Macrobrachium rosenbergii total clutch size prediction model, method of constructing and use thereof

Abstract

The present application relates to the field of aquatic breeding technology, and particularly relates to a macrobrachium rosenbergii total clutch weight prediction model, a construction method thereof and application. The present application tracks multiple spawning of female macrobrachium rosenbergii individuals, collects growth traits and reproductive indicators of the female macrobrachium rosenbergii individuals under different clutch times, and combines correlation analysis, path analysis and multiple regression modeling to systematically evaluate the growth and reproductive performance change rule of the female macrobrachium rosenbergii individuals after multiple spawning. The results show that the body length, body weight and reproductive performance of the macrobrachium rosenbergii are significantly improved in the first three spawnings, and the total egg weight is the key trait affecting the total clutch weight, and the body weight, abdominal length and abdominal width indirectly affect the total clutch weight by affecting the total egg weight. The present application provides a theoretical basis and a quantitative model for screening and breeding of high-reproductive macrobrachium rosenbergii parents, has the advantages of simple operation, strong applicability and high selection efficiency, and is widely applicable to technical scenes such as freshwater shrimp breeding, germplasm improvement and reproductive management.

Description

Technical Field

[0001] This invention relates to the field of aquatic breeding technology, specifically to a prediction model for the total egg-carrying capacity of giant freshwater prawns, its construction method, and its application. Background Technology

[0002] Giant freshwater prawns (Macrobrachium rosenbergii) are highly favored by consumers for their tender texture and rich nutritional value. Their meat is rich in high-quality protein and various essential amino acids, while being low in fat, making it an excellent source of protein. Furthermore, giant freshwater prawns are rich in minerals and vitamins, such as calcium, phosphorus, iron, and B vitamins, which play an important role in enhancing immunity, promoting metabolism, and maintaining bone health. Therefore, the market demand for giant freshwater prawns continues to grow, directly driving the rapid development of the giant freshwater prawn industry.

[0003] Currently, the main farming method for giant freshwater prawns is pond culture, supplemented by rice-fish integrated farming. In recent years, the breeding of new varieties and the maturation of artificial breeding techniques have promoted the increase in seedling production. At the same time, the giant freshwater prawn industry chain has gradually improved, including seedling production, breeding, feed supply, processing, and sales. Many regions have formed centralized industrial clusters, facilitating resource integration and technology sharing. However, there is currently a high market demand for stress-resistant varieties of giant freshwater prawns, while highly resistant seedlings are scarce and in short supply. Further breeding of new varieties suitable for different industry needs is still needed.

[0004] However, several key issues remain to be addressed in the promotion of improved varieties. Some small-scale breeding farms use commercially raised shrimp from ponds for broodstock, resulting in inconsistent seed quality and severely hindering the development of the giant freshwater prawn (Macrobrachium rosenbergii) industry. Currently, the breeding objectives for several new varieties mainly focus on rapid growth, with less emphasis on developing new varieties with resistance to stress and disease. Furthermore, some regions experience seasonal imbalances in seed supply, failing to meet the market's year-round demand for a stable supply. These problems not only limit the large-scale development of the giant freshwater prawn industry but also affect its market competitiveness and sustainability. Therefore, developing a model to predict the reproductive performance of giant freshwater prawns for genetic breeding research, and optimizing and improving broodstock cultivation and seedling raising techniques, are crucial to solving the current industry problems. Summary of the Invention

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] This invention provides a method for constructing a prediction model for the total egg-carrying capacity of the giant freshwater prawn, comprising the following steps:

[0007] Significance analysis was performed on the reproductive performance parameters of female giant freshwater prawns that successfully completed at least three berthing cycles. These reproductive performance parameters included single egg weight, average egg long diameter, average egg short diameter, total egg weight, total number of eggs carried, relative number of eggs carried, natural hatching rate, artificial hatching rate, number of hatched larvae, and accumulated temperature. The key parameters that showed significant differences among different berthing cycles were identified through significance analysis. The key parameters were total number of eggs carried and total egg weight.

