Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Greenhouse gas mitigation in pasture-based dairy production systems in New Zealand: A review of mitigation options and their interactions(Elsevier B.V., 2025-08) Kalehe Kankanamge E; Ramilan T; Tozer PR; de Klein C; Romera A; Pieralli SReducing greenhouse gas (GHG) emissions from dairy farming is crucial for mitigating climate change and enhancing the environmental credentials of New Zealand's dairy exports. This paper aims to explore potential GHG mitigation measures and their interactive effects when combined within New Zealand context, emphasising the practicality of these combinations, particularly focusing on recent studies of pasture-based dairy systems. The review assesses various mitigation options across animal, manure management, feed-based, soil-related, and system-related interventions and identifies immediately applicable mitigation options based on specific criteria. It also discusses the implementation costs, implications on emissions, and the combined effects of these options when applied as bundles in pasture-based systems using a combination matrix. It is indicated that mitigation options on New Zealand's dairy farms can yield diverse outcomes and costs based on farming characteristics. By analysing different combinations of short-listed, it was found that although most mitigation options are compatible, some may have a lower overall reduction potential because of interaction effects. Integrating lower N fertiliser use, low-emission feed, and reduced stocking rates with high-performing animals provides a practical approach for GHG reductions and potential cost savings. However, implementing compatible mitigation bundles requires better quantification of their interactions, economic viability, and compatibility with existing farming systems which need further research.Item Plantain-mixed pasture collected in different climatic seasons produced less methane and ammonia than ryegrass–white clover pasture in vitro(CSIRO Publishing, 2025-06-23) Sivanandarajah K; Donaghy D; Molano G; Horne D; Kemp P; Navarrete S; Ramilan T; Pacheco D; Jonker AContext Plantain (PL) is recognised for reducing nitrate leaching and nitrous oxide emissions in pastoral systems. Evidence has shown that cows fed pure PL produced less methane (CH4) than cows fed ryegrass. However, it is unclear if the CH4 reduction can be achieved with PL in mixed pasture. Aim The study evaluated the in vitro rumen fermentation profiles of ryegrass–white clover (RWC) and medium-level PL (PLM, containing ~40% PL) pasture collected during different climatic seasons, to determine whether this inclusion level influences CH4 and rumen ammonia (NH3) production. Methods Substrates were selected from samples with various proportions of PL. Samples were categorised into three climatic seasons (i.e. spring, summer and autumn) and two pasture types (PLM and RWC). Representative samples for these scenarios were tested in an automated in vitro rumen batch culture system for gas, CH4 (mL/g DM) and NH3 (mM/g DM) production. Key results In summer samples, PLM produced approximately 8%, 14% and 19% less CH4 at 12 h, 24 h and potential CH4 production (PCH4), respectively. Although gas production (GP) was similar at 12 and 24 h, PLM had 13% lower potential GP than RWC (P < 0.05). In spring samples, PLM had approximately 11% greater GP and CH4 production at 12 h. For the autumn samples, GP and CH4 production were similar between PLM and RWC (P > 0.05). Net NH3 production from PLM substrates was significantly lower in spring (27%) and autumn (17%) samples, with no differences in summer, despite higher crude protein levels in the selected PLM. Conclusions Compared with RWC, PLM changed rumen fermentation parameters that could translate to potential environmental benefits: PLM produced less net NH3 in spring and autumn samples (27% and 17%, respectively), and up to 19% less CH4 production in summer samples. Implications Incorporating ~40% PL into RWC pasture showed a promising reduction of CH4 emissions and nitrogen losses in vitro. If the in vitro results translate to cows grazing pasture, this could offer greater environmental benefits with minimal input costs. In vitro results suggest that PLM’s potential to mitigate CH4 emissions can be influenced by seasonal variations in pasture quality compared with RWC. However, further animal studies are needed to fully comprehend the CH4 mitigation potential of this forage.Item Effects of Plantain (Plantago lanceolata L.) Metabolites Aucubin, Acteoside, and Catalpol on Methane Emissions In Vitro(American Chemical Society, 2025-05-21) Sivanandarajah K; Donaghy D; Kemp P; Navarrete S; Horne D; Ramilan T; Molano G; Pacheco DPlantain (PL) contains plant secondary metabolites (PSM), such as acteoside, aucubin, and catalpol, known for their bioactive properties. While acteoside and aucubin have been linked to reducing nitrogen losses in grazed pastures, their effects on enteric methane (CH4) emissions remain unexplored. Three in vitro batch culture experiments were conducted to assess the effects of PSM on rumen fermentation, using PL pastures with varying PSM concentrations, purified PSM compounds, and/or their combinations added to ryegrass (Lolium perenne, RG), which does not contain these PSM. Aucubin addition to RG extended the time to reach halftime for gas production (GP) and CH4 by 15-20% due to its antimicrobial effects. Acteoside, alone or with aucubin, promoted propionate production, an alternative hydrogen sink, which reduced the acetate to propionate ratio, increased GP by up to 13%, and decreased CH4 proportion in gas by 