Livestock farming is, in most cases in Europe, unsupervised, thus making it difficult to ensure adequate control of the position of the animals for the improvement of animal welfare. In addition, the geographical areas involved in livestock grazing usually have difficult access with harsh orography and lack of communications infrastructure, thus the need to provide a low-power livestock localization and monitoring system is of paramount importance, which is crucial not for a sustainable agriculture, but also for the protection of native breeds and meats thanks to their controlled supervision. In this context, this work presents an Internet of things (IoT)-based system integrating low-power wide area (LPWA) technology, cloud, and virtualization services to provide real-time livestock location monitoring. Taking into account the constraints coming from the environment in terms of energy supply and network connectivity, our proposed system is based on a wearable device equipped with inertial sensors, Global Positioning System (GPS) receiver, and LoRaWAN transceiver, which can provide a satisfactory compromise between performance, cost, and energy consumption. At first, this article provides the state-of-the-art localization techniques and technologies applied to smart livestock. Then, we proceed to provide the hardware and firmware co-design to achieve very low energy consumption, thus providing a significant positive impact to the battery life. The proposed platform has been evaluated in a pilot test in the northern part of Italy, evaluating different configurations in terms of sampling period, experimental duration, and number of devices. The results are analyzed and discussed for packet delivery ratio, energy consumption, localization accuracy, battery discharge measurement, and delay.

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model’s precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.

Passive immunity transfer has a pivotal role in newborn lambs, where the colostrum represents the primary source of immunoglobulins. This study hypothesized that the high content in polyphenolic compounds, mono- and poly-unsaturated fatty acids, and vitamin E of hazelnut skin affects blood and colostrum immunoglobulin G (IgG) concentration and related gamma-glutamyl-transferase (GGT) and lactate dehydrogenase (LDH) levels in sheep and their lambs. In the last 45 days of pregnancy, ewes were divided into a control (CTR) and a hazelnut skin supplemented group (HZN). Blood and colostrum were collected from ewes and lambs before the first suckling, at 24 and 48 h after birth, then IgG concentration, GGT and LDH activity levels were measured. IgG concentration in the colostrum and in lamb’s serum were significantly greater in HZN than CTR. No significant difference was detected for ewe’s blood. A significant positive correlation was found between IgG and GGT in lambs’ serum and colostrum, between IgG and LDH, as well as between GGT and LDH in lambs’ serum and colostrum. Our results suggest that hazelnut skin supplementation influences IgG colostrum concentration, with improved immune passive transfer to the suckling lambs. The transfer of maternal derived immune factors is confirmed by the GGT and LDH enzyme activity levels.

: In brief: Melatonin plays a crucial role in enhancing reproductive performance in small ruminants. This paper reveals the effects of exogenous melatonin on the placental and endometrial rearrangement in early pregnancy in sheep. Early pregnancy losses cause 25% of pregnancy failures in small ruminants because of asynchrony between conceptus and uterine signals. In this context, melatonin plays a crucial role in sheep reproductive dynamics, but little is known about its effects during the peri-implantation period. We hypothesized that melatonin supports embryo implantation by modulating the uterine microenvironment. This study aimed to assess the effects of exogenous melatonin on the endometrial and early placental rearrangement. Ten multiparous ewes either did (MEL, n = 5) or did not (CTR, n = 5) receive a subcutaneous melatonin implant (18 mg) 50 days before a synchronized mating. On day 21 of pregnancy, the sheep were euthanized. MEL ewes exhibited a higher prolificity rate (2.8 vs 2.0 embryos/ewe) and plasma progesterone levels (3.84 vs 2.96 ng/mL, P < 0.05) than did CTR ewes. Groups did not differ significantly in embryo crown-rump length. MEL placentas had significantly (P < 0.001) more binucleated trophoblast cells in the chorion region, and ovine placental lactogen expression was significantly (P < 0.05) more strongly upregulated than in CTR. Exogenous melatonin increased significantly (P < 0.05) gene expression of angiogenic factors (VEGFA, VEGFR1, IGF1R), IFNAR2, and PR in the caruncular endometrium. Expression of the MT2 receptor in the endometrium and placenta was significantly (P < 0.05) higher in the MEL group. These results indicate that melatonin implants acted differentially on uterine and placental rearrangement. Melatonin increases differentiation in the placenta and induces changes that could promote vessel maturation in the endometrium, suggesting that it enhances the uterine microenvironment in the early stage of pregnancy in sheep.

: In brief: Fibroblast growth factor-2 (FGF2) is essential for early placenta development in sheep. This study shows that the mechanistic target of rapamycin is the key modulator of trophoblast adaptive response under FGF2 modulation. During the early stage of placentation in sheep, normal conceptus development is affected by trophoblast cell functionality, whose dysregulation results in early pregnancy loss. Trophoblast metabolism is supported mainly by histotrophic factors, including fibroblast growth factor-2 (FGF2), which are involved in cell differentiation and function through the modulation of specific cellular mechanisms. The mechanistic target of rapamycin (mTOR) is known as a cellular ‘nutrient sensor’, but its downstream regulation remains poorly understood. The hypothesis was that during trophoblast development, the FGF2 effect is mediated by mTOR signalling pathway modulation. Primary trophoblast cells from 21-day-old sheep placenta were characterised and subjected to FGF2 and rapamycin treatment to study the effects on cell functionality and gene and protein expression profiles. The model showed mainly mononuclear cells with epithelial cell-like growth and placental morphological properties, expressing typical trophoblast markers. FGF2 promoted cell proliferation and migration under normal culture conditions, whereas mTOR inhibition reversed this effect. When the mTOR signalling pathway was activated, FGF2 failed to influence invasion activity. mTOR inhibition significantly reduced cell motility, but FGF2 supplementation restored motility even when mTOR was inhibited. Interestingly, mTOR inhibition influenced endocrine trophoblast marker regulation. Although FGF2 supplementation did not affect ovine placenta lactogen expression, as observed in the control, interferon-tau was drastically reduced. This study provides new insights into the mechanism underlying mTOR inhibitory effects on trophoblast cell functionality. In addition, as mTOR is involved in the expression of hormonal trophoblast markers, it may play a crucial role in early placenta growth and fetal–maternal crosstalk.

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of Lora link performance in spatial dimensions and ascertain theextent of reliable communication coverage achievable by gateways in mountainous environments. Additionally an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model’s precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.