A method for dynamic prediction of metabolic energy requirements

Abstract

The application discloses a metabolic energy requirement dynamic prediction method, relates to the technical field of metabolic energy requirement prediction, and comprises the following steps: 1, body composition or egg composition determination; 2, metabolic energy factor analysis method model establishment in a growth period or an egg laying period; and 3, input variable correlation model in the growth period or the egg laying period respectively. The application is based on the more scientific factor analysis method principle, dynamically predicts the metabolic energy requirement according to the actual production level, thereby performing individualized nutrition guidance on different farms, improving the comprehensive income of the farm, reducing environmental pollution, and having extremely strong industrial value.

Description

Technical Field

[0001] This invention relates to the field of metabolic energy requirement prediction technology, specifically a method for dynamic prediction of metabolic energy requirement. Background Technology

[0002] Feed energy is the core of livestock and poultry nutrition, and the three major nutrients (carbohydrates, fats, and proteins) are the primary forms of energy. In nutrition, gross feed energy (GE) is defined as the energy released from the complete oxidation of feed. However, due to the varying efficiency of digestion, absorption, and utilization in livestock and poultry, GE cannot be completely digested, absorbed, and utilized. Therefore, a more appropriate energy assessment system is needed. Poultry, with their cloaca, excrete a mixture of feces and urine. In poultry nutrition, the energy obtained by subtracting the GE from the ingested feed is defined as metabolizable energy. The metabolizable energy system comprehensively considers the energy loss from feed digestibility and metabolism in poultry, thus providing a more reasonable assessment of the effective energy value of feed and is widely used in animal nutrition.

[0003] In recent years, with the national promotion of soybean meal reduction and substitution and low-protein diet technology, net energy systems have received considerable attention. Net energy systems are built upon metabolizable energy systems, further deducting the additional heat production energy loss (such as heat gain) in livestock and poultry to more accurately assess energy efficiency. In fact, net energy systems were first used in ruminants. Due to the fermentation effect of rumen microorganisms, rumen heat production and heat gain account for a large proportion of energy loss in ruminants, making net energy systems significantly superior to metabolizable energy systems. However, the heat production and heat gain effects of hindgut microorganisms in poultry are relatively small, especially in yellow-feathered broilers where breed variation is significant. Furthermore, measurement errors are inevitably introduced during the evaluation of net energy systems, and the net energy database for poultry feed is not complete, limiting the application and promotion of net energy systems in poultry. Therefore, although net energy systems are gradually gaining importance, poultry still primarily rely on metabolizable energy systems.

[0004] Current poultry farming models are diverse, and the establishment of metabolizable energy requirement models requires precision and dynamism to be applicable to different production levels. The comprehensive method is a traditional approach in my country for assessing livestock and poultry nutritional requirements. It treats experimental animals as a "black box," conducting animal experiments with diets at different nutrient gradient levels, and determining the recommended nutrient requirements based on the optimal production performance. The United States uses factorial methods to model livestock and poultry nutritional requirements. Factorial methods classify nutrient utilization based on different growth and development goals of livestock and poultry, effectively opening the "black box" of experimental animals for categorized modeling and analysis. However, factorial methods are rarely used in domestic nutrition research. Most domestic livestock and poultry nutritional requirement databases are based on the comprehensive method, and the resulting requirement parameters are theoretically only applicable to the production level at the time of the experiment and lack universality. Therefore, there is an urgent need to establish a nutritional requirement database based on the factorial method.

[0005] In 2020, our organization published the agricultural industry standard NY / T3645-2020, "Nutritional Requirements for Yellow-feathered Broilers" (hereinafter referred to as the "Standard"). The Standard lists the metabolizable energy values ​​of feed ingredients and the metabolizable energy requirements for different types and growth stages of yellow-feathered broilers. The metabolizable energy requirements for yellow-feathered broilers in the "Standard" are based on the net energy values ​​of feed ingredients for white-feathered broilers. The metabolizable energy values ​​of formulated feeds from literature were recalculated, and then modeling and analyzing the metabolizable energy was performed to obtain the required amounts. Therefore, the metabolizable energy requirements for yellow-feathered broilers listed in the "Standard" still have the following limitations:

[0006] 1) There is a deviation in predicting the metabolizable energy requirement of yellow-feathered broiler chickens by using the metabolizable energy value of white-feathered broiler chicken feed ingredients, because the growth rate of white-feathered broiler chickens (42 days to market) is significantly faster than that of yellow-feathered broiler chickens (60-150 days to market).

