A carbohydrate model for thinning apples is catching on rapidly in eastern apple growing areas.
“This model is fantastic. It is driving people to the NEWA network as never before,” said Dr. Juliet Carroll, who is in charge of NEWA, the Network for Environment and Weather Applications, at Cornell University, New York.
For years, NEWA has offered apple growers access to forecast models for three diseases, six insects, and irrigation scheduling. But the newest one—the one Carroll says is driving growers to the network as never before—is the carbohydrate model for apple thinning.
This carbohydrate model, which was developed from research by Cornell’s Dr. Alan Lakso and field verification by Dr. Terence Robinson, predicts how sensitive the tree may be to thinners, based on recent weather and the forecast.
The researchers found that apple thinning responses were stronger during periods of carbohydrate deficit, for example on hot, cloudy days. During those times, apples are easier to thin because the plant is having difficulty providing an adequate supply of carbohydrate to support all the rapidly growing young fruits.
Carbohydrate deficit can come from periods of high carbohydrate demand (due to warm temperatures and a lot of fruit) or low carbohydrate supply, and these in turn are caused primarily by weather.
Both Lakso and Carroll spoke to growers at the Mid-Atlantic Fruit and Vegetable Convention in Hershey, Pennsylvania, in January.
NEWA started at Cornell in 1995 and has since spread to 291 stations in seven states. Growers in Minnesota want to connect 12 stations there to the network.
Growers in Michigan use the carbohydrate model as well, using data from the state’s Enviro-weather network of some 70 stations across Michigan to work the model. But Enviro-weather is not formally part of the NEWA network. Michigan State University extension fruit educator Phil Schwallier works the model for Michigan growers.
Access to NEWA
Growers anywhere can access the NEWA website (newa.cornell.edu). They can find a nearby weather station where weather would be about the same as on their farm, and then use the models.
But more and more, growers are finding it to their benefit to put in their own stations. And since this data then is shared, the more stations there are, the better the data becomes.
Carroll tells growers to buy only RainWise AgroMET and IP100 Weather Stations, which cost $1,890. The stations come with software, a solar panel for power, an Ethernet interface that transmits data by line-of-sight transmission, and eight integrated sensors.
The sensors monitor temperature, relative humidity, rainfall, leaf wetness, solar radiation, wind speed and direction, and barometric pressure. Data are sent every 15 minutes to NEWA’s server.
Carroll said weather stations need to be calibrated every two years, and that it is not something that can be done in the orchard. “You have to send them back to RainWise,” she said. “You can’t do it in the field.”
How models work
Models can be developed after scientists figure out what makes things tick. The development of many insects is driven by temperature, Carroll said, so models for insects like codling moth, oriental fruit moth, obliquebanded leafroller, plum curculio, and apple maggot can be built from knowledge of when eggs are laid relative to accumulated growing-degree days, for example.
Calculations are based on a biofix date, which may be determined by first trap catches or by plant growth stage, such as petal fall, or whatever is a good base point from which to monitor the activity of an insect or disease.
Diseases like apple scab, sooty blotch, and flyspeck develop according to temperature and leaf wetness, and fire blight organisms also multiply according to temperature and moisture conditions. The biofix date for fire blight, for example, is the date of first flower opening.
In the case of the carbohydrate model, Lakso said, the basic biofix date is green tip, the date of budbreak, which is the starting of canopy development. It takes leaves and photosynthesis to provide carbohydrate to the plant for root, shoot, and fruit growth.
“Once that date is established, the only required inputs are daily maximum and minimum temperatures and daily radiation,” Lakso said.
“Basically, sunlight drives carbohydrate supply, and temperature drives carbohydrate demand,” he said.
Ideal conditions for thinning are when weather is warm, driving demand for carbohydrate, but radiation is low, providing little new carbohydrate supply. Warm nights create demand but no supply at all.
Other factors that make thinning easier include heavy bloom and heavy crop, humid conditions, weak spurs, and vigorous shoot growth, Lakso said.
It is hard to thin when weather is cool (lower demand) but sunny (high supply), he said. Light bloom, strong spurs, and slow shoot growth make thinning harder.
The model works pretty well, but a key tree response complicates the situation.
“Weather conditions in the three days after the thinner is applied are more important than conditions before the thinner is applied,” he said. “So we need to use weather forecast data to estimate what the post-treatment period will be like for carbohydrates. That can be a real limitation no matter how accurate the model is. Forecasting radiation is the hardest.”
The basic decision the model offers is not whether to thin but how much to adjust the thinner concentration, compared to what a grower would normally use in a thinning program.
There are seven possible results of the model on any given day, depending on the carbohydrate balance calculated. Only one of these results displays for the end user, eliminating much of the guesswork or calculations the grower would otherwise need to do. The recommendation is based on number of grams of carbohydrate the tree is “out of balance” in the positive direction.
Growers would use their standard thinning program when the tree is generally in balance or moderately negative. If the four-day carbohydrate balance is expected to be positive (producing more carbohydrate than the plant needs), increase thinner rate by 15 or 30 percent. Growers would decrease their thinner rate by 10, 20, or 30 percent depending on how great the deficit is. For severe deficits, no thinner would be recommended, as many fruits could be expected to fall off naturally.
Lakso cautioned that the model requires high quality data—good weather stations, properly located and calibrated for accurate temperatures, and especially sunlight. Good weather forecasts help as well, but these are beyond grower control. Growers in New York and other areas who have used the model have found it to be a useful addition to the information and experience needed to make good thinning decisions, he said.
Growers should get some indication of how effective their thinning was by looking at the actual weather data for the four days after they applied the thinner. But they can also monitor it by using a method developed with Dr. Duane Greene at University of Massachusetts.
In that system, growers choose fruits to measure before and after thinner is applied. After thinning, fruits that are growing at less than half of the rate of the largest fruit will almost certainly fall off, although it may take several days for that to happen. •