Infertile Eggs Research Articles

Minimum sperm dose for optimal fertility after artificial insemination in ostriches

Artificial insemination (AI) in ostriches may present potential solutions to high proportions of infertile eggs commonly recorded on commercial farms and assist in reducing the number of males for breeding purposes thereby leading to a more economical and efficient farming system. Although non-invasive methods to collect semen and to artificially inseminate female ostriches have been developed, the insemination dose for maximum fertility of eggs remains unknown. This study was thus conducted to determine the minimum sperm dose that would promote fertility of eggs following AI in female ostriches. A total of 22 South African black ostriches (7 males and 15 females) aged between 2 and 9 years old were used. Semen samples were collected using the dummy female method and diluted 1:4 (semen: diluent) with an ostrich specific semen diluent. Females were inseminated with various sperm doses of diluted semen from the same male three times a week, every second day resulting in a total sperm dose of A: <2.5 × 109, B: 2.5–5 × 109, C: 5–7.5 × 109 and D: 7.5–9.6 × 109 sperm/week. Eggs produced after insemination were opened to determine the fertilization status of the germinal disc (GD) with an unaided eye. The fertile period was then calculated as the number of days fertilized eggs were laid after the last AI. Furthermore, the number of sperm trapped in the outer perivitelline membrane (spermOPVL) above the GD region was counted under fluorescent light, following staining with 4′,6-diamidino-2-phenylindole to determine the rate of sperm loss and the number of days up to when the last egg containing sperm was laid. On average, a mean (±sd) of 35.34 ± 25.72% eggs produced after AI were fertilized. Fertility was lower (mean ± se) when sperm dose A was used (6.71 ± 9.40%), as compared to sperm dose B (46.01 ± 6.71%), C (37.34 ± 6.60%) and D (37.75 ± 8.36%) (P < 0.05). No significant difference was recorded in the latter three doses (P > 0.05). Furthermore, the length of the fertile period and the rate of sperm loss did not differ significantly between sperm doses (P > 0.05). Hence, a sperm dose of between 2.5 and 5 × 109 sperm/week is recommended to optimize fertility after AI in ostriches, as increasing the sperm dose would not benefit fertility. Further studies are, however, needed to determine the frequency of insemination that would maintain fertility throughout the breeding season as well as hatchability of eggs laid after AI.

Development of an environmental contamination model to simulate the microbial bloom that occurs in commercial hatch cabinets

Microbial blooms that emerge in commercial hatch cabinets consist of apathogenic and pathogenic microorganisms, including Escherichia coli, Enterococcus faecalis, and Aspergillus fumigatus. Objectives of the present study included the development of a multipathogen contamination model to mimic commercial conditions and optimization of sampling methods to quantify bacterial or fungal presence within the hatch cabinet. The pathogen challenge mix (PM) was recreated from select bacterial or fungal isolates recovered from an egg homogenate (EH) derived from the contents of infertile eggs and late embryonic mortalities. Isolates selected for PM included Enterococcus faecalis (∼108 CFU/egg), Staphylococcus aureus (∼107 CFU/egg), Staphylococcus chromogenes (∼107 CFU/egg), Aspergillus fumigatus (∼106 spores/egg), and 2 Escherichia coli (∼108 CFU/egg) isolates. Challenge (100 μL of PM or EH) was administered using a sterile loop to a 28 mm area on the blunt end of the eggshell at day 19 of embryogenesis (DOE). In 3 experiments, microbiological data were collected from environmental hatcher samples (open-agar plate method), fluff samples, postmortem whole-body chick rinse samples, and gastrointestinal tract (GIT) samples to evaluate select bacteria and fungi circulating within the hatch cabinet and colonization of GIT. Cumulative bacterial and fungal recovery from the PM hatching environment from DOE20 to hatch was higher than the nonchallenged group (NC) and EH group at ∼860 and ∼1,730 CFU, respectively. Bacterial recovery from GIT, fluff, and chick rinse samples were similar for the PM and EH group in Exp. 1. However, Aspergillus fumigatus recovery from fluff and chick rinse samples for the PM group was significantly (P < 0.001) higher than the NC and EH group. In Exp. 2 and 3, PM challenge significantly (P < 0.05) increased Gram-negative bacterial recovery from the GIT, fluff and chick rinse samples compared to both the NC and EH group. These data suggest this innovative multispecies environmental contamination model using PM could be utilized to evaluate strategies to mitigate microbial contamination in commercial hatch cabinets in a laboratory setting.

