High Labor Costs Research Articles

Trends in Input-Output Relation and Structural Changes in Input Cost of Major Crops in West Bengal

"The structural change of input cost refers to the change in the intensity of labour and capital (seed, fertilizers, pesticides, machineries etc.) used in agriculture. An attempt has been made to analyse the structural changes of input cost along with the trends in input-output relation of the three major crops (paddy, jute and potato) in West Bengal during 1999-00 to 2019-20 by using data of the Cost of Cultivation of Principal crops in India, Directorate of Economics & Statistics (DES), Ministry of Agriculture and Farmers Welfare, Government of India. It is found that the structural intensity of input cost shifts towards a more capital-intensive direction during 1999-00 to 2009-10, but then the situation is reversed to more labourintensive in terms of input cost during 2009-10 to 2019-20. To investigate the reasons, the study found that the higher labour cost is the main reason for this. Though the physical unit of labour per hectare decreased during the second phase (2009-10 to 2019-20), the high wage rate increased the share of labour cost and the total cost of cultivation. In the trend of the input-output relation of the state’s agricultural production, the study observed a decline in the input-output ratio (profitability from cultivation) for the crop paddy and jute, but it increased for the potato. The higher growth of input cost over output price during reference periods is the main reason for the decline in profitability of the crops in West Bengal."

INVESTMENT ENVIRONMENT OF THE NETHERLANDS

The paper offers a comprehensive review of the investment environment in the Netherlands, a nation noted for its advanced economy, robust international trade links, and a solid legal framework that supports business activity. The Netherlands features a stable political environment, transparent governance, and administrative efficiency, creating a reliable backdrop for investment. This climate is conducive to innovation and attracts foreign direct investment, especially due to an advantageous tax system. The workforce in the Netherlands is highly skilled and multilingual, which is a critical asset, though the high labor costs could be seen as a deterrent. However, these costs are often offset by the high productivity and the business-friendly policies of the government. The Dutch market is well-connected and integral to the European trade landscape, making it a strategic entry point into the European Union. Transport and logistics infrastructure, particularly the Port of Rotterdam—the largest port in Europe—plays a crucial role in facilitating trade not only across Europe but globally. This infrastructure is supported by a top-tier digital network and service sector, with the Netherlands boasting one of the highest broadband penetration rates in Europe. Despite its many advantages, the investment climate in the Netherlands does face challenges such as regulatory complexity and stringent environmental regulations. These challenges are balanced by the country’s commitment to sustainability and the green economy, which opens up new investment opportunities in renewable energy and sustainable technologies. Furthermore, the Netherlands is proactive in fostering a favorable business environment through various government initiatives. These include incentives for research and development, enhancements in sustainable business practices, and subsidies for green energy projects, all aimed at maintaining the country’s competitive edge. The Dutch government’s approach to international business is also characterized by a network of treaties designed to protect foreign investments and prevent double taxation, making the Netherlands an attractive destination for multinational corporations. The presence of a strong legal system ensures that property rights and contracts are well protected, which is a critical consideration for investors. In conclusion, the Netherlands presents a balanced spectrum of opportunities and challenges. While the high cost of operations and regulatory demands pose hurdles, the strategic advantages, robust infrastructure, and government incentives create a favorable environment for significant investment returns. This detailed analysis serves as an essential guide for potential investors, providing insights into both the macroeconomic stability and sector-specific prospects within the Dutch market.

VAEAT: Variational AutoeEncoder with adversarial training for multivariate time series anomaly detection

High labor costs and the requirement for significant domain expertise often result in a lack of anomaly labels in most time series. Consequently, employing unsupervised methods becomes critical for practical industrial applications. However, prevailing reconstruction-based anomaly detection algorithms encounter challenges in capturing intricate underlying correlations and temporal dependencies in time series. This study introduces an unsupervised anomaly detection model called Variational AutoeEncoder with Adversarial Training for Multivariate Time Series Anomaly Detection (VAEAT). Its fundamental concept involves adopting a two-phase training strategy to improve anomaly detection precision through adversarial reconstruction of raw data. In the first phase, the model reconstructs raw data to extract its basic features by training two enhanced variational autoencoders (VAEs) that incorporate both the long short-term memory (LSTM) network and the attention mechanism in their common encoder. In the second phase, the model refines reconstructed data to optimize the reconstruction quality. In this manner, this two-phase VAE model effectively captures intricate underlying correlation and temporal dependencies. A large number of experiments are conducted to evaluate the performance on five publicly available datasets, and experimental results illustrate that VAEAT exhibits robust performance and effective anomaly detection capabilities. The source code of the proposed VAEAT can be available at https://github.com/Du-Team/VAEAT.