5-15%. Aucubin reduced ruminal net ammonia (NH3) production by up to 46%, with a similar reduction observed when combined with acteoside. This study highlights the potential of PSM to mitigate CH4 emissions and reduce nitrogen losses from dairy cows, warranting in vivo evaluation of PSM and targeted breeding of PL pastures with increased PSM content.Item Synthetic hyperspectral reflectance data augmentation by generative adversarial network to enhance grape maturity determination(Elsevier B V, 2025-08) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval ENon-destructive and rapid grape maturity detection is important for the wine industry. The ongoing development of hyperspectral imaging techniques and deep learning methods has greatly helped in non-destructive assessing of grape quality and maturity, but the performance of deep learning methods depends on the volume and the quality of labeled data for training. Building non-destructive grape quality or maturity testing datasets requires damaging grapes for chemical analysis to produce labels which are time consuming and resource intensive. To solve this problem, this study proposed a conditional Wasserstain Generative Adversarial Network (WGAN) with the gradient penalty data augmentation technique to generate synthetic hyperspectral reflectance data of two grape maturity categories (ripe and unripe) and different Total Soluble Solids (TSS) values. The conditional WGAN with the gradient penalty was trained for a range of epochs: 500, 1000, 2000, 8000, 10,000, and 20,000. After training of 10,000 epochs, synthetic hyperspectral reflectance data were very similar to real spectra for each maturity category and different TSS values. Thereafter, contextual deep three-dimensional CNN (3D-CNN), Spatial Residual Network (SSRN) and Support Vector Machine (SVM) are trained on original training and syn- thetic + original training datasets to classify grape maturity. The synthetic hyperspectral reflectance data, incrementally added to the original training set in steps of 250, 500, 1000, 1500, and 2000 samples, consistently resulted in higher model performance compared to training solely on the original dataset. The best results were achieved by augmenting the training dataset with 2000 synthetic samples and training with a 3D-CNN, yielding a classification accuracy of 91 % on the testing set. To better assess the effectiveness of GAN-based data augmentation methods, two widely used regression models: Partial Least Squares Regression (PLSR) and one-dimensional CNN (1D-CNN) were used based on same data augmentation method. The best result was achieved by adding 250 synthetic samples to the original training set when training 1D-CNN model, yielding an R2 of 0.78, RMSE of 0.63 ◦Brix, and RPIQ of 3.36 on the testing set. This study indicated that deep learning models combined with conditional WGAN with the gradient penalty data augmentation technique had a good application prospect in the grape maturity assessment.Item Hyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture(MDPI AG, 2025-03-21) Cushnahan T; Grafton M; Pearson D; Ramilan T; Hasenauer HItem Hyperspectral Data Can Classify Plant Functional Groups Within New Zealand Hill Farm Pasture(MDPI AG, 2025-03-21) Cushnahan T; Grafton M; Pearson D; Ramilan T; Hasenauer HReliable evidence of species composition or habitat distribution is essential to advance pasture management and decision making, including the definition of fertiliser rates for aerial top dressing. This is more difficult in a diverse environment such as New Zealand hill country farms. The simplification of the landscape character using plant functional types and species dominance has proven useful in ecological studies and in modelling grasslands. This study used hyperspectral imagery to map hill country pasture into plant functional groups (PFGs) as a proxy for pasture quality. We validated a farm scale map generated using support vector machines (SVMs), with ground reference data, to an overall accuracy of 88.75%. We discuss how that information can improve on-farm decision making and allow for better coordination with off-farm consultants. This form of farm-wide mapping is also critical for the successful application of variable-rate aerial topdressing technology as input for the allocation of fertiliser rates.Item Non-destructive and on-site estimation of grape total soluble solids by field spectroscopy and stack ensemble learning(Elsevier, 2025-02-20) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval EItem Enhancing climate resilience in northern Ghana: A stochastic dominance analysis of risk-efficient climate-smart technologies for smallholder farmers(Elsevier B.V., 2024-07-17) Ahiamadia D; Ramilan T; Tozer PRNorthern Ghana is a semi-arid region characterised by a unimodal rainfall pattern, and hot and dry weather conditions. Heavy reliance on rain-fed agriculture and the lack of resources for irrigation, makes smallholder farmers in the region increasingly vulnerable to climate-related crop failures. In recent years, climate-smart technologies (CSTs) such as changing planting dates (PD), compartmental bunding (CB), mulching (M), and transplanting (TP) have been recommended to minimise yield losses. However, there is limited information on the most risk-efficient CSTs for crops cultivated in the region. This study used a stochastic dominance approach to identify the most risk-efficient CSTs for maize, rice, and sorghum. The stochastic modelling process employed the Aqua-crop model to simulate climate-related yield variability using Ghana climate data, and gross margin variability with crop budgets from