Increasing the planting density of mango orchards appears promising for obtaining higher yields, particularly during the first productive years. However, the challenge is to maintain a good balance between vegetative growth and fruit production in the longer term. The objective of this study was to decipher the effects of planting density, training system and cultivar on young mango trees’ growth and production. The experiment, conducted in North Queensland, consisted of five combinations of planting density and training system applied to the cultivars Keitt, Calypso and NMBP-1243. The planting densities were low (208 tree ha−1), medium (416 tree ha−1) and high (1250 tree ha−1). The closed vase conventional training system was applied at each density. Single leader and espalier on trellis training systems were applied at medium and high densities, respectively. The tree canopy dimensions were measured every 6 months from planting, and tree production was recorded from the third to the fifth years after planting. Vegetative growth and fruit production were the results of complex interactions between planting density, training system, cultivar and/or time. The expected increase in orchard yield with higher planting density was observed from the first productive year, despite lower individual tree production at high planting density. Lower vegetative growth and fruit production at high planting density were probably caused by competition between trees. NMBP-1243 and Keitt showed more rapid vegetative growth. Keitt was the most productive cultivar during the first three productive years. The detailed results of this study provide avenues to further explore the behaviour of mango trees at high planting densities.

This study reports on the wavelength dependence of near infrared (NIR) radiation penetration depth into 'Hass' avocado fruit for both transmission and reflectance modes in the wavelength range from 12,820-4,000 cm-1 (780-2,500 nm). For transmission mode, depths of penetration less than 3 mm occurred in the 5,369 to 7,413 cm-1 (1,862-1,348 nm) range, while the maximum depth of penetration of approximately 12 mm occurred in the region 11,293 to 11,987 cm-1 (885-834 nm). Two other wavelength regions were identified with significant depth of penetration in transmission mode, one around 9,272 cm-1 (1,078 nm) and the second at approximately 7,899 cm-1 (1,265 nm) with approximately 10 and 4.5 mm penetration respectively. In reflectance mode, despite the difference in the optical paths sampled, qualitatively similar trends in wavelength dependence of the depth of penetration were observed, with a lower penetration depth of 3.3 mm occurring around 9,311 cm-1 (1,074 nm) and maximum depth of penetration of approximately 4.8 mm occurring in the 11,625 to 11,987 cm-1 (860-834 nm) region.

Banana production in Australia is under threat from Fusarium wilt Tropical Race 4 (TR4), and the option of growing a disease-resistant variety is sought after by many farmers. Goldfinger is one such alternative; however, it was previously rejected by Australian consumers. In Phase I of this investigation, mutagenesis was employed as a practical method to generate large numbers of novel variants. In Phase II, 631 variants were established in a field trial where agronomic and postharvest assessments, including taste-testing, identified 20 improved selections. Phase III involved a replicated field evaluation of the selections with favourable mutations, where further taste-testing revealed five selections with the most promising eating characteristics. In Phase IV, the five selections underwent large-scale consumer and sensory evaluations where four of them performed as well as the industry standard controls ‘Williams’ Cavendish and Lady Finger. The sensory analysis revealed differences in the fruit flavour profile and textural attributes, and these features will play a key role in bringing a new banana variety to market and for promotion to consumers. Screening of the variants’ resistance to yellow Sigatoka was integrated into the project, but further work is needed to ensure these four selections have retained their TR4 resistance.

Trading partners often require phytosanitary or quarantine treatments for fresh horticultural produce to ensure no economically important pest species are moved with the imported product. When developing such treatments, it is essential that the level of treatment efficacy can be determined. This is often based on the mortality of the total number of target pests exposed to treatment, but in naturally infested products this number is not always known. In such cases, the infestation rate and subsequently an estimate of the number of pests are obtained directly from a set of untreated control samples of the host product. The International Plant Protection Convention (IPPC) Secretariat has provided 2 formulas for these situations that place an interval around the point estimate obtained from the control samples to obtain an estimate of the infestation rate. However, these formulas do not allow a confidence level to be assigned to the estimate, and there are concerns with the assumptions regarding the distribution and the measure of variability used in the formulas. In this article, we propose 2 alternative formulas. We propose that the lower one-sided confidence limit should be applied to all infestation datasets that are approximately normally distributed. As infestation data are sometimes skewed, it is proposed the lower one-sided modified Cox confidence limit is applied to data approximately log-normal distributed. These well-recognized formulas are compared to the formulas recommended by the IPPC and applied to 3 datasets involving natural infestation.

