All-season breeding method for lion-head geese

Abstract

The invention provides a lion-head goose all-season breeding method, and relates to the technical field of lion-head goose breeding, and the lion-head goose all-season breeding method comprises the following steps: S1, breeding goose selection, S2, feed management, S3, breeding preparation, S4, poultry house temperature prediction, S5, incubation management, S6, hatching and nursing, and S7, gosling management. According to the method, key factors of the air temperature of a lion-head goose poultry house are screened, a support vector regression (SVR) prediction model structure is simplified, then a PCA-SVR prediction model is trained and predicted, finally, an ARMA residual error correction model is utilized to predict a PCA-SVR prediction error sequence, important information implied in residual errors is deeply mined, and the prediction accuracy is improved. The ARMA pair residual error prediction result and the PCA-SVR prediction result are subjected to geometric summation to obtain a final prediction result, the model is corrected through the ARMA residual error, the error of the PCA-SVR prediction result can be effectively reduced, and the prediction precision and stability of the whole combined model are improved.

Description

Technical Field

[0001] The invention relates to the technical field of lionhead goose breeding, and in particular to a full-season breeding method for lionhead geese. Background Art

[0002] The Lion-head goose is a local fine goose breed in China. It is native to Raoping County, Chaozhou City, Guangdong Province. It is named because the feathers on its head resemble a lion's mane. The Lion-head goose is large in size, grows fast, and has delicious meat. It is one of the important poultry breeds in southern China.

[0003] At present, when the market has a high demand for all-season goslings or meat geese, farmers may choose to carry out all-season breeding. During all-season breeding, suitable temperature conditions can promote the growth and development of lionhead geese, improve their immunity and production performance. By predicting the temperature in the poultry house, it can be ensured that the temperature in the poultry house can be maintained within the suitable range required by the lionhead geese under different seasons and weather conditions, thereby providing them with a good growth environment. First, the basic temperature requirements of the lionhead geese at different growth stages are clarified. These requirements are usually based on the physiological characteristics and environmental adaptability of the lionhead geese, and are an important basis for predicting and adjusting the temperature of the poultry house. Then a temperature prediction model is established. The model can be based on meteorological data, poultry house structure, ventilation conditions, stocking density and other factors for comprehensive consideration. The temperature of the poultry house is predicted by mathematical methods or machine learning algorithms. Since the model may not be able to fully capture all the complex factors affecting the temperature of the poultry house and their interactions, it increases the error of the prediction results.

[0004] Therefore, it is necessary to provide a new all-season breeding method for lionhead geese to solve the above-mentioned technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for breeding lionhead geese in all seasons.

[0006] The present invention provides a method for breeding lionhead geese in all seasons, comprising the following steps:

[0007] S1. Selection of breeding geese: healthy, strong and genetically defect-free adult Lionhead geese are selected as breeding geese, and the male-female breeding ratio is usually 1:3 or 1:5;

[0008] S2. Feed management: provide high-quality feed to ensure that the breeding geese get adequate nutrition and feed them regularly and quantitatively. The Lionhead goose all-season breeding goose compound feed is divided into laying period feed and rest period feed.

[0009] S3. Prepare for breeding, provide spacious poultry houses and clean water sources, and ensure that the environment is suitable during the breeding season;

[0010] S4. Prediction of poultry house temperature. By organically combining PCA, SVR and ARMA models, a nonlinear combination prediction model of poultry house temperature for Lionhead goose breeding is constructed based on PCA-SVR-ARMA. The idea is to first repair and standardize the environmental data of Lionhead goose breeding, and then use PCA as the pre-preprocessing system of support vector regression machine to screen the key factors of Lionhead goose poultry house temperature, simplify the structure of support vector regression machine SVR prediction model, and then train and predict the PCA-SVR prediction model. Finally, the ARMA residual correction model is used to predict the PCA-SVR prediction error sequence, so as to deeply explore the important information hidden in the residual, and geometrically sum the ARMA residual prediction results with the PCA-SVR prediction results to obtain the final prediction results.

