Technical articles, research summaries, tutorials, and insights from the intersection of agriculture and data science.
From working with farmers in Chitwan to advanced crop modeling at UF — the experiences that shaped my approach to agricultural data science.
A practical guide to DSSAT calibration based on real-world experience, covering the most common pitfalls researchers face.
How machine learning is transforming crop yield prediction, and a comparison of models I've tested in my research.
Key takeaways from the 23rd DSSAT Development Sprint at the University of Kentucky — new features, community insights, and the future of crop modeling.
A beginner-friendly explanation of how crop simulation models like DSSAT convert field data into yield predictions.
My experience at Ragolski & Son's and the advanced techniques that could benefit Nepali agriculture.
A step-by-step guide to using QGIS for site selection, spatial analysis, and crop monitoring in agricultural research.
How decision support tools can optimize nitrogen application for rice cultivation — lessons from my research in Chitwan.
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