Labor Requirements Research Articles

Determination of Factors Affecting the Cumin Production Decision of Agricultural Enterprises: The Case of Konya Province

Turkey has vast agricultural lands and diverse climatic conditions, and different plant species can be cultivated. Especially, Konya province, located in the Central Anatolia Region, is an important agricultural centre in Turkey. Konya province, which has a polyculture production pattern, stands out in the production of medicinal and aromatic plants due to its favourable climatic conditions and soil structure. In Turkey, 26.33% of cumin, which is among the medicinal and aromatic plants, is produced in Konya province. This study aims to determine the factors affecting cumin production. For this purpose, 65 cumin producers determined by proportional sampling method were interviewed. The data obtained through these interviews were analysed by using the Analytic Hierarchy Process (AHP) method to determine the weights of the criteria affecting the cumin production decision of the agricultural enterprises. In the process of determining the criteria influencing the cumin production decision, the opinions of subject experts working in universities, public institutions and organizations and producers were taken. Among the criteria determined as yield, price, labour requirement, water requirement, ease of marketing, mechanisation, input costs, cultivation knowledge, soil structure and subsidies, the most important criterion was found to be price with 28.11%. Price criterion is followed by input cost 22.57%, water requirement 12.13%, yield 8.71%, cultivation knowledge 8.43%, subsidies 6.82%, ease of marketing 4.74%, soil structure 3.63%, mechanisation 2.54% and labour requirement 2.25%. It is thought that the solution of the mechanisation problem will make a significant contribution to cumin production as it will reduce the need for labour force.

Unlocking synergies and managing trade-offs: how climate actions in Indian agriculture support the Sustainable Development Goals

This paper assesses four relevant climate actions in Indian agricultural sector through the lens of United Nation’s Sustainable Development Goals (SDGs) to understand how climate actions (SDG 13) are linked with other SDGs and their targets and what measures can be taken to scale up these actions. Four board categories of climate actions are considered for this exercise: shift to less-flooded paddy water management practices; minimizing overuse of nitrogen-based fertilizer, system of rice intensification and climate smart agricultural practices. A mixed method has been followed: first, scoping review of literature between 2010 and 2022 is undertaken to capture the growing body of literature and then a normalized weighted score is used to understand the net strength of the linkages. Scoping review helped in identifying 100 studies spanned across Indo-Gangetic Plain (IGP) and the eastern part of Peninsular India. Findings from the scoring method suggest that these practices have linkages with 15 SDGs and as many as 43 SDG targets. Almost all linkages are positive (synergy) but there are a few negative linkages (trade-offs) as well. Trade-offs include exacerbating inequalities including gender, high upfront cost, requirement of skilled labor, lack of access to information and finance. This paper provides a detailed description of these trade-offs and suggests measures to tackle and minimize them.

Current Status of Patent Research on Picking End Effector

Background: Fruit and vegetable picking, as the most important part of fruit and vegetable production, is a labor-intensive industry. Fruit and vegetable picking robots can effectively improve the efficiency of picking and reduce labor requirements. As the most important part of the picking robot, the picking end-effector effectively plays a key role in improving the picking efficiency and ensuring the wholeness of the fruit. Therefore, the development trend of picking end effectors has received more and more attention. Objective: The study aimed to solve the problems of high labor consumption and low picking efficiency of manual picking; thus, the grasping method of the picking end effector has been continuously improved. Methods: This article has presented various representative patents related to the picking end effector, such as the suction end effector, multi-finger end effector, and bionic end effector. Results: Through the investigation of a large number of picking end effector patents, the current problems, such as low accuracy and large clamping damage, are summarized and analyzed. In addition, the future trends of fruit and vegetable picking end effectors are discussed. Conclusion: The optimization of the picking end effector is conducive to reducing manual labor and improving the picking rate of fruits and vegetables. More relevant patents will be invented in the future.

