• Asian-Australasian Journal of Animal Sciences
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• v.32 no.8_spc
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• pp.1244-1255
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• 2019

Dairy goat production continues to be a socially, economically and culturally important part of the livestock industry in North, Central and South America and the Caribbean islands. Goat milk, cheese and other dairy products offer consumers food products with nutritional, health and environmental benefits. In North America, Mexico produces the greatest volume of goat milk, but most is for family or local consumption that is typical of a mixed farming system adopted by subsistence farmers in dry areas. The United States is not yet a large global goat milk producer, but the sector has expanded rapidly, with dairy goat numbers doubling between 1997 and 2012. The number of dairy goats has also increased dramatically in Canada. Commercial farms are increasingly important, driven by rising demand for good quality and locally sourced goat cheese. In South America, Brazil has the most developed dairy goat industry that includes government assistance to small-scale producers and low-income households. As of 2017, FAO identified Haiti, Peru, Jamaica, and Bolivia as having important goat milk production in the Western Hemisphere. For subsistence goat producers in the Americas on marginal land without prior history of chemical usage, organic dairy goat production can be a viable alternative for income generation, with sufficient transportation, sanitation and marketing initiatives. Production efficiency, greenhouse gas emission, waste disposal, and animal welfare are important challenges for dairy goat producers in the Americas.

• The Journal of Industrial Distribution & Business
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• v.2 no.2
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• pp.5-14
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• 2011

Opening agricultural market expansion, reduced purchases through wholesale markets, expanding the influence large retailers of consumer's market such as changes in the distribution system to the farmer's market conditions are changing rapidly. Because of this, retailers of the scaled and chain-store operations was centered on distribution environmental changes of the consumer market place. In producing area due to changes in market conditions in the agricultural production of in producing area distribution organization and the size distribution can not be put off no longer challenge is imminent. If it do not raise forces banded together, the producer is bound to remain as the weak. To support the distribution of this production was introduced in 2000 enable the Activation Project of in producing area distribution. Recent in producing area Changes of Agricultural conditions in order to cope with the Small-scale farmers and small individual farmers are becoming Scaled and specialized. Also, is specific to each item and regional is showing aspects. Government support for Activation Project of in producing area distribution is greatly improved, but in terms of competitiveness on the market still is showing the limitations. The most common of these problems, the market response if in producing area producer's organization and scale of the problem. Equipped for the purpose of consumer market place responsiveness unreasonable propelled outward from the Painter-sized weakens the organizational power. also, Difficult to succeed organizational size is a dissolution or anything within a few years, farmers around the best producer organizations, such as deviation occurs is exposed to a variety of issues. In this study, previous studies refer to the recent changes in agricultural retail environment, background and needs of organization·scaled, Determine the status of the domestic in producing area organizations and derived Problems, look into Domestic and overseas of in producing area organization with best practices for enhancing the competitiveness of the proposed improvement are intended to. In the future, in producing area distribution policy would like to provide direction to the development. The results of the study showed the follwing : 1) enhance utilization and orrganized through the diversification of the agricultural Collection systems. 2) Scaled to achieve through Items of specialized a wide area marketing. 3) Management operating units, such as installation and operating that overseas the best practices " Comite Economique Agricole Regional 'Fruits et Legumes' de Bretagne". 4) To establish a support system that in producing area distribution organization model development for appropriate domestic. In particular, in case of domestic in producing area distribution organization, through the analysis of various case study that a successful organization and scaled. The process of the various challenges arising in organizational scaled and generalization, and by the way he goes about trying to overcome is required. At the end of the study's limitations and future research directions suggested.

• 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.

• Journal of Distribution Science
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• v.12 no.12
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• pp.65-73
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• 2014

