Labor Costs Research Articles

SCENARIO ANALYSIS OF WHOLESALE AND RETAIL TRADE IN BANGLADESH: AN EMPIRICAL EVIDENCE

Wholesale and retail trade is considered as one of the essential activities contributing to the domestic product, labour force and linkage between producers and consumers. The main objective of the study is to assess the status of input cost, gross output, gross value added of wholesale and retail trade in Bangladesh during the period of COVID-19 pandemic and also compare the volatility between wholesale and retail trade. The secondary data of wholesale and retail trade survey-2021 with 15,258 establishment have been collected from Bangladesh Bureau of Statistics (BBS). Both the quantitative and qualitative methods were applied in this study. Scenario analysis was employed to find the current status of input cost, gross output, gross value added of wholesale and retail trade in Bangladesh. The study reveals that industrial cost was increased due to several constraints, including market issues, high labour costs, transportation issues, and logistical shortcomings. The findings of the volatility between wholesale and retail trade represent that retail businesses have more flexibility in expenses, while wholesale enterprises often have more output and value added. Finally, it has been found that gross output significantly depends on gross value addition, whereas COVID-19 affects all scenarios. The findings of this study would be helpful for the planners and policy makers of the trade sectors to take appropriate policies regarding wholesale and retail trade in Bangladesh.

Analysis of Determining the Cost of Goods Production Using the Full Costing Method at UD. Aqqib Jaya Collection

This research aims to find out how to calculate production costs and determine the selling price of bags at UD. Aqqib Jaya Collection. The method used is a qualitative approach by conducting interviews, documentation and observation to collect information. The business owner is the main informant in the interview. The research results show that MSMEs calculate production costs manually without taking depreciation costs into account. Production costs are calculated by adding up raw material costs, direct labor costs, and other costs related to bag production. In the process of calculating production costs and determining the selling price of bags, business actors still experience obstacles related to understanding the field of accounting, especially in including depreciation costs.

Improvement of the UCP method for estimating labor costs in the implementation of IT-projects

An important stage of planning in the implementation of IT-projects is the assessment of the complexity of software development. An accurate estimate of labor costs at the early stage of the project life cycle significantly affects the allocation of resources, time planning and budget costs. Therefore, regardless of the large number of methods used to estimate labor costs during the implementation of an IT-project, the task of increasing the accuracy of this predictive estimate is relevant. The paper examines in detail the possibilities of using the well-known method for estimating labor costs Use Case Points (UCP), which is based on the use of use cases. It belongs to algorithmic methods, which makes it quite reliable and predictable in use. It also allows you to take into account the functional requirements for the system, which is especially useful in the early stages of project implementation. In addition, UCP is not tied to specific technologies or programming languages, which indicates its universality and flexibility. The purpose of the study is to improve the UCP method of estimating labor costs necessary for the successful implementation of an IT-project, which will allow taking into account interval estimates of technical complexity factors and external factors that affect the estimation of the amount of software and, accordingly, the estimation of the project's labor intensity. To experimentally verify the operability and efficiency of the proposed modified UCP method, a dataset containing information on 71 real projects was used. The results of computer modeling and comparative analysis of the accuracy of estimates of labor costs for the implementation of the project obtained using the proposed and original UCP methods are given. The results of the experiments showed that the proposed modification of the UCP method allows obtaining more accurate predictive estimates of labor costs in the implementation of IT-projects, which can significantly affect the efficiency of planning and project management processes.