[0007] 2) The metabolizable energy requirement for yellow-feathered broilers listed in the standard is a fixed value, and it is impossible to make personalized predictions of the metabolizable energy requirement based on the specific production level of the farm.

[0008] In addition, body weight (BW) and daily weight gain (ADG) can be used to predict metabolic energy requirements: ME = a × BW 0.75 +b×ADG. Predicting metabolizable energy requirements using body weight and daily weight gain is essentially based on regression analysis using a "comprehensive method." However, daily weight gain is not an ideal indicator for predicting deposited metabolizable energy because the difference between body protein and body fat deposition is significant. Yellow-feathered broilers primarily deposit body protein in the early growth stage, while the rate of body fat deposition increases significantly in the later growth stage. Using only daily weight gain to predict deposited metabolizable energy can lead to large deviations and may even result in reduced feeding efficiency and increased costs, contradicting the theory of precision nutrition. Summary of the Invention

[0009] To address the shortcomings of existing technologies, this invention provides a method for dynamically predicting metabolizable energy requirements. Based on the principle of factorial analysis, and by correlating actual production indicators, carcass indicators, and egg production indicators as input variables to the model, it can quickly and accurately dynamically assess the metabolizable energy requirements under different production levels and farming models.

[0010] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The present invention provides a method for dynamically predicting metabolizable energy requirements, the specific steps of which are as follows:

[0011] Step 1, determination of body or egg composition: Before modeling, determine the water, protein, fat and total energy content in the body or egg to calculate the amount of energy and nutrients deposited in the body or egg.

[0012] Step 2: Establishment of the metabolizable energy factorization model:

[0013] Step 2.1, Factorial model of metabolic energy requirement:

[0014] During the growth period, ME = ME g +ME m ;

[0015] During the egg-laying period, ME = ME e +ME m ;

[0016] Step 2.2: Predicting the depositional metabolic energy (ME) g :

[0017] Step 2.2.1:

[0018] (1) During the growth period, predict the body deposition metabolite energy ME g It is necessary to first measure the net energy (NE) of body protein deposition. pro Net energy (NE) from body fat deposition fat :

[0019] The net energy (NE) of body deposition is obtained by detecting the energy, body protein content, and body fat content in body components. g Protein deposition and fat deposition:

[0020] Net Energy of Volume Deposition (NE) g = Carcass mass × Carcass energy value;

[0021] Body protein or fat deposition = carcass mass × carcass body protein or fat content;

[0022] Net Energy of Deposition (NE) g The energy values ​​of body protein and body fat were calculated by performing a multiple linear regression analysis on their correlation with body protein deposition (Protein) and body fat deposition (Fat).

[0023] NE g =NE pro +NE fat = a × Protein + b × Fat;

[0024] (2) During the laying period, predict the metabolic energy (ME) of egg deposition. e It is necessary to first measure the net energy (NE) of protein deposition in the egg. pro And net energy (NE) from fat deposition in eggs fat :

[0025] The net energy (NE) of egg deposition is obtained by detecting the energy, protein, and fat content in egg components. g Protein and fat deposition in eggs:

[0026] Egg deposition net energy NE e = Daily egg production × Total egg energy value;

[0027] Protein or fat deposition in eggs = Daily egg production × Protein or fat content in eggs;

[0028] Establishing net energy (NE) from egg deposition e The energy values ​​of protein and fat in eggs were calculated by performing a multiple linear regression analysis on the protein and fat deposition amounts in the eggs.

[0029] NE e =NE pro +NE fat = a × Protein + b × Fat;

[0030] Step 2.2.2:

[0031] (1) During the growth period, maintain metabolic energy (ME) was measured. m Energy efficiency of body protein deposition k p Energy efficiency of body fat deposition (k) f :

[0032] Establish metabolizable energy intake (ME) i Net energy (NE) from body protein deposition pro and body fat deposition can NE fat The multiple linear regression relationship was used to calculate the body protein k. p and body fat K f Energy efficiency of deposition:

[0033]

[0034] (2) During the egg-laying period, measure the maintenance metabolic energy (ME). m Energy efficiency of protein deposition in eggs (k) p Energy efficiency k of fat deposition in eggs f The linear regression relationship during the egg-laying period is the same as that during the growth period;

[0035] Step 2.2.3: Calculate the metabolic energy requirement:

[0036] (1) During the growth period, the energy value a and energy efficiency k of somatic protein deposition were obtained by establishing a multiple linear regression model. p Energy value b and energy efficiency k of body fat deposition f Maintaining metabolic energy ME m ,but

[0037] The daily metabolic energy requirement is:

[0038]

[0039] The dietary metabolizable energy requirement is:

[0040]

[0041] ADFI is the average daily feed intake.

