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Physicochemical characteristics, techno-functionalities, and amino acid profile of Prionoplus reticularis (Huhu) larvae and pupae protein extracts
Get PDFThe amino acid profile, techno-functionalities (foaming stability/capacity, emulsion stability/capacity, solubility, and coagulation), and physicochemical characteristics (colour, particle size, surface hydrophobicity, Fourier-transform infrared spectroscopy, and differential scanning calorimetry) of protein extracts (PE) obtained from Prionoplus reticularis (Huhu grub) larvae (HLPE) and pupae (HPPE) were investigated. Total essential amino acid contents of 386.7 and 411.7 mg/g protein were observed in HLPE and HPPE, respectively. The essential amino acid index (EAAI) was 3.3 and 3.4 for HLPE and HPPE, respectively, demonstrating their nutritional equivalence. A unique nitrogen-to-protein conversion constant, k, and the corresponding protein content of the extracts were 6.1 and 6.4 and 72.1% and 76.5%, respectively. HLPE (37.1 J/g) had a lower enthalpy than HPPE (54.1 J/g). HPPE (1% w/v) exhibited a foaming capacity of 50.7%, which was higher than that of HLPE (41.7%) at 150 min. The foaming stability was 75.3% for HLPE and 73.1% for HPPE after 120 min. Both protein extracts (1% w/v) had emulsifying capacities that were 96.8% stable after 60 min. Therefore, protein extracts from Huhu larvae and pupae are of a good nutritional quality (based on their EAAI) and have techno-functional properties, such as foaming and emulsification, that afford them potential for certain food technology applications
Time to bucket up!
No full textBucket up! Can large private properties provide substantial coastal city flood mitigation under climate change?
Protecting coastal cities from flooding under climate change is challenging. Conventional approaches like walls and dykes are inadequate and there is risk of under or over investment. Green stormwater infrastructure can help, but public land is often missing. Small buckets on private land may not be enough. We need a more strategic approach for larger buckets to protect communities. Can industrial lands provide a better solution? We assessed the potential of large industrial parcels in Christchurch for flood mitigation under different climate change projections. Industrial parcels could provide substantial flood mitigation within three of six study catchments, reducing the severity of climate change-induced increased runoff volume to current flood protection level under any climate scenario in the immediate future (2030-2050). Industrial land in two of these catchments could also reduce runoff to this level in the distant future (2080-2100), and under larger storm events, though not for all climate scenarios.
Finding the right bucket in the right place and at the right time: A novel methodology for developing adaptive flood mitigation strategies with climate change
Traditional flood prevention is difficult to plan for one-time investment due to high uncertainty of climate change projections in the long term. Networks of Green stormwater infrastructure (GSI), in contrast, can be designed and rolled out when needed as climate change impacts increase. Although GSI on large developed land can provide substantial flood mitigation, parcels have different flood mitigation capabilities over time. However, an effective methodology to evaluate and compare them is missing. Here, we present the Hydrology-based Land Capability Assessment and Classification (HLCA+C) methodology which builds on the strengths of existing methodologies. It was applied to a Christchurch catchment, leading to the identification of an adaptive GSI flood mitigation network for the next 80 years that can mitigate flooding almost as effectively as the major climate change scenario
Meat me for lunch: Consumer attitudes to alternative proteins
No full textPresentation at the CETA Workshop held on 3 February 2023
Does institutional quality affect the impact of public agricultural spending on food security in Sub-Saharan Africa and Asia?
No full textDue to declining or stagnating agricultural productivity and rising food insecurity, African countries agreed to dedicate ten percent of public spending to agriculture under the Maputo and Malabo declarations in 2003 and 2014, respectively. Similarly, Asian countries increased spending on agriculture via input subsidies for crop production, greater mechanisation, and research and development funds. These spending increases are happening in countries with varying levels of institutional quality, and hence, differences in observed outcomes. Using cereal prodution as proxy for the availability dimension of food security, we conduct a comprehensive empirical assessment of the impact of public agricultural spending on food security under heterogeneous institutional quality regimes. We estimate fixed effects models with robust standard errors using an unbalanced panel of 25 countries in Sub-Saharan Africa and Asia over 22 years. The preponderance of evidence suggests that institutional quality plays a significant mediating role on the impact of public agricultural spending on food security. Overall, the magnitude of change in cereal production due to public agricultural spending is larger for countries with higher institutional quality than for those with lower institutional quality. While public investment in agriculture enhances food security, its effectiveness is predicated on improvements in the quality of public institutions. High quality public institutions protect private physical and intellectual property and guide prudent use of public resources
Can large private properties in Christchurch provide substantial coastal flood mitigation under climate change?
