Managing Global Resources for a Secure Future

2017 Annual Meeting | Oct. 22-25 | Tampa, FL

49-3 Utilizing Remote Sensing for Variable-Rate Nitrogen and Irrigation Management in Potato.

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: Agricultural Remote Sensing General Oral (includes student competition)

Monday, October 23, 2017: 9:35 AM
Tampa Convention Center, Room 5

Tyler J. Nigon1, Brian J. Bohman2, Carl J. Rosen1 and David Mulla1, (1)Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN
(2)University of Minnesota, Shoreview, MN
Abstract:
In-season adaptive management of nitrogen [N] and supplemental irrigation [IRR] depends on high resolution spatial and temporal data, often obtained through remote sensing technologies. These data can be utilized within existing crop management frameworks, such as split-applications of N-fertilizer and water balance based IRR management, to incorporate spatial heterogeneities and changes over time, improve nutrient use efficiencies and prevent N-leaching losses. A study was carried out on Russet Burbank potatoes grown on an irrigated, coarse-textured soil in central Minnesota and data were collected using two remote sensing platforms, CROPSCAN narrowband Multispectral Radiometer (MSR-16R) and Sentek Systems GEMS broadband Multispectral Sensor. These measurements were compared with leaf level measurements using the SPAD Chlorophyll Meter. A total of six N-treatments and two IRR-treatments were applied in a factorial design. Crop growth was monitored using Normalized Difference Vegetation Index [NDVI] and Soil Adjusted Vegetation Index [SAVI]. Information from these indices were used to adjust the crop coefficient used in evapotranspiration [ET] calculations to incorporate spatial and temporal heterogeneity and determine the feasibility for variable timing and rates of IRR. Crop N status was monitored using the MERIS Terrestrial Chlorophyll Index [MTCI], Simple Ratio 8 [SR8], Green Ratio Vegetation Index [GRVI], and SPAD index. Information from these indices were used with the Nitrogen Sufficiency Index [NSI] approach to identify areas of relative N-stress and guide variable-rate N applications. Together, these remote sensing approaches facilitated an integrated management of variable rate and timing of N and IRR possible.

See more from this Division: ASA Section: Climatology and Modeling
See more from this Session: Agricultural Remote Sensing General Oral (includes student competition)