Role of Artificial Intelligence in Vegetable Production: A Review

Role of Artificial Intelligence (AI) in vegetable production, emphasizing its potential to address critical challenges such as climate change, population growth, and resource scarcity. AI technologies, including machine learning, computer vision, and robotics, are revolutionizing agricultural practices. AI-driven innovations in crop management, pest control, and soil analysis enhance productivity, reduce labour costs, and ensure sustainable farming practices. Notable advancements include precision spraying by Blue River Technology, significantly reducing herbicide use, and deploying autonomous tractors and drones for efficient farm management. AI applications, such as PEAT's Plantix and Trace Genomics, provide accurate diagnostics for soil health and pest management. Satellite-based solutions like Farm Shots and aWhere offer real-time crop monitoring and weather prediction, optimizing resource use and mitigating risks. The review highlights the importance of making AI technologies more affordable and accessible to farmers, particularly in developing regions. Collaboration between researchers, industry stakeholders, and policymakers is crucial to harness AI's full potential in agriculture. As AI continues to evolve, its integration into vegetable production promises a more efficient, resilient, and sustainable agricultural sector, contributing to global food security and environmental preservation. The aim of the study is to evaluate the impact and effectiveness of Artificial Intelligence (AI) in vegetable production, focusing on how AI technologies enhance productivity, efficiency, and sustainability. The objectives are to assess current AI applications, analyze their benefits and challenges, and provide recommendations for future improvements and wider adoption in the agricultural sector. The research methodology for the study on the role of Artificial Intelligence in vegetable production involves a comprehensive literature review of existing AI technologies and their applications in agriculture, coupled with the analysis of case studies to evaluate real-world implementations. Additionally, expert interviews and surveys with farmers and industry professionals will be conducted to gather insights on the benefits, challenges, and future potential of AI in this sector. The theoretical implications of the study on the role of Artificial Intelligence in vegetable production include advancing the understanding of AI's capabilities in agricultural optimization and contributing to the academic discourse on sustainable farming practices. Practically, the study provides actionable insights for farmers and agribusinesses on implementing AI technologies to enhance crop yields, reduce resource wastage, and improve overall farm management efficiency.

The U.S. Low-Wage Structure: A McWage Comparison

Abstract Thanks to standardized work protocol and technology of McDonald's restaurants, the hourly wage of McDonald's Basic Crew enables wage comparisons under near-identical skill inputs and hedonic job conditions. McWages capture labor costs in entry-level jobs, while the Big Macs (earned) Per Hour (BMPH) index measures corresponding purchasing power of wages. We document large and growing geographical wage differences in standardized jobs using data covering most U.S. counties during 2016-2023. Before the Covid-19 pandemic, there was no BMPH growth where minimum wages stayed constant, but the pandemic wage increase, which diminished the importance of minimum wages, was stronger in these areas.

Impact of productivity drivers on the performance of manufacturing firms

A considerable number of research indicates that a nation's industrial sector plays a crucial role in its economic growth. Therefore, it is crucial to research the productivity drivers among businesses engaged in the manufacturing sector to create a sound, inclusive, long-lasting, and innovative industry. Thus, as a micro-perspective of industrialization, this study looks into the main productivity drivers of manufacturing firms in Nigeria. The study analyses an imbalanced panel firm-level dataset from 2007 to 2019 using the simultaneous approach of the stochastic frontier to achieve its goal. Our research shows that Nigerian manufacturing firms deal with growing returns to scale and a high labour intensity. For policy actions, it was recommended that a realistic industrialization strategy has to be micro-founded. This strategy should prioritise large-scale industrial companies, promote exports, and provide a steady supply of electricity. The findings also show that small and micro firms in Nigeria are more productive than large manufacturing firms, even though the frequency of power outages and average labour costs impede the productivity of these firms.

Analisis dan Perancangan Usaha Pembukaan Cabang Baru pada Konveksi XYZ di Kota Bandung

Konveksi specific industry or agency. The problem with convection piling up and not making convection operational activities efficient. The aim of this study is to consider market aspects, technical aspects and financial aspects. By carrying out this feasibility analysis, convection can find out what needs to be prepared and know what risks will occur in order to avoid or minimize existing risks in the longer term. This financial aspect approach uses calculations of the Internal Rate of Return (IRR), Net Present Value (NPV), Payback Period (PBP), and Profitability Index (PI). The benefit of the proposed design results is that it can estimate the size of market demand for the opening of a new branch of XYZ Convection in Bandung City, namely 8,513 pcs in 2025, it can design the technical and operational aspects of opening a new branch of XYZ Convection in Bandung City by determining an investment of IDR 153,738,772, it can measure the financial feasibility of opening a new branch of Konveksi sensitivity of four variables, including sensitivity to decreasing demand levels, decreasing product selling prices, decreasing, increasing raw material costs, and increasing direct labor and indirect labor costs. Can identify the risks of opening a new branch of Konveksi XYZ in Bandung City with a percentage of 4.9%, and for entrepreneurs, it can be used as a consideration in making decisions in the business of opening a new branch of Konveksi. Based on the results of a feasibility study carried out taking into account market aspects, technical aspects and financial aspects, opening a new branch at XYZ Convection in Bandung City is feasible.

