• Korean Journal of Organic Agriculture
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• v.20 no.2
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• pp.185-200
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• 2012

It has been reported that world population continues to increase so that a matter of food security can be a world-wide problem for mankind. An anticipated rise in world population of 30% and the subsequent increased demand for food brings with it challenges in terms of global resource usage and food security. However, ruminant livestock production and consumption make a large contribution to the greenhouse gas (GHG) emissions which can be attributable to food production. Given the association between GHG and climate change, this is clearly of great concern to the livestock industry worldwide. Nevertheless, ruminant livestock also play an important role in global food security as they can convert the plant cell wall materials and non-protein nitrogen compounds, found widely in plants but indigestible to all monogastric animals including man, into high value proteins for human consumption. Much effort has been made to maximize animal production, feed conversion ratio, and to improve animal breeding in ruminant agriculture. In addition improving feed formulation techniques, developing chemical additives, plant extracts, and new plant varieties for grazing have been tested. Future ruminant production systems will need to capitalize on important benefits of ruminants. It is therefore suggested that ruminant agriculture has a key role to play in maintaining and enhancing provision of quality proteins and essential nutrients for human being but the challenge of reducing GHG emissions, and methane in particular, needs to be successfully addressed.

• Nutrition Research and Practice
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• v.18 no.1
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• pp.149-164
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• 2024

BACKGROUND/OBJECTIVES: The economic recession caused by the coronavirus disease 2019 pandemic disproportionately affected poor and vulnerable populations globally. Better uunderstanding of vulnerability to shocks in food supply and demand in the Asia Pacific region is needed. SUBJECTS/METHODS: Using secondary data from rapid assessment surveys during the pandemic response (n = 10,420 in mid-2020; n = 6,004 in mid-2021) in India, Indonesia, Myanmar, and Vietnam, this study examined the risk factors for reported income reduction or job loss in mid-2021 and the temporal trend in food security status (household food availability, and market availability and affordability of essential items) from mid-2020 to mid-2021. RESULTS: The proportion of job loss/reduced household income was highest in India (60.4%) and lowest in Indonesia (39.0%). Urban residence (odds ratio [OR] range, 2.20-4.11; countries with significant results only), female respondents (OR range, 1.40-1.69), engagement in daily waged labor (OR range, 1.54-1.68), and running a small trade/business (OR range, 1.66-2.71) were significantly associated with income reduction or job loss in three out of 4 countries (all P < 0.05). Food stock availability increased significantly in 2021 compared to 2020 in all four countries (OR range, 1.91-4.45) (all P < 0.05). Availability of all essential items at markets increased in India (OR range, 1.45-3.99) but decreased for basic foods, hygiene items, and medicine in Vietnam (OR range, 0.81-0.86) in 2021 compared to 2020 (all P < 0.05). In 2021, the affordability of all essential items significantly improved in India (OR range, 1.18-3.49) while the affordability of rent, health care, and loans deteriorated in Indonesia (OR range, 0.23-0.71) when compared to 2020 (all P < 0.05). CONCLUSIONS: Long-term social protection programs need to be carefully designed and implemented to address food insecurity among vulnerable groups, considering each country's market conditions, consumer food purchasing behaviors, and financial support capacity.

• Korean Journal of Community Nutrition
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• v.1 no.2
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• pp.277-290
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• 1996

• The monthly packaging world
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• s.104
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• pp.89-93
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• 2001

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.11
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• pp.1840-1841
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• 2018

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.7-7
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• 2022

• Water for future
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• v.56 no.11
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• pp.8-13
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• 2023

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.5
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• pp.603-621
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• 2017

The current review focuses on characterization and conservation efforts vital for the development of breeding programmes for indigenous beef cattle genetic resources in Southern Africa. Indigenous African cattle breeds were identified and characterized using information from refereed journals, conference papers and research reports. Results of this current review reviewed that smallholder beef cattle production in Southern Africa is extensive and dominated by indigenous beef cattle strains adaptable to the local environment. The breeds include Nguni, Mashona, Tuli, Malawi Zebu, Bovino de Tete, Angoni, Landim, Barotse, Twsana and Ankole. These breeds have important functions ranging from provision of food and income to socio-economic, cultural and ecological roles. They also have adaptive traits ranging from drought tolerant, resistance to ticks and tick borne diseases, heat tolerance and resistance to trypanosomosis. Stakeholders in the conservation of beef cattle were also identified and they included farmers, national government, research institutes and universities as well as breeding companies and societies in Southern Africa. Research efforts made to evaluate threats and opportunities of indigenous beef cattle production systems, assess the contribution of indigenous cattle to household food security and income, genetically and phenotypically characterize and conserve indigenous breeds, and develop breeding programs for smallholder beef production are highlighted. Although smallholder beef cattle production in the smallholder farming systems contributes substantially to household food security and income, their productivity is hindered by several constraints that include high prevalence of diseases and parasites, limited feed availability and poor marketing. The majority of the African cattle populations remain largely uncharacterized although most of the indigenous cattle breeds have been identified.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.294-294
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• 2022

In 2013, the Asian Development Bank classified the Philippines among the countries facing high food security risks. Evidence has suggested that climate change has affected agricultural productivity, and the effect of extreme climatic events notably drought has worsened each year. This had resulted in serious hydrological repercussions by limiting the timely water availability for the agriculture sector. Laguna is the 3^rd most populated province in the country, and it serves as one of the food baskets that feed the region and nearby provinces. In addition to climate change, population growth, rapid industrialization, and urban encroachment are also straining the delicate balance between water demand and supply. Studies have projected that the province will experience less rainfall and an increase in temperature, which could simultaneously affect water availability and crop yield. Hence, understanding the composite threat of climate change for crop yield and water consumption is imperative to devise mitigation plans and judicious use of water resources. The water footprint concept elaborates the water used per unit of crop yield production and it can approximate the dual impacts of climate change on water and agricultural production. In this study, the water footprint (WF) of six main crops produced in Laguna were estimated during 2010-2020 by following the methodology proposed by the Water Footprint Network. The result of this work gives importance to WF studies in a local setting which can be used as a comparison between different provinces as well as a piece of vital information to guide policy makers to adopt plans for crop-related use of water and food security in the Philippines.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.182-190
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• 2021

Deep learning is an advanced technology for large-scale data analysis, with numerous promising cases like image processing, object detection and significantly more. It becomes customarily to use transfer learning and fine-tune a pre-trained CNN model for most image recognition tasks. Having people taking photos and tag themselves provides a valuable resource of in-data. However, these tags and labels might be noisy as people who annotate these images might not be experts. This paper aims to explore the impact of noisy labels on fine-tuning pre-trained CNN models. Such effect is measured on a food recognition task using Food101 as a benchmark. Four pre-trained CNN models are included in this study: InceptionV3, VGG19, MobileNetV2 and DenseNet121. Symmetric label noise will be added with different ratios. In all cases, models based on DenseNet121 outperformed the other models. When noisy labels were introduced to the data, the performance of all models degraded almost linearly with the amount of added noise.