No full textPresentation given to Christchurch City Council and Environment Canterbury, 7 June 202
Using ensemble learning to analyze hurricane images to identify damage
No full textThe classification of hurricane images looking for damages includes analysing satellite or radar images of the areas affected by hurricanes and categorizing them into two classes (damaged/not damaged) based on their characteristics. This can be done using deep learning techniques where a deep neural network is trained on a large dataset of satellite images labelled as damaged or not damaged to recognize patterns and make predictions on new images. Convolutional Neural Networks (CNNs) are a type of neural network commonly used for image classification tasks. The classification process includes image preprocessing, feature extraction, and classification. In image preprocessing, the images are usually resized or filtered to remove noise and enhance quality. Feature extraction involves identifying relevant features in the images, such as building structures, that can be used to distinguish the damages of hurricanes.
The primary objective of this research is to find whether ensemble learning can be used to improve the accuracy of the classification of hurricane images. Ensemble learning is a machine learning technique that combines multiple models to improve the accuracy and robustness of predictions by taking advantage of the complementary strengths of each model.
We used the hurricane image dataset from https://ieee-dataport.org/open-access/detecting-damaged-buildings-post-hurricane-satellite-imagery-based-customized (Cao and Choe 2018). The training dataset has 7000 images (50% - damaged), and the testing dataset has 1200 images (50% - damaged). The experiments are performed with CNN and TPOT (Tree-based Pipeline Optimization Tool) (Olson, et al. 2016), an automated machine learning tool. It is designed to automate the entire machine-learning pipeline, including data pre-processing, feature selection, and hyperparameter tuning, to find the best machine-learning model for a given problem. TPOT cannot be used to analyse colour images since it does not take 3-dimensional datasets. Hence, we combined the CNN with TPOT, where the 3-dimensional dataset is sent into a model developed using CNN and features were extracted. The features were then given to the TPOT classifier as the input. The best pipeline selected by the TPOT classifier uses Recursive Feature Elimination for feature selection, two ExtraTreesClassifiers with 100 trees, and StackingEstimator to stack the models together into a single ensemble model. In ExtraTreesClassifier, extra-trees build an ensemble of decision trees by fitting each tree to a random subset of the training data. The overall predictive accuracy is improved by averaging the output of each tree.
The accuracy of the model built with 5 convolutional 2D layers, 3 max-pooling layers, 5 batch normalization layers, 5 activation layers, and 1 flatten layer was 0.89. The new approach replaced the output layer with the ensemble model, and the accuracy could be improved to 0.95. The CNN model took 8,910 seconds for training and the ensembled model took 36,408 seconds. Both approaches took 10 seconds to predict 1200 images. Despite of the increase in training time, time for prediction is not affected by the new development.
