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Impact of dairy calf management practices on the intestinal tract microbiome pre-weaning
Get PDFIntroduction. Microbiota in the gastrointestinal tract (GIT) consisting of the rumen and hindgut (the small intestine, cecum and colon) in dairy calves play a vital role in their growth and development. This review discusses the development of dairy calf intestinal microbiomes with an emphasis on the impact that husbandry and rearing management have on microbiome development, health and growth of pre-weaned dairy calves.
Discussion. The diversity and composition of the microbes that colonize the lower GIT (small and large intestine) can have a significant impact on the growth and development of the calf, through influence on nutrient metabolism, immune modulation, resistance or susceptibility to infection, production outputs and behaviour modification in adult life. The colonization of the calf intestinal microbiome dynamically changes from birth, increasing microbial richness and diversity until weaning, where further dynamic and drastic microbiome change occurs. In dairy calves, neonatal microbiome development prior to weaning is influenced by direct and indirect factors, some of which could be considered stressors, such as maternal interaction, environment, diet, husbandry and weaning practices. The specific impact of these can dictate intestinal microbial colonization, with potential lifelong consequences.
Conclusion. Evidence suggests the potential detrimental effect that sudden changes and stress may have on calf health and growth due to management and husbandry practices, and the importance of establishing a stable yet diverse intestinal microbiome population at an early age is essential for calf success. The possibility of improving the health of calves through intestinal microbiome modulation and using alternative strategies including probiotic use, faecal microbiota transplantation and novel approaches of microbiome tracking should be considered to support animal health and sustainability of dairy production systems
Make African grasslands climate-change resilient
Get PDFClimate change has negatively impacted grassland
productivity in Africa. Climate-smart technologies
such as forage grass, legume, and herb mixtures
could enhance grassland productivity and resilience, offering a sustainable solution for African pasture-based livestock systems. Grasslands (intensive and extensive) are Africa’s dominant land use type, accounting for 44.8% of the total land area, which provides feed for livestock and wild animals. Approximately 70% of people in rural Africa
depend on livestock for their livelihoods. As human populations increase, grasslands are increasingly being transformed into arable land and other uses. The remaining grasslands often experience overgrazing due to livestock production, resulting in significant land degradation. This is exacerbated by climate change, with shifting weather patterns and increased frequency of extreme weather events (e.g. drought and flooding), the spread of invasive species, and bush encroachment. Consequently, there is a significant reduction in forage quality and quantity, increased livestock disease vulnerability, and mortality rates, threatening regional food security. Given the importance of livestock production to smallholder farmers’ livelihoods
in Africa, we believe in adopting sustainable practices that could enhance the productivity of intensively and extensively managed African grasslands for economic, social, and environmental benefits. The Global Farm Platform initiative, a community of collaborative practitioners investigating sustainable ruminant livestock systems around the globe (www.globalfarmplatform.org), highlighted management strategies for sustainable livestock systems. Here, we argue that various forage species mixtures
could enhance the sustainability of agricultural grasslands in Africa
Trade-offs between forage nutrition and ruminant carrying capacity in response to fertiliser application – Findings from the Park Grass long-term experiment (1860–2020)
Get PDFContext: Rothamsted Research’s Park Grass Experiment, established in 1856, is the longest-running grassland
study globally. Naturally regenerating grassland swards are grown in plots with varying applications of fertiliser
including ammonium sulphate and sodium nitrate (at varying application rates), organic fertiliser, minerals (K,
Mg, Na, P), and lime, which is mown twice a year. As the world’s most widely produced crop, grass is predominantly used to feed ruminants, however, the nutritional properties and carrying capacities of these plots
have not previously been quantified.
Objective: The objective of this study was to characterise the nutritional profile of forage gathered from the Park
Grass plots from 1860 to 2020 and the ruminant carrying capacity that the plots would support. The study further
aimed to explore the trade-offs between productivity, forage nutritional quality, and biodiversity.
Method: Dried PGE herbage samples were taken from the Rothamsted sample archive at decade intervals from
1860 to 2020, representing a range of plot treatments. Proximate analysis and XRF elemental analysis were
performed, and the data was used to estimate ruminant carrying capacity of plots based on metabolisable energy
and crude protein requirements for production.