Effect of curing conditions on cement based self-compacting mortar produced with mortar waste aggregate

Concrete and mortar wastes, which have a large volume and economic value among construction demolition wastes, are the most targeted demolition waste group to be recycled. An important area where construction demolition waste can be utilized is self-compacting mortar (SCM) systems. SCMs are innovative and economical systems designed to minimize the labor requirements that are difficult to meet in the production process. In this study, mortar waste aggregate (MWA) obtained by mechanical crushing and grinding was used in SCM elements by substituting different ratios (5-10-20-30-40 %) by mass to aggregate. In this way, it was aimed to evaluate both the sustainability of MWAs and the usability of MWAs in SCMs, which are considered as a new production technology. The fresh and hardened mortar tests performed in the study are presented comparatively. The physical (dry unit volume weight, porosity), durability (capillary water absorption) and mechanical properties (flexural tensile, compressive strength) of the hardened SCM elements are based on the determinations made at 3, 7 and 28 test days and according to different curing conditions (water curing, air curing and heat curing).In addition, X-ray Diffractometer (XRD) analysis was performed on specimens obtained from 0 %, 10 %, 20 % and 40 % MWA substituted specimens after heat curing (after 7 days water curing) and 28 days water curing. In the light of the data obtained, it is reported that SCM production with 10 % MWA substitution is feasible in terms of sustainability and engineering properties evaluated in this study.

Trait development and genetic parameters of resilience indicators based on variability in milk consumption recorded by automated milk feeders in North American Holstein calves

The implementation of automated milk feeders (AMF) on precision dairy farms has enabled efficient management of large numbers of group-housed replacement calves with reduced labor requirements and improved calf welfare. In this study, we investigated the feasibility of deriving calf resilience indicators based on variability in milk consumption using data from 10,076 North American Holstein calves collected between 2015 and 2021. We modeled and evaluated deviations in observed and predicted daily milk consumption trajectories as indicators of resilience to environmental perturbations. We also analyzed average milk intake and the number of treatments for bovine respiratory disease (BRD) and their genetic correlations with the derived resilience parameters. Milk consumption was recorded using the Förster-Technik AMF. Deviations in cumulative milk intake were modeled using various methods, including quantile regression and the Gompertz function. Ten resilience indicators were derived to quantify the degree and duration of perturbations, including amplitude, perturbation time, recovery time, and deviation velocities. After data editing, genomic data from 9,273 calves and pedigree information from 10,076 calves with 321,388 phenotypic records were used to estimate genetic parameters for 12 traits, including 10 calf resilience indicators as well as average milk intake and treatments for bovine respiratory disease. Substantial phenotypic variability was observed for all calf resilience indicators derived and genetic parameters related to these novel resilience indicators were estimated. The heritability estimates for the resilience traits are as follows: amplitude of the deviation (in L) 0.047 (0.032, 0.064) (HPD interval), perturbation time of deviation (in d) 0.011 (0.0056, 0.016), recovery time of the deviation (in d) 0.025 (0.016, 0.035), maximum velocity of perturbation (L/d) 0.039 (0.024, 0.053), average velocity of perturbation (L/d) 0.038 (0.022, 0.050), area between the curves (L x d) 0.039 (0.027, 0.054), recovery ratio 0.053 (0.036, 0.072), deviation variance 0.049 (0.32, 0.068), log-deviation variance 0.027 (0.016, 0.044), deviation auto-correlation 0.010 (0.0042, 0.017) and number of deviation occurrences 0.023 (0.0094, 0.036). Some of the highlighted genetic correlations observed with average milk consumption include amplitude: 0.569 (0.474, 0.666), perturbation time: -0.534 (-0.73, -0.342), and average velocity: 0.554 (0.432, 0.672). Similarly, the genetic correlations between the number of times treated for BRD with perturbation time was 0.494 (0.251, 0.723), -0.294 (-0.52, -0.095) with number of deviations, and 0.348 (0.131, 0.578) with deviation autocorrelation. This study highlights the genetic influence on various resilience traits in calves, including amplitude, perturbation time, recovery time, and velocity measures of the perturbation. Our findings suggest the need for prioritizing genetic selection based on traits like recovery time, which exhibits higher heritability and a moderate genetic correlation with the number of times a calf is treated for BRD. The combination of AMF data, mathematical modeling, and genomic evaluation provides a comprehensive framework for assessing and breeding more resilient dairy calves in the face of environmental and health challenges.

