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What animal has fat with the highest energy density?

What animal has fat with the highest energy density?


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Fat is more energy-dense than protein and carbohydrates, it is not only an energy deposit but also an organ with many functions such as cushioning and metabolism regulations. I want to know are animal fats similar across species (composition & function), specifically what animal has fat with the highest energy density?


In both humans and animals, the body fat stores appear as triglycerides, which can be composed of different fatty acids, but they all have about 9 kilocalories per gram (USDA).

There can be different amounts of fat in different fat cells, so there can be slightly different amounts of fat in 100 grams of different types of fat tissue.

Calories in 100 g of fat from different animals:

  • Lard = 902
  • Beef tallow = 902
  • Chicken fat = 900
  • Turkey fat = 900
  • Goose fat = 900

The fat mentioned above was white fat, which appears as a single fat droplet in a fat cell. Humans and some animals, especially rodents and bats, also have different amounts of brown fat in which the fat cells contain a lot of mitochondria and the fat is present in multiple droplets, so, logically, brown fat tissue has lower energy density.


Energy density

In physics, energy density is the amount of energy stored in a given system or region of space per unit volume. It may also be used for energy per unit mass, though a more accurate term for this is specific energy (or gravimetric energy density).

Often only the useful or extractable energy is measured, which is to say that inaccessible energy (such as rest mass energy) is ignored. [1] In cosmological and other general relativistic contexts, however, the energy densities considered are those that correspond to the elements of the stress–energy tensor and therefore do include mass energy as well as energy densities associated with the pressures described in the next paragraph.

Energy per unit volume has the same physical units as pressure, and in many circumstances is a synonym: for example, the energy density of a magnetic field may be expressed as (and behaves as) a physical pressure, and the energy required to compress a compressed gas a little more may be determined by multiplying the difference between the gas pressure and the external pressure by the change in volume. A pressure gradient has the potential to perform work on the surroundings by converting internal energy to work until equilibrium is reached.


Human Brain Size

The distinguishing factor between humans and our primate ancestors is our brain size.

(Although not all of us were blessed with this adaptation…)

Between 6 million and 2.5 million years ago, our brain size was fairly consistent. Since then, it has quadrupled in size.

According to Viljammur Stefansson in The Fat of the Land, the forest lands turned into dry prairies around 2 million years ago from climate change.

The pre hominid diet of fruits and seeds would no longer suffice. The cooler grasslands made plant foods more difficult to procure because of their seasonality .

Some pre hominids just turned to lower quality plant foods. To be fair, this was the easier approach and not even our ancestors were immune to laziness.

But fossil evidence shows that by 1.2 million years ago, these ancestors of ours died out , while othermore “entrepreneurial” hominids found new ways to survive and obtain sufficient energy.


Protein

Protein is used in every cell of your body, and you need the stuff for healthy muscles and skin. Protein is made up of amino acids, and your body needs 20 types of these to function. You make 11 on your own, but nine amino acids are essential, meaning you must get them from food. Animal products contain all essential amino acids, as do soy beans. However, most plant foods don't. Combining complementary plant-based proteins such as rice and beans will allow you to meet amino acid needs. About 10 to 35 percent of your total daily calories should be protein 46 grams per day is sufficient for most women.


Carbs are one of your best friends when it comes to losing weight. Why? Because if you choose wisely, carb foods can be filling, nutritious and also low-calorie. Fruits, vegetables and some whole-grain foods are rich in carbs and low in caloric density. According to Penn State nutrition researcher Dr. Barbara Rolls, foods with higher amounts of water generally have lower energy density. Many fresh fruits and vegetables contain between 75 and 95 percent water, reports the University of Kentucky Cooperative Extension Service. The fiber content of fruits and vegetables also contributes to feeling full, as fiber provides bulk to the diet. Fruits and veggies are also rich sources of vitamins and minerals ChooseMyPlate.gov recommends half of your mealtime plate be filled with fruits and vegetables.

According to Ellie Whitney and Sharon Rolfes in “Understanding Nutrition,” protein has the greatest satiating ability of any macronutrient. Digested slowly, it helps keep you feeling full longer. Despite the fact that the caloric contribution of protein is the same as carbs, it has a lower water content than most fruits and veggies. Common protein foods are animal products that contain some fat, which raises the caloric density of the food. Examples include beef, eggs, pork, chicken, turkey and fish. The Institute of Medicine recommends that 10 to 35 percent of the calories in your diet come from protein.

