Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA

ABSTRACT

Cropland fallowing is choosing not to plant a crop during a season when a crop is normally planted. It is an important component of many crop rotations and can improve soil moisture and health. Knowing which fields are fallow is critical to assess crop productivity and crop water productivity, needed for food security assessments. The annual spatial extent of cropland fallows is poorly understood within the United States (U.S.). The U.S. Department of Agriculture Cropland Data Layer does provide cropland fallow areas; however, at a significantly lower confidence than their cropland classes. This study developed a methodology to map cropland fallows within the Northern Great Plains region of the U.S. using an easily implementable decision tree algorithm leveraging training and validation data from wet (2019), normal (2015), and dry (2017) precipitation years to account for climatic variability. The decision trees automated cropland fallow algorithm (ACFA) was coded on a cloud platform utilizing remotely sensed, time-series data from the years 2010–2019 to separate cropland fallows from other land cover/land use classes. Overall accuracies varied between 96%-98%. Producer’s and user’s accuracies of cropland fallow class varied between 70-87%.

KEYWORDS:

• Agriculture
• machine learning
• cloud computing
• image classification algorithms
• food security
• water security

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data used and generated in this publication is available from the USGS ScienceBase (Oliphant et al. 2024).

Additional information

Funding

This research was supported by the U.S. Geological Survey (USGS) National Land Imaging program of the Core Science Systems Mission Area of the USGS. The major research support for this project came from the water SMART (Sustain and Manage America's Resources for Tomorrow) project funded by the Department of Interior (DOI) through USGS. The research was conducted in the science facilities of the USGS Western Geographic Science Center (WGSC). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.