Mango productivity and fruit quality in Australia can be improved through transforming low-density plantings to high-density plantings and intensive training systems. Several planting density and training systems were established in Australia to investigate optimizing light interception and distribution, yield and fruit quality, and to reduce tree vigor and biennial bearing through the manipulation of canopy architecture. In this research, we studied light relations and yield in conventional low-density commercial orchards of different ages and investigated light relations and yield of ‘National Mango Breeding Program NMBP1243’, ‘Keitt’ and ‘Calypso’ grafted to ‘Kensington Pride (KP)’ rootstock in a replicated experiment. Trees were trained to five planting systems: high-density espalier and conventional, medium-density single leader and conventional, and low-density conventional. Our study in commercial orchards showed that maximum yield was ~16,000 kg/ha when light interception reached ~49% and declined at higher levels of light interception. In the high-density intensive training systems, we found that light interception increased with canopy volume, with high-density espalier training systems intercepting more available light compared to medium-density training systems and low-density conventional trees. Yield/ha increased to ~50,000 kg/ha in espalier training systems when light interception was ~40%. Light interception, canopy volume and yield/ha varied between varieties.

Queensland lemons are exported at low (i.e. <3°C) temperature to meet market access protocols. While the fruit generally tolerate these conditions, skin damage consistent with chilling injury (CI) is occasionally encountered and results in significant economic and reputational loss. The robustness of fruit to withstand export handling is inherently variable and difficult to predict. This study evaluated the potential of hyperspectral imaging to detect and predict CI in fruit before it was visible to the human eye. Seeded and seedless Eureka lemons were sourced from a north Queensland orchard. They were treated with either 2.6 or 18.5 parts per million (ppm) ethylene gas at 20.4 or 28.9oC for 5 days to hasten the loss of green skin colour, as per commercial degreening practice. The degreened fruit were stored at 1.9oC for 5 weeks to simulate seafreight to export markets, followed by 10 days at 20oC to assess CI under typical retail display conditions. Spectral data was collected within 2-3 h after harvest, degreening or cold storage. Three near infrared (NIR) devices were used; Bruker Matrix-F for spot assessment; Pika NIR 320 and Pika XC2 for hyperspectral imaging, covering wavelengths from 830-2500 nm, 900-1700 nm and 400-1000 nm, respectively. The supervised classification method of linear discriminate analysis was applied to the spectra collected from the Bruker Matrix-F, whereas partial least squares discriminant analysis (PLS-DA) was used to analyse spectra and images obtained from the Pika XC2 and NIR 320 instruments. The results showed that there is potential for NIR hyperspectral imaging to predict CI in seeded and seedless Eureka lemons, when assessed after degreening and particularly after cold shipment. Spectra collected after degreening and cold storage demonstrated reasonable levels (i.e. 70%) of correct classification. The highest correct classification was obtained for scans taken after storage and is likely due to CI having been induced in the fruit. Spectra collected 2-3 h after harvest using the Bruker Matrix-F device were not analysed due to fruit damage from the technique itself. Future research is needed to explore wavelength selections to refine, verify and develop models against an independent prediction set that combines the two varieties. The maximum level of spatial and/or spectral binning for the hyperspectral images that results in minimal loss of information should also be investigated. Once models are tested and validated successfully at a laboratory level, there would be scope for RD&E investment in a model that could be integrated into handheld or in-line NIR sensors to assist industry in grading out sensitive fruit batches that lack robustness to withstand export.