[0008] Correlation analysis was performed on total incubation count, total egg weight, and growth traits, including body length, body weight, abdominal length, abdominal width, and abdominal height. Important parameters that were significantly correlated with incubation count were screened out, namely body weight, abdominal length, abdominal width, and total egg weight.

[0009] Using total egg-carrying capacity as the dependent variable and body weight, abdominal length, abdominal width, and total egg weight as independent variables, stepwise regression analysis was performed to establish a predictive model for total egg-carrying capacity of Macrobrachium rosenbergii.

[0010] This invention, for the first time, systematically constructs a multiple linear regression prediction model for total egg-carrying capacity (TNE) based on phenotypic traits such as individual body weight (BW), abdominal length (AL), abdominal width (AW), and total egg weight (EW) of the giant freshwater prawn (Macrobrachium rosenbergii). This fills the research gap in the lack of a dynamic quantitative model for reproductive performance in the context of multiple spawning cycles in giant freshwater prawns. Compared with existing models, this invention overcomes technical bottlenecks such as fragmented reproductive data collection, subjective variable selection, and insufficient predictive ability. It is the first to introduce path analysis into the modeling of reproductive traits in giant freshwater prawns, establishing a predictive system that is theoretically clear, structurally stable, highly accurate, and highly operable. This provides a scientific basis and technical support for parent selection, improving reproductive efficiency, and making decisions regarding breeding management.

[0011] Furthermore, the method for selecting the important parameters is as follows:

[0012] After performing correlation analysis on total number of eggs, total egg weight and growth traits, body length, which had no significant correlation with total number of eggs, was removed, and the remaining growth traits were ranked according to their correlation with the number of eggs carried each time.

[0013] During the first brooding period, the ranking of traits was as follows: total egg weight > abdominal width > abdominal length > abdominal height > body weight;

[0014] During the second brooding period, the ranking of traits was as follows: total egg weight > body weight > abdominal height > abdominal width > abdominal length;

[0015] During the third brooding period, the ranking of traits was as follows: total egg weight > abdominal length > body weight > abdominal width > abdominal height;

[0016] In the correlation ranking of three broods, the growth traits that appear at least twice in the top three or not in the last one are selected as the important parameters: body weight, abdominal length, abdominal width, and total egg weight.

[0017] The present invention also provides a prediction model for the total egg-carrying capacity of Macrobrachium rosenbergii obtained by the construction method described above. The prediction model is: TNE1=4650.641-50.584BW-58.608AL-9.737AW+7590.647EW, where TNE1 is the predicted total egg-carrying capacity of Macrobrachium rosenbergii during its first egg-carrying, BW is the body weight, AL is the abdominal length, AW is the abdominal width, and EW is the total weight of the eggs.

[0018] The present invention also provides a prediction model for the total egg-carrying capacity of Macrobrachium rosenbergii obtained by the construction method described above. The prediction model is: TNE2=-629.851+115.264BW-70.445AL+213.332AW+6419.687EW, where TNE2 is the predicted total egg-carrying capacity of Macrobrachium rosenbergii during its second egg-carrying period, BW is the body weight, AL is the abdominal length, AW is the abdominal width, and EW is the total weight of the eggs.

[0019] The present invention also provides a prediction model for the total egg-carrying capacity of Macrobrachium rosenbergii obtained by the construction method described above. The prediction model is: TNE3=-11981.927-197.874BW+337.891AL-7.511AW+8166.778EW, where TNE3 is the predicted total egg-carrying capacity of Macrobrachium rosenbergii during its third egg-carrying period, BW is the body weight, AL is the abdominal length, AW is the abdominal width, and EW is the total weight of the eggs.

[0020] Furthermore, the prediction model R 2 =0.925.

[0021] Furthermore, the prediction model R 2 =0.789.

[0022] Furthermore, the prediction model R 2 =0.725.