Technology and growth in the South African mining industry: An assessment of critical success factors and challenges

The mining industry has yet to fully accept and embrace the strategic role of technology and innovation in successful business planning and execution. In this study, we empirically determine the factors that influence acceptance of technology solutions in the mining industry in South Africa, assess critical success factors influencing adoption of advanced technology in the South African mining industry, and explore challenges faced by the mining industry in South Africa in adopting advanced technology in its operations. Thematic content analysis is employed in the data analysis. Results show that critical success factors to the adoption of mining technology include learning culture, knowledge sharing, high labour costs, and behaviour of other firms in the industry; commonly identified challenges include inadequate engagement with external stakeholders, uncertainties, the cyclical nature of the sector, and high risk related to the adoption of unproven technology and performance systems focused on volumetric production. The study encourages mining companies to implement usage of technology. This can be properly done through regular engagement with the Minerals Council South Africa.

Design of an Arduino-based intelligent logistics cart

With the achievement of the first centenary goal, we will strive for another 15 years on this basis, aiming to achieve socialist modernization by 2035. The concept of intelligent production is receiving increasing attention, with the logistics industry being a representative sector. Currently, traditional logistics operations are unable to meet the high requirements of modern society due to problems such as high labor costs, poor safety, and low efficiency. In order to change the current situation of the traditional logistics industry, more intelligent equipment is needed to replace labor-intensive manual handling work. In this era, an Arduino-controlled intelligent logistics cart emerges as a development trend. This paper designs and develops an Arduino-controlled intelligent logistics cart, taking the Yunnan Province University Student Engineering Training Comprehensive Ability Competition as an application scenario. It achieves functions such as automatic tracking, intelligent recognition of block colors, automatic scanning of QR codes to obtain tasks, and all-round grasping of objects. The design of this intelligent logistics cart involves hardware selection, system design, and structural design.

Economic efficiency of rice production among the small-scale women farmers in the Federal Capital Territory, Nigeria

This study evaluated the economic efficiency of rice production among the small-scale women farmers in the Federal Capital Territory, Nigeria. A multi-stage sampling technique was used to select 100 small-scale women rice farmers. The primary data were collected with the aid of a well-designed and well-structured questionnaire. The data were analyzed using descriptive statistics, farm budgeting technique, financial analysis, the Stochastic production frontier efficiency model, and the Tobit dichotomous regression model. The results show that 88% of women rice farmers were below 50 years old. The mean age was 44 years. Averagely, they were small-scale farmers with 1.31 hectares of farm land. The labor input in man-days constituted the highest percentage of about 50.8% of the total costs of activities involved in rice production. The net farm income and gross margin ratio was estimated at 416,800 Naira and 0.63 respectively. This shows that the rice production by women farmers was profitable and worthwhile. The mean allocative, economic, technical efficiency scores were 50.3%, 50.8%, and 51.20% leaving the efficiency gaps of 49.7%, 49.2%, and 48.8% for improvement respectively. The significant factors influencing the economic efficiency of the rice production among women farmers include the following: – farm size, labor input, household size, seed input, fertilizer input, chemical input, farm experience, and access to credit. The major constraints encountered by women rice farmers include the following: inadequate credit facilities (1st), high cost of labor (2nd), high cost of fertilizers (3rd) and high cost of herbicides (4th). The study recommends that farm inputs such as improved seeds, fertilizer input, chemical input and herbicides should be made available to women farmers at affordable prices. Credit facilities devoid of rigorous administrative procedures at low interest rate should be made available to women farmers Keywords: economic efficiency, rice production, Tobit regression model, small-scale women farmers, Abuja, Nigeria

Impact Evaluation and Adoption of Climate Resilience and Nutrition Orange Fleshed Sweet Potato in Burundi: A Case Study of the Rural Bujumbura Province

