Weather Data Source and Apple Scab DSS Model Output – Does it Make a Difference?

 Jon Clements (UMass Extension) and Daniel Cooley (Stockbridge School of Agriculture)

Weather data source, the location of a station, and DSS each make a difference in numbers of fungicide applications to control primary apple scab. So why the difference? Is the weather data different? Probably. Are the models interpreting the data differently? Probably. So it’s a combination of both? Likely.

INTRODUCTION
In the Northeast it is not possible to produce apples commercially without timely fungicide sprays to control apple scab. Decision Support System (DSS) models based on weather data allow more targeted and potentially better scab control with fewer fungicide applications than a calendar spray schedule during the primary phase of apple scab infection. But does the source of weather data input or the DSS make a difference in predicting infection periods? To answer that question, we compared weather data collected in 2020 from several weather stations and a virtual weather data source at the UMass Orchard in Belchertown, MA.

METHODS
Weather data was collected from four on-site weather stations at the UMass Orchard in Belchertown, MA. Two stations are situated close to one another, and the other two farther apart, as shown in Table 1. Weather stations included two RX3000 and one U30 weather station from Onset Computer Corporation (onsetcomp.com), and one Rainwise (rainwise.com) weather station. All collect temperature, wetness, and precipitation data, requisites to run the primary apple scab infection model on the Network for Environment and Weather Applications (NEWA). The NEWA apple scab model is based on Mills Table infection events with modification. We also looked at one “virtual” weather data service (meteoblue.com) feeding weather data into RIMpro (rimpro.eu), a cloud-based DSS running its own (proprietary) version of the apple scab infection model. RIMpro was also run using data from the Rainwise weather station.

Table 1 - Name (DSS used in this blog post), Station hardware, Lat./Lon., Elevation, and DSS of weather stations or virtual weather data source at the UMass Orchard, Belchertown, MA. (Click on Table to see larger.)

RESULTS

Examples of how and when infection periods for NEWA and RIMpro were determined are shown in in Figures 1. and 2. respectively. Then, all apple scab primary infection periods evaluated by each system is graphically illustrated in Figure 3. Red color-filled table cells with an ‘x’ in them are scab infection periods (by day). Yellow color-filled cells are presumed fungicide spray infection prevention events that are based on a few simple rules: a preventive fungicide spray before every infection event; a post-infection (kickback) spray during longer infection periods and/or during the accelerated phase of ascospore maturity/development; and for RIMpro, following the previous two rules, but only when the RIM infection value exceeded 100 (daily RIM value indicated by the number). From Figure 3., all primary infection periods from green tip to 100% ascospore maturity and final release of spores, and proposed spray events were counted for each Decision Support System and are shown in Table 2.

Figure 1 - Example of how apple scab infection periods are counted in NEWA. (Click on Figure to see larger.)

Figure 2 - Example of how apple scab infection periods are counted in NEWA.

Figure 3 - Apple scab infections (in red) predicted by DSS and suggested spray events (in yellow) for the primary appe scab season beginning 1-Apr iland ending 30-June. (Click on Figure to see larger.)

Table 2 - From Figure 3, total number of Infection Periods and Proposed Sprays during the primary apple scab season by DSS (Name).

DISCUSSION

Primary apple scab infection periods ranged from three infection events for RIMpro up to ten infections for NEWA (Table 2). Spray events followed similarly, ranging from three fungicide sprays to twelve sprays. Thus, the number of infection periods and proposed fungicide sprays vary significantly, from three to ten and three to twelve respectively. There are several sources of variability that could result in such different DSS outputs, including: virtual vs. hardware weather station (including manufacturer), location in the orchard, and different DSS interpretations of the scab model, particularly NEWA vs. RIMpro. Several assumption, particularly when it comes to spray events, have been made too, which could be argued. But these differences are concerning and could result in a grower taking different actions, and experiencing different outcomes, like getting scab, depending on how weather data is collected and what DSS is used. Therefore, there is a need for further field testing using fungicide applications applied according to each Decision Support System.

For further reading:

Clements, J., and D. Cooley. 2013. A Comparison of Two Sources of Environmental Data and Three Model Outputs for Primary Apple Scab in 2012 at the UMass Cold Spring Orchard. Fruit Notes, Volume 78, Spring 2013. http://umassfruitnotes.com/v78n2/a2.pdf

Garofalo, E., A. Tuttle, J. Clements, and D. Cooley. 2016. Discrepancies Between Direct Observations of Apple Scab Ascopore Maturation and Disease Model Forecasts in the 2014 and 2015 Growing Seasons. Fruit Notes, Volume 81, Spring, 2016. http://umassfruitnotes.com/v81n2/a2.pdf

Weather data in, DSS out for apple scab infection period model -- does it make a difference? (YouTube video.) A comparison of four on-site weather stations and one virtual weather service as data sources in 2020 for the apple scab infection period model in two Decision Support Systems at the UMass Orchard in Belchertown, MA. A presentation at the 62nd Annual New England, New York, and Canada Pest Management Conference for Extension, research, and consultants, October 19, 2020 via Zoom. ©2020 Jon Clements and the UMass Fruit Advisor, umassfruit.com. Or play here...

