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ARS Home » Plains Area » Brookings, South Dakota » Integrated Cropping Systems Research » Research » Publications at this Location » Publication #141730
Title: DETECTING NITROGEN AND PHOSPHORUS STRESS IN CORN USING MULTI-SPECTRAL IMAGERY

Author
item Osborne, Shannon
item Schepers, James
item Schlemmer, Michael

Submitted to: Communications in Soil Science and Plant Analysis
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/16/2003
Publication Date: 2/1/2004
Citation: Osborne, S.L., Schepers, J.S., Schlemmer, M.R. 2004. Detecting nitrogen and phosphorus stress in corn using multi-spectral imagery. Communications in Soil Science and Plant Analysis 35:505-516.

Interpretive Summary: In-season detection of nutrient stress and/or the ability to predict grain yield could be beneficial to producers. This type of information could help producers make various management decisions. These management decisions could lead to a reduction in environmental pollution due to over fertilization. A field study was conducted to evaluate the use of multi-spectral imagery for estimating in-season production and quality, and grain yield and quality (grain nitrogen (N) and phosphorus (P) content) under varying levels of N and P nutrient management. The experiment was conducted in a continuous corn system utilizing linear drive irrigation. There were four N rates (0, 60, 120, and 240 lb N ac-1) and four P rates (0, 20, 40, and 60 lb P ac-1). Multi-spectral imagery was collected throughout the growing season using a four (blue, green, red and near-infrared (NIR)) band sensor. Grain yield, in-season biomass and N concentration increased with increasing N rate for all sampling dates. Biomass production differences due to P deficiency were present only for the early (June) sampling dates. The 1998 imagery was better for estimating plant and grain characteristic compared to the 1997 imagery, due to differences in sensor sensitivity and increased plant response to applied nutrients. The normalized difference greenness vegetation index (GNDVI), a ratio of green and NIR reflectance, was the best for evaluating grain yield. This study demonstrated the utility of remote sensing for estimating grain production and nutrient deficiency helping producers with in-season nutrient management decisions.

Technical Abstract: The ability to evaluate in-season nutrient deficiencies and/or estimate grain yield could be beneficial to producers in helping make various management decisions. Proper nutrient management decisions could lead to decreased environmental pollution due to over fertilization. A field experiment was established to evaluate the use of multi-spectral imagery for estimating in-season plant biomass, plant nitrogen (N) and phosphorus (P) concentration, grain yield and grain N and P concentration with varying degrees of N and P nutrition. The experiment was a randomized complete block design with four replications using a factorial arrangement of treatments in an irrigated continuous corn (Zea mays L.) system. There were four N rates (0, 67, 134, and 269 kg N ha-1) and four P rates (0, 22, 45, 67 kg P ha-1). Multi-spectral imagery was collected throughout the growing season using a four (blue, green, red and near-infrared (NIR)) band sensor. Grain yield, in-season biomass and N concentration increased with increasing N rate for all sampling dates. Biomass production differences due to P deficiency were present only for the early (June) sampling dates. The 1998 imagery had higher regression correlation for in-season biophysical characteristics and grain yield compared to the 1997 growing season, due to differences in sensor sensitivity and incrased plant response to applied nutrients. The normalized difference greenness vegetation index (NGDVI) generally had the highest r2 with grain yield. This study demonstrated the utility of multi-spectral imagery for estimating grain production and nutrient deficiency helping producers with in-season nutrient management decisions.