Dayenu Ltda.

San Fernando, Chile

Dayenu Ltda.

San Fernando, Chile

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Martinez Vega M.V.,Copenhagen University | Wulfsohn D.,Dayenu Ltda. | Clemmensen L.H.,Technical University of Denmark | Toldam-Andersen T.B.,Copenhagen University
Scientia Horticulturae | Year: 2013

We report on the performance of a novel sampling method for determining fruit quality variability and yield from an orchard, which focus on its applicability for the fruit industry. We used the 'fractionator' tree sampling method to investigate the quality variability of a small, representative sample of 'Granny Smith' (Malus x domestica cv. 'Granny Smith') apples obtained from a 17ha orchard based on a final sample of 74 fruit. Estimates of fruit marketable yield and fruit size distribution agreed well with packing house records. The estimated marketable yield was 356.6±89.2t compared to 374.9t of fruit packed for export. Distributions of starch (S), soluble solids content (SSC) and flesh firmness (F) were also estimated from the sample. The distribution of starch (S) and fruit mass (M) showed high variability (CVS=SD/mean=0.32 and CVM=0.23), whereas SSC and flesh firmness showed moderate variability (CVSSC=0.11 and CVF=0.10). The average within-tree variabilities were estimated as CVM=0.04, CVSSC=0.10, CVS=0.15 and CVF=0.07. Between-tree variabilities were similar to the within-tree variabilities, except for starch (CVtM=0.04, CVtSSC=0.13, CVtS=0.29 and CVtF=0.09). From the quality characteristics studied only fruit mass could be significantly related to position of the fruit in the canopy, represented by height of the fruit above ground, the fruit position along the branch and position relative to the tree row orientation in the orchard. Variations in starch, SSC and flesh firmness could not be explained by position of the fruit in the canopy. The methods used in this paper are proposed as tools for studies aimed at understanding sources of quality variability as well as for management purposes. Further research is needed to determine recommended sample sizes to accurately describe the distribution of various quality variables of apples at the orchard scale. © 2013 Elsevier B.V.


Martinez Vega M.V.,Copenhagen University | Sharifzadeh S.,Technical University of Denmark | Wulfsohn D.,Dayenu Ltda | Skov T.,Copenhagen University | And 2 more authors.
Journal of the Science of Food and Agriculture | Year: 2013

BACKGROUND: Visible-near infrared spectroscopy remains a method of increasing interest as a fast alternative for the evaluation of fruit quality. The success of the method is assumed to be achieved by using large sets of samples to produce robust calibration models. In this study we used representative samples of an early and a late season apple cultivar to evaluate model robustness (in terms of prediction ability and error) on the soluble solids content (SSC) and acidity prediction, in the wavelength range 400-1100nm. RESULTS: A total of 196 middle-early season and 219 late season apples (Malus domestica Borkh.) cvs 'Aroma' and 'Holsteiner Cox' samples were used to construct spectral models for SSC and acidity. Partial least squares (PLS), ridge regression (RR) and elastic net (EN) models were used to build prediction models. Furthermore, we compared three sub-sample arrangements for forming training and test sets ('smooth fractionator', by date of measurement after harvest and random). Using the 'smooth fractionator' sampling method, fewer spectral bands (26) and elastic net resulted in improved performance for SSC models of 'Aroma' apples, with a coefficient of variation CVSSC = 13%. The model showed consistently low errors and bias (PLS/EN: R2 cal=0.60/0.60; SEC = 0.88/0.88°Brix; Biascal=0.00/0.00; R2 val=0.33/0.44; SEP = 1.14/1.03; Biasval=0.04/0.03). However, the prediction acidity and for SSC (CV = 5%) of the late cultivar 'Holsteiner Cox' produced inferior results as compared with 'Aroma'. CONCLUSION: It was possible to construct local SSC and acidity calibration models for early season apple cultivars with CVs of SSC and acidity around 10%. The overall model performance of these data sets also depend on the proper selection of training and test sets. The 'smooth fractionator' protocol provided an objective method for obtaining training and test sets that capture the existing variability of the fruit samples for construction of visible-NIR prediction models. The implication is that by using such 'efficient' sampling methods for obtaining an initial sample of fruit that represents the variability of the population and for sub-sampling to form training and test sets it should be possible to use relatively small sample sizes to develop spectral predictions of fruit quality. Using feature selection and elastic net appears to improve the SSC model performance in terms of R2, RMSECV and RMSEP for 'Aroma' apples. © 2013 Society of Chemical Industry.


Zamora F.A.,Dayenu Ltda. | Tellez C.P.,Dayenu Ltda. | Wulfsohn D.,Copenhagen University | Zamora I.,Dayenu Ltda. | Garcia-Finana M.,University of Liverpool
American Society of Agricultural and Biological Engineers Annual International Meeting 2010, ASABE 2010 | Year: 2010

Early estimation of expected fruit tree yield is important for the market planning and for growers and exporters to plan for labour and boxes. Large variations in tree yield may be found, posing a challenge for accurate yield estimation. We evaluated a multilevel systematic sampling procedure for fruit yield estimation. In the Spring of 2009 we estimated the total number of fruit in several rows in each of 14 commercial fruit orchards growing apple, kiwi, and table grapes in central Chile. Survey times were 10-100 minutes for apples, 85 minutes for table grapes, and up to 150 minutes for kiwis. At harvest in the Fall, the fruit were counted to obtain the true yield. Yields ranged from lows of several thousand (grape bunches), to highs of more than 40 thousand fruit (apples, kiwis). In 11 orchards, true errors less than 10% were obtained. In two highly variable orchards we obtained absolute true errors of about 20%. An analysis based on systematic sub-sampling of sample data across each sampling stage was used to determine how to distribute sampling effort to acheive the desired precision.


Wulfsohn D.,Dayenu Ltda | Wulfsohn D.,Copenhagen University | Zamora F.A.,Dayenu Ltda | Tellez C.P.,Dayenu Ltda | And 2 more authors.
Precision Agriculture | Year: 2012

Early forecasting of fruit orchard yield is important for market planning and for growers and exporters to plan labour, bins, storage and purchase of packing materials. Large variations in tree yield pose a challenge for accurate yield estimation. We evaluated a three-level systematic sampling procedure for unbiased estimation of fruit number for yield forecasts. In the Spring of 2009 we estimated the total number of fruit in several rows of each of 14 commercial fruit orchards growing apple (11 groves), kiwifruit (two groves), and table grapes (one grove) in central Chile. Survey times were 10-100 min for apples (depending on vigour), 85 min for the table grapes, and 85 and 150 min for the kiwifruit. During harvest in the Fall, the fruit were counted to obtain the true number. Yields ranged from lows of several thousand (grape bunches), to highs of more than 40 000 fruit (apples, kiwifruit). Absolute true errors (defined as the absolute difference between the estimate and the true value, divided by the true value) were less than 5% in six orchards, between 5 and 10% in a further five orchards and 13% in one orchard. In two apple orchards we obtained absolute true errors of about 20%. Error analysis based on systematic sub-sampling across each sampling stage was used to determine how to distribute sampling effort to achieve a total coefficient of error of 10%. We discuss the extension of the procedure for yield estimation at the full orchard scale for any target precision. © 2011 Springer Science+Business Media, LLC.

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