[0042] (2) During the egg-laying period, the energy value 'a' and energy efficiency 'k' of protein deposition in the egg were obtained by establishing a multiple linear regression model. p The energy value b and energy efficiency k of fat deposition in eggs f Maintaining metabolic energy ME m The calculation model for the egg-laying period is the same as that for the growth period.

[0043] Step 3: Associate the input variables of the model:

[0044] Step 3.1: Predict the metabolic energy maintenance requirement (ME) m :

[0045] Nonlinear regression analysis was used to correlate body weight with metabolic energy maintenance requirement (ME). m :

[0046] ME m =d×BW e ;

[0047] Step 3.2: Predict the amount of metabolizable energy required for deposition:

[0048] (1) During the growth period, the production indicators directly related to body protein deposition are daily weight gain (ADG), breast percentage, or leg muscle percentage (Thigh%), while the production indicators related to body fat deposition are daily weight gain (ADG) and abdominal fat percentage. Therefore, a multiple linear regression model was established:

[0049] Daily body protein deposition

[0050] Protein%=a×Thigh%+b×Breast%+c

[0051] Protein = ADG × Protein%

[0052] Daily body fat deposition

[0053] Fat%=a×Abdominal fat%+c;

[0054] Fat = ADG × Fat%

[0055] (2) During the laying period, the indicators directly related to protein and fat deposition in eggs are daily egg production and protein or fat content in eggs. Therefore, the prediction model is established as follows:

[0056] Protein or fat deposition in eggs = Daily egg production × Protein or fat content in eggs;

[0057] Step 3.3: Establish a model for predicting total metabolizable energy requirements:

[0058] (1) During the growth period, combining the amount of body protein and body fat deposition in step 3.2 and the prediction model obtained in step 2.2.3, the metabolic energy requirements of farms at different growth stages and production levels are predicted by body weight, pectoral muscle rate, leg muscle rate or abdominal fat rate.

[0059] (2) During the egg-laying period, combining the protein and fat deposition in eggs in step 3.2 and the prediction model obtained in step 2.2.3 during the egg-laying period, the metabolic energy requirement of farms at different production levels is predicted based on the daily egg production and the protein or fat content in eggs.

[0060] Furthermore, in step 1, the moisture content was measured according to the national standard GB / T6435-2014, the protein content was measured according to the national standard GB / T6433-2006, the fat content was measured according to the national standard GB / T6433-2006, and the total energy content was determined using an oxygen bomb energy meter.

[0061] Furthermore, in step 2.2.1, a and b are multiple linear regression coefficients, and the biological significance of a and b is the energy value of protein and fat.

[0062] Furthermore, in step 2.2.2, k p and k f k represents the coefficients of the multiple linear regression. p and k f The biological significance is the energy efficiency of protein and fat deposition, with the constant C representing the metabolic energy (ME) for maintenance. m .

[0063] Furthermore, in step 3.1, d and e are nonlinear regression coefficients, and BW is body weight.

[0064] Furthermore, in step 3.2 during the growth period, Protein% and Fat% represent body protein and body fat content, Thigh%, Breast% and Abdominal fat% represent leg muscle percentage, chest muscle percentage and abdominal fat percentage, and a, b and c are the linear regression parameters of body protein and body fat, respectively.

[0065] Compared with the prior art, the present invention provides a method for dynamically predicting metabolic energy requirements, which has the following beneficial effects:

[0066] Metabolizable energy systems are widely used energy assessment systems in current poultry production. Currently, the metabolizable energy requirements provided by nutritional standards for yellow-feathered broilers are fixed values, making it difficult to meet the diverse needs of current yellow-feathered broiler breeds and the varied feeding methods and farming levels. This invention patent, however, provides personalized and precise predictions of metabolizable energy requirements based on the actual production levels of farms. It uniquely links production, carcass, and egg production performance as input variables to the model, making the metabolizable energy prediction accurate and dynamic. This allows for rapid prediction of metabolizable energy requirements, providing personalized nutritional guidance to different farms, improving overall farm profitability, reducing environmental pollution, and possessing significant industrial value. Attached Figure Description

[0067] Figure 1 is a flowchart of the dynamic prediction of metabolic energy requirements according to the present invention. Detailed Implementation

[0068] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0069] As shown in Figure 1, this invention provides a method for dynamically predicting metabolic energy requirements, the specific steps of which are as follows:

[0070] (1) During the growth period:

[0071] Step 1, Body composition determination: Before modeling, the water, protein, fat and total energy content in the body are determined to calculate the amount of energy and nutrients deposited in the body.

