Skip to main content
ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publications at this Location

Publications at this Location

ARS scientists publish results of their research projects in many formats. Listed below are the publications from research projects conducted at this location.

Clicking on a publication title will take you to more information on the publication. Clicking on the reprint icon Repository URL will take you to the publication reprint.

2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998 | 1997 | 1996 | 1995 |

2022 Publications
(listed by order of acceptance date)

Current View: Peer Reviewed Publications Only

Show All Publications || Peer Reviewed Journal Publications Only

Displaying 1 to 9 of 9 Records

Conditions potentially affecting corn ear formation, yield, and abnormal ears: a review
(Peer Reviewed Journal)
Integrating partitioned evapotranspiration data into hydrologic models: vegetation parameterization and uncertainty quantification of simulated plant water use
(Peer Reviewed Journal)
Corn emergence uniformity estimation and mapping using UAV imagery and deep learning Reprint Icon
(Peer Reviewed Journal)
Deep transfer learning of global spectra for local soil carbon monitoring Reprint Icon
(Peer Reviewed Journal)
A new perspective when examining maize fertilizer nitrogen use efficiency, incrementally Reprint Icon
(Peer Reviewed Journal)
Maize leaf appearance rates: a synthesis from the US Corn Belt Reprint Icon
(Peer Reviewed Journal)
Extraction of reflectance spectra features for estimation of surface, subsurface, and profile soil properties Reprint Icon
(Peer Reviewed Journal)
Quantifying the effects of soil texture and weather on cotton development and yield using UAV imagery
(Peer Reviewed Journal)
Corn nitrogen nutrition index prediction improved by integrating genetic, environmental, and management factors with active canopy sensing using machine learning Reprint Icon
(Peer Reviewed Journal)