846 resultados para Farm income.


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Krishin Vigyan Kendras-KVKs (Farm Science Centres) have been established by the Indian Council of Agricultural Research in 569 districts. The trust areas of KVKs are refinement and demonstration of technologies, and training of farmers and extension functionaries. Imparting vocational trainings in agriculture and allied fields for the rural youth is one of its mandates. The study was undertaken to do a formative and summative (outcome and impact) evaluation of the beekeeping and mushroom growing vocational training programmes in the Indian state of Punjab. One-group pre and post evaluation design was employed for conducting a formative and outcome evaluation. The knowledge tests were administered to 35 beekeeping and 25 mushroom cultivation trainees, before and after the training programmes organized in 2004. The trainees significantly gained in knowledge. A separate sample of 640 trainees, trained prior to 2004, was selected for finding the adoption status. Out of 640, a sample of 200 was selected by proportionate sampling technique out of three categories, namely: non-adopters, discontinued-adopters and continued-adopters for evaluating the long-term impact of these training programmes. Ex-post-facto one-shot case study design was applied for this impact analysis. The vocational training programmes have resulted in continued-adoption of beekeeping and mushroom cultivation enterprises by 20% and 51% trained farmers, respectively. Age and trainee occupation had significant influence on the adoption decision of beekeeping vocation, whereas education and family income significantly affected the adoption decision of mushroom cultivation. The continued adopters of beekeeping and mushroom growing had increased their family income by 49% and 24%, respectively. These training programmes are augmenting the dwindling farm income of the farmers in Indian Punjab.

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This paper uses the data of 1338 rural households in the Northern Mountainous Region of Vietnam to examine the extent to which subsidised credit targets the poor and its impacts. Principal Component Analysis and Propensity Score Matching were used to evaluate the depth of outreach and the income impact of credit. To address the problem of model uncertainty, the approach of Bayesian Model Average applied to the probit model was used. Results showed that subsidised credit successfully targeted the poor households with 24.10% and 69.20% of clients falling into the poorest group and the three bottom groups respectively. Moreover, those who received subsidised credit make up 83% of ethnic minority households. These results indicate that governmental subsidies are necessary to reach the poor and low income households, who need capital but are normally bypassed by commercial banks. Analyses also showed that ethnicity and age of household heads, number of helpers, savings, as well as how affected households are by shocks were all factors that further explained the probability at which subsidised credit has been assessed. Furthermore, recipients obtained a 2.61% higher total income and a 5.93% higher farm income compared to non-recipients. However, these small magnitudes of effects are statistically insignificant at a 5% level. Although the subsidised credit is insufficient to significantly improve the income of the poor households, it possibly prevents these households of becoming even poorer.

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Farming systems research is a multi-disciplinary holistic approach to solve the problems of small farms. Small and marginal farmers are the core of the Indian rural economy Constituting 0.80 of the total farming community but possessing only 0.36 of the total operational land. The declining trend of per capita land availability poses a serious challenge to the sustainability and profitability of farming. Under such conditions, it is appropriate to integrate land-based enterprises such as dairy, fishery, poultry, duckery, apiary, field and horticultural cropping within the farm, with the objective of generating adequate income and employment for these small and marginal farmers Under a set of farm constraints and varying levels of resource availability and Opportunity. The integration of different farm enterprises can be achieved with the help of a linear programming model. For the current review, integrated farming systems models were developed, by Way Of illustration, for the marginal, small, medium and large farms of eastern India using linear programming. Risk analyses were carried out for different levels of income and enterprise combinations. The fishery enterprise was shown to be less risk-prone whereas the crop enterprise involved greater risk. In general, the degree of risk increased with the increasing level of income. With increase in farm income and risk level, the resource use efficiency increased. Medium and large farms proved to be more profitable than small and marginal farms with higher level of resource use efficiency and return per Indian rupee (Rs) invested. Among the different enterprises of integrated farming systems, a chain of interaction and resource flow was observed. In order to make fanning profitable and improve resource use efficiency at the farm level, the synergy among interacting components of farming systems should be exploited. In the process of technology generation, transfer and other developmental efforts at the farm level (contrary to the discipline and commodity-based approaches which have a tendency to be piecemeal and in isolation), it is desirable to place a whole-farm scenario before the farmers to enhance their farm income, thereby motivating them towards more efficient and sustainable fanning.

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A Bayesian method of classifying observations that are assumed to come from a number of distinct subpopulations is outlined. The method is illustrated with simulated data and applied to the classification of farms according to their level and variability of income. The resultant classification shows a greater diversity of technical charactersitics within farm types than is conventionally the case. The range of mean farm income between groups in the new classification is wider than that of the conventional method and the variability of income within groups is narrower. Results show that the highest income group in 2000 included large specialist dairy farmers and pig and poultry producers, whilst in 2001 it included large and small specialist dairy farms and large mixed dairy and arable farms. In both years the lowest income group is dominated by non-milk producing livestock farms.