The new orange-fleshed sweet potato varieties have been adopted with the aim of increasing production, farmers' welfare, and combating chronic malnutrition. This study analyzed the impact of orange-fleshed sweet potato adoption on production in Gihanga commune using the propensity score matching method. A survey of 385 households was carried out on April 2023, including 161 orange-fleshed sweet potato adopters and 224 non-adopters. The determinants and rate of adoption of orange-fleshed sweet potato were also analyzed using the logit model and the Average Treatment effect on the Treated (ATT) method respectively, while Kendall's Concordance Coefficient was used to determine adoption constraints. Thus, unpredictable weather and climatic conditions, lack of capital, unavailability of PDCO cuttings, insufficient arable land as well as high labor costs are the main constraints to orange-fleshed sweet-potato adoption. The adoption rate for orange-fleshed sweet potatoes is estimated at 42%. Adoption of PDCO was positively and significantly influenced by the household head's level of education, family labor, access to credit, access to extension services and membership of producer organizations, while the household head's age and household size had a negative impact on adoption. The ATT revealed that the adoption of orange-fleshed sweet potatoes increased production per adopter household by an average of 369.88 kg, although the difference was not statistically significant. The government and other players in the agricultural sector should do their part to raise awareness, disseminate, facilitate access and mobilize farmers to adopt sweet potato varieties in order to improve their productivity.

Effect of Crude Oil Spillage and Constraints Associated with Artisanal Fishing in Oil-Spilled Areas of Bayelsa State

This study considered the effect of crude oil spillage and constraints associated with artisanal fishing in oil-spilled areas of Bayelsa State. The study's goals were to find out the respondents' socioeconomic status, determine how the spill of crude oil affected artisanal fishing, and pinpoint the obstacles to artisanal fishing output in particular oil-spilled areas. A simple random selection technique was employed to select 100 respondents, from whom a questionnaire was used to collect data for the study. Descriptive statistics were used to analyze the data that was collected. Men made up the majority of respondents (77%), according to the study's main findings. In the region where crude oil spilled, 55% of the artisanal fishermen were between the ages of 45 and 55. Married respondents made up 84% of the sample, and 60% had finished secondary school. The respondents had a mean of 25.4 years of experience and an average household size of 6. The main obstacles to artisanal fishing in the areas affected by the spill of crude oil were the following: water pollution, rising consumption, lack of government support, issues with marketing, spoiled fish, funding and credit issues, absence of extension services, climate change and flooding, poor processing facilities, high labour costs, health issues, and a scarcity of fishing baits. This study makes several recommendations, including better processing facilities, increased access to bank loans at lower interest rates for farmers, expanded and strengthened extension services, and the establishment of policies by the government, private citizens, and corporate entities that will boost output and improve the profitability of artisanal fishing.

Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists

Multiple Myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of plasma cells within the bone marrow. Diagnosing MM presents considerable challenges, involving the identification of plasma cells in cytology examinations on hematological slides. At present, this is still a time-consuming manual task and has high labor costs. These challenges have adverse implications, which rely heavily on medical professionals’ expertise and experience. To tackle these challenges, we present an investigation using Artificial Intelligence, specifically a Machine Learning analysis of hematological slides with a Deep Neural Network (DNN), to support specialists during the process of diagnosing MM. In this sense, the contribution of this study is twofold: in addition to the trained model to diagnose MM, we also make available to the community a fully-curated hematological slide dataset with thousands of images of plasma cells. Taken together, the setup we established here is a framework that researchers and hospitals with limited resources can promptly use. Our contributions provide practical results that have been directly applied in the public health system in Brazil. Given the open-source nature of the project, we anticipate it will be used and extended to diagnose other malignancies.

Betel Vine Marketing in Tamil Nadu, India: Price Spread Analysis

Tamil Nadu is one the leading betel leaf cultivating states in the country. Besides, it faces significant challenges in producing betel vines due to risk of pests and diseases. These risks have a negative impact on the farm economy. Disease outbreaks can be incredibly concerning and have an impact on a company's profitability. The higher labour costs, pest and disease and natural calamities have made it difficult for farmers to produce their crops. Therefore, it was essential to study the marketing cost, price spread and marketing efficiency of betel vine in Tamil Nadu. The use of a well-structured interview schedule allowed for the collection of information from farmers regarding the cost and return of producing betel vines. From the local marketplaces in Thanjavur District, village traders, commission agents, wholesalers and retailers were chosen. Price spread and marketing efficiency were used to know about the marketing cost and the price received by the farmer. The efficient marketing channel was channel III (Producer → Retailer → Consumer) because it has the lowest price spread of 18.9 per cent which be due to a smaller number of intermediaries. The marketing efficiency in channel III (16.1) was higher than in channel I (10.6) and channel II (6.3) respectively. Thus, marketing of betel leaf in channel III was more efficient for farmers.

