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Canopy sensors improve prediction of corn N requirement

By Jacob Nederend

Full story here: All figures are the property of the University of Minnesota.

A study conducted by the University of Minnesota’s Precision Agriculture Center during the 2014 and 2015 growing seasons provides evidence to support the use of multispectral sensors for corn nitrogen management. PhD student Gabriel Dias Paiao provided an in-depth look at how the handheld sensors Minolta SPAD metre, Trimble GreenSeeker NDVI, and RapidSCAN NDVI and NDRE compare for fertilizer management.

When should I apply N?

Delayed application of nitrogen better synchronizes supply with crop demand, but questions remain about the optimal timing. A later application at V12 risks yield loss due to lack of precipitation needed to incorporate the fertilizer and transport it to the crop roots, while pre-plant and early post-emergence before V4 risks nitrogen losses due to heavy precipitation. Sidedress timing between V4 and  V8 did not affect corn grain yield (Figure 1). The NDRE outperformed NDVI at all timings.

When should I take canopy measures?

Given the flexibility in application timing, the next step was  to determine when canopy sensors should be used to best predict nitrogen deficiency. The GreenSeeker outputs the popular Normalized Difference Vegetation Index (NDVI), while RapidSCAN can do both NDVI and the Normalized Difference Rededge Index (NDRE). SPAD meter actually measures the transmittance of light through a leaf, and is highly correlated to chlorophyll content, and therefore nitrogen status. None of the sensors were strongly related to nitrogen deficiency at or before V4 (Figure 2). All reflectance sensors provided conservative estimates of nitrogen status, meaning growers should apply a base rate at planting to sustain the crop through to the mid-vegetative stages.

Does soil nitrate add any predictive power?

Since canopy sensors can only predict the demand side of the equation, the Pre-Sidedress Nitrate Test (PSNT) could be combined to integrate supply-side data. The PSNT had the greatest benefit the V4 stage (Figure 4; smaller bars are better), but the previous results indicate that this timing is too early to adequately identify nitrogen deficiency, and nitrogen applications face a risk of poor estimation and moisture-related losses. While the PSNT is a good predictor, the high density (<1ac./core) of samples pulled in this experimental setting are not practical in production agriculture. These results support further investigation of techniques that enable the use of low-density soil samples by integrating them with lower-cost canopy measures.