Yield data is the data collected from different farm machinery like combines, planters, and harvesters, that evaluates the quality and quantity of the crops that have been produced within a given field or area. Agriculture mapping software plays a vital role in this. Accurate yield data helps farmers in making informed decisions. When they are armed with detailed yield data, farmers can tailor their practices to maximize productivity.
For instance, if a specific area of a field produces lower yields, they can investigate the reasons, like soil health or irrigation problems, and take remedial action. Also, it drives precision agriculture management practices. By mapping the variations in crop performance across the fields, farmers can tailor their input application, like pesticides and fertilizers, to specific areas. The targeted approach will not only optimize the resource use but also reduce the environmental impacts.
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Agriculture Mapping Software – Importance of Automated Yield Data Cleaning and Calibration
According to the Food and Agriculture Organization (FAO), global agricultural production needs will increase by 60% by 2050 to cater to the growing demand for food. Yield data is instrumental in achieving this target. To ensure accurate yield data, it is imperative to ensure accuracy and reliability. Calibrating the yield data set is a functionality that corrects the distribution of values in alignment with mathematical principles. It helps to enhance the overall integrity of the data and empowers better decision-making. Also, the data set becomes much more valuable for further in-depth analysis.
Some Use Cases of Automated Yield Data Cleansing and Calibration
- Synchronizing data when multiple harvesters have worked over several days, thus ensuring consistency.
- Creating a homogeneous and accurate dataset by smoothing variations.
- Removing data noise and any unnecessary information that clouds judgment.
- Eradicating turnarounds or abnormal geometrics that can distort the actual patterns and trends in the field.
Why is it Crucial to Clean and Calibrate Yield Data?
Yield data is collected by the yield monitors and sensors attached to harvesters. These devices measure the mass flow rate and the moisture content of the harvested crop and use GPS coordinates for georeferencing the data. However, these evaluations include certain factors that can affect the performance of the equipment or crop conditions. These include:
1. Equipment variations –
Farm machinery, like the combines and harvesters, have variations that lead to discrepancies in data collection. These variations can create a difference in machinery calibration and sensor sensitivity. Some monitors use a linear relationship between voltage and mass flow rate, while others use a nonlinear method. There are sine sensors that are sensitive to dust and dirt. These variations cause discrepancies in yield data throughout different fields or machines.
2. Environmental factors –
The weather conditions, topography, and soil types play a crucial role in crop yields. If they are not accounted for, the environmental factors add inaccuracies to the yield data. For example, the sandy soils or steep slopes cause lower yields than the loamy soils or flat terrains. Similarly, the areas with higher crop density will have higher yields than areas with low density.
3. Sensor inaccuracies –
Despite the precision, the sensors might drift over time, offering inaccurate readings if they are not calibrated regularly. For instance, a faulty load cell or loose wiring might cause inaccurate flow rate readings. The moisture sensor might get dirty, giving inaccurate moisture content.
All these factors result in yield datasets that can be inaccurate or inconsistent. If the data is not cleaned and calibrated correctly, it will lead to uninformed and potentially harmful decisions. For instance, using uncleaned data to create yield maps can result in false identification of high or low-yielding areas in a field. Using uncalibrated yield data sets to compare yields across fields can result in wrong comparison. Using uncleaned or uncalibrated yield data sets for calculating nutrient balance or crop inputs can result in over or underapplication of pesticides or fertilizers. Therefore, it is crucial to perform automated yield data cleaning and calibration.
Automated yield data cleansing and calibration is crucial because it empowers farmers in making better decisions. Farmers can optimize the farming practices, reduce soil erosion, and increase crop productivity. Using agriculture mapping software, automated yield data cleansing and calibration, and better fertilizer recommendations help with more high-quality crop yields. For the best and most accurate digital soil mapping, contact SoilOptix® today! Visit https://soiloptix.com/ for details.