a solid baseline, the exploration of XGBoost and Holt-Winters indicates a desire to capture non-linear relationships and incorporate external factors more effectively. Feature engineering, as highlighted, is paramount; weather data, acreage, imports, and even broader economic indicators can all contribute to more accurate predictions. It’s interesting to note the absence of discussion around incorporating unstructured data sources like news reports or social media sentiment – areas ripe for future exploration. The challenges around commodity pricing are particularly complex, often influenced by factors beyond purely supply and demand dynamics, such as geopolitical events or speculative trading. Successfully forecasting these requires a deeper dive into econometrics and potentially incorporating alternative data sources. The conversation also subtly touches on a larger question: how to effectively validate and deploy these models in a rapidly changing environment – a concern echoed in [Should I Commit and Publish the Results? [R]](/post/should-i-commit-and-publish-the-results-r-cmqa6mvq300zjtqtwfy39ur7y), where the challenges of model evaluation and stakeholder buy-in are discussed.
What makes this post particularly compelling is its genuine, relatable nature. It’s not a boast of expertise but a sincere request for guidance from a practitioner actively learning and applying these techniques. This reflects a growing understanding that machine learning isn't a magic bullet but a powerful toolkit that requires careful application and iterative refinement. The focus on practical implementation – production-grade frameworks – points toward a shift from academic experimentation to real-world impact within the agricultural industry. The prevalence of these discussions, as seen in our community exploring [What will be the next breakthrough in ASR? [D]](/post/what-will-be-the-next-breakthrough-in-asr-d-cmqa6mle500yttqtwccat2h9c), emphasizes the ongoing evolution of the field and the need for continuous learning and adaptation. It also highlights the importance of community support and knowledge sharing, particularly as organizations navigate the complexities of implementing AI-powered solutions.
Ultimately, this conversation underscores the transformative potential of AI-native spreadsheet technology in industries like agriculture. The ability to accurately forecast crop volumes and pricing can have profound implications for supply chain management, resource allocation, and ultimately, profitability. The challenge lies not just in building sophisticated models but in seamlessly integrating them into existing workflows and empowering users to leverage data-driven insights. As we move forward, it will be crucial to focus on building accessible, user-friendly tools that democratize access to these powerful forecasting capabilities, enabling businesses of all sizes to thrive in an increasingly data-driven world. What strategies will prove most effective in bridging the gap between complex machine learning models and the practical needs of agricultural professionals?
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.
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