Browsing by Author "Bretherton M"

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    Dry matter yield, nutritive value and tiller density of tall fescue and perennial ryegrass swards under grazing
    (New Zealand Grassland Association, 31/12/2016) Hendricks S; Donaghy D; Matthew C; Bretherton M; Sneddon N; Cosgrove G; Christensen C; Kaufononga S; Howes J; Osborne M; Taylor P; Hedley M
    Alternative pasture species with the potential to supply quality forage during summer feed shortages, such as tall fescue (TF), are of interest to dairy farmers. A paddock scale study was undertaken to compare performance of TF managed on a shorter rotation similar to perennial ryegrass (RG) (TF-RG) with TF managed on a longer rotation more consistent with its morphology of 4 live leaves/tiller (TF-TF), and with RG (RG-RG). Accumulated dry matter (DM) yields were similar for the three treatments. Patch grazing was observed during the first spring, with more long patches in TFTF than in either TF-RG or RG-RG. Sown-species leaf area index (LAI) was greater in TF-TF compared with TF-RG and RG-RG (2.25, 1.56 and 0.90, respectively; P<0.05). The proportions of grass weeds were higher in the TF-RG (P<0.05) compared with TF-TF and RG-RG treatments (302, 207 and 164 g/kg DM, respectively). A soil fertility gradient with distance along the paddock away from the farm race was recorded, with Olsen P declining at 0.130 mg/kg/m with distance from the farm race. Tiller density, LAI and yield of sown species and total yield sampled were all positively correlated with Olsen P. Overall, this study highlights the importance of managing TF pastures according to its specific growth habits. However, attaining longer grazing rotations under field conditions whilst trying to maintain cow intakes, is likely to continue to prove elusive.
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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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    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 the use of UAV-derived Vegetation Indices and Environmental Variables for Grapevine Water Status Monitoring based on Machine Learning Algorithms and SHAP Analysis
    (1/06/2022) Wei H-E; Grafton MC; Bretherton M; Irwin M; Sandoval E; Horne, D; Singh, R
    Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. The spatial and temporal variation of GWS, is conventionally characterized by using a pressure bomb. This is laborious and time-consuming, which limits the number of samples. Although unmanned aerial vehicles (UAVs) can provide mapping across the entire vineyards efficiently, most commercial UAV-based multispectral sensors do not contain a shortwave infrared band which makes the monitoring of GWS problematic. As GWS is an integrated response to vegetation characteristics, temporal trends, weather conditions, and soil/terrain data, it is assumed that these ancillary variables have the potential to enhance the capability of UAVs for GWS monitoring. The goal of this study is to explore whether and which of these ancillary variables may improve the accuracy of GWS estimation using UAV, and provide insights into the contribution that each selected variable contributes to the variation in GWS. A UAV was flown over two Pinot Noir vineyards in New Zealand to generate aerial images with 4.3 cm resolution, and 18 vegetation indices (VIs) were computed for every sampled grapevine. The strongly correlated VIs were used as the core input for later GWS modeling. Ancillary data included soil/terrain, weather, and temporal variables. Slope and elevation were extracted from a digital elevation model, and apparent electrical conductivity (ECa) was obtained from an EM38 survey. A local weather station provided continuous air temperature, humidity, rainfall, wind speed, and irradiance data, which were computed as variables at weekly and daily intervals. Day of the year (DOY) was used to represent the temporal trend along the growing season. Three machine learning algorithms (elastic net, random forest regression, and support vector regression) were used to regress the predictors against stem water potential (Ψstem), measured by a pressure bomb and used as a proxy for GWS. Shapley Additive exPlanations (SHAP) analysis (a statistical tool that weighs the importance of each variable in a model) was used to assess the relationship between selected variables and Ψstem. The results show that Transformed Chlorophyll Absorption Reflectance Index (TCARI) and Excess Green Index (ExG) are the best correlated VIs, but their correlation with Ψstem is poor (rTCARI = 0.6; rExG = 0.58). The coefficient of determination (R2) of the TCARI-based model increased from 0.35 to 0.7 when DOY and elevation were included as ancillary inputs. R2 of the ExG-based model increased from 0.3 to 0.74 when DOY, elevation, slope, ECa, and daily average windspeed, were included as ancillary inputs. Support vector regression was the best model to describe the relationship between Ψstem and selected predictors. This study has provided proof of the concept of developing GWS estimation models that potentially enhance the monitoring capacities of UAVs for GWS, as well as provide individual GWS mapping at the vineyard scale. This may enable growers to improve irrigation management, leading to controlled vegetative growth and optimized berry quality.
