The Alice : "Follow the White Rabbit" : parasites of farm rabbits based on coproscopy

The aim of the study, conducted in the years 2011–2013, was to determine the level of gastrointestinal parasites infection in New Zealand White rabbits, kept at the Experimental Station of the University of Agriculture in Krakow. The study showed rabbits protozoan infection with the genus Eimeria, belonging – based on the sporulation method – to the following species: E. magna, E. media, E. perforans, E. stiedae and E. irresidua. The highest prevalence of infection, as well as the intensity of oocysts output (OPG – oocysts per gram of faeces), was noted for E. magna and E. media – respectively 31.4 % (19477.3 OPG), and 40.0 % (14256.07 OPG). The infection of rabbits with Eimeria spp. differed significantly between years. With regard to oocysts output, the level of infection was strongly connected with the age of rabbits, being higher in young animals. However, the range of infection was highest among adults. Among nematodes, Passalurus ambiguus pinworm was regularly found (prevalence reached 21.9%), other species – Trichuris leporis, and Graphidium strigosum were rarely noted. The overall infection with nematodes did not differ between years. Similarly, as in the case of Eimeria older individuals were more often infected by nematodes. We observed some trends in parasite oocysts/eggs output; the protozoan oocysts were recorded more often in faecal samples collected in the evenings, whereas the nematodes eggs occurred frequently in the mornings. This situation may be related to the phenomenon of coprophagy occurring in the mammals of Lagomorpha order. The results of the study indicate that especially coccidiosis constitute permanently throughout the years an important problem in the rabbitry examined

The genetic history of the Southern Arc: a bridge between West Asia and Europe

By sequencing 727 ancient individuals from the Southern Arc (Anatolia and its neighbors in Southeastern Europe and West Asia) over 10,000 years, we contextualize its Chalcolithic period and Bronze Age (about 5000 to 1000 BCE), when extensive gene flow entangled it with the Eurasian steppe. Two streams of migration transmitted Caucasus and Anatolian/Levantine ancestry northward, and the Yamnaya pastoralists, formed on the steppe, then spread southward into the Balkans and across the Caucasus into Armenia, where they left numerous patrilineal descendants. Anatolia was transformed by intra–West Asian gene flow, with negligible impact of the later Yamnaya migrations. This contrasts with all other regions where Indo-European languages were spoken, suggesting that the homeland of the Indo-Anatolian language family was in West Asia, with only secondary dispersals of non-Anatolian Indo-Europeans from the steppe

H-Bond Mediated Phase-Transfer Catalysis: Enantioselective Generating of Quaternary Stereogenic Centers in β-Keto Esters

In this work, we would like to present the development of a highly optimized method for generating the quaternary stereogenic centers in β-keto esters. This enantioselective phase-transfer alkylation catalyzed by hybrid Cinchona catalysts allows for the efficient generation of the optically active products with excellent enantioselectivity, using only 1 mol% of the catalyst. The vast majority of phase-transfer catalysts in asymmetric synthesis work by creating ionic pairs with the nucleophile-attacking anionic substrate. Therefore, it is a sensible approach to search for new methodologies capable of introducing functional groups into the precursor’s structure, maintaining high yields and enantiomeric purity

Modelling of Mechanical Properties of Fresh and Stored Fruit of Large Cranberry Using Multiple Linear Regression and Machine Learning

The study investigated the selected mechanical properties of fresh and stored large cranberries. The analyses focused on changes in the energy requirement up to the breaking point and aimed to identify the apparent elasticity index of the fruit of the investigated large cranberry fruit varieties relating to harvest time, water content, as well as storage duration and conditions. After 25 days in storage, the fruit of the investigated varieties were found with a decrease in mean acidity, from 1.56 g⋅100 g−1 to 1.42 g⋅100 g−1, and mean water content, from 89.71% to 87.95%. The findings showed a decrease in breaking energy; there was also a change in the apparent modulus of elasticity, its mean value in the fresh fruit was 0.431 ± 0.07 MPa, and after 25 days of storage it decreased to 0.271 ± 0.08 MPa. The relationships between the cranberry varieties, storage temperature, duration of storage, x, y, and z dimensions of the fruits, and their selected mechanical parameters were modeled with the use of multiple linear regression, artificial neural networks, and support vector machines. Machine learning techniques outperformed multiple linear regression

Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

Modelling plays an important role in identifying and solving problems that arise in a number of scientific issues including agriculture. Research in the natural environment is often costly, labour demanding, and, in some cases, impossible to carry out. Hence, there is a need to create and use specific “substitutes” for originals, known in a broad sense as models. Owing to the dynamic development of computer techniques, simulation models, in the form of information technology (IT) systems that support cognitive processes (of various types), are acquiring significant importance. Models primarily serve to provide a better understanding of studied empirical systems, and for efficient design of new systems as well as their rapid (and also inexpensive) improvement. Empirical mathematical models that are based on artificial neural networks and mathematical statistical methods have many similarities. In practice, scientific methodologies all use different terminology, which is mainly due to historical factors. Unfortunately, this distorts an overview of their mutual correlations, and therefore, fundamentally hinders an adequate comparative analysis of the methods. Using neural modelling terminology, statisticians are primarily concerned with the process of generalisation that involves analysing previously acquired noisy empirical data. Indeed, the objects of analyses, whether statistical or neural, are generally the results of experiments that, by their nature, are subject to various types of errors, including measurement errors. In this overview, we identify and highlight areas of correlation and interfacing between several selected neural network models and relevant, commonly used statistical methods that are frequently applied in agriculture. Examples are provided on the assessment of the quality of plant and animal production, pest risks, and the quality of agricultural environments

