Corn nitrogen rate forecasting based on soil water and expected yield‐increase

Abstract

Water supply is an important factor for high grain corn (Zea mays L.) yield and in nitrogen fertilizer management. It is the objective of this paper to develop a model for N‐rate forecasting based on yield‐increase (Yi) as related to plant available soil water (PAW) and precipitation.

In the zone studied maximum yield (YM) is more influenced by soil N fertility and climatic factor variability than by added N fertilizer. The same N‐rate gave high variation in yield from one year to another. In contrast, the Yi, due to nitrogen fertilization, is less related to soil N fertility but highly related to added N fertilizer and water supply. Thus, the correlation coefficient between Yi and PAW within 0–150 cm ranged from 0.75* with March 1st PAW to 0.85* with June 1st PAW. In dry‐winter years, these correlations were even higher. The correlation of Yi with N‐rate is also high (r = 0.94**). Because of its significant correlation with both N‐rate and water supply the expected Yi as related to PAW and precipitation was taken into account in forecasting N‐rate. Two forecasts of expected Yi were proposed: one in early spring, before sowing using the PAW within 0–150 cm on March 1st (X₁) and another at mid June when the plants have generally 6–8 leaves, using X₁, June 1st PAW at the same depth (X₂) and June precipitation (X₃). The following regressions based on 8‐year data were used for Yi forecasting:

-	before sowing
-
-	and in June
-

where Yi is given in q/ha and X in mm. Then, the N‐rates were calculated using a ratio of 10 kg of nitrogen per each quintal of forecasted yield‐increase. These forecasted N‐rates and the respective yields were very closely related to the observed (optimum) ones. Accurate prediction of N‐rate based on yield increase and water supply at the beginning of the growing season may provide a better nitrogen fertilizer management for obtaining maximum yield.

Key words:

• Nitrogen
• soil water
• precipitation
• prediction model
• dry climate
• zea mays L.