Saturday, February 16, 2019

iPiPE iPMX5

Last week (February 5-6, 2019) I attended iPMX5, the 5th Annual iPiPE 'Mixer' at North Carolina State University in Raleigh, NC. This was my third iPMX5 because for two years I have been New England (and New York) Apple Crop Pest Program Coordinator (CPPC) for iPiPE. In a nutshell, iPiPE "Progress thru Sharing" is a USDA/NIFA funded initiative who's mission states "There is a critical need to develop a national infrastructure of professionals who routinely monitor crop health and pest incidence then share this knowledge enabling dissemination of mitigation measures to limit food security impairment." More on iPiPE here and Apple CPPC (New England-New York) here. Note that anyone can join (and contribute to) iPiPE here.

As New England Apple CPPC for two years, I employed a graduate student (Liz Garofalo) who in-turn employed/engaged a total of four undergraduate students -- two in 2017 (Nicole Foley and Paul O'Connor) and two in 2018 (Cam Olanyk and Lyndsey Ware) -- to primarily scout apple orchards in Massachusetts and enter pest incidence data into iPiPE. Among many other miscellaneous work duties as assigned... 😀
Cam Olanyk pruning peaches at UMass Orchard, May 2018

I was not alone at iPMX5 as Liz, Cam, and Lyndsey accompanied me along with UMass colleague Katie Campbell-Nelson (Vegetable CPPC) and her student Avi Flynn. Also, UConn Grape CPPC Mary Concklin and her student interns Evan Lentz and Casey Lambert were there along with other iPiPE CPP's and student interns from across the country. I want to highlight here though the fact that at iPMX5 our own Cam and Lyndsey won several awards (which included cash!) from the iPiPE leadership team.

Lyndsey presents her honorable mention poster at iPMX5
First, Cam and Lyndsey got Honorable Mentions each for their Intern posters presented at iPiPE. The undergraduate student intern poster presentation session is an important event at the Mixer, and all the posters by these undergraduate students highlighting their iPiPE intern work and research experience were quite exceptional. Cam and Lyndsey's posters were titled 'A Grower's Perspective on Data Sharing and IPM.' and 'The Apple Press - Pests, Plants, & People and Using Models Effectively' respectively. Although not best in show, Liz and I were pleased with their performance and the amount of work they put into their posters, particularly when they also had current semester class and homework commitments ongoing. (Well, I understand Cam was doing quite a bit of snowboarding too!) You can see Cam's poster here, and Lyndsey's poster here, although Lyndsey's hard copy poster had an extra-special 3-D element to it that can't be reproduced w/o being there. I should note that at iPMX4 in 2018 Nicole Foley and Paul O'Connor also won a Poster award, second place I believe. You can see their poster from 2018 here.

Lyndsey and Cam share stakeholder engagement award
with fellow NJ iPiPE intern at iPMX5
Second, Cam and Lyndsey shared first and second place (along with a fellow iPiPE intern from NJ) in the iPiPE 'Stakeholder Engagement' card/publication judging. Their entry was unique compared to others, it was a small field note-taking booklet, with front and back cover links to the iPiPE website and a QR code that leads to the iPiPE Lite App. Cam and Lyndsey actually handed out the booklet to growers at the Massachusett Fruit Growers' Association Summer Meeting held at the UMass Cold Spring Orchard on July 10, 2018. Hence, stakeholder engagement. You can see the booklet cover and instructions for using iPiPE Lite here.
Lyndsey with iPiPE blog 1st place award!

Finally, Lyndsey was awarded best/first iPiPE intern blogger for her posts during the 2018 season. Lyndsey has journalism training and is quite adept at wording and looking at her work in a different way. For now, the intern blog is private. It was on Google+, which is being discontinued, so iPiPE is looking for a different host. But, you can get a flavor for Lyndsey's blog posts here and here and here.

Very proud of these students, and the work they did for us, and thanks to Liz for honchoing their activities over two field seasons. There is lots more to iPiPE, but that is another story. Here I just wanted to talk about the iPiPE Mixer, iPMX5, and the fine job our UMass student interns did. Congratulations...👍❤💥💪

Monday, January 14, 2019

MFGA meeting and Malusim app

Last Thursday, January 10, 2019 the Massachusetts Fruit Growers' Association met for their Annual Meeting at the Great Wolf Lodge in Fitchburg, MA. The meeting program included presentations by UMass Extension faculty and staff as well as Dan Donahue from Cornell's Eastern New York Commercial Horticulture Program.