Because fat is the most concentrated source of calories in your diet, limit fat intake to no more than 35 percent of calories, warns the Institute of Medicine. This is critical if you are trying to lose weight. If you have the opposite problem, and want to gain weight, make more of your food choices calorie-dense. Even so, remember that moderation is key when eating high-fat foods, as high-fat diets are associated with heart disease, stroke and development of some cancers. The type of fat you choose is also important. The 2010 Dietary Guidelines for Americans recommend that saturated fats be limited to no more than 10 percent of calories. Trans fat intake should be as low as possible, but not more than 1 percent of calories.


RESULTS

Flights

Birds successfully completed 20 flights (11 HEWL, nine LEWL) ranging in duration from 28 min to 600 min (10 h), and flight durations were not significantly different (t=−0.30, d.f.=16.56, P=0.77) between the treatment groups [HEWL, mean 246 min (47–600 min) LEWL, mean 373.7 min (28–600 min)].

Body composition changes in flight

Before flight, birds had a mean (±s.d.) mass of 12.67±1.33 g (10.37–15.04 g) with average fat load of 1.98±0.98 g (0.533–3.68 g), which corresponds to a mean percentage fat of 15.16% (4.84–26.40%) and an average fat-free mass of 10.68±0.77 g (9.66–12.19 g). There were no significant differences in mass (F1,18=0.448, P=0.512), fat mass (F1,18=0.858, P=0.367) or fat-free mass between the treatment groups prior to flight (F1,18=0.001, P=0.991). After flight, birds had an average mass of 11.53±1.24 g (10.04–14.19 g), with an average fat load of 1.32±0.91 g (0.26–3.59 g), which corresponds to a mean percentage fat of 11.50% (2.60–26.75%) and an average fat-free mass of 10.12±0.70 g (9.04–11.40 g).

During flight, birds under the HEWL conditions lost significantly more mass than birds under the LEWL conditions (F1,17=6.99, P=0.017), and mass losses increased with flight duration (F1,17=128.57, P<0.001). Total fat loss increased with flight duration (F1,17=282.58, P<0.001), and there were no significant differences in total fat loss in response to different humidity treatments (F1,17=0.410, P=0.530). Total fat-free mass loss increased with increasing flight duration overall (F1,17=10.27, P=0.005), and birds under the HEWL treatment lost more fat-free mass than those under the LEWL treatment (F1,17=9.75, P=0.006 Fig. 1 post fat-free HEWL, 10.05 g post fat-free LEWL, 10.26 g). Birds under the HEWL conditions lost fat-free mass at a rate of 0.249±0.16 g h −1 , whereas birds under the LEWL conditions lost fat-free mass at a rate of 0.227±0.27 g h −1 . Overall, the rate of fat mass loss was 0.144±0.05 g h −1 . Mean flight cost was 1.85±0.58 W (1.19–3.36 W) and there were no significant differences in flight costs between treatments (F1,17=0.176, P=0.680), but flight costs did decrease with increasing flight duration (F1,17=6.043, P=0.024).

Reduction in mass, fat mass and fat-free mass with increasing flight duration in Setophaga coronata. (A–C) Reductions in mass (A), fat mass (B) and fat-free mass (C) increased as flight duration increased. Mass loss and fat-free mass loss were significantly greater in the high evaporative water loss (HEWL red N=11 individuals) treatment group compared with the low evaporative water loss (LEWL blue N=9 individuals) treatment group (P<0.05), but there was no significant interaction between treatment groups in C and no significant difference in fat mass loss between the treatment groups.

Reduction in mass, fat mass and fat-free mass with increasing flight duration in Setophaga coronata. (A–C) Reductions in mass (A), fat mass (B) and fat-free mass (C) increased as flight duration increased. Mass loss and fat-free mass loss were significantly greater in the high evaporative water loss (HEWL red N=11 individuals) treatment group compared with the low evaporative water loss (LEWL blue N=9 individuals) treatment group (P<0.05), but there was no significant interaction between treatment groups in C and no significant difference in fat mass loss between the treatment groups.