[0011] S5. Incubation management: use professional incubation equipment to incubate and ensure that the temperature and humidity in the incubation equipment are appropriate;

[0012] S6. Hatching and care: The incubation period is generally 28-30 days. When the goslings are about to hatch, they will break out of the shell on their own. The ducklings should be immediately transferred to the brooding room after hatching.

[0013] S7. Gosling management: Use heat lamps or infrared lamps to maintain the temperature in the brooding room, and group the goslings according to their growth conditions, while providing high-quality gosling feed.

[0014] Preferably, the specific steps of predicting the temperature in the poultry house of Lionhead goose breeding are as follows:

[0015] S10, collect online the time series data of the poultry house environment of lion-head goose through the waterfowl farming IoT cloud platform, perform repair and standardization processing, and divide the sample training set and test set;

[0016] S20, using principal component analysis and screening of key influencing factors of temperature in poultry houses for lion-head geese, eliminating multivariate redundant information and streamlining the prediction model structure;

[0017] S30, initializing the PCA-SVR prediction model parameters, based on the sample training set, training and optimizing the PCA-SVR model through trial and error and cross validation methods, to obtain the best SVR prediction model;

[0018] S40. Use the test set to predict the performance of the PCA-SVR model, and use the ARMA residual correction model to predict the predicted residual time series data. Perform geometric summation on the residual prediction structure and the PCA-SVR prediction result to obtain the final prediction result. Then, apply the optimized PCA-SVR-ARMA model to the field of Lionhead goose breeding environment prediction to achieve accurate prediction of the future temperature of Lionhead goose breeding poultry houses.

[0019] Preferably, the poultry house is designed with a brick wall and steel frame structure, with a sliding window on the north wall and the glass painted black. The south side of the poultry house is shielded by black roller blinds and is equipped with an amphibious sports field. A fixed pergola is built on the amphibious sports field, and an artificial sports pool is set outside the sports field with a water depth of 0.8m to 1.0m and an area of ​​15 breeding geese per square meter. A drinking water device is set next to the pool, separate from the sports pool.

[0020] Preferably, the infertility feed is a mixture of raw materials in the following proportions: 13.5 to 14.0 parts of crude protein, 11.0 MJ / kg to 11.5 MJ / kg of metabolizable energy, 0.9 to 1.4 parts of calcium, 0.7 parts of total phosphorus, and 0.25 parts of salt.

[0021] Preferably, the egg-laying feed is a mixture of raw materials in the following proportions: 16.0 to 16.5 parts of crude protein, 11.5 MJ / kg to 12.0 MJ / kg of metabolizable energy, 2.0 to 2.5 parts of calcium, 0.7 parts of phosphorus, and 0.3 parts of salt.

[0022] Preferably, the breeding geese are restricted in the short natural daylight period in early December each year, and the long-light treatment is carried out with a daily light duration of 18.5 hours for the first 25 days, 18 hours for the 26th day to the 40th day, 17.5 hours for the 41st day, and 16.5 hours for the 41st to 52nd day.

[0023] Preferably, short-light treatment is applied to male geese starting from the 53rd day after long-light treatment, and the daily light time is shortened from 16.5h to 12h. From the 81st day, male and female geese are grouped together so that the female geese also end the 12h daily light treatment. After the female goose lays the first clutch of eggs, the light time is shortened to 10.5h per day until the end of the egg-laying year.

[0024] Preferably, the light intensity of the long photoperiod is maintained at 150-200 lux, and the light intensity of the short photoperiod is maintained at above 150 lux.

