Application of Machine Learning Algorithms to Describe the Characteristics of Dairy Sheep Lactation Curves.

In recent years, machine learning (ML) algorithms have emerged as powerful tools for predicting and modeling complex data. Therefore, the aim of this study was to evaluate the prediction ability of different ML algorithms and a traditional empirical model to estimate the parameters of lactation curves. A total of 1186 monthly records from 156 sheep lactations were used. The model development process involved training and testing models using ML algorithms. In addition to these algorithms, lactation curves were also fitted using the Wood model. The goodness of fit was assessed using correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and relative root mean square error (RRSE). SMOreg was the algorithm with the best estimates of the characteristics of the sheep lactation curve, with higher values of r compared to the Wood model (0.96 vs. 0.68) for the total milk yield. The results of the current study showed that ML algorithms are able to adequately predict the characteristics of the lactation curve, using a relatively small number of input data. Some ML algorithms provide an interpretable architecture, which is useful for decision-making at the farm level to maximize the use of available information.

Prevalence of Parasitic Infections with Zoonotic Potential in Tilapia: A Systematic Review and Meta-Analysis.

Tilapia has a high socioeconomic value in many countries worldwide. However, it has been identified as a zoonotic parasite reservoir. A systematic literature search and meta-analysis were carried out in order to estimate the global prevalence of zoonotic parasites that affect tilapia. The search was performed by three field experts to avoid reviewer bias. Polled prevalence was estimated using a logistic-normal random-effect regression model in the R software. We dealt with the heterogeneity among studies through subgroup analysis, taking into account the continent, country, genus of the host, parasite taxonomic group, sample origin, and type of diagnostic test as moderator variables. Fifty-two eligible articles were identified covering five tilapia genera with a pooled prevalence of 0.14 (95% CI: 0.10−0.20) showed significant heterogeneity (I2 = 98.4; p < 0.001). The subgroup analysis revealed that the most affected host was Sarotherodon, with a prevalence of 0.42 (95% CI: 0.22−0.65). Cestode was the taxonomic group with the largest prevalence (0.40; 95% CI:0.32−0.48), followed by amoeba (0.24; 95% CI: 0.16−0.35) and nematode (0.22; 95% CI: 0.11−0.38), among which, Schyzocotyle spp., Opistorchis spp., Gnathostoma spp. and Vermamoeba spp. have an impact on public health. Significant differences (p < 0.004) were found among continents and countries, with the highest value of prevalence detected in the African continent (0.28; 95% CI: 0.20−0.37), specifically in Tanzania (0.56; 95% CI: 0.22−0.87) and Egypt (0.43; 95% CI: 0.20−0.55). The origin of samples had a significant effect (p < 0.0001) on the detected prevalence, especially from those that showed the highest prevalence (0.24; 95% CI: 0.17−0.33). Finally, there were no differences in prevalence according to the diagnostic test (p = 0.97). Our results provide useful information on the development of epidemiological programs for the control of zoonoses associated with parasites in tilapia and in the design, planning, and implementation of future research.

The economic effects of grazing in small-scale lamb fattening production systems in central México through a scenario analysis.

The objective was to assess grazing as an element of profitability and competitiveness of a small-scale lamb fattening production system in central México and compare its economic performance by means of an analysis of scenarios. Two scenarios were analysed to assess the contribution of grazing on profitability and competitiveness. The first analysis was when grazing was the feed base, and secondly, costs of opportunity and economic impacts were studied under the assumption that sheep do not graze, and total feed has to be bought from external suppliers. The economic effect of grazing on the profitability was analysed by means of the Policy Analysis Matrix. Differences were found between strata; farmers with more than 70 sheep have the best profitability indices and the least vulnerability under the non-grazing scenario. Grazing had a positive effect reducing the cost of production and increasing competitiveness in the four strata assessed. However, farmers with higher technical level, specialised breeds and larger flocks (strata 3 and 4) have higher economic profits. The conclusion was that the profitability in fattening sheep is linked to taking advantage of grazing. However, to keep and improve the current financial performance, there is a need for the adoption of strategies for an integral improvement of the system and the adoption of better grazing management to further reduce production costs.

Effect of soybean grain (Glycine max L.) supplementation on the production and fatty acid profile in milk of grazing cows in the dry tropics of Mexico.

Improving the lipid profile in milk of cows with the use of soybean grain (Glycine max L.) can be favored in the grazing systems in the dry tropics of Mexico. The objective was to evaluate the milk production, the chemical composition, and the fatty acids profile (FAs) of the milk of cows in continuous grazing and supplemented with and without ground roasted soybean in the dry tropics of Mexico. Ten cows randomly distributed in two equal groups were used. Daily during confinement for milking, the cows individually received the treatments on dry basis T0: supplement with 4.6 kg commercial concentrate® without soybean, T1: supplement with 3.7 kg commercial concentrate® with 380 g of soybean. During the 78 days of the experiment, milk production was measured in all cows, and samples were collected to determine the chemical composition and FAs profile. Milk production, protein, milk total fat, lactose, and non-fat solids did not vary with treatment (p >0.05). Linoleic acid content (C18: 2, cis, cis-∆9, ∆12) increased by 22% in milk fat of cows of the T1 (p ˂0.05). The sum of the mono- and polyunsaturated FAs 29.1%, the ratio of saturated-unsaturated FAs (1.65), and the atherogenicity index (1.71) also improved in the milk of cows supplemented with T1 (p ˂0.05). It was concluded that ground roasted soybean supplement in the diet of grazing dairy cows did not affect production and did improve the lipid profile in milk fat with favorable index to promote human health.

Estimation of body weight in hair ewes using an indirect measurement method.

The aim of the present study was to develop and evaluate an equation to predict body weight (BW) using hip width (HW) in Pelibuey ewe lambs and ewes. Five hundred seventy-seven 2-month-old to 3-year-old, non-pregnant, non-lactating, clinically healthy ewe lambs and adult ewes with a mean BW of 34.7 ± 12.4 kg and HW of 15.6 ± 3.4 cm were considered. Three equations were evaluated: BW (kg): - 19.17 + 3.46 × HW (Eq. 1), BW (kg): - 17.79 + 3.25 × HW + 0.007 × HW2 (Eq. 2) and BW (kg): 0.39 × HW1.63 (Eq. 3). Independent data from 80 animals with similar characteristics (BW of 23.4 ± 10.9 kg and HW of 12 ± 3.1 cm) were also considered to evaluate the developed equations. The evaluation was based on the relationship between the observed and predicted values of BW analysed using a linear regression, the mean squared error of prediction (MSEP), the root MSEP (RMSEP) and the concordance correlation coefficients (CCCs). Additionally, cross-validation analyses were performed using the k-folds validation (k = 10) procedure. The correlation coefficient (r) between BW and HW was 0.94 (P < 0.001). The parameters for precision and accuracy showed that the proposed equations had high precision (R2 > 0.95%), accuracy (Cb > 0.98) and reproducibility (CCC > 0.96) in predicting the BW of ewe lambs and adult ewes. Equation (1) accurately predicted observed BW, with a bias (observed - predicted) of 4.3 kg and RMSEP of 9.68% with respect to the observed BW (random error of 84.23%); it also generated the best prediction according to the residual mean squared prediction error, coefficient of determination and mean absolute error. In conclusion, the highly correlated relationship between BW and HW in Pelibuey ewe lambs and adult ewes under humid tropic conditions enabled the development of mathematical models herein to estimate BW with an adequate goodness of fit. The linear model showed the best performance according to the goodness-of-fit evaluation and internal and external validation; hence, this model is proposed for use in both the experimental and commercial farms.