Overnight RMR

There was a significant reduction in whole-animal RMR after flight (F1,9=8.04, P=0.020) and fat-free mass was a significant covariate (F1,9=5.79, P=0.039 Fig. 2), whereas structural size was not significant (P=0.628). Before flight, birds had an average RMR of 0.256±0.038 W, which is greater than that predicted allometrically (0.189 W McKechnie and Wolf, 2004) and there was no significant relationship between RMR and mass (P=0.23), fat-free mass (P=0.49) or structural size (P=0.80). After flight, RMR was 0.220±0.034 W, which represents a 14.35% reduction compared to pre-flight levels (Fig. 2), and corresponds with a 9.0% reduction in total mass and a 4–6% loss of fat-free mass (see above). Post-flight RMR was significantly related to mass (F1,12=12.968, P=0.004) and to fat-free mass [F1,12=8.07, P=0.015 logRMR, −4.633+1.27 log(mass) logRMR, −4.423+1.23 log(fat-free mass)]. The reduction in RMR was not related to flight duration (F1,9=1.61, P=0.236 Fig. 3A), did not differ between humidity treatments (F1,9=2.67, P=0.136) and was not related to change in fat-free mass (F1,9=1.41, P=0.265 Fig. 3B).

Reduction in whole-animal and mass-specific resting metabolic rate (RMR) after long-duration flight in S. coronata. (A,B) Whole-animal RMR (A) and mass-specific RMR (B) were significantly reduced (P<0.05) after long-duration flight in a wind tunnel (pre-flight N=12, post-flight N=14) (see text for details). Gray lines connect measurements within each individual. In boxplots, the box represents the interquartile range and the line represents the median whiskers indicate 95% confidence interval.

Reduction in whole-animal and mass-specific resting metabolic rate (RMR) after long-duration flight in S. coronata. (A,B) Whole-animal RMR (A) and mass-specific RMR (B) were significantly reduced (P<0.05) after long-duration flight in a wind tunnel (pre-flight N=12, post-flight N=14) (see text for details). Gray lines connect measurements within each individual. In boxplots, the box represents the interquartile range and the line represents the median whiskers indicate 95% confidence interval.

Changes in RMR after long-duration flight are not associated with the flight duration or changes in fat-free mass in S. coronata. (A,B) The reduction (post-flight RMR – pre-flight RMR) in RMR after flight was not explained by flight duration (A) or changes in fat-free mass (B) after long-duration flight in a wind tunnel (N=13 total, N=9 LEWL, N=4 HEWL).

Changes in RMR after long-duration flight are not associated with the flight duration or changes in fat-free mass in S. coronata. (A,B) The reduction (post-flight RMR – pre-flight RMR) in RMR after flight was not explained by flight duration (A) or changes in fat-free mass (B) after long-duration flight in a wind tunnel (N=13 total, N=9 LEWL, N=4 HEWL).

Birds had a mean PMR of 1.54±0.33 W before flight and a mean PMR of 1.49±0.31 W after flight, and these values were not significantly different (F1,17=0.23, P=0.637 Fig. 4). There was no effect of humidity treatment on PMR (F1,18=0.42, P=0.525), and overall PMR was significantly related to fat-free mass (F1,18=22.638, P<0.001). Change in PMR was unaffected by flight duration (F1,15=0.50, P=0.4877), humidity (F1,15=0.006, P=0.940) or change in fat-free mass during flight (F1,15=0.129, P=0.725).

Whole-animal and mass-specific peak metabolic rates (PMRs) do not change after long-duration flight in S. coronata. (A,B) Whole-animal (A) and mass-specific (B) PMRs recorded pre-flight were not different from those recorded after long-duration flight in a wind tunnel (pre-flight N=19, post-flight N=20). Gray lines connect measurements within each individual. In boxplots, the box represents the interquartile range and the line represents the median whiskers indicate 95% confidence interval.

Whole-animal and mass-specific peak metabolic rates (PMRs) do not change after long-duration flight in S. coronata. (A,B) Whole-animal (A) and mass-specific (B) PMRs recorded pre-flight were not different from those recorded after long-duration flight in a wind tunnel (pre-flight N=19, post-flight N=20). Gray lines connect measurements within each individual. In boxplots, the box represents the interquartile range and the line represents the median whiskers indicate 95% confidence interval.

