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The effect of traffic and tillage management systems on soil organic carbon dynamics and crop performance
Get PDFThere is an increased interest in implementing soil compaction mitigation strategies in sustainable agricultural practices to promote soil health, crop productivity and resilience. However, knowledge gaps still exist on the long-term effects of alternative traffic systems, and their interaction with different tillage systems, on soil organic matter (SOM) dynamics and crop yield. This thesis aimed to determine the effects of three traffic systems—Standard Tyre Pressure (STP), Low ground Tyre Pressure (LTP), and Controlled Traffic Farming with 30% trafficked area (CTF)—interacting with three tillage systems (Deep 25 cm, Shallow 10 cm and Zero tillage) on SOM dynamics and crop performance, in a long-term 3×3 factorial field experiment with four replicates on sandy loam soil. After 12 years, the non-trafficked crop area of CTF with Zero tillage had significantly higher SOM concentration (0-30 cm), storing 5 Mg/ha more SOC stocks on equivalent soil mass than other treatments. This combination stored ~26% more particulate organic matter carbon (POM-C) and ~6% more mineral-associated organic matter carbon (MAOM-C). After introducing a C4 millet crop, the POM δ13C was 4.5% higher and MAOM δ13C was 0.4% higher than under the previous C3 crop, indicating that carbon storage was driven by the POM fraction.
Crop yield was significantly higher only for Spring oats, which yielded ~ 14% higher than STP Deep and ~ 10% higher than STP Shallow. CTF and LTP systems produced significantly higher yields than STP systems (~ 9% more for Winter wheat and ~ 7% more for Spring oats). Tillage effects on yield were not significant, indicating that long-term Zero tillage maintained equivalent yields. However, calculating for a more realistic CTF with 15% trafficked area provided ~4% additional grain yield increase
Detection of Aspergillus flavus contamination in peanut kernels using a hybrid convolutional transformer-feature fusion network: A macro-micro integrated hyperspectral imaging approach and two-dimensional correlation spectroscopy analysis
No full textAspergillus flavus contamination in peanut kernels poses significant health risks and economic losses, hence requiring accurate and fast detection methods to ensure postharvest safety and quality. This study investigated the detection of Aspergillus flavus contamination in peanut kernels using visible-near infrared (VNIR) hyperspectral imaging and hyperspectral microscopic imaging (HMI). The research explored the structural damage to peanut kernel cells and tissue caused by contamination, as revealed through both electron microscopy and hyperspectral imaging. Generalized two-dimensional correlation spectroscopy analysis was applied to determine the sequence of molecular changes, providing insights into fungal metabolism. The spatial-spectral features of the peanut kernels and peanut kernel sections were extracted, and a hybrid convolutional transformer-feature fusion network (HCT-FFN) was employed for features integration and classification. The model demonstrated superior accuracy compared to classic deep learning models, with test accuracy of 100.00 % for both VNIR hyperspectral imaging and HMI. Using smaller regions of interest in peanut kernel sections maintained high accuracy and improved the efficiency of the model. The study concluded that Aspergillus flavus contamination significantly altered peanut kernel structure and spectral properties. The HCT-FFN model proved highly effective for detecting and classifying contamination with minimal computational costs, highlighting its potential as a valuable tool for ensuring the safety and quality of postharvest nuts
Exploring consumer acceptance of grass-derived proteins in the UK: A structural equation modelling approach
Get PDFGrass-derived proteins, as a novel and sustainable source of nutrition, offer potential solutions for food security and environmental sustainability but face challenges in consumer adoption. This study investigates the factors influencing consumer acceptance and intentions to consume grass-derived proteins in the United Kingdom using a Structural Equation Modelling (SEM) approach to capture the complex relationships among psychological, social, and product-related variables. Data were collected via a cross-sectional survey of 990 participants, capturing attitudes, subjective norms, perceived behavioural control, facilitators and food neophobia. The findings reveal that facilitators such as perceived health benefits, nutritional value, and safety significantly enhance consumer willingness to adopt grass-derived ingredients. Further, negative attitudes reduce positive attitudes towards meat preferences which in turn leads to positive intentions to consume grass-derived proteins. A multigroup analysis of the meat avoiders-reducers and regular meat consumers reveals different pathways influencing their behavioural intentions. Facilitators emerge as the strongest predictors of intention for both groups, but differences in the strength of pathways underscore the need for tailored marketing and policy interventions. For avoiders-reducers, direct pathways from facilitators to intention dominate, while indirect pathways involving attitudes towards meat hold minimal influence.