[0072] Furthermore, body water was measured according to the national standard GB / T6435-2014, protein was measured according to the national standard GB / T6433-2006, fat was measured according to the national standard GB / T6433-2006, and total energy content was determined using an oxygen bomb energy meter.

[0073] Step 2: Establishment of the metabolizable energy factorization model:

[0074] Step 2.1, Factorial model of metabolic energy requirement:

[0075] ME = ME g +ME m .

[0076] Step 2.2: Predicting the depositional metabolic energy (ME) g :

[0077] Step 2.2.1: Predict the volumetric deposition metabolite energy (ME) g It is necessary to first measure body protein deposition (NE). pro ) and net energy from body fat deposition (NE) fat ):

[0078] The net energy deposited in the body is obtained by detecting the energy, protein, and fat content in the body components. g ), body protein deposition (Protein) and body fat deposition (Fat):

[0079] Net Energy of Volume Deposition (NE) g (kcal) = Carcass mass (kg) × Carcass energy value (kcal / kg);

[0080] Body protein or fat deposition (g) = Carcass mass (g) × Carcass body protein or fat content (%)

[0081] Due to the net energy of body protein deposition (NE) pro ) and body fat deposition energy (NE fat ) is the net energy of volume deposition (NE) g The two specific manifestations of ) therefore establish net energy of solid deposition (NE) g The energy values ​​(cal / g) of body protein and body fat were calculated by performing a multiple linear regression analysis on their correlation with body protein and body fat deposition.

[0082] NE g =NE pro +NE fat = a × Protein + b × Fat;

[0083] Where a and b are the multiple linear regression coefficients, and the biological meaning of a and b is the energy value (cal / g) of body protein and body fat.

[0084] Step 2.2.2: Measure the maintenance metabolic energy (ME). m Energy efficiency of body protein deposition (k) p ) and body fat deposition energy efficiency (k f ):

[0085] Establish metabolizable energy intake (ME) i ) and net energy of body protein deposition (NE) pro ) and body fat deposition energy (NE fat The multiple linear regression relationship of ) was used to calculate the body protein (k) p ) and body fat (kJ) f Energy efficiency of deposition (%):

[0086]

[0087] Where k p and k f k represents the coefficients of the multiple linear regression. p and k fThe biological significance of this is the energy efficiency (%) of body protein and body fat deposition, where the constant C is the metabolic energy (ME) for maintenance. m .

[0088] Step 2.2.3: Calculate the metabolic energy requirement:

[0089] By establishing a multiple linear regression model, the energy value (a) and energy efficiency (k) of somatic protein deposition were obtained. p ), and the energy value (b) and energy efficiency (k) of body fat deposition. f ), and maintain metabolic energy ME m (Regression model constant C). Therefore, the daily metabolic energy requirement (kcal / d) is:

[0090]

[0091] The dietary metabolizable energy requirement (kcal / kg) is:

[0092]

[0093] ADFI stands for Average Daily Feed Intake (kg / d).

[0094] Step 3: Associate the input variables of the model:

[0095] Step 3.1: Predict the metabolic energy maintenance requirement (ME) m :

[0096] The maintenance requirements of animals are power-law related to body weight; therefore, nonlinear regression analysis can be used to correlate body weight with metabolic energy maintenance requirements (ME). m :

[0097] ME m =d×BW e ;

[0098] Where d and e are nonlinear regression coefficients, and BW is body weight.