Multifidelity operator learning for predicting displacement fields of tunnel linings under external loads

In practical tunnel projects, the deformation of tunnel linings affects the service performance and structural reliability of tunnels. However, currently, several monitoring points must be installed to represent the specific deformation patterns of tunnel linings accurately, incurring high labor and financial costs. In recent years, the rapid development of artificial intelligence has provided potential solutions to this issue. To solve the aforementioned problem, this study proposed a multifidelity DeepONet framework, which comprised two neural networks. The first low-fidelity network was trained with data provided by a macro-level numerical model validated by experimental campaigns to learn the physical deformation patterns of tunnel linings. The second network was trained with limited high-fidelity monitoring data to learn the correlations between observations and numerical models. Even with very limited monitoring data, the proposed framework could still predict the mechanical behavior of tunnel linings under different loading scenarios. In this study, data collected from noncircular tunnel projects were used as case studies. The results demonstrated that the final output conformed to the deformation pattern obtained with the numerical simulations and was consistent with the actual measurements, achieving seamless fusion of the experimental campaigns and numerical models.

Many birds, one stone: Medical image segmentation with multiple partially labeled datasets

Medical image segmentation is fundamental in the field of medical image analysis and has wide clinical applications in disease diagnosis and surgical planning etc. Current prevalent solution is to train a deep network in a fully supervised way with a large-scale fully labeled dataset. However, due to the high labor cost and requirement on medical expertise, such dataset is always absent. Instead, there are multiple partially labeled datasets which are originally established for specific purposes. To make full use of these partially labeled datasets, we propose a novel partially supervised segmentation network, named PSSNet, which consists of a task-specific feature learning network followed by a cross-task attention module (xTA) to exploit task dependencies to enhance task-specific features. To solve the challenges raised by unlabeled classes and domain shift across datasets, we propose an adversarial self-training strategy. We conduct experiments on two medical image segmentation tasks. One is the fine-grained fundus image segmentation aiming to simultaneously segment four-class lesions, OD and OC, and vessels. Validation on seven datasets demonstrates that our PSSNet performs the best among three baselines and three state-of-the-arts. The other is the multiple abdominal organ segmentation in CT images. Our PSSNet is trained on three partially labeled datasets, i.e., LiTS, KiTS and Spleen. Validation on one fully labeled dataset, i.e., BTCV, demonstrates that our PSSNet achieves better performances than four state-of-the-arts. The code is publicly available at https://github.com/CVIU-CSU/PSSNet.

The hidden strength of small business: Social networks and wet market vendors in China

AbstractThroughout contemporary China, consumers have the option of purchasing food products from “traditional” Chinese wet markets or “modern” supermarket chains. Wet market vendors are economically disadvantaged as the operators of small‐scale businesses with limited investment and high labor costs. Nevertheless, wet market vendors continue to thrive in urban China, despite the continued expansion of supermarket chains. This article combines ethnographic data and social network analysis to examine how social networks provide wet market vendors with some important competitive advantages. The article draws on fourteen months of fieldwork in Sanya City, China, and provides an analysis of the composition, structure, and strength of the network ties of 113 wet market vendors. Vendors tend to have strong and dense ties to individuals who help them establish their businesses. Vendors also rely on strong ties with regular suppliers to help them reduce costs and ensure a regular supply of goods. These strong ties are balanced by weaker ties with a diverse range of customers, including small businesses and household consumers. A combination of both strong and weak network ties contributes to the collective resilience of wet market vendors, allowing them to confront crises, reduce risk and uncertainty, and improve performance.

