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Gluten-free voices: How are consumers navigating the prices, the aisles, the struggles and the hype?
No full textA mixed methods assessment of an online mental health and resilience program for agricultural sector students
No full textFinancial, environmental, and socio-cultural challenges affect the mental health and wellbeing of those working and living on-farm. Education programs can help in improving mental health and overall wellbeing, but most of these programs are offered to established farmers in a face-to-face format, leading to a gap in offerings to many subgroups, including young agricultural entrants. To address these gaps, this paper assesses the value of an online mental health and resilience education program for young agricultural entrants. An explanatory sequential mixed methods approach was applied. Firstly, a quantitative survey was conducted with 172 first year agricultural tertiary students, aged 18–20, to assess the knowledge and skills gained due to program participation. Regressions and propensity score matching were used to determine the effect of program participation. Subsequently, seven interviews were conducted with program participants to provide insights into their experiences of the program. Thematic coding was applied to analyze the qualitative data. Findings from the quantitative survey show a significant increase in mental health knowledge and skills amongst program participants compared to participants in the control group. The qualitative interviews show the ability to work through the material in their own time and space, creates a safe environment for online students. Other mechanisms contributing to learning are using “normal language”, a peer voice in communicating wellbeing, and building on learnings in previous education. This paper addresses a gap in literature by being the first study to evaluate online mental health education for agricultural tertiary students. It provides educators and other program designers with valuable information for future program design to support the wellbeing of young agricultural workers
Online food shopping and nutrition inequality among rural children
No full textPrevious research has identified nutritional inequality among rural children as a pressing issue, yet effective strategies to mitigate this disparity remain underexplored. This study examines the impact of online food shopping—a practical means of accessing online markets—on nutritional inequality among rural children. We measure nutritional inequality using the Gini coefficients of four anthropometric indicators, including stunting, underweight, overweight, and obesity. We employ the Recentered Influence Function regression to estimate survey data from rural China. The results indicate that online food shopping significantly reduces nutritional inequality related to stunting and obesity. However, the effects exhibit notable gender and age disparities: while online food shopping substantially decreases stunting- and obesity-related inequality among young children (ages 0–6) and girls, its impact on older children (ages 6 and above) and boys is statistically insignificant. These findings suggest that diversifying online food shopping channels and implementing age- and gender-sensitive interventions could be crucial in addressing nutritional inequality in rural areas
House prices during the COVID-19 pandemic: the impact of “panic” returnees migrants to New Zealand
No full textPurpose
This paper aims to investigate the effects of the “panic” returnees who quickly returned back into New Zealand during the COVID-19 pandemic, given that the country was a perceived “safe haven” during that time and thus having some impacts on its national housing market.
Design/methodology/approach
This empirical study combines the autoregressive distributed lag framework and the instrumental variable approach to analyse the causal relationship between “panic” returnees and housing market dynamics in New Zealand during the COVID-19 pandemic.
Findings
The findings reveal that returnees were the major driver of increases in both short- and long-term house prices, although other longstanding factors such as rental prices, mortgage rates and supply constraints on new housing developments are also important. Policy recommendations and housing programmes are accordingly discussed.
Originality/value
To the best of the authors’ knowledge, it is the first study on what is termed “panic” returnees. Therefore, it contributes to the understanding of housing market dynamics in the context of international mobility and economic disruptions, in addition to paving the way for targeted policy recommendations and housing programmes to address affordability challenge
Late pregnancy is associated with an increase in regulatory cytokines and a decline in nematode-specific antibody levels in sheep infected with Teladorsagia circumcincta
No full textThe gastrointestinal nematode Teladorsagia circumcincta is a prevalent and economically important parasite of sheep. Sheep develop acquired immunity to T. circumcincta, characterised by nematode-specific antibody production and a type 2 T-helper (Th) cell response. During late pregnancy and early lactation, ewes experience increased nematode faecal egg count (FEC) known as the peri-parturient rise (PPR). The PPR is associated with a decline in nematode-specific antibodies, but research on the role of Th cell-mediated immunity in the PPR is limited. Here, we characterised the cellular and humoral immune responses of T. circumcincta-infected ewes fed at two dietary levels during the peri-parturient period. Diet had a limited effect on any trait, but all ewes experienced a pronounced PPR. The PPR was associated with a decline in nematode-specific IgG, and antigen-dependent declines of IgA and IgE around parturition. Levels of the Th1-associated cytokine IFN-γ and the Th2-associated cytokine IL-4 showed antigen-dependent declines during the peri-parturient period. In contrast, the regulatory cytokine IL-10 increased around parturition in response to a generic mitogen, nematode antigens and Heptavac® vaccine, suggesting a generalised regulatory immune phenotype. Our results provide a comprehensive view of the immunological changes that contribute to the PPR and reveal the contribution of cell-mediated immunity
Insights into low-temperature strategies for preserving cooked pork quality in ready-to-eat meat products through processing and reheating studies
No full textThis study investigated how low-temperature storage and reheating affect the quality of ready-to-eat (RTE) pork dishes. Cooked lean and fatty pork were stored via refrigeration (4 °C), freezing (−18 °C, −35 °C), or liquid nitrogen (LN) rapid chilling, then reheated. Refrigeration preserved texture but increased cooking loss (3.15 % lean, 4.2 % fatty) and peroxide values (PV), with PV rising 3.5-fold in lean and 1.4-fold in fatty meat. Freezing at −35 °C minimized lipid oxidation (TBARS 0.16 mg MDA/kg in fatty pork) but raised reheating loss (38.2 % lean, 44.5 % fatty). LN caused the most damage—hardness dropped 52.4 %, fatty meat lost 51.5 % fat and showed a 16-fold TBARS rise. Color values (a*, b*) and taste compounds (NMS, ANS) also declined with LN. Freezing at −18 °C best preserved lean meat, while −35 °C was optimal for fatty cuts. LN chilling is unsuitable for RTE pork. These findings offer benchmarks to optimize freezing, reheating strategies for RTE pork quality, stability