[0025] Compared with the related art, the all-season breeding method of lionhead geese provided by the present invention has the following advantages:

[0026] Beneficial effects:

[0027] The present invention organically combines PCA, SVR and ARMA models to construct a nonlinear combined prediction model for the temperature of a poultry house for lion-head geese based on PCA-SVR-ARMA. The model is based on the idea that the environmental data of lion-head geese breeding are first repaired and standardized, and then PCA is used as a pre-preprocessing system of a support vector regression machine to screen key factors of the temperature of the lion-head geese poultry house, simplify the structure of the support vector regression machine SVR prediction model, and then the PCA-SVR prediction model is trained and predicted. Finally, the ARMA residual correction model is used to predict the PCA-SVR prediction error sequence, so as to deeply mine the important information hidden in the residual, and the ARMA residual prediction result is geometrically summed with the PCA-SVR prediction result to obtain the final prediction result. The ARMA residual correction model can effectively reduce the error of the PCA-SVR prediction result, and improve the prediction accuracy and stability of the entire combined model. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A flowchart of a method for breeding lionhead geese in all seasons provided by the present invention;

[0029] Figure 2 A flowchart of the temperature prediction of the poultry house for Lionhead geese breeding provided by the present invention;

[0030] Figure 3 This is a flowchart of the model residual correction provided by the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0032] Please refer to Figure 1 , Figure 2 as well as Figure 3 ,in, Figure 1 A flowchart of a method for breeding lionhead geese in all seasons provided by the present invention; Figure 2 A flowchart of the temperature prediction of the poultry house for Lionhead geese breeding provided by the present invention; Figure 3 This is a flowchart of the model residual correction provided by the present invention.

[0033] In the specific implementation process, Figures 1 to 3 As shown, the following steps are included:

[0034] S1. Selection of breeding geese: healthy, strong and genetically defect-free adult Lionhead geese are selected as breeding geese, and the male-female breeding ratio is usually 1:3 or 1:5;

[0035] It should be noted that the breeding geese are selected from individuals between 1 and 3 years old, as geese of this age have strong reproductive capacity;

[0036] S2. Feed management: provide high-quality feed to ensure that the breeding geese get adequate nutrition and feed them regularly and quantitatively. The Lionhead goose all-season breeding goose compound feed is divided into laying period feed and rest period feed.

[0037] It should be noted that the rest period feed is a mixture of raw materials with the following proportions: 13.5 to 14.0 parts of crude protein, 11.0 MJ / kg to 11.5 MJ / kg of metabolizable energy, 0.9 to 1.4 parts of calcium, 0.7 parts of total phosphorus, and 0.25 parts of salt;

[0038] Egg-laying feed: The egg-laying feed is mixed with the following raw materials in the following proportions: 16.0 to 16.5 parts of crude protein, 11.5 MJ / kg to 12.0 MJ / kg of metabolic energy, 2.0 to 2.5 parts of calcium, 0.7 parts of phosphorus, and 0.3 parts of salt;

[0039] Meanwhile, the feed formula for the off-season is as follows: 62.5% corn, 16% wheat bran, 14.4% soybean meal, 2% corn gluten meal, 1.6% calcium hydrogen phosphate, 0.2% salt, 2.3% stone powder, and 1% premix;

[0040] The feed formula during the laying period is as follows: 65% corn, 6.6% wheat bran, 14.8% soybean meal, 6% corn gluten meal, 2.34% calcium hydrogen phosphate, 0.26% salt, 4% stone powder, and 1% premix;

[0041] S3. Prepare for breeding, provide spacious poultry houses and clean water sources, and ensure that the environment is suitable during the breeding season;

[0042] It should be noted that the poultry house is designed with a brick wall and steel frame structure, with sliding windows on the north wall and black glass. The south side of the poultry house is covered with black roller blinds and equipped with a water and land playground. A fixed pergola is built on the land playground. An artificial sports pool is set outside the playground with a water depth of 0.8m to 1.0m and an area of ​​15 breeding geese / ㎡. A drinking water device is set up next to the pool, separate from the sports pool.

[0043] The goose house is built with longitudinal negative pressure ventilation facilities, with ventilation holes set at one end of the longitudinal direction. On the opposite side, low-noise, high-volume air coolers for livestock and poultry houses are installed according to the volume of the goose house. The number and duration of the air coolers are controlled according to the temperature in the house (the temperature in the house is controlled below 30°C during the summer egg-laying period). The stocking density is 4 to 6 breeding geese per square meter, and the manure in the goose house is cleaned once a day.