Relating PMR to RMR

PMR was 6.27±1.25-fold higher than RMR before flight, and 6.91±1.67-fold higher after flight, but this difference was not significant (F1,10=0.90, P=0.366), and differences between the metabolic rates after flight were not explained by flight duration, humidity treatment, change in mass or change in fat-free mass (all P>0.05).

PMR was significantly related to RMR overall [F1,10=25.79, P<0.001 PMR (W)=5.98 × (RMR W)+0.157 Fig. 5], and there was no difference in slope or intercept from before flight to after flight (P>0.05), indicating a functional linkage between RMR and PMR.

PMR is significantly associated with RMR after long-duration flight in S. coronata. PMR was significantly related to RMR overall (P<0.001 see text for statistics and parameter estimates), but the response of PMR was highly variable among individuals. Gray dot-dashed lines connect pre- and post-flight measures within individuals (N=15 groups).

PMR is significantly associated with RMR after long-duration flight in S. coronata. PMR was significantly related to RMR overall (P<0.001 see text for statistics and parameter estimates), but the response of PMR was highly variable among individuals. Gray dot-dashed lines connect pre- and post-flight measures within individuals (N=15 groups).


Discussion

The data show that food groups and subgroups differ widely in terms of nutritional quality and in terms of cost per MJ. The meat and the fruit and vegetables groups that offered the highest NDS overall were also the most expensive in terms of cost per MJ. Conversely, added fats provided dietary energy at a very low cost and had both a low NDS and a high content of negative or disqualifying nutrients. Mixed dishes, snacks, and dairy products were intermediate in rank, both in terms of nutritional quality and in terms of cost of energy. Sweets and salted snacks had a lower nutritional quality than would be expected from their relatively high cost of energy.

Both fish and vegetables and fruit had good nutrient profiles, as indicated by very high NDS and by low LIM. However, they were also associated with higher costs per MJ and therefore with higher diet costs. On the other hand, as our previous studies showed ( 33), vegetables and fruit provided an affordable package of nutrients (as opposed to energy) per unit cost.

Overall, starches and grains had very favorable nutritional quality-to-price ratio. These foods appear to be a good choice, particularly whole or unrefined staples, which provide adequate nutrition at a moderate cost. Whole-grain cereals generally provided twice the amount of nutrients than refined cereal products, but at twice the price. It will be interesting to determine whether the food choices made by lower income and food insecure persons, high in grains and starches and low in vegetables and fruit ( 7), is a rational behavior in response to economic constraints, or whether tradition and education are mainly involved in these choices.

Although a clear ranking of nutrient-to-price ratios was found among food groups, food subgroups showed more diversity. Although several food subgroups had a high nutritional quality, they were not the most expensive ones within their group. These subgroups, particularly milk, organ meats, and eggs, had a very good nutritional quality-to-price ratio. Vegetable fats, dried fruit, and nuts also showed good nutritional quality-to-price ratios. Interestingly, diets obtained using a computer to attain the whole set of nutritional recommendations at the lowest cost preferentially contained foods belonging to the groups and subgroups identified in the present study as having good nutritional quality-to-price ratios ( 36). This does not mean that low-income consumers should select only grains and starches and stay away from fruit, vegetables, and fish. On the contrary, the good quality-to-price ratio of grains and starches leaves a substantial amount of the budget for high-cost, nutrient-dense foods such as fruit, vegetables, and fish. Modeling studies using both cost and nutritional constraints showed that including important amounts of unrefined starches in the diet actually made it possible to fulfill all nutritional requirements for people on a moderate food budget ( 36). Interestingly, such modeled diets also included important amounts of fruit, vegetables, and fish.

The analysis of the link between diet cost and nutritional quality has been hampered for a long time by methodological limitations. Economists, who typically analyze household budgets surveys, lack information about individual consumption and about the nutritional composition of purchased foods. Conversely, nutritional epidemiologists lack information on the price of foods actually consumed by individuals. Associating a mean price to foods in food consumption surveys (as well as the mean nutritional composition associated with them) has allowed investigators to solve this methodological issue and to estimate the daily cost of each individual diet ( 15). Although this approach only roughly estimates individual expenditures, it seemed valid, in our study, to evaluate mean expenditures for food consumed at home, insofar as the mean daily cost estimated from the present data (4.7 €/d or $6.20/d) was very close to that from the last French household budget survey ( 37). The price of a given food varies according to stores, season, brand, size, packaging, and according to whether it is prepared at home or bought ready to eat. The use of a mean price partially hid this variability although frequently consumed foods weighted higher in the mean price calculation. For instance, the mean price of green beans was closer to the price of the processed items rather than to that of the fresh ones. Likewise, the mean price of a given fruit was closer to the price in full season rather than to the price out of season.

