Implementing effective animal-based measurements for assessing animal welfare on farms and slaughter plants.

This chapter describes how to write clear animal welfare standards and guidelines that will be interpreted the same way by different people; the difference between animal-based outcome measures and input resource-based standards; how to determine the most important core criteria or critical points to prevent abuse or neglect; easy-to-use measures for assessing body condition, lameness, injuries, condition of haircoat/feathers, animal handling, hygiene, heat and cold stress and the presence of abnormal behaviour and how to set up effective animal welfare auditing programmes.

Significant Applications of Machine Learning for COVID-19 Pandemic

Machine learning is an innovative approach that has extensive applications in prediction. This technique needs to be applied for the COVID-19 pandemic to identify patients at high risk, their death rate, and other abnormalities. It can be used to understand the nature of this virus and further predict the upcoming issues. This literature-based review is done by searching the relevant papers on machine learning for COVID-19 from the databases of SCOPUS, Academia, Google Scholar, PubMed, and ResearchGate. This research attempts to discuss the significance of machine learning in resolving the COVID-19 pandemic crisis. This paper studied how machine learning algorithms and methods can be employed to fight the COVID-19 virus and the pandemic. It further discusses the primary machine learning methods that are helpful during the COVID-19 pandemic. We further identified and discussed algorithms used in machine learning and their significant applications. Machine learning is a useful technique, and this can be witnessed in various areas to identify the existing drugs, which also seems advantageous for the treatment of COVID-19 patients. This learning algorithm creates interferences out of unlabeled input datasets, which can be applied to analyze the unlabeled data as an input resource for COVID-19. It provides accurate and useful features rather than a traditional explicitly calculation-based method. Further, this technique is beneficial to predict the risk in healthcare during this COVID-19 crisis. Machine learning also analyses the risk factors as per age, social habits, location, and climate.

Exploring the Drivers and the Interventions towards Sustainable Food Security in the Food Supply Chain

The sustainability of food security is a global concern, and one of the priorities related to it is the ability to identify effective efforts that can protect food security along the food supply chain (FSC). The present study demystifies the relation of sustainability and food security and structurally identifies the sustainable drivers, and the pragmatic interventions that can critically contribute to the sustainability of food security within each stage of the FSC. A systematic review of articles from six databases was implemented. Thematic analysis was engaged to detect the sustainable drivers and interventions practised at each stage in the FSC for food security purposes. Six main themes of the drivers and interventions were derived from the analysis: food security governance involvement, input resource management, output management, information sharing, income, and technology. The result identifies the potential that quality management has on the mediating effect towards sustainable food security, through which a conceptual model for sustainable food security in the FSC was thus developed. It is deduced that mapping and categorizing the drivers and intervention of sustainable food securities could help deepen the understanding of effective and innovative practices towards food security in the FSC.

Public-Private Partnerships for agricultural innovation: opportunities and challenges in world practice

The importance of Public Private Partnership (PPP) in agriculture is understood in terms of a shared mechanism among partners for input, resource, market, risk, technology and benefits. In addition, review of various studies indicated the visibility of PPP in various facets of knowledge management, capacity building of youth, development of high end technologies, processing and market promotion in agriculture. The partnership approach apart from developing certain technologies also empowered farmers in terms of enhanced access to technology and market in Russia through organized farmers groups. The limitations of PPP such as focus mainly on high end technologies, high profit margin areas and crops, perceived mistrust and lack of transparency and non-adherence to agreement among partners could be overcome through appropriate working mechanism and policy support. Establishing PPP cell at research and development organizations would spearhead the growth of PPP and thereby sustainable agriculture and livelihood of millions of farm in Russia. The article will be interesting and useful for students of law and economic faculties, it helps them to understand the latest techniques relevant to a particular sector in PPP and or to understand how responses developed in jurisdictions where PPP is firmly established might be applied to new markets.

Miscanthus x giganteus Stem versus Leaf-derived Lignins Differing in Monolignol Ratio and Linkage

As a renewable industrial crop, Miscanthus offers numerous advantages, namely high photosynthesis activity (as a C4 plant) and exceptional CO2 fixation rate. These properties make Miscanthus very attractive for industrial exploitation, such as lignin generation. Here, we present a systematic study analyzing the correlation of the lignin structure with Miscanthus genotype and plant portion (stem versus leaf). Specifically, the ratio of the three monolignols and corresponding building blocks as well as the linkages formed between the units have been studied. Depending on the Miscanthus genotype and plant component (leaf versus stem), correlations between chemical structure and properties of the lignins have been determined, i.e. correlations in molecular weight, polydispersity and decomposition temperature. Lignin isolation was performed using non-catalyzed organosolv pulping and the structure analysis includes NREL, FTIR, UV-Vis, HSQC-NMR, TGA, pyrolysis GC/MS. Structural differences were found for stem and leaf-derived lignins. Compared to beech wood lignins, Miscanthus lignins possess lower molecular weight and narrow polydispersities (< 1.5 Miscanthus vs. > 2.5 beech) corresponding to improved homogeneity. In addition to conventional univariate analysis of FTIR spectra, multivariate chemometrics revealed distinct differences for aromatic in-plane deformations of stem versus leaf-derived lignins. These results emphasize the potential of Miscanthus as low-input resource and Miscanthus-derived lignin as promising agricultural feedstock.

Evaluate the Efficiency of Chinese Regional Quality Development Based on DEA and Malmquist Index

This paper puts forward a kind of method to evaluating the efficiency of quality development based on DEA .By Making a static efficiency evaluation for the quality development of 31 provinces in China with DEA model and Malmquist Index based on the quality competitiveness evaluation index system, the efficiency of the development of Chinese quality based on the dynamic time sequence have been got. The results showed that the input resource allocation of most provinces is unreasonable on the static time sequence. A low quality development efficiency has been leaded to as a result. The technical efficiency is the most influential in the all, it increased unstably but potentially from 2008 to 2012 based on the dynamic time consider. Therefore, we should pay more attention to technical innovation, in order to developing the development efficiency of quality.