A Digital Project Management Framework for Transnational Prefabricated Housing Projects

Compared with an ordinary prefabricated housing project (PHP), a transnational PHP tends to involve more uncertainties, with major stakeholders residing in different countries. This study proposes a novel digital project management framework that integrates building information modeling to enhance information utilization. This framework also incorporates innovative design concepts of modulor, modulus, module, model, durability, and recyclability for enhanced user comfort, housing industrialization, and extended lifespan. It was demonstrated how planning, design, manufacture, and transportation processes can be streamlined in transnational PHP delivery. A case study was performed in a typical transnational PHP between the Kingdom of Saudi Arabia and China for validation. By applying the framework, this PHP could install a single house within 24 h, improve precast level by about 20%, and reduce project cost per square meter by 5.2%, because of integrated design concept, reduced labor cost, effective material cost control, and enhanced information management.

PSXI-30 Economic impact of pre-training on dairy cattle automatic milking system acclimation

Abstract Pre-training in automatic milking systems (AMS) has the potential to increase milk production and milk quality. In a recent study, cows that experienced pre-training (TRT; i.e. intentional training access with an AMS prior to first AMS milking) had greater milk yield and fat percentages compared with no pre-training (38.4 vs. 37.6 ± 0.2 kg/d; 4.75 vs. 4.38 ± 0.06%; P < 0.01, and < 0.01 respectively). Similarly, cows without pre-training had greater somatic cell count (SCC; 298,860 vs. 188,220 ± 10,700 cells/mL; P < 0.001). The time required for pre-training could be a limiting factor (40 ± 10 h/wk), and the economic implications of labor versus long-term benefits must be examined. To that end, a stochastic simulation tool was developed to consider the increased costs of labor and the potential benefits of improved milk yield, milk fat percentage, and decreased somatic cell count. The GRKS distribution with minimum, mean, and maximum values was used to model increases in milk yield (0, 1.4, 1.8 kg), fat percentage (0, 3.5%, 4.5%), and labor cost ($0.0011, $0.0016, $0.0020 per h) and decreases in SCC (0, 120, 130 x 103 cells/mL) per hundredweight of milk produced. Baseline values for milk production ranged from 14.7 to 59.2 kg/cow/d in 4.4 kg increments with fat percentage of 3.5% and SCC of 312,000 cells/mL. The model assumed a minimum increase across milk yield, fat, and SCC of 0 (no change). The Simetar add-on in Microsoft Excel simulated 500 iterations across three herd size scenarios (60, 120, and 480 cows) and calculated the marginal return on investment across a 90-d period for all milk production ranges. Regardless of starting milk production level, pre-training had a positive economic impact across all levels (30 to 50 h) and herd sizes (Table 1). Financial impact per cow per d with input of 30, 40, and 50 h ranged from $0.58 to $0.73, $0.56 to $0.71, and $0.54 to $0.70. The majority of simulations returned a positive marginal impact for pre-training. If pre-training resulted in a loss, it ranged from $242 to $1,937, $323 to $2,585, $405 to $3,246, across 30, 40, and 50 hours, respectively. Based on these results, the investment in pre-training time was consistently beneficial for simulated herds.

