The data-information-knowledge-wisdom (DIKW) hierarchy as a pyramid to manage knowledge. Reproduced with permission from Tedeschi (2019).

The data-information-knowledge-wisdom (DIKW) hierarchy as a pyramid to manage knowledge. Reproduced with permission from Tedeschi (2019).

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• The improvement of nutrition efficiency in sheep and goats is more challenging than for other species. Because of their nutritional and environmental adaptability, sheep and goats are reared in very diverse farming (from extensive to highly intensive) and feeding (from grazing and browsing to total mixed diets) systems, in wide-ranging geographic...

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... learning is needed to develop ideas to gain knowledge. Much of the Platonic thinking on knowledge was incorporated into the data-information-knowledge-wisdom hierarchy (Figure 6), which acts as a lighthouse that has guided much scientific research. At the beginning of research endeavors in any science field, scientists lacked data and frequently blamed that lack on our inability to make adequate predictions or forecasts. ...
Context 2
... intelligence misses the wisdom in the data-information-knowledge-wisdom hierarchy because wisdom requires judgments that are unique to individuals who assimilate information and knowledge simultaneously to make intelligent decisions and novelty creations. The scientific road we have traveled since the 1940s has had its ups and downs (Tedeschi, 2019), reflecting our appetite for scientific data, the need to understand the unknown, and the desire to make rational decisions to improve our livelihoods, assuming that greater knowledge and wisdom would reduce the risks of being wrong ( Figure 6). Despite our ignorance of how artificial intelligence works its way through data in developing its sets of neural network weights for the inputs-what some call learning-it is a powerful advancement in predictive analytics. ...

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... In arid zones, small body-sized and dwarf goats can survive better than other breeds (Gaughan et al. 2019). Recently, there has been an increased goat population in Africa and Asia (FAOSTAT 2017), with the total world number of small ruminants growing at a faster rate (Cannas et al. 2019). In an indirect response to the growing climate change, global goat and sheep population post-millennium has increased by more than 282 million and 142 million, respectively, compared to 177 million in cattle (FAOSTAT 2017). ...
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... The FAO statistical database (FAOSTAT, 2020; http://www.fao.org/faostat/en/#home; accessed on 15 March 2021) reports 1239 million sheep heads in the global population in 2020, which increased by approximately 15% in the last 15 years [5]. However, this estimated population size has remained almost stable over the last three decades (FAOSTAT, 2020). ...
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... The generated reinforcing loop, labeled "flock size", is expected to drive exponential growth of the system ( Figure 3.1). Since regional flock maintenance is highly associated with methane emissions (Cannas et al. 2019;Atzori et al. 2020), an exponential growth in flock size will also drive exponential growth in environmental impact (Figure 3.1). Exponential growth is not a sustainable pattern in systems (Ford 1999;Turner et al. 2016). ...
... that would respect the current cheese and milk market conditions and the land use capacity of Sardinia. Indeed, a constant increase in production efficiency would push for a reduction in sheep heads, which would lead to a positive reduction in environmental impact Cannas et al. 2019;Marino et al. 2016). ...
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... An interesting finding is that the extensive efficient farms seem to depend more on home-grown feed, whereas the intensive and semi-intensive farms depend more on purchased feed. Sheep farmers even under the same production system apply different feeding strategies; some produce home-grown feed, whereas others prefer to purchase a large part of their feed from markets and a debate regarding which strategy is the most profitable differ [29,40]. Another interesting result is that the fixed cost, which is the most important source of production cost followed by feeding cost, was much higher in the inefficient farms. ...
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... There is ample research literature on energy and protein requirements, and some modern feeding models for sheep have been published or updated in the last decade [2][3][4]. Still, none of the existing feeding standards or models reports optimal dietary fiber (NDF) concentrations for sheep. ...
... The Cornell Net Carbohydrate and Protein System (CNCPS) for sheep [4], as well as the Small Ruminant Nutrition System (SRNS) for sheep and goats based on CNCPS [8], suggest an optimum dietary NDF concentration of 200-245 g/kg DM. Although small ruminants adapt their feeding behavior to the fiber proportion of the offered diet [7,9], a reduction in fiber concentration below 20% has been shown to increase the risk of ruminal acidosis in feedlot cattle, which can challenge animal health and welfare [10]. ...
... Increasing dietary starch inclusion may also decrease dry matter intake, initially through poor digestibility of NDF and physical NDF filling and later through chemical satiety depending on dietary and animal variables [13]. Given the nutritional models have been evaluated based on meta-analyses of microbial protein flow [2,4]. Not accounting for depressed efficiency of microbial protein synthesis with higher starch diets or reduced DMI could overpredict microbial protein supply, limiting metabolizable protein. ...
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... This population growth is related to these animals ability to adapt to different environmental conditions and nutritional plans. As observed by Cannas et al. (2019), small ruminants are distributed across the most diverse geographical areas around the world, where different feeding systems and breeds are being used. ...
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... Because of the high requirements of high-yielding sheep and goats, dietary formulation should be carefully carried out and monitored. Although there is a vast research literature on energy and protein requirements in sheep and goats, and some modern feeding models for these species have been published or updated in the last decade (see review of Cannas et al., 2019), none of the existing feeding systems reports optimal dietary fibre (NDF) and non-fibre carbohydrate (sugars, starch and pectin) concentrations. Thus, it becomes difficult to translate energy requirements into practical formulation of diets, considering that rumen function and microbial efficiency are markedly affected by these nutrients. ...
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This review discusses the most relevant aspects of nutritional, reproductive and health management, the three pillars of flock efficiency, production and sustainability regarding the intensification of production in sheep and goats. In small ruminants, reproductive management is dependent on seasonality, which in turn depends on breed and latitude. Nutrition represents the major cost for flocks and greatly affects their health, the quality of their products and their environmental impact. High-yielding sheep and goats have very high requirements and dietary intake, requiring nutrient-dense diets and sophisticated nutritional management that should always consider the strong interrelationships among nutrition, immunity, health, reproduction, housing and farm management. The reproductive pattern is to a great extent assisted by out-of-season breeding, facilitating genetic improvement schemes, and more recently by advanced reproductive technologies. Heath management aims to control or eradicate economic and zoo-notic diseases, ensuring animal health and welfare, food safety and low ecosystem and environmental impacts in relation to chemical residues and pathogen circulation. In highly producing systems, nutrition, genetic and hazard factors assume a complex interrelationship. Genomic and management improvement research and technological innovation are the keys to sustain sheep and goat production in the future.
... To the best of our knowledge, there is no available data on lamb nutrition and corresponding costs for our case study. Data from other regions are possibly not appropriate to be used because the nutritional requirements depend on genetic, environmental and managerial factors, which can differ significantly between regions (Cannas et al., 2019). Though limited, our simplified model allows for considering a feeding cost-prolificacy linkage. ...
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