MFGA Annual Meeting, 10-Jan 2019, Great Wolf Lodge, Fitchburg, MA
But, as a result of some feedback, I wanted to highlight one of my presentations, "Precision thinning using the Malusim app: trials and tribulations." I am going to follow with the individual "slides" and what I should have said during my presentation, but of course then I did not have time to smooth it out like I will here. Not to mention I only got 15 minutes. So here goes...


slide 1
Slide 1 - Today I want to talk to you about the Malusim app and how I used it (hence trials and tribulations) at the UMass Orchard in 2018 to practice "precision thinning."


slide 2
Slide 2 - So exactly what is precision thinning? It uses the fruit growth rate model co-developed by Duane Greene at UMass, and Alan Lasko and Terence Robinson at Cornell University to help predict if chemical thinners have been effective and thus achieve a target crop load per apple tree. AKA predicting fruit set. (See: A Method to Predict Chemical Thinner Response in Apples.) You can see the required steps here, which include: 
  • Determine desired crop load
  • Count flower clusters at bloom (7 trees per variety per orchard block)
  • Tag and mark fruitlets at about 5 to 6 mm (7 trees times 15 spurs times 5 fruits per spur equals 525 fruits)
  • Begin measuring each fruit with a caliper and record results, keep track of each fruit with each measurement, go home and enter into Excel spreadsheet 
  • Spray thinner, repeat above (several times) until desired crop load achieved (number of fruit or % fruit set), and additional thinners (if necessary) have been applied.
Are we having fun yet? Is anyone actually doing this?


slide 3
Slide 3 - If you really do want to use the predicting fruit set procedure, it is all outlined here in a 7 page document: https://www.canr.msu.edu/apples/horticulture/ Just keep in mind too it really needs to be done on every variety in every block! I ask again: are we having fun yet? Is anyone out there actually doing this?


slide 4
Slide 4 - Enter the Malusim app. An app developed by Poliana Francescatto and co-workers at Cornell University to help put precision thinning/predicting fruit set and the in the palm of your hands. Literally. First note that the Malusim app works in your browser where you create an account an set up your orchard blocks, as can be seen here in a browser window. As for the iOS and Android apps, we have been beta testing them but the app should be available to download this spring in the respective app stores. Note that Malusim also includes an Irrigation Model and has the ability to keep chemical thinner spray and irrigation application records.


slide 5
Slide 5 - So what did I do this year? Note that I have experience predicting fruit set and have wrote two articles on jmcextman.blogspot.com highlighting my results. But this year, I used the Malusim app to (hopefully) facilitate the process. Which included:
  • Selecting six (6) varieties in six different blocks: Pazazz, Fuji, Gala, Honeycrisp, McIntosh, and Empire
  • All trees were dwarf, tall-spindle (more-or-less) except Empire (slender-spindle)
  • Five trees were selected, five (only five!) spurs per tree selected and measured on 4 measurement dates
  • All data entered using Malusim app on Android (Google) phone using (the experimental) voice input
  • Only a petal fall thinning application was applied: NAA (or Maxcel) plus carbaryl

slide 6
Slide 6 - All fruit measurement data was entered using the Malusim app on a mobile device either using voice input or the device keyboard. I want to say it was easy, so simple even UMass Stockbridge School of Agriculture undergraduate students could do it! (Thanks Cam and Lindsey!)


slide 7
Slide 7 - Here are some screen shots using the phone app. On the left, the Locations menu in the ?. Middle, fruit diameter data entry screen, including the voice input icon - press and your are prompted to speak the measurement which is automatically entered into the Fruitlet # field. And last on right, the results showing target fruit number and % fruit set, and predicted set (number and %) based on each measurement date. At bottom of this screen you can also see when the chemical thinning spray was applied, also indicated by the vertical gray line on chart (to left).


slide 8
Slide 8 - and here, for example, is the interface (browser window) you would get when logging into malusim.org from your computer. When logged into your account, the app completely displays and syncs across phone, tablet, and computer.


slide 9
Slide 9 - Now, let's look at the results. First up Pazazz, a managed variety grown in WA, MN, WI, NY, and Nova Scotia. These are screen captures from the browser window. At the bottom, you can see when the thinning spray was applied, which is also depicted by the vertical gray line on the left of the chart. You can also see there the Potential fruit per tree and the Target fruit per tree (number of fruit and % set). These are determined when the block is initially set up, after counting the flower clusters and deciding how many fruit per tree is wanted. Then, four fruit measurements were made, with predicted fruit set shown by the blue bars beginning/after the second measurement date on 30-May. At the top, I counted the number of fruit per tree left at harvest, averaged across the 5 trees. So, on the last measurement date (8-Jun) predicted fruit set was 159 fruits per tree. Actual fruit per tree at harvest was 117. Humph. Definitively over-cropped were these trees, which affected quality -- look, I only wanted 50 fruit per tree on these smallish Bud.9 rootstocks. That's how it works. Now let's look how the rest of the varieties worked out?