The age of whole otoliths from barramundi (Lates calcarifer) obtained from the southern Gulf of Carpentaria were estimated using Fourier transform near-infrared (FT-NIR) spectroscopy. Otoliths from 1716 barramundi collected in 2006, 2009 and 2012–2015 were used in this study. Partial least-squares regression models (PLS-R) and multiple linear regression models (MLR) were developed from the diffuse reflectance spectra and the age was obtained from traditional sectioned otoliths. Calibration models were built up over consecutive years (2012–2015) by using a subset of the samples and used to predict the age of the remaining samples and samples from the following year. Results suggest that when seasonal (temporal) variability is incorporated into the calibration model, FT-NIR has the ability to predict barramundi age (validation R2 ranged from 0.73 to 0.78; RMSEP ranged from 6.92 to 7.64 months). The predicted age class was within 1 year of the reference age in over 96% of the samples. These models were also able to predict the age of otoliths from 2006 and 2009, which were retrieved from long-term storage (validation R2 ranged from 0.77 to 0.84; RMSEP ranged from 8.66 to 10.88 months). The results from this study have shown the potential for barramundi from the southern Gulf of Carpentaria to be aged quickly and accurately by using FT-NIR.

The amount of light intercepted by a tree and its distribution within the canopy is critical in optimizing tree photosynthesis efficiency, carbon partitioning and productivity. Here we compare light relations in experimental high-density mango orchard systems with current commercial orchards. A baseline study of current commercial mango orchards showed a maximum light interception of approximately 67% was reached in trees aged between 26-31 years old, with canopy volume of approximately 15,187 m3/ha. Light interception did not significantly increase beyond 67% regardless of increase in canopy volume per hectare or the increase in orchard age. In these conventional orchards, maximum yields were reached approximately 16 t/ha when light interception was around 49% and as light interception increased above 49%, yields declined. In the high density orchard systems, light interception increased with canopy volume, with high density systems intercepting more available light than medium and low density orchard systems. In 5 year old high-density, espalier trained trees with approximately 5000 m3/ha canopy volume, yields reached close to 50 t/ha at 40% light interception. Trees trained as single leader or espalier had more evenly distributed light in the inner canopy compared to conventionally closed vase trained trees. in current commercial orchards, increasing the canopy volume and light interception above the maximums noted above had no benefit to increase economic returns whereas in high density mango orchards with alternate tree training, yields increases more than four times early in the orchards development. Further productivity gains are likely as the experimental high density have yet to fill their allocated space in the orchard.

BACKGROUND:A rapid non-destructive in-line grading system that can rapidly and accurately assess individual avocado fruit for internal quality attributes, including bruises and rots, would allow the avocado industry to provide a more consistent fruit quality to the consumer, optimise market distribution and ensure maximum yield for the producer and retailer. Fourier transform-near-infrared (FT-NIR) spectroscopy was investigated to detect bruises and rot susceptibility as an indication of shelf-life in avocado fruit at both the sprung stage of ripeness and eating ripe fruit.RESULTS:The classification models (principal component linear discriminant analysis, partial least squares discriminant analysis and support vector machine) for each of three growing seasons found hard green fruit that were deliberately bruised could be correctly detected with 70-78% accuracy after 2-5 h following impact damage and with 83-89% accuracy after 24 h. For eating ripe fruit, the accuracy was 60-100% after 2-5 h following impact damage and 66-100% after 24 h. The ability of the classification models to accurately predict rot development into two classes, ≤10% and >10% of flesh affected, ranged from 65% to 84% over the three growing seasons. When the rot classes were defined as ≤30% and >30% the accuracy was 69-77%.CONCLUSIONS:The results of the study highlight the potential of FT-NIR reflectance spectroscopy for application in a commercial, in-line setting for the non-destructive evaluation of impact damage and rot susceptibility of whole avocado fruit. The study indicates that fruit should be held for approximately 24 h prior to scanning to allow bruise development to occur, particularly in hard fruit (i.e., stage 2) prior to bruise assessment. © 2018 Society of Chemical Industry.

