Method for estimating net energy value required by growth of largemouth bass

Abstract

The invention provides a method for estimating a net energy value required by growth of largemouth bass, and the method comprises the steps: carrying out the calculation processing based on the experimental data of multiple parallel fasting experiments, and obtaining the maintenance net energy data of target fingerlings of different specifications; performing calculation processing based on experimental data of different feeding level experiments to obtain unit weight intake energy, unit weight deposition energy and growth net energy data; performing regression analysis on the unit weight intake energy and the unit weight deposition energy to obtain a regression equation; performing analysis and comparison processing in combination with the net energy maintaining data and the regression equation, and determining a basic metabolism value; combining the basic metabolism value and the growth net energy data to construct a model for predicting a net energy value required by growth; and inputting the predicted daily gain into a model for predicting the net energy value required by growth to obtain the net intake growth energy value required by the target fingerling. By means of the method, the efficiency of the feed is improved, unnecessary waste of the feed is reduced, economic benefits are improved, and sustainable development of aquaculture is promoted.

Description

Technical Field

[0001] The invention relates to the technical field of aquaculture, and in particular to a method for estimating the net energy value required for the growth of largemouth bass. Background Art

[0002] Energy is the foundation of feed formulation. Only when energy is adequate and other nutrients are appropriately combined according to the animal species and physiological characteristics can an effective feed formulation be formulated. Different energy systems have different energy distribution and relationships with other nutrients, so the choice of energy system plays a crucial role in optimizing diet formulation. Net energy best reflects an animal's energy needs and provides a precise definition of the energy required for growth. It serves as a crucial reference for designing precise fish feed formulations and a crucial benchmark for precision cost reduction. Furthermore, the net energy system further refines energy utilization compared to existing metabolizable energy systems, potentially reducing energy waste. However, limited research has further explored net energy requirements in fish, and the relationship between fish growth and net energy remains unclear. Therefore, the rational and correct use of net energy systems remains a challenge and a pressing need for aquaculture producers.

[0003] Largemouth bass, as a farmed animal with high protein and high energy intake, needs to have its actual energy intake and requirements evaluated to accurately predict the growth of farmed animals, thereby optimizing farming management and improving production efficiency to further increase economic benefits. Summary of the Invention

[0004] In view of this, the present invention provides a method for estimating the net energy required for the growth of largemouth bass to solve the above problems.

[0005] The present invention provides a method for estimating the net energy value required for the growth of largemouth bass, comprising: performing calculation processing based on experimental data of multiple parallel fasting experiments to obtain maintenance net energy data of target fish species of different specifications; performing calculation processing based on experimental data of experiments with different feeding levels to obtain energy intake per unit body weight, energy deposition per unit body weight and net energy for growth data; performing regression analysis on the energy intake per unit body weight and the energy deposition per unit body weight to obtain a regression equation; performing analysis and comparison processing on the maintenance net energy data and the regression equation to determine a basal metabolic value; constructing a model for predicting the net energy value required for growth based on the basal metabolic value and the net energy for growth data; and inputting the expected daily weight gain into the model for predicting the net energy value required for growth to obtain the net energy value required for growth intake of the target fish species.

[0006] In another implementation of the present invention, the experimental data of the multiple parallel fasting experiments include body weight, daily energy consumption and daily energy consumption per unit body weight data.

[0007] In another embodiment of the present invention, the experimental data of the experiments with different feeding levels include initial weight, final weight, weight gain, feed intake, terminal energy, deposited energy, energy intake and standard body weight.

[0008] In another implementation of the present invention, the regression equation is expressed as:

[0009] y=0.3831*x-40.093

[0010] Here, x represents the energy intake and y represents the energy deposition.

[0011] In another implementation of the present invention, the model for predicting the net energy required for growth is expressed as:

[0012] NER=7.0816*ADG+NEm

[0013] Where ADG represents the energy gained from daily growth, in kJ / kg / day; NEm represents the net energy for maintenance, in MJ;

[0014] NEm=a*BW 0.78 / d

[0015] Where a is the metabolic constant of the target fish species, in kJ / kg; BW is the weight of the target fish species; BW 0.78 Metabolic weight.