slide 10
Slide 10 - Next up, Fuji. Target 80 fruit. Predicted (after last meausurement) 137 fruit. Actual, 125. Close, but over-cropped.


slide 11
Slide 11 - Gala. These Gala trees were a bit odd, with variable bloom and final crop. So, not putting much stock in it, but Target 65, Predicted 50, Actual 61. Not bad. I have come to the conclusion you almost need to be just below your target upon the final measurement to come up right.


slide 12
Slide 12 - Honeycrisp. We love to hate it. Target 50 fruit, predicted 90 apples, harvest 101 apples. Ugh. Way over-cropped = lousy tasting Honeycrisp. More chemical thinner should have been applied, need to see that final blue bar BELOW the pink shade?


slide 13
Slide 13 - McIntosh. 100 fruit target. 114 fruit predicted. 160 apples at harvest. What? McIntosh are different. But small Macs are good, right? Not sure I would waste my time doing this on Macs, which habitually crop every year and did I say small Macs aren't a bad thing? But do they make any money? 😟


slide 14
Slide 14 - Empire, I add here only because there is a side story (in two slides). This was not my experiment you will see. I don't have much to say here, except wait for slide 16.


slide 15
Slide 15 - So, the general tendency is for the trees to be over-cropped at harvest, and that has a tendency to be indicated by the predicting fruit set results. All is good on that front, but you have to wonder why too many fruit? Don't forget the Malusim app can also display the results of the Carbohydrate (CHO) Balance model at your orchard. Wait a minute, that is if you have a NEWA site because the app pulls the results of the CHO model real-time from NEWA. Note here the CHO balance is not particularly severe when the chemical thinners were applied. Therefore, one would expect modest chemical thinning (at best). Another/more chemical thinners should have been applied.


slide 16
Slide 16 - Now the Empire story, here Paul O'Connor, UMass PhD student works with a technician from Carnegie Mellon University at the UMass Orchard to visualize fruitlet growth using a hyperspectral(?) camera. Over the course of a week they took several visualizations of fruit growth using this special (and expensive and heavy) camera with the goal of seeing if indeed growth rate can be visualized and calculated based on these images. Indeed, preliminary results suggest that this is the case, and last I heard, they are working to see if the same growth rate learning model can be applied ot images taken with, shall I say it, a smart phone? An idea I have had for quite some time...😲


slide 17
Slide 17 - Conclusions. The Malusim app has the potential to make the job of precision thinning and predicting fruit set notably easier. One needs to be a bit of a technophobe, however, and it's not for everyone. Plus it's kind of in beta. (Cornell, please figure out the future path of Malusim.) Don't forget, however, there is a whole irrigation model built-in too. I encourage you to go to malusim.org, sign-up with an account, and give it a try in 2019. I will say at the very least you will learn a lot by going out and looking at a small sub-set of your growing (ot not growing) fruitlets, which will make you "seat of the pants" chemical thinning decisions a little less so. Good luck and feel free to submit feedback to the Malusim team, whoever that is...


















Wednesday, January 2, 2019

NC-140 rootstocks gone rogue?

2014 NC-140 Honeycrisp planting at UMass Cold Spring Orchard, 13-May, 2018
While recently working with colleagues on a rather comprehensive article (for Fruit Notes and Horticultural News, soon to be published) on data results from the 2014 NC-140 Honeycrisp planting at the UMass Cold Spring Orchard in Belchertown, MA I was shaking my head. Yea, we got lots of nice large tables with lots of numbers via measurements we took in the field, and they are all nice and statistically separated, but how does the average (non-scientist?) person (apple grower?) sort through it all? And what is the take-home message?

So, I may be going rogue here -- or to put it another way "some NC-140 results for dummies" -- but here is how I/we can look at it in a way I hope may be more useful to the average grower thinking about using any of these rootstocks. (If you really want more in-depth, be sure to visit the NC-140 website.)

First, let me point out a few details of the planting: tree spacing is app. 3 feet by 16 feet, planted in 2014, data collection started in 2014 (tree size only) and then beginning in 2015 including fruit yield. You will see what rootstocks are in the planting shortly, but Honeycrisp is the variety, and there are 10 replications of the rootstocks, i.e., 10 trees of each rootstock that are randomized down the row. This NC-140 planting objective is to look at some as-of-yet unreleased Vineland (V.) rootstocks compared to some common commercial rootstocks (M.9, M.26, and B.9) as well as including most of the recently released Geneva (G.) rootstocks.