[0099] Step 3.2: Predict the amount of metabolizable energy required for deposition:

[0100] Since the metabolic energy required for deposition can be divided into metabolic energy for depositing body protein and metabolic energy for depositing body fat, the key is to establish predictive models for body protein and body fat deposition, and then use the respective energy values ​​and energy deposition efficiencies (kJ / kb) of body protein and body fat. p and k fThe required metabolic energy for deposition is then determined. Production indicators directly related to body protein deposition are daily weight gain (ADG), chest muscle percentage (Breast%), or leg muscle percentage (Thigh%). Production indicators related to body fat deposition are daily weight gain (ADG) and abdominal fat percentage (Abdominal fat%). Therefore, a multiple linear regression model can be established:

[0101] Daily body protein deposition (g / d)

[0102] Protein%=a×Thigh%+b×Breast%+c

[0103] Protein = ADG × Protein%

[0104] Daily body fat deposition (g / d)

[0105] Fat%=a×Abdominal fat%+c;

[0106] Fat = ADG × Fat%

[0107] Wherein, Protein% and Fat% are the percentages of body protein and body fat, respectively; Thigh%, Breast% and Abdominal fat% represent the percentages of leg muscle, chest muscle, and abdominal fat, respectively; and a, b, and c are the linear regression parameters of body protein and body fat, respectively.

[0108] Step 3.3: Establish a model for predicting total metabolizable energy requirements:

[0109] Combining the body protein and body fat deposition levels in step 3.2 with the prediction model obtained in step 2.2.3, the metabolizable energy requirements of farms at different growth stages and production levels can be predicted using body weight, pectoral muscle percentage, leg muscle percentage, or abdominal fat percentage.

[0110] Example 1: Using a model to quickly predict the metabolizable energy requirements of yellow-feathered broiler farms:

[0111] This study aims to estimate the metabolizable energy requirement (kcal / kg) of a third-month-old Qingyuan Ma chicken (hen) farm to guide feed formulation. Assuming a predictive model for the metabolizable energy requirement of third-month-old Qingyuan Ma chickens is established using the above methodology, the following model is proposed:

[0112] (1) Maintenance metabolic energy requirements

[0113] ME m =45×BW 0.7

[0114] Where BW is body weight (kg), and 45 and 0.7 are parameters obtained from modeling.

[0115] (2) Depositional metabolic energy requirements

[0116]

[0117] Protein% and Fat% represent the body protein and body fat content of Qingyuan Ma chicken, respectively. 0.65 and 0.8 are the body protein and body fat energy deposition efficiencies obtained based on the above methodology, and 8.4 and 12.3 are the obtained body protein and body fat energy values ​​(kcal / kg).

[0118] (3) Body protein content prediction model

[0119] Protein%=0.6×Thigh%+0.65×Breast%+0.007

[0120] Among them, Protein% is the body protein content of Qingyuan Ma chicken, Thigh% is the leg muscle percentage, Breast% is the breast muscle percentage, and 0.6, 0.65, and 0.007 are the parameters obtained from modeling.

[0121] (4) Body fat percentage prediction model

[0122] Fat%=6.3×Abdominal fat%+0.03

[0123] Among them, Fat% is the body fat content of Qingyuan Ma chicken, Abdominal fat% is the abdominal fat percentage, and 6.3 and 0.03 are the parameters obtained from modeling.

[0124] The specific steps are as follows:

[0125] Step 1: Collect production data of Qingyuan Ma chickens at the farm at three months of age, namely, average initial and final body weight (BW), average daily gain (ADG), and average daily feed intake (ADFI) at three months of age. Calculate the average body weight (BW) based on the average initial and final body weight at three months of age. 均 ).

[0126] Assuming the average initial and final weight of the third-month-old group is 800g and 1200g respectively, the average weight of the third-month-old group is 1000g, the average daily weight gain is 13.33g / d, and the average daily feed intake is assumed to be 60g / d.

[0127] Step Two: Select a puppy in the middle of its third month of age (around 75 days old), based on its weight and group body size. 均 Two to three healthy Qingyuan Ma chickens were slaughtered and their breast muscle percentage (Breast%), leg muscle percentage (Thigh%), and abdominal fat percentage (Abdominal fat%) were measured.

[0128] Assume the measured average chest muscle percentage, leg muscle percentage, and abdominal fat percentage are 10%, 18%, and 2.7%, respectively.

[0129] Step 3: Based on the above model, predict the required metabolic energy to maintain the following:

[0130] ME m =45×BW 0.7 =45 × 1.0 0.7 = 45 kcal / d.

[0131] Step 4: Based on the obtained model, predict the body protein and body fat content as follows:

[0132] Protein%=0.6×Thigh%+0.65×Breast%+0.007

[0133] = 0.6 × 18% + 0.65 × 10% + 0.007 = 18%

[0134] Fat%=6.3×Abdominal fat%+0.03=6.3×2.7%+0.03=20%.