Machine Vision-based Trajectory Tracking of Honey Bee Crawling Motion

With the maturity of machine vision technology in artificial intelligence and the rise of the concept of "smart agriculture", machine vision-based methods are widely interested in studying biological behavior, but the technology for acquiring biological behavior information is still unsound. In this paper, we propose a machine vision-based bee crawling trajectory tracking method, which automatically records the bee crawling trajectory in the video information and obtains the description of bee crawling behavior. The method establishes the description of bee crawling motion through bee target detection, bee crawling motion tracking and bee crawling motion prediction as a method to study bee crawling behavior through artificial intelligence techniques. The method has the advantages of high timeliness and low labor cost, and reduces the problems of traditional research through manual observation records and bioassays methods with high influence of human subjective judgment factors and high application costs.

Does innovation matter for asymmetric cost behaviour? Evidence from forward citations to the patents

AbstractThis study investigates how innovation affects cost stickiness. Utilising data on patents of US firms between 1991 and 2022, we find that the extent of cost stickiness is greater with a greater number of forward citations for patents. We also find that the main relationship is more pronounced in high‐tech industries, high labour cost intensity, and market competition. In addition, we find that firms with a higher number of patents and forward citation experience a subsequent increase in sales. Overall, this makes a valuable addition to the extant literature, proposing that patents serve as an alternative driver of cost stickiness.

IPO suspensions and labor employment decisions: Evidence from China

The Chinese regulator occasionally suspends all initial public offerings (IPOs) to maintain stability in the capital markets and facilitate capital market reform. These suspensions temporarily prevent firms that have obtained IPO approval from going public. We hypothesize that this suspension-induced listing delay influences firms’ labor employment decisions. Consistent with this argument, we find that a suspension-induced listing delay is associated with an increase in labor costs, both in terms of total and average employee payments. This positive relationship is more pronounced for human capital-intensive firms and non-state-owned enterprises. We also find that there is an increase in employment and employee turnover for firms facing suspension-induced listing delays. Moreover, IPO suspensions result in lower labor productivity and firm performance. Overall, our results indicate that government policy uncertainty leads firms to incur high labor costs and reduces capital allocation efficiency.

A novel jujube tree trunk and branch salient object detection method for catch-and-shake robotic visual perception

Visual perception has become a prerequisite for automated jujube harvesting robot operations under complex orchard conditions. Catch-and-Shake harvesting, as the most efficient and common harvesting method, has widely been applied on various manually operated harvesters to complete large-area jujube fruit harvesting. However, the main factors restricting the development of existing harvesters are labor shortage, high labor cost, and low operating efficiency. To address the issues, we designed a catch-and-shake harvesting robot for jujube tree trunks and branches visual perception that can provide a barrier-free catch-and-shake operation area and guide the manipulator to reach the area to complete the harvesting operation. Meanwhile, a visual perception system including tree trunks and branches detection, skeleton extraction, catch-and-shake area confirmation was presented to guide robot intelligent operations. In the visual perception system, a novel salientobjectdetectionmodel called feature intersection and fusion Transformer (FIT-Transformer) network was proposed to split branches and background to provide reference for determining safe catch-and-shake areas. Moreover, we designed a diverse feature aggregation (DFA) and an attention feature fusion module (AFFM) to strengthen feature learning capabilities and obtain robust perception models. Comparative experimental results showed that our proposed FIT-Transformer model outperformed 12 state-of-the-art (SOTA) algorithms including C2FNet, RAS, BASNet, U2Net, SCRNet, PiCANet, EDRNet, EGNet, ICONR, VST, TransSOD and ABiU_Net. Specifically, the segmentation accuracy of jujube tree trunks and branches using our method showed the satisfactory result on five evaluation indexes under natural environment (the EM, SM, WF, FM and MAE reached 0.9713, 0.8991, 0.8854, 0.8905, and 0.0302, respectively). Field experiments also proved that our method could meet the requirements of operational accuracy and real-time operations.

IMPACT OF CASHLESS POLICY ENFORCEMENT AND NAIRA REDESIGN ON FRESH CATFISH MARKETING IN MAKURDI LOCAL GOVERNMENT AREA OF BENUE STATE, NIGERIA