[0023] The present invention has the following beneficial effects:

[0024] The total egg-carrying capacity prediction model for giant freshwater prawns provided by this invention solves the problems of difficulty in directly measuring total egg-carrying capacity, large errors in manual statistics and neglect of individual differences in traditional aquaculture, as well as the problems of unscientific variable selection, low prediction accuracy and inability to adapt to dynamic changes in multiple spawning cycles in existing models. It establishes a multiple linear regression prediction system based on key growth traits (body weight, abdominal length, abdominal width, and total egg weight), which has important theoretical and practical significance for studying the reproductive performance mechanism of giant freshwater prawns, parent selection strategies, and the construction of breeding decision-making models. Attached Figure Description

[0025] Figure 1This is a map showing the locations where some growth traits were measured.

[0026] Figure 2 The graphs show the effects of the number of brood cycles on growth traits. A represents the body length, B the body weight, C the abdominal length, D the abdominal width, and E the abdominal height.

[0027] Figure 3 The graphs show the impact of the number of incubation cycles on reproductive performance. A represents the weight of a single egg, B represents the long diameter of the egg, C represents the short diameter of the egg, D represents the total number of incubated eggs, E represents the relative number of incubated eggs, F represents the natural hatching rate, J represents the artificial hatching rate, H represents the number of hatched larvae, and I represents the accumulated temperature required for hatching. Different lowercase letters indicate significant differences. P <0.001. Detailed Implementation

[0028] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments, but should not be construed as such.

[0029] Limitations. Unless otherwise specified, the technical means used in the following embodiments are conventional means well known to those skilled in the art, and the materials, reagents, etc. used in the following embodiments, unless otherwise specified, can be obtained commercially.

[0030] They obtained it through business channels.

[0031] This invention, through significance analysis of reproductive performance parameters across different brooding cycles, found significant differences in total egg load and total egg weight across different brooding cycles. Correlation analysis was then performed with growth traits body length, body weight, abdominal length, abdominal width, and abdominal height. Since there was no significant correlation between body length and total egg load during the second brooding cycle, further analysis was not conducted. The ranking of significant correlations across the three brooding cycles is shown below:

[0032] During the first brooding period, the ranking of traits was as follows: total egg weight > abdominal width > abdominal length > abdominal height > body weight;

[0033] During the second brooding period, the ranking of traits was as follows: total egg weight > body weight > abdominal height > abdominal width > abdominal length;

[0034] During the third brooding period, the ranking of traits was as follows: total egg weight > abdominal length > body weight > abdominal width > abdominal height;

[0035] In the three correlation analyses of oviposition, growth traits and reproductive performance parameters that appear at least twice in the top three or not in the last one are selected. Therefore, this invention selects total oviposition as the dependent variable and body weight, abdominal length, abdominal width and total egg weight as independent variables to conduct stepwise regression analysis and establish a prediction model for total oviposition of Macrobrachium rosenbergii.

[0036] Based on the above research results, the present invention provides a prediction model for the first egg-carrying period as TNE1=4650.641-50.584BW-58.608AL-9.737AW+7590.647EW, where TNE1 is the predicted total number of eggs carried by the giant freshwater prawn during its first egg-carrying period, BW is the body weight, AL is the abdominal length, AW is the abdominal width, and EW is the total weight of the eggs.

[0037] The prediction model for the second egg-carrying period is TNE2 = -629.851 + 115.264BW - 70.445AL + 213.332AW + 6419.687EW, where TNE2 is the predicted total number of eggs carried by the giant freshwater prawn during its second egg-carrying period, BW is the body weight, AL is the abdominal length, AW is the abdominal width, and EW is the total weight of the eggs.

[0038] The prediction model for the third egg-carrying period is TNE3 = -11981.927 - 197.874BW + 337.891AL - 7.511AW + 8166.778EW, where TNE3 is the predicted total number of eggs carried by the giant freshwater prawn during its third egg-carrying period, BW is the body weight, AL is the abdominal length, AW is the abdominal width, and EW is the total weight of the eggs.

[0039] Example 1

[0040] I. Materials and Methods.