[0044] S4. Prediction of poultry house temperature. By organically combining PCA, SVR and ARMA models, a nonlinear combination prediction model of poultry house temperature for Lionhead goose breeding is constructed based on PCA-SVR-ARMA. The idea is to first repair and standardize the environmental data of Lionhead goose breeding, and then use PCA as the pre-preprocessing system of support vector regression machine to screen the key factors of Lionhead goose poultry house temperature, simplify the structure of support vector regression machine SVR prediction model, and then train and predict the PCA-SVR prediction model. Finally, the ARMA residual correction model is used to predict the PCA-SVR prediction error sequence, so as to deeply explore the important information hidden in the residual, and geometrically sum the ARMA residual prediction results with the PCA-SVR prediction results to obtain the final prediction results.

[0045] It should be noted that the specific steps for predicting the temperature in the poultry house for Lionhead geese are as follows:

[0046] S10, collect online the time series data of the poultry house environment of lion-head goose through the waterfowl farming IoT cloud platform, perform repair and standardization processing, and divide the sample training set and test set;

[0047] S20, using principal component analysis and screening of key influencing factors of temperature in poultry houses for lion-head geese, eliminating multivariate redundant information and streamlining the prediction model structure;

[0048] S30, initializing the PCA-SVR prediction model parameters, based on the sample training set, training and optimizing the PCA-SVR model through trial and error and cross validation methods, to obtain the best SVR prediction model;

[0049] S40, using the test set to predict the performance of the PCA-SVR model, and predicting the prediction residual time series data through the ARMA residual correction model, geometrically summing the residual prediction structure and the PCA-SVR prediction result to obtain the final prediction result, and then applying the optimized PCA-SVR-ARMA model to the field of Lionhead goose breeding environment prediction, so as to achieve accurate prediction of the temperature of the Lionhead goose breeding poultry house in the future;

[0050] PCA (Principal Component Analysis) is a commonly used data dimensionality reduction technique. Its main purpose is to transform the original data into a set of linearly independent representations of each dimension through linear transformation. It can be used to extract the main characteristic components of the data and is often used for dimensionality reduction of high-dimensional data.

[0051] Support Vector Regression (SVR) is a new machine learning method based on the VC dimension and structural risk minimization principle. Its goal is to construct an optimal hyperplane so that the error of training samples from the optimal hyperplane is minimized, so as to achieve better fitting performance and generalization ability for unknown samples. For a given Lionhead goose breeding environment data training sample is the input vector, is the target output value for temperature prediction, and the optimization problem corresponding to its SVR is expressed as follows:

[0052]

[0053] In the formula, and b are weight and bias respectively, penalty factor C>0, non-negative slack variable ξ i , ε is the insensitive loss function parameter, st is the constraint condition;

[0054] At the same time, according to the KKT condition, the Lagrangian function is introduced, and the regression expression is as follows:

[0055]

[0056] In the formula, K(x,x i )=φ(x)φ(x i ) is the kernel function, and a i ≥0 are Lagrangian function multipliers;

[0057] KKT conditions (Karush-Kuhn-Tucker Conditions), also known as Karush-Kuhn-Tucker conditions, are a necessary and sufficient condition for nonlinear programming problems in mathematics and an important tool for solving optimization problems.

[0058] Lagrangian function is a very important concept in optimization theory and control theory, especially when dealing with optimization problems with constraints.

[0059] Because the Gaussian radial basis kernel function has the advantages of few parameters, low computational complexity, and easy implementation, this study selects the radial basis function as the kernel function of the SVR regression function to improve the temperature prediction performance. The radial basis kernel function expression is as follows:

[0060]

[0061] Where δ is the width parameter of the RBF kernel function, xi For any input sample vector, x j is the center of Gaussian RBF kernel function;

[0062] ARMA model, full name is Autoregressive Moving Average Model, is a commonly used model in time series analysis, and through SVR to establish the Lionhead goose breeding poultry house temperature prediction model, although the historical time series data can be used to train the SVR prediction model to obtain better prediction results, but still get a series of error time series data through SVR, ARMA model its expression ARMA (p, q) is as follows:

[0063]

