ABSTRACT
Milk production in the Andean highlands is variable over space and time. This variability is related to fluctuating environmental factors such as rainfall season which directly influence the availability of livestock feeding resources. The main aim of this study was to develop a time-series model to forecast milk production in a mountainous geographical area by analysing the dynamics of milk records thorough the year. The study was carried out in the Andean highlands, using time-series models of monthly milk records collected routinely from dairy cows maintained in a controlled experimental farm over a 9-year period (2008-2016). Several statistical forecasting models were compared. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percent Error (MAPE) were used as selection criteria to compare models. A relation between monthly milk records and the season of the year was modelled using seasonal autoregressive integrated moving average (SARIMA) methods to explore temporal redundancy (trends and periodicity). According to white noise residual test (Q = 13.951 and p = 0.052), Akaike Information Criterion and MAE, MAPE, and RMSE values, the SARIMA (1, 0, 0) x (2, 0, 0)12 time-series model resulted slightly better forecasting model compared to others. In conclusion, time-series models were promising, simple and useful tools for producing reasonably reliable forecasts of milk production thorough the year in the Andean highlands. The forecasting potential of the different models were similar and they could be used indistinctly to forecast the milk production seasonal fluctuations. However, the SARIMA model performed the best good predictive capacity minimizing the prediction interval error. Thus, a useful effective strategy has been developed by using time-series models to monitor milk production and alleviate production drops due to seasonal factors in the Andean highlands.
Subject(s)
Milk , Models, Statistical , Animals , Female , Cattle , Seasons , Incidence , ForecastingABSTRACT
The reproductive physiology in camelid species has its particularities. The present study aimed to characterize the ovarian follicular dynamics and its functional significance in relation to follicular deviation, vaginal cytological characteristics, and sexual hormone profiles in llamas as the first report in South American camelids. Non-pregnant, multiparous llamas (Lama glama; n = 10; age: 48−72 mo.; BCS: 2.5−3.0) were enrolled in the study. The ultrasonographic assessment was carried out transvaginally and follicular ablation was performed (day 0) when follicles were larger than 7 mm. The follicle number and diameter were scored daily throughout the process for a proper evaluation of the deviated follicles and to monitor the presence of new follicle pools (1.5 to 2.5 mm diameter). Vaginal cytological evaluation (parabasal, intermediate, and superficial cells) was performed every other day until day 6. Endocrine profiles (17ß estradiol, anti-Mullerian hormone, testosterone, and progesterone) during pre- and post-follicular deviation were determined by using the ELISA assay. Differential follicular dynamics both in the presence of a single dominant follicle (DF) and in codominance during the follicular deviation process were detected in llamas (p < 0.05). The percentage of superficial cells was the most related to the follicular wave phase. However, the percentage of parabasal, intermediate, and superficial cells was not related to the phases of follicular growth, dominance, and regression (p > 0.05). Differential patterns among the different hormone concentration levels regarding the 17ß estradiol, anti-Mullerian hormone, progesterone, and testosterone during follicular deviation were observed, with the latter being significantly different along the deviation process (p < 0.05). In conclusion, the use of vaginal cytology assessment would not be sufficient to determine the follicular phases in llamas. Therefore, complementary analyses, such as ultrasonography and endocrine assessment, are strongly recommended to determine follicular dynamics during the follicular deviation.