Near infrared (NIR) spectroscopy was investigated as a potential rapid method of estimating fish age from whole otoliths of Saddletail snapper (Lutjanus malabaricus). Whole otoliths from 209 Saddletail snapper were extracted and the NIR spectral characteristics were acquired over a spectral range of 800–2780 nm. Partial least-squares models (PLS) were developed from the diffuse reflectance spectra and reference-validated age estimates (based on traditional sectioned otolith increments) to predict age for independent otolith samples. Predictive models developed for a specific season and geographical location performed poorly against a different season and geographical location. However, overall PLS regression statistics for predicting a combined population incorporating both geographic location and season variables were: coefficient of determination (R2) = 0.94, root mean square error of prediction (RMSEP) = 1.54 for age estimation, indicating that Saddletail age could be predicted within 1.5 increment counts. This level of accuracy suggests the method warrants further development for Saddletail snapper and may have potential for other fish species. A rapid method of fish age estimation could have the potential to reduce greatly both costs of time and materials in the assessment and management of commercial fisheries.

In this study, a collection of 24,840 expressed sequence tags (ESTs) generated from five mango (Mangifera indica L.) cDNA libraries was mined for EST-based simple sequence repeat (SSR) markers. Over 1,000 ESTs with SSR motifs were detected from more than 24,000 EST sequences with di- and tri-nucleotide repeat motifs the most abundant. Of these, 25 EST-SSRs in genes involved in plant development, stress response, and fruit color and flavor development pathways were selected, developed into PCR markers and characterized in a population of 32 mango selections including M. indica varieties, and related Mangifera species. Twenty-four of the 25 EST-SSR markers exhibited polymorphisms, identifying a total of 86 alleles with an average of 5.38 alleles per locus, and distinguished between all Mangifera selections. Private alleles were identified for Mangifera species. These newly developed EST-SSR markers enhance the current 11 SSR mango genetic identity panel utilized by the Australian Mango Breeding Program. The current panel has been used to identify progeny and parents for selection and the application of this extended panel will further improve and help to design mango hybridization strategies for increased breeding efficiency.

Assessment of genetic diversity is an essential component in germplasm characterisation and utilisation. In this study the genetic diversity of mango was determined among 254 Mangifera indica L. accessions and related Mangifera species originating from 12 diverse geographic areas using eleven known simple sequence repeat (SSR) markers from mango. A total of 133 alleles were detected, ranging from eight (LMMA12) to 16 (MIAC-5) alleles per locus with a mean value of 12.36 and an average polymorphism information content (PLC) of 0.72. The mean number of alleles (8.45) was highest in the South East Asian accessions (Indonesia/Malesia) and lowest in the accessions from the Philippines (2.55). Diversity analysis divided the accessions into four major nodes broadly representing their geographical origins. The genetic diversity of 'Kensington Pride' was confirmed as being very low and no parents for this cultivar were identified. No association could be established between SSR markers analysed and embryony. Ten synonymous accessions were identified with matching genetic identity with at least one other accession at all SSR loci examined. Twenty-two unique genotypes were identified for 50 trees previously assigned different accession names. The remaining accessions were genetically distinct from each other. This increased understanding of genetic diversity in the Australian National Mango Genebank will assist breeders to better select parents with the potential to contribute desired genes to the progeny and thus more rapidly deliver improved cultivars to industry to meet consumer demand. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

[Extract] Quality and safety evaluation of agricultural products has become an increasingly important consideration in market/commercial viability and systems for such evaluations are now demanded by customers, including distributors and retailers. Unfortunately, most horticultural products struggle with delivering adequate and consistent quality to the consumer. Removing inconsistencies and providing what the consumer expects is a key factor for retaining and expanding both domestic and international markets. Most commercial quality classification systems for fruit and vegetables are based on external features of the product, for example: shape, colour, size, weight and blemishes. However, the external appearance of most fruit is generally not an accurate guide to the internal or eating quality of the fruit. Internal quality of fruit is currently subjectively judged on attributes such as volatiles, firmness, and appearance. Destructive subjective measures such as internal flesh colour, or objective measures such as extraction of juice to measure sweetness (°Brix) or assessment of dry matter (DM) content are also used, although obviously not for every fruit – just a sample to represent the whole consignment.