Results: Fertiliser applications increased carrying capacity due to yield improvements but reduced crude protein
while increasing cellulose and hemicellulose. Increased growth appeared to have a dilution effect on some
essential minerals, particularly Ca, Mg, Mn, and P. Sodium nitrate produced higher carrying capacities per unit of
nitrogen compared to ammonium sulphate or organic manure.
Conclusions: The findings highlight trade-offs in improved grasslands between forage quality, quantity, biodi�versity, and management inputs. Results show that fertiliser applications enhance carrying capacity by increasing
forage yield but potentially at the cost of reduced nutritional quality and species diversity. This study also
provides the first comprehensive nutritional analysis of the Park Grass plots, revealing how historical fertiliser
treatments influenced forage quality and ruminant carrying capacity over 160 years.
Significance: Studying the trade-offs and gradients within grassland systems is essential for understanding the
balance between productivity and biodiversity. This study also contributes to the rich dataset available on the
Park Grass Experiment, providing future opportunities and insight, whilst also highlighting the importance of
long-term experimental studies in the agricultural and environmental science
Production Agglomeration and Spatiotemporal Evolution of China’s Fruit Industry over the Last 40 Years
Get PDFThis study analyzes the dynamics of China’s fruit industry using a range of analytical tools, including the location Gini coefficient, industry concentration ratio, spatial
autocorrelation index, specialization index, and the industry gravity model. It explores the industry’s evolving characteristics and trends since the economic reforms, culminating in a trajectory map that highlights shifts in the industry’s gravitational center. This study also offers a qualitative analysis of the factors influencing the agglomeration and relocation of fruit production centers. The findings show a steady increase in both total output and
yields per unit area within China’s fruit industry over time. Although the overall degree of agglomeration has decreased, regional agglomeration effects remain significant. Furthermore, the data reveal significant spatial autocorrelation in fruit production, indicating a long-term westward shift in core production areas. Different geographic areas exhibit varying levels of gradational shifts, with marked differences in production concentration patterns across different fruit types. This study provides a comprehensive framework for understanding production agglomeration, integrating interdisciplinary methods from statistics and geography
A viewpoint on the role of artificial intelligence in food processing and production: promise, pitfalls, and the path forward
No full textArtificial intelligence (AI) has the potential of being used in food processing and production across multiple dimensions that could include process optimisation, quality control, supply-chain efficiency, sustainability, and product innovation, among others. However, the rise of AI and its use is often met by criticism and scepticism due to a number of ethical, socioeconomical and technical challenges. Therefore, this viewpoint aims to explore how AI technologies such as machine learning, computer vision, predictive analytics, and robotics can be used in enhancing operational performance and meeting the growing demands for safe, nutritious, and sustainable food systems. Herein, key challenges of AI that include issues to do with the quality of data in addition, standardization, algorithmic bias, and transparency; job displacement and workforce transformation; cybersecurity and data privacy risks; and regulatory and ethical uncertainty are addressed. An evidence-based roadmap for responsible and inclusive AI adoption in the food sector are also addressed, where human-AI collaboration are explored as well as the need for interdisciplinary research, ethical design, robust governance, and education discussed. In this work, we show that even though AI holds promise to revolutionizing food production and processing similar to what mechanization did in the 20th century, its benefits will only be realized if its deployment and use in food industry is guided by values of transparency, ethical use, fairness, and sustainability. As such this work aims to contribute to the existing knowledge on the use and application of AI in food processing and production and its impact on food systems
Utilisation of single and multiple species cover crops for the suppression of soil-borne nematodes of Narcissus