Hurricane damage identification is an important task for analyzing potential damages to buildings and tracking the path of hurricanes, and for studying the characteristics and behaviour of these powerful storms
Does mobile payment adoption really increase online shopping expenditure in China: A gender-differential analysis
No full textMobile payments are ubiquitous in China and facilitate myriads of offline and online transactions daily. However, little is known about the spending effects of mobile payment adoption. Accordingly, this study is devoted to examining the effects of mobile payment adoption on household online shopping expenditure. Focusing on gender differentials, we analyze the 2017 Chinese General Social Survey data using the instrumental-variable-based Tobit model. Our results show that mobile payment adoption significantly increases household online shopping expenditure for females, while it does not affect online shopping expenditure for males—the spending effects of mobile payment adoption are not gender-neutral. Thus, gender-specific promotional strategies and initiatives may help increase mobile payment adoption and boost online shopping. The positive spending effect of mobile payment adoption on households’ online shopping points to synergies between mobile payment and e-commerce platforms. Policymakers should consider regulations that encourage the integration of these platforms and, at the same time, foster innovation and competition in the industry
A global database of soil plant available phosphorus
Get PDFSoil phosphorus drives food production that is needed to feed a growing global population. However, knowledge of plant available phosphorus stocks at a global scale is poor but needed to better match phosphorus fertiliser supply to crop demand. We collated, checked, converted, and filtered a database of c. 575,000 soil samples to c. 33,000 soil samples of soil Olsen phosphorus concentrations. These data represent the most up-to-date repository of freely available data for plant available phosphorus at a global scale. We used these data to derive a model (R² = 0.54) of topsoil Olsen phosphorus concentrations that when combined with data on bulk density predicted the distribution and global stock of soil Olsen phosphorus. We expect that these data can be used to not only show where plant available P should be boosted, but also where it can be drawn down to make more efficient use of fertiliser phosphorus and to minimise likely phosphorus loss and degradation of water quality
‘As a farmer you've just got to learn to cope’: Understanding dairy farmers' perceptions of climate change and adaptation decisions in the lower South Island of Aotearoa-New Zealand
Get PDFThe impacts and implications of climate change – such as floods, droughts, heavy rainfall and increased regulation – are affecting dairy farming practices in the lower South Island (Te Waipaounamu) of Aotearoa-New Zealand. Adapting to these changes, in an equitable and transformational manner, is dependent on understanding the underlying root causes of vulnerability alongside local knowledge and values. We apply an intersectional values-based and contextual analysis to describe how past and present processes of agrarian change interact across different farmer identities to influence adaptive pathways. Local knowledge, place-based experience, values and perceptions of fairness intersect with different facets of a farmer's identity – such as financial capacity, land ownership status, debt arrangements, age and gendered participation – to enable or constrain adaptive action. Notably, notions of fairness, whether real or perceived, vary across farmer groups, and influence the kinds of adaptation activities that dairy farmers are willing, or potentially able, to engage in. The results call for more contextualised engagement with farming communities, and highlight the need to build a shared understanding of the complex historical, social, economic, cultural and environmental drivers of past, present and future change, in this highly productive, yet risky, agricultural landscape
What are effective design guidelines for protecting small, low-lying New Zealand coastal towns from climate-change induced flooding? : A dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Landscape Architecture at Lincoln University
Get PDFIn Aotearoa New Zealand more than 75% of the population are situated within 10 kilometres of the coast (Hayward, 2008). With global temperatures on the rise, climate change-related hazards are projected to increase in frequency and severity. Additionally, sea levels are expected to rise by 0.3-1.1 meters by the year 2100 (Hernández-Delgado, 2015; NIWA, n.d; O'Donoghue et al., 2021; Oppenheimer et al., 2019; Rouse et al., 2017). Low-lying, small coastal communities face an amplified risk of flooding caused by climate change, due to a lack of infrastructure and limited funding available to local councils, compared to larger cities (James M. Fitton et al., 2021).
At present, larger cities have greater access to mitigation strategies and tools for coping with coastal flooding in the face of climate change (Lamb et al., 2019). Furthermore, little is known about the effectiveness of design guidelines to assist landscape architects to implement appropriate design strategies in small coastal towns. Strategies include design guidelines. Design guidelines refer to recommendations or principles that designers can use to ensure that their designs are effective for their intended purpose (Nijhuis & de Vries, 2019).
Firstly, a narrative literature review was conducted to develop an understanding of what was known and not known about strategies for mitigating climate change induced flooding, as well as determining whether design guidelines for flooding in small coastal towns exist. Secondly, an evaluation of existing guidelines was conducted following Kennedy Evans (2019) ‘best practice Landscape Architecture design guideline criteria' to determine their effectiveness. A set of suggestions to improve existing guidelines were proposed based on the results of the evaluation in conjunction with the narrative literature review.
Māpua, New Zealand was chosen as the case study site for this research as it is highly prone to flooding due to its biophysical characteristics. An inventory and analysis was made of all existing biophysical and land-use characteristics to determine to what extent Māpua met the best practice flooding design guidelines that were proposed. The guidelines were then applied to the site at a regional, masterplan and intermediate scale to demonstrate how the improved design guidelines can be applied to protect a small coastal town. Thus demonstrating what effective guidelines are to protect small, low-lying coastal towns from climate-change induced flooding