63 Impacts of protein supplementation frequency on animal performance, rumen fermentation and methane emission from growing beef cattle

Abstract Nutrient supplementation is one of largest expenditure in forage-based beef production systems, especially when forage quality is poor or limited. Decreasing supplementation frequency may have a role in improving sustainability by increasing animal performance and minimizing cost of labor and fuel. This study evaluated the impacts of protein supplementation frequency (canola meal pellet; CMP) on animal performance, rumen fermentation, and enteric methane production from beef cattle fed forage-based diet. A total of 60 crossbred backgrounding steers [body weight (BW) = 278 ± 10 kg) was randomly assigned to one of the three treatments (n = 20/treatment): i) control (no CMP supplementation), ii) control + everyday CMP supplementation (0.83 kg/d per steer), and iii) control + every other day supplementation of CMP. The study was comprised of 21 d of adaptation followed by three 21-d periods, with enteric methane measured for 7 d per period. Animals were fed grass hay-based diets once a day at 0800 h and remained on their respective diets throughout the 63-d study. Diets were formulated considering animal BW, feed nutrient density and environmental condition in accordance with the National Academies of Sciences, Engineering and Medicine. Each treatment group was assigned to a pen, with the cattle and diets rotated among pens weekly to allow the animals to access the GreenFeed system stationed in one of the pens for enteric methane measurement. For measurement of rumen fermentation products, three ruminally canulated steers (BW = 350 ± 20 kg) were allocated to the three treatments using a 3 x 3 Latin Square Design with 21-d periods so that one animal received all the treatments over the three experimental periods. Animals were adapted to the treatments for 17 d and rumen samples were collected between d 19 and 21 at 0700 h, 1000h, 1300 h, 1600 h and 1900 h. Data were analyzed using the Mixed procedure of SAS. Relative to the control treatment (9.9 kg/d), DM intake was 4 to 5% greater (P < 0.001) following CMP supplementation regardless of supplementation frequency. Similarly, average daily gain was greater (7 to 11%, P = 0.01) for CPM supplemented treatments as compared with control (1.04 kg/d) irrespective of supplantation frequency. Rumen fermentation was affected by treatments. Total VFA concentration was greater (P = 0.01) for the every other day supplementation of CMP as compared with control and daily supplementation treatments. Conversely, rumen ammonia concentration was not affected by treatments. The control group had greater acetate and reduced propionate molar proportions that resulted in a 5 to 6% greater acetate:propionate ratio (P < 0.01) as compared with the CMP supplemented groups. Enteric methane production (g/d) was greater for the daily CMP supplementation (P = 0.02) while yield (g/kg daily weight gain) was not affected by treatment. Our results indicated that reduction of protein supplementation frequency improved animal performance without negatively impacting rumen fermentation.

Labour costs and the decision to hire the first employee

Firms without paid employees account for up to 80 % of all firms, but only a small minority ever hires. This paper investigates the relationship between labour costs and the decision to hire a first employee and become an employer. Leveraging a unique policy in Belgium that permanently reduced the labour cost of the first employee by 13 %, we find that the number of new, first-time employers jumped by 31 % immediately following the reform. The elasticity of the probability to hire the first employee with respect to the labour cost is −2.39 [95 % CI: −3.45, −1.25].

Cold sintering of α- and γ-modifications of aluminum oxohydroxides: A low-temperature route to porous corundum ceramics

Objectives. To obtain porous corundum ceramics using an innovative cold sintering process starting from different phase modifications of aluminum oxohydroxide — boehmite γ-AlOOH and diaspore α-AlOOH; to study the phase and structural properties of the resulting materials; and to assess their permeability to water.Results. Cold sintering enables the formation of single-phase corundum ceramics with an open porosity of 47.9% directly from the initial boehmite powder with the addition of 5 wt % corundum in the presence of 20 wt % water at a temperature of 450°C, mechanical pressure of 220 MPa, and isothermal exposure for 30 min. Under the same conditions of cold sintering, a mixture of diaspore and boehmite was transformed into α-AlOOH ceramics. This then turned into corundum with an open porosity of 39% when calcined in air at 600°C for 1 h. The resulting materials had permeability for pure water above 5000 L/(m2∙h∙bar).Conclusions. Cold sintering is a promising approach to producing porous corundum ceramics which can be used in filtration systems. Compared to traditional ceramic technology, the new approach reduces energy, time, and labor costs in the material manufacturing. It also eliminates the need to use auxiliary substances (binders, pore-forming agents, etc.).

