Effects of heat-stress on production in dairy cattle.

Economic losses from heat stress by us livestock industries

The ability of individual organisms to alter morphological and life-history traits in response to the conditions they experience is an example of phenotypic plasticity which is fundamental to any population's ability to deal with short-term environmental change. We currently know little about the prevalence, and evolutionary and ecological causes and consequences of variation in life history plasticity in the wild. Here we outline an analytical framework, utilizing the reaction norm concept and random regression statistical models, to assess the between-individual variation in life history plasticity that may underlie population level responses to the environment at both phenotypic and genetic levels. We discuss applications of this framework to date in wild vertebrate populations, and illustrate how natural selection and ecological constraint may alter a population's response to the environment through their effects at the individual level. Finally, we present future directions and challenges for research into individual plasticity.

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1420-9101.2007.01300.x

The evolutionary ecology of individual phenotypic plasticity in wild populations.

Despite many challenges faced by animal producers, including environmental problems, diseases, economic pressure, and feed availability, it is still predicted that animal production in developing countries will continue to sustain the future growth of the world's meat production. In these areas, livestock performance is generally lower than those obtained in Western Europe and North America. Although many factors can be involved, climatic factors are among the first and crucial limiting factors of the development of animal production in warm regions. In addition, global warming will further accentuate heat stress-related problems. The objective of this paper was to review the effective strategies to alleviate heat stress in the context of tropical livestock production systems. These strategies can be classified into three groups: those increasing feed intake or decreasing metabolic heat production, those enhancing heat-loss capacities, and those involving genetic selection for heat tolerance. Under heat stress, improved production should be possible through modifications of diet composition that either promotes a higher intake or compensates the low feed consumption. In addition, altering feeding management such as a change in feeding time and/or frequency, are efficient tools to avoid excessive heat load and improve survival rate, especially in poultry. Methods to enhance heat exchange between the environment and the animal and those changing the environment to prevent or limit heat stress can be used to improve performance under hot climatic conditions. Although differences in thermal tolerance exist between livestock species (ruminants > monogastrics), there are also large differences between breeds of a species and within each breed. Consequently, the opportunity may exist to improve thermal tolerance of the animals using genetic tools. However, further research is required to quantify the genetic antagonism between adaptation and production traits to evaluate the potential selection response. With the development of molecular biotechnologies, new opportunities are available to characterize gene expression and identify key cellular responses to heat stress. These new tools will enable scientists to improve the accuracy and the efficiency of selection for heat tolerance. Epigenetic regulation of gene expression and thermal imprinting of the genome could also be an efficient method to improve thermal tolerance. Such techniques (e.g. perinatal heat acclimation) are currently being experimented in chicken.

Adaptation to hot climate and strategies to alleviate heat stress in livestock production.

Environmentally induced periods of heat stress decrease productivity with devastating economic consequences to global animal agriculture. Heat stress can be defined as a physiological condition when the core body temperature of a given species exceeds its range specified for normal activity, which results from a total heat load (internal production and environment) exceeding the capacity for heat dissipation and this prompts physiological and behavioral responses to reduce the strain. The ability of ruminants to regulate body temperature is species- and breed-dependent. Dairy breeds are typically more sensitive to heat stress than meat breeds, and higher-producing animals are more susceptible to heat stress because they generate more metabolic heat. During heat stress, ruminants, like other homeothermic animals, increase avenues of heat loss and reduce heat production in an attempt to maintain euthermia. The immediate responses to heat load are increased respiration rates, decreased feed intake and increased water intake. Acclimatization is a process by which animals adapt to environmental conditions and engage behavioral, hormonal and metabolic changes that are characteristics of either acclimatory homeostasis or homeorhetic mechanisms used by the animals to survive in a new ‘physiological state’. For example, alterations in the hormonal profile are mainly characterized by a decline and increase in anabolic and catabolic hormones, respectively. The response to heat load and the heat-induced change in homeorhetic modifiers alters post-absorptive energy, lipid and protein metabolism, impairs liver function, causes oxidative stress, jeopardizes the immune response and decreases reproductive performance. These physiological modifications alter nutrient partitioning and may prevent heat-stressed lactating cows from recruiting glucose-sparing mechanisms (despite the reduced nutrient intake). This might explain, in large part, why decreased feed intake only accounts for a minor portion of the reduced milk yield from environmentally induced hyperthermic cows. How these metabolic changes are initiated and regulated is not known. It also remains unclear how these changes differ between short-term v. long-term heat acclimation to impact animal productivity and well-being. A better understanding of the adaptations enlisted by ruminants during heat stress is necessary to enhance the likelihood of developing strategies to simultaneously improve heat tolerance and increase productivity.

https://dspace.unitus.it/bitstream/2067/1536/1/FINAL%20ANIMAL%20repo_A77rkGLY.pdf

Metabolic and hormonal acclimation to heat stress in domesticated ruminants.

