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Algorithms for In-Season Nutrient Management in Cereals

D. Franzen,N. Kitchen,K. Holland,J. Schepers,W. Raun

Published 2016 in Agronomy Journal

ABSTRACT

The demand for improved decision-making products for cereal production systems has placed added emphasis on using plant sensors in-season, and that incorporate real-time, site specific, growing environments. The objectives of this work were to describe validated in-season sensor-based algorithms presently being used in cereal grain production systems for improving nitrogen use efficiency (NUE) and cereal grain yields. A review of research programs in the central Great Plains that have developed sensor-based N recommendations for cereal crops was performed. Algorithms included multiple land-grant university, government, and industry programs. A common thread in this review is the use of active sensors, particularly those using the normalized difference vegetation index (NDVI) for quantifying differences in fertilized and non-fertilized areas, within a specific cropping season. In-season prediction of yield potential over different sites and years is possible using NDVI, planting date, sensing date, cumulative growing degree days (GDD), and rainfall. Other in-season environment-specific inputs have also been used. Early passive sensors have advanced to by-plant N fertilization using active NDVI and by-plant statistical properties. Most recently, sensor-based algorithm research has focused on the development of generalized mathematical models for determining optimal crop N application. The development and promotion of fee-based modeling approaches for nutrient management continues. Nonetheless, several algorithms using active sensors for in-season N management are available from state and government sources at no cost and that have been extensively field tested and can be modified by producers.

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