• Agribusiness and Information Management
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• v.8 no.1
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• pp.17-26
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• 2016

India is largest producer of banana in the world producing 29.72 million tonnes from an area of 0.803 million ha with a productivity of 35.7 MT ha-1 and accounted for 15.48 and 27.01 per cent of the world's area and production respectively (www.nhb.gov.in). In India, Tamil Nadu leads other states both in terms of area and production followed by Maharashtra, Gujarat and Andhra Pradesh. In Rayalaseema region of Andhra Pradesh, Kurnool district had special reputation in the cultivation of banana in an area of 5765 hectares with an annual production of 2.01 lakh tonnes in the year 2012-13 and hence, it was purposively chosen for the study. On $23^{rd}$ November 2003, the Government of Andhra Pradesh has commenced a comprehensive project called 'Andhra Pradesh Micro Irrigation Project (APMIP)', first of its kind in the world so as to promote water use efficiency. APMIP is offering 100 per cent of subsidy in case of SC, ST and 90 per cent in case of other categories of farmers up to 5.0 acres of land. In case of acreage between 5-10 acres, 70 per cent subsidy and acreage above 10, 50 per cent of subsidy is given to the farmer beneficiaries. The sampling frame consists of Kurnool district, two mandals, four villages and 180 sample farmers comprising of 60 farmers each from Marginal (<1ha), Small (1-2ha) and Other (>2ha) categories. A well structured pre-tested schedule was employed to collect the requisite information pertaining to the performance of drip irrigation among the sample farmers and Data Envelopment Analysis (DEA) model was employed to analyze the performance of drip irrigation in banana farms. The performance of drip irrigation was assessed based on the parameters like: Land Development Works (LDW), Fertigation costs (FC), Volume of water supplied (VWS), Annual maintenance costs of drip irrigation (AMC), Economic Status of the farmer (ES), Crop Productivity (CP) etc. The first four parameters are considered as inputs and last two as outputs for DEA modelling purposes. The findings revealed that, the number of farms operating at CRS are more in number in other farms (46.66%) followed by marginal (45%) and small farms (28.33%). Similarly, regarding the number of farmers operating at VRS, the other farms are again more in number with 61.66 per cent followed by marginal (53.33%) and small farms (35%). With reference to scale efficiency, marginal farms dominate the scenario with 57 per cent followed by others (55%) and small farms (50%). At pooled level, 26.11 per cent of the farms are being operated at CRS with an average technical efficiency score of 0.6138 i.e., 47 out of 180 farms. Nearly 40 per cent of the farmers at pooled level are being operated at VRS with an average technical efficiency score of 0.7241. As regards to scale efficiency, nearly 52 per cent of the farmers (94 out of 180 farmers) at pooled level, either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Majority of the farms (39.44%) are operating at IRS and only 29 per cent of the farmers are operating at DRS. This signifies that, more resources should be provided to these farms operating at IRS and the same should be decreased towards the farms operating at DRS. Nearly 32 per cent of the farms are operating at CRS indicating efficient utilization of resources. Log linear regression model was used to analyze the major determinants of input use efficiency in banana farms. The input variables considered under DEA model were again considered as influential factors for the CRS obtained for the three categories of farmers. Volume of water supplied ($X_1$) and fertigation cost ($X_2$) are the major determinants of banana farms across all the farmer categories and even at pooled level. In view of their positive influence on the CRS, it is essential to strengthen modern irrigation infrastructure like drip irrigation and offer more fertilizer subsidies to the farmer to enhance the crop production on cost-effective basis in Kurnool district of Andhra Pradesh, India. This study further suggests that, the present era of Information Technology will help the irrigation management in the context of generating new techniques, extension, adoption and information. It will also guide the farmers in irrigation scheduling and quantifying the irrigation water requirements in accordance with the water availability in a particular season. So, it is high time for the Government of India to pay adequate attention towards the applications of 'Information and Communication Technology (ICT) and its applications in irrigation water management' for facilitating the deployment of Decision Supports Systems (DSSs) at various levels of planning and management of water resources in the country.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2000.10a
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• pp.543-549
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• 2000

Water quality was monitored from a watershed with small-scale livestock production farms. To evaluate pollution potential, land use, population, the size of livestock production of each farm, and livestock management were surveyed. Climate and stream flow data were gathered. Water samples were taken periodically for base conditions and some storm events. Pollutant loading was estimated by flow volume and concentrations of constituents.