Let's start with tree size, which of course is of inherent interest in these NC-140 apple rootstock plantings. Tree size (trunk diameter) is measured at app. 12 inch height above the graft union every year at the end of the growing season. Every tree, and then averaged across the individual rootstocks. At the end of 2018, here is what it looks like:
Trunk diameter (in inches) at end of 2018 growing season,
NC-140 Honeycrisp planting at UMass Cold Spring Orchard
OK, you can see which trees are larger -- G.890, V.6, V.5, V.7 and G.30, and they need to be planted at a lower density, let's say 6 feet apart, app. 520 trees per acre. Then tree size kind of breaks at V.1, G.969, G.214, M.26, G.935, and G.41. I figure these trees need to be planted at about 4.5 feet apart, app. 800 trees per acre. And then there are the smallest trees on G.11, M.9, and G.202. These could be planted 3 feet apart at circa 1,320 trees per acre. I want to note here that G.202 should be bigger, I think there was something wrong(?) with these trees in this planting, so I would discount here whatever you see on G.202.

Now let's look at apple yield. The following chart shows fruit yield per tree in 2018 and cumulative fruit yield per tree from 2015 to 2018. Rootstocks are ordered top to bottom by decreasing apple yield (in pounds) in 2018. Now, consider some trees are larger (G.890) and therefore are going to have more apples compared to smaller trees like G.11. Note G.969 and G.30 (and arguably G.890, but it's a bigger tree!) stand out in cumulative yield. Interesting. The rest of this story later, and you might surmise where we are headed here with this already?
Fruit yield and cumulative yield (in lbs.) per tree at end of 2018 growing season,
NC-140 Honeycrisp planting at UMass Cold Spring Orchard
But before we get there, let's look at a measure of productivity regardless of tree size, where it's a fact that generally the bigger tree, the more fruit yield, and vice-versa (smaller tree = less fruit per tree). This correction factor is called yield efficiency, a measure of fruit yield per unit of trunk area. Next up we have a chart of yield efficiency and cumulative yield efficiency. Yield efficiency here is measured by lbs. of fruit produced per square inch trunk area (per tree). Once again, from top to bottom, rootstocks are ordered by decreasing 2018 yield efficiency. In general, higher yield efficiency is better. Note how the order is more-or less reversed from the absolute per-tree yield chart above. This is because, typically the more dwarfing rootstocks produce more apples per unit of trunk area. They grow fruit, not wood, that is why we like them. G.969 again looks like a standout. But G.11, M.9, G.214, G.935, G.41, and G.30 look good too. (G.30 mostly because cum. yield efficiency looks very good.)
2018 yield efficiency and 2015-18 cumulative yield efficiency (in lb. per sq. in. trunk area),
NC-140 Honeycrisp planting at UMass Cold Spring Orchard

One more thing before we get to the gold mine. It's useful to also look at the number of apples per unit of trunk area to assess productivity. Here, because we typically view a range of 4 to 8 apples per square centimeter of trunk area as 'ideal' I am going to have to give you the results in metric units (number of apples per square centimeter of trunk area). For Honeycrisp, I like to see about 5 to 6 apples per square centimeter trunk area for optimum fruit quality and size, as well as to help prevent biennial bearing. You can see belwo what rootstocks were in this crop load range in 2018. G.969 at 10 apples per square centimeter trunk area was over-cropped and apples were notably green in skin color on these trees. Most else was good, although once we start dropping much below five apples per sqare centimeter trunk area one could argue the trees are under-cropped. (Note to self here: have not fully explored biennial bearing tendence of these rootstocks, would not be hard to do, just need to do it. Maybe an update to this blog post someday?)
Number of apples per sq. cm. trunk area at end of 2018 growing season,
NC-140 Honeycrisp planting at UMass Cold Spring Orchard

OK, now just one more thing. Really what matters is how much money you can make, i.e., what is the production going to be worth given tree spacing (trees per acre) and how much each rootstock has produced through the 5th-leaf growing season? Well, that is easy to do -- simply multiply tree density (either 520, 800, or 1,320 trees per acre per above discussion) by the cumulative yield in pounds per tree, and assign a dollar value per pound. To make this super-easy, let's use $1.00 per pound value of Honeycrisp apples across the board, certainly doable, a little high for some, quite low for some others. So what do we come up with in terms of production value per acre over the five years? Humph. See chart below. I like G.41 and G.11. A lot. But consider it's going to cost more per acre to put in a G.11 orchard because of closer tree spacing. G.969 looks real good, but I did not like the lack of color on those fruit in 2018. (I assumed 100% pack-out for this analysis, why would I want to grow anything less? G.969 would not have given me good pack-out at that crop load, fruit too green.) G.935, G.214, and G.30 deserve consideration too. I want to say year-in, year-out G.30 has looked real good when I harvested it, but it root suckers profusely. With the exception of V.1, the Vineland roostocks are simply too big and not very yield efficient. (But consider they may have other attributes.) I already mentioned G.202 seems to be an anamoly in this planting. M.9 looks good, but who wants to lose trees to fire blight? Ditto for M.26, that one needs to be buried (literally). And now you have the rest of the story...
Predicted cumulative dollar value of appled produced 2015-18 in the NC-140 Honeycrisp planting
at UMass Cold Spring Orchard

Monday, December 24, 2018

Camera traps? Interesting but what about the future?