Price variability within a single category of food may alter the nutritional quality-to-price ratio of foods considered individually. Actually, a British study showed that branded foods generally cost 2.5 times the price of economy-line foods, but do not contain more nutrients, so that the quantity of nutrients bought for one shilling of food was always clearly higher with economy-line products ( 38). We considered that this intrafood cost variability would not alter the nutritional quality-to-price hierarchy among main food groups, but this requires further investigation. Another possible drawback was the evolving nature of the indicators used to estimate the nutritional quality of food groups. The present NDS was based on 23 nutrients with a known RDA. Although only some of these nutrients are implicated in public health problems, the European Commission takes into account those nutrients that are scientifically recognized as having an effect on health. That list is still not finalized, especially insofar as nutritional problems are not the same in all countries because of different food habits, availability, and different enrichments and supplementation practices. We therefore preferred a more universal score than a country-specific score. On the other hand, one could also argue that our score does not consider enough different nutrients. Actually, several bioactive compounds, including polyphenols and some trace elements, were not included in the NDS, either because the nutrient composition database was not available or because the nutritional requirement was not yet defined. Furthermore, we calculated only those nutrients naturally present in foods and not those introduced by enrichment. This was done to avoid direct comparisons between a fortified food and a nonfortified food with a similar nutrient content.

The nutritional quality-to-price hierarchy presently found between food groups probably explains the positive association observed between the nutritional quality of the diet and its cost ( 15, 18– 20). Notwithstanding, the wide disparity of nutritional quality and prices observed within food groups is compatible with the fact that improving diet quality is not necessarily associated with increased diet costs in intervention studies implicating nutrition education ( 39– 41). Our results suggest that, by preferentially selecting subgroups that have the highest nutritional quality-to-price ratio, healthy diets can be obtained at a moderate cost. However, such low-cost nutritionally adequate diets ( 39– 41) deviated considerably from the typical food habits of the population ( 36). Although nutrition education could make such diets more attractive, they may not be palatable enough or socially acceptable. In addition, there is a threshold cost under which it is impossible to obtain a nutritionally adequate diet, estimated at ∼3.5 €/d per adult in France ( 36) and at $116/wk for a 4-person family in the U.S. ( 42). Many studies have emphasized that food budgets of the poor are often under this threshold ( 16, 17, 36, 43). The fact that food groups with the more favorable nutrient profile were also the more expensive sources of energy suggests that the present structure of food prices does not favor the adoption of food-based dietary guidelines, at least by low-income people.

Although nutritionally balanced diets can be obtained at limited cost ( 36, 39– 41), often they are neither palatable nor convenient. It is a major challenge for public health nutrition to link public health imperatives with economic realities of life in ensuring that nutritionally adequate and socially acceptable foods are affordable and available to all. A refinement of food and agriculture policies and food assistance programs is one potential strategy for change ( 44– 46, and unpublished data by Z. Rambeloson, N. Darmon, and E. L. Ferguson). Effective dietary guidance must take into account both the nutrient profile of foods and their nutrient and energy costs. These considerations will allow consumers to identify and select optimal diets at an affordable cost.


Contents

Many governments require food manufacturers to label the energy content of their products, to help consumers control their energy intake. [6] In the European Union, manufacturers of packaged food must label the nutritional energy of their products in both kilocalories and kilojoules, when required. In the United States, the equivalent mandatory labels display only "Calories", [7] often as a substitute for the name of the quantity being measured, food energy an additional kilojoules figure is optional and is rarely used. In Australia and New Zealand, the food energy must be stated in kilojoules (and optionally in kilocalories as well), and other nutritional energy information is similarly conveyed in kilojoules. [8] [9] The energy available from the respiration of food is usually given on labels for 100 g, for a typical serving size (according to the manufacturer), and/or for the entire pack contents. [ citation needed ]

