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Title: Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt

Abstract

Crop rotations (the practice of growing crops on the same land in sequential seasons) reside at the core of agronomic management as they can influence key ecosystem services such as crop yields, carbon and nutrient cycling, soil erosion, water quality, pest and disease control. Despite the availability of the Cropland Data Layer (CDL) which provides remotely sensed data on crop type in the US on an annual basis, crop rotation patterns remain poorly mapped due to the lack of tools that allow for consistent and efficient analysis of multi-year CDLs. This study presents the Representative Crop Rotations Using Edit Distance (RECRUIT) algorithm, implemented as a Python software package, to select representative crop rotations by combining and analyzing multi-year CDLs. Using CDLs from 2010 to 2012 for 5 states in the US Midwest, we demonstrate the performance and parameter sensitivity of RECRUIT in selecting representative crop rotations that preserve crop area and capture land-use changes. Selecting only 82 representative crop rotations accounted for over 90% of the spatio-temporal variability of the more than 13,000 rotations obtained from combining the multi-year CDLs. Furthermore, the accuracy of the crop rotation product compared favorably with total state-wide planted crop area available from agricultural censusmore » data. The RECRUIT derived crop rotation product was used to detect land-use conversion from grassland to crop cultivation in a wetland dominated part of the US Midwest. Monoculture corn and monoculture soybean cropping were found to comprise the dominant land-use on the newly cultivated lands.« less

Authors:
ORCiD logo; ; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1290414
Report Number(s):
PNNL-SA-107429
Journal ID: ISSN 0168-1699; KP1601050
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Computers and Electronics in Agriculture
Additional Journal Information:
Journal Volume: 108; Journal ID: ISSN 0168-1699
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
Cropland data layer; Crop rotations; US Midwest; RECRUIT algorithm; Prairie pothole region

Citation Formats

Sahajpal, Ritvik, Zhang, Xuesong, Izaurralde, Roberto C., Gelfand, Ilya, and Hurtt, George C. Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt. United States: N. p., 2014. Web. doi:10.1016/j.compag.2014.08.005.
Sahajpal, Ritvik, Zhang, Xuesong, Izaurralde, Roberto C., Gelfand, Ilya, & Hurtt, George C. Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt. United States. https://doi.org/10.1016/j.compag.2014.08.005
Sahajpal, Ritvik, Zhang, Xuesong, Izaurralde, Roberto C., Gelfand, Ilya, and Hurtt, George C. 2014. "Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt". United States. https://doi.org/10.1016/j.compag.2014.08.005.
@article{osti_1290414,
title = {Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt},
author = {Sahajpal, Ritvik and Zhang, Xuesong and Izaurralde, Roberto C. and Gelfand, Ilya and Hurtt, George C.},
abstractNote = {Crop rotations (the practice of growing crops on the same land in sequential seasons) reside at the core of agronomic management as they can influence key ecosystem services such as crop yields, carbon and nutrient cycling, soil erosion, water quality, pest and disease control. Despite the availability of the Cropland Data Layer (CDL) which provides remotely sensed data on crop type in the US on an annual basis, crop rotation patterns remain poorly mapped due to the lack of tools that allow for consistent and efficient analysis of multi-year CDLs. This study presents the Representative Crop Rotations Using Edit Distance (RECRUIT) algorithm, implemented as a Python software package, to select representative crop rotations by combining and analyzing multi-year CDLs. Using CDLs from 2010 to 2012 for 5 states in the US Midwest, we demonstrate the performance and parameter sensitivity of RECRUIT in selecting representative crop rotations that preserve crop area and capture land-use changes. Selecting only 82 representative crop rotations accounted for over 90% of the spatio-temporal variability of the more than 13,000 rotations obtained from combining the multi-year CDLs. Furthermore, the accuracy of the crop rotation product compared favorably with total state-wide planted crop area available from agricultural census data. The RECRUIT derived crop rotation product was used to detect land-use conversion from grassland to crop cultivation in a wetland dominated part of the US Midwest. Monoculture corn and monoculture soybean cropping were found to comprise the dominant land-use on the newly cultivated lands.},
doi = {10.1016/j.compag.2014.08.005},
url = {https://www.osti.gov/biblio/1290414}, journal = {Computers and Electronics in Agriculture},
issn = {0168-1699},
number = ,
volume = 108,
place = {United States},
year = {Wed Oct 01 00:00:00 EDT 2014},
month = {Wed Oct 01 00:00:00 EDT 2014}
}