Using pheromone 'wing' traps to monitor and set the first sustained trap catch (aka biofix) is a first-line IPM strategy to manage lepidoptera pests such as Oriental fruit moth, codling moth, and obliquebanded leafroller in apple orchards. For example, see MODEL BUILDING: the obliquebanded leafroller biofix/degree-day model for controlling first-generation larvae.

For several years now, I have been experimenting (trialing?) automated pheromone traps originally made by Spensa Technologies. At first, their 'Z-traps' literally did 'Zap' and kill (render flightless, at least temporarily) flying male moths entering the trap (attracted by appropriate pheromone), and the on-board electronics would count these moths. That information would then be sent to their cloud-based 'MyTraps' platform where the user could access and visualize the trap data on a 'dashboard' after logging in to their account on the web. (Note that their overall Spensa Agronomic Platform, in addition to MyTraps, also has many other features, particularly a pest scouting and recordkeeping interface available on a mobile device.) In 2018 Spensa was aquired by DTN.

Before 2018 my experience with the Z-Traps was kind of 'meh.' It was primarily a black-box driven set of hardware/software devices, the set-up and transmission of collected data being kind of onerous. Plus, trap distance from the base station, which needed to be hard-wire connected to the internet via router, was limiting. Trapped and killed moths, however, once 'zapped' could be seen and counted after being funneled into a collection device. But I don't want to talk about the old set-up because that has been discontinued.

In 2018, Spensa/DTN introduced a new camera trap, dubbed a 'Smart Trap,' the hardware being nearly identical to before, but instead replaced the 'zapping' mechanism with a simple sticky bottom, just like a traditional pheromone wing trap. Then, a camera located just above the sticky trap bottom would take a daily picture of the bottom and trapped moths, and transmit that to the DTN web dashboard. In addition to being able to remotely visualize what was caught, the Spensatech software could actually isolate the moths, differentiate between old catches and new catches, and chart/plot the results. Overall, based on my experience it (mostly) works!

Smart Trap at UMass Cold Spring Orchard, Belchertown, MA on 27-June, 2018

Sticky bottom of Smart Trap with OBLR capture; note camera points down
at sticky bottom at bottom of white rectangular electronics box
I know it works because in the spring of 2018 I placed two Smart Traps -- one pheromone-baited for codling moth (CM), and one for obliquebanded leafroller (OBLR) -- in a central Massachusetts orchard that is an hour drive from my office in Belchertown, MA. After logging into my DTN Agronomic Platform (AP) dashboard, the traps were automatically placed on a map (after I deployed them in the field, the traps communicating by a cellular connection), and I could configure them by choosing the pest being trapped, when pheromone was added, etc. Then I waited and sure enough pictures of the sticky bottom started appearing daily on my dashboard. Over the days, old catches had red squares placed around them, and then new (daily) catches had green squared placed over the caught moths. In addition the count(s) and graph(s) were automatically updated. Pretty cool! I found over time the flight patterns of both CM and OBLR were matching more-or-less what was being observed in other Massachusetts locations.

Cumulative OBLR caught in Smart Trap; red outlines are
previously caught moths, green outlines are moths caught in past day

DTN AP dashboard chart of season-long OBLR trap catch in one Smart Trap

Season-long OBLR trap catch using conventional pheromone trap(s);
note rough match with chart of moths caught using Smart Trap above
Now you have the gist of what's going on, I would like to point out what I think are the pros and cons of these Smart Traps. I would also like to say I am a little dubious (worried?) about the acquisiton of Spensa (a relatively small tech start-up) by a much larger agronomic platform (DTN), therefore what the future of these traps and the DTN AP will be. There is a lot of promise here to make such pest monitoring eaiser and more widespread, I hope it does not get lost and that the price becomes more palatable for more users.

Smart Traps/DTN Agronomic Platform PROS and CONS

PROS

  • SmartTrap set up is simple and communication generally reliable (although one trap out of three I had went dead after awhile, was replaced by DTN)
  • SmartTraps can be installed anywhere there is a decent cellular GSM signal (AT&T, T-Mobile I believe, NOT Verizon)
  • DTN/AP/MyTraps is robust (but has a rather steep learning curve)
  • much time and money and travel can be saved by not having to manually check trap counts
  • trap counts come in daily, which unless the traps are on-site, typically is done only weekly if done manually
  • good season-long record and visualization of trap data with no additional effort (entering data, etc.)

