Researchers from Cornell University, New York, are teaming up with apple growers to boost the efficiency of chemical thinning in orchards. The research program called ‘Precision Chemical Thinning’ aims at optimizing the spraying of plant growth inhibitors according to an optimum number of apples per tree, in view of enhancing fruits’ marketability and raising orchard rentability.
In apple growing, thinning refers to the partial shedding of the fruitlets produced by a tree in order to make room for the growth of others and achieve an optimized fruit size and tree crop load. Thinning usually happens in June in the Northern Hemisphere. It can be performed either manually – which requires between 100 to 200 hours of work – or chemically. Chemical thinning consists of spraying an active substance to regulate plant growth and ripeness, abscising the trees’ frailest fruit. An example of a product sprayed for chemical thinning is ethephon – a regulator which, through decaying ethylene, fosters both selective ripping and falling of unwanted fruitlets.
Researchers from Cornell University recently discovered that apple trees’ susceptibility to fruit growth inhibitors is higher during periods of carbohydrate deficit – which mostly happen during hot and cloudy days, as carbohydrate demand rises with temperatures while carbohydrate synthesis requires light to unfold.
Since the efficiency of chemical thinning varies according to temperature and sunlight, growers in the US states of New York and Michigan are starting to take into account weather data in their thinning decisions. The underlying model enables apple growers to match the timing and intensity of thinning sprays with low carbohydrates levels in trees. This eventually increases the predictivity of both apple size and fruit density before harvests. This so-called ‘carbohydrate model’ is supplemented by a second model called the ‘fruit growth-rate model’, where growers perform measurements on fruit diameters before and after inhibitor spraying. These measurements then feed into an agronomic formula that calculates growth rates in fruits and calculates chemical thinning decisions accordingly.
The combination of both decision support tools – based on fruit growth rates and the integration of carbohydrate levels – into one single protocol called ‘Precision Chemical Thinning’ is gaining ground in North American apple orchards – particularly in the states of Michigan and New York. The Precision Chemical Thinning protocol is supported by a research program led by Poliana Francescatto, a postdoctoral researcher at the University of Cornell. While the model still needs refining, it harbours promising opportunities for the profitability of apple orchards, enabling growers to better align their thinning strategies with a targeted crop load and a preferential fruit size and weight.
Precision chemical thinning in apple orchards – how can digitization contribute?
In 2017, Poliana Francescatto and her team took their model to the next level by integrating Bluetooth-enabled callipers for measuring fruit bud diameters. Instead of being collected through pencil, measurement data is now sent straight to smartphones: this enables participants in the Precision Chemical Thinning program to save time and increase the reliability of their thinning strategy.
How IoT supports orchard farmers in the quest towards precision agriculture
The Precision Chemical Thinning model is only one of the many applications of precision agriculture in fruit growing. The concept is now being spearheaded by the advent of the Internet of Things (IoTs), through which massive volumes of field data are captured, analysed and fed into decision-making protocols.
For example, by combining weather data with soil moisture data, IoT technologies lie the basis for decision support tools that allow farmers to save costs on intermediate input applications, from irrigation water to fertilizers, as nutrient uptake in apple trees requires the right amount of soil wetness to kick in.
Further information on how fruit growers are taking-up precision farming.