The Data on the Reproductive Biology of the Satanic Leaf-Tailed Geko, Uroplatus phantasticus (Squamata, Gekkonidae), at the BION Terrarium Center as a Contribution to ex situ Offspring Programs

This paper represents the results of a project on mass breeding of satanic leaf-tailed geckos (Uroplatus phantasticus Boulenger, 1888) in controlled laboratory conditions of BION Terrarium Center (Kyiv, Ukraine) in 2020. Given the growing popularity of U. phantasticus among hobbyists all over the world, it is important to develop a method of mass breeding of the species in conditions of herpetoculture. The work provides statistical data on the duration of gestation and incubation of eggs, obtained from a fairly large sample. The mean duration of gestation period for females (n = 119; exact counts of inter-clutch periods) was 34 days (min = 12, max = 64). The average duration of incubation was 94 days (min = 67, max = 130). The work also contains information on peculiarities of mating behavior, breeding ecology, copulation, percentage of infertile eggs and experience of maintaining a breeding stock with number of females exceeding that of males. Such information and its analysis are of particular scientific and practical value for the development of management plans for the conservation of the species, both in the wild and ex situ.

Assessment of Sperm Viability and Computer-Assisted Motility Analysis in Budgerigars (Melopsittacus undulatus): Effect of Several In Vitro Processing Conditions.

In order to preserve endangered psittacine species, more basic and applied research in reproductive biology is required. Assisted reproductive technologies such as artificial insemination play an important role in parrots species conservation programs to overcome the problem of infertile eggs and male infertility. The aim of this study was to define an effective in vitro protocol in order to standardize the sperm quality evaluation in psittacines, studying Melopsittacus undulatus as model species. Semen was collected from twenty adult males by massage technique from May to June. Sperm concentration was measured by the spectrophotometric method. Sperm quality (sperm membrane integrity (SMI), motility, and kinetic parameters) was assessed on fresh semen. Three different experimental protocols were performed to compare the effects of various processing conditions on SMI, motility, and kinetic parameters. In protocol 1, test was performed by Lake extender with three different pH, 7.4 versus 8.2 versus 8.4, and two different equilibration temperatures after dilution of fresh semen (4°C versus 25°C). In protocol 2, two dilution rates of semen after collection were valuated, 1 : 3 versus 1 : 4, as well as three different semen storage temperatures (4°C versus 25°C versus 38°C) before sperm motility analysis with the computer-assisted sperm analysis (CASA). In protocol 3, two different Makler chamber temperatures (38 versus 41°C) during motility analysis were tested. A significant progressive improvement in spermatozoa motility and kinetic parameters was registered with pH 8.4. Progressive motility and all kinetic parameters were higher at 4°C equilibration temperature. Straightness (STR) kinetic parameter was better with 1 : 4 dilution rate. Total motile sperm was higher in 41°C Makler chamber. In this study, for the first time, the effects of different processing protocols on psittacines seminal quality analysis were investigated. Significant differences conditioning the effectiveness of analysis protocols have been described.

The Effects of a Male Audience on Male and Female Mating Behaviour in the Blood-Sucking Bug Rhodnius prolixus

Social context is a key factor affecting sexual behaviour and cannot be neglected in gregarious species, such as triatomine blood-sucking bugs. Here we study the influence of the social context on the sexual reproductive behaviour of males and females of Rhodnius prolixus Stål. Specifically, we identify and compare the frequencies and sequence of sexual behaviours exhibited by a focal pair in presence or absence of a male audience. We expect that in presence of a male audience females increase their selectiveness level since the risk of losing infertile eggs decreases in presence of more candidates and females can benefit from copulating with a better quality male. Besides, in presence of potential rivals, we expect changes in the sexual behaviour of focal males, associated to a reduction in the risk of sperm competition. As expected, in presence of a male audience, females significantly increased the exhibition of rejection behaviour. Moreover, focal males exhibited shorter latencies to mount the female, longer duration of copula, and differences in their stereotyped behaviour exhibited during copula. We discuss the influence of the social context on the reproductive behaviour of females and males of R. prolixus.