Conversely, meat consumers exhibit stronger resistance tied to cultural perceptions of grass-derived products. These findings suggest emphasizing strategies to enhance consumer familiarity and address sensory concerns while leveraging the environmental and health benefits of grass-derived proteins. By addressing group-specific drivers and barriers, these efforts can foster broader acceptance of sustainable food innovations, contributing to global goals for food security and environmental sustainability
Ruminant livestock farmers and industry are leading innovation to deliver human nutrition and improved environmental outcomes through sector lifecycle collaboration: a review of case studies
Get PDFImplications
Well-managed systems like adaptive grazing and silvopasture enhance soil health, biodiversity, and water retention while reducing greenhouse gas emissions.
Intensive feeding practices, from supplementation to feedlots, increase meat production and manage emissions effectively through controlled feeding and manure strategies.
Combining sustainable grazing with intensive systems balances land use, nutrition, and emissions reduction, addressing global food demand.
Livestock’s up-cycling efficiency converts inedible grasses and by-products into nutrient-dense food, critical for food security.
Farmers and industry leaders, through innovation and life-cycle analysis, use data-driven decisions to optimize sustainability, showcasing livestock’s essential role in achieving environmental and nutritional goals in agriculture
Understanding the structure and function in reduced fat cheese using double emulsion technology
Get PDFReduced fat cheese often suffers from inferior sensory and functional qualities compared to its full fat counterpart, which limits consumer acceptance despite the increasing demand for reduced fat products. Double emulsions, such as water-in-oil-in-water (W/O/W) are a novel technology, which use synthetic emulsifiers such as polyglycerol polyrincoleate (PGPR), can be utilised to improve the sensory and functional properties of reduced fat cheeses. However, challenges persist in identifying alternative natural lipophilic surfactants and minimise the use of synthetic emulsifiers.
In this study, the development of stable, small droplet (3 – 4 m) double emulsions was explored for their application in reduced fat cheese production. The process involved creating primary emulsions, forming double emulsions and subsequently incorporating them into cheese formulations. Various analyses including nutritional, functional and sensory evaluations were conducted.
Initial attempts using polyphenol crystals curcumin and quercetin, as lipophilic surfactants were unsuccessful in achieving the desired small droplet sizes. However, sunflower lecithin proved effective, stabilising droplets at approximately 12 m in sunflower oil. Transitioning from sunflower oil to milk fat with sunflower lecithin alone presented production challenges and resulted in larger droplet sizes. Nevertheless, partially replacing PGPR with sunflower lecithin in ratio of P1.5:L0.5 and P1:L1 produced stable droplets of around 3.6 m. Further method development for skimmed milk-based double emulsions allowed for the successful encapsulation of reduced PGPR with sunflower lecithin, maintaining stable double emulsions for two hours under optimised conditions (35:65 W1/O:W2, 6000 rpm for 10 minutes), resulting in droplet size of 14 to 17 m suitable for reduced fat cheese production.
