Implicación en el sector primario

Storage of kiwifruit lines at 20 °C substantially accelerated the softening losses during 21 d (Section 4.3.1.3 and Section 6.4.1.2). GLs demonstrated variability in their softening patterns. In two seasons, some GLs maintained an extended initial lag phase (18 - 21 d) while others did not (Figure 5.4 and Figure 6.3). The initial lag phase of kiwifruit softening has usually been assumed to be influenced by fruit maturity at- harvest (White et al., 2005). For example very immature fruit (very early harvest) may not soften completely to eating firmness of 0.6 - 0.8 kgf until treated with ethylene (Burdon and Lallu, 2011). Such lines raised a question whether the arbitrary chosen 20 °C was the optimum way to accelerate softening losses in AFL (Section 7.3.1). Application of ethylene concentration could be a potential alternative to accelerate the softening of GLs (Section 7.3.2). To represent AFL data, calculation of mean firmness could be avoided by using population distribution descriptors (Section 7.3.3). This section further discusses the options to generate and analyse losses information.

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7.3.1 High temperature

Kiwifruit maturity stage and storage temperature influences the softening rate (Ritenour et al., 1999). Use of 20 °C was chosen in this study due to the ease of application in comparison to other options like gas manipulation. It is possible that both rapid (Figure 5.6B) and very slow declines (Figure 5.6A and Figure 6.3) in firmness observed at 20 °C in some GLs may not reflect softening behaviour in optimal storage. Higher temperature (> 20 °C) can reduce the time differences between prematurely soft and healthy fruit to reach the same final firmness (Davie, 1997). A lower AFL temperature (e.g. 15 °C) would comparatively slow down the softening process (Schotsmans et al., 2008). Alternatively, GLs with extended initial lag phase may require longer time or temperature higher than 20 °C to completely soften. Early harvested fruit may exhibit significant difference in softening patterns when stored at different temperatures, while late harvested fruit may not exhibit substantial differences in softening patterns (Schotsmans et al., 2008). Expression of all softening phases depends upon capacity of fruit to soften in relation to storage conditions (e.g. temperature) and time. Use of high temperature (> 20 °C) could accelerate the softening loss of early to late harvested GLs, including those which do not complete softening curve during AFL monitoring. Meanwhile it may reduce the differences between GLs for slow and rapid softening. Therefore, amount and rate of losses for early to late harvested kiwifruit can be studied at different temperatures to define optimum temperature for AFL establishment.

7.3.2 Ethylene application

Storage of kiwifruit at higher temperature (20 °C) results in initiation of autocatalytic ethylene production and accelerated ripening (Antunes and Sfakiotakis, 2000; Antunes, 2007). The magnitude of ethylene production also depends on the number of very soft, damaged or rotten fruit in a box (Section 7.2.3). Perhaps, rapidly softening GLs may have produced more ethylene than others.

Applied ethylene could potentially accelerate fruit softening during AFL monitoring. The magnitude of acceleration will be a function of concentration, duration of application and temperature. Correct concentration of ethylene perhaps accelerates the softening process (from 2 - 3 weeks to 6 - 7 d) and would provide an opportunity to

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reduce the AFL monitoring duration. Previously, application of 0.5 ppm ethylene reduced the time to reach firmness of 2.6 kgf by 75% at 10 °C (Arpaia et al., 1986). Application of 10 ppm ethylene for 24 h at 20 °C resulted decrease in firmness to 1.36 kgf within 4 - 5 d (Crisosto et al., 1997). At higher temperatures (e.g. > 15 °C), ethylene application can cause kiwifruit to reach their climacteric peak more rapidly (Antunes, 2007) and can result in accelerated softening. However, it seems highly likely that ethylene application would over-ride inherent differences between GLs in softening rate, such as we saw in first attempt of AFL monitoring in 2010 (Section 3.4.3.1). Ethylene application protocol (concentration and duration) in AFL would have to be tested to see if it could accelerate softening losses of kiwifruit while retaining the differences between GLs.

7.3.3 Data manipulation

In this study, mean firmness of 36 fruit in the AFL data was used to follow softening patterns of GLs. However, means of population do not express the within batch fruit variability for firmness. Feng et al. (2003a) reported that substantial variation exists within batches for fruit firmness. Variability between fruit of any batch or population can be because of differences in growth conditions, mineral concentrations, flowering time and physiological maturity (Feng et al., 2003a; Jordan and Loeffen, 2013). While use of mean is effective for locating a population on a particular axis of quality, unfortunately, averages are of limited use in characterising acceptability of an entire population. Depending upon variability among members of a population, it is quite

possible that a significant proportion of a population’s individuals are unacceptable

even if the average seems quite acceptable (Banks, 2003). Two populations with identical average firmness just above the soft threshold of 1 kgf may have different numbers of soft fruit (Adams et al., 2010). Use of averages to compare biological populations is also criticised by De Ketelaere et al. (2003). Moreover, in an industrial scenario where during fruit quality checking, rotten fruit are usually removed from the population, interpretation of batch quality based on mean value would be biased because of variable population sizes at different measurement occasions.

In the first season, along with average firmness, other parameters like maximum firmness value, firmness range, standard deviation (representing variability) and 3rd

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quartile (representing distribution) also showed potential to indicate seasonal life of GLs (Table 3.2 - 3.4). Such parameters are more representative of extremes (e.g. maximum firmness value and range of firmness) in a population. In industry the number of fruit with low firmness (< 1 kgf) in a population directly influences fruit loss leading to higher re-packing cost while achieving export standards (SF > 1 kgf). Perhaps a change to parameters which describe the wider firmness distribution and variability during softening could provide a better insight about the fruit population behaviour and hence a better prediction of GL storability. However, means are the most stable parameter, whereas extremes become less stable to estimate. In addition, population size is also important to estimate variability parameters. Smaller sample size (36 fruit at each measurement) used in later attempts (in 2011 and 2012) of AFL methodology may not truly represent existing extremes and could restrict the potential of using variability parameters to describe population behaviour or pattern in storage. The larger sample size (300 fruit for each measurement occasion) used by industry, as per ISO-2859 regulations, provide an opportunity of using population extremes (e.g. SF) to differentiate batches. However, use of a small sample size could be appropriate for observing average firmness change of kiwifruit GLs during AFL monitoring.