Sero-detection of antibodies to Avian metaavulavirus 2 in peri-domestic birds, Nigeria

ABSTRACT Avian metaavulavirus 2 (AMAV-2) previously known as the avian paramyxovirus-2 causes mild to severe respiratory disease, reduced hatchability and infertility of eggs, including increase in white-shelled eggs in chickens and Turkey breeders. When exacerbated by secondary pathogens and environmental stresses, infection is more severe leading to significant economic losses. This study was conducted to determine, if any, the presence of antibodies to Avian metaavulavirus 2 (AMAV-2) in peri-domestic birds in Bauchi State, Nigeria. In all, one hundred sera samples from pigeons (n = 10) and doves (n = 90 were collected in Bauchi, Nigeria. Based on hemagglutination-inhibition (HI) test, overall seroprevalence of 27.0% (27/100) was recorded. In pigeon, the seroprevalence was 80.0% while 21.1% was recorded for dove with HI antibody titers ranging from 3log2 to 8log2. There was statistical significance obtained between dove and pigeon sera tested (p < .05). Until now and to the best of our knowledge, there are no reports on AMAV-2 in poultry or wild birds in Nigeria. This study, thus, provides preliminary information on AMAV-2 seroprevalence in Nigerian peri-domestic birds. The need to conduct further studies in other avian species and wild birds in Nigeria is highlighted.

Research on Detection of Fertility of Group Hatching Eggs Based on Adaptive Image Segmentation

HighlightsAn adaptive group eggs segmentation was proposed, which separates a single egg from the group eggs image.Key color features and texture features were extracted from the RGB and HSI histograms, respectively.The Support Vector Machine (SVM) model and Least Squares Support Vector Machine (LS-SVM) model were built to distinguish infertile egg and dead-embryo egg, respectively.LS-SVM model reached 100% accuracy for infertile eggs on day 4 of incubation, and dead-embryo eggs on day 10 of incubation.Abstract. For the incubation factory, it is of vital importance to detect infertile eggs and dead-embryo eggs in the industrial egg trays as early as possible. In this article, an activity detection computer vision system was proposed and evaluated. Due to the dense layout of eggs in industrial egg trays, the image segmentation task to separate each single egg becomes difficult. To this end, an adaptive image segmentation method for group eggs was proposed. Firstly, the binary image was obtained by the Canny operator using dynamic threshold and processed to reduce redundant information. Then ellipse fitting was employed to obtain the egg contour of the single egg. Moreover, the Red, Green, Blue (RGB) and Hue, Saturation, Intensity (HSI) histograms were selected for feature extraction. According to the analysis on color features and texture features of the images, 13 features [positions for peak values of R and I in histogram, peak values of G and I within histogram, averages of R and G, variances of R and G, contrast, roughness, inverse different moment (IDM), correlation and angular second moment (ASM)] were chosen as the criterion for detecting dead-embryo eggs. Meanwhile, 12 features (positions for peak values of R, G, H, and I in histogram, averages of R and G, slope and variance of G, contrast, roughness, IDM, and correlation) were selected for detecting infertile eggs. Lastly, the Support Vector Machine (SVM) model and Lease Squares Support Vector Machine (LS-SVM) model were built to distinguish infertile egg and dead-embryo egg, respectively. According to the comparison, the LS-SVM model reached higher accuracy in determining infertile egg and dead-embryo egg than the SVM model, with less time consumed. The LS-SVM model reached 100% accuracy in detection for infertile eggs on day 4 of incubation, as well as for dead-embryo eggs on day 10 of incubation. The result demonstrated that the proposed method can conduct the activity detection for group eggs both accurately and fast, which meets the commercial requirement for non-destructive detection in the hatchery industry. Keywords: Adaptive image segmentation, Computer vision, Fertility, Group hatching eggs, LS-SVM, Non-destructive detection, SVM

Non-Destructive Egg Fertility Detection in Incubation Using SVM Classifier Based on GLCM Parameters