[0041] 1. Experimental Materials: Breeding shrimp bred in 2023 by the breeding center of Jiangsu Shufeng Aquatic Seed Industry Co., Ltd. were selected and re-harvested into the breeding workshop in mid-September for aquaculture management. Healthy individuals with intact appendages and strong vitality were selected, with females not yet carrying eggs. The average weight of the selected breeding shrimp was 23.69±8.91g, with a total of 200 shrimp (female to male ratio 3:1). To track individuals, each female shrimp was injected with a different combination of fluorescent markers. All tagged breeding shrimp to be tested were temporarily held in a 19m² room in the breeding workshop. 2 In the cement pool.

[0042] 2. Feeding and Management: Commercial feed was given daily at 7:30 AM and 5:30 PM, with the daily feed amount being approximately 10% of the total shrimp weight. Ribbonfish and snail meat were alternated daily for intensive cultivation. Feeding was conducted daily at 7:00 AM after sludge removal. During the experiment, micro-aeration was maintained for 24 hours, with water temperature at 24±1℃, pH at 7.8±0.1, dissolved oxygen at 8~11 mg / L, ammonia nitrogen at 0.1~0.4 mg / L, and nitrite at 0.01~0.05 mg / L. The egg-carrying status of female shrimp was observed daily at 8:30 PM. Egg-carrying female shrimp were individually placed in shrimp cages for easy observation of their development.

[0043] 3. Experimental Methods: From September 17, 2023 to January 24, 2024, the growth and development of 150 female shrimp were observed daily, morning and evening. Egg-bearing females were promptly removed and isolated. After the first batch of broodstock eggs developed to the gray egg stage, they were removed before feeding, their weight measured, and placed in incubation tanks for natural incubation. After hatching, their weight was measured again (the difference was the egg weight). Records were made (date, individual number, growth phenotype data, tank number, etc.), and the shrimp were returned to the tank for intensive nutrition for subsequent sampling or observation of their re-carrying status (male shrimp were introduced proportionally at the same time). A portion of the eggs from each individual were weighed, and the egg diameter (including major and minor diameters) was measured under a microscope. The number of eggs was counted, and the average weight of a single egg for that individual was obtained to estimate the total number of eggs carried. The relative number of eggs carried was estimated based on the total weight of the eggs and the individual's body weight. To ensure a more consistent environment and minimize interference with hatching rates, a small portion of the mature fertilized eggs from each shrimp were disinfected with alcohol and placed in an artificial incubation device for artificial hatching, and the hatching rate was calculated. Subsequent shrimp that matured with eggs underwent the same treatment.

[0044] 4. Statistics on growth parameters: The growth traits and reproductive performance parameters of female shrimp that successfully completed three berthing cycles were statistically analyzed. This included data on body length (BL), body weight (BW), abdominal length (AL), abdominal width (AW), and abdominal height (AH) at each berthing cycle, as well as reproductive performance parameters such as weight per egg (WPE), average egg length (AEL), average egg width (AEW), total egg weight (EW), total number of eggs (TNE), relative fecundity (RF), natural hatching rate (NHR), artificial hatching rate (AHR), number of larvae (NL), accumulated temperature (AT), and the time interval between berthing cycles.

[0045] 5. Statistical analysis: Correlation analysis was used to select body weight, abdominal length, abdominal width, and total egg weight for statistical analysis.

[0046] Using total egg-carrying capacity as the dependent variable and body weight, abdominal length, abdominal width, and total egg weight as independent variables, stepwise regression analysis was performed to establish a predictive model for total egg-carrying capacity of Macrobrachium rosenbergii.

[0047] Analysis of variance was performed using the repeated measures method in the general linear model. Significance analysis was conducted with the number of laparoscopic cycles as a factor, and the results were examined. F Value and P The value is used to determine the significance of differences between the indicators. If P A value less than 0.05 indicates a significant difference in this indicator across different berths. Furthermore, correlation analysis was performed on female shrimp carrying eggs at each time, selecting total egg load (TNE) as the dependent variable and body length (BL), body weight (BW), abdominal length (AL), abdominal width (AW), abdominal height (AH), and total egg weight (EW) as independent variables. Pearson correlation coefficients were calculated among these variables to determine significant correlations. A multiple regression prediction model for total egg load was constructed, and path analysis was used to decompose the direct and indirect effects of each variable on total egg load.