Design and Fabrication of Automated Accumulator for Waste Management

Abstract: In today's society, the escalating issue of improper waste disposal looms large. As population density swells, so does the volume of waste produced, straining landfill capacities, polluting the environment, and posing grave health risks. Traditional waste collection approaches fall short, proving costly, laborious, and inadequate in addressing the mounting problem. This underscores the urgent need for innovative solutions. Enter the automated accumulator: a pioneering system designed to revolutionize waste management by streamlining processes and boosting efficiency. Combining cutting-edge sensor technology, robotics, and data analytics, the automated accumulator redefines waste management efficiency. Through instantaneous waste identification and categorization, it revolutionizes resource distribution, slashes manual labor requirements, and curtails environmental pollution.

Lathe Workshop Layout Design Analysis Using Blocplan and SLP Methods

An inappropriate layout can result in excessive material movement and increase costs significantly. Therefore, it is important to carefully plan the facility layout so that related activities can be coordinated according to the applicable departments and workflows. Sinar Abadi Lathe Workshop is a company engaged in the machining business, especially in the lathe process. This process involves working with materials by cutting or modifying workpieces using a lathe. This research analyses the layout of the lathe workshop using the SLP and BLOCPLAN methods in order to reduce moving costs, and optimism the use of space in the context of the lathe workshop. The implementation of the BLOCPLAN method in the lathe shop showed labor efficiency with less labor required per production cycle compared to the initial layout. However, a complete change from the original layout may result in higher moving costs. This highlights the importance of cost considerations in choosing a layout method. For layout changes with minimal moving costs, the SLP Layout is proposed as a cost-efficient alternative, as it considers various factors, including distance, material transportation, and labor requirements in planning the layout.

Thermal performance and life cycle analysis of 3D printed concrete wall building

Automated construction process with extrusion-based 3D concrete printing (3DCP) is widely recognised due to its ability to construct complex freeform geometrical shapes. Furthermore, the automation process reduces labour and minimises material wastage, thus improving productivity. The structural performance of printed structures has been widely discussed; however, addressing the knowledge gap in energy performance and their environmental impact needs further investigation. Thus, this study investigated and compared the insulation characteristics, life cycle cost and environmental impact assessment of 3DCP structures with traditional reinforced concrete members. The evaluation of the insulation properties of 3D printable concrete allows the characterisation of operational costs and greenhouse gas emissions incurred from the additional heating and cooling systems. Furthermore, this study also suggested a method to enhance the thermal resistance of 3DCP with recycled fibre recovered from face mask waste. A comparison between face mask fibre-reinforced 3D-printed wall element and conventional reinforced concrete wall is also performed across the thermal, life cycle cost and environmental impact parameters. The results showed that the insulation properties of 3DCP walls were increased by 49.5% with the addition of face mask fibres compared to RC walls. This results in reduced energy consumption from additional heating and cooling systems along with a reduction in greenhouse gas emissions, thereby improving the energy performance of the building. Further, face mask fibre-reinforced printed walls showed a reduction in the life cycle cost, mainly in the operation stage, due to the low labour requirement and elimination of formwork. Moreover, a significant reduction in the depletion of resources and ozone depletion was observed for 3DCP face mask fibre-reinforced walls, along with the reduced impact on human health and ecosystem damage. Consequently, the incorporation of face mask fibres into the 3DCP process paves the way for its potential in constructing energy-efficient and environmentally sustainable buildings.

Legislating Nurse Staffing: Projected Impact on Hospital Economics, Process Flow, and Hospital-Associated Infections in Montana.