The research presented in this paper aims at egg fertility detection during incubation based on the presence of the embryo. To obtain an optimal detection method that does not adversely affect the incubation process, the presented approach uses computer technology to analyze the images obtained from the egg candling process, and to obtain information about the presence of an egg embryo. This study applies the Support Vector Machine (SVM) classifier method using the second-order statistical feature extraction input to detect the chicken egg's fertility. The feature extraction is based on the Gray-Level Co-occurrence Matrix (GLCM) approach with 6 parameters: Energy (En), Contrast (Ct), Entropy (Et), Variance (V), Correlation (Cr), and Homogeneity (H). This study develops a manual detection process that takes a long time and improves the research accuracy of the backpropagation method. The dataset used consisted of 100 images of chicken eggs, including 50 images for each type: fertile and infertile. The preprocessing of the images included cropping, grayscaling, and image enhancement using CLAHE (Contrast Limited Adaptive Histogram Equalization) and HE (Histogram Equalization) methods. Such process can improve the image to get its GLCM parameters. The extracted GLCM parameters are input to the egg fertility detection process performed by the SVM classifier-based method. The presented SVM-GLCM approach was able to detect fertile and infertile eggs with a success rate of 98.20%, which was an improvement over the previous research. This research can be a reference for implementing the detection of chicken egg fertility in the incubation machine to obtain optimal hatching results.

Effect of inclusion of hatchery discarded infertile egg meal with shell on In vitro digestibility of puppy food

Effect of inclusion of hatchery discarded infertile egg meal with shell on In vitro digestibility of puppy food

Penerapan Algoritma Convolutional Neural Network dalam Klasifikasi Telur Ayam Fertil dan Infertil Berdasarkan Hasil Candling

Fertile chicken eggs are eggs that can hatch because these eggs have a development in the form of dots of blood and blood vessels or can be called an embryo, while infertile chicken eggs are a type of egg that cannot be hatched because there is no embryo development in the hatching process. Inspection of infertile chicken eggs must be carried out especially for breeders who will carry out the selection and transfer of fertile chicken eggs and infertile chicken eggs. However, currently, the selection of fertile and infertile chicken eggs is still using a less effective way, namely only by looking at the egg shell or called candling, this process is certainly less accurate to classify which eggs are fertile and infertile eggs because not all breeders are able to see the results of the eggs properly. candling so that the possibility of prediction errors. Therefore, in this study, a classification of fertile chicken eggs and infertile chicken eggs will be carried out based on candling results using the Convolutional Neural Network method. From the results of the classification carried out, the percentage of accuracy obtained for the classification of fertile and infertile chicken eggs is 98% and an error of 5%.

Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction

This study aims to identify chicken eggs fertility using the support vector machine (SVM) classifier method. The classification basis used the first-order statistical (FOS) parameters as feature extraction in the identification process. This research was developed based on the process’s identification process, which is still manual (conventional). Although currently there are many technologies in the identification process, they still need development. Thus, this research is one of the developments in the field of image processing technology. The sample data uses datasets from previous studies with a total of 100 egg images. The egg object in the image is a single object. From these data, the classification of each fertile and infertile egg is 50 image data. Chicken egg image data became input in image processing, with the initial process is segmentation. This initial segmentation aims to get the cropped image according to the object. The cropped image is repaired using image preprocessing with grayscaling and image enhancement methods. This method (image enhancement) used two combination methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE). The improved image becomes the input for feature extraction using the FOS method. The FOS uses five parameters, namely mean, entropy, variance, skewness, and kurtosis. The five parameters entered into the SVM classifier method to identify the fertility of chicken eggs. The results of these experiments, the method proposed in the identification process has a success percentage of 84.57%. Thus, the implementation of this method can be used as a reference for future research improvements. In addition, it may be possible to use a second-order feature extraction method to improve its accuracy and improve supervised learning for classification.

