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	Nitrogen Use Efficiency In an Illinois Corn field

 

The field maps shown here summarize data from one growing season. Data was acquired from normal field operations for both nitrogen application and harvest near Geneseo, Illinois.

 

Data layers were obtained using the same Case AFS GPS/DGPS receiver at both sidedress and harvest. At sidedress time, Variable rate application data was obtained from a 28% N Soil Doctor®-equipped 16 row Henniker cultivator. Harvest data was obtained from an 8 row Case Combine employing the Case AFS-derived variant of the AgLeader yield monitor.

 

Scale yield data from the field were used to confirm monitor calibration. Because data from both field operations is acquired at a fixed sampling time (1 second GPS update interval), using different equipment widths, data points are not at identical spots in the field, and comparison points must be interpolated to obtain directly comparative locations.

 

Examining these interpolated data layers, in a true GIS with analytical capabilities, provided the ability to derive commonly used data indices to assess the efficacy of the Soil Doctor® system on production practices within farm fields. All Maps herein are Courtesy of Weber Beef, Geneseo, IL and CTI.

 

The example to the right reveals the efficiency ratio of LBS N applied per Bushel of corn produced. It illustrates that the traditional view of actually applying 1.25 LBS N/bushel of corn yield desired is clearly not the most efficient application method, and that the vast majority of this field actually required less than half that amount --in a variable rate distribution-- demonstrating the economic advantages that the Precise Soil Doctor® device actually, not speculatively, provides to its owners.

 

The bottom line on any Precision Farming technique is its ability to improve production economics, although some have been trying really, really hard, for several years, to change the subject from net profit increases to "other benefits".

 

And, most growers agree that if all the effort and expense that they invest in a so-called "precise device" doesn't result in an improvement in their bottom line, then that technique isn't all that precise after all.

 

So, let's compare the traditional N application in this field, where 160 LBS N/acre would have been the normal practice, to what the Soil Doctor system variable rate applied. This map illustrates just part of the economic advantage of the Soil Doctor® system, the N Fertilizer savings. Readily visible, Soil Doctor® Precision Makes its Owners More Money.

Beyond Confirming Soil Doctor® Applicator Efficacy,

Is there more that can be learned from

The acquired data and data analysis process?

 

First of All, Examine your "Raw Data" (as recorded, before analysis), before you begin your analysis operations.

 

The ground-track maps below (Application data on the left) and (Yield Data on the right) clearly show the difference in data layers obtained using both the sixteen row cultivator and the eight row combine described previously,

 

These figures illustrate that only through a great deal of luck could the soil, application, and harvest data be located spatially at the same, identical point, for layer-to-layer analysis. Consequently, interpolation is always required for mathematical comparisons. Unfortunately, there is no scientifically proven interpolation methodology (not even the much-touted kriging) that can be employed to precisely predict values at intermediate, unsampled locations. One is invariably required to make assumptions to extrapolate these data, and many analysts use weighted linear (inverse distance) interpolation.

 

The above data were gridded, and the originally acquired data values were retained wherever possible . A larger interpolation smoothing interval was required for the application data than for the combine data due to the differences in equipment width and travel speeds.

 

We  noted that there were yield data that did not lie within the same field boundaries as the application data. An unknown portion of the harvest data set example was compromised due to seasonal GPS/DGPS position inaccuracy near a wooded area adjacent to the west side of the field. It is not possible for any valid yield data to exist outside of the field boundary sidedressed because there is forest in the indicated location through which the combine cannot pass. This data layer is an artifact (ERROR) associated with the reception of the GPS/DGPS system due to the blockage by the forest on the west side of the field. Foliage, time of day, and the season of the year influence the location precision obtainable from geo-referenced location systems at this point in GPS technology development. This distortion of true geo-referenced field boundaries did not occur when the field was sidedressed.

 

Looking closer, one can also see that ground tracks within the field shift laterally from time-to-time. This is not a sloping field, nor was it planted, sidedressed, or harvested, slipping on wet soils. Rather than an indication of how well the field was managed, the lateral shifting illustrates the position differences resulting from computations reflecting DGPS corrections. There are also data drop outs, where GPS reception was temporarily lost. Slight GPS/DGPS registration differences are also obvious when examining the sidedress and harvest field shapes relative to the latitude (North-South) axis.

 

Present Value of Mapping Technology

 

Despite many GPS/DGPS distortions, one can construct maps today which provide a good qualitative feel for what is going on in the field. That is the inherent value that current mapping systems provide. (Also see "Is Precision Farming for You?")

 

But additional data processing must still be employed. Each data layer has a time/distance distortion that must be corrected by a specific model. In all cases, this distortion is first removed using a simple first order lag correction. On farm equipment, GPS receiver antennae are not located at individual Soil Doctor® parameter sensor locations, but are displaced ahead of the sensors. Combine receivers are similarly not located at the head where the grain is harvested.

 

Moreover, combine yield values must also be adjusted using a mixing transport delay model and, in the case of the Soil Doctor® applicator, the flow of nitrogen fertilizer is averaged and accumulated over a standard 2 second interval.

 

 

 

The interpolated maps reveal that the highest yields were obtained at points in the field where the least N fertilizer was applied, again confirming the efficacy of Soil Doctor® Prescription Application Technology.

 

Yes, but what else can producers learn from these data?

* The Wide Spatial Range of Yields (over 3 to 1),

* The Marked Range of VRT Nitrogen Application resulting in the Highest Yields where Applied N was the Least, and  

* The Depression of Yield near Field Boundaries (foliage shading and nutrient removal) are all apparent from these data, inviting intriguing focus for additional, but focused on-farm studies.

The only locations within the field where N use levels approached standard (non-precision) recommendations was at the field boundaries adjacent to standing timber. Here, however, normal field yield goals are inappropriate, because the microclimate is markedly deliterious to reaching such goals. Not only is photosynthesis in the corn crop inhibited due to shade, but trees constantly extract nutrients from those soils. Some of the highest yields were obtained at the eastern edge of the field which has no adjacent timber.

 

Setting aside the microclimate (the "edge effects"), yield variations are directly related to soil type and nutrient variations within the field. The Soil Doctor system examines both, generating an index which measures the nutrient potential of the local soil before additional nitrogen is applied. When the index is high, little nitrogen is applied, and when the index is lower, significantly higher N levels are applied. When mapped, the nutrient potential of the soil is clearly seen to be directly related to the yield patterns within the field.

Bottom Line on State-of-the-Art Mapping

 

In the final analysis, the resulting accuracy of today's precision agriculture maps is measured in tens of feet. Compare that to crop agronomic application requirements. Agronomic requirements are less forgiving, requiring sub-meter accuracy to place both seed and fertilizer where they are needed from the plants "point of view", the plant's limited, sub-meter root system.

 

 

 

CTI Soil Sensor Index Reveals Yield Potential

 

The future value of any precision agriculture data layer is directly related to its ability to be both descriptive and spatially explicit. As precision agriculture systems continue to develop, data layer co-registration accuracy remains a critical issue that cannot always be overcome by interpolation routines and appealing visual presentations.

 

Today's precision agriculture data sets do not readily lend themselves to rigorous quantitative analysis on a true site-specific basis, but they can be visually, qualitatively analyzed.

 

Improvements in mapping technology will make it easier for the producer to clearly show the benefits that Soil Doctor® Right-Now technology provides foot-to-foot, as well as the performance of other technologies, and will aid all map-based solutions in approaching the benefits that real-time soil sensors have been providing producers --for over a decade.

 

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