[0016] The method of the present invention for estimating the net energy value required for the growth of largemouth bass predicts the energy requirements of fish growth through linear regression and fasting metabolism, providing a theoretical and data basis for better optimizing feed energy formulas, reducing protein levels in feeds, and reducing the impact of nitrogen in feeds on water quality, thereby reducing water treatment costs and the risk of disease. At the same time, characterized by a precise breeding model, it accurately breeds fish, reduces unnecessary feed feeding, improves economic benefits, and provides strong support for the sustainable development of the aquaculture industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. By reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. In the drawings:

[0018] Figure 1 This is a flow chart of a method for estimating the net energy required for growth of largemouth bass according to one embodiment of the present invention.

[0019] Figure 2FIG1 is a schematic diagram of a regression equation based on energy intake per unit body weight and energy deposition per unit body weight according to an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and detailedly described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in the embodiments of the present invention should fall within the scope of protection of the embodiments of the present invention.

[0021] Figure 1 A flow chart of a method for estimating the net energy required for growth of largemouth bass provided by an embodiment of the present invention is shown as follows: Figure 1 As shown, this embodiment mainly includes:

[0022] S101. Calculate and process experimental data from multiple parallel fasting experiments to obtain net energy maintenance data for target fish species of different sizes.

[0023] S102. Calculate and process experimental data based on experiments at different feeding levels to obtain data on energy intake per unit body weight, energy deposition per unit body weight, and net energy for growth.

[0024] For example, the net energy for growth data is obtained by performing regression analysis on the weight gain and energy deposition data of target fish species of different sizes in the experimental data.

[0025] S103 , performing regression analysis on the energy intake per unit body weight and the energy deposited per unit body weight to obtain a regression equation.

[0026] S104: Analyze and compare the net energy maintenance data and the regression equation to determine a basal metabolic value.

[0027] For example, the above experimental data are analyzed and compared to determine the most reasonable value and range as the basal metabolic value.

[0028] S105 , combining the basal metabolic value and the net energy data for growth to construct a model for predicting the net energy required for growth.

[0029] S106: Input the expected daily weight gain into the model for predicting the net energy required for growth to obtain the net energy intake required for growth of the target fish species.

[0030] The method of the present invention for estimating the net energy value required for the growth of largemouth bass predicts the energy requirements of fish growth through linear regression and fasting metabolism, providing a theoretical and data basis for better optimizing feed energy formulas, reducing protein levels in feeds, and reducing the impact of nitrogen in feeds on water quality, thereby reducing water treatment costs and the risk of disease. At the same time, characterized by a precise breeding model, it accurately breeds fish, reduces unnecessary feed feeding, improves economic benefits, and provides strong support for the sustainable development of the aquaculture industry.

[0031] In another implementation of the present invention, the experimental data of the multiple parallel fasting experiments include body weight, daily energy consumption and daily energy consumption per unit body weight data.

[0032] For example, the experimental data of the multi-parallel fasting experiment specifically include body weight, daily energy consumption, and daily energy consumption per unit body weight. As shown in Table 1, although there are certain differences in the specifications of the two treatment groups, their related trends are similar. As the number of fasting days increases, the body weight continues to decrease, and the rate of weight loss gradually slows down. The average daily consumption of fish in the later stage of fasting is less than that in the early stage. In addition, the daily consumption per unit body weight of fish of different sizes after fasting for a specific period of time is similar, and the energy consumption per unit day ranges from 28.41 to 43.05 kJ / kg BW 0.78 / d.

[0033] Table 1. Data on the energy consumption per unit body weight of fish of different sizes after fasting for a specific period of time

[0034]

[0035]

[0036] Daily energy consumption = (initial energy - final energy) / number of experimental days

[0037] Energy consumption per unit day = daily energy consumption / unit body weight

[0038] In another embodiment of the present invention, the experimental data of the experiments with different feeding levels include initial weight, final weight, weight gain, feed intake, terminal energy, deposited energy, energy intake and standard body weight.

