Resumen Se reconoce que el ambiente térmico es uno de los factores ecológicos más importantes para determinar el crecimiento, el desarrollo y la productividad de animales domésticos con efectos directos e indirectos sobre su fisiología y comportamiento. A pesar de tener una adaptación específica e individual, es muy común que, dentro de las variaciones de temperatura estacionales o diurnas, los animales se enfrenten a situaciones fuera de su zona de confort térmico. Debido al estrés calórico, las vacas lecheras reducen su producción y fertilidad, y por lo tanto, llevan a cabo un aumento en la incidencia de trastornos metabólicos, causando bajos ingresos para la finca a corto y mediano plazo. Debido al cambio climático mundial, se espera un mayor impacto de las variables meteorológicas en las respuestas de los animales para el futuro. Este estudio tuvo como objetivo describir y comprender las interacciones de las variables asociadas con el estrés calórico en el ganado lechero. El enfoque dinámica de sistemas aplicado a este mecanismo fisiológico específico ayuda a entender el estrés calórico en vacas lecheras cuando se relacionan los aspectos de pastoreo y las necesidades del animal. Se usó una anotación de diagrama de bucle causal para conectar las variables biológicas incluidas en los límites del sistema. Las conexiones causales se validaron con citas bibliográficas sobre la influencia del estrés calórico. Las interconexiones de retroalimentación más importantes resaltadas, subrayan la estructura dominante y los patrones esperados. Se encontraron cuatro bucles balance implicados en los mecanismos fisiológicos utilizados por los animales para reducir la producción de calor metabólico y para regular la homeostasis de la temperatura interna.

Abstract Thermal environment is recognized to be one the most important ecological factor to determine domestic animal growth, development and productivity for direct and indirect effects on its physiology and behavior. Despite having specific and individual adaptation, is very common, within seasonal or diurnal temperature variations, animals deal with situations outside their thermal comfort zone. Due to heat stress, dairy cows reduce milk production and fertility, and therefore, had achieved an increasing in metabolic disorders incidence, causing low revenues to farm in short and medium periods. Due to world climate change more impact of the meteorological variables on animal responses is expected for the future. This study aimed to describe and understand the interactions of variables associated with heat stress in dairy cattle. System thinking approach to this specific physiological mechanism might help to better focus managerial aspects related to grazing conditions and animal needs. A causal loop diagram annotation used to connect biological variables included in system boundaries. Causal connections were validated with literature citations on the heat stress influence. The most important feedback loops highlighted underline dominant structure and expected patterns. Four balancing loops involved in physiological mechanisms used by animals in order to reduce metabolic heat production and to regulate homeostasis of the internal temperature, were found.

Resumen La participación global de Colombia en emisiones de gases de efecto invernadero (GEI) es baja (0.37%); pero de este porcentaje, el 38% es atribuido a la agricultura y ganadería. Son pocos los trabajos sobre emisiones de gases a nivel regional para actividades agrícolas, por lo cual, el objetivo de este documento es estimar dinámicamente los GEI y huella de carbono producto de la ganadería bovina en el departamento del Valle del Cauca. Las estimaciones de GEI se realizaron siguiendo el nivel II de la metodología del IPCC (2006) y el comportamiento poblacional se simuló mediante dinámica de sistemas. Según el inventario ganadero del departamento en los últimos años (2010-2015), las emisiones promedio fueron de 673 millones de kg CO2eq.año-1, con una intensidad de emisión (IE) de 5.58 kgCO2eq.kg leche y 3.54 kgCO2eq.kgcarne-1. Simulando el comportamiento de la población bovina (2016-2035), según tendencias históricas, el número de animales y sus emisiones propendían a la baja. La IE simulada estuvo entre 4.1 y 4.3 kgCO2eq.kgleche-1 y entre 3.7 y 3.9 kgCO2eq.kg.carne-1. Al aumentar la natalidad (83 a 90%), disminuir la edad al primer parto (34 a 30 meses) y mejorar la producción de leche vaca.día-1 (5.33 a 10 kg), aumentó la producción de leche y carne en 100 y 4 millones de kg anuales, respectivamente. Con disminución en IE de 1.4 unidades para leche y 2 para carne. Esta investigación demostró que seleccionando animales eficientes (productiva y reproductivamente), se puede producir más proteína de origen animal con menor huella de carbono.

