Apples are among the most important commercial fruit crops grown worldwide. Meeting market demands to provide fruit of the highest quality is an ongoing challenge for apple producers.
Optimising crop load through the agronomic practice of thinning can improve fruit size and quality. In commercial orchards the application of phytochemicals, which cause fruit drop, followed by a manual thinning adjustment are used to optimise the fruit load. The entire operation is costly and time consuming.
Picture 1: IAD measurements in the field with a DA-Meter.
Optimising crop load can also reduce biennial, or alternate, bearing in apples, where high fruit numbers in one year are followed by reduced fruit numbers the following year. Growers can lose up to 30 per cent of their income every year due to biennial bearing. The ability to even out biennial bearing would be extremely valuable and assist with industry wide planning.
With these outcomes in mind, an international collaborative project between Germany (University of Hohenheim, Stuttgart) and Australia (Agriculture Victoria Research) with co-investment from Horticulture Innovation Australia Limited using the Apple and Pear research and development levy and funds from the Australian Government, was initiated. The aim of this project (currently in its fourth year) is to advance knowledge of the factors that cause the limited flower bud induction that leads to biennial bearing patterns. The Australian component of the research focusses on the application of crop load gradients to trees in a commercial orchard setting in the Yarra Valley, Victoria, to study tree physiological and fruit quality response to crop load. In addition, practical innovations are being studied to assist growers with chemical thinning decisions.
The role of crop load
High crop load can unbalance the chemical composition of the fruit, especially calcium accumulation, resulting in reduced storage capability and higher frequency of postharvest disorders. Fruit maturity at harvest is very important affecting storage disorders since immature fruit are prone to superficial scald while over-mature fruit are prone to internal browning. Maturity also affects fruit texture, sweetness, aroma and overall satisfaction at the time of consumption.
As part of the Australian experiment a range of crop loads, from extremely low to over double the common practice, were applied to the cultivar ‘Nicoter’ (marketed as Kanzi®), normally considered as mildly susceptible to biennial bearing, and the cultivar ‘Rosy Glow’ (marketed as Pink Lady®, normally considered as non-biennial bearing. Trees, on M9 rootstock, were spaced at 1.0 metre and trained on open Tatura trellis as spindles. Row spacing was 4.0metre. Five crop load treatments were applied yearly starting in the 2015-16 growing season with six replications, for a total of 30 trees per cultivar. Crop load treatments, as number of fruit/cm2 of trunk cross sectional area (TCSA), were imposed by hand thinning immediately after full bloom and consisted of:
(I) 10 fruit left on the trees (1 fruit/cm2 for both cultivars);
(II) half the standard grower practice (3 and 4 fruit/cm2 for ‘Nicoter’ and ‘Rosy Glow’ respectively);
(III) standard grower practice (6 and 8 fruit/cm2 for ‘Nicoter’ and ‘Rosy Glow’ respectively);
(IV) one and half standard grower practice (9 and 12 fruit/cm2 for ‘Nicoter’ and ‘Rosy Glow’ respectively); and
(V) double standard grower practice (12 and 16 fruit/cm2 for ‘Nicoter’and ‘Rosy Glow’ respectively).
Figure 1: Effect of crop load on fruit maturity during the growing seasoned measured as IAD for cultivar ‘Nicoter’. Legend shows crop load as number of fruit/cm2 TCSA.
A series of tree growth variables were regularly measured during the growing season, such as shoot and fruit growth, leaf conductance
(as an indication of transpiration and photosynthesis), light interception (Picture 2), TCSA, pruning wood at summer (if needed) and winter time, and return bloom the following season.
Fruit maturity was measured as flesh chlorophyll concentration using an Index of Absorbance Difference (IAD) DA-Meter (Model 53500 T.R. Turoni, Forli, Italy) (Picture 1). The DA-meter is a measures chlorophyll concentration in the fruit flesh. IAD was measured regularly starting at least four weeks prior to harvest. The usage of tools to non-destructively monitor fruit development and maturity during the growing season could assist in the determination of fruit maturity variability within trees and blocks. This would also assist in the prediction of optimal harvest times.
This article reports the results to date of the effect of crop load on fruit maturity during development and fruit quality at harvest.
Figures 1 and 2 show the effect of crop load on fruit maturity development during the three growing seasons completed for both cultivars. There is a clear tendency to delayed maturity with increasing crop load. Differences in maturity delay between the crop loads were more evident in the first year of application (season 2015-16) for both cultivars, probably as a reactive response of the trees to the first application of the crop load treatments; ‘Nicoter’ showed close to two weeks delay between the two extreme crop loads to reach IAD = 0.4 (considered the optimal value for harvesting) (Figure 1), while ‘Rosy Glow’ showed a delay of over 20 days to reach the ideal harvesting IAD value of 1.1 (Figure 2).
Figure 2: Effect of crop load on fruit maturity during the growing season measured as IAD for cultivar ‘Rosy Glow’. Legend shows crop load as number of fruit/cm2 TCSA.
In the following seasons, the difference in maturity at harvest between the crop loads was less noticeable, both between the crop loads and between the cultivars. Growing season weather conditions influence the timing of fruit maturation mainly due to the level of carbohydrate accumulated and distributed to the fruit sink by the trees. This could be one of the reasons why the low to mid-crop load levels (II, III and IV) were mostly grouped together at harvest.
Overall, from our experiment, it is clear that high crop load delays maturity. This is true not only at a canopy level, but also at the branch level or in sections of trees, increasing the variability between fruit. It becomes very useful then to have methods to understand the amount of fruit quality variability in the orchard and to monitor fruit maturity during the season and between seasons.
DA-meter simplifies harvest planning
The DA-Meter allows the non-destructive monitoring of fruit maturity during the season. Fruit can be measured in situ or after being picked. The conversion of IAD values to ethylene production is available for some cultivars enabling the exact identification of the fruit physiological stage and the ideal harvesting time for best postharvest performance. If the correlation of IAD values with ethylene is not available, growers can build a correlation with firmness and flesh starch.This would not be as accurate as the ethylene, but it would still give a rough estimation of fruit maturity.
In any case, monitoring IAD values during the season, starting roughly 4 – 6 weeks prior to estimated harvest, would give an understanding of the speed of fruit maturity. Using historical data in subsequent seasons would provide comparison in the speed of maturity and a better prediction of harvest date up to three weeks in advance, which would help in planning the logistics of harvest and storage facilities.
About the authors:
Dario Stefanelli, Agriculture Victoria, Department of Economic Development, Jobs, Transport and Resources, AgriBio, Australia
Tim Plozza, Agriculture Victoria, Department of Economic Development, Jobs, Transport and Resources, Macleod, Australia
Rebecca Darbyshire, New South Wales Department of Primary Industries, Queanbeyan, Australia
Henryk Flachowsky, Institute for Breeding Research on Fruit Crops, Julius Kuhn-Institut, Federal Research Centre for Cultivated Plants, Germany
Jens Wünsche, University of Hohenheim, Department of Crop Science, Crop Physiology of Specialty Crops, Germany