Se estimaron parametros geneticos para caracteres de ultrasonido para distintas exigencias de nivel promedio en el espesor de grasa subcutanea (uEGS) del grupo  de contemporaneos. Las heredabilidades para peso al destete, area del ojo del bife (uAOB), uEGS, grasa intramuscular (uMarb) y aptitud materna obtenidas para la base de datos sin edicion (C) fueron: 0,34, 0,34, 0,16, 0,07 y 0,23, respectivamente. Al emplear un nivel de exigencia intermedia (GC25 > 2,5 mm), las estimaciones de heredabilidad fueron 0,35, 0,34, 0,25, 0,09 y 0,24. Y, cuando el criterio de edicion fue mas restrictivo (GC3 > 3 mm), las respectivas heredabilidades fueron 0,37, 0,34, 0,29, 0,18 y 0,25, respectivamente. El mayor nivel de exigencia implico un uso de apenas el 13% de las observaciones de uEGS y uMARB. Las correlaciones de los meritos geneticos predichos entre C y los diferentes niveles de exigencia fueron 0,95 con GC25 y 0,82 con GC3 para uEGS, mientras que para uMARB fueron 0,93 con GC25 y 0,81 con GC3. Se evaluo que la incertidumbre en las estimaciones obtenidas con el archivo  GC3 fue demasiado alta como para concluir sobre la utilidad de emplear esta alternativa. Se concluye que no amerita incorporar a la evaluacion genetica nacional la edicion empleada al obtener GC25 y los parametros correspondientemente estimados debido al bajo efecto que tuvo sobre el ordenamiento de los reproductores y a las escasas modificaciones en las precisiones calculadas.

Parámetros genéticos de caracteres de canal por ultrasonido para Angus en el Uruguay

A mechanistic, dynamic whole farm simulation model was developed to evaluate the effect of farming strategies on the productivity of dairy grazing systems. The model integrates local available information on pasture growth and quality and current knowledge on animal nutrition and metabolism. The pastoral component simulates the pasture rotation structure of the farm, with variable number and size of paddocks, to which the user must assign a pasture type from an available database. Each pasture type is represented by initial herbage mass (HM) and two vectors: monthly dry matter (DM) growth rate values and organic matter digestibility (OMD) values. The model is driven by pasture growth rate (PGR) on a monthly interval step. Several pasture production and management strategies can be defined as a per paddock basis. The cows are defined in terms of their potential for milk production (MPP), body condition score (BCS, scale 1-5), biotype Frame (body weight with BCS of 3), calving date, and contents of fat and protein in milk. These variables are used to characterize the average of up to six groups of adult cows which are defined by the user to represent the current situation of a dairy farm or a theoretical system. Average grazing DM intake (DMI) of each calving group of cows is estimated considering animal factors: Frame, MPP and days in milk (DIM); pasture factors: OMD, pre-grazing HM (pg-HM) and substitution rate (SR) of supplementary feed. The model is based on metabolisable energy (ME) and environmental thermo neutrality is assumed. Total ME intake (MEI) is partitioned among body functions following a defined priority: maintenance, pregnancy, milk production potential and body reserves (BR). One distinct feature of this model is that the approach used implies an active role of BR in defining the partition of MEI. If ME balance for potential milk is not achieved then BR are mobilized at a constant rate (κ) to give an absolute amount which is proportional to the current size of estimated mass of BR, whose initial level is set when inputting the initial BCS. Another feature of this model is that it can manage decisions taken at different system levels (pasture rotation structure, annual DM yield and seasonal distribution, reserves production and supplementation strategies, variables stocking rates, effects of animal size, BCS, milk potential, etc.), to quantitatively assess the impact of these decisions on cows and farm productivity. The model output was initially validated at the "cow biotype level" using published farmlet trials. The relative prediction error (RPE) and concordance correlation coefficient (CCC) were used as measures of fitness; models with values of RPE less than 10% and values of CCC greater than 0.90 were considered to have significant predictive power. Daily milk yield per cow, live weight and BCS change through the l actation were validated using a set of 12 monthly values for each trait, obtained from cows of contrasting body sizes (Heavy and Light).The RPE and CCC were 16% and 0.94 in Heavy, 20 % and 0.87 in Light cows for milk yield; 3 % and 0.72 in Heavy, 2 % and 0.81 in Light cows for live weight; 6 % and 0.18 in Heavy and 9% and -0.47 in Light cows for BCS change. Monthly intake of pasture per ha was validated using another independent set of 12 average monthly values for each of 5 farmlet stocking rates treatments (2.2; 2.7; 3.1; 3.7 and 4.3 cows/ha). RPE and CCC were: 13% and 0.77; 9% and 0.87; 12% and 0.93; 13 % and 0.91; 16 % and 0.88 respectively. The model was responsive to contrasting cow type and farming management. These results show that the model has acceptable predictive power and can be used to better understand actual farming systems and also to evaluate the expected productive impact of some technical changes introduced at the farm level.

/pdf/development-and-validation-of-a-mechanistic-whole-dairy-farm-52ktvm2mrp.pdf

O. Ravagnolo

Papers

Parámetros genéticos de caracteres de canal por ultrasonido para Angus en el Uruguay

Development and validation of a mechanistic whole dairy farm model to evaluate farming strategies under grazing conditions in Uruguay.