[0135] The daily deposition of body protein and body fat are as follows:

[0136] Protein=ADG×Protein%=13.33×18%=2.4g / d

[0137] Fat=ADG×Fat%=13.33×20%=2.7g / d.

[0138] Step 5: Based on the obtained model, predict the required depositional metabolic energy:

[0139]

[0140] Step Six: Based on the principle of factorial analysis, the daily requirement for metabolizable energy is:

[0141] ME daily =ME g +ME m =73+45=118kcal / d.

[0142] Step 7: Based on the average daily feed intake of the third-month-old infant, convert the daily metabolizable energy requirement (kcal / d) into the dietary metabolizable energy requirement (kcal / kg):

[0143]

[0144] (2) During the egg-laying period:

[0145] Step 1, Egg composition determination: Before modeling, the moisture, protein, fat and total energy content of the egg are determined to calculate the amount of energy and nutrients deposited in the egg.

[0146] Furthermore, the moisture content of the egg was measured according to the national standard GB / T6435-2014, the protein content of the egg was measured according to the national standard GB / T6433-2006, the fat content of the egg was measured according to the national standard GB / T6433-2006, and the total energy content was determined using an oxygen bomb energy meter.

[0147] Step 2: Establishment of the metabolizable energy factorization model:

[0148] Step 2.1, Factorial model of metabolic energy requirement:

[0149] ME = ME e +ME m .

[0150] Step 2.2: Predicting the depositional metabolic energy (ME) g :

[0151] Step 2.2.1: Predict the metabolic energy (ME) of egg deposition. e It is necessary to first measure the protein deposition (NE) in the egg. pro ) and net energy from fat deposition in eggs (NE fat ):

[0152] The energy, protein, and fat content of an egg are measured to obtain the net energy (NE) of egg deposition. g Protein and fat deposition in eggs:

[0153] Egg deposition net energy NE e = Daily egg production × Total egg energy value;

[0154] Protein or fat deposition in eggs = Daily egg production × Protein or fat content in eggs;

[0155] Establishing net energy (NE) from egg deposition e The energy values ​​(cal / g) of protein and fat in eggs were calculated by performing a multiple linear regression analysis on the protein and fat deposition amounts in the eggs.

[0156] NE g =NE pro +NE fat = a × Protein + b × Fat;

[0157] Where a and b are multiple linear regression coefficients, and the biological significance of a and b is the energy value (cal / g) of protein and fat in the egg.

[0158] Step 2.2.2: Measure the maintenance metabolic energy (ME).m Energy efficiency of protein deposition in eggs (k) p ) and fat deposition energy efficiency (k f ):

[0159] Establish metabolizable energy intake (ME) i ) and net energy of protein deposition in eggs (NE) pro ) and fat deposition energy (NE fat The multiple linear regression relationship of ) was used to calculate the protein content (k) in the egg. p ) and fat (k f Energy efficiency of deposition (%):

[0160]

[0161] Where k p and k f k represents the coefficients of the multiple linear regression. p and k f The biological significance of this is the energy efficiency (%) of protein and fat deposition in the egg, where the constant C is the metabolic energy (ME) for maintenance. m .

[0162] Step 2.2.3: Calculate the metabolic energy requirement:

[0163] By establishing a multiple linear regression model, the energy value (a) and energy efficiency (k) of protein deposition in eggs were obtained. p ), and the energy value (b) and energy efficiency (k) of fat deposition in eggs. f ), and maintain metabolic energy ME m (Regression model constant C). Therefore, the daily metabolic energy requirement (kcal / d) is:

[0164]

[0165] The dietary metabolizable energy requirement (kcal / kg) is:

[0166]

[0167] ADFI stands for Average Daily Feed Intake (kg / d).

[0168] Step 3: Associate the input variables of the model:

[0169] Step 3.1: Predict the metabolic energy maintenance requirement (ME) m :

[0170] Nonlinear regression analysis was used to correlate body weight with metabolic energy maintenance requirement (ME). m :

[0171] ME m =d×BWe ;

[0172] Where d and e are nonlinear regression coefficients, and BW is body weight.

[0173] Step 3.2: Predict the amount of metabolizable energy required for deposition:

[0174] The indicators directly related to protein and fat deposition in eggs are daily egg production and the protein or fat content of eggs. Therefore, the predictive model is established as follows:

[0175] Protein or fat deposition in eggs = daily egg production × protein or fat content in eggs.