Sustainable practices for cotton production in Bangladesh: economic and environmental perspectives

Cotton is a major cash crop in the agriculture of Bangladesh, primarily fueling its substantial textile industry. Although local production is limited, cotton has the potential to bolster the country’s economic growth by supporting local economies and improving agricultural sustainability. An in-depth sustainability assessment of this integral crop is essential to ensure its long-term economic and environmental viability. This study aims to evaluate the sustainability of cotton production in Bangladesh and suggest strategic enhancements and policy measures to improve its environmental, economic, and social aspects. Data were collected from 60 farmers in two villages of Jashore district using a simple random sampling technique. A combination of descriptive, mathematical and statistical techniques was used to analyze the data. The findings highlighted a profitable benefit–cost ratio of 1.88 for cotton production. Positive impacts on cotton profitability were notably linked to the costs of labor, land preparation, and insecticides and pesticides. Despite this, 60% of farmers faced substantial challenges, including financial barriers, limited access to modern agricultural technologies, and a lack of training in sustainable practices. Economic constraints were a major barrier, although the environmental benefits of sustainable practices were widely acknowledged. To address these issues, the study recommends optimizing inputs like labor, land preparation, and pest management, and advocates for policy actions such as subsidies, tax incentives, and enhanced training programs to support the widespread adoption of sustainable practices across Bangladesh.

A DUF21 domain-containing protein regulates plant dwarfing in watermelon.

Dwarf or semi-dwarf plant structures are well-suited for intensive farming, maximizing yield, and minimizing labor costs. Watermelon (Citrullus lanatus) is classified as an annual vine plant with elongated internodes, yet the mechanism governing watermelon dwarfing remains unclear. In this study, a compact watermelon mutant dwarf, induced by the insertion of T-DNA, was discovered. Through re-sequencing, a gene named domain of unknown function 21 (ClDUF21), located downstream of the T-DNA insertion site, was identified as the candidate gene for the dwarf mutant, and its functionality was subsequently confirmed. Watermelon mutants generated through CRISPR/Cas9-mediated knockout of ClDUF21 revealed that homozygous mutants displayed a pronounced dwarfing phenotype, and protein-protein interaction analysis confirmed the direct interaction between ClDUF21 and ClDWF1. Subsequently, we employed CRISPR/Cas9 technology to precisely modify the homologous gene CsDUF21 in cucumber (Cucumis sativus) and performed protein interaction validation between CsDUF21 and CsDWF1, thereby demonstrating that the CsDUF21 gene also exhibits analogous functionality in plant dwarfing. These findings demonstrate that ClDUF21 governs plant dwarfism by modulating the brassinosteroid synthesis pathway via ClDWF1.

Monocular Visual Pig Weight Estimation Method Based on the EfficientVit-C Model

The meat industry is closely related to people’s daily lives and health, and with the growing global population and increasing demand for meat, the development of efficient pig farming technology is particularly important. However, China’s pig industry still faces multiple challenges, such as high labor costs, high biosecurity risks, and low production efficiency. Therefore, there is an urgent need to develop a fast, accurate, and non-invasive method to estimate pig body data to increase production efficiency, enhance biosecurity measures, and improve pig health. This study proposes EfficientVit-C model for image segmentation and cascade several models to estimate the weight of pigs. The EfficientVit-C network uses a cascading group attention module and improves computational efficiency through parameter redistribution and structured pruning. This method uses only one camera for weight estimation, reducing equipment costs and maintenance expenses. The results show that the improved EfficientVit-C model can segment pigs accurately and efficiently the mAP50 curve convergence is 98.2%, the recall is 92.6%, and the precision is 96.5%. The accuracy of pig weight estimation is 100 kg +/− 3.11 kg. On the Jetson Orin NX platform, the average time to complete image segmentation for each 640*480 resolution image was 4.1 ms, and the average time required to complete pig weight estimation was 31 ms. The results show that this method can quickly and accurately estimate the weight of pigs and provide guidance for the subsequent weight evaluation procedures of pigs.