These double emulsions, when incorporated into reduced fat cheese, enhanced texture and meltability. Sensory evaluations indicated positive outcomes, with similar aroma and flavour profiles across samples, though the mouth feel remained akin to that of reduced fat control cheese. This study demonstrates the potential of double emulsions with reduced synthetic emulsifiers to improve the functionality and sensory properties of reduced fat cheese
Novel nocturnal insect pest monitoring for sustainable crop protection using ensemble augmented deep learning classification
Get PDFVine weevil, Otiorhynchus sulcatus F. (Coleoptera: Curculionidae), is an economically important pest of soft fruit and ornamental crops globally. Its management has historically relied on broad-spectrum synthetic insecticides, but this has shifted toward integrated pest management compatible methods such as entomopathogenic nematodes and fungi that target soil-dwelling larvae. These methods require reliable pest monitoring tools to be practically effective and economically viable. Existing monitoring methods rely on detecting the nocturnal adult weevils as a proxy for larval presence, however, these are unreliable and time-consuming to implement. This may be addressed by developing an identification algorithm for adult weevils. Here we present results that show improved machine learning models can identify adult vine weevils under laboratory and semi-field conditions. Specifically, we employ a lightweight network model and use ensemble enhancement techniques to address potential issues such as color variations, occlusions, and deformations in the data labels. The proposed framework strategically integrates a lightweight network model with adaptive ensemble augmentation mechanisms to comprehensively address three core data challenges: (1) chromatic variance under varying illumination conditions, (2) partial occlusion from pest aggregation, and (3) morphological deformation during specimen collection. This is the first report of such technologies specifically developed for a nocturnal insect pest. It demonstrates the feasibility of an automated monitoring approach, which could benefit growers as it will provide more timely information about pest populations in their crops and better inform management decisions
Empowering professional identity and positive outcomes through Third Space collaboration: A subject lecturer and EAP practitioner case study
Get PDFEnglish for Academic Purposes (EAP) staff frequently find themselves sidelined in higher education (HE), where they can be perceived as operating on the edge of academia, or even outside of it. Proactively claiming a role in the third space (Whitchurch, 2008) potentially supports recognition of their professional identity, value, and contribution. This case study reflects on a collaboration between a Lecturer with a professional services background, and an EAP Practitioner, incorporating perspectives from both staff members. The collaboration took place at all three levels identified by Dudley-Evans and St John (1998) for this type of shared work: cooperation, collaboration, and then team teaching. The third level of team teaching was achieved through a co-delivered assessment workshop. This was designed to allow the EAP Practitioner’s expertise to scaffold the students towards asking clear questions of the Lecturer, in a safe space, supporting understanding and assessment performance, while minimising concerns about inappropriate challenge or loss of face. Both staff members benefitted from this third space collaboration, building professional confidence, with the EAP Practitioner feeling empowered in their expertise and practice, which can be challenging for third-space professionals with previous negative experiences of attempted collaboration. The student outcomes appeared positive, and this collaboration led to other activities that further cemented the collaborative working relationship and demonstrated the value of activity within the third space
Agricultural practices can threaten soil resilience through changing feedback loops
Get PDFSoil has supported terrestrial food production for millennia; however, agricultural intensification may affect its resilience. Using a systems-thinking approach, we reviewed the impacts of conventional-agriculture practices on soil resilience and identified alternative practices that could mitigate these effects. We found that many practices only affect soil resilience with their long-term repeated use. Lastly, we ranked the impacts that pose the greatest threats to soil resilience and, consequently, food and feed security
Vignette 4.5: Hybrid fieldwork approaches: The independent study of ‘place’ in teaching and learning
No full textFinite element optimization of a flexible fin-ray-based soft robotic gripper for scalable fruit harvesting and manipulation
Get PDFOn the path to achieving fully autonomous farming, the use of grasping devices for fruit picking and handling remains an open challenge. Current solutions are designed for specific fruits and robot manipulators, often without considering the intrinsic interaction between the gripper's fingers and the fruit. This work explores the use of fin-ray-based flexible grippers, which mimic human fruit-picking movements, for harvesting and pick-and-place operations involving medium-sized fruits. Optimal gripper characteristics were determined through a Finite Element Analysis methodology. To achieve the harvesting objective, the grippers were integrated into a vision-based system and a robotic manipulator, with testing conducted under laboratory conditions. The harvesting study focused on apples, while the manipulation task was tested with apples, oranges, and lemons. The findings indicate that while all grippers demonstrated a suitable performance, one particular design emerged as the most effective, meeting all criteria and outperforming the others in experiments and performance metrics