Get PDFDitylenchus dipsaci, Pratylenchus penetrans, and Aphelenchoides subtenuis parasitise Narcissus. Cover crops reduce plant parasitic nematodes through several mechanisms, including non/poor host, allelopathy and trap cropping. This study assessed the impact of cover crops on plant parasitic nematodes associated with Narcissus and beneficial nematode communities. Several cover crops were tested under greenhouse conditions for their host suitability to P. penetrans, and ten were rated as poor hosts (Chapter 3). Four selected cover crops (French marigold, oilseed radish, alfalfa and forage chicory) were tested in three field experiments (Chapter 6). The abundance of Pratylenchus spp., fungivorous and bacterivorous nematodes, was monitored before planting, three months after planting and six weeks post-incorporation. All cover crops significantly reduced Pratylenchus spp. and increased the abundance of fungivorous and bacterivorous nematodes (Chapter 6). The impact of cover crops on soil nematode communities and soil food web indices was assessed using High-throughput Sequencing targeting the 18S rRNA gene (Chapter 7). Cover crop treatments did not impact beta diversity; therefore, no adverse effects on nematode communities. To better understand how cover crops reduce P. penetrans, in vitro assays were conducted to assess the nematodes' behavioural responses after exposure to root exudates (Chapter 4). Root exudates from forage chicory and alfalfa did not affect the behaviour of P. penetrans. Finally, in vitro assays were conducted to test the nematicidal potential of three different brassica isothiocyanates against P. penetrans (Chapter 5). Benzyl was the most toxic (LD50=3.2 μg ml-1), 2-Phenylethyl (LD50=5.2 μg ml-1) was the second and lastly Allyl (LD50=9.9 μg ml-1). Collating the results of the experimental work in this PhD thesis strongly suggests that French marigold, oilseed radish, forage chicory, and alfalfa are potential options for managing Pratylenchus spp. in Narcissus fields without deleterious effects on non-target nematodes. Moreover, P. penetrans is associated with various cash crops grown in the UK; therefore, these cover crops could become part of integrated nematode management and in rotations to confer agroecosystem services, such as improved soil fertility
Utilising on-farm risk assessment data for the management of Johne’s disease in dairy cattle in Northern Ireland
No full textJohne’s disease (JD) causes weight loss, diarrhoea, and reduced milk yields in clinically infected cattle. In 2020, Animal Health and Welfare Northern Ireland (AHWNI) launched a voluntary JD control programme (JDCP) which focuses on bio-exclusion, biocontainment and market reassurance. Authorised veterinary practitioners (AVPs) conduct a Veterinary Risk Assessment and Management Plan (VRAMP) and use this information to make up to three recommendations. Between August 2022 and January 2024, 2274 herds enrolled in the NI JDCP and conducted up to three VRAMPs. This study characterised the JD-related risks and veterinary recommendations, identified the risks related to confirmed cases of JD and assessed if farmers changed their practices in response to AVP recommendations. AVPs assigned risk scores to management practices. Practices related to the calving area, particularly an absence of or delayed snatch calving, demonstrated the highest average risk score. Thematic analysis highlighted five main themes within AVP recommendations, including the use of diagnostic testing and management of calving areas. Multivariable binomial logistic regression identified five management practices which significantly increased the likelihood of herds having had a confirmed case of JD, including the segregation of clinically infected or test-positive cows from the rest of the herd in the calving area. Analysis of the risk scores and responses to closed questions from 278 herds which conducted first and second VRAMPs suggested that farmers had not changed their JD-related management practices in response to AVP recommendations. These findings simultaneously outline the challenges in JD control, reinforce the use of VRAMPs in identifying JD-related risks, demonstrate the harmonisation in AVP recommendation themes and provide data which can be considered by industry and policy makers
Availability and Purchasing of Gluten-Free Cereal Products in a Polish Population of Female Celiac Disease Patients
Get PDFBackground/Objectives: The problems with following a gluten-free (GF) diet result from the high cost of GF products, their limited availability for celiac disease (CD) patients, and their disputable quality. The aim of this cross-sectional study was to assess the frequency of buying and availability of GF cereal products in a population of Polish female CD patients. Methods: This study was conducted in a population of Polish female CD patients who were members of the Polish Celiac Society, and n = 547 respondents were included in this study. Participants were asked about the frequency of buying and problems with the availability of GF cereal products, which were compared by sub-groups stratified by age, place of residence, place of purchasing major grocery shopping and purchasing GF products online. Results: The majority of the studied female CD patients declared often purchasing GF flour, pasta, and bread, as well as never purchasing GF puff pastry, fried baked goods, dumplings, and crackers. The only product