Greenflation, a myth or fact? Empirical evidence from 26 OECD countries

Among the various effects of the green transition on the macroeconomy, we examined the unintended effects of the green transition on inflation, the so-called “greenflation”. We analyzed panel data from 26 OECD countries covering the period from 2005 to 2019 using four models based on the New Keynesian Phillips curve model. To date, only a few studies have examined greenflation, particularly in terms of empirical investigation. The main contribution of this study to the literature is to provide empirical evidence of greenflation. We also provide empirical evidence on the issues in the NKPC literature that the output gap better explains inflation dynamics than unit labor costs and confirm the demise of the Phillips curve. Estimates from the Common Correlated Effect Mean Group (CCEMG), Augmented Mean Group (AMG), and Panel Corrected Standard Error (PCSE) models indicate that the green transition has an upward inflationary impact on headline inflation, but no significant impact on core inflation. In addition, we have examined four circumstances under which the green transition may affect inflation that have not been sufficiently discussed in the literature: increased green transition spending, carbon pricing, increased demand for critical minerals, and the low energy density of renewable energy. Each of these circumstances has the potential to exert both upward and downward pressures simultaneously. Our suggestion to policymakers is that they do not yet need to change their current view of monetary policy with respect to greenflation, but they should carefully consider policy designs that take into account the unintended effects of the energy transition.

Enhancing Disease Detection in Electronic Medical Records: Integrating Human Expertise and Large Language Models with Application to Diabetes, Hypertension, and Acute Myocardial Infarction

ObjectiveElectronic medical records (EMR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EMR is labour-intensive, requiring advanced medical informatics knowledge, and is challenging without effective data extraction tools. This study developed a high-throughput pipeline to detect diseases in EMRs. MethodsWe developed a pipeline that leverages a generative large language model (LLM) to analyze, understand, and interpret EMR notes by following clinical experts’ designed prompts. The pipeline was applied to detect diabetes, hypertension, and acute myocardial infarction (AMI) from the EMRs for a cardiac patient cohort in Calgary, Canada. The performance was compared against clinician-validated diagnoses as the reference standard. ResultsThe cohort consisted of 3,413 patients with 551,095 clinical notes. The prevalence was 27.8%, 66.3%, and 54.3% for diabetes, hypertension, and AMI, respectively. The performance for detecting conditions varied: diabetes had 90.5% sensitivity, 83% specificity, and 67% positive predictive value (PPV); hypertension had 94.2% sensitivity, 30.2% specificity, and 73.8% PPV; and AMI had 86.4% sensitivity, 61% specificity, and 75.3% PPV. The monthly prevalence trends between the detected cases and reference standard showed similar patterns. ConclusionThe proposed pipeline demonstrated reasonable accuracy and high efficiency in disease detection without manually curated labels, indicating the potential for automated real-time disease surveillance using EMRs. ImplicationVariations of documentation practices in clinical note can impact the detection performance of different diseases. Hence, an automated pipeline integrating LLMs with expert knowledge may improve detection accuracy with reduced labour costs while indicating documentation quality.

