Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Evaluation of the use of two-stage calibrated PlanetScope images and environmental variables for the development of the grapevine water status prediction model
    (24/05/2023) Wei H-E; Grafton M; Bretherton M; Irwin M; Sandoval E
    Abstract Grapevine water status (GWS) assessment between flowering and veraison plays an important role in viticulture management in terms of producing high-quality grapes. Although satellites and uncrewed aerial vehicles (UAV) have successfully monitored GWS, these platforms are practically limited because data transfer is delayed due to post processing and UAV operation is weather dependent. This study focuses on addressing two issues: the unreliability of GWS estimation using satellite images with low-moderate spatial resolution and the inaccessibility of real-time satellite data. It aims to predict the temporal variation of GWS based on a prediction model using spectral information (calibrated PlanetScope (PS) images), soil/topography data (apparent electrical conductivity, elevation, slope), weather parameters (rainfall and potential evapotranspiration), cultivation practices (irrigation, fertigation, plucking, and trimming), and seasonality (day of the year) as predictors. Stem water potential (Ψstem) was used as a proxy for GWS. Two-stage calibration, including an initial calibration of UAV images with measured Ψstem and a subsequent calibration of satellite images with calibrated UAV data, was applied to calibrate the PS images. Three machine learning models (random forest regression, support vector regression, and multilayer perceptron) were used in the calibration and modeling process. The results showed that a two-stage calibration can generate reliable reference data, with a root mean square error of 113 kPa and 59 kPa on the test sets during the first and second calibration stage, respectively. The prediction model described the temporal variation of block Ψstem when compared with the measured Ψstem. In the similarity analysis, the Pearson correlation coefficient was 0.89 and 0.87 between predicted and reference Ψstem maps across four dates for the two study vineyards. This study supports the concept of developing an approach to predict grapevine Ψstem, which would enable growers to acquire Ψstem variation in advance during the growing season, leading to improved irrigation scheduling and optimal grape quality.
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    Evaluation of Point Hyperspectral Reflectance and Multivariate Regression Models for Grapevine Water Status Estimation
    (MDPI AG, 12/08/2021) Wei H-E; Grafton M; Bretherton M; Irwin M; Sandoval E
    Monitoring and management of plant water status over the critical period between flower-ing and veraison, plays a significant role in producing grapes of premium quality. Hyperspectral spectroscopy has been widely studied in precision farming, including for the prediction of grapevine water status. However, these studies were presented based on various combinations of transformed spectral data, feature selection methods, and regression models. To evaluate the performance of different modeling pipelines for estimating grapevine water status, a study spanning the critical period was carried out in two commercial vineyards at Martinborough, New Zealand. The modeling used six hyperspectral data groups (raw reflectance, first derivative reflectance, second derivative reflectance, continuum removal variables, simple ratio indices, and vegetation indices), two variable selection methods (Spearman correlation and recursive feature elimination based on cross-validation), an ensemble of selected variables, and three regression models (partial least squares regression, random forest regression, and support vector regression). Stem water potential (used as a proxy for vine water status) was measured by a pressure bomb. Hyperspectral reflectance was undertaken by a handheld spectroradiometer. The results show that the best predictive performance was achieved by applying partial least squares regression to simple ratio indices (R2 = 0.85; RMSE = 110 kPa). Models trained with an ensemble of selected variables comprising multicombination of transformed data and variable selection approaches outperformed those fitted using single combinations. Although larger data sizes are needed for further testing, this study compares 38 modeling pipelines and presents the best combination of procedures for estimating vine water status. This may lead to the provision of rapid estimation of vine water status in a nondestructive manner and highlights the possibility of applying hyperspectral data to precision irrigation in vineyards.
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    The importance of incorporating geology, soil, and landscape knowledge in freshwater farm planning in Aotearoa New Zealand
    (2/09/2022) Bretherton M; Burkitt L
    Over half of Aotearoa New Zealand’s (NZ’s) land area is under agriculture or forestry production. Long term monitoring has shown declines in freshwater quality in regions with the most intensive agriculture. The New Zealand government has historically focused on reducing the impact of agriculture on water quality through its Resource Management Act 1991. Lack of improvement in freshwater quality has resulted in the 2020 Essential Freshwater package of reforms which will require all pastoral farms >20 ha in size and all arable farms > 5 ha in size to develop a Freshwater Farm Plan (FFP) by a certified Freshwater Farm Planner. As far as we are aware, New Zealand is the first country in the world to mandate compulsory FFPs. This paper describes the key geological, soil, and landscape factors that need to be considered in an FFP for it to be successful in meeting the 2020 Essential Freshwater objectives. We argue that a greater emphasis should be placed on understanding a farm’s natural resources, as they provide the physical interface between the farming system and both the freshwater and atmospheric ecosystems. Documenting our learning in this area could assist other countries considering Freshwater Farm Planning as a strategy to reduce the impact of agriculture on freshwater quality.