Abstract The contribution of Colombia to global emissions of greenhouse gases (GHGs) is low (0.37%); from this percentage, 38% is attributed to agriculture. There are few studies on GHGs emissions at the regional level for agriculture. Therefore, the aim of this research is to estimate dynamically GHG and carbon footprint produced by cattle in the department of Valle del Cauca, Colombia. GHG estimates were established as follows: Tier II of IPCC methodology (2006), and population trends, which were simulated using dynamic system. According to the departmental livestock inventory in recent years (2009-2015), average emissions were 673 million of CO2eq. year-1, with emissions intensity (EI) of 5.58 kgCO2eq.kg.milk-1 and 3.54 kgCO2eq.kgmeat-1. Simulating the behavior of the bovine population (2016-2035), based on historical trends, the number of animals and emissions tended to reduce. The simulated EI was in 4.1 - 4.2 kgCO2eq.kg.milk-1 and 3.7 - 3.9 kgCO2eq.kg.meat-1. Increasing birth rates (83 to 90%), lower age at first birth (34 to 30 months), increased milk production per cow per day (5.33 to 10 kg), increased production of milk and meat in 100 and 4 million kg per year, respectively; with decrease in EI of 1.4 for milk and 2 units for meat. This study showed selecting efficient animals (productively and reproductively), can produce more animal protein with less carbon footprint.

ABSTRACT If the world population continues to increase exponentially, wealth and education inequalities might become more pronounced in the developing world. Thus, offering affordable, high-quality protein food to people will become more important and daunting than ever. Past and future challenges will increasingly demand quicker and more innovative and efficient solutions. Animal scientists around the globe currently face many challenging issues: from ensuring food security to prevent excess of nutrient intake by humans, from animal welfare to working with genetic-engineered animals, from carbon footprint to water footprint, and from improved animal nutrition to altering the rumen microbiome. Many of these issues are most likely to continue (or to exacerbate further) in the coming years, but animal scientists have many options to surmount the obstacles posed to the livestock industry through tools that are presently available. The frequency, interval, and intensity of livestock impacts, however, differ across regions, production systems, and among livestock species. These differences are such that the generalization of these issues is impossible and dangerous. For instance, when we discuss domesticated ruminant nutrition in the human food context, we look for the most efficient ruminant feeds that complement, rather than compete with, grains grown for direct human nutrition. Greater scrutiny and standardization are needed when developing and validating methodologies to assess short- and long-term impacts of livestock production. Failure in correctly quantifying these impacts may lead to disregard and disbelief by the livestock industry, increased public confusion, and the development of illusionary solutions that may amplify the impacts, thereby invalidating its original intent.

The majority of sheep milk produced in the world is transformed into cheese. Feeding is a major factor affecting the quality of sheep milk and, therefore, of sheep cheese. Because fat is the main compound of cheese, this review gives an update on the effects of feeding and nutrition on milk fat content and deeply discusses feeding strategies aimed at increasing the levels of healthy fatty acids (FA), such as conjugated linoleic acid and omega-3 FA, in milk and cheese in the human diet. In addition, the use of alternative feed resources such as by-products, aromatic plants, and phenolic compounds in the sheep diet and their effects on milk and cheese FA composition are also discussed. Among feeding strategies, grazing and the use of supplements rich in oils seem to be the best and the cheapest strategies to improve the nutritional value of the fatty acid profile in sheep cheese.

This review deals with the most relevant limits and developments of the modeling of intake of sheep and goats reared intensively and extensively. Because small ruminants are normally fed ad libitum, voluntary feed intake is crucial in feeding tactics and strategies aimed at optimal animal production. The effects of genetic, neuroendocrine, hormonal, feed and environmental factors on voluntary feed intake were discussed. Then, several mathematical models to estimate dry matter intake (DMI) were examined, with emphasis on empirical models for sheep and goats in intensive farm systems or in extensive areas under pasture or rangeland conditions. A sensitivity analysis of four models of prediction of DMI in housed lactating dairy sheep and meat sheep breeds was also presented. This work evidenced a large variability in the approaches used and in the variables considered for housed sheep and goats. Regarding the estimation of feed intake for grazing sheep and browsing goats, the accuracy of estimates based on empirical models developed so far is very low when applied out of the boundaries of the studied system. Feeding experiments indoors and outdoors remain fundamental for a better modeling and understanding of the interactions between feeds and small ruminants. However, there is a need for biological and theoretical frameworks in which these experiments should be carried out, so that appropriate empirical or mechanistic equations to predict DMI could be developed.