Date of Graduation

5-2016

Document Type

Thesis

Degree Name

Master of Science in Crop, Soil & Environmental Sciences (MS)

Degree Level

Graduate

Department

Crop, Soil & Environmental Sciences

Advisor

Larry Purcell

Committee Member

Trenton L. Roberts

Second Committee Member

Douglas Karcher

Third Committee Member

Jason Kelly

Keywords

Biological sciences; Nitrogen; Optimization; Soil fertility

Abstract

Nitrogen (N) is one of the most limiting factors for maize (Zea mays L.) production worldwide. Over-fertilization of N may decrease yields and increase NO3- contamination of water. The Dark Green Color Index (DGCI) is a quantitative measure of greenness that is closely related to leaf N concentration. Previous research determined that DGCI values from maize at V6 to V10 could be used to predict the amount of N fertilizer to recover 90 or 95% of the potential yield. These DGCI algorithms were used by Spectrum Technologies Company to develop a smartphone application (app-method) for predicting maize N needs. Our objectives were to (1) evaluate the N recommendations based upon DGCI values made at V6 to V10 using the app-method and the previously developed method using a digital camera (camera-method) with standard N recommendation from the University of Arkansas Cooperative Extension Service (246 kg ha-1); (2) determine if DGCI values made by the app-method agreed with values determined by the camera-method; and (3) identify potential sources of error leading to discrepancies between the camera- and the app-methods for determining DGCI. Field and laboratory experiments were conducted to answer these objectives. When residual soil N was high and the crop was unresponsive to additional N fertilizer, the app still recommended additional N to be applied. The evaluation of the camera-method at low- and medium-residual N soils showed that DGCI predicted significantly less N than the standard recommendation without affecting yield potential. There was a relatively poor agreement between DGCI values made on the same leaves, resulting in large differences in the recommended amounts of N to apply using the camera- and the app-methods (r2=0.52, 0.67). Two major sources were identified that were responsible for the discrepancies between the camera and the app-methods. First, the DGCI values of the internal standards used by the two methods were different from their reported nominal values. Second, the camera-method uses the entire leaf blade in calculating DGCI whereas the app calculates DGCI from only a small portion of the leaf. Dissection of leaf blades indicated that N concentration increased from the basal to the apical portion of leaves and that DGCI values were different across the leaf blade. Future research should improve the relationship between the camera- and the app-methods in predicting DGCI values.

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