CONS

  • cost, about $400 per trap per year (the traps are not owned but leased), mostly to cover the cost of cellular data transmission and to support the DTN cloud and dashboard (note, however, the dashboard has other general scouting uses in addition to supporting Traps)
  • DTN/AP/MyTraps web application has a rather steep learning curve (but is quite robust); probably not for everyone
  • reliability, some issues still need to be improved because as noted one out of three traps failed mid-season (but was readily replaced by DTN)
  • sustainability of Smart Traps and DTN/AP is unknown at this time after aquisition of SpensaTech
  • not having to manually check traps takes out (sometimes) the personal interaction with growers, and perhaps train future pest scouts?
  • will I sign up again in 2019? remains unclear...







Saturday, November 3, 2018

New York, New England, Canada Fruit Pest Management Conference

I'm on a roll. So, in mid-October 2018 I was up in Burlington, VT with a group of fellow-minded (mostly tree fruit) Extension, research, and consultant folks from the New England states (mostly), New York, and our friends up north, Canada (mostly Quebec). This is the our 82nd get-together, believe it or not. I've been probably close to 25 times beginning with my work at University of Vermont and continuing with my 18 years at UMass, during which I don't think I have missed a year? There were just over 30 of us, as evidenced by this group photo:

Northeastern IPM Center Tree Fruit Working Group, 24-October, 2018.
Photo byCornell's Art Agnello, our facilitator at far left.
We reported on special orchard problems during the past growing season, gave research and Extension reports/updates, and much enjoyed a regional beverage tasting, all at the Bishop Booth Conference Center on the shore of beautiful Lake Champlain. You can further investigate this Tree Fruit Working Group on the Northeastern IPM Center website, including reports and research presentations. (2018 to be posted soon I hope.)

But we had a special guest, Peter Triloff, a consultant from the Lake Constance region of southern Germany. Peter was being hosted by Vincent Philion up in Quebec, so they came to the meeting togeher, and Peter gave an update on canopy adjusted spraying. Wow, kind of blew away some conventional wisdom I had, such as going slower equals better spray coverage (not necessarily) and that air-induction (AI) nozzles are NOT the way to go, because drift reduction, although important, should not be priority when spraying. What is priority? Better coverage while still controlling drift. AI nozzles do not give you as good spray coverage compared to regular hollow cone nozzles with finer droplets. So how do they achieve higher spray coverage with smaller droplets while controlling drift? Peter says by modifying the air flow to match the canopy, which includes throttling down while speeding up.
One screen of Peter Triloff's presentation at Interpoma as mentioned below
Now, there are many fine points, but there is one major talking point: radial fans are a disaster if we are talking tall-spindle type trees. (And why would we talk anything else?) A tower (vertical air flow pattern) is the only way to go. I certainly get the impression spray technology over there is way ahead of us. And they have restrictions, such as wind speed limits (app. 8 mph?), sprayer inspections, and full sprayer clean-out between tank mixes. Peter's talk also honed in on the money and time saving that could be achieved by speeding up while still maintaing good coverage. Diesel fuel savings too which they seem to be into out there. AND, pesticide use reduction by about 2/3 because spray is not going on the ground or drifting! Target the canopy and save money, time, and reduce pesticide use. What's not to like? Here is a presentation on this subject from 2014 given by Peter Triloff at Interpoma in Italy. The presentation he gave in Vermont was updated and quite thought provoking. Mostly, we could do a lot better with our spray application technology here in Massachusetts, New England, and the Northeast? I present my case:
















IFTA New Zealand 2018

I admit to being lame and not publishing a blog post in almost a year. It's been a busy year with lot's going on, so I ought to make an attempt to get back in the saddle. (No promises.) Will start with a big deal, I was lucky enough to spend almost two weeks in New Zealand last February with the International Fruit Tree Association. Mostly I want to share with you a photo album I put together, but here's a (very) quick synopsis of my trip.

IFTA New Zealand 2018 attendees.

First stop Napier, on the North Island. I liked the public water pool/hot tubs after a long series of flights that spanned two days. One-day IFTA Conference to kick things off, intro to New Zealand apple production and some pretty technical production talks. Of course you know NZ is now famous for Jazz, Pacific Rose, and more recently Envy but don't forget Granny Smith, Gala, and Braeburn also originated in New Zealand! Two full days or orchard tours in the Hawke's Bay apple growing region. Many highlights including NZ Plant & Food Research (Future Orchard Production System), Prevar, and Rockit apples. Very technical and detail-oriented apple orchards, apple quality is key given the money is in export apples. We had a day off with guided activity options, I chose a bike ride along the coast and sheep country topped off by lunch at a winery. With wine of course! Made the bike ride into a stiff wind back to point of origin kind of onerous, but overall the day was a great one.