Comparing the Malusim app to the ‘Schwallier’ and ‘Ferri’ XLS spreadsheet versions of the fruitlet growth rate model in 2020 to predict fruit set in Gala, Honeycrisp, and Pazazz® apples

INTRODUCTION

Chemical thinning sprays are the most trying and most important decisions an apple orchardist can make. Factors that influence chemical thinner application include weather, carbohydrate balance, and fruitlet growth rate. The Malusim app (malusim.org) uses the fruitlet growth rate and carbohydrate balance models to better inform chemical thinning decisions. Two XLS (Microsoft Excel) spreadsheets are also available for inputting fruit measurements and predicting fruit set based on the fruitlet growth rate model.

METHOD

Five tall-spindle apple trees in each of three varieties – Gala, Honeycrisp, and Pazazz® –  were selected at the UMass Orchard in Belchertown, MA. In May 2020, bloom (total number of flower clusters) in each of the five trees was counted to get an estimate of potential fruit set, and fourteen flower/fruit clusters were selected and tagged for fruitlet growth measurements. Fruitlet measurements were started on 27-May, and then made on 31-May, 4-June, and 12-June. Fruitlet measurements were entered using the Malusim app smartphone (iPhone) voice recognition feature and results calculated in Malusim (malusim.org) to get predicted fruit set. From Malusim the same data was exported and used in the Schwallier and Ferri XLS spreadsheets/apps to get predicted fruit set. The Ferri XLS spreadsheet is a modification of the Schwallier sheet by Tom and Joe Ferri, T&K Orchard, Clarksburg, Ontario, Canada and not publicly available, but available on request. The fruitlet growth rate model output included percent fruit predicted to set and fruit numbers per tree on each measurement date so that the need for a chemical thinning spray could be better assessed.

Honeycrisp trees selected for counting bloom and measuring fruitlets

Cluster selected and tagged for subsequent measuring apple fruitlets

Digital caliper used for measuring fruitlets

Sample Malusim app output

Sample Schwallier XLS spreadsheet output

Sample Ferri (modification of Schwallier) XLS spreadsheet output

RESULTS

For each variety, all three predictions of fruit set were similar within variety. Therefore, any of the three “apps” could be used to predict fruit set. In the end, however, final fruit set, as counted by the number of apples left on each tree, was less than predicted by the apps except for Pazazz®. And actual fruit number per tree counted at harvest was less than the target number of fruit per tree. (Ugh.) A severe carbohydrate deficit at the time of chemical thinner application – as indicated by the Carbohydrate Balance in Malusim – is the likely culprit.

Gala predicted fruit set. Target was 80 fruit per tree, actual at harvest was 45 apples.

Honeycrisp predicted fruit set. Target was 70 fruit per tree, actual at harvest was 26 apples.

Pazazz® predicted fruit set. Target was 70 fruit per tree, actual at harvest was 12 apples.

Significant carboydrate deficit in the Malusim app during the chemical thinning window

DISCUSSION

Although the fruitlet growth rate model is a useful tool to help guide thinning decisions, setting it up and measuring fruits is an onerous process and has not been widely adopted by growers. What’s needed is a faster and simpler method of assessing fruit growth rate during the chemical thinning window. To that end we are investigating, and in collaboration with Carnegie Mellon University, computer imaging and learning to visualize and calculate fruit growth rate. Early results are promising.

Marssonina blotch

Recently I visited a block of EverCrisp apple trees in an orchard in the hilltowns of western Massachusetts west of the Connecticut River. I went to look at what is probably the largest planting of EverCrisp apples in Massachusetts. A few acres, with more in the works to be planted. The trees are 3-4 years old and on Geneva 41 rootstock. A couple of observations and even more questions.

First, crop load management is essential as EverCrisp can go somewhat biennial if over-cropped. And apple quality is not what it should be on over-cropped trees. What is the best crop load (number of apples) and chemical thining recommendation for EverCrisp?

