Browsing by Author "Yule I"

Now showing 1 - 6 of 6
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    A review of practices in precision application of granular fertilisers
    Grafton MCE; Yule I; Manning M; Nelson, W
    There is an implicit assumption in cited literature on precision agriculture (PA) that spreading of fertiliser is performed perfectly in the field leading to uniform application, this is not true. Variation can be large and often the actual performance of spreading equipment used has never been measured or verified. In various countries around the world there are quality assurance (QA) systems designed to achieve a prescribed level of performance. Even within these QA schemes limited testing is undertaken and always under perfect or near perfect conditions. The test methods are designed to establish an acceptable bout width which meets an acceptable evenness of spread if driven accurately. The test does not take into account wind conditions (except for requiring less than 15kmhr-1 for testing), humidity, slope, terrain or the instrumentation to maintain the desired bout width. This paper examines the effect of the farm environment and the physical characteristics of fertilisers on the spread patterns of fertilisers in the field. Fertilisers with heterogeneous particle size distributions proved to have more robust spread patterns under field conditions than those with homogeneous particle size distributions.
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    Integrating soil moisture measurements into pasture growth forecasting in New Zealand’s hill country
    Hajdu I; Yule I; Bretherton M; Singh R; Grafton M; Nelson, W
    Forecasting pasture growth in hill country landscapes requires information about soil water retention characteristics, which will help to quantify both water uptake, and its percolation below the root zone. Despite the importance of soil moisture data in pasture productivity predictions, current models use low-resolution estimates of water input into their soil water balance equations and plant growth simulations. As a result, they frequently fail to capture the spatial and temporal variability of soil moisture in hill country soils. Wireless Sensor Networks (WSN) are promising in-situ measurement systems for monitoring soil moisture dynamics with high temporal resolution in agricultural soils. This paper presents the deployment of a soil moisture sensing network, utilising WSN technology and multi-sensor probes, to monitor soil water changes over a hill country farm in the northern Wairarapa region of the North Island. Processed capacitance-based raw data was converted to volumetric water content by means of a factory calibration function to assess sensor accuracy and to calculate soil water storage within the pasture root zone. The derived volumetric soil moisture data was examined in terms of its dependence on the variability and influences of hill country landscape characteristics such as aspect. The integration of spatially distributed sensors and multi-depth soil moisture measurements from various hillslope positions showed that slope and aspect exerted a significant impact on soil moisture values. Furthermore, considerable differences were identified in soil water profile responses to significant rainfall events and subsequent soil water redistribution. Initial indications are that high-resolution time series of accurate multi-depth soil moisture measurements collected by a WSN are valuable for investigating root zone water movement. Sensor evaluation and data analysis suggest that these devices and their associated datasets are able to contribute to an improved understanding of drying and wetting cycles and soil moisture variability. Potentially, this will create an opportunity to generate improved pasture growth predictions in pastoral hill country environments.
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    Segregation of ‘Hayward’ kiwifruit for storage potential using Vis-NIR spectroscopy
    (Elsevier BV, 2022-07) Li M; Pullanagari R; Yule I; East A
    Kiwifruit 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.
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    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 S
    This 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.
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    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 S
    The 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.