Smallholders Beef Cattle Commercialization in Southwest Ethiopia

In this study, the beef cattle markets in Southwest Ethiopia are analyzed based on a survey of 172 producers. The first part emphasized the characterization and commercialization of the beef cattle market in the study area. The second part is dedicated to identifying the factors affecting households’ participation in the beef cattle market using the Heckman two-step selection model. In the findings, the beef cattle market is characterized by the dominance of few traders, asymmetric information, lack of contract enforcement, lack of transparency among market actors, and poorly developed market infrastructure. There is very low net commercial off-take rate of cattle for smallholder farmers in the study area. The result from the Heckman two-step selection model revealed that having positive stock of cattle, better access to extension service & feed, and a better level of literacy enhanced market participation and sales volume. On the contrary, market participation and sales volume were negatively affected by cattle keeper’s age, non-livestock income, and poor road and health infrastructure. The study suggested that improving the market and health infrastructure, providing capacity building for producers, and improving access to feed could enhance the intensity of smallholder beef cattle market participation.

Protease Produced by Endophytic Fungi: A Systematic Review

The purpose of this systematic review was to identify the available literature of production, purification, and characterization of proteases by endophytic fungi. There are few complete studies that entirely exhibit the production, characterization, and purification of proteases from endophytic fungi. This study followed the PRISMA, and the search was conducted on five databases: PubMed, PMC, Science Direct, Scopus Articles, and Web of Science up until 18 May 2021, with no time or language restrictions. The methodology of the selected studies was evaluated using GRADE. Protease production, optimization, purification, and characterization were the main evaluated outcomes. Of the 5540 initially gathered studies, 15 met the inclusion criteria after a two-step selection process. Only two studies optimized the protease production using statistical design and two reported enzyme purification and characterization. The genus Penicillium and Aspergillus were the most cited among the eleven different genera of endophytic fungi evaluated in the selected articles. Six studies proved the ability of some endophytic fungi to produce fibrinolytic proteases, demonstrating that endophytic fungi can be exploited for the further production of agents used in thrombolytic therapy. However, further characterization and physicochemical studies are required to evaluate the real potential of endophytic fungi as sources of industrial enzymes.

Intelligent Time-Stepping for Practical Numerical Simulation

We present a novel reservoir simulator time-step selection approach which uses machine-learning (ML) techniques to analyze the mathematical and physical state of the system and predict time-step sizes which are large while still being efficient to solve, thus making the simulation faster. An optimal time-step choice avoids wasted non-linear and linear equation set-up work when the time-step is too small and avoids highly non-linear systems that take many iterations to solve. Typical time-step selectors use a limited set of features to heuristically predict the size of the next time-step. While they have been effective for simple simulation models, as model complexity increases, there is an increasing need for robust data-driven time-step selection algorithms. We propose two workflows – static and dynamic – that use a diverse set of physical (e.g., well data) and mathematical (e.g., CFL) features to build a predictive ML model. This can be pre-trained or dynamically trained to generate an inference model. The trained model can also be reinforced as new data becomes available and efficiently used for transfer learning. We present the application of these workflows in a commercial reservoir simulator using distinct types of simulation model including black oil, compositional and thermal steam-assisted gravity drainage (SAGD). We have found that history-match and uncertainty/optimization studies benefit most from the static approach while the dynamic approach produces optimum step-sizes for prediction studies. We use a confidence monitor to manage the ML time-step selector at runtime. If the confidence level falls below a threshold, we switch to traditional heuristic method for that time-step. This avoids any degradation in the performance when the model features are outside the training space. Application to several complex cases, including a large field study, shows a significant speedup for single simulations and even better results for multiple simulations. We demonstrate that any simulation can take advantage of the stored state of the trained model and even augment it when new situations are encountered, so the system becomes more effective as it is exposed to more data.

PSXV-3 The influence of the dams genotype on the indicators of the breeding value of offspring

Aberdeen Angus cattle of Australian breeding is a new ecological and genetic genotype and it is currently in the process of formation and differentiation, the genealogy of the breeding stock is represented by the leading related groups of bulls-leaders of domestic and imported breeding. The aim of this work was to study the breeding value of Bismarck5682bovine cows of Australian selection (n = 20) and Design1015 (n = 20) of domestic reproduction of the Aberdeen-Angus breeding and their influence on the selection and genetic parameters of the productivity of heifers-daughters. It was revealed that the representatives of the Design line had an advantage in live weight (615.4 kg,lim-705-495kg) by 9.8% (P < 0.001), milk productivity (219.7 kg,lim-241-184kg) by 2.4% (P > 0.05) and height insacrum (134.5 cm,lim-140cm-128cm) by 4.1% (P < 0.001) compared to peers of Bismarck line.The ability of animals to realize their breeding potential is determined by many factors. So, it was revealed that the heifers of Design lineexceeded their peers from Bismarck group in live weight in 15 months (359.8 kg, Cv-5.24%) by 5.2%, (P >0.05), average daily gain (652.8 g, Cv-7.86%) - 10.6% (P< 0.001), body conformation score (19.7 points, Cv-3.72)- 14.5% (P< 0.01). It should be noted that heifers – daughters of the Bismarck genealogical line gave in sacrum height but had a better estimate of meat forms by 2.9% with reliable values. These results allow us to conclude that the offspring of the Bismarck 5682 genealogical line of Australian breeding are more affected by the negative factors of the new breeding zone. Thus, a step-by-step selection evaluation of the breeding stock will allow more reliable identification of breeding bulls with a better ability to improve valuable distinguishing features in the generations of cows during their linear breeding. The research was carried out within the framework of RAS 0526-2021-0001.