The aim of this study was to project the impact of legislated nurse staffing ratios on patient-, staff-, and system-level outcomes for Prospective Payment System (PPS) hospitals in Montana. In 2023, House Bill 568 was introduced in Montana focused on legislating hospital safe nursing standards. A quantitative design was used for a convenience sample of Montana PPS hospitals. Data were gathered through a newly developed survey and from other publicly available sources for the years 2018 to 2022. Independent t tests were conducted when appropriate with the significance threshold set at 0.05. Projections indicate no significant change in patient outcome metrics accompanied by increases in labor requirements, slower emergency department throughput times, and decreases in hospital operating margins. In Montana, legislating nurse staffing ratios would have downstream implications inconsistent with the intended impact on patient safety, emphasizing the complexity of variables within and external to the healthcare system that drive patient-, staff-, and system-level outcomes.

Goods market desirability of minimum wages

AbstractThis paper presents a general equilibrium model incorporating heterogeneous firms and a perfectly competitive labour market to explore the desirability of minimum wages. We demonstrate that a low minimum wage could enhance social welfare, assuming equal weighting for all individuals. This occurs because the introduction of minimum wages has the potential to mitigate the goods market distortions arising from imperfect competition, firm heterogeneity and free entry. Additionally, we illustrate that the optimal minimum wage is positively associated with the preference intensity for differentiated products relative to the numeraire and population size, while it negatively correlates with the degree of love for variety, entry cost, and upper bound of marginal labour requirements.

Analysis of Productivity and Labor Requirements of Palm Oil (Elaeis guineensis Jacq.) Harvesting at Afdeling 4 in PT. Alam Jaya Persada

In facing increasingly competitive competition, every company needs quality human resources (HR) who have high competence. The challenges that are often faced by every company are the demand for production, which is quite high, and the quality of the products produced. Based on this, human resource management is needed according to the needs of the company. This study aims to determine the characteristics of harvesting employees, the productivity of harvesting employees, and the number of effective oil palm harvesting labor requirements in Afdeling 4 at PT. Alam Jaya Persada. This study uses two data sources, namely primary data consisting of interviews, direct observation, and documentation, as well as secondary data consisting of company documents and literature related to research activities. Respondents in this study amounted to 22 harvest employees who were determined using saturated sampling techniques. Research data collection was carried out through direct observation, interviews, and collecting all documents related to research. Data analysis used in managing research data is descriptive analysis. Based on the characteristics of harvest employees, 22 people are all male with a percentage of 100%; the average education of employees, namely elementary school (SD), is 10 employees with a percentage of 45%; based on an average age of 31–40 years, namely 9 employees with a percentage of 41%; and based on the average length of work of 1-2 years, namely 11 employees with a percentage of 50%. The results showed that of the 22 harvesting employees of Afdeling 4, the average productivity of harvesting employees was 163 beds/HK, and the basis set by the company was 120 beds/HK. This shows that Afdeling 4 employees can be said to be very productive. The calculation of the need for effective employees in Afdeling 4 is 18 harvesting employees with an area of 728 ha, but the number of employees in the field is 22 or has exceeded the labor requirements for the area of the land.

Duration and resource constraint prediction models for construction projects using regression machine learning method

