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Making better protein maps

T Jensen, R Kelly, W Strong and D Butler

Department of Primary Industries, Toowoomba, QLD 4350
Email : jensent@dpi.qld.gov.au

Abstract

Protein mapping is set to provide a unique dimension in site-specific management that will enable better use of yield maps. As part of a GRDC funded project, we have collected coincidental yield and protein maps from grain crops over 7 seasons throughout the northern cereal belt.

The capacity to protein map has resulted from the development of a mechanised grab sampler that enables a grain sample to be collected from the clean grain elevator and geo-referenced. A sample can be collected every 15-30 seconds. This is a much coarser resolution than being collected to produce the yield map (one reading every 1-2 seconds).

The project is in the process of installing an on-the-go protein analyser from NIR Technology Australia. This apparatus has the capacity to sample protein at a greater frequency than the mechanised grab sampler. This intensity will more closely match the yield sampling frequency.

Introduction

Protein mapping is set to provide a unique dimension in site-specific management that will enable better use of yield maps (refer to paper by Kelly that is presented at this conference). As part of a GRDC funded project, we have collected coincidental yield and protein maps from grain crops over 7 seasons throughout the northern cereal belt.

Prior to the commencement of the project in 1997, we were led to believe that an on-the-go protein monitor would be soon available. This was not the case and these sensors are still not available.

The capacity of our project to map protein resulted from the development of a mechanised grab sampler that is detailed by Jensen (2001).

Mechanised grab sampler

The sampler consisted of a modified John Deere GreenStarTM moisture sensor. In normal operating mode, the sensor diverted a small continuous grain flow from the clean grain elevator. The grain passed over capacitance plates (used to determine a moisture content) and was returned into the elevator via a paddle wheel driven by a direct current motor. To meet our needs, we removed the capacitance plates and electronics of the moisture sensor. The polarity of the voltage supply to the motor driving the paddle wheel was controlled enabling it to rotate in both directions. A slot was cut in the existing housing that enabled a sample to be diverted to the operator for capture.

The handshake lines (RTS and DTR) of the serial port of the palm-top computer were used to control the direction of the motor driving the paddle. These signals, via optical isolation, were used to operate relays, which control the polarity of the supply voltage to the motor and thus the direction of the paddle. The relays were housed in a sealed box that also acted as a junction for other cabling.

Over 800 site-specific grain samples were collected using this sampling apparatus from a 40 ha barley field at Jimbour (27.10S, 151.20E) in November 1999. Yield was collected by an AgLeader mass flow monitor corrected to position with an Omnistar differential GPS. Grain protein was determined in the laboratory using near infrared spectroscopy, and samples were geo-referenced to the location file captured at harvest. The combination of yield and protein maps are shown in figure 1 a and b respectively.

Figure 1. Yield (a) and protein (b) maps derived from a barley paddock near Jimbour, 1999.

There are limitations associated with this apparatus. The sampling procedure requires a person to sit on side of header to collate the samples and to oversee the operation of the equipment. This is a harsh environment for equipment and operator.

There is a considerable expense (time and money) when collecting samples from several paddocks across different regions. Whilst sitting on the side of the header there is plenty of time to contemplate a better way to achieve the same results.

New Equipment

Currently, we are in the process of installing a ‘Cropscan 2000H’ on-the-go protein analyser that was purchased from NIR Technology Australia (see figure 2). This apparatus has the capacity to sample at a greater frequency than we can with our apparatus. This intensity will more closely match the yield sampling frequency.

Figure 2. The Cropscan 2000H on-the-go protein analyser with the on-header attachment (black device on the right).

We are having some commissioning problems with the prototype on-header attachment of the instrument. As our use is more demanding than its intended use, we are going to great lengths to rigorously test the unit. We want to ensure that there is no contamination between samples and that we take the exact sample that this instrument has used to calculate protein back to the laboratory to process using our NIR machine. It is also important to know precisely from whence in the paddock the sample came. By going through this process, we are assisting with the prototype development of the on-header apparatus.

It is hoped that this apparatus will allow us to obtain high-accuracy protein readings, investigate the effect of sampling method, and the role of sample frequency on the protein variation found in the maps produced. We plan to test the system on a late plot of sorghum with a fully functioning system ready for the winter crop harvest at the end of this year.

Conclusions

The ‘Cropscan 2000H’ on-the-go protein analyser will vastly increase the capacity to capture coincidental yield and protein data on a site-specific basis. This will provide the farm manager with the potential to investigate and review their approach to N management, moisture usage, and economic viability. The unit, when operational, will save considerable project resources (time and money) and make the collection of data much easier allow much larger areas to be mapped.

Acknowledgments

The authors are grateful to GRDC and QDPI for provision of funds for this project. We also wish to thank Jamie Grant, Rob Taylor, Mike Smith, and Richard Prior for access to their fields and too Wesfarmers Landmark and RDS Technologies for their in-kind support.

References

Jensen T., Kelly R. and Strong W. (2001),Using protein mapping to define yield-limiting factors”, Third European Conference on Precision Agriculture, Montpellier, France, June 2001.

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