[0176] Step 3.3: Establish a model for predicting total metabolizable energy requirements:

[0177] Combining the protein and fat deposition in eggs from step 3.2 with the prediction model obtained from the laying period in step 2.2.3, the metabolizable energy requirements of farms at different production levels are predicted based on daily egg production and the protein or fat content in eggs.

[0178] Example 2: Using a model to quickly predict the metabolizable energy requirements of yellow-feathered broiler breeder farms during the laying period:

[0179] To estimate the daily metabolizable energy requirement (kcal / kg) of a Qingyuan Ma chicken (breeding hen) farm during the egg-laying period, in order to guide feed formulation.

[0180] Step 1: Building the Model

[0181] Assuming the above methodology has been used to establish a predictive model for the metabolizable energy requirements of breeding hens during their egg-laying period, the following model is proposed:

[0182] Metabolic energy requirement (ME) m =32×BW 0.7

[0183] Where BW is body weight (kg), and 32 and 0.7 are parameters obtained from modeling.

[0184] Egg deposition metabolic energy requirements

[0185]

[0186] Wherein, Protein and Fat represent the daily deposition of protein and fat in eggs from breeder hens, respectively, and 0.65 and 0.78 are the egg protein (kJ) values ​​obtained based on the above methodology. p ) and fat (k f Energy deposition efficiency (%), 8.2 and 12.2 are the energy values ​​(kcal / kg) of egg protein (a) and fat (b).

[0187] Predictive models for daily protein and fat deposition in eggs:

[0188] Protein=egg production×egg protein%;

[0189] Fat=egg production×egg fat%;

[0190] Protein and Fat refer to the daily protein and fat deposition in eggs from breeding hens, respectively. Egg production refers to the daily egg production (g / d), and egg protein and egg fat% refer to the average protein and fat content of eggs.

[0191] Step Two: Collect the average body weight data of the laying hens of Qingyuan Ma chickens at this farm, assuming an average body weight (BW) of 2.5 kg. The required metabolic energy for maintenance is:

[0192] ME m =32×BW 0.7 =32 × 2.5 0.7 =61kcal / d;

[0193] Step 3: Collect egg production performance data of breeding hens. Assuming the feed restriction of breeding hens is 110g / d and the average daily egg production is 45g / d, the average protein content of the sample eggs is 13% and the fat content is 12%. Based on the obtained model, predict the daily deposition of protein and fat in eggs as follows:

[0194] Protein=egg production×egg protein%=45×13%=5.85g / d;

[0195] Fat=egg production×egg fat%=45×12%=5.4g / d;

[0196] Step 4: Based on the above model, predict the daily metabolic energy requirement for egg deposition as follows:

[0197]

[0198] Step Six: Based on the principle of factorial analysis, the daily requirement for metabolizable energy is:

[0199] ME daily =ME e +ME m =158 + 61 = 219 kcal / d;

[0200] Step 7: Based on an average daily feed intake of 100g / d, convert the daily metabolizable energy requirement (kcal / d) into a dietary metabolizable energy requirement (kcal / kg):

[0201]