• Journal of Agricultural Extension & Community Development
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• v.22 no.4
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• pp.455-464
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• 2015

The purposes of this study are to explain and identify the frame of structural relations of learning orientation, self-efficacy, learning transfer and job performance of farmers who participated in the strong and small farms education. This is an experimental research with the data collected from 495 farmers who have taken the farm education. Based on the collected data, the study conducted a structural equation modeling(SEM) to confirm the validity and analyze the structural relations of the suggested model. Using measured and latent variables drew from the analyses, the study set a structural equation model and tested the model by analysis of the structural equation modeling with AMOS 18.0. The results found from the empirical analysis can be summarized as follows. 1) Learning orientation and self-efficacy positively influenced job performance through learning transfer. 2) The hypothesis that learning orientation would have direct impact on job performance was not supported. 3) The strong and small farms education is useful to expand learning transfer and to enhance job performance. So, government policy support has to reinforce learning support on farmers in order to achieve high performance of learning and job management through farm educations.

• Korean Journal of Environmental Biology
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• v.40 no.4
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• pp.374-386
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• 2022

Phytoplankton communities, with emphasis on picoplankton and nanoplankton, were investigated in Gamak Bay, South Korea, where freshwater input and coastal water intrusion shape ecosystem functions. Shellfish farms and fish farms are located in the inner bay and outer bay, respectively, and tides translocate uneaten food and urine production from aquaculture farms toward the inner bay. Water masses were distinctly different based on a significantly different density between the surface and bottom layer and among three water masses, including the inner bay, outer bay, and Yeosu Harbor. Phytoplankton communities were quantified using flow cytometry and size-fractionated chlorophyll-a (chl-a) was measured. Salinity was a principal variable separating phytoplankton communities between the surface and bottom layer, whereas Si(OH)₄ controlled the communities in the inner bay, and NH₄⁺ and PO₄^3- governed the outer bay communities. While phycocyanin-containing (PC) cyanobacteria dominated in the outer bay, phycoerythrin-containing (PE) cyanobacteria dominance occurred with cryptophyte dominance, indicating that nutrients affected the distribution of pico- and nanoplankton and that cryptophytes potentially relied on a mixotrophic mode by feeding on PE cyanobacteria. Interestingly, picoeukaryotes and eukaryotes larger than 10 ㎛ were mostly responsible for the ecological niche in the western region of the bay. Given that chl-a levels have historically declined, our study highlights the potential importance of increased small phytoplankton in Gamak Bay. Particularly, we urge an examination of the ecological role of small phytoplankton in the food supply of cultivated marine organisms.

• Journal of Animal Environmental Science
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• v.15 no.3
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• pp.251-262
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• 2009

In this research dairy cattle barn facilities what are 4,198 houses hold over 50 heads were surveyed by scale and province. Full-time farms hold over 50 heads breed total 344,514 heads. Each of Farms holds 50 to 99 heads were 79.8 percent and breed average 82.1 heads. Dairy cattle barns were constructed september 1995 averagely. Each of barns have $1,740.0\;m^2$ scale. The construction type of dairy cattle barn was almost litter barn type 84.0%, freestyle type 5.1%, mooring+litter ground type 17.3% and other types 4.4%. The litter barn type was popular in small farms. But in large farms, freestyle type was popular than small farms. The construction type of dairy cattle barn was almost litter barn type 84.0%, freestyle type 5.1%, moohng+ltter ground type 17.3% and other types 4.4%. Type of dairy cattle robotic milking system was pipeline 41.5%, herringbone 22.8% and tandem 35.8%. The pipeline type was popular in small farms which have 50~99 heads. But in large farms which have over 200 heads, tandem type was popular than small farms. Proportion of floor type of dairy cattle barn was almost litter type 94.9%. Scraper type was popular in large farms than in small farms. Proportion of roof type of dairy cattle barn was slate 32.5%, vinyl 16.3%, sunlight 11.1%, panel 10.9, zinc plate 8.8 and steel plate 8.3%. Roof type was lots of slate type before 1995. But vinyl type is increasing after 1995. Proportion of wall type of dairy cattle barn was almost open type 83.3% and winch-curtain 26.8%. Utilization period of dairy cattle barn was 9.2 years about milker, 7.9 years about automatic feeder, 9.2 years about waterer and 10.4 years about electric facilities. In this results, there were lots of improvements about automatic feeder.

