Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.

Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Investigating the use of Active Crop Canopy Sensors for Soybean Management in Field Research and Production

Joshua Jay Miller, University of Nebraska - Lincoln

Abstract

Approximately one-third of soybean yield gain is a result of improved agronomic practices, which includes disease and insect management. Treatments containing fungicide, insecticide, biological, and nutrient components were evaluated in Nebraska soybean fields during 2013 through 2015 to determine effects on soybean yield and profitability. The greatest yield (4.83 Mg ha -1, p=0.019) was achieved with a complete seed and pod set treatment, but resulted in the second lowest calculated net return (US$151 ha -1, p=0.019) after accounting for fixed and variable costs at a soybean market price of US$0.367 kg-1. The most profitable treatment was the fungicide seed treatment followed by no pod set treatment (US$241 ha-1, p=0.019). The use of pod set treatments in the absence of significant disease and insect pressure was not profitable in most instances. ^ Crop canopy reflectance was measured several times throughout the season during 2014 and 2015 to evaluate normalized difference red edge (NDRE) index to predict soybean productivity. The NDRE values were used to calculate a cumulative reflectance value through the R6 growth stage, defined as area under the reflectance progress curve (AURPC). The AURPC values and seed yield were classified as top 25%, middle 50%, or bottom 25% by location. Multinomial regression determined that bottom AURPC values correctly predicted bottom yield 52.5% of the time (p=0.033), but ranged from 46.7 to 86.2% by location. Misclassifications by incorrectly identifying a bottom yield within the top AURPC ranged from 0.0% to 16.7% by location. The AURPC offers a novel method to delineate management zones in soybean production fields.^ Soybean canopy reflectance was also evaluated for the relationship between NDRE and soybean response to soybean cyst nematode (SCN; Heterodera glycines Ichinohe) infection. SCN-resistant and -susceptible varieties were planted in SCN-infested and non-infested sites during 2015 and 2016. Susceptible varieties yielded more than the resistant varieties at the non-infested sites by 245 kg ha-1 (p=0.004), and resistant varieties yielded more than the susceptible varieties at the SCN-infested sites by 340 kg ha -1 (p=0.0021). Measured NDRE values at R4 and R5 were different between resistant and susceptible varieties, but were not correlated with yield.^

Subject Area

Agronomy|Plant pathology|Remote sensing

Recommended Citation

Miller, Joshua Jay, "Investigating the use of Active Crop Canopy Sensors for Soybean Management in Field Research and Production" (2017). ETD collection for University of Nebraska - Lincoln. AAI10272395.
http://digitalcommons.unl.edu/dissertations/AAI10272395

Share

COinS