[0202] This invention relates to a method for dynamically predicting the metabolizable energy requirements of yellow-feathered broilers. Since the methodology is applicable to other poultry (white-feathered broilers, ducks, geese, pigeons, breeding poultry, laying hens, etc.), its application to other poultry species also falls within the scope of this invention. The basic principles, main features, and advantages of this invention have been shown and described above. Those skilled in the art should understand that this invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made without departing from the spirit and scope of this invention, and all such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for dynamically predicting metabolic energy requirements, characterized in that: The specific steps are as follows: Step 1, determination of body or egg composition: Before modeling, determine the water, protein, fat and total energy content in the body or egg to calculate the amount of energy and nutrients deposited in the body or egg. Step 2: Establishment of the metabolizable energy factorization model: Step 2.1, Factorial model of metabolic energy requirement: During the growth period ; During the egg-laying period ; Step 2.2, Predicting depositional metabolic energy: Step 2.2.1: (1) During the growth period, predict the body deposition metabolic energy ME g It is necessary to first measure the net energy of body protein deposition. and net energy from body fat deposition : The net energy of body deposition is obtained by detecting the energy, body protein content, and body fat content in body components. Protein deposition and fat deposition: ； ； Net energy of solid deposition The energy values ​​of body protein and body fat were calculated by performing a multiple linear regression analysis on their correlation with body protein deposition (Protein) and body fat deposition (Fat). ； a and b are multiple linear regression coefficients, and the biological significance of a and b is the energy value of protein and fat, respectively. (2) During the laying period, predict the metabolic energy (ME) of egg deposition. e It is necessary to first measure the net energy of protein deposition in the egg. and net energy from fat deposition in eggs : The energy, protein, and fat content of an egg are measured to obtain the net energy of egg deposition. Protein and fat deposition in eggs: Egg deposition net energy NE e =Daily egg production × Total egg energy value; Protein or fat deposition in eggs = Daily egg production × Protein or fat content in eggs; Establishing net energy (NE) from egg deposition e The energy values ​​of protein and fat in eggs were calculated by performing a multiple linear regression analysis on the protein and fat deposition amounts in the eggs. ； Step 2.2.2: (1) During the growth period, measure maintenance metabolic energy. Energy efficiency of body protein deposition Energy efficiency of body fat deposition : Establish metabolizable energy intake Net energy from body protein deposition and body fat deposition The multiple linear regression relationship was used to calculate body protein levels. and body fat Energy efficiency of deposition: ； (2) During the egg-laying period, measure the maintenance metabolic energy. Energy efficiency of protein deposition in eggs and energy efficiency of fat deposition in eggs The linear regression relationship during the egg-laying period is the same as that during the growth period; Step 2.2.3: Calculate the metabolic energy requirement: (1) During the growth period, the energy value 'a' and energy efficiency of somatic protein deposition were obtained by establishing a multiple linear regression model. Energy value b and energy efficiency of body fat deposition Maintain metabolic energy ,but The daily metabolic energy requirement is: ； The dietary metabolizable energy requirement is: ； ADFI is the average daily feed intake. (2) During the egg-laying period, the energy value 'a' of protein deposition in the egg and the energy efficiency were obtained by establishing a multiple linear regression model. The energy value b and energy efficiency of fat deposition in eggs Maintain metabolic energy The calculation model for the egg-laying period is the same as that for the growth period. Step 3: Associate the input variables of the model: Step 3.1: Predict metabolic energy maintenance requirements : Nonlinear regression analysis was used to correlate body weight and metabolic energy maintenance requirements. : ； Where d and e are nonlinear regression coefficients, For body weight; Step 3.2: Predict the amount of metabolizable energy required for deposition: (1) During the growth period, the production indicators directly related to body protein deposition are daily weight gain (ADG) and pectoral muscle percentage. or leg muscle rate Production indicators associated with body fat deposition are daily weight gain (ADG) and abdominal fat percentage. Therefore, a multiple linear regression model is established: Daily body protein deposition ； ； Daily body fat deposition: ； ； a, b, and c are the linear regression parameters for body protein and body fat, respectively. (2) During the laying period, the indicators directly related to protein and fat deposition in eggs are daily egg production and protein or fat content in eggs. Therefore, the prediction model is established as follows: Protein or fat deposition in eggs = Daily egg production × Protein or fat content in eggs; Step 3.3: Establish a model for predicting total metabolizable energy requirements: (1) During the growth period, combined with the amount of body protein and body fat deposition in step 3.2 and the prediction model obtained in step 2.2.3, the metabolic energy requirement of farms at different growth stages and different production levels is predicted by body weight, pectoral muscle rate, leg muscle rate or abdominal fat rate. (2) During the egg-laying period, based on the amount of protein and fat deposition in eggs in step 3.2 and the prediction model obtained in step 2.2.3 during the egg-laying period, the metabolic energy requirement of farms at different production levels is predicted by the daily egg production and the protein or fat content in eggs.

2. The method for dynamically predicting metabolic energy requirements according to claim 1, characterized in that: In step 1, moisture content was measured according to national standard GB / T 6435-2014, protein content was measured according to national standard GB / T 6433-2006, fat content was measured according to national standard GB / T 6433-2006, and total energy content was determined using an oxygen bomb energy meter.

3. The method for dynamically predicting metabolic energy requirements according to claim 1, characterized in that: In step 2.2.2 and These are the coefficients of the multiple linear regression. and The biological significance is the energy efficiency of protein and fat deposition, with the constant C representing the metabolic energy required to maintain metabolism. .

4. The method for dynamically predicting metabolic energy requirements according to claim 1, characterized in that: During the growth period in step 3.2 and This refers to the content of body protein and body fat. , , This indicates leg muscle percentage, chest muscle percentage, and abdominal fat percentage.

Images