Public pool/hot tub beachside in Napier. Sweet.

After a short Island hop courtesy of a chartered Air New Zealand flight we landed in Nelson. Nelson is a rather cool place with a downtown filled with shops, bars and restaurants. Two full days of orchard tours in Nelson/Tasman Region. Again, highly technical apple growing, including Drape Net demonstration, 2D apple canopies, and grafting. Another day off with a group trip to Abel Tasman National Park. Choice of activities, I opted for Catamaran ride. Needless to say it was great. While in Nelson, I also caught a glimpse of the "Crux" aka Southern Cross constellation one evening.

Dr. Greg Lang. You think he is enjoying the Cat?
Departing Nelson on bus, destination Christchurch, we stopped in Kaikura. Again, choice of group activities, I went swimming with dolphins. Whoaa, that was an experience, wet suit and mask/snorkel, flippers and all. Whisked off back of boat, saw dolphins, whistled back on boat. Several/many times. Exhausting, ever seen the movie "Open Water?" Thought crossed my mind.


OK, one day of orchard tour out of Christchurch to Timaru Region, one orchard, M A Orchards, first (and only?) Honeycrisp planted in NZ in 2012. Large red apples being harvested, bound for North America. Of concern was some significant drop. Reflective fabric in place. Very interesting.

Bin of harvested NZ Honeycrisp, 7-March, 2018

Thanks to IFTA, all our NZ hosts and sponsors, and Onward Travel for making it all a pretty seamless experience with good orchards, good food, good company and entertainment, good scenery, good recreation. And to WSU's Karen Lewis who did two rounds of hosting over a period of a month.  Every stop/activity times two. She has special skill, knowledge, and stamina to be able to go the mile (kilometer?), and we all appreciated her presence many times over.

Karen Lewis, fearless leader. The end is near...
And be sure to check out the photo album here...


Wednesday, January 3, 2018

Precision chemical apple thinning in MA in 2017

OK, I have been through this before, the whole measuring apple fruitlets thing and entering data into the precision thinning spreadsheet to determine if I needed to do more chemical thinning (or not) routine, you can read all about it here: http://jmcextman.blogspot.com/2016/06/precision-chemical-apple-thinning-in-ma.html But in 2017 I decided to ease the workload AND at the same time do more varieties. Yea, right.

To that end, I used four groups of dwarf apple trees -- Honeycrisp, Gala, Fuji and DS-41 (Pazazz) -- at the UMass Orchard in Belchertown, MA. These trees were either on M.9 or B.9 rootstock, and six to ten years in age, in other words pretty much settled down tall-spindle type trees planted three feet apart. The Gala/M.9 were tallest (at the top wire), the 10-year old Honeycrisp/B.9 the shortest (barely 7 feet).

Instead of doing the recommended 15 spurs per 5 trees, for 75 spurs total, I said to myself "surely 5 spurs per tree, 5 trees, 25 spurs total will be enough in these trees and give me a good enough idea of what's going on and save me a lot of work, right?" Yea, right.

I ended up making measurements three different times, and deciding -- likely w/o much input from anyone else, and probably not enough commiserating about it -- that after two chemical thinning sprays*, I was done. No more measurements, no more chemical thinning sprays. It looked kind of good, see Figs. 1-4 from the precision thinning spreadsheet.

*I think, can't really verify with spray operator, but it was something like petal fall carbaryl, followed by a carbaryl/Fruitone spray at modest rates, let's just say it was pretty much a "grower standard" for 2017. Pretty lame, huh?

Fig. 1 -- Honeycrisp predicted fruit set


Fig. 2 -- Gala predicted fruit set

Fig. 3 -- Fuji predicted fruit set

Fig. 4 -- DS-41 (Pazazz) predicted fruit set

Some explanation on what you are seeing in the Figures:
  • 2 and 3 on the x-axis are the measurement times, app. 7-10 days apart, after the first fruit measurement (which is not shown)
  • the blue-purple bars (y-axis) are the predicted number of fruit setting based on the measurements and fruitlet growth rate model
  • the green bars are the target number of fruits per tree, which was initially decided on when bloom clusters were counted; it was decided that 50 fruit per tree (60 for Pazazz, large fruit size an issue) was the target crop load, which is about 10% of all the flowers setting (app. 100 flower clusters per tree, times 5 flowers per cluster, equals 500 flowers, if 10% set equals a crop load of 50 fruit, right...admittedly a bit fuzzy?)
Now that you got it, well it sure looked to me like we were getting some pretty good thinning across the board with the second chemical thinning spray that was made between measurement 2 and 3. So, as already mentioned, enough was enough, right? Yea right.