[0039] For example, as shown in Table 2, the effects of different feeding levels on fish growth-related parameters are specifically demonstrated, wherein as the feeding level changes, the energy intake per unit body weight and the energy deposited per unit body weight produce obvious differences, which increase significantly with the increase of intake.

[0040] Table 2. Fish growth parameters under different feeding levels

[0041]

[0042]

[0043] Weight gain = final weight - initial weight; initial energy = initial weight * water * dry weight energy

[0044] Final energy = Final weight * Water * Dry weight energy

[0045] Accumulated energy = final energy - initial energy

[0046] Standard weight = body weight ^ 0.78

[0047] Energy intake per unit body weight = energy intake / standard body weight

[0048] In another implementation of the present invention, the regression equation is expressed as:

[0049] y=0.3831*x-40.093

[0050] Here, x represents the energy intake and y represents the energy deposition.

[0051] For example, Figure 2 As shown, when returning to zero, the basal metabolism is 40.093kJ / kgBW. 0.78 / d.

[0052] In another implementation of the present invention, the model for predicting the net energy required for growth is expressed as:

[0053] NER=7.0816*ADG+NEm

[0054] Where ADG represents the energy gained from daily growth, in kJ / kg / day; NEm represents the net energy for maintenance, in MJ;

[0055] NEm=a*BW 0.78 / d

[0056] Where a is the metabolic constant of the target fish species, in kJ / kg; BW is the weight of the target fish species; BW 0.78 Metabolic weight.

[0057] In another implementation of the present invention, the net energy value model required for daily growth of fish was investigated and tested, and the specific data are shown in Table 3.

[0058] Table 3. Input and output results of the model for predicting the net energy required for growth

[0059] Experimental Group 1 Experimental Group 2 Experimental Group 3 Experimental Group 4 Net energy of feed (kJ) 8.63 8.27 8.98 8.75 Daily intake (g) 0.46 0.47 0.47 0.49 Net energy intake per day (kJ) 3.31 3.21 3.57 3.69 Actual daily growth (g) 0.47 0.46 0.51 0.53 Estimated daily growth (g) 0.47 0.49 0.53 0.54

[0060] Daily intake = total intake / number of days of breeding

[0061] Net energy intake per day = total intake * net energy of feed

[0062] Actual daily growth = total growth / number of days of cultivation

[0063] Estimated daily growth = (net energy intake - 40.3 kJ / kg BW 0.78 ) / 7.0816

[0064] As can be seen from the table, the actual values of different net energy levels and net energy intake by fish are relatively close to the estimated values.

[0065] Net energy for fish growth = Net energy for daily weight gain + Net energy for maintenance

[0066] = Net energy gained during growth + Net energy consumed by basal metabolism

[0067] The maintenance net energy and growth net energy of fish were explored by comparing three different slaughter modes to further construct a fish growth model. The maintenance net energy range of fish is 39kJ / kg~43kJ / kg BW 0.78 / d, preferably 40.3 kJ / kgBW 0.78 / d.

[0068] The present invention has the following advantages or beneficial effects:

[0069] Compared with traditional fish digestibility determination, the method for estimating the net energy value required for largemouth bass growth provided by the present invention is simpler and faster, and can further accurately determine the actual energy value required for fish growth.

[0070] The model provided by the present invention for estimating the net energy value required for the growth of largemouth bass can further explore the energy required for the growth of largemouth bass, so as to further optimize the feed formula and reduce costs while achieving nutritional balance.

[0071] The model for estimating the net energy value required for the growth of largemouth bass provided by the present invention can better reduce the utilization of feed protein, thereby reducing the impact of fish nitrogen excretion on water quality, reducing costs and reducing pollution to the environment.

[0072] In summary, the present invention provides a method and model for estimating the net energy value required for the growth of largemouth bass, which can further predict the growth of largemouth bass and make certain constructive adjustments to the feed formula to further promote the effectiveness of the feed, reduce unnecessary waste in the feed, achieve healthy aquaculture, improve economic benefits, and promote the sustainable development of aquaculture.