Purpose - Distribution of agricultural products is collapsing due to recent changes in the environment balance between retailers and producers. Further, the increase in the imports of agricultural products due to free trade has caused revenue problems for producers. Agricultural producers are faced with increasing difficulties, and the organized producing center's importance is growing each day. Korean organized producing centers' policies have been conducted since 2000, and have thus been sustained for more than 10 years. Organizing a producing center's business is focused on agricultural cooperative and farming association corporation this has already have been carried out. In addition, it has achieved significant results through these organizations, but it still has many problems. Research design, data, and methodology - Organized producing centers were classified for each type. The survey covered 90 organizations, based on factor analysis, analysis of balance, and logistic regression analysis. The results of the conducted factor analysis are as follows. Production, marketing, human resources management, organizational skills, post management, safety, and scale were classified as the seven kinds of factors. These are the factors that affect the organized producing centers through the variance analysis and logistic regression analysis. The purpose of such research and analysis is to suggest the direction for the future organized producing centers' policies. Result - Results of the analysis of the variance are listed below. Items by type, production, marketing, human resource management, and organizational skills are shown to depict differences between the factors. Organization form results show that production and marketing showed a difference between the factors. The size of the organization, production, and marketing showed the difference between the factors. The factors affect organized producing centers differently depending on the type shown, as seen from the results. In contrast, the logistic regression results of the analysis are as follows. Considering the results by the type of item, and the horticulture type of marketing, human resource management has a (+) influence. Grain type and production showed a (+) influence. New types of income affecting organizational skills have a (+) influence. Considering the results for each type of organization, the agricultural cooperative type of marketing and human resource management have (+) influence. Farming association corporation type of production has a (+) influence. As a result, the size of the organization is as follows. Large organizations are production, expressed in marketing with a (+) impact. Medium-size organizations are expressed with their size factor having a (+) impact. Small organizations are expressed with a safety factor having a (+) impact. Conclusions - The types of factors display different effects. One factor is that the type of a (+) represents the influence of other factors (-) as per factor symptomatology. Therefore, the type of measures to organize a strategy to maximize the benefits of your organization should be pursued. In addition, based on the support for small organizations, improving the quality of the producer organization should be considered.

• Korean Journal of Agricultural Science
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• v.45 no.3
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• pp.325-353
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• 2018

Agricultural systems in South Asian countries are dominated by smallholder farmers. Additionally, these farmers have limited access to pre- and post-harvest technologies due to their high initial cost. The lack of these technologies in postharvest handling is responsible for 20% to 44% of fruit and vegetable losses. These high losses are largely the result of a generally weak basic postharvest infrastructure for the preservation of products, which avoids damage from improper handling, transportation, packaging, and storage. High postharvest losses of products negatively affect food availability, food security, and nutrition, as the producer is able to sell less of the farm yield and the net availability of these food commodities for consumption is reduced. An underlying cause of these postharvest losses is the limited awareness and knowledge bases of stakeholders (researchers, farmers, governments, non-governmental organizations, and merchants) in the traditional supply chains in which these losses occur. The analysis presented in this paper explores the state of postharvest practice in South Asian countries and discusses options for low-cost postharvest technologies in the region that can support small-scale farmers and provide a viable pathway for supply to the market, joining with modern value chains and bringing about individual and regional reduction in postharvest losses of fruits and vegetables. The improvement of basic and simple low-cost technologies through precise research efforts has the potential to prevent such huge losses of products, and help meet the ever-increasing demand for food in South Asian countries.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.5
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• pp.67-77
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• 2007

The images of MODerate resolution Imaging Spectroradiometer (MODIS) that provide wider swath and shorter revisit frequency than Land Satellite (Landsat) and Satellite Pour I' Observation de la Terre (SPOT) has been used fer land cover classification with better spatial resolution than National Oceanic and Atmosphere Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR)'s images. Due to the advantages of MODIS, several researches have conducted, however the results for the land cover classification using MODIS images have less accuracy of classification in small areas because of low spatial resolution. In this study, uncertainty of paddy fields classification using MODIS images was conducted in the region of Gyeonggi-do and the relation between this uncertainty of estimating paddy fields and topographical factors was also explained. The accuracy of classified paddy fields was compared with the land cover map of Environmental Geographic Information System (EGIS) in 2001 classified using Landsat images. Uncertainty of paddy fields classification was analyzed about the elevation and slope from the 30m resolution Digital Elevation Model (DEM) provided in EGIS. As a result of paddy classification, user's accuracy was about 41.5% and producer's accuracy was 57.6%. About 59% extracted paddy fields represented over 50 uncertainty in one hundred scale and about 18% extracted paddy fields showed 100 uncertainty. It is considered that several land covers mixed in a MODIS pixel influenced on extracted results and most classified paddy fields were distributed through elevation I, II and slope A region.