Less than half of the nitrogen fertilizer that is applied to farm fields is actually used by the growing crops; the rest is either leached from the soil as nitrate, which contaminates surface and ground water, or volatilized from the soil, which contributes to air pollution and global warming.
The inefficient use of nitrogen fertilizer by crops is a big economic loss for farmers and a big environmental problem. While precision agriculture has made significant advancements in the last several decades, on-the-go precision nitrogen fertilizer management remains a challenge. Previous research has shown that a narrow window of opportunity exists in the late spring (V6 growth stage of corn) when soil nitrate concentrations are predictive of crop nitrogen fertilizer needs. Thus precision nitrogen fertilizer management is possible if the spatial distribution of soil nitrate levels can be rapidly measured during that narrow window (about two weeks).
Although the pre-sidedress soil nitrate test has been shown to be effective for improving nitrogen use efficiency and reducing nitrate leaching losses, the pre-sidedress test is not widely used by farmers because labor for soil sampling and analysis is expensive, its low spatial resolution misses substantial in-field variability, and the time delay between soil sampling and the availability of a nitrogen fertilizer prescription increases risk for farmers. The bottom line for most farmers is that the soil testing for nitrates is too impractical, too expensive, and too risky for large-scale use on production agriculture fields.
Our team is developing a soil senor system based on Fourier Transform Infrared sensor technology that can be attached onto a nitrogen fertilizer applicator and used to modulate side-dress N fertilizer application rates in real-time on-the-go as the applicator moves through a field. The proposed soil nitrate sensor system has the potential to transform the pre-sidedress soil sampling protocol into a simple and practical tool for precision management of nitrogen fertilizer, because it would eliminate the need for soil sampling and the time delay between sampling and fertilizer application and it would provide much greater spatial resolution than is possible with the current sampling protocol.
Light Detection and Ranging, otherwise known as Lidar, is used to map and create elevation models for any landscape. This data is gathered from an aerial source using a laser, receiver scanner, and GPS. The reflection of light off the surface is measured and stored as elevation models: lidar maps.
Lidar is a very important stepping stone to many other landscape attributes that are useful for interpretations and predictions of farmland. Geology features such as Slope, Aspect, Curvature, and more are built off Lidar's Elevation data. These are used in tandem with a farmland's history and soil types to reach important end goals such as fertilizer management zones, seasonal Corn Yield predictions, farm CSR2 ratings, etc.
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