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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #330330
Title: Will algorithms modified with soil and weather information improve in-field reflectance-sensing corn nitrogen applications?

Author
item Kitchen, Newell
item Sudduth, Kenneth - Ken
item BEAN, GREGORY - University Of Missouri
item Drummond, Scott
item Yost, Matt

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/20/2016
Publication Date: 7/31/2016
Citation: Kitchen, N.R., Sudduth, K.A., Bean, G.M., Drummond, S.T., Yost, M.A. 2016. Will algorithms modified with soil and weather information improve in-field reflectance-sensing corn nitrogen applications? In: International Conference on Precision Agriculture Proceedings. Available: https://ispag.org/proceedings/?action=abstract&id=2078&search=types.

Interpretive Summary: Canopy reflectance sensing has been used on many farmers’ fields for assessing corn nitrogen (N) health to help decide how much N fertilizer to apply during the growing season to achieve the economic optimal yield. However, farmers have not always had satisfactory results with this technology. In another study the performance of canopy sensing for variable-rate N fertilization was improved by including soil and weather factors when making the decision of how much to apply. The objective of this investigation was to validate the performance of these weather- and soil-modified recommendations using independent data. Generally, N rate recommendations were not improved by including soil and weather information when all soil types were examined together. This was true when examining within-field variability of corn N need or when averaging corn N across the whole field. However, the relationship between economic N rate and the N recommendation did improve with soil and weather information for claypan soils. These results provide evidence that soil and weather information may help improve canopy sensing N applications in some cases, but additional development and validation is still needed. Farmers will benefit from this research because they can reduce excess N applications, which should save them money. If fertilizer can be better matched with crop need, N fertilizer loss to the environment will be reduced, thus helping to protect soil, water, and air resources.

Technical Abstract: Nitrogen (N) needs to support corn (Zea mays L.) production can be highly variable within fields. Canopy reflectance sensing for assessing crop N health has been implemented on many farmers’ fields to side-dress or top-dress variable-rate N application, but at times farmers report the performance of this approach unsatisfying. Another study has shown promise that the performance of canopy sensing algorithms for rate N fertilization can be improved by including soil and weather factors. The objective of this investigation was to validate the performance of weather and soil modified corn algorithms using an independent dataset. The validation dataset was a 16-field investigation conducted over four growing seasons (2004-2007) on three major soil areas of Missouri: alluvium, deep loess, and claypan. Multiple blocks of randomized N rate response plots were arranged end-to-end so that blocks traversed the length of each field (400 to 800 m in length). Each block consisted of eight N treatments from 0 to 235 kg N/ha on 34 kg N/ha increments, side-dressed sometime between vegetative growth stages V7 and V11. Canopy sensing was done at the time of side-dress application. From these, the economic optimal N rate (EONR) was calculated and compared to the un-adjusted, weather-adjusted, and weather+soil-adjusted algorithm N recommendation rates. Generally, N rate recommendations were not improved by the adjusted algorithms. This was true when examined by individual blocks or when EONR was calculated at the field level (average over all blocks). While on average recommendations did not improve with the adjusted algorithms, the relationship between EONR and algorithm N recommendation did improve on claypan soils (r^2 values of 0.40, 0.82, and 0.89 for unadjusted, weather, and weather+soil algorithms, respectively). These results hint that soil and weather information may help improve canopy sensing N applications in some cases, but additional algorithm development and validation is still needed.