Correlation between milk composition and selected blood indicators of liver function in ewes during lactation

The objective of this study was to research correlations between milk composition and selected blood indicators of liver function in ewes during lactation. The research was conducted with samples of milk and blood from Travnik Pramenka ewes (n = 99). Samples were collected in the area of western Slavonia in Croatia from lactating sheep grazing on natural pastures. Chemical composition of sheep milk was analysed as well as biochemical parameters in serum. Positive correlation between ALB : TGC, ALB : ALT, GUK : GGT, CHOL : ALT and AST : GGT was determined, while significant negative correlation was determined between GUK : ALB, GUK : TGC, GUK : ALT, ALB : GGT as well as ALT : GGT. Significant positive correlation was determined between blood and milk indicators, like ALB protein (r = 0.243), ALB : DMNF (r = 0.309) and ALB : SCC (r = 0.249), as well as negative correlation between TGC : MY (r = -0.264), ALT : protein (r = -0.258), lactose : TGC (r = -0.274) and ALT : DMNF (r = -0.234). The relations between indicators of milk composition, milk production and ewes blood indicators as well as their mutual connections indicate the justification of using the analysis of liver status indicators. Accordingly, by using these relations metabolic pathways of liver status indicators could be better monitored, which is important for practice regarding prevention of possible feeding errors and prevention of major production losses while maintaining the health of lactating sheep.

Modeling the Effect of Bovine Viral Diarrhea Virus in Australian Beef Herds

Bovine viral diarrhea virus (BVDV) is an economically important disease in Australian beef farming. The disease typically results in low-level production losses that can be difficult to detect for several years. Simulation modeling can be used to support the decision to control BVDV; however, current BVDV simulation models do not adequately reflect the extensive farming environment of Australian beef production. Therefore, the objective of this study was to develop a disease simulation model to explore the impact of BVDV on beef cattle production in south-east Australia. A dynamic, individual-based, stochastic, discrete-time simulation model was created to simulate within-herd transmission of BVDV in a seasonal, self-replacing beef herd. We used the model to simulate the effect of herd size and BVDV introduction time on disease transmission and assessed the short- and long-term impact of BVDV on production outputs that influence the economic performance of beef farms. We found that BVDV can become established in a herd after a single PI introduction in 60% of cases, most frequently associated with the breeding period. The initial impact of BVDV will be more severe in smaller herds, although self-elimination is more likely in small herds than in larger herds, in which there is a 23% chance that the virus can persist for >15 years following a single incursion in a herd with 800 breeders. The number and weight of steers sold was reduced in the presence of BVDV and the results demonstrated that repeat incursions exacerbate long-term production losses, even when annual losses appear marginal. This model reflects the short- and long-term production losses attributed to BVDV in beef herds in southeast Australia and provides a foundation from which the influence and economic utility of BVDV prevention in Australian beef herds can be assessed.

Autonomous Corrosion and Scale Management in Electric Submersible Pump Wells

Producers find a considerable amount of their operating expense (OPEX) comes from managing risks associated with corrosion and scale. Monitoring and chemical adjustment workflows are typically manual, and performed at low frequencies, leading to delays in event detection. As a result, the potential for negative events such as production shutdowns and well failures increase. This project's scope integrates chemistry domain experience with edge analytics, machine learning models, and intelligent equipment, to transform manual processes into an autonomous solution. The goal is to optimize operations, reduce well failures and workover costs, and maximize production. This solution is currently deployed in an oilfield, that has been historically challenged with a high number of electric submersible pump (ESP) failures due to corrosion and scale that resulted in significant production losses and unforeseen workover costs. The designed digital architecture supports autonomous management of scale and corrosion through remote monitoring and automated chemical injection. Real-time data is acquired from connected equipment, processed in an edge device running artificial intelligence, and autonomously sent to chemical pumps. Data from sensors, connected devices, and models are visualized in cloud applications, or integrated into existing client systems for end user analysis and full visibility of the entire process. The results show highly accurate models, precise chemical injection, and a reduction of well failures.