I could tell by mid-summer that most of the trees were clearly overset. But I left them to harvest, and then picked and counted all the fruit off each of the five trees. Here is the average number of fruit left per tree (across the 5 trees) by variety compared to the target number of fruit and what the spreadsheet was telling me after the last measurement (see Figs. 1-4 above).

no. fruit at harvest
predicted fruit set
target no. fruit
Honeycrisp
180
133
50
Gala
132
120
50
Fuji
117
132
50
DS-41 (Pazazz)
80
88
60

Except for DS-41, all the trees had more fruit on them at harvest than what was predicted on the last measurement date and all had way more fruit than the target number. (I knew that about mid-July!) 

One other data observation before I get into what went wrong. I counted the number of fruit left at harvest on each of the 5 tagged spurs. (Except for Honeycrisp, it dawned on me this would be a good idea after I had already picked the fruit. D'oh!) The results are kind of interesting, because, think about it -- if five flowers, 25 fruit set potential, 10% (the already defined target) then there should only be 2.5 fruit left per 5 flower/fruit spurs counted. Or 12.5 fruit per 25 clusters counted (125 fruit potential). My results on the 5 trees (25 clusters) are: Gala, 21 fruits left; Fuji, 16 fruits left; DS-41 (Pazazz), 11 fruits left. Remember, there should only be 12.5 fruits left. This somewhat mirrors the results above, in that DS-41 actually had the best thinning compared to the others. I could say more about this, but suffice it to say the flower clusters I tagged and picked to measure fruits were pretty representative of the end result.

OK, so what went wrong here? I have several (five to be exact) observations:
  1. How often have you over-thinned using chemical thinners? my experience is the first time you think you have thinned enough, hit them again with another chemical thinner application! (Caveat, watch the weather forecast if heat and clouds are forecast, be more careful.)
  2. Maybe using just five spurs per tree is not a good (big?) enough sample? if one or two spurs per tree are "duds" -- meaning they are not representative of all spurs on the trees, maybe they are damaged and/or readily shed their fruitlets? -- then it is going to throw the whole prediction off? Next time (if there is one) I will look at a minimum 10 spurs per tree (or 5 spurs over 10 trees?); I've had this issue/theory before...
  3. If after the last measure, the bar chart is still showing more fruit than the target, chemical thin again? Only when the bar shows less fruit than the target can you stop?
  4. You could always hand thin to the target fruit number to be on the safe side :-)
  5. Maybe the predicting fruit set model needs some kind of revision? But I will defend it here, because I introduced a lot of error into my procedure in an attempt to make it easier and do more; shortcuts are not good in this case, maybe?
Speaking of shortcuts, I would like to point out that I procured an inexpensive ($80) Bluetooth caliper on Amazon and paired it up with a custom spreadsheet I developed on my iPhone to enter data automatically in the field (Fig. 5).

Fig. 5 -- using a Bluetooth caliper and iPhone to enter fruit growth measurement data

It worked OK, just OK. I had to keep an eye on it to make sure it recorded data properly. If just one observation was skipped, then all the entries from thereon would be off, which would not be good. It has to be drop-dead reliable/accurate to be really useful. Needs work. Speaking of which, with all the talk about machine learning now, why not an app that can snap picts of fruit clusters and analyze which fruitlets are growing (or not)? I don't think that is too far-fetched?

Where do I/we go from here? As I've said before, it is a useful exercise to get out and measure fruitlet growth to help assess what is going on with your thinning program. And if you largely grow solid blocks of a few varieties, by all means go whole-hog and fully follow the procedure outlined here. It will work. Unfortunately, if you have many discrete blocks (tree age, variety, site, etc.) it's just too onerous/time consuming to implement the procedure in all blocks? (But do it in high value blocks, don't waste your time with McIntosh...) What's really needed is a bullet-proof way to measure and enter data quickly (and accurately) w/o having to employ too much man/woman power? Ideally in a mobile package, that also gives immediate feedback? Perhaps a chemical thinner spray has to be made sooner than later (because of weather conditions?) and having that immediate feedback would be useful (vs. having to go to home/office and entering the data into XLS spreadsheet)? Can we do that? I understand Cornell folks are working on an app that might help, keep your eye out for it in 2018. I just hope it is simple enough to be truly useful. I have some say, I am a beta-tester for Jaume and Poli at Cornell...

Alas, chemical thinning decisions are never "simple," are they? Good luck in 2018...