At a snail’s pace: the influence of habitat disturbance on terrestrial snail movement using experimentally manipulated mesocosms

The Anthropocene marks great changes to environments and the animals that inhabit them. Changes, such as disturbance, can affect the manner in which animals interact with their environments, such as moving and selecting habitats. To test how animals might respond to changing disturbance regimes, we employ an experimental approach to movement ecology. We used integrated step selection analysis (iSSA) to test the behavioural responses of individually-marked grove snails (Cepaea nemoralis) exposed to a gradient of physical disturbance in their habitat. We used a before-after control-impact (BACI) experimental design within semi-controlled mesocosms to manipulate edge and disturbance variables by altering the area of the mesocosm covered by bricks. We showed that grove snails perceive edges of enclosures and edges of bricks as risks, and responded to such risks by altering their movement. Grove snails displayed a bimodal response to risk by taking shelter in place or moving faster to be farther from the disturbance. Furthermore, individuals tended to modulate their behavioural response to the degree of risk. Our study highlights the usefulness of experimental mesocosms in movement ecology and in determining the effects of habitat alteration and human-imposed risk on movement behaviour.

Money also is sunny in a retiree’s world: financial incentives and work after retirement

This paper assesses the impact of financial incentives on working after retirement. The empirical analysis is based on a large administrative individual career data set that includes information about 2% of all German employees subject to social security or in marginal employment until age 67 and their employers in the period 1975–2014. We use the classical labor supply model and differentiate between the impact of (potential) labor and non-labor (pension entitlements) income. A Heckman-type two step selection model corrects for endogeneity. We show that labor income has a positive and non-labor income a negative impact on the decision to work after retirement. Especially individuals who can substantially increase their earnings in comparison to their pension entitlements accordingly have a higher probability to work. Men are more attracted by labor earnings incentives than women. Also individuals who work until retirement are easier attracted to work after retirement by higher labor income than those with gaps between employment exit and retirement. Our results allow the calculation of the impact of changes in taxes on labor and non-labor income and changes in earnings offers by employers on work after retirement for different demographic groups.

Some Memories Never Fade: Inferring Multi-Scale Memory Effects on Habitat Selection of a Migratory Ungulate Using Step-Selection Functions

Understanding how animals use information about their environment to make movement decisions underpins our ability to explain drivers of and predict animal movement. Memory is the cognitive process that allows species to store information about experienced landscapes, however, remains an understudied topic in movement ecology. By studying how species select for familiar locations, visited recently and in the past, we can gain insight to how they store and use local information in multiple memory types. In this study, we analyzed the movements of a migratory mule deer (Odocoileus hemionus) population in the Piceance Basin of Colorado, United States to investigate the influence of spatial experience over different time scales on seasonal range habitat selection. We inferred the influence of short and long-term memory from the contribution to habitat selection of previous space use within the same season and during the prior year, respectively. We fit step-selection functions to GPS collar data from 32 female deer and tested the predictive ability of covariates representing current environmental conditions and both metrics of previous space use on habitat selection, inferring the latter as the influence of memory within and between seasons (summer vs. winter). Across individuals, models incorporating covariates representing both recent and past experience and environmental covariates performed best. In the top model, locations that had been previously visited within the same season and locations from previous seasons were more strongly selected relative to environmental covariates, which we interpret as evidence for the strong influence of both short- and long-term memory in driving seasonal range habitat selection. Further, the influence of previous space uses was stronger in the summer relative to winter, which is when deer in this population demonstrated strongest philopatry to their range. Our results suggest that mule deer update their seasonal range cognitive map in real time and retain long-term information about seasonal ranges, which supports the existing theory that memory is a mechanism leading to emergent space-use patterns such as site fidelity. Lastly, these findings provide novel insight into how species store and use information over different time scales.

An Operator-Based Scheme for the Numerical Integration of FDEs

An operator-based scheme for the numerical integration of fractional differential equations is presented in this paper. The generalized differential operator is used to construct the analytic solution to the corresponding characteristic ordinary differential equation in the form of an infinite power series. The approximate numerical solution is constructed by truncating the power series, and by changing the point of the expansion. The developed adaptive integration step selection strategy is based on the controlled error of approximation induced by the truncation. Computational experiments are used to demonstrate the efficacy of the proposed scheme.

Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

Background New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. Results We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. Conclusions Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.