[0048] II. Results

[0049] 1. Statistics on the number of egg-carrying events, egg-carrying intervals, and incubation days: As shown in Table 1, during the 129-day culture experiment, 150 female shrimp experienced a total of 328 egg-carrying events. The number of shrimp carrying eggs for the first time was 138, for the second time 106, and for the third time 72, gradually decreasing to 10 and 2 respectively thereafter. Overall, with the increase in the number of egg-carrying events, the average egg-carrying interval gradually decreased from 40.80 days initially to 22.50 days after the fifth egg-carrying event. The average incubation period for different egg-carrying events was approximately 19 days, showing relative stability.

[0050] Table 1: Interval between different incubation cycles and incubation days

[0051]

[0052] Note: n is the sample size, / indicates no data, and the same applies to subsequent tables.

[0053] 2. Analysis of the effect of the number of egg-carrying cycles on growth traits: As shown in Table 2, data were obtained from 47 female shrimp that successfully laid eggs three times consecutively. The growth traits of each female shrimp were measured during each egg-carrying cycle. Figure 2 As shown in Figures A, B, and C, there were highly significant differences in body length, weight, and abdominal length among individuals with different berths of brooding. Subsequent multiple comparisons indicated that body length, weight, and abdominal length increased with increasing berths of brooding, and all differences were highly significant. Figure 2As shown in D and E, there were highly significant differences in abdominal width and abdominal height between the first and second berths, and between the first and third berths, but no significant difference between the second and third berths. Since these six growth traits were significant in each of the three berth intervals, body length, weight, abdominal length, abdominal width, and abdominal height were selected for subsequent normality tests and correlation analyses.

[0054] Table 2: Mean and standard error of growth traits under different brooding counts

[0055]

[0056] Note: Different superscript letters in the same row indicate significant differences between data points, and the same applies to subsequent tables.

[0057] 3. Analysis of the impact of the number of brooding cycles on reproductive performance: The reproductive performance parameters for each number of brooding cycles are shown in Table 3. Figure 3 Results A, C, and I showed no significant differences in single egg weight, egg short diameter, and accumulated temperature required for incubation among different brooding cycles. Multiple comparisons revealed... Figure 3 The results of the D study showed significant differences in the total number of follicles between the first, second, and third trials. Figure 3 As shown in Figures B, E, F, J, and H, there were no significant differences in egg length diameter, relative egg load, natural hatching rate, artificial hatching rate, and number of hatched larvae between the first and second brooding cycles. However, significant differences were observed between the first and third brooding cycles, and between the second and third brooding cycles. The total egg load and total egg weight showed significant differences across the three brooding cycles, indicating that these indicators effectively reflect the changing trends in female shrimp reproductive capacity during different breeding cycles. Therefore, total egg load and total egg weight were selected for subsequent normality and correlation analyses.

[0058] Table 3: Mean and standard error of reproductive performance under different brooding counts

[0059]

[0060] Note: There are significant differences between different lowercase letter identifiers, p<0.05.

[0061] 4. Statistics on growth traits and total number of eggs carried at different berths: As shown in Table 4, total number of eggs carried was collected at 91, 82 and 60 berths respectively in the three berths. Analysis showed that the coefficient of variation of total number of eggs carried was the highest in the three different berths. The coefficient of variation of body length was the lowest in the first and third berths, followed by abdominal length and abdominal height.

[0062] Table 4: Statistics on growth traits, total egg weight, and total number of eggs carried by giant freshwater prawns at different egg-carrying counts

[0063]

[0064] 5. Before performing linear regression analysis, the total number of follicles across the three follicle phases was used as the dependent variable, and the Kolmogorov–Smirnov method was employed to test for normality. The results showed that the total number of follicles across the three follicle phases all conformed to a normal distribution.