Feeding kampong chickens with infertile eggs: effects on 6-weeks performance

The present study evaluated the effect of dietary inclusion levels of infertile eggs powder (IEP) from the hatchery processing industry on the performance of kampong chickens measured at 6 weeks. A total of 120 birds at 21 d (327±18 gram) were equally distributed to receive 10%, 20%, and 30% of IEP with control treatment without IEP. Each treatment group was replicated five times with eight birds in each, giving three dietary treatments including control. Diets were formulated to meet the Indonesia National Standard of the nutrient requirement for kampong chickens. Results showed that increasing dietary IEP at 10% and 20% significantly increased average daily gain (ADG), final body weight (BW), and decreased feed conversion when compared to control (p<0.05). When given at 30%, no effect on production performance parameters was observed. In conclusion, the present study suggests that infertile eggs powder can be partially used to substitute conventional protein sources in kampong chickens. Dietary inclusion of IEP up to 20% given in mixed with corn in the formula improves the performance of kampong chickens without negatively affecting feed intake.

Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing

This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.

Determination of metabolizable energy and amino acid digestibility in various hatchery byproducts for broiler chickens

The objective of the present experiment was to determine ME concentrations and amino acid (AA) digestibility in various hatchery byproducts (HBPs) for broiler chickens. In experiment 1, a total of forty 60-day-old female broiler chickens were allotted to 1 of 5 dietary treatments with 8 replicates and used to measure ME concentrations in HBPs. The basal diet was prepared to contain corn, soybean meal, corn oil, and other non-energy ingredients. Additional 4 experimental diets were prepared to contain 10% of infertile eggs (IFE), unhatched eggs (UHE), low-grade or dead chicks (LDC), and mixture (MIX; 55% IFE, 10% UHE, 10% LDC, and 25% hatched eggshells). In experiment 2, a total of seven hundred and sixteen 1-day-old mixed-sex broiler chickens (1:1 ratio of males and females) were allotted to 1 of 5 dietary treatments with 7 replicates per treatment and used to determine apparent ileal digestibility (AID) and standardized ileal digestibility (SID) of AA in HBPs. The experimental diets consisted of a nitrogen-free diet and 4 diets containing IFE, UHE, LDC, or MIX as a sole source of AA. Results indicated that AME and AMEn values were greater (P < 0.05) for LDC than for IFE, which had greater (P < 0.05) AME and AMEn values for UHE and MIX. The AID and SID of most AA in LDC were greater (P < 0.05) than those in MIX, whereas IFE and UHE had intermediate AID and SID of those AA as compared to LDC and MIX. Average SID of essential AA in LDC was greater (P < 0.05) than in UHE and MIX, but the average SID of nonessential AA did not differ among 4 HBPs. In conclusion, LDC has the greatest ME concentrations and AA digestibility among 3 individual HBPs (IFE, UHE, and LDC). The mixture of HBPs has the least ME concentrations and AA digestibility in broiler chickens. The ME and AA digestibility of HBPs are likely affected by inclusion amounts of hatched eggshells. However, high concentrations of ME and available AA demonstrate that individual HBPs and their mixture are potential protein ingredients for broiler diets.

Unpaired Adult California Condors (Gymnogyps californianus) can Serve as Foster Parents in Captivity

Despite efforts to recover the California Condor (Gymnogyps californianus) in the past 30 yr, the species is still Critically Endangered due in part to dependence on field management and captive breeding. Captive breeding focuses not only on the quantity of young produced every year, but also on the quality of these birds, to ensure their future success in the wild. In this study, The Peregrine Fund pioneered the use of unpaired adult condors as foster parents to increase the number of young being raised by conspecifics, and therefore likely increase their chances of success in the wild. A total of 17 young have been raised to fledging by unpaired adults with previous rearing experience at The Peregrine Fund's World Center for Birds of Prey from 2012 to 2019. Three unpaired males successfully raised eight young. An unpaired female raised young in 2018 and 2019 after her infertile eggs were switched with hatching eggs. We also separated a pair of condors and had each attempt to raise young on their own, for a total of seven young raised. The adult that remained in the original chamber always raised young successfully. The adult that was separated, however, often rejected the dummy egg that was offered in the new chamber. The use of unpaired adults as foster parents provides the recovery effort with additional benefits, such as having experienced breeders raising young, having “backup” parents, keeping unpaired adults occupied with breeding tasks, and preventing them from being housed as surplus animals.

