Yield mapping challenges
The acreage performance of modern harvesters has improved continuously over the past few years. This is one reason why combine and forage harvesters are often used to harvest multiple farms – machinery syndicates and agricultural contractors have specialised in providing these services. The data generated during the harvesting is extremely important when planning crop cultivation measures. So that farm managers can use the data, yield mapping possibilities have also evolved.
The harvest yields from agricultural land generally exhibit some localised differences, the causes for which can be traced back to the soil characteristics. Other factors relate to the cultivation measures applied, such as the nutrients provided and differences in sowing methods. Yield data and other types of data can be used to capture these site-specific variations. As a result, yield mapping offers a data-driven approach to decision making that enables crops to be cultivated site-specifically. It is important to note, however, that yield mapping alone does not provide definitive information about the causes of yield variations. Comparing data from various sources in order to draw valid conclusions about the potential of individual field sections is a much more constructive approach. Solutions that detect growth differences in crops, for example, can be applied here. The Crop View component offers one such solution. It uses multi-year satellite data to calculate the soil potential and create application maps. It also incorporates soil sample data, which is used to create application maps too.
Measurement systems for determining crop yields
Data from multiple crop years is extremely relevant when it comes to creating meaningful yield maps. This data pool is used to determine zones that differ with regards to the yield potential. Before farmers can even start to capture this data, they need to have the right measurement technology in the form of sensors and weighing systems in place. Farmers have been recording yield quantities and documenting yield data for some time now. The practice has been developing in parallel with technical advancements in sensor technology.
Another important aspect of yield mapping is the precise positioning of measurement points in the coordinates system for each field. Technologies such as real-time kinematic (RTK) and differential global positioning systems (DGPS) are used for this. They can define the position of a machine to the exact centimetre. Improvements have also been made in this sector over the years, enabling position data to be captured with ever greater precision. DGPS works using static reference stations with precisely defined positions. Measurement stations are used to correct and optimise GPS satellite position data. They are absolutely critical in yield mapping in order to evaluate data. Various systems are available for measuring yield quantities.
Yield mapping is most commonly used in arable farming. High-tech combine harvesters are equipped with a multitude of crop analysis tools. Crop data is continuously recorded via light sensors and power/impulse measurements in the machine’s grain elevator. They provide information about the grain volume and mass. Other sensors generate information about the crop’s moisture content. The measurements can be used to correct yield data so that the overall values can be more accurately illustrated. Data on routes, cutting widths, ground speed and mass flow allow conclusions to be drawn about the acreage performance.
Appropriate technologies such as mobile and stationary weighing systems are also available for grassland and maize harvesting. Mobile systems determine the weight of the crop via sensors installed in the axles of transport trailers and grain wagons. Stationary systems can be used near the field or in the area around silos. However, neither of these systems enable site-specific cultivation because the crop cannot be attributed to an exact position on the field. To solve this problem, forage harvesters record the volume flow of the crop. The flow is determined based on roller position data and the crop intake speed. Calibration is required in order to accurately determine the crop yield. To do this, the actual harvested quantity is weighed on the transport wagon and then entered into the system via the forage harvester. The on-board computer then uses these values to determine the yield data. For ultra-precise results, the calibration should be repeated every time the crop variety or field changes. The values generated for yield mapping as part of this process can also be used for precision farming measures.
With the help of DGPS, the continuously recorded data from sensors and measuring instruments is assigned to a specific point on the field so that it can then be used for site-specific cultivation. As this process uses so-called ‘point data’, the values used for yield mapping must be transformed into field data. We’ll discuss the challenges arising from the interpolation process in the following chapter.
The challenges of interpolating point data
In order to define the performance of field sections based on yield points, the potential sources of error need to be incorporated into the calculation. For example, measurement errors can come from the data on the harvester’s position, ground speed, cutting width and throughput measurements, all of which are used for mapping. Differences emerge in relation to the cutting width because for various reasons the effective working width is usually lower than the actual value. When it comes to remaining areas in particular, the actual values fluctuate significantly below the provided data. Another source of error is the fact that the crop needs a certain amount of time to reach the sensors. The amount of time required is inconsistent and depends on the harvester’s design and the throughput of the crop. This, coupled with changing ground speeds, results in the measured values being assigned to inaccurate positions. These deviations are difficult to calculate and correct. The ground speed of the harvester also causes inaccuracies due to the different measurement processes in the wheel, radar and GPS sensors.
Nevertheless, position data is now significantly less error prone thanks to the optimised measurement processes. Automatic steering systems and precise field planning solutions also help to minimise potential driving errors. The bottom line is that all this results in improved yield mapping methods. The collected yield data also provides a reliable source of information that enables site-specific cultivation in farming.
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