[0065] 6. Correlation analysis among traits with different brooding counts: As shown in Table 5, during the first brooding, body length, body weight, abdominal length, abdominal width, abdominal height, and total egg weight were all significantly positively correlated with the total number of eggs carried. P <0.01), the correlation coefficients between each trait and total brood size were in the following order: total egg weight > abdominal width > body length > abdominal length > abdominal height > body weight. Total egg weight had the greatest impact on total brood size, while body weight had the least impact. During the second brood period, except for body length, there was no significant correlation between total brood size and total brood size. P >0.05), and the other 5 traits were all highly significantly correlated with the total number of broodstock (>0.05). P <0.01). The correlation coefficients between each trait and the total number of eggs carried were in the following order: total egg weight > body weight > abdominal height > abdominal width > abdominal length > body length. At the third brood stage, only abdominal height showed a significant correlation with the total number of eggs carried. P <0.05), and all other traits showed a highly significant positive correlation with the total number of eggs carried ( ). P <0.01), the correlation coefficients between each trait and total brood weight are in the following order: total egg weight > body length > abdominal length > body weight > abdominal width > abdominal height.

[0066] Table 5: Correlation coefficients among phenotypic traits of Macrobrachium rosenbergii

[0067]

[0068] Note: ** At the 0.01 level (two-tailed), the correlation is highly significant; * At the 0.05 level (two-tailed), the correlation is significant.

[0069] 7. When constructing a linear regression model, in order to improve the model's stability and practical usability, and to ensure a high degree of stability... R ² To ensure the model fits well, some traits are selected and indicators are simplified to improve the model's operability and scalability in actual production scenarios.

[0070] Since there was no significant correlation between body length and total number of brooding eggs during the second brooding period, further analysis was not conducted, and the ranking was removed. The significant correlation ranking among the three brooding periods is shown below:

[0071] During the first brooding period, the ranking of traits was as follows: total egg weight > abdominal width > abdominal length > abdominal height > body weight.

[0072] During the second brooding period, the ranking of traits was: total egg weight > body weight > abdominal height > abdominal width > abdominal length.

[0073] During the third brooding period, the ranking of traits was as follows: total egg weight > abdominal length > body weight > abdominal width > abdominal height.

[0074] In the three correlation analyses of oviposition, growth traits that appeared at least twice in the top three or did not appear in the last one were selected. Therefore, the total egg weight, body weight, abdominal length and abdominal width of the giant freshwater prawn at different oviposition times were finally selected for stepwise regression analysis of the total oviposition amount. The model is summarized in Table 6.

[0075] Table 6: Model Overview and Output Results

[0076]

[0077] The results are shown in Table 7. The total weight of eggs at all three brooding events had a highly significant impact on the total number of eggs carried. Based on the stepwise regression analysis, a multiple regression equation for the total number of eggs carried was derived, with the following equations:

[0078] TNE1=4650.641-50.584BW-58.608AL-9.737AW+7590.647EW

[0079] TNE2=-629.851+115.264BW-70.445AL+213.332AW+6419.687EW

[0080] TNE3=-11981.927-197.874BW+337.891AL-7.511AW+8166.778EW

[0081] In multiple regression analysis, when R 2 A value ≥0.60 is generally considered a good model fit, indicating that the independent variables can explain most of the variation in the dependent variable. In the regression analysis of body length, body weight, and total egg weight of *Macrobrachium rosenbergii* with different egg-carrying counts, the first egg-carrying count... R 2 (0.925), second egg holding R 2 (0.789), third egg holding R 2 (0.725), all satisfy the condition. R 2 The regression equation established under the condition ≥0.60 is reliable.

[0082] Table 7: Partial regression coefficients, standardized regression coefficients, and significance test results obtained from stepwise regression analysis

[0083]

[0084] Note:** At the 0.01 level (two-tailed), the correlation is highly significant.