Second, EverCrisp appears to be quite susceptible to the fungal disease Marssonina coronaria causing the symptom Marssonina blotch. Now, the big question is how important is it to keep this disease under control until the fall harvest? These EverCrisp had not been treated with a fungicide in well over a month, and groups of trees showed signifiacnt Marssonina blotch. Even some partial defoliation. The grower acknowledged that Marssonina blotch has been observed on these EverCrisp trees in the past. A standard fungicide program for apple scab -- that includes Captan and mancozeb fungicides, because it appears these fungicides have good activity against Massonina -- should keep it at bay for the majority of the growing season. Slacking off on fungicide applications towards harvest, however, can result in Marssonina blotch becoming rather "ugly." So my questions include:

1. Will letting the disease build up -- it overwinters in leaf litter on the orchard floor -- make it more difficult to control in future years? (Remember, sanitation is a basic tenet of plant disease control.) 
2. What are the ramifications of late-season "leaf blotch" and partial defoliation on tree health and productivity?
3. What fungicides have best action against Marssonina, and how late into the summer or early fall should fungicide application continue?
4. What weather conditions are most favorable for Marssonina infection? Surely moisture is an essential ingredient, and in fact, RIMpro has a Marssonina coronario infection risk model. Migh be worth heeding.

I know some of these questions are currently trying to be answered by University researchers in the Northeast, but Marssonina is a relatively new apple disease here and EverCrisp appears particularly susceptible. Both it's parents, Honeycrisp and Fuji, are known to be susceptible to Marssonina. So keep an eye out on those EverCrisp blocks!

Marssonina leaf symptoms on Evercrisp apple, 20-October, 2020

Under magnification, dark spots are diagnostic for Marssonina

Typical Marssonina "hot spot" in EverCrisp trees on 20-October, 2020; trees in background are less afflicted; defoliation of heavily diseased trees is occurring

RIMpro Marssonina model for the UMass Orchard, Belchertown, MA

2015 Modi Organic NC-140 Apple Rootstock Trial and Drapenet Demonstration

Blogger note:waiting too long for this to appear in Fruit Notes/Horticultural News. Sorry Wes and Win...

Jon Clements, Elizabeth Garofalo, and Wesley Autio

This NC-140 (http://nc140.org) rootstock planting in a commercial “Certified Naturally Grown” (CNG, https://www.cngfarming.org/) orchard gets more disappointing every year. In 2019, now in its fifth-leaf, more trees are dying or failing, and fruit quality and yield in 2019 was pretty abysmal. It’s unclear if low fruit set and yields are a result of pollination issues or the “organic” management regimen? In 2018 there were virtually no apples, but the entire rest of the CNG orchard was light too. In 2019 the CNG orchard had a good crop, but these Modi trees had a light to moderate crop (at best) of apples. Another problem was the amount of insect damage, mostly plum curculio and internal lep worms (codling moth or Oriental fruit moth) which made the CNG apples quite deformed and small in size. Weed control and fertilization remain organic orchard issues. My take home to date is that G.890, because of its vigor, is a good choice for organic orchards. Although G.30, G.202, and G.41 are acceptable too. (Maybe throw G.969 and G.214 in the ring?) G.16 is not right in this planting, and M.9 has really under-performed. G.935 has some issues, wondering if it is the virus/rootstock/scion interaction? Liberty trees on G.935 planted between replications and as guard trees have all died. Marssonina leaf spot was confirmed in September, and has been causing early defoliation of these Modi trees.

In 2019 a Drapenet (https://drapenetnorthamerica.com/) was installed over replications 1-6 (and not 7-12, there are two rows) the primary objective being to see if insect damage could be reduced. (Although there was a lot of hail around in 2019.) The Drapenet was installed on May 19, 2020 during late bloom, and was secured to the bottom wire with plastic wire ties. Inspection of the apples in late June showed that it was pretty much wholly ineffective at preventing plum curculio damage, however, a more formal harvest survey of 100 fruit per treatment (covered with Drapenet vs. uncovered) for damage showed that internal worms, mostly likely caused by codling moth or Oriental fruit moth, were greater in the uncovered (35% damage) vs. covered (12% damage) replications. But, as already mentioned, PC damage was greater in covered (80% damage) vs. uncovered (51% damage). Interestingly, the incidence of apple maggot fly injury was also greater in the covered (26%) vs. uncovered (5%) apples. Sooty blotch/flyspeck was also greater in the Drapenet apples (59% for sooty blotch, 21% for flyspeck) than the uncovered apples (19% and 12% respectively for sooty blotch and flyspeck). Note that at the UMass Orchard Modi performs just fine, and in fact, was one of the most beautiful apple crops I have ever seen. (Modi apple pictured above.)

