Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Segregation of ‘Hayward’ kiwifruit for storage potential using Vis-NIR spectroscopy(Elsevier BV, 2022-07) Li M; Pullanagari R; Yule I; East AKiwifruit are often harvested unripe and kept in local coolstores for extended periods of time before being marketed. Many pre-harvest factors contribute to variation in fruit quality at harvest and during coolstorage, resulting in the difficulty in segregating fruit for their storage potential. The ability to forecast storage potential, both within and between populations of fruit, could enable segregation systems to be implemented at harvest to assist with inventory decision making and improve profitability. Visible-near infrared (Vis-NIR) spectroscopy is one of the most commonly used non-destructive techniques for estimation of internal quality of kiwifruit. Whilst many previous attempts focused on instantaneous quantification of quality attributes, the objective of this work was to investigate the use of Vis-NIR spectroscopy utilised at harvest to qualitatively forecast storage potential of individual or batches of kiwifruit. Commercially sourced ‘Hayward’ kiwifruit capturing large variability of storability were measured non-destructively at harvest using Vis-NIR spectrometer, and then assessed at 75, 100, 125 and 150 days after coolstorage at 0 °C. Machine learning classification models were developed using at-harvest Vis-NIR spectral data, to segregate storability of kiwifruit into two groups based on the export FF criterion of 9.8 N. The best prediction was obtained for fruit stored at 0 °C for 125 days: approximately 54% of the soft fruit (short storability) and 79% of the good fruit (long storability) could be predicted. Further novelty of this work lies within an independent external validation using data collected from a new season. Kiwifruit were repacked at harvest based on their potential storability predicted by the developed model, with the actual post-storage performance of the same fruit assessed to evaluate model robustness. Segregation between grower lines at harvest achieved 30% reduction in soft fruit after storage. Should the model be applied in the industry to enable sequential marketing, significant costs could be saved because of reduced fruit loss, repacking and condition checking costs.Item The Deviation between Dairy Cow Metabolizable Energy Requirements and Pasture Supply on a Dairy Farm Using Proximal Hyperspectral Sensing(MDPI (Basel, Switzerland), 2021-03-12) Duranovich F; Lopez-Villalobos N; Shadbolt N; Draganova I; Yule I; Morris SThis study aimed at determining the extent to which the deviation of daily total metabolizable energy (MEt) requirements of individual cows from the metabolizable energy (ME) supplied per cow (DME) varied throughout the production season in a pasture-based dairy farm using proximal hyperspectral sensing (PHS). Herd tests, milk production, herbage and feed allocation data were collected during the 2016–2017 and 2017–2018 production seasons at Dairy 1, Massey University, New Zealand. Herbage ME was determined from canopy reflectance acquired using PHS. Orthogonal polynomials were used to model lactation curves for yields of milk, fat, protein and live weights of cows. Daily dietary ME supplied per cow to the herd and ME requirements of cows were calculated using the Agricultural Food and Research Council (AFRC) energy system of 1993. A linear model including the random effects of breed and cow was used to estimate variance components for DME. Daily herd MEt estimated requirements oscillated between a fifth above or below the ME supplied throughout the production seasons. DME was mostly explained by observations made within a cow rather than between cows or breeds. Having daily estimates of individual cow requirements for MEt in addition to ME dietary supply can potentially contribute to achieving a more precise fit between supply and demand for feed in a pasture-based dairy farm by devising feeding strategies aimed at reducing DME.Item The Relative Importance of Herbage Nutritive Value and Climate in Determining Daily Performance per Cow in a Pasture-Based Dairy Farm(MDPI (Basel, Switzerland), 2021-05-14) Duranovich F; Shadbolt N; Draganova I; Lopez-Villalobos N; Yule I; Morris SThe objective of this study was to assess the relative importance of herbage nutritive value (NV), herbage quantity and climate-related factors in determining daily performance per cow in a pasture-based dairy farm. Data on milk production, live weight, body condition score, weather, herbage NV and herbage quantity were regularly collected from August 2016 to April 2017 and from July 2017 to April 2018 at Dairy 1, Massey University, Palmerston North, New Zealand. Data were analyzed using multiple linear regression. Results indicated herbage NV was of higher relative importance in explaining the variation in performance per cow than herbage quantity and climate factors. The relative importance of the interaction between herbage metabolizable energy (ME) and crude protein (CP) on explaining variation in yields of milk, fat and protein was high (0.11 ≤ R2 ≤ 0.15). Herbage ME was of high relative importance in determining milk urea and body condition score, while neutral detergent fiber was a key driver of milk urea and liveweight (0.12 ≤ R2 ≤ 0.16). The quantity of herbage supplied at Dairy 1 might have been high enough to not limit cow performance. Developing feeding strategies aimed at improving the efficiency of cow feeding by exploiting the daily variation in herbage NV to better match supply and demand of nutrients may be useful to improve the overall performance per cow of pasture-based dairy farms.