[0085] 8. To further analyze the influence weights of the four independent variables in the regression equation, this invention conducted a path analysis. The results of the path analysis are shown in Table 8. From the perspective of direct action coefficients, the influence strength of each variable (measured by absolute value) during the first berth was as follows: total egg weight (0.986) > body weight (-0.028) > abdominal length (-0.021) > abdominal width (-0.002); during the second berth, the influence was: total egg weight (0.845) > body weight (0.070) > abdominal width (0.060) > abdominal length (-0.035); and during the third berth, the influence was: total egg weight (0.855) > body weight (-0.103) > abdominal length (0.097) > abdominal width (-0.001). In all three berths, total egg weight had the highest influence, followed by body weight. However, from the perspective of indirect effects, during the first brooding episode, abdominal width (0.507) > abdominal length (0.458) > body weight (0.451) > total egg weight (-0.025); during the second brooding episode, body weight (0.366) > abdominal length (0.361) > abdominal width (0.326) > total egg weight (0.039); and during the third brooding episode, body weight (0.578) > abdominal width (0.445) > abdominal length (0.398) > total egg weight (-0.006). Therefore, it can be seen that total egg weight directly affects the total number of eggs carried. However, from the perspective of indirect effects, the indirect impact of total egg weight is lowest in all three brooding episodes. Body weight, abdominal length, and abdominal width indirectly affect the total number of eggs carried by influencing total egg weight.

[0086] Table 8: Path analysis of the effects of certain traits of giant freshwater prawns with different egg-carrying counts on total egg-carrying capacity.

[0087]

[0088] 9. Data Validation

[0089] As shown in Table 9, the paired-samples t-tests showed that the mean differences between the predicted and measured values ​​were not significant. P The correlation coefficient >0.05 indicates that the model prediction has no systematic bias. Meanwhile, the correlation analysis results in Table 10 show that the predicted values ​​are highly correlated with the measured values ​​(r=0.915-0.982, ...). P The value <0.001 indicates that the regression equation has high reliability in predicting the number of follicles.

[0090] Table 9: Paired-samples t-test

[0091]

[0092] Table 10: Correlation between paired samples

[0093]

[0094] III. Discussion of Results.

[0095] Experimental results showed that after the first egg-carrying cycle, the growth of the giant freshwater prawn (Macrobrachium rosenbergii) does not stop; its body length and weight continue to increase with each subsequent egg-carrying cycle, although the growth rate gradually slows down. Significant differences were observed in the body length, weight, and abdominal length of the giant freshwater prawn among the three egg-carrying cycles. Like most crustaceans, the giant freshwater prawn transports fertilized eggs to its abdomen for incubation. After the first reproductive cycle, the abdominal space expands, and the abdominal characteristics do not change significantly during subsequent egg-carrying cycles.

[0096] With increasing number of berried cycles, the total number of eggs carried by the giant freshwater prawn (Macrobrachium rosenbergii) showed a significant upward trend. To reduce the influence of body weight on the total number of eggs carried, relative egg-carrying capacity was statistically analyzed and subjected to analysis of variance. The results showed that the relative egg-carrying capacity in the third berried cycle was significantly higher than that in the first and second cycles. Similar trends were observed in egg length diameter, natural hatching rate, artificial hatching rate, and the number of hatched larvae. This may be because after the first two breeding cycles, the female prawns become more mature, with increased body length and weight. Through repeated egg-carrying processes, the female prawns gradually adapt and optimize their reproductive behavior and energy allocation, thereby improving their reproductive performance.

[0097] In production, the trait of total oviposition is difficult to measure and has a large coefficient of variation. By controlling nutrition and environmental conditions, the relationship between body length, body weight, total egg weight, and total oviposition in *Macrobrachium rosenbergii* at different oviposition cycles was analyzed. The results showed that these traits had a significant correlation with total oviposition at all three oviposition cycles. Therefore, using total oviposition as the dependent variable, it was found that as the number of oviposition cycles increased, the influence of body size on reproductive capacity gradually increased, especially at the third oviposition cycle, where the effects of body length and body weight became significant. Furthermore, it was found that total egg weight is the most critical factor affecting the total oviposition in *Macrobrachium rosenbergii*, while body weight and body length indirectly affect the total oviposition by influencing total egg weight.

