Here is the MaluSim simulation for April 26 (pdf) that I presented at the In-depth Fruit School in Winchester last night. As you can see from both the weather data and the simulation, it looks like the next 3-day period where there is a carbohydrate deficit will be next week. Chemical thinners applied at the earlier end of the deficit will likely have more activity than those applied over the weekend. Since the model predictions are only as good at the forecast data that are used as inputs, it is be important to keep an eye on the temperatures towards the end of next week. I’ll run another simulation on Monday. In previous years, the model has shown that carbohydrate deficits of less than -60 g CHO/day can cause over thinning.
As I discussed last night, the MaluSim model should be used as another tool in the toolbox for understanding the interaction between environmental conditions (primarily temperature and sunlight) and thinning. At this point in time, the model is not able to predict the actual amount of thinning you can expect for any given application. But it does provide some indication as to whether to expect greater or lesser activity from your thinning chemicals.
Please let me know how you are using this information for making management decisions. In future years, we hope to be able to further refine the model to account for more specific inputs (for example, cultivar, crop load, frosts and other physiological stresses). Your input will help us understand how the model is being used and how we can increase its utility.
In the linked pdf file, I also include data on the current fruitlet size for several varieties that we grow at the AREC (fruitlet sizes listed on the simulations are for Empire, which is the standard tree used for the model).
Also included is a slide on year to date data on precipitation. Despite some rains last week and over the weekend, soil conditions are still abnormally dry.
I updated the pdf to show the running average of carbohydrate balance, as opposed to the running average of the input variables. This appears to be a more robust method of understanding carbohydrate balance in the tree.