The cost of over thinning is much less than the cost of under thinning. Over thinned trees produce larger fruit that command better prices; while under thinned trees produce a lot of small fruit that command lower prices and the added problem of less return bloom for the following year.
This spring we published the Cornell MaluSim carbohydrate model results for several locations around the state. We hope that the model helped you in your thinning decision process. It seems that weather patterns are becoming more erratic and it is tough to factor all the myriad of factors into a cohesive thinning program. The carbohydrate model is another tool that you can look at that we hope will help you in your decision making process. Some growers have had some questions about the effects of this spring's weather on how it is factored into the recommendations.
Thanks for your note. We need feedback on the model to see when it works and when it does not. There are good reasons for both. The first few paragraphs here are some general points about model that I sent to Rob a bit earlier, then I address drought effects.
I need to emphasize a couple of points about the model that I am not sure get to the growers clearly. First the model assumes a healthy tree that does not have any particular stress (I.e. Adequate nutrition, good water status, no frost damage, etc). It is complex enough to try to model the apple tree in a healthy state. Plus, stresses are so variable that it is almost impossible to really model them for each orchard or year. So it is certainly possible that the heat and dry conditions you experienced affected the results and the model would not take them into account.
Second, fruit growth, drop and response to thinners can be limited by many factors. Carbon supply is one that seems to be important if there is a deficit but is clearly not the only one that can limit fruit development. So when there is a carbon deficit, normally the carbon then is most likely the limiting factor. However, when we estimate a good carbon balance or better, it means that the carbon is probably not the most limiting factor. So the weather for the carbon supply may be good, but another stress like low nitrogen may limit fruit growth and give a strong thinner response that the model as we use it would not account for.
The model seems to work in many cases so we think it is generally valid, but we are looking for cases when it does not correlate with thinning and trying to figure out if the model is not correct or if another factor, either a stress or perhaps the weather station input is not representative of the orchard, or as often the case the forecast data was off some. But you have done a good job to check that in your case. Also an area of concern with the model is the high temperature end as we do not have much high temp info on tree responses to high temps around thinning time.
Of course the model is just a tool and one source of data that a grower needs to integrate to make decisions, so one concern is the over-reliance on the model. If everything is good, it may help but if there is some other stress then we need to be skeptical. Understanding its limitations is a big part of it.
The environmental parameters used in the model are only daily maximum and minimum temperatures and daily total radiation received. These were limited so that most weather stations could provide data that would drive the model. Although there are many interactions between temperature and radiation, over a short time such as a week temperature primarily controls the demand for carbon by affecting growth while the radiation primarily affects the carbon supply.
Drought effects - As mentioned above, drought is not included for several reasons although we are working on a drought effect sub model. There has been some research showing that drought early in the season does reduce final fruit set though there was not precise analysis of why. From research in general we know that drought reduces growth rates of shoots and fruit. How exactly that affects carbon balance is not known.
One reason it is not modeled is that we have inadequate data and knowledge of the effects of drought on all the growth and physiology that we have in the model. Also drought responses generally vary at different stages of growth (growth by cell division is very sensitive, so shoot and root growth and early growth of apples are more sensitive than mid and late season stress). But how it affects root growth and respiration of organs is not really known.
Second, it is difficult to model a situation that is extremely variable such as water availability. The daily radiation is uniform across an orchard so all trees get the same. But in the case of water, there is rainfall but also a large reservoir of soil water, so there are multiple sources. Additionally the soil can vary greatly across an orchard and finally the apple root systems are variable as to how much soil water they access. So predicting the actual water status of the trees is very difficult, and we rarely have the kind of data on soil moisture or tree water status needed.
Nutrient status has similar issues though we do generally have leaf nutrient levels once a year that could be useful. Cold injury is not in the model.
In your situation if it was dry enough early it may have reduced the leaf area that developed so there might less potential light interception and thus supply. Also drought will close stomatal pores on leaves and reduce the photosynthesis, so a double effect on carbon supply. Water stress likely reduced fruit growth which would reduce demand but slow fruit growth leads to more drop. The very heavy initial set you had also gave a high demand which likely led to slower fruit growth in combination with the direct effect and the reduced carbon supply. So the combination of drought and very heavy initial crop probably led to the very strong response.
If you utilized the information from the MaluSim carbohydrate model in your thinning decisions and have some observations, please do not hesitate to pass them on to me and I will relay them to Alan.