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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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    PLANNING FOR CHANGES IN TOPSOIL C AND N STOCKS–SIGNIFICANCE IN C AND N BUDGETS
    Calvelo Pereira R; Hedley MJ; Bretherton M; Conland N; Tressler A
    New Zealand has a history of rapid land use change as trends in global commodity markets influence primary sector financial sustainability. Traditionally, low sheep and beef returns accelerate extensive pastoral land use change to forest, particularly if supported by afforestation schemes (e.g.AGS and ETS). High dairy payout accelerate forest change to intensive pasture. Current debate around the agricultural sector participating in a carbon(C) economy is spreading in New Zealand, coincident with debate on de-intensification to reduce impacts on water quality. Farms including planted forest lands may be rewarded if they are able to show a decrease in nitrogen (N) loss to water and an increase in the terrestrial sink of C. While soil carbon change is not accounted for in the ETS a change from forest to pasture penalises the landowner for the reduction in biomass C with no reward or penalty for change in soil organic matter C and N. To account for soil carbon change, protocols to measure and monitor topsoil organic C and N storage at the farm level are needed. Evidence for consistent quantifiable change is required to support inclusion of soil organic matter change in both C and N accounting. Previous research in the Taupo (Central North Island) area has shown that conversion of forest land back to productive permanent pasture caused a fast accumulation of soil organic C (6.1 t C/ha/year)and of N (450kgN/ha/year) as a response to fertiliser addition and plant productivity. In this paper we provide a case study of topsoil organic matter change in a forest to pasture conversion in the Taupo region. 42 paddocks from three sites (Tainui, Tauhara and Waimana; Wairakei Estate, Taupo)were monitored in 2017. The paddocks are currently under pasture management after recent (2-11yearsago) conversion from former planted forest. Marked differences in the storage of C (38to51tC/ha15cm) and N (1.8to 3.4 t N /ha15cm; Waimana site)were detected. The relevance of these changes to C and nutrient budgeting are discussed in relation to how such large and important changes can be accounted for.
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    Short-term effects of deep ploughing on soil C stocks following renewal of a dairy pasture in New Zealand
    (14/08/2018) Calvelo Pereira R; Hedley MJ; Hanly J; Bretherton M; Horne D; Bishop P; Beare M; McNally S
    In New Zealand’s high producing permanent pastures the topsoil constitutes a large reservoir of soil organic carbon (SOC), which shows a marked stratification with depth. As consequence, sub-surface layers can contain 10 times less carbon than the surface soil. In permanent pastures with high carbon inputs, the formation and decomposition of these surface SOC stocks are often at equilibrium and C storage shows little change over time. Pastoral based dairy systems utilising ryegrass plus clover cultivars require renewal every 7-10 years to avoid reversion to less productive grasses. This may involve spring cultivation (either no-till, shallow till or full cultivation), summer forage cropping and autumn re-grassing. It has been hypothesised that SOC stocks can be increased by inverting the soil profile at pasture renewal through infrequent (once in 25-30 years) deep mouldboard ploughing (up to 30 cm depth). Increased C sequestration occurs when the new grass quickly rebuilds SOC stocks in the new topsoil (exposed low C sub-soil) at a rate faster than the decomposition of SOC in the rich former topsoil transferred to depth (now below 15 cm). However, benefits form accelerated C storage may be offset if crop and pasture production is adversely affected by the ploughing event (e.g., as result of compaction or excessive drainage). Hence, the aim of this work was to assess the short-term effects of infrequent inversion tillage of long-term New Zealand pastoral-based dairy soils under summer crop management and autumn re-grassing. An imperfectly drained Typic Fragiaqualf under dairy grazing was deep ploughed (approx. 25 cm) and re-sown with turnip in October 2016; other treatments included were shallow (< 10 cm) cultivation and no-till. The site was core sampled (0-40 cm) before cultivation and after 5 months of turnip growth to assess changes in SOC. Plant growth, herbage quality, and nutrient leaching were monitored during the 5-month period; root growth was assessed at the end of the crop rotation. Full cultivation transferred SOC below 10 cm depth, as expected. Soil bulk density decreased whereas root mass increased (10-20 cm depth; P < 0.05) under deep cultivation only. Besides, losses of mineral N were attenuated under deep tillage, resulting in a relative increase in crop yield. The potential for infrequent inversion tillage increasing soil C sequestration as a greenhouse gas (GHG) mitigation tool is currently being tested at other sites in New Zealand.
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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.
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    Towards better irrigation management for vineyards based on machine learning algorithms and SHAP analysis: a case study in New Zealand
    (19/09/2022) Wei H-E; Grafton MC; Bretherton M; Irwin M; Sandoval E
    Monitoring and management of grapevine water status (GWS) over the critical period between flowering and veraison plays a significant role in producing grapes of premium quality. GWS monitoring is critical to vineyard management, and so determines which variables are the main drivers of GWS variation. The goal of this study is to provide viticulturists with an approach to simulate the complex relationship between canopy GWS with vegetation, weather, day of the year (DOY), and soil/terrain variables, along with the interpretation of these relationships. A case study done in Martinborough, New Zealand is used for illustration. A UAV was flown over two Pinot Noir vineyards to generate aerial images with 4.3 cm resolution, and the vegetation index, Transformed Chlorophyll Absorption Reflectance Index (TCARI), was computed for every sampled grapevine. Slope and elevation were extracted from a digital elevation model, and apparent electrical conductivity (ECa) was obtained from an EM38 survey. A local weather station provided continuous air temperature, humidity, rainfall, wind speed, and irradiance data, which were computed as variables at weekly and daily intervals. DOY was used to represent temporal trends along the growing season. Hierarchical clustering and three machine learning algorithms (elastic net, random forest regression, support vector regression) were used to regress predictors against stem water potential (Ψstem), measured by a pressure bomb and used as a proxy for GWS. Shapley Additive exPlanations (SHAP) analysis (a statistical tool that weighs the importance of each variable in a model) was used to interpret the relationship between selected predictors and Ψstem. Our results showed that the coefficient of determination (R2) of the best-performed model reached 0.7 when simulated by support vector regression using TCARI, DOY, and elevation as inputs. This study has provided proof of concept of developing regression models that would be beneficial for grapevine irrigation systems via continuous GWS monitoring, while the identification and clarification of the relationship between statistically dominant variables would assist in decision-making to attain optimal grape quality.