Part I.
In the soybean leaf samples
1. T he damage caused was most likely due to insects with chewing type mouthparts. This is the only explanation for the missing portions of the leaf centers and edges. In the week 3 blog where we had to identify insects that were collected within a soybean leaf sample from Scandia, Ks, the majority of the insects that were found exhibited chewing type mouthparts. Some of the different orders were either Orthoptera adults or Lepidoptera larvae. In this portion of the activity, all of the damage looked as though insects had caused it at some point, so therefore it is indirect circumstantial injury to the soybean leaves. I would describe this damage as
2. The sampling unit in this case was a single plant leaf from a soybean plant (may have been many whole plants involved). The sampling size that was used in this lab for the soybean leaves was 50 leaves. I would say that in a scouting situation a collection of at least 5-10 plants from an affected area, if it was a field wide issue, then more samples may be necessary. From these plants then you would need to analyze the plants based upon the parts that were injured (in this case, the leaves). The method that I utilized to quantify the damage was by calculating the percentage of leaf loss based on the total estimated leaf area. From this I would then try and quickly gauge what the rest of the leaves seemed like in comparison for the rest of the set.
In the Corn ears and Sunflower heads
1. The corn ear showed external chewing on some kernels and also some boring of insects into the kernels. The corn damage was probably caused mainly by larval Lepidoptera that would eat into the ear before the corn was fully matured and dried. The sunflower heads had what looked like the tops of the seeds had been sliced off or dug out, this could be caused by chewing insects (or a knife). This type of damage would probably be done by some Lepidoptera larvae or possibly an Orthoptera adult also, the fully removed seeds would probably be caused by birds. Typically in sunflowers the damage that occurs from insect larvae is actually inside the stem or head where they are safe to feed without the risk of predation.
2. The sampling units in the corn case were corn ears and in the sunflowers it was a fully mature sunflower head. The sampling size was 30 ears of corn and 10 sunflower heads. For the corn it was quite a bit more difficult to estimate the damage since it was not just laying out flat. In order to estimate the damages I tried to roughly calculate any damages that I saw into a total percentage of damage, I was not very successful at this. In the sunflower heads it wasn’t too hard to gauge the grain damages since it was layed out in a flat surface. To estimate the damage I also tried to gauge the damaged portions versus the unaffected portions, I was fairly accurate but not perfect by any means.
3. This method would be a relative measure of the amount of leaf tissue that was missing; it would be very difficult to do an absolute measurement in one field, let alone multiple fields. The measure of the leaf loss would be categorized as absolute if the leaves were analyzed more accurately with measurements of the total leaf area vs the missing parts. A faster method of accurate measurement would require scanning the leaves into a file on the computer and calculating the missing material with a specialized computer program that can make the calculations of the leaf losses.
Part II.
4. In this experience with the accuracy of damage prediction in 3 crops, I would say that as more samples were added to the sample unit that my accuracy dropped. In the soybean example there were 50 samples to look at which made consistency key to keep the accuracy high. The corn and sunflowers had much fewer samples so it would be expected for this damage analysis to be more accurate.
5. In comparison with the rest of the class I did worse than I would have hoped, as notated by the next three graphs.
For the soybeans I was accurate to a level of 1.49 and precise to an R2 of 0.88, since this was a fairly large sample size I was fairly pleased with this result. The best score was from Michael Davidson: slope of 1.27 R2 of 0.93, he overestimated on average but was still in the 93% accurate range. Since I was somewhat higher on the overestimation, I could have become more accurate with a lower level of estimation.
The corn samples were from a smaller sample size but due to the type of sample (corn on cob) it was more difficult overall for the class to properly judge what percentage of damage was incurred. I achieved 0.34 accuracy and an R2 of 0.19. This was not very good but it was not the worst in the class. The best judge for the corn damages was Tanner Robbins: slope of 1.07 R2 of damage 0.69.
For the sunflower example, we had the lowest sample size of only 10. I was very accurate on this sample at 1.00 and a 93% precision rate. The best analysis in the class came from Kim Kerschen: slope 1.17 and R2 of 0.99, a lot of the class was able to do well on this crop and I attribute this to the smaller sample size.
If we were to conduct another exercise like this I would definitely ask the top damage estimators from the class what their methods were so that I could improve upon my ability to estimate crop damages.
6. This exercise was useful in the sense that it can show someone how easy it is to over or underestimate crop damages in the field. In the event that it was necessary to make a quick decision on whether or not to utilize a control method upon a pest, precision would be a must in order to properly advise a producer. Higher accuracy when analyzing how much damage has been caused is an integral part of making a precise reading.
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