These results are just investigatory, as the covered vs. uncovered was not randomized and replicated for statistical analysis. But a recent article in Fruit Quarterly (http://nyshs.org/fruit-quarterly/) also showed (research conducted at Michigan State University) that Drapenet is effective at reducing/minimizing flying moth damage (codling moth, Oriental fruit moth, oblique-banded leafroller).

Note that Modi is not available to apple growers outside of a California packing house (https://modiappleusa.com/). It was bred in Italy, a cross of Gala X Liberty and is scab-resistant. It has been marketed in Europe as an enviro-friendly apple (http://www.modiapple.com/about-us).

Installation of Drapenet on 15-May, 2019 over Modi apple trees in the
2015 NC-140 Organic Apple Roostock Trial in a CNG orchard.

Tree and yield characteristics in 2019 of Modi apple trees in the 2015 NC-140 Organic Apple Rootstock Trial in a CNG orchard.

Trunk cross-sectional area (sq. cm. trunk area) and cumulative yield efficiency (2017-19, kg. apple per sq. cm. trunk area) in 2019 of Modi apple trees in the 2015 NC-140 Orgamic Apple Rootstock trial.

Typical insect damage (and russet, Septmber 2019) on Modi grown in in a CNG orchard, includng plum curculio, Oriental fruit moth, and apple maggot fly.

Improvements to MaluSim (Cornell Apple Carbohydrate Thinning Model)

In the most recent Fruit Quarterly (Vol. 28, No. 1, Spring 2020) Dr. Terence Robinson and co-authors introduce some improvements to the Cornell Apple Carbohydrate Thinning Model, also known as MaluSim. If you remember, MaluSim is a decision support tool to help make effective chemical thinning applications based on predicted thinning efficacy. Inputs to the model require temperature and sunlight which are derived from a NEWA weather station. Outputs include a daily Thinning Index and recommendation to increase or decrease chemical thinner rates. Many apple growers have indicated the MaluSim (Apple Carbohydrate Thinning) is one of the most widely used decision support tools on NEWA: 

• Apple Carbohydrate Thinning v2019 on NEWA
• 2019 NEWA Impact Report for Massachusetts

The rationale behind Robinson making these changes/improvements to MaluSim are based on their annual study from 2000 to 2011 where experimental thinning treatments (using carbaryl, NAA, and 6-BA) were applied to apple trees in Geneva, NY and annual data on flower bud density and then cropping (yield, fruit size) was recorded. Weather data was input into MaluSim where a daily carbohydrate balance during the chemical thinning period was calculated and compared to the crop load at harvest. It turns out:

• The greatest effect on fruit set was timing of chemical thinning application, with the best thinning occurring at 200 to 250 degree days (Base 39 degrees F.) Note that king fruit diameter centered about 12 mm during this window. (I remember my MSU colleauge Phil Schwallier, who has done many chemical thinning trials over the years, saying he has consistently got the best results when chemical thinners were applied when fruitlet size was 10 to 12 mm.)
• Initial flower counts (bloom intensity) have to be taken into the equation too. When there are more flowers, more aggressive thinning is needed vs. having fewer flowers.
• Carbohydrate balance also had an effect on fruit set, but was much reduced (or non-existent) outside of this degree-day window of 200-250 DD’s.
• And, the actual daily carbohydrate balance should be expanded to a longer period before and after the thinning application compared to the “old” MaluSim which used a 4 day running average to compute the daily carbohydrate balance.

So, based on this research the new Cornell Apple Carbohydrate Model on NEWA (Apple Carbohydrate Thinning v2019) was modified as follows:

• Users must input % flowering spurs before running the model, with four choices: 76-100%, 51-75%,, 26-50%, or 0-25%. (Note the user must also input green tip and bloom dates. Don't accept the NEWA default green tip date, enter your own. Bloom date should be when 80% of the flowers are open on the north side of trees.)
• Degree Days are automatically calculated, summed, and highlighted in the DD column when they are in the range of 200-250 DD’s (Base 39 degrees F.) from bloom.
• Calculation of the “Thinning Index” (daily carbohydrate balance) is expanded to seven days (two days before the day of thinning to four days after)
• And, thinning recommendation, taking into account % of spurs that are flowering, DD’s from bloom, and carbohydrate balance over seven days (all per above) will be color coded red=high risk of over-thinning, yellow=caution, possible over-thinning, green=expect good thinning, and blue=little or no thinning expected.

In 2019 the older Cornell Carbohydrate Thinning Model will be replaced by the new and improved Apple CHO Thinning v2019 MaluSim model and you are advised to use that. Note that CHO thinning is also available in the Malusim app available on both iOS and Android smartphones for mobile access to thinning recommendations.

Cornell Apple CHO v2019 NEWA interface

Cornell Apple CHO v2019 NEWA output