Algorithms for In-Season Nutrient Management in Cereals

1775 Over application of N fertilizer in cereal production systems continues to be problematic (Biello, 2008). Th e environmental costs of over applying fertilizer N are highlighted by iconic examples of hypoxia in the Gulf of Mexico and Chesapeake Bay (Ribaudo et al., 2011). Th is work further noted that N applied at rates that exceed crop needs has a greater risk of leaving the fi eld and degrading water supplies. For Iowa (largest tonnage of fertilizer N purchased and applied in the United States), this has become somewhat uncomfortable as within-state lawsuits have been fi led against maize (Zea mays L.) producers surrounding the Des Moines and Raccoon rivers for over applying N (Charles, 2015). Solutions exist but involve practices that will require a signifi cant investment in equipment and management (Roberts et al., 2012). Use of sensors in agriculture has advanced from measuring transpiration rates in 1917 (Briggs and Shantz, 1917) to onthe-go sensing and application of fertilizer on a by-plant scale (Kelly et al., 2015). Other work has suggested that the highest precision in N management for maize can be achieved through in-season N applications that are based on early-season N dynamics using models that dynamically simulate soil and crop processes (van Es et al., 2007). Th e adoption of sensor-based nutrient management has been slow, but consistent with the delayed adoption of other agricultural technologies (Fuglie and Kascak, 2015). Th is work further noted that diff usion of new agricultural technologies improved with increased farm size and producer education. Work by Holland and Schepers (2010) reports a function that delivers N fertilizer recommendations based on in-season remote sensing and local production information. Solie et al. (2012) developed a methodology using a sensor-based approach that is applicable for both wheat (Triticum aestivum L.) and maize, and that works over diff erent stages of growth. Even so, both of these approaches rely on in-season measurements of a growing crop canopy. Algorithms for In-Season Nutrient Management in Cereals