The amount of food energy associated with a particular food could be measured by completely burning the dried food in a bomb calorimeter, a method known as direct calorimetry. [10] However, the values given on food labels are not determined in this way. The reason for this is that direct calorimetry also burns the dietary fiber, and so does not allow for fecal losses thus direct calorimetry would give systematic overestimates of the amount of fuel that actually enters the blood through digestion. What are used instead are standardized chemical tests or an analysis of the recipe using reference tables for common ingredients [11] to estimate the product's digestible constituents (protein, carbohydrate, fat, etc.). These results are then converted into an equivalent energy value based on the following standardized table of energy densities. [4] [12] However "energy density" is a misleading term for it once again assumes that energy is IN the particular food, whereas it simply means that "high density" food needs more oxygen during respiration, leading to greater transfer of energy. [1] [13]

Note that the following standardized table of energy densities [12] is an approximation and the value in kJ/g does not convert exactly to kcal/g using a conversion factor.

Food component Energy density
kJ/g kcal/g
Fat 37 9
Ethanol (drinking alcohol) 29 7
Proteins 17 4
Carbohydrates 17 4
Organic acids 13 3
Polyols (sugar alcohols, sweeteners) 10 2.4
Fiber 8 2

All the other nutrients in food are noncaloric and are thus not counted.

Increased mental activity has been linked with moderately increased brain energy consumption. [14] Older people and those with sedentary lifestyles require less energy children and physically active people require more.

Recommendations in the United States are 2,600 and 2,000 kcal (10,900 and 8,400 kJ) for men and women (respectively) between 31 and 35, at a physical activity level equivalent to walking about 2 to 5 km ( 1 + 1 ⁄ 2 to 3 mi) per day at 5 to 6 km/h (3 to 4 mph) in addition to the light physical activity associated with typical day-to-day life, [15] with French guidance suggesting roughly the same levels. [16]

Recognizing that people of different age and gender groups have varying daily activity levels, Australia's National Health and Medical Research Council recommends no single daily energy intake, but instead prescribes an appropriate recommendation for each age and gender group. [17] Notwithstanding, nutrition labels on Australian food products typically recommend the average daily energy intake of 2,100 kcal (8,800 kJ).

According to the Food and Agriculture Organization of the United Nations, the average minimum energy requirement per person per day is about 7,500 kJ (1,800 kcal). [18]

The human body uses the energy released by respiration for a wide range of purposes: about 20% of the energy is used for brain metabolism, and much of the rest is used for the basal metabolic requirements of other organs and tissues. In cold environments, metabolism may increase simply to produce heat to maintain body temperature. Among the diverse uses for energy, one is the production of mechanical energy by skeletal muscle to maintain posture and produce motion.

The conversion efficiency of energy from respiration into mechanical (physical) power depends on the type of food and on the type of physical energy usage (e.g., which muscles are used, whether the muscle is used aerobically or anaerobically). In general, the efficiency of muscles is rather low: only 18 to 26% of the energy available from respiration is converted into mechanical energy. [19] This low efficiency is the result of about 40% efficiency of generating ATP from the respiration of food, losses in converting energy from ATP into mechanical work inside the muscle, and mechanical losses inside the body. The latter two losses are dependent on the type of exercise and the type of muscle fibers being used (fast-twitch or slow-twitch). However, alterations in the structure of the material consumed can cause modifications in the amount of energy that can be derived from the food i.e. caloric value depends on the surface area and volume of a food. For an overall efficiency of 20%, one watt of mechanical power is equivalent to 4.3 kcal (18 kJ) per hour. For example, a manufacturer of rowing equipment shows calories released from 'burning' food as four times the actual mechanical work, plus 300 kcal (1,300 kJ) per hour, [20] which amounts to about 20% efficiency at 250 watts of mechanical output. It can take up to 20 hours of little physical output (e.g., walking) to "burn off" 4,000 kcal (17,000 kJ) [21] more than a body would otherwise consume. For reference, each kilogram of body fat is roughly equivalent to 32,300 kilojoules of food energy (i.e., 3,500 kilocalories per pound). [22]

In addition, the quality of calories matters because the energy absorption rate of different foods with equal amounts of calories may vary. [ citation needed ] Some nutrients have regulatory roles affected by cell signaling, in addition to providing energy for the body. [23] For example, leucine plays an important role in the regulation of protein metabolism and suppresses an individual's appetite. [24]

