An Algorithm for Corn Nitrogen Recommendations Using a Chlorophyll Meter Based Sufficiency Index

Abstract

Nitrogen fertilizer continues to be the major input influencing corn (Zea mays L.) yield in the Midwest. Improved N recommendations should result in greater N use efficiency and producer profit while reducing surface and groundwater contamination. This study was conducted to develop a plant‐based technique to detect and correct N deficiencies during the season. Chlorophyll meter readings and grain yield were collected from corn in irrigated monoculture corn and soybean [Glycine max (L.) Merr.]–corn cropping systems with four hybrids and five N fertilizer application rates in the Platte Valley near Shelton, NE. Normalized chlorophyll meter readings (sufficiency index, SI) were calculated from data collected at three vegetative stages, defined by thermal time accumulation after planting, during each of the 10 yr of study (1995–2004). Highly significant linear correlations between SI and relative yield (normalized yield) indicated both responded similarly to N fertilizer application. Relationships between N rate and SI (at each of the three vegetative stages and combined over stages) were described by quadratic models. The combined model [(SI = 0.8073 + 0.002(N rate) − 0.0000056(N rate)2, R2 = 0.70)] can be used to compute N needed to achieve maximum yield. Our procedure gives producers the tools to determine if N is needed, and if so, the amount of N required for maximum yield. In addition if SI is computed for specific areas of the field, N applications can be tailored to those areas, thereby reducing the potential of introducing more N into the system than needed to achieve maximum yield, with spatial and temporal constraints.