PurposeThe construction projects are highly subjected to uncertainties, which result in overruns in time and cost. Realistic estimates of workforce and duration are imperative for construction projects to attain their intended objectives. The aim of this study is to provide accurate labor and duration estimates for the construction projects, considering actual uncertainties.Design/methodology/approachThe dataset was formulated from the information collected from 186 construction projects through direct interviews, group discussions and questionnaire methods. The actual uncertainties and exposure conditions of construction activities were recorded. The data were verified with the standard guideline to remove the outliers. The prediction model was developed using support vector regression (SVR), a machine learning (ML) method. The performance was evaluated using the widely adopted regression metrics. Further, the cross validation was made with the visualization of residuals and predicted errors, ridge regression with transformed target distribution and a Gaussian Naive Bayes (NB) regressor.FindingsThe prediction models predicted the duration and labor requirements with the consideration of actual uncertainties. The residual plot indicated the appropriate use of SVR to develop the prediction model. The duration (DC) and resource constraint (RC) prediction models obtained 80 and 82% accuracy, respectively. Besides, the developed model obtained better accuracy for the training and test scores than the Gaussian NB regressor. Further, the range of the explained variance score and R2 was from 0.95 to 0.97, indicating better efficiency compared with other prediction models.Research limitations/implicationsThe researchers will utilize the research findings to estimate the duration and labor requirements under uncertain conditions and further improve the construction project management practices.Practical implicationsThe research findings will enable industry practitioners to accurately estimate the duration and labor requirements, considering historical uncertain conditions. A precise estimation of resources will ensure the attainment of the intended project outcomes.Social implicationsDelays in construction projects will be reduced by implementing the research findings, which significantly ensures the effective utilization of resources and attainment of other economic benefits. The policymakers will develop a guideline to develop a database to collect the uncertainties of the construction projects and relatively estimate the resource requirements.Originality/valueThis is the first study to consider the actual uncertainties of construction projects to develop RC and DC prediction models. The developed prediction models accurately estimate the duration and labor requirements with minimal computational time. The industry practitioners will be able to accurately estimate the duration and labor requirements using the developed models.

Seismic behavior of modular buildings with reinforced concrete (RC) structural walls as seismic force resisting system

Modular construction is becoming more popular worldwide due to its reduced construction time, lower construction waste, decreased on-site labor requirements, among other benefits. However, the unique structural characteristics of modular buildings, such as discrete floor diaphragms, limited connections between modules, may result in different behavior during earthquakes compared to conventional buildings. However, the seismic response of modular buildings is not well understood, and the force demand in seismic collectors (i.e., connections between modules and seismic force resisting elements) is not quantified. To address this problem, a 9-story prototype building with reinforced concrete (RC) walls was used to investigate the structural properties and responses of modular buildings. Firstly, a nonlinear numerical model was created to accurately model the RC walls, modular frames, inter-module connections, seismic collectors, and other key structural elements. Elastic modal analysis and nonlinear response history analyses were conducted to assess the building's behavior under seismic loads. Subsequently, a simplified numerical model was developed to investigate the effect of three key parameters: the stiffness of seismic collectors, RC wall strength, and earthquake intensity. It was found that modular buildings with RC walls in the center are torsional flexible. Reducing the seismic collector's rigidity can lead to greater force response in the collector. The maximum forces experienced by seismic collectors at the first floor are sensitive to earthquake intensity but not significantly affected by the RC wall strength. Both an increase in the of RC wall strength and earthquake intensity can lead to an increase in the maximum forces experienced by seismic collectors. The method in ASCE 7–16 substantially underestimated the maximum acceleration coefficients at stories below 80 % of the structural height.

PbS Colloidal Quantum Dots: Ligand Exchange in Solution

PbS colloidal quantum dots (CQDs) have the advantages of adjustable band gap, large exciton Bohr radius, controllable size, easy synthesis, and potential multi-exciton effect, making them attractive for photodetectors and solar cells. However, the long ligand chain wrapped on PbS CQDs limits carrier transport, and defect states of as-synthesized CQDs increase non-radiative recombination, negatively affecting photovoltaic performance. Surface properties determine the characteristics of CQDs, so ligand exchange processes are crucial. Because solution phase ligand exchange reduces labor and time requirements, it is more advantageous than solid phase ligand exchange. This review discusses the solution phase ligand exchange process of PbS CQDs, emphasizing the impact of surface ligands on conformation and conductivity.