for which the majority of the studied participants declared problems with availability was dumplings. For younger respondents, a higher share declared often buying GF pasta (p = 0.0073), chips, crisps and puffs (p < 0.0001), and Asian-style noodles (p = 0.0269), as well as declared problems with the availability of GF wraps/tortillas (p = 0.0001), puff pastry (p = 0.0294), fried baked goods (p = 0.0008), biscuits/cookies (p = 0.0148), and Asian-style noodles (p = 0.0046) compared to older respondents, while for older respondents, a higher share declared often buying GF flour (p = 0.0358), and never buying GF wraps/tortillas (p = 0.0181). For respondents living in big cities, a higher share declared problems with the availability of GF pasta compared to respondents living in small towns/villages (p = 0.0245). For respondents purchasing major grocery shopping in hypermarkets, a higher share declared often buying GF biscuits/cookies compared to respondents purchasing in other shops (p = 0.0039), while for respondents purchasing in other shops, a higher share declared never buying puff pastry (p = 0.0076), dumplings (p = 0.0002), and wraps/tortillas (p = 0.0038), as well as declared problems with availability of GF puff pastry (p = 0.0246), biscuits/cookies (p = 0.0002), and breakfast cereals (p = 0.0011). For respondents not purchasing GF products online, a higher share declared never buying GF fried baked goods compared to respondents purchasing online at least occasionally (p = 0.0284), as well as a lower share declared problems with the availability of GF wraps/tortillas (45% vs. 33%, p = 0.0411). Conclusions: The population of Polish female CD patients seems quite diverse in terms of the chosen GF cereal products, with age, primary place of purchasing major grocery shopping and purchasing GF products online, but not the place of residence, as the major determinants. The declared problems with the availability of GF products are probably associated with two diverse mechanisms—either frequent purchasing (as individuals not purchasing may not be interested in such a product at all) or rare purchasing (which may result from poor availability). Increasing the availability of GF cereal products for a population of Polish female CD patients may allow them to obtain a more diverse diet
The rumen microbiota and metabolism of dairy cows are affected by the dietary rate of inclusion of Yucca schidigera extract
Get PDFNatural plant compounds can be used to supplement livestock diets, improving feed efficiency, production, and health, while also reducing environmental impact. In the present study, a Yucca schidigera (Mohave Yucca) extract was added at four rates of inclusion (ROI) of 0, 5, 15, or 30 g/day to a ryegrass and maize silage-based diet and fed to dairy cows in a 4 × 4 Latin square experimental design. Each period was 28 days in duration, with sampling undertaken during the final week of each period. Solid phase digesta (SPD) and liquid phase digesta (LPD) samples were collected via a rumen cannula and analyzed for volatile fatty acids (VFAs), ammonia N, and microbiome using 16S rRNA gene sequencing. Total fecal and urine collection was undertaken over a 3-day period. Rumen microbial diversity was not affected by ROI (LPD: P = 0.180; SPD: P = 0.059). However, discriminant analysis found a decrease in Methanobrevibacter millerae (linear discriminant analysis, LDA = 2.15) and an increase in an unclassified species of Proteobacteria (LDA = 2.10) associated with ROI. Univariate analysis also revealed differential abundance of operational taxonomic units classified as Prevotellaceae and Fibrobacteraceae by ROI (P 0.05) by ROI; however, there was a linear increase in milk fat content from 38.9 to 42.0 g/kg with ROI (P < 0.05)
Dielectric feature-driven dual prediction of moisture content and bulk density for rice quality assessment
No full textRapid and accurate determination of moisture content and bulk density in rice is essential for quality assessment, storage, and processing in the agri-food industry. In this study, a microwave-based sensing system was developed to simultaneously predict both moisture content and bulk density in rice cultivars using dielectric parameters as input features. Key dielectric features, including phase shift (ϕ), attenuation (A), dielectric constant (ε’), loss factor (ε’’), and loss tangent (tanδ), were measured in the frequency range of 2–4 GHz. The nine machine learning regressors were implemented to develop predictive models, and their performance was comprehensively evaluated. The k-nearest neighbors and random forest models exhibited the highest predictive accuracy. The models achieved high accuracy in predicting moisture content (correlation coefficient (R) = 0.98, root mean square error (RMSE) = 0.015–0.024) and the most significant results for bulk density (R = 0.92, RMSE = 0.600–1.200). These findings provide potentially encouraging routes for integrating the method into larger digital-agriculture systems and state-of-the-art data storage infrastructure