• Korean Journal of Organic Agriculture
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• v.25 no.3
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• pp.643-651
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• 2017

The purpose of this study was to analyze the purpose, effectiveness and problems of HACCP system implementation for small, medium and large scale of swine farms implementing HACCP. Small and medium scale swine farms stated that the primary purpose of HACCP implementation was to receive government funding, but large scale swine farm was answered to improve the farm's competitiveness. In the case of small farms, the effect of HACCP implementation responded to systematic farm management as the top priority, while mid-scale and large-scale swine farms were said to improve farm sanitation. In addition, the biggest problems caused by the implementation of HACCP were the difficulty of improving the consciousness of the employees (small farm) and the records management (medium scale, large farm) by HACCP implementation. In order to solve these problems, it is necessary to adjust the number of HACCP evaluation items of pig farm and HACCP education for employees.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.263-269
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• 1996

In indian agricultural , the energy use pattern has played dominant role in influencing the level of mechanization . Besides that the agro-climatic factors as well as the size of holdings do affect the degree of mechanization . Nearly 30 percent of total cultivated area is owned by l76 percent of the small and tiny land holders each owning even less than a hectare. On the other hand, about 2 percent of land owners cultivate land. These variabilitieshave greatly influenced the ownership of power sources on Indian farms. Small farmers, employ human and animal energies with the use of hand tools and animal drawn equipments. Whereases, the use of tractors, power tillers, electric motors, etc. on small farms is on a marginal scale. There are few pockets and also extensive wheat growing regions where mechanical and electrical power sources are extensively used in production agriculture leading to about 185% of cropping intensity . In that region, the animal energy is employed for on the farm transport of fertilizers, fodders and fuel to support milch animals and other household activities . Inspite of high degree of mechanization, the harvesting of crops is done by human labour with few exceptions of harvesting wheat crops by combines in few pockets. In overall assessment of mechanization, the following conclusions are drawn : ⅰ) Farm operation which show a growing trend of mechanization are (a) tillge, (b) seedling (c) Irrigation (d) Plant protection application (e) Threshing and (f) Transport . ⅱ) Crop cultivation system in respect of wheat, maize and sorghum have been greatly mechanized. ⅲ) The least mechanized cropping systems are (a) vegetable production and (b) cultivation of sugarcane, cotton, rice and pulses. ⅳ) Annual production of tractor has touched the figure of 280.000 by 1995 and the total number has crossed 1.5million on Indian farms.

• Agribusiness and Information Management
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• v.8 no.2
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• pp.1-8
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• 2016

The present study has aimed at analyzing the technical and scale efficiencies of credit utilization by the farmer-borrowers in Chittoor district of Andhra Pradesh, India. DEA approach was followed to analyze the credit utilization efficiency and to analyze the factors influencing the credit utilization efficiency, log-linear regression analysis was attempted. DEA analysis revealed that, the number of farmers operating at CRS are more in number in marginal farms (40%) followed by other (35%) and small (17.5%) farms. Regarding the number of farmers operating at VRS, small farmers dominate the scenario with 72.5 per cent followed by other (67.5%) and marginal (42.5%) farmers. With reference to scale efficiency, marginal farmers are in majority (52.5%) followed by other (47.5%) and small (25%) farmers. At the pooled level, 26.7 per cent of the farmers are being operated at CRS, 63 per cent at VRS and 32.5 per cent of the farmers are either performed at the optimum scale or were close to the optimum scale (farms having scale efficiency values equal to or more than 0.90). Nearly 58, 15 and 28 percents of the farmers in the marginal farms category were found operating in the region of increasing, decreasing and constant returns respectively. Compared to marginal farmers category, there are less number of farmers operating at CRS both in small farmers category (15%) and other farmers category (22.5%). At the pooled level, only 5 per cent of the farmers are operating at DRS, majority of the farmers (73%) are operating at IRS and only 22 per cent of the farmers are operating at CRS indicating efficient utilization of credit. The log-linear regression model fitted to analyze the major determinants of credit utilization (technical) efficiency of farmer-borrowers revealed that, the three variables viz., cost of cultivation and family expenditure (both negatively influencing at 1% significant level) and family income (positively influencing at 1% significant level) are the major determinants of credit utilization efficiency across all the selected farmers categories and at pooled level. The analysis further indicate that, escalation in the cost of cultivation of crop enterprises in the region, rise in family expenditure and prior indebtedness of the farmers are showing adverse influence on the credit utilization efficiency of the farmer-borrowers.

