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Extensible Framework for Analysis of Farm Practices and Programs

Authors :
Sandeep Puthanveetil Satheesan
Gowtham Naraharisetty
Christopher M. Navarro
Jong S. Lee
Rabin Bhattarai
Gary D. Schnitkey
Lisa Gatzke
Hanseok Jeong
Shannon Bradley
Rishabh Gupta
M. Ondrejcek
Yan Zhao
Jonathan Coppess
Source :
PEARC
Publication Year :
2019
Publisher :
ACM, 2019.

Abstract

We present an open source extensible web framework for the analysis of different farm practices and programs and easy dissemination of their results to the users. Currently, this framework is being applied to two use cases --- a web-based decision support system for cover crop management and a web-based farm program analysis tool to assist farmers, academics, and policymakers to understand programs and policies surrounding the Farm Bill. Through the first use case, we address the problem of bridging the gap between the scientific research that happens in labs and experimental plots and the day to day practices and decisions taken by the farmers in the fields. Specifically, this use case focuses on the practice of cover crops, their management, and the impact on reducing nutrient runoff into water bodies. Through the second use case, we address the problem of predicting the expected payment amounts and measured risk or probability of payment for different government insurance programs authorized by the 2018 Farm Bill, namely the Agriculture Risk Coverage (ARC) and Price Loss Coverage (PLC). This helps the farmers compare these programs based on forecasted crop yields and prices. In this paper, we describe the overall architecture of the framework and its major components, the use cases that are currently benefiting from using this framework and share screenshots of the web applications developed using this framework for those use cases. We also share our plans for future work and conclusions about applying this framework to the two use cases.

Details

Database :
OpenAIRE
Journal :
Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)
Accession number :
edsair.doi...........682efd8d0ce7d4826e6120bdb23d2541
Full Text :
https://doi.org/10.1145/3332186.3337063