Promoting sustainable smallholder farming systems in China

CONTEXTHundreds of millions of smallholder farmers account for 84% of farming households globally. Owing to the limited access to knowledge and resources, smallholder farming systems under sub-optimal management are facing multiple challenges such as adverse environmental impacts and difficulties in meeting growing food demand. Although substantial advanced techniques and practices have been developed, applying these practices on the ground at a large scale remains a great challenge. OBJECTIVESThis study aims to assess the sustainability of typical soil-crop management practices in order to recommend best practice bundles. The study also deploys a farmer survey in order to identify barriers hindering the adoption of sustainable practices and propose ways to overcome these barriers. METHODSTo achieve these aims, we develop a generalizable framework combining technical innovations and social realization pathway for sustainable transformation of smallholder farming systems. We apply this framework in the North China Plain as a case study, propose target-oriented practice bundles and subsequent realization roadmaps from smallholder farmer's perspectives and interests. RESULTS AND CONCLUSIONSOur results show that nitrogen reduction has the greatest positive effect on the overall sustainability of smallholder farming systems (12–14%), followed by formula fertilizer (6–10%) and optimal crop practices (2–4%). Thus, our recommended practice bundles include reducing fertilizer inputs and optimal sowing date and rate. However, our farmer survey shows that the implementation of these recommended practices faces barriers associated with economic costs, labor requirements, and farmer's preferences and acceptance. We suggest ways to overcome these barriers, including multi-stakeholder platforms better connecting smallholders, scientists, government, and industry. SIGNIFICANCEOur framework can be amended to fit different contexts and targets, therefore it can be adopted and tweaked by other regions worldwide dominated by smallholder agriculture to promote sustainable transformation.

Holistic analysis of cropping diversity and intensity implications for productive, environmental, and nutritional performance of smallholder farms in Bihar, India

The agricultural productivity and sustainability in Eastern Gangetic Plain Zones of India are threatened because of the inefficiency of current production practices, shortage of resources, and socioeconomic constraints. We hypothesized the potential impact of intensified cereal systems with mung bean as a third crop within the annual cropping cycle. We assessed economic, social, and environmental indicators for intensified and current cropping system management practiced by different farm types in the region using the FarmDESIGN model. Building on a farm typology constructed for the region in our past research, we used five types of farmers: part-time (PT), well-endowed (WE), small-scale (SS) crop and livestock mix, medium-scale (MS), and resource-poor farmers (RP) in this study. The performance indicators of the 229 original cropping systems cultivated within the 43 farms varied strongly in the eight performance indicators. This variability of cropping systems performance within the farm types resulted in the absence of significant differences between the types. Compared to the original cropping systems, the intensified cropping systems with mung bean not only performed high in dietary energy (DE) production and organic matter (OM) inputs into the soil but also had high application rates of biocides and minimized losses of nitrogen (N). The intervention systems were low in labor requirement and scored at an intermediate level for crop gross margin, water use, and greenhouse gas (GHG) emissions. The ranges of areas of maize- and rice-based systems that could be replaced by intensified systems were largest for the WE and RP farm types. This was reflected in large ranges of change in the performance indicators, but no significant differences in response were found between the farm types. The intensification of maize- and rice-based systems with the proposed intervention cropping systems involving mung bean would result in increased profitability, higher DE yield, and lower requirements for labor and water as the proportion of the farms being converted increases. However, the use of biocides would increase, while the intervention cropping systems would have no significant effect on OM input, GHG emissions, and soil N losses.

Estimating near-roadway air pollution from multi-frequency noise measurements

Air pollution is a major environmental problem and its monitoring is essential for regulatory purposes, policy making, and protecting public health. However, dense networks of air quality monitoring equipment are prohibitively expensive due to equipment costs, labor requirements, and infrastructure needs. As a result, alternative lower-cost methods that reliably determine air quality levels near potent pollution sources such as freeways are desirable. We present an approach that couples noise frequency measurements with machine learning to estimate near-roadway particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) at 1-min temporal resolution. The models were based on data collected by co-located noise and air quality instruments near a busy freeway in Long Beach, California. Model performance was excellent for all three pollutants, e.g., NO2 predictions yielded Pearson's R = 0.87 with a root mean square error of 7.2 ppb; this error represents about 10 % of total morning rush hour concentrations. Among the best air pollutant predictors were noise frequencies at 40 Hz, 500 Hz, and 800 Hz, and meteorology, particularly wind direction. Overall, our method potentially provides a cost-effective and efficient approach to estimating and/or supplementing near-road air pollutant concentrations in urban areas at high temporal resolution.