Avenues To Reduce Carbon Emission In Cement Fabrication To Acquire Carbon Credit

The cement industry accounts for roughly 8% of global carbon dioxide emission, and for every tonne of cement procured, a ton of carbon dioxide is released into the air. Cement industry is the backbone of global infrastructure development with no sign of slowing down, as such, it provides the most promising testbed for a carbon credit market to start up. Carbon credit functions as a conversion of money as payment to offset carbon dioxide emission, and can be acquired by reducing carbon emission or its greenhouse equivalents. This carbon credit can then be sold on the carbon market which provides financial incentive for companies to reduce their carbon emission. Acquiring carbon credit can be a viable capital gain strategy in developing nations due to their relatively low labor and overhead costs. This study aims to provide avenues to reduce carbon dioxide emission in cement fabrication as a way to acquire carbon credit for capital gain purposes to developing nations worldwide. This study uses literature review with a descriptive qualitative methodology, with data from relevant books and journals of renowned and reputable publishers online as validity. This study identified three promising avenues to reduce carbon dioxide emission in cement fabrication: alternative cement aggregate material fly ash and bottom ash, carbon capture and storage technology, and alternative fuel in cement fabrication. Three avenues provide pathways for players in the cement industry to adopt according to their capabilities in order to effectively reduce their carbon dioxide emission for acquiring carbon credit.

A SWOT Analysis of Organizations in the Agri-Food Chain Sector from the Northern Region of Portugal Using the PESTEL and MEETHS Frameworks

Research on modern agri-food chains aims to enhance flexibility by analyzing supply chain aspects to identify improvement opportunities. A SWOT analysis of 39 agri-food sector organizations using a SWOT analysis organized using the PESTEL and MEETHS categories was conducted to evaluate the stakeholders’ needs in this sector of activity in Northern Portugal. Logistic regressions were used to compute inferential statistics, which were complemented with a qualitative analysis. Cooperatives and primary sector companies often claim superior product quality without clear evidence, while corporations integrated into competitive national markets, like those with smoked products, adapt better to dietary trends. Small- and medium-sized enterprises struggle with competitive wages, leading to high turnover and difficulty retaining skilled workers. High costs hinder many organizations, particularly cooperatives, from adopting modern communication technologies affecting competitiveness. Challenges include identifying market opportunities and managing global competition for raw materials, like wild fish. Fishing and meat sectors depend heavily on modern distribution and are dominated by large retailers. Low labor costs boost competitiveness but reflect the struggle to add value. Larger organizations are more optimistic though many face challenges with the cost and volatility of key products, like pork and milk. This study offers the following key recommendations: invest in technology and innovation while balancing short-term gains with long-term sustainability; strengthen strategic planning and collaboration among corporations, cooperatives, associations, and academic institutions; and adapt to regulatory changes, invest in market and technological capabilities, and address resource limitations. Research and collaboration with policymakers and academic institutions will support tailored solutions, enabling the sector to anticipate challenges and capitalize on opportunities.

Evaluation of Tractor Mounted Axial Flow Combine Harvester for Mung Bean (Vigna radiata L.)

A cost-effective, tractor mounted axial flow combine harvester designed to minimize harvesting losses was evaluated. Performance evaluation of the machine was done on summer mung bean (Vigna radiata L. Wilczek) under various operational conditions: three forward speeds (FS): 2.1, 3.0, and 4.8 km h-1, three-cylinder peripheral speeds (PS): 11.08, 13.16, and 14.79 m s-1, and days after irrigation (DAI): 2, 4, and 6 days after last irrigation. Threshing efficiency, cleaning efficiency, broken percentage, shattering losses, and non-collectable losses were estimated. Cost of operation of the combine harvester was compared with manual harvesting and subsequent threshing using a spike tooth type thresher. The results show that the threshing efficiency and grain breakage increased by 1.1% and 1.01%, respectively, with an increase in DAI and PS, however, both these parameters decreased with an increase in FS. The non-collectable loss increased by 0.9% with an increase in FS and DAI; however, PS did not affect the non-collectable losses. The cleaning efficiency increased with an increase in FS, but it decreased with an increase in PS and DAI. The shattering loss increased with an increase in FS and DAI, although PS had no impact. The best combination of operational variables for threshing operation was found for treatment combination of 6 days after last irrigation, with a cylinder peripheral speed of 14.79 m s-1 and a forward speed of 3.0 km h-1. At this combination, threshing efficiency, non-collectable losses, grain breakage, and cleaning efficiency were 99.28%, 4.53%, 2.96%, and 95.89%, respectively. This combine-harvester saved 91.75% in labor costs and 53.71% in harvesting costs compared to the combination of manual harvesting and subsequent threshing using a spike tooth type thresher.