Response of Maize Varieties (Zea mays L.) to the Application of Classic and Stabilized Nitrogen Fertilizers—Nitrogen as a Predictor of Generative Yield

The study presents the results of a 3-year field trial aimed at assessing the yield and efficiency indicators of nitrogen application in the cultivation of three maize cultivars differing in agronomic and genetic profile. The advantages of the UltraGrain stabilo formulation (NBPT and NPPT) over ammonium nitrate and urea are apparent if a maize cultivar capable of efficient nutrient uptake in the pre-flowering period and effective utilization during the grain filling stage is selected. Therefore, the rational fertilization of maize with urea-based nitrogen fertilizer with a urease inhibitor requires the simultaneous selection of cultivars that are physiologically profiled for efficient nitrogen utilization from this form of fertilizer (“stay-green” cultivar). The interaction of a selective cultivar with a high genetically targeted potential for nitrogen uptake from soil, combined with a targeted selection of nitrogen fertilizer, is important not only in terms of production, but also environmental and economic purposes

Comparative Analysis of Plant Growth-Promoting Rhizobacteria (PGPR) and Chemical Fertilizers on Quantitative and Qualitative Characteristics of Rainfed Wheat

The indiscriminate use of hazardous chemical fertilizers can be reduced by applying eco-friendly smart farming technologies, such as biofertilizers. The effects of five different types of plant growth-promoting rhizobacteria (PGPR), including Fla-wheat (F), Barvar-2 (B), Nitroxin (N1), Nitrokara (N2), and SWRI, and their integration with chemical fertilizers (50% and/or 100% need-based N, P, and Zn) on the quantitative and qualitative traits of a rainfed wheat cultivar were investigated. Field experiments, in the form of randomized complete block design (RCBD) with four replications, were conducted at the Qamloo Dryland Agricultural Research Station in Kurdistan Province, Iran, in three cropping seasons (2016–2017, 2017–2018, and 2018–2019). All the investigated characteristics of rainfed wheat were significantly affected by the integrated application of PGPR chemical fertilizers. The grain yield of treated plants with F, B, N1, and N2 PGPR plus 50% of need-based chemical fertilizers was increased by 28%, 28%, 37%, and 33%, respectively, compared with the noninoculated control. Compared with the noninoculated control, the grain protein content was increased by 0.54%, 0.88%, and 0.34% through the integrated application of F, N1, and N2 PGPR plus 50% of need-based chemical fertilizers, respectively. A combination of Nitroxin PGPR and 100% of need-based chemical fertilizers was the best treatment to increase the grain yield (56%) and grain protein content (1%) of the Azar-2 rainfed wheat cultivar. The results of this 3-year field study showed that the integrated nutrient management of PGPR-need-based N, P, and Zn chemical fertilizers can be considered a crop management tactic to increase the yield and quality of rainfed wheat and reduce chemical fertilization and subsequent environmental pollution and could be useful in terms of sustainable rainfed crop production

Degree of Biomass Conversion in the Integrated Production of Bioethanol and Biogas

The integrated production of bioethanol and biogas makes it possible to optimise the production of carriers from renewable raw materials. The installation analysed in this experimental paper was a hybrid system, in which waste from the production of bioethanol was used in a biogas plant with a capacity of 1 MWe. The main objective of this study was to determine the energy potential of biomass used for the production of bioethanol and biogas. Based on the results obtained, the conversion rate of the biomass—maize, in this case—into bioethanol was determined as the efficiency of the process of bioethanol production. A biomass conversion study was conducted for 12 months, during which both maize grains and stillage were sampled once per quarter (QU-I, QU-II, QU-III, QU-IV; QU—quarter) for testing. Between 342 L (QU-II) and 370 L (QU-I) of ethanol was obtained from the organic matter subjected to alcoholic fermentation. The mass that did not undergo conversion to bioethanol ranged from 269.04 kg to 309.50 kg, which represented 32.07% to 36.95% of the organic matter that was subjected to the process of bioethanol production. On that basis, it was concluded that only two-thirds of the organic matter was converted into bioethanol. The remaining part—post-production waste in the form of stillage—became a valuable raw material for the production of biogas, containing one-third of the biodegradable fraction. Under laboratory conditions, between 30.5 m3 (QU-I) and 35.6 m3 (QU-II) of biogas per 1 Mg of FM (FM—fresh matter) was obtained, while under operating conditions, between 29.2 m3 (QU-I) and 33.2 m3 (QU-II) of biogas was acquired from 1 Mg of FM. The Biochemical Methane Potential Correction Coefficient (BMPCC), which was calculated based on the authors’ formula, ranged from 3.2% to 7.4% in the analysed biogas installation