Assessment of microbial diversity in various saline soils driven by salt content
No full textThe Yellow River Delta, as an important reserve land resource area, faces soil salinization problems. Understanding the bacterial community composition in saline soils is an important foundation for control and utilization of saline soils. However, few studies have been conducted on the composition of bacterial communities in soils with different degrees of salinization. Thus, saline soils categorized into low-salinity (LS), medium-salinity (MS), and high-salinity (HS) based on electrical conductivity (EC) were collected. The 16S rRNA high-throughput sequencing analysis was performed to analyze the effects of salinities on soil bacterial community patterns, as well as the relationships between soil bacterial communities and environmental factors. The results showed that Actinobacteriota, Proteobacteria, Chloroflexi, Firmicutes, Acidobacteriota, Gemmatimonadota and Bacteroidota accounted for almost 90 % of all the bacterial community. The linear discriminant analysis effects (LDA > 3.7) showed that 6, 5 and 3 biomarkers were present in LS, MS and HS soils, respectively, which indicated EC was an important factor influencing the saline soil bacterial community patterns. Redundancy analysis further revealed that the primary environmental parameters impacting the bacterial community were pH, EC, nitrate nitrogen, available phosphorus, total phosphorus, and soil organic matter. According to network analysis, the microbial network complexity was increased steadily with increasing of soil salinity. These findings together revealed that bacterial communities could serve as a reliable way to assess and improve the quality of salinized soils
Does non-native diversity mirror Earth's biodiversity?
Get PDFAim: Human activities have introduced numerous non-native species (NNS) worldwide. Understanding and predicting large-scale NNS establishment patterns remain fundamental scientific challenges. Here, we evaluate if NNS composition represents a proportional subset of the total species pool available to invade (i.e. total global biodiversity), or, conversely, certain taxa are disproportionately pre-disposed to establish in non-native areas.
Location: Global.
Time period: Present day.
Major taxa studied: Global diversity.
Methods: We compiled one of the most comprehensive global databases of NNS (36,822 established species) to determine if NNS diversity is a representative proportional subset of global biodiversity.
Results: Our study revealed that, while NNS diversity mirrors global biodiversity to a certain extent, due to significant deviance from the null model it is not always a representative proportional subset of global biodiversity. The strength of global biodiversity as a predictor depended on the taxonomic scale, with successive lower taxonomic levels less predictive than the one above it. Consequently, on average, 58%, 42% and 28% of variability in NNS numbers were explained by global biodiversity for phylum, class and family respectively. Moreover, global biodiversity was a similarly strong explanatory variable for NNS diversity among regions, but not habitats (i.e. terrestrial, freshwater and marine), where it better predicted NNS diversity for terrestrial than for freshwater and marine habitats. Freshwater and marine habitats were also greatly understudied relative to invasions in the terrestrial habitats. Over-represented NNS relative to global biodiversity tended to be those intentionally introduced and/or ‘hitchhikers’ associated with deliberate introductions. Finally, randomness is likely an important factor in the establishment success of NNS.
Main conclusions: Besides global biodiversity, other important explanatory variables for large-scale patterns of NNS diversity likely include propagule and colonization pressures, environmental similarity between native and non-native regions, biased selection of intentionally introduced species and disparate research efforts of habitats and taxa
Transit-oriented development and spatial data infrastructure in African cities
No full textInfrastructural deficiency remains a major challenge for Africa in achieving its full potential. As one of the global hubs growing at unprecedented rate into economic centres, attending to the demand for key infrastructure has become an exigency based on the fact that inadequate transport infrastructure can add up to 40% to the costs of goods traded among African countries (Export- Import Bank of India, 2018). In an increasingly urbanised, technology-driven and overstretched world with its attendant challenges, the need to attain prosperity, frantically combat environmental degradation due to transportation and ensure sustainable growth contends with digitisation, which is driving new patterns of transport for goods, services and people (Smith et al., 2017). As transport needs and challenges are increasing world over, the unavailability of a data infrastructure framework needed to make optimal use of different data sets has further made prevailing challenges more overwhelming for major stakeholders in the transportation sector such as the urban/transport planners, investors, policy makers and commuters
Development of an advanced deep learning and neural network method for automatic early detection of mastitis in dairy cattle : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University
No full textMastitis, a costly and prevalent disease in dairy cows, reduces milk yield, quality, and animal welfare, while increasing treatment costs. Early detection, especially in the subclinical stage, is crucial for controlling the disease and maintaining milk production. This study explores advanced methods for early mastitis detection using data from a robotic milking system. Initially, Self-Organising Map (SOM) was employed to capture the mastitis spectrum. Then, Fuzzy C-Means (FCM) clustering was applied to the SOM map, identifying five health states—healthy, early subclinical, subclinical, late subclinical and clinical—despite the absence of specific labels for the subclinical stages. Building on the insights gained from unsupervised learning with SOM and FCM, we then trained a Bidirectional Long Short-Term Memory (BiLSTM) network to forecast the cow health state for the following day using supervised learning. The BiLSTM model showed high efficacy in forecasting cow health states, with precision, recall, and F1-scores for each state ranging from 0.92 to 1.00. This approach advances mastitis detection by integrating SOM-FCM for spectrum capture and BiLSTM for temporal forecasting, improving early diagnosis and enabling targeted interventions, thus promoting precision dairy farming and better economic outcomes