• Journal of Veterinary Science
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• v.23 no.3
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• pp.36.1-36.14
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• 2022

Background: Since 2003, the H5 highly pathogenic avian influenza (HPAI) subtype has caused massive economic losses in the poultry industry in South Korea. The role of inland water bodies in avian influenza (AI) outbreaks has not been investigated. Identifying water bodies that facilitate risk pathways leading to the incursion of the HPAI virus (HPAIV) into poultry farms is essential for implementing specific precautionary measures to prevent viral transmission. Objectives: This matched case-control study (1:4) examined whether inland waters were associated with a higher risk of AI outbreaks in the neighboring poultry farms. Methods: Rivers, irrigation canals, lakes, and ponds were considered inland water bodies. The cases and controls were chosen based on the matching criteria. The nearest possible farms located within a radius of 3 km of the case farms were chosen as the control farms. The poultry farms were selected randomly, and two HPAI epidemics (H5N8 [2014-2016] and H5N6 [2016-2017]) were studied. Conditional logistic regression analysis was applied. Results: Statistical analysis revealed that inland waters near poultry farms were significant risk factors for AI outbreaks. The study speculated that freely wandering wild waterfowl and small animals contaminate areas surrounding poultry farms. Conclusions: Pet birds and animals raised alongside poultry birds on farm premises may wander easily to nearby waters, potentially increasing the risk of AI infection in poultry farms. Mechanical transmission of the AI virus occurs when poultry farm workers or visitors come into contact with infected water bodies or their surroundings. To prevent AI outbreaks in the future, poultry farms should adopt strict precautions to avoid contact with nearby water bodies and their surroundings.

• Journal of Animal Reproduction and Biotechnology
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• v.38 no.4
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• pp.215-224
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• 2023

Background: Reproductive management practices play crucial roles to maximize the reproductive performance of cows, and thus contribute to farm profitability. We aimed to assess the reproductive management of cows currently practiced in the dairy farms in an urban farming system. Methods: A total of 62 dairy farms were randomly selected considering all size of farms such as small (1-5 cattle), medium (6-20 cattle) and large farms (> 20 cattle) from selected areas of Dhaka city in Bangladesh. The reproductive management-related parameters viz. estrus detection, breeding method, pregnancy diagnosis, dry cow and parturition management, vaccination and treatment of reproductive problems etc. were obtained in a pre-defined questionnaire during the farm visit. Results: The visual observation method was only used (100.0%; 62/62) for estrus detection irrespective of size of the farms; while farmers observed cows for estrus 4-5 times a day, but only for 20-60 seconds each time. Regardless of farm size, 89.0% (55/62) farms used artificial insemination (AI) for breeding the cows. Intriguingly, all farms (100.0%) routinely checked the cows for pregnancy at 35-40 days post-breeding using rectal palpation technique by registered veterinarian. However, only 6.5% (4/62) farms practiced dry cow management. Notably, all farms (100.0%) provided nutritional supplements (Vit D, Ca and P) during late gestation. However, proper hygiene and cleanliness during parturition was not practiced in 77.4% (48/62) farms; even though 96.7% (60/62) farms treated cows by registered veterinarian for parturition-related problems. Conclusions: While farmers used AI service for breeding and timely check their cows for pregnancy; however, they need to increase observation time (30 minutes/ observation, twice in a day: early morning and early night) for estrus detection, consider dry cow management and ensure hygienic parturition for maximizing production.