Swings in body temperature – either hotter or cooler – increase the metabolic rate, thus burning more energy. Prolonged exposure to extremely warm or very cold environments increases the basal metabolic rate (BMR). People who live in these types of settings often have BMRs 5–20% higher than those in other climates. [ citation needed ] Physical activity also significantly increases body temperature, which in turn uses more energy from respiration. [ citation needed ]


Keywords

R. Pérez-Escamilla is a professor of epidemiology and public health director, Office of Community Health, Yale School of Public Health, Yale University, New Haven, CT

J. E. Obbagy is a nutritionist/project manager, Evidence Analysis Library Division, Center for Nutrition Policy and Promotion, US Department of Agriculture, Alexandria, VA

J. M. Altman is a nutritionist/project manager, Evidence Analysis Library Division, Center for Nutrition Policy and Promotion, US Department of Agriculture, Alexandria, VA

E. V. Essery is a nutritionist/project manager, Evidence Analysis Library Division, Center for Nutrition Policy and Promotion, US Department of Agriculture, Alexandria, VA

M. M. McGrane is a nutritionist/project manager, Evidence Analysis Library Division, Center for Nutrition Policy and Promotion, US Department of Agriculture, Alexandria, VA

Y. P. Wong is a research librarian, Evidence Analysis Library Division, Center for Nutrition Policy and Promotion, US Department of Agriculture, Alexandria, VA

J. M. Spahn is director/nutritionist, Evidence Analysis Library Division, Center for Nutrition Policy and Promotion, US Department of Agriculture, Alexandria, VA

C. L. Williams is a nutrition/science consultant and president, Healthy Directions, Inc, Scarsdale, NY

Meets Learning Need Codes 4000, 4030, 5370, and 9020. To take the Continuing Professional Education quiz for this article, log in to www.eatright.org, click the “MyProfile” link under your name at the top of the homepage, select “Journal Quiz” from the menu on your myAcademy page, click “Journal Article Quiz” on the next page, and then click the “Additional Journal CPE Articles” button to view a list of available quizzes, from which you may select the quiz for this article.

STATEMENT OF POTENTIAL CONFLICT OF INTEREST No potential conflict of interest was reported by the authors.


How to improve dairy cow fertility through nutrition

Falling fertility rates are a big concern for dairy farmers. You want a profitable, sustainable business. And that requires fertile cows that you can count on to maintain reliable ovulation cycles, develop viable embryos and give birth to healthy calves.

It sounds simple on paper, doesn&rsquot it? Yet as dairy milk yields have increased over the years, dairy herd fertility rates have decreased. That challenge can be tackled through nutrition - and in this article we explain what you can do to feed your cows fertile.


Improved milk yields, decreased fertility

Up until recently, the fertility of UK dairy cows had declined for some forty years. (Just ask the University of Nottingham.) Over this time, scientists have been trying to find out why improved milk yields seemed to reduce fertility rates, with first service conception rates often falling below 40%.

Now we have some of the answers.

Back in 2002 Claire Wathes and Vicky Taylor published research in the Holstein Journal that proved the strong relationship between milk yield, fertility and conception intervals. Their studies found that a peak milk yield of about 42 kg/day seemed to be the point above which fertility became adversely affected.

During the study the cows producing less than 42 kg/day all conceived within 150 days and most took less than 100 days to get back in-calf. In contrast, only half of the higher yielding cows were back in-calf by 100 days and around a quarter took more than 150 days.

Yet high milk yields and good fertility don&rsquot have to be mutually exclusive. You don&rsquot always have to choose between one or the other. The secret is to get nutrition right - ensuring your herd receives the right nutrients, at the right time and in the right quantities. Simple!


The danger of negative energy balance

After calving it&rsquos almost inevitable that your cows will enter negative energy balance (NEB). During early lactation cows cannot eat enough to meet the high energy demands of milk production. This results in a loss of body condition.

Faced with negative energy balance, cows&rsquo livers produce less of an important metabolic hormone called insulin-like growth factor 1 (IGF-1). This hormone plays a key role in regulating levels of fertility and can delay the resumption of a regular oestrous cycle. Low levels of IGF-1 are associated with fewer follicles developing on the ovaries and fewer follicles being ovulated.