Production Enhancement in Intermittent Gas Lift Wells Using Sweeping Pipe Bend - Successful Case Histories from Various Onshore Fields in India

Objectives In wells which are producing on intermittent gas lift (IGL), the injected gas cannot sweep the entire liquid volume to the surface from the bottom of the tubing as there is continuously some fluid falling back in the tubing. The fallback can be described as the difference between the volume of the slug at the start of the gas injection and the volume of the actual produced slug at the surface. This fallback of liquid happens due to the fact that the gas has a tendency to flow through the liquid slug and letting the liquid to fall. The intensity of the liquid fallback increases more when there is increase in back pressure at wellhead. In order to minimize this liquid falling back in wells on intermittent gas lift, the sweeping pipe bend technology has been used in the various onshore fields operated by ONGC which has resulted in substantial gains and has been brought out in the paper. Process Gas break through and fallback are affected by three factors including the development of the gas bubble, the velocity of the slug flowing upward in the tubing, and wellhead restrictions caused due to presence of many 90-degree bends. To prevent gas breakthrough and to optimize the liquid fallback to minimum 5-7 % per 1000 feet of lift, it is recommended to maintain 1000 feet/min of minimum velocity of slug. Slower is the velocity of the slug which is moving up in the tubing, the longer time it takes for the gas to break through the liquid. At 1000 feet/min velocity, the wellhead restrictions can result in fallback losses due to breakthrough of gas in the well. In general, the flow path through the Christmas tree into the flowline is rather tortuous, moving first through a tee to the wing valve, then through other 90-degree ells before finally reaching the flowline. These restrictions further result in slowdown of the velocity of the slug thus resulting in more liquid to fallback and subsequently in significant production losses. Results In order to overcome the aforementioned problem and to reduce fallback in an intermittent gas lift well, sweeping pipe bend technology was considered and in the first phase implemented in 5 identified wells of different fields of ONGC Assets. With the help of sweeping pipe bend, the flow pattern becomes streamlined and number of 90-degree bends reduces or eliminates resulting in substantial reduction in the back pressure thus reducing the fall back. The implementation of the technology has resulted in an average liquid gain of 20.3% per well. Various guidelines for successful application of sweeping pipe bend have also been brought out in the paper. Additive Information 650 candidate wells operating on intermittent gas lift have been identified for the implementation of Sweeping Pipe Bends. As per the analysis, the implementation of Sweeping Pipe Bend is likely to result in a liquid gain of about 1000 m3/day from these wells.

Symptoms and interrelationships of macro and micronutrients available for soybean

Nutrients have differences in their functions as metabolic and structural constituents in plant organs. The specific identification of the symptoms of excess or deficiency of nutrients is essential for the correct management to be carried out in order to avoid production losses. In this context, this research aimed to evaluate the symptoms of deficiency and excess of nutrients in soybean. The experiment was carried out on a bench, with 3-liter containers, in which uniformly germinated seedlings were selected for implantation. Initially, the seedlings were subjected to a complete nutrient solution to allow for a uniform and unrestricted initial development over a period of one week. Then, the plants were subjected to solutions with twice as much nutrient, absence of nutrients, complete solution and nutrient restriction, individual omissions resulted in morphological changes, which translated into visual symptoms characteristic of the nutritional deficiency of the respective nutrient. The solution with twice the nutrient concentration of the complete solution showed an increase in the absorption of N, Mg, K and Fe, for Cu it was twice the absorption and for Zn five times more. There was a decrease in the absorption of Ca and Mn and, with that, it is concluded that the availability of twice as many nutrients did not result in double their absorption.

Phylogenetic relationship of coffee leaf rust in the central jungle of Peru

Coffee leaf rust is the main disease that causes significant losses in Coffea arabica. In Peru, this disease caused epidemics between 2008 and 2013 with production losses of 35 %. The objective was to identify H. vastatrix using a morphological and molecular approach based on a phylogenetic species concept. Coffee leaf samples with symptoms of chlorotic lesions with the presence of yellow uredospores at different severity stages of different cultivars were collected from 11 locations in the departments of Pasco and Junin during 2017-2018. DNA was purified as proposed by Cristancho and coworkers. The major subunit of ribosomal DNA was amplified with universal primers LR0R and LR5, and sequenced by Macrogen and deposited in GenBank. Sequences from the genera Achrotelium, Blastospora, Cystopsora, Hemileia, and Mikronegeria were included for phylogenetic analysis. The results showed that the rust was distributed in coffee growing regions of Pasco: Villa Rica (Catimor, Caturra, and Gran Colombia); Oxapampa (Yellow Caturra), and Junín: San Luis de Shuaro (Catimor), Chanchamayo (Catimor), San Ramón (Catimor), Vitoc (Caturra), Pichanaki (Caturra), Río Negro (Caturra), Pangoa (Yellow Caturra, Gran Colombia, Limani). It was also grouped into a single clade with isolated H. vastatrix from Mexico and Australia, suggesting that they come from a common ancestor. This is the first confirmed report using molecular barcoding of H. vastatrix in the central jungle of Peru.