[0098] The differences in growth and reproductive performance of *Macrobrachium rosenbergii* during its first three berried stages were systematically analyzed. The results showed that during the first three berried stages, with increasing berried stages, the body length, weight, abdominal length, abdominal width, and abdominal height of female shrimp all significantly increased. P <0.001), with the most significant increases in body length and weight. Regarding reproductive performance, total egg load, natural hatching rate, and number of hatched larvae also significantly increased with increasing egg load during the first three egg loads, while single egg weight and accumulated temperature required for hatching showed no significant differences among different egg load counts. Correlation and path analyses showed that total egg weight was the most significant factor affecting total egg load during the first three egg loads, and further influenced total egg load indirectly through body length and weight. Multiple regression models were established based on the number of egg loads. This provides a scientific basis for the screening and aquaculture management of high-quality breeding populations of Macrobrachium rosenbergii.

[0099] It should be noted that when numerical ranges are mentioned in the claims of this invention, it should be understood that the two endpoints of each numerical range and any value between the two endpoints can be selected. To avoid redundancy, the present invention describes preferred embodiments.

[0100] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the invention.

[0101] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for constructing a prediction model for the total egg-carrying capacity of Macrobrachium rosenbergii, characterized in that, Includes the following steps: Significance analysis was performed on the reproductive performance parameters of female giant freshwater prawns that successfully completed at least three berthing cycles. These reproductive performance parameters included single egg weight, average egg long diameter, average egg short diameter, total egg weight, total number of eggs carried, relative number of eggs carried, natural hatching rate, artificial hatching rate, number of hatched larvae, and accumulated temperature. The key parameters that showed significant differences among different berthing cycles were identified through significance analysis. The key parameters were total number of eggs carried and total egg weight. Correlation analysis was performed on total incubation count, total egg weight, and growth traits, including body length, body weight, abdominal length, abdominal width, and abdominal height. Important parameters that were significantly correlated with total incubation count were screened out, namely body weight, abdominal length, abdominal width, and total egg weight. Using total egg-carrying capacity as the dependent variable and body weight, abdominal length, abdominal width, and total egg weight as independent variables, stepwise regression analysis was performed to establish a predictive model for the total egg-carrying capacity of the first, second, and third egg-carrying stages of the giant freshwater prawn. The method for selecting the important parameters is as follows: After performing correlation analysis on total brood count, total egg weight, and growth traits, body length, which had no significant correlation with total brood count, was removed, and the remaining growth traits were ranked according to their correlation with total brood count for each period. During the first brooding period, the ranking of traits was as follows: total egg weight > abdominal width > abdominal length > abdominal height > body weight; During the second brooding period, the ranking of traits was as follows: total egg weight > body weight > abdominal height > abdominal width > abdominal length; During the third brooding period, the ranking of traits was as follows: total egg weight > abdominal length > body weight > abdominal width > abdominal height; In the correlation ranking of three broods, the growth traits that appear at least twice in the top 3 or not in the last 3 are selected, and the important parameters are body weight, abdominal length, abdominal width and total egg weight. The prediction model for the total number of eggs carried during the first brooding event is: TNE1 = 4650.641 - 50.584BW - 58.608AL - 9.737AW + 7590.647EW. R 2 =0.925, where TNE1 is the predicted total number of eggs carried by the giant freshwater prawn during its first egg-carrying period, BW is the body weight, AL is the abdominal length, AW is the abdominal width, and EW is the total weight of the eggs; The prediction model for the total number of eggs carried during the second brooding phase is: TNE2 = -629.851 + 115.264BW - 70.445AL + 213.332AW + 6419.687EW. R 2 =0.789, where TNE2 is the predicted total number of eggs carried by the giant freshwater prawn during its second egg-carrying period, BW is the body weight, AL is the abdominal length, AW is the abdominal width, and EW is the total weight of the eggs; The prediction model for the total number of eggs carried during the third brooding event is: TNE3 = -11981.927 - 197.874BW + 337.891AL - 7.511AW + 8166.778EW. R 2 =0.725, where TNE3 is the predicted total number of eggs carried by the giant freshwater prawn during its third spawning, BW is the body weight, AL is the abdominal length, AW is the abdominal width, and EW is the total weight of the eggs.

2. The application of the total egg-carrying capacity prediction model of giant freshwater prawn as described in claim 1 in giant freshwater prawn farming.

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