P–090 Cumulative live birth rates (CLBR) per embryo transfer, embryo transferred and inseminated oocyte are not affected by paternal age in infertile male donor egg cycles

Abstract Study question Is reproductive success measured as CLBR per inseminated oocyte, per embryo transfer and per embryo transferred affected by paternal age in donor egg IVF-ICSI cycles? Summary answer Paternal age does not significantly affect reproductive outcomes measured by CLBR per inseminated oocyte, embryo transfer and embryo transferred in donor egg IVF-ICSI cyles. What is known already In recent years, the delay in the start of formation of a family has led to an increase of the average male age at which the first child is conceived. Therefore, there is a growing interest on the study of the impact of male age on the reproductive outcomes in assisted reproduction cycles (ART). Several studies have evaluated the effect of paternal age on reproductive outcomes using donor egg cycles to control for female factors. However, the results obtained on this topic are still controversial leading to a need of more research about it, which this study tries to address. Study design, size, duration This retrospective observational multicentric cohort study has included donor IVF-ICSI treatments (n = 1539) performed to couples with etiology of male infertility (non-normozoospermic) in Spain IVIRMA clinics between January 2008 and March 2020 using patients’ own sperm sample. Paternal age ranged from 28 to 74 years. The study population was categorized in 5 groups following the criterion of homogenizing the number of observations between groups 28–38 (A), 38–41 (B), 41–44 (C), 44–48 (D) y 48–74 (E) years. Participants/materials, setting, methods Considering that male age could be a factor affecting reproductive outcomes, we evaluated men with different age that performed a donor IVF-ICSI treatment with their own semen, etiology of male infertility and known age. Data was exported in order to obtain the clinical database and Kaplan-Meier was used for data analysis. P &amp;lt; 0.05 was considered statistically significant. We measured reproductive success by CLBR per embryo transfer, per embryo transferred and per inseminated oocytes until live birth. Main results and the role of chance This study considered approximately 836 patients and 1411 embryo transfers. The CLBR per inseminated oocyte showed no significant difference between the study groups: A (3.09%, 42.61%, 71.96%), B (4.53%, 39.76%, 84.19%), C (5.89%, 47.04%, 78.61%), D (2.99%, 46.7%, 73.15%) and E (3.89%, 38.39%, 78.85%) for 7, 12 and 17 inseminated oocytes, respectively. In terms of CLBR per embryo transfer, the results obtained for each of the age groups were: A (51.55%, 70.69%, 92.18%), B (50.40%, 78.13%, 100.00%), C (53.68%, 71.69%, 100.00%), D (51.71%, 79.72%, 100.00%) and E (46.69%, 60.02%, 70.01%) for 3, 5 and 7 embryo transfers, respectively. No statistically significant differences were found among the studied age groups. CLBR per embryo transferred also did not show statistically significant differences between the age groups: A (8.43%, 53.34%, 72.13%), B (8.55%, 44,67%, 71.65%), C (10.40%, 53.94%, 72.49%), D (7.25%, 43.61%, 75.12%) and E (8.21%, 45.99%, 64.55%) for 1, 4 and 7 embryos transferred, respectively. Therefore, no significant differences were found in the number of inseminated oocytes, embryo transfers and embryos transferred needed to achieve a live birth between the age groups (p &amp;gt; 0.05), suggesting that maybe paternal age has no relevant clinical effect on donor egg cycles with our categorization. Limitations, reasons for caution The retrospective nature of this study leads to biases derived from the clinical practice and to the presence of missing data (limiting sample size). Moreover, this study included donor egg cycles for controlling female factors, so this limits the generalization of our results to a population of young women. Wider implications of the findings: Our study showed that the reproductive success measured as CLBR per embryo transfer, embryo transferred and inseminated oocytes was not statistically significant different among the studied age groups in donor egg cycles. Therefore, considering our study setting, paternal age does not affect reproductive success, however further studies should be done. Trial registration number NA

Influences of the feature extraction area of duck egg image for the identification accuracy on the unfertilized duck eggs on the hatching tray

AbstractHowever, feature parameter extraction region of duck egg image could affect the identification results of model and the speed of image processing. Therefore, the image skeleton‐maximum inscribed circle algorithm and Hough gradient algorithm (2‐1HT) were proposed to segment duck egg image feature paramer region. Support vector machine (SVM) and naive bayes (NB) models were established to evaluate accuracy of feature parameters extracted by different algorithms. The comparison shows that the SVM model based on feature parameters extracted by image skeleton maximum inscribed circle algorithm has the best discriminant performance, with the value of Q, SE, and SP being 98.81%, 98.44%, and 100.00%, respectively. Moreover, the process speed and prediction accuracy of image skeleton‐maximum inscribed circle algorithm were much higher than that of Hough gradient algorithm.Practical ApplicationsTherefore, optimizing the image feature parameter extraction algorithm of a single duck egg in grouped duck eggs was of great significance to improve the discrimination accuracy of the model and realize the automatic recognition of infertile duck eggs.