Climate vulnerability assessment in semi-arid and arid region of Rajasthan, India: An enquiry into the disadvantaged districts

This paper assesses the district level climate vulnerability in the state of Rajasthan using largescale data on climate and socio-economic variables.More than thirty indicators segregated into four components of exposure, sensitivity, adaptive capacity and crop production loss were combined to develop a composite index of vulnerability and homogenous districts were clustered into three categories, viz.low, medium and high.Wide inter-district variations were observed across the calculated indices. The result reveals that highest production losses occurred in Ganganagar district followed by Hanumangarh and Bharatpur. Pali was least exposed to the climatic variability, whereas Bundi had the maximum exposure.Jaisalmer rated the maximum sensitivity level. Further, Pratapgarh followed by Jaisalmer and Banswara had the lowest degree of adaptive capacity. On the whole, districts like Hanumangarh, Jaisalmer, Ganganagar, Bundi, Bharatpur, Jodhpur, Bikaner, Chittorgarh, Alwar, Baran and Pratapgarh exhibit high level of vulnerability to climatic change. While on the other spectrum Sirohi district was least vulnerable due to lower exposure, sensitivity, crop production loss and high adaptive capacity. The analysis, suggests the need for prioritizing vulnerable areas to arrest regional imbalances by encouraging need/location based interventions for moderating the degree of vulnerability, whilst making agro-ecosystem in Rajasthan resilient to climatic aberrations.

An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation

A great deal of information related to rice cultivation has been published on the web. Conventionally, this information is studied by end-users to identify pests, and to prevent production losses from rice diseases. Despite its benefits, such information has not yet been encoded in a machine-processable form. This research closes the gap by modeling the knowledge-bases using ontologies and semantic technologies. Our modeled ontologies are externalized from existing reliable sources only, and offer axioms that describe abnormal appearances in rice diseases (and insects) and the corresponding controls. In addition, we developed an expert system called RiceMan, based on our ontologies, to support technical and non-technical users for diagnosing problems from observed abnormalities. We also introduce a composition procedure that aggregates users’ observation data with others for realizing spreadable diseases. This procedure, together with ontology reasoning, lies at the heart of our methodology. Finally, we evaluate our methodology practically with four groups of stakeholders in Thailand: senior agronomists, junior agronomists, agricultural students, and ontology specialists. Both ontologies and RiceMan are evaluated to verify their correctness, usefulness, and usability in various aspects. Our experimental results show that ontology reasoning is a promising approach for this domain problem.

Metabolic Changes in Scylla paramamosain During Adaptation to an Acute Decrease in Salinity

The mud crab Scylla paramamosain is an important euryhaline mariculture species. However, acute decreases in salinity seriously impact its survival and can result in large production losses. In this study, we evaluated metabolic changes in S. paramamosain exposed to an acute salinity reduction from 23 psu to 3 psu. After the salinity decrease, hemolymph osmolality declined from 726.75 to 642.38 mOsm/kg H2O, which was close to the physiological equilibrium state. Activities of osmolality regulation-related enzymes in the gills, including Na+-K+-ATPase, CA, and V-ATPase all increased. Using LC-MS analysis, we identified 519 metabolites (mainly lipids). Additionally, 13 significant metabolic pathways (P < 0.05) were identified via enrichment analysis, which were mainly related to signal pathways, lipids, and transportation. Our correlation analysis, which combined LC-MS and previous GC-MS data, yielded 28 significant metabolic pathways. Amino acids and energy metabolism accounted for most of these pathways, and lipid metabolism pathways were insignificant. Our results showed that amino acids and energy metabolism were the dominant factors involved in the adaptation of S. paramamosain to acute salinity decrease, and lipid metabolites played a supporting role.