[0064] In the formula, p, q are the orders of the ARMA model, U t (t=1,2,…,q) are the autoregressive parameter and the moving average parameter respectively, satisfying EU t y t-1 =0,φ i The local steps of model residual correction (i=1, 2, ..., q) are as follows:

[0065] Step 1: Use the Augmented Dickey-Fuller (ADF) to perform a time series diagram and unit root test on the residual time series data of the temperature prediction of the Lionhead goose breeding poultry house to determine whether the residual time series changes are stable. If so, go to step 3, otherwise go to step 2;

[0066] Step 2: According to the unbalanced characteristics of the residual time series data of the temperature prediction of the lion head goose breeding poultry house, the logarithmic transformation method, decomposition method, smoothing method and difference method are selected for stabilization processing;

[0067] Step 3: Calculate the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the residual time series data of the temperature prediction of the poultry house of Lionhead goose breeding, and determine that the residual prediction model is the ARMA (p, q) model;

[0068] Step 4: Determine the optimal ARMA model orders p and q according to the minimum Akaike Information Criterion (AIC) value and the Bayesian Information Criterion (BIC) value;

[0069] Step 5: Use the least squares method to estimate the autoregressive parameters, moving average parameters and parameters to be estimated of the model, and minimize the residual sum;

[0070] Step 6: Perform model significance and parameter significance tests respectively to determine whether the residual time series is a white noise sequence. If not, go to step 2 and execute repeatedly until the optimal ARMA (p, q) model is obtained.

[0071] Step 7: Use the constructed optimal ARMA (p, q) model to predict the residual value;

[0072] S5. Incubation management: use professional incubation equipment to incubate and ensure that the temperature and humidity in the incubation equipment are appropriate;

[0073] It should be noted that the temperature is generally maintained at around 37.8°C, the humidity is between 50% and 60%, and in the late incubation period (around the 18th day), the temperature is appropriately lowered and the eggs are cooled to promote embryonic development;

[0074] S6. Hatching and care: The incubation period is generally 28-30 days. When the goslings are about to hatch, they will break out of the shell on their own. The ducklings should be immediately transferred to the brooding room after hatching.

[0075] It should be noted that after hatching, the goslings need to stay in the incubator for a while and transfer them after their down is dry.

[0076] S7. Gosling management: Use heat lamps or infrared lamps to maintain the temperature in the brooding room, group the goslings according to their growth conditions, and provide high-quality gosling feed;

[0077] In early December each year, the natural daylight is short, so the breeding geese are restricted and the long-light treatment is carried out. The light time is 18.5 hours per day for the first 25 days, 18 hours per day from the 26th day to the 40th day, 17.5 hours per day from the 41st day, and 16.5 hours per day from the 41st day to the 52nd day.

[0078] Preferably, short-light treatment is applied to male geese starting from the 53rd day after long-light treatment, and the daily light time is shortened from 16.5h to 12h. From the 81st day, male and female geese are grouped together so that the female geese also end the 12h daily light treatment. After the female goose lays the first clutch of eggs, the light time is shortened to 10.5h per day until the end of the egg-laying year.

[0079] Preferably, the light intensity of the long photoperiod is maintained at 150-200 lux, and the light intensity of the short photoperiod is maintained at above 150 lux.

[0080] The circuits and controls involved in the present invention are all prior art and will not be described in detail here.