Additivity of digestible energy and nutrient concentrations in hatchery byproducts fed to nursery pigs.

ObjectiveThe objective was to test additivity of digestible energy and nutrient concentrations in the hatchery byproduct mixture fed to nursery pigs.MethodsIn the previous studies, energy, phosphorus, calcium, and amino acid digestibility of infertile eggs, unhatched eggs, culled chicks, and a mixture of 3 hatchery byproduct ingredients was determined in nursery pigs (initial body weight = 9.4 to 14.2 kg). An additivity test was conducted using these determined values.ResultsNo difference was observed between determined and predicted metabolizable energy values in the mixture (3,998 and 3,990 kcal/kg as-is basis, respectively). Measured standardized total tract digestible phosphorus in the mixture was less than the predicted value (4.5 vs 5.3 g/kg as-is basis, respectively; p<0.05). Measured standardized total tract digestible calcium in the mixture was greater compared with the predicted value (40.0 vs 31.7 g/kg as-is basis, respectively; p<0.05). Measured standardized ileal digestible tryptophan in the mixture was greater than the predicted value (3.7 vs 3.1 g/kg as-is basis, respectively; p<0.05) whereas other amino acid values were additive.ConclusionEnergy and most of amino acid concentrations in hatchery byproducts are additive in the mixture fed to nursery pigs.

Description of factors decreasing egg incubation of creole hens (Gallus gallus domesticus)

Objective: To describe the factors that affect the incubation of fertile eggs of Creole hens (Gallus gallus domesticus) at Mezquital Valley, Hidalgo, Mexico. Design/Methodology/Approach: 448 eggs of Creole hens were incubated in a Casser model 7622 incubator, in which the number of infertile and fertile eggs was evaluated; within the number of fertile eggs, the number of dead embryos was determined; due to dehydration or damage caused by fungi and bacteria. From the live hatched chickens, those that died due to omphalitis were recorded. Results: 272 of 448 incubated eggs were fertile; 119 of them hatched and 153 were dead embryos. While 176 were infertile. Among the dead embryos, 84 of dehydration, 69 due to fungal and bacterial infection and 11 live hatched chicks due to omphalitis. Limitations/implications: The literature review found little information on factors affecting the hatching of fertile Creole chicken eggs in backyard flocks. Findings/conclusions: Infection by fungi and bacteria increase embryonic mortality, while omphalitis increases mortality during the first days of life of the backyard Creole chickens. High storage temperature in the nest or incubator causes embryo dehydration.

Assessment of hatching properties for identifying multiple duck eggs on the hatching tray using machine vision technique

Since the hatching characteristics (fertile and infertile) of multiple duck eggs on the hatching tray were difficult to be recognized intelligently, a method of identifying infertile duck eggs by machine vision was proposed in this study. Based on the image analysis of the duck eggs hatched for 7 days, region of interest (ROI) mask was created to extract the image feature parameter vector with 4 features. This study investigated the optical change of duck eggs during different hatching time, and used Linear Discriminant Analysis (LDA), Naïve Bayes (NB) and Support Vector Machine (SVM) methods to establish discriminant models for different incubation periods. SVM classifier yielded the best results with 4 features for validation set on day 5. Classification accuracy, sensitivity (SE), specificity (SP), positive predictive value (PPV) and negative predictive value (NPV) are all 100%. Compared with artificial candling recognition, the identification time for the fertility and infertility was 2 days earlier using SVM model. The prediction accuracy for the new unknown duck eggs was 92.06% by the established SVM model of day 5. Machine vision techniques combined with the SVM model of high classification accuracy on day 5 provided an appropriate method to identify the infertile duck eggs on the hatching tray.