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This paper explores the financial implications of converting to organic farming in Great Britain through a case study of farmers considering conversion in 2002. Most study farmers were motivated to convert for financial, not ideological or life-style reasons; organic meat production was the most common planned enterprise, although those choosing to produce milk, vegetables and cereals were also studied in depth. At the time of study, organic beef and sheep meat production was particularly profitable. It was found that, in these product sectors, a large improvement in Family Farm Income would result if organic production was introduced on the case study farms. With few exceptions, a fall in Family Farm Income during the conversion period would not be an obstacle to farmers changing to organic methods. Fixed cost changes would also not deter conversion but expensive investment in new livestock and appropriate buildings would be required by some of those businesses studied. These findings are, however, dependent upon the price premia assumptions used and, whilst these premia have dropped slightly since the time of study, this would lessen the financial shortfall during the conversion period. There is also the possibility that reversion to conventional agricultural production might occur, perhaps at a faster rate than the original conversion process that was taking place around the turn of the century.

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This report summarizes the financial and production records of 139 dairy farms from throughout Michigan in 2006. To be included, the farms must have produced at least 50 percent of gross cash farm income from milk and dairy animal sales. The records came from Michigan State University’s TelFarm project and the Farm Credit Service system in Michigan. The values were pooled into averages for reporting purposes. The farms are larger than would be the average of all dairy farms in Michigan. While considerable variation in the data exists, average values are reported in the summary tables and discussion that follows.

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This extension circular is an income statement form that covers the following areas: Cash Farm Income (grain/hay sales, livestock sales, livestock product sales, government payments, custom work); Cash Farm Expenses (cash operating, breeding livestock purchases, gross cash farm expenses); Adjustment (inventory, machinery/equipment depreciation, fixed farm improvements depreciation, capital gain or loss on machinery/equipment, gross sales of machinery/equipment, real estate sold); and Non-Farm Income (operators's wage, wife's wage, interest/dividend income, gifts/inheritances, gain or loss on security, non-farm inventory change, net income on other farms owned and non-farm real estate).

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In Chile, small-scale farmers are classified according to old approaches from 1993 that do not include changes occurred in the last two decades. Maule is the region with most rural population in Chile which represents a significant stratum for development, innovation and competitiveness. This study explores a new approach of small-scale farmers -associated with Family Farm Agriculture (AFC) - classification in Chile and it describes a commercial profile or AFC-1 for famers of the Maule Region. A Cluster analysis to determine AFC-1 farmers is used. The analysis includes four association variables: Total Assets, Farm Income, Production Costs and Management Indicators. The results suggest that 16.4% of the farmers have a commercial profile and they could stay out support provided by the National Institute for Agricultural Development (INDAP). This group of farmers would not belong to AFC in short terms. This fact could bring restriction to AFC-1 farmers such as lack of credit access, less investment incentives and technical assistance. Thus, it would expect low process of technology adoption and welfare improvement. New agrarian policies must be warranted to support this important group of famers with a commercial profile.

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In order to stabilise and improve their income situation, rural households are strongly encouraged to diversify their activities both within and outside the agricultural sector. Often, however, this advice is only moderately pursued. This paper addresses issues of rural household income diversification in the case of Poland. It investigates returns from rural household income strategies using propensity score matching methods and extensive datasets spanning 1998-2008. Results suggest that returns from combining farm and off-farm activities were lower than returns from concentrating on farming or on self-employment outside agriculture. This differential is stable over time although returns from diversification have relatively improved after Poland’s accession to the EU. This is also visible in the fact that since 2006 returns from combining farm and off-farm activities have evened with returns from relying solely on hired off-farm labour, thus smoothing the difference observed before the accession. Further, over the analysed period, households pursuing the diversification strategy performed better than those relying solely on unearned income. Finally, in general, the income in households combining farm and off-farm activities was higher than in those combining two off-farm income sources.

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"Constitutes report no. 4 under contract no. 12-17-07-5-1651, June 24, 1976 (ERS-192-B-76)"

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Cover title.

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Begun in 1936 as a cooperative enterprise, with the Agricultural Adjustment Administration, the Bureau of Home Economics, and the Bureau of Agricultural Economics participating.

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Title from cover.

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"Issued November, 1930."

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Contribution from Bureau of Human Nutrition and Home Economics in cooperation with the College of Home Economics, Univ. of Tennessee.