Avocado fruit maturity and quality characteristics are often variable resulting in variation within a shipment in ripening rates, shelf-life and quality. Inferior fruit quality is seen as one of the key factors impacting on supply chain efficiency and profitability (Margetts 2009). Consumer surveys show that only 30% of Australian’s eat avocados and that they expect to discard one in every four pieces of fruit they purchase because of poor internal quality (Avocados Australia Limited and Primary Business Solutions 2005). Surveys reveal that consumers prefer avocado fruit with at least 25% dry matter (DM) (Harker et al. 2007) and select bruising as the major defect, followed by rots (Harker 2009). Research has shown that if a consumer is dissatisfied with fruit quality then that consumer will not purchase that commodity for another 6 weeks (Embry 2009). To expand domestic and international sales the industry must be able to supply the discerning and demanding consumer with a consistent high quality product. Therefore a rapid non-destructive system that can accurately and rapidly monitor avocado quality attributes would allow the industry to provide better, more consistent eating quality fruit to the consumer, thus improving industry competitiveness and profitability. This paper presents the current research findings of developing a non-invasive near infrared spectroscopy assessment tool which uses optical light for detecting bruises and for predicting both avocado DM content and rot susceptibility as an indication of shelf-life.

BACKGROUND:The inability to consistently guarantee internal quality of horticulture produce is of major importance to the primary producer, marketers and ultimately the consumer. Currently, commercial avocado maturity estimation is based on the destructive assessment of percentage dry matter (%DM), and sometimes percentage oil, both of which are highly correlated with maturity. In this study the utility of Fourier transform (FT) near-infrared spectroscopy (NIRS) was investigated for the first time as a non-invasive technique for estimating %DM of whole intact 'Hass' avocado fruit. Partial least squares regression models were developed from the diffuse reflectance spectra to predict %DM, taking into account effects of intra-seasonal variation and orchard conditions.RESULTS:It was found that combining three harvests (early, mid and late) from a single farm in the major production district of central Queensland yielded a predictive model for %DM with a coefficient of determination for the validation set of 0.76 and a root mean square error of prediction of 1.53% for DM in the range 19.4-34.2%.CONCLUSION:The results of the study indicate the potential of FT-NIRS in diffuse reflectance mode to non-invasively predict %DM of whole 'Hass' avocado fruit. When the FT-NIRS system was assessed on whole avocados, the results compared favourably against data from other NIRS systems identified in the literature that have been used in research applications on avocados.

Most commercial quality classification systems for fruit and vegetables are based on external features of the product, for example: shape, colour, size, weight and blemishes. For avocado fruit, external colour is not a maturity characteristic. Also its smell is too weak, and appears later in its maturity stage.1 Because maturity is a major component of avocado quality and palatability, it is important to harvest mature fruit, so as to ensure that fruit will ripen properly and have acceptable eating quality. Currently, commercial avocado maturity estimation is based on destructive assessment of the percentage of Dry Matter (%DM), and sometimes percent oil, both of which are highly correlated with maturity.2, 3 A rapid and non-destructive system that can accurately and rapidly monitor internal quality attributes would allow the avocado industry to provide better, more consistent eating quality fruit to the consumer, and thus improve industry competitiveness and profitability. The aim of this study was to assess the potential of FT-NIR diffuse reflectance spectroscopy as an objective non-invasive method to determine Hass avocado maturity and thereby eating quality, based on %DM, and its ability to predict over several growing seasons.