Submitted to: Fluid Fertilizer Foundation Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 1/31/2007
Publication Date: 2/15/2007
Citation: Kitchen, N.R. 2007. Incorporating nutrient sensing technology in production agriculture. 2007 Fluid Fertilizer Forum, February 18-20, 2007, Scottsdale, AZ, p.35-40 Interpretive Summary:
Technical Abstract: The greatest impediment to using manual soil sampling followed by laboratory measurement for crop nutrient management is the time and expense associated with sampling, transportation, and analysis of the sample. While improvements in fertilizer nutrient use efficiency have been made relying on these conventional soil sampling methods, the perceived value for many farmers in doing this type of sampling has been marginal. In grain crop production areas of the U.S. only moderate adoption of soil sampling for spatial characterization of soil nutrients can be found. More efficient and less expensive tools and procedures are needed before managing with-in field nutrient variability will be widely adopted. The most logical approach is assessing crop nutrient need in situ with the aid of on-the-go sensors. Research developments suggest this is the future. Sensor-based technologies for crop nutrient management can provide improved accuracy (with higher resolution), responsiveness to temporal factors, and better economics. Three plant-based sensor approaches that are currently being tested for nitrogen (N) management in production agriculture are 1) the hand-held chlorophyll meter, 2) ground-based spectral radiometers, and 3) aerial imaging. The advantages and disadvantages of each are reviewed. With each approach, a sufficient-N reference is needed to determine mid-season fertilizer N recommendations, regardless of the crop in question. The reference clearly identifies the need for additional N above that available to the crop up to that point in the growing season when the mid-season N management decision is made. Algorithms for processing sensor information into N input decisions have been developed, but refinements are needed in order to account for the management, soil, and climate differences within croplands. Sensor-based methodologies are progressive, but adoption of these new approaches will likely be accelerated as they are integrated within strategies that provide visual feedback to farmers as they work in their fields.