ChatGPT is a Large Language Model (LLM), which is a subset of Artificial Intelligence (AI), as is Machine Learning (ML), Deep Learning (DL), etc.

What you did was to ask a LLM to answer a question more appropriate to ML. To answer a broad question, like "Which varieties should I plant?," it's going to fall back on its Web-based training data. It may not be the best tool for the job.

I recall the frustration back in the 90s, when I suddenly had a lot of yield and fertility data to work with -- and ordinary stats weren't up to the job. Later, I started using ML algorithms and my predictive models began showing some skill. With enough data, ML could easily answer your "Which varieties?" question. But one answer leads to three more questions -- and the need for more data.

I knew one important component had been missing from my ML analyses: location. We're dealing with spatial data. And if you just dump it all into a hopper, stripped of spatial context, you are going to miss most of the information in your data.

So, about ten years ago I started a deep dive into geostatistics. I can report that it's a little daunting. But I now think the best method for ag analytics will use geostats to prepare the feedstock to ML algorithms. There's a lot of potential here...and I wish I were thirty years younger.