[0081] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for breeding lion-head geese in all seasons, characterized in that: The following steps are involved: S1. Selection of breeding geese: healthy, strong and genetically defect-free adult Lionhead geese are selected as breeding geese, and the male-female breeding ratio is 1:3 or 1:5; S2. Feed management: provide high-quality feed to ensure that the breeding geese get adequate nutrition and feed them regularly and quantitatively. The Lionhead goose all-season breeding goose compound feed is divided into laying period feed and rest period feed. S3. Breeding preparation: provide spacious poultry houses and clean water sources, and ensure that the environment is suitable during the breeding season; S4. Prediction of poultry house temperature. By organically combining PCA, SVR and ARMA models, a nonlinear combination prediction model of poultry house temperature for Lionhead goose breeding is constructed based on PCA-SVR-ARMA. The idea is to first repair and standardize the environmental data of Lionhead goose breeding, and then use PCA as the pre-preprocessing system of support vector regression machine to screen the key factors of Lionhead goose poultry house temperature, simplify the structure of support vector regression machine SVR prediction model, and then train and predict the PCA-SVR prediction model. Finally, the ARMA residual correction model is used to predict the PCA-SVR prediction error sequence, so as to deeply explore the important information hidden in the residual, and geometrically sum the ARMA residual prediction results with the PCA-SVR prediction results to obtain the final prediction results. S5. Incubation management: use professional incubation equipment for incubation and ensure that the temperature and humidity in the incubation equipment are appropriate; S6, hatching and care, immediately transfer the ducklings to the brooding room after hatching; S7. Gosling management: Use heat lamps or infrared lamps to maintain the temperature in the brooding room, and group the goslings according to their growth conditions, while providing high-quality gosling feed.

2. The all-season breeding method of Lionhead geese according to claim 1, characterized in that: The specific steps for predicting the temperature in the poultry house for Lionhead geese are as follows: S10, collect online the time series data of the poultry house environment of lion-head goose through the waterfowl farming IoT cloud platform, perform repair and standardization processing, and divide the sample training set and test set; S20, using principal component analysis method and screening of key influencing factors of temperature in poultry houses for Lionhead geese; S30, initializing the PCA-SVR prediction model parameters, based on the sample training set, training and optimizing the PCA-SVR model through trial and error and cross validation methods, to obtain the best SVR prediction model; S40, using the test set to perform a prediction test on the performance of the PCA-SVR model, and using the ARMA residual correction model to predict the prediction residual time series data, performing a geometric summation on the residual prediction structure and the PCA-SVR prediction result to obtain the final prediction result.

3. The all-season breeding method of Lionhead geese according to claim 2, characterized in that: The poultry house is designed with a brick wall and steel frame structure. A sliding window is left on the north wall with black glass. The south side of the poultry house is covered with black roller blinds and is equipped with an amphibious sports field. A fixed pergola is built on the amphibious sports field. An artificial sports pool is set up outside the sports field with a water depth of 0.8m to 1.0m and an area of ​​15 breeding geese per square meter. A drinking water device is set up next to the pool, separate from the sports pool.

4. The all-season breeding method of Lionhead geese according to claim 3, characterized in that: Feed for the rest period is a mixture of raw materials in the following proportions: 13.5 to 14.0 parts of crude protein, 11.0 MJ / kg to 11.5 MJ / kg of metabolizable energy, 0.9 to 1.4 parts of calcium, 0.7 parts of total phosphorus, and 0.25 parts of salt.

5. The all-season breeding method of Lionhead geese according to claim 4, characterized in that: Feed for laying period: Feed for laying period is a mixture of raw materials in the following proportions: 16.0 to 16.5 parts of crude protein, 11.5 MJ / kg to 12.0 MJ / kg of metabolic energy, 2.0 to 2.5 parts of calcium, 0.7 parts of phosphorus, and 0.3 parts of salt.

6. The all-season breeding method of Lionhead geese according to claim 5, characterized in that: In early December each year, the natural daylight is short, so the breeding geese are restricted and the long-light treatment is carried out. The light time is 18.5 hours per day for the first 25 days, 18 hours per day from the 26th day to the 40th day, 17.5 hours per day from the 41st day, and 16.5 hours per day from the 41st day to the 52nd day.

7. The all-season breeding method of Lionhead geese according to claim 6, characterized in that: From the 53rd day after the long-light treatment, the ganders were given short-light treatment, with the daily light time shortened from 16.5h to 12h. From the 81st day, the ganders and female geese were grouped together so that the female geese also ended their 12h daily light treatment. After the female geese laid their first clutch of eggs, the light time was shortened to 10.5h per day until the end of the egg-laying year.

8. The all-season breeding method of Lionhead geese according to claim 7, characterized in that: The light intensity of long photoperiod is maintained at 150-200 lux, and the light intensity of short photoperiod is maintained at above 150 lux.

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