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In-Season Nitrogen Requirement For Maize Using Model And Sensor-Based Recommendation Approaches
1
L. J. Stevens,
2
R. B. Ferguson,
3
D. W. Franzen,
4
N. R. Kitchen
1. University of Nebraska, Lincoln, NE
2. University of Nebraska, Lincoln, Nebraska
3. North Dakota State University, Fargo, North Dakota
4. USDA-ARS Cropping Systems and Water Quality Research Unit, Columbia, Missouri
Nitrogen (N), an essential element, is often limiting to plant growth. There is great value in determining the optimum quantity and timing of N application to meet crop needs while minimizing losses. Low nitrogen use efficiency (NUE) has been attributed to several factors including poor synchrony between N fertilizer and crop demand, unaccounted for spatial variability resulting in varying crop N needs, and temporal variances in crop N needs. Applying a portion of the N fertilizer alongside the growing crop allows fertilizer availability to coincide more closely with the time in which the crop needs the most N and is expected to increase NUE. This in-season application also allows for adjustments which can be responsive to actual field and weather conditions which result in varying N needs. Simulation models have been identified as a precision management technique which has potential to maximize the synchrony of crop demand for N and fertilizer N supply. The Maize-N model was developed to estimate economically optimum N fertilizer rates for maize by taking into account soil properties, indigenous soil N supply, local climatic conditions and yield potential, crop rotation, tillage and fertilizer formulation, application method and timing. Strategies which detect crop N status at early growth stages can also improve NUE. Active crop canopy sensors monitor the N status of the crop, allowing growers to make management decisions that are reactive to actual growing season conditions. In a study conducted in 2012 and 2013, an active crop canopy sensor provided a normalized difference red edge index (NDRE) which was used to generate a sufficiency index (SI) for each plot. The SI was then used in the modified algorithm developed by Holland and Schepers to determine an in-season N application rate. Four replications of randomized complete blocks were conducted at each of 6 sites over a 3-state region including Missouri, Nebraska, and North Dakota. The objective of this study was to evaluate these two approaches for determining in-season N rates: Maize-N model and an active crop canopy sensor. Additionally, the study investigated effects of maize hybrid and population on the efficacy of the two N recommendation strategies. The model-based and sensor-based approaches were evaluated for yield, nitrogen partial factor productivity, agronomic efficiency, and profitability. For 10 of 12 site years, in-season N application rates for model-based treatments exceeded those of sensor-based treatments. Additionally, sensor-based treatments had significantly higher nitrogen use efficiency as seen by partial factor productivity than model-based treatments for 10 out of 12 site years. Given $5.00 corn prices and $0.50 fertilizer prices, profitability considering only N fertilizer costs was calculated. The sensor-based approach was significantly more profitable than the model-based approach for two site years, and the model-based approach was significantly more profitable than the sensor-based approach for two site years. Seven site years showed no significant difference in profitability between the sensor-based and model-based approaches.
L. J. Stevens
R. B. Ferguson
D. W. Franzen
N. R. Kitchen
Sensor Application in Managing In-season CropVariability
Oral
2014
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