literature sources. From the study's findings, changing planting date from April to May was the most risk-efficient choice for maize and sorghum under farmers' and recommended practices. In contrast, transplanting was the most risk-efficient technology for rice farming in the study area. The study also highlights the importance of considering the risk-averse nature of smallholder farmers when selecting CSTs. By identifying the most risk-efficient CSTs, the study can help improve the resilience of smallholder farmers. These findings have important implications for the development and adoption of CSTs in northern Ghana.Item Assessing the Leaf Blade Nutrient Status of Pinot Noir Using Hyperspectral Reflectance and Machine Learning Models(MDPI (Basel, Switzerland), 2023-03-08) Lyu H; Grafton M; Ramilan T; Irwin M; Sandoval E; Díaz-Varela RAMonitoring grape nutrient status, from flowering to veraison, is important for viticulturists when implementing vineyard management strategies, in order to produce quality wines. However, traditional methods for measuring nutrient elements incur high labour costs. The aim of this study is to explore the potential of predicting grapevine leaf blade nutrient concentration based on hyperspectral data. Leaf blades were collected at two Pinot Noir commercial vineyards at Martinborough, New Zealand. The leaf blade spectral data were obtained with a handheld spectroradiometer, to evaluate surface reflectance and derivative spectra in the spectrum range between 400 and 2400 nm. Afterwards, leaf blades nutrient concentrations (N, P, K, Ca, and Mg) were measured, and their relationships with the hyperspectral data were modelled by machine learning models; partial least squares regression (PLSR), random forest regression (RFR), and support vector regression (SVR) were used. Pearson correlation and recursive feature elimination, based on cross-validation, were used as feature selection methods for RFR and SVR, to improve the model’s performance. The variable importance score of PLSR, and permutation variable importance of RFR and SVR, were used to determine the most sensitive wavelengths, or spectral regions related to each biochemical variable. The results showed that the best predictive performance for leaf blade N concentration was based on PLSR to raw reflectance data (R2 = 0.66; RMSE = 0.15%). The combination of support vector regression with the Pearson correlation selected method and second derivative reflectance provided a high accuracy for K and Ca modelling (R2 = 0.7; RMSE = 0.06%; R2 = 0.62; RMSE = 0.11%, respectively). However, the modelling performance for P and Mg, by different feature groups and variable selection methods, was poor (R2 = 0.15; RMSE = 0.02%; R2 = 0.43; RMSE = 0.43%, respectively). Thus, a larger dataset is needed for improving the prediction of P and Mg. The results indicated that for Pinot Noir leaf blades, raw reflectance data had potential for the prediction of N concentration, while the second-derivative spectra were more suitable to predict K and Ca. This study led to the provision of rapid and non-destructive measurements of grapevine leaf nutrient status.Item Using Remote and Proximal Sensing Data and Vine Vigor Parameters for Non-Destructive and Rapid Prediction of Grape Quality(MDPI (Basel, Switzerland), 2023-11-19) Lyu H; Grafton M; Ramilan T; Irwin M; Wei H-E; Sandoval E; Zhang C; Liu DThe traditional method for determining wine grape total soluble solid (TSS) is destructive laboratory analysis, which is time consuming and expensive. In this study, we explore the potential of using different predictor variables from various advanced techniques to predict the grape TSS in a non-destructive and rapid way. Calculating Pearson’s correlation coefficient between the vegetation indices (VIs) obtained from UAV multispectral imagery and grape TSS resulted in a strong correlation between OSAVI and grape TSS with a coefficient of 0.64. Additionally, seven machine learning models including ridge regression and lasso regression, k-Nearest neighbor (KNN), support vector regression (SVR), random forest regression (RFR), extreme gradient boosting (XGBoost), and artificial neural network (ANN) are used to build the prediction models. The predictor variables include the unmanned aerial vehicles (UAV) derived VIs, and other ancillary variables including normalized difference vegetation index (NDVI_proximal) and soil electrical conductivity (ECa) measured by proximal sensors, elevation, slope, trunk circumference, and day of the year for each sampling date. When using 23 VIs and other ancillary variables as input variables, the results show that ensemble learning models (RFR, and XGBoost) outperform other regression models when predicting grape TSS, with the average of root mean square error (RMSE) of 1.19 and 1.2 °Brix, and coefficient of determination (R2) of 0.52 and 0.52, respectively, during the 20 times testing process. In addition, this study examines the prediction performance of using optimized soil adjusted vegetation index (OSAVI) or normalized green-blue difference index (NGBDI) as the main input for different machine learning models with other ancillary variables. When using OSAVI-based models, the best prediction model is RFR with an average R2 of 0.51 and RMSE of 1.19 °Brix, respectively. For NGBDI-based model, the RFR model showed the best average result of predicting TSS were a R2 of 0.54 and a RMSE of 1.16 °Brix, respectively. The approach proposed in this study provides an opportunity to grape growers to estimate the whole vineyard grape TSS in a non-destructive way.
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