In short: cows in too much of a negative energy balance in early lactation can be incredibly difficult to get back in-calf. In fact if cows lose more than one BCS point in early lactation, chances of conception are significantly reduced. Fertility falls by around 10% for every 0.5 loss in BCS.

This problem is compounded by the fact that many dairy cows have been genetically selected based on their ability to mobilise body tissue to maintain high milk yields - e.g. milk off their own backs. Add in poor nutritional management and before long you are looking at losses in body condition score (BCS) across your herd. Such cows duration of negative energy balance (NEB) could last for up to 20 weeks or more after calving.

To combat this issue, it may be tempting to fatten up your cows before calving. This also has its problems. Fat cows are more likely to develop fertility problems than cows with a healthy BCS (2.5 to 3.5). These problems include cystic ovaries, metritis and increased risk of calving problems due to pelvic fatty deposits.


Make dietary changes gradually

Good nutrition can reverse fertility problems while maintaining high milk yields. But before we take a look at some specific recommendations, it&rsquos important to remember that dairy cows must be exposed to dietary changes gradually. It takes the rumen at least two weeks to adapt to changes. Radical changes in diet can make fertility problems worse, not better.


Feed your herd fertile

On paper, using nutrition to feed your herd fertile is simple. It&rsquos no more complex than making sure your herd&rsquos diet meets the necessary protein, fat, energy and mineral requirements. In practice, getting it right is a little more tricky.


One for all versus one for each

All cows behave differently in the way they mobilise body reserves during lactation. It&rsquos a complex equation based on genetics, metabolic condition and disease status. In an ideal world each cow in your dairy herd would have a diet that&rsquos personalised to their specific metabolic requirements. Of course that&rsquos not quite practical in real life.

Remember Wathes and Taylor from the beginning of the article? Their research revealed an interesting dichotomy. Cows in negative energy balance could enhance their fertility rates with diets that stimulated insulin. Yet while that proved to boost rates of ovulation, too much insulin was detrimental to oocyte quality.

In short by feeding diets to stimulate insulin in early lactation and lower insulin during the mating period, you can increase pregnancy rate without compromising milk yield or cow health. A simple takeout from that is to promote insulin when cows are not cycling, then lower insulin for cows that are cycling but conceiving.


Optimum BCS

AHDB Dairy recommends that for optimum fertility, cows should calve down at a BCS of 2.75-3.0 and lose no more than half a point by service. Don&rsquot rely on making changes in body condition during the dry period.


Beware excessive protein

It takes a lot of energy to digest protein. That can exacerbate energy deficits, so it may be better to consider making up for negative energy balances with other macronutrients such as fat.


Fat is your friend. As long as you use it properly.

Pound-for-pound fat has the highest energy density of any nutrient, with around 2.5-times the energy concentration of cereals. That makes fat an incredibly efficient way to meet those enhanced energy demands. Certain fats are also known to increase progesterone &ndash a hormone that plays an important role in facilitating healthy pregnancies.

Yet feeding fats is not without risks. Unprotected or rumen-active fats and oils decrease fibre digestion in the rumen leading to lower energy availability from the diet. That&rsquos the opposite of what you&rsquore trying to achieve. The answer is to supplement diets with ingredients that don&rsquot break down in the rumen. And that&rsquos where Megalac comes in.

Megalac is a rumen-protected fat supplement that combines natural plant oil with calcium. It&rsquos highly digested in the acidic lower gut - with the enrgy intake going straight to where the cow needs it most. Megalac has the highest independently-measured net energy value of any feedstuff and is perfect to feed during early lactation when cows experience negative energy balance. Brand new research is also suggesting that the combination of fatty acids in Megalac influence the production of insulin in a positive way, ensuring a balanced partitioning of nutrients between milk and body fat.

It&rsquos one of several reasons Megalac has been proven to improve fertility.


Make sure feed is freely available (and tastes nice)

It sounds obvious, but you need to make sure your feed is palatable and freely available across your farm. When you make it easy for your herd to eat, they are likely to eat more. And remember the aim to prevent fertility losses is to mitigate against negative energy balance during early lactation.

  • Fertility is mostly influenced by events around calving and early lactation, rather than during actual service period
  • Fertility loss is caused by complications associated with negative energy balance and BCS loss
  • Most critical periods to maintain regular fertility are during dry period and during first few weeks of lactation


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>> Meet the fats that can transform dairy cows&rsquo health and productivity



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