The study examined the impact of Cashless Policy Enforcement and Naira Redesign on Fresh Catfish Production and Marketing in Makurdi Local Government Area of Benue State, Nigeria. Multi-stage sampling procedure was employed to select 100 respondents for the study. Data were collected with the aid of structured questionnaire and analyzed using descriptive statistics and farm budgeting techniques. The result revealed that fresh catfish producers were in their productive age with mean age of 34years. Majority (76%) of the fresh catfish producers were males and 84% married. The average household size and farming experience were 5 persons and 6 years, respectively. The results of the profitability analysis of catfish marketing in the study area before and during the cashless policy enforcement and Naira redesign revealed that before the policy, the catfish producers’ total variable costs and total fixed costs were N1701379 and N665689.22, respectively, making a total cost of N2367068.22. The results further revealed that total revenue of N2659284.66 and a net farm income of N292216.440 were realised. The profitability ratio shows that for every one naira invested, the catfish producers made about 89 kobo. However, the result revealed that, amid the cashless policy enforcement and Naira redesign, the total variable cost and total fixed cost were N827791.66 and N712688.47, respectively, making a total cost of N1540475.13. The results revealed total revenue of N1650800 and a net farm income of N110384.87. The profitability ratio shows that for every one naira invested, the catfish producers made about 92 kobo. The result of the impact of catfish marketing on the income level of the marketers in the study area before and during the cashless policy enforcement and Naira redesign revealed that before the policy, 30%t of the catfish marketers had an income of over N300, 000. However, during the policy, only 26% of the catfish farmer marketers had an income of over N300, 000 and 15% had an income of less than N50, 000. The result of the constraints to catfish marketing before and during the cashless policy enforcement and Naira redesign revealed that before the policy, the most constraints to catfish marketing were high electric charges (12.3%) and lack of credit facilities (9.1%). However, during the policy, the most constraints to catfish marketing were inadequate capital (8.3%), seasonality of commodity (7.8%), high cost of feed (7.4%), high cost of production (7.3%), high cost of labour (7.3%) and low demand of product (7.0%). The study concluded that Catfish production and marketing in the study area was more profitable before the cashless policy enforcement and Naira redesign. The study recommended that provisions for agricultural incentives such as short and long-term loans, with a single digit interest rate for the marketers in order to solve the problem of lack of credit facility. Also, Marketers should form marketing cooperatives to increase their access to credit from financial institutions and hence alleviate the problem of inadequate capital.

RURAL WOMEN FARMERS PARTICIPATION IN AGRICULTURAL COOPERATIVE SOCIETIES IN BAUCHI AND TAFAWA BALEWA LOCAL GOVERNMENT AREAS OF BAUCHI STATE

The Study analyzed rural women farmers’ participation in agricultural cooperative societies in Bauchi and Tafawa Balewa local government areas of Bauchi State. A Multi-Stage Sampling Technique was used to select 195 women farmers. Data were collected using structured questionnaire and analyzed using both descriptive and inferential statistics. The study revealed that the average age of women farmers was 36.5 years and majority (55.4%) were married with an average household size of 13 persons. While most (81.0%) had crop farming as primary occupation and (29.1%) of the farmers had attained secondary education. The results showed that women farmers had average farming experience of 13 years with an average farm size of 6.6 hectares. The coefficient of determination (R2) was 0.723 implying that 72.3% of the variation participation in cooperative societies in the study accounted by the factors included in the model. Education, household size, membership duration, extension visit and access to credit were significant at p<0.001 in the level of participation while, primary occupation, amount of loan obtained and adult education were significant at p<0.05. Constraints affecting agricultural activities were; high cost of inputs (66.4%), inadequate capital (82.8%), and high cost of labour (91.1%). The study concluded that, It was discovered from the study that the majority of the women farmers were not too aged, meaning they are still active and productive, they were mainly married and were literate at least up to primary education level. However, their participation is limited by pressure from household head in the study. The study recommended that, cooperative principles like continuous education and training should be arranged in order to improve women’s participation in cooperative activities. More female extension workers should be trained to help female farmers.

Constraints of Sugarcane Farmers During Cultivation and Marketing in Kerala, India

The study analysed the constraints faced by farmers during cultivation and marketing of sugarcane in Kerala. A multi-stage random sampling technique was adopted for the data collection and information was obtained from 120 sugarcane farmers and 10 marketing intermediaries. The farmers ranked and used Garrett's ranking technique to analyze the main production and marketing constraints. The major production constraints identified were wild animal and rodent attack, followed by shortage of labour and high labour cost, high cost of inputs and delay in harvesting. Price fluctuations of sugarcane and absence of regulated markets were major marketing constraints. Valuable suggestions were implementing effective measures to mitigate wildlife damage, government should purchase farmers products at reasonable price.