[0073] In another aspect of the present invention, an electronic device includes a processor, a memory, a communication bus, and a communication interface.

[0074] in:

[0075] The processor, memory and communication interface communicate with each other through a communication bus.

[0076] Communication interface, used to communicate with other electronic devices or servers.

[0077] The processor is used to execute a program, specifically to execute the steps of any one of the methods for estimating the net energy required for growth of largemouth bass in the above embodiments.

[0078] Specifically, the program may include program codes including computer operation instructions.

[0079] The processor may be a central processing unit (CPU) or a graphics processing unit (GPU). The one or more processors included in a smart device may be processors of the same type, such as one or more CPUs, or processors of different types, such as one or more CPUs and one or more GPUs.

[0080] Memory is used to store programs. The memory may include high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk storage.

[0081] The program can be specifically configured to cause a processor to execute the steps of any of the methods for estimating the net energy required for growth of largemouth bass described in the embodiments. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units executed in any of the aforementioned methods for estimating the net energy required for growth of largemouth bass, and will not be repeated here. Those skilled in the art will clearly understand that, for ease and brevity of description, the specific operating processes of the devices and modules described above can refer to the corresponding process descriptions in the aforementioned method embodiments.

[0082] The above method according to the embodiment of the present invention can be implemented in a server equipped with a central processing unit (CPU) and an image processing unit (GPU).

[0083] Thus far, specific embodiments of the present invention have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. Furthermore, the sample types corresponding to the training datasets in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results.

[0084] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, back, etc.) are only used to explain the relative position relationship between the components in a certain specific order (as shown in the accompanying drawings). If the specific order changes, the directional indication will also change accordingly.

[0085] In the description of the present invention, the terms "first" and "second" are used solely to facilitate description of different components or names and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the quantity of the technical features being described. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one of such features.

[0086] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0087] It should be noted that although the specific embodiments of the present invention are described in detail in conjunction with the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Within the scope described by the claims, various modifications and variations that can be made by those skilled in the art without creative effort still fall within the scope of protection of the present invention.

[0088] The examples of the embodiments of the present invention are intended to briefly illustrate the technical features of the embodiments of the present invention so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to improperly limit the embodiments of the present invention.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for estimating the net energy required for growth of largemouth bass, characterized in that: include: Based on the experimental data of multiple parallel fasting experiments, the net energy data for maintaining target fish species of different sizes were obtained; Based on the experimental data of different feeding level experiments, the energy intake per unit body weight, energy deposition per unit body weight and net energy for growth were obtained. Performing regression analysis on the energy intake per unit body weight and the energy deposited per unit body weight to obtain a regression equation; Analyzing and comparing the net energy maintenance data and the regression equation to determine the basal metabolic value; Combining the basal metabolic value and the net energy data for growth, constructing a model for predicting the net energy required for growth; The expected daily weight gain is input into the net energy value model for predicting growth to obtain the net energy value required for growth of the target fish species.

2. The method according to claim 1, characterized in that The experimental data of the multiple parallel fasting experiments include body weight, daily energy consumption and daily energy consumption per unit body weight data.

3. The method according to claim 1, characterized in that The experimental data of the experiments with different feeding levels include initial weight, final weight, weight gain, feed intake, terminal energy, deposited energy, energy intake and standard body weight.

4. The method according to claim 3, characterized in that The regression equation is expressed as: y=0.3831*x-40.093 Here, x represents the energy intake and y represents the energy deposition.

5. The method according to claim 4, characterized in that The model for predicting the net energy required for growth is expressed as: NER=7.0816*ADG+NEm Where ADG represents the energy gained from daily growth, in kJ / kg / day; NEm represents the net energy for maintenance, in MJ / day; NO=a*BW 0.78 / d Where a is the metabolic constant of the target fish species, in kJ / kg; BW is the weight of the target fish species; BW 0.78 Metabolic weight.

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