Genomic surveillance of SARS-CoV-2 evolution by a centralised pipeline and weekly focused sequencing, Austria, January 2021 to March 2023

Background The COVID-19 pandemic was largely driven by genetic mutations of SARS-CoV-2, leading in some instances to enhanced infectiousness of the virus or its capacity to evade the host immune system. To closely monitor SARS-CoV-2 evolution and resulting variants at genomic-level, an innovative pipeline termed SARSeq was developed in Austria. Aim We discuss technical aspects of the SARSeq pipeline, describe its performance and present noteworthy results it enabled during the pandemic in Austria. Methods The SARSeq pipeline was set up as a collaboration between private and public clinical diagnostic laboratories, a public health agency, and an academic institution. Representative SARS-CoV-2 positive specimens from each of the nine Austrian provinces were obtained from SARS-CoV-2 testing laboratories and processed centrally in an academic setting for S-gene sequencing and analysis. Results SARS-CoV-2 sequences from up to 2,880 cases weekly resulted in 222,784 characterised case samples in January 2021–March 2023. Consequently, Austria delivered the fourth densest genomic surveillance worldwide in a very resource-efficient manner. While most SARS-CoV-2 variants during the study showed comparable kinetic behaviour in all of Austria, some, like Beta, had a more focused spread. This highlighted multifaceted aspects of local population-level acquired immunity. The nationwide surveillance system enabled reliable nowcasting. Measured early growth kinetics of variants were predictive of later incidence peaks. Conclusion With low automation, labour, and cost requirements, SARSeq is adaptable to monitor other pathogens and advantageous even for resource-limited countries. This multiplexed genomic surveillance system has potential as a rapid response tool for future emerging threats.

Refugees, Welfare Organizations and Compulsory Labour in Postwar Austria

During the Second World War, Germany employed 13.5 million men, women and children as forced and slave labourers throughout Europe, including on Austrian territory. Compulsory labour in Austria did not end with the Allied occupation. Despite the aspiration to adopt the values of a liberal democracy, the Austrian federal government enacted a statute on compulsory labour (the Arbeitspflichtgesetz) in 1946. This legislation required everyone residing in the country on a permanent or temporary basis to perform mainly manual work. At the time, one in every five individuals in Austria was displaced from her or his country of origin. They included foreigners who refused to be repatriated and over 200,000 Jews who sought to rebuild their lives far from Europe. Strict quotas in the countries where they wished to settle thwarted their plans. Jewish and non-Jewish refugees found themselves trapped in Austria for months and years, subject to the authority of the occupying military forces and UNRRA. To what extent did the compulsory labour requirement apply to them? An examination of this issue provides an insight into the very gradual transition of Austria from authoritarianism to democracy and the shaping of refugee policies by both the Austrian government and relief organizations.

A comparative economics of natural farming and conventional farming of rice cultivation in Arunachal Pradesh

The study was conducted in the state of Arunachal Pradesh, East Siang district was purposively selected due to its reliance on agriculture as the primary livelihood with rice being the main crop. Primary data was collected from 45 respondents each from natural and conventional farming making the sample total 90, for the year 2022-23 of kharif season. The growth rate in area, production and productivity at the world, India, East Siang district as well as state of Arunachal Pradesh were found to be positive. Significance difference was observed on cost of bullock labour, machine labour, seeds and plant protection in natural farming. The total cost of rice cultivation was lower in natural farming by 29.49 per cent. Although the yield and gross returns was lower in natural farming but the net returns was significantly higher than conventional farming by mean difference of ? 6119.78 per hectare. The study also highlights potential gender-based variations in labor requirements, observing natural farming as labour intensive farming. In both natural and conventional farming, the per cent of total female labour utilization was observed to be higher than total male labour.