Clean data are the foundation of any statistical analysis. In order to ensure that the results of any analysis are meaningful, it is critical that the data used in those analyses are valid and reliable. The best way to ensure valid and reliable data is to properly calibrate your measurement device. In the spring, this means ensuring that the hybrids that you are planting are correctly entered into your monitor and that your seed sensors are working properly.  In the fall, this means calibrating your yield monitor. We usually recommend calibrating monitors at the start of harvest, each time you change crops, and whenever the grain loses 3-4 percentage points of moisture. We are here to help with each of these processes. We can help setup your planting monitor in the spring, and we can bring out a weigh wagon to calibrate your yield monitor in the fall.

Of course, we realize that sometimes you will encounter problems during data collection. We can help at those times too. For instance, if you accidentally forget to match the hybrid in your planter to that in your planting monitor, we can adjust the data to correct for that problem. Perhaps even more importantly, though, we can clean the data that is collected by your yield monitor. Yield monitors have trouble taking accurate readings when the flow of grain through the combine is low, such as at the start of each swath, and when the head is down but the grain has already been harvested. Notice all of the red near the start of each swath in the raw yield map.

We have developed a program to clean those bad data points. It is superior to most existing methods, which tend to rely on either mean imputation or deletion to fix extremely high and low readings. As an alternative to these methods, our program utilizes the readings from adjacent data points both to determine which points are out of whack and to fix those readings. This preserves variance in the field while not allowing those data points to skew the results of any analyses conducted on those data. Notice how much easier it is to see the variation in yield across the field in the cleaned yield map, as compared to the raw yield map from the monitor. In the process of cleaning the data, we can also adjust the data to match the actual yield of each farm taken from scale tickets or weights from a grain cart. The cleaning process can usually be completed in 24-48 hours after the data is retrieved from the yield monitor.

Does your farm have sufficient in-field variation to benefit from variable rate technology? What about multi-hybrid planting? How much yield are you losing on wet years from not tiling? Or how much yield are you using on dry years from not irrigating? These are all questions that can be addressed through multi-year yield analysis.

If you have yield data spanning multiple years (at least 3), you may be able to draw on those data to gain insights into how to improve your operation. For instance, by comparing yield data from wet years to yield data from dry years, you can estimate the ROI from adding tile to a field. Or, by looking at average yield variation within your fields, you can estimate the ROI from investing in variable rate technology.

To conduct multi-year yield analysis, we will need access to historical yield data (at least 3 years). We will clean those data and combine them into layers that summarizes the performance of each field. The specific layers created will depend on the reason for the multi-year analysis. Note that this can be a time consuming process, depending on the number of years included in the analysis, the size of the operation, and the reason for the analysis. As a result, we typically conduct these analyses in the winter months and ask for at least a month to prepare the report.

How efficient is your operation? Do all the plants in all of your fields produce full size ears? How does the performance of your operation compare to your neighbors? These are questions that we can help you answer, and in doing so, we can help give you a sense of how your operation is performing relative to others’ operations.

As we continue to move towards variable rate technology, yield is no longer the best indicator of performance because crop inputs are not applied in a way that attempts to maximize the yield of every acre. Variable rate technology explicitly recognizes that some acres have higher productivity than others and applies inputs accordingly. As a result, we need a different metric to evaluate the performance of our fields.

At our agency, we use bushels/1,000 seeds planted as an alternative to the traditional bushels/acre. Bushels/1,000 seeds planted provides farmers utilizing variable rate technology a metric for evaluating the efficiency of the plants growing in their fields. For corn, if every stalk has a full sized ear, then we would expect about 8 bushels/1,000 seed planted. A score of 6 on this measure would indicate that only about 75% of plants had a full sized ear. Looking at variation in the bushels/1,000 seeds planted across fields or across one’s farming operation can reveal clues about where management practices can be improved.

We also provide benchmarking at an operation level. To do so, we have created a tool that compares farmers’ yields to the estimated yield reported by the USDA. Anyone who has at least three years of yield data can use this tool. It only requires one to enter their yield data into a spreadsheet and to select the area (i.e. the county, region, state, or nation) which they want to compare to their operation. The output is a line graph that compares the average productivity gains or losses on one’s operation to the productivity gains of the area of interest.

Variable-rate prescriptions have become commonplace in agriculture. Prescriptions allow farmers to increase the efficiency of their operations by maximizing the productivity of each acre.

At our agency, we have the capability to write custom prescriptions using any data layers requested. On our farm, we primarily rely on historical yield data and irrigation layers for our planting prescriptions. We have provided example single-hybrid and multi-hybrid prescriptions, which are based on the analyses found on the multi-year yield analysis tab. In addition, we can write fertilizer and nitrogen prescriptions, based on soil tests, mathematical models, or crop health imagery.

Defensive Hybrid