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نشرة المشاورة بشأن المعلومات الزراعية ، المجلد 1

Year of publication 2003

Place of publication روما، ايطاليا ;

Product type Document

Category Bulletin

Edition 1

Language Arabic

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    This paper documents a relationship between non-farm income (primarily earnings and pensions) and agricultural outlays in Bulgaria, using the 2003 Multitopic Household Survey. The outcomes analyzed are expenditures on working capital (variable inputs such as feed, seed, and herbicides) and investment in livestock. I find that while non-farm income has no significant effect on the probability of purchasing variable inputs, it does have an effect on the amount spent if positive, with an estimated elasticity of 0.14. Non-farm income also has an effect on the number of households that purchase farm animals, with an estimated elasticity of 0.35. The use of non-farm income for farm investment is consistent with the presence of credit constraints, as is the fact that less than one per cent of farmers report outstanding debts for agricultural purposes. Yet it is also noted that many farm households take out large unsecured loans for other purposes, suggesting that a lack of demand for agricult ural borrowing may also be part of the problem.
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    Short-term projection of global fish demand and supply gaps 2017
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    A short-term projection model is developed to assess and monitor potential future fish demand and supply gaps at the country (nearly 200 countries or territories), regional (about 40 country groups), and global levels for nine species groups. Salient results at the global, regional and country levels are presented in the main text. Key results for all countries and all the nine species groups (including both standard and conservative projections) are documented in the appendix. The results indic ate that: (i) if fish prices and consumer preferences remain the same, income growth would drive world per capita fish demand up from 20 kg/year in the mid-2010s to 25 kg/year in the early 2020s (or 23 kg/year under the conservative projection); (ii) the income-driven per capita fish demand hike, combined with population growth, would drive world fish demand up by 47 million tonnes (or 31 million tonnes under the conservative projection); (iii) the 19-million-tonne fish supply growth generated b y the trend growth of world aquaculture production would cover only 40 percent of the projected demand growth (or 62 percent of the conservative projection), leaving a fish demand-supply gap of 28 million tonnes (or 16 million tonnes under the conservative projection) in the early 2020s; (iv) the demand-supply gap for shellfish (i.e. crustaceans and molluscs) would be bigger than that for finfish – they would account for, respectively, 55 percent and 45 percent of the 28-million-tonne fish deman d-supply gap; (v) while world aquaculture production following its recent trend would grow 4.5 percent annually from the mid-2010s to the early 2020s, it would take a 9.9 percent annual growth (or 6.9 percent under the conservative projection) to fill the world fish demand-supply gap in the early 2020s; (vi) the trend aquaculture growth in only 17 countries (or 24 countries under the conservative projection) would be sufficient to cover the demand growth driven by population and income growth; e xcess demand is expected to occur in 170 countries (or 163 countries under the conservative projection); and (vii) should the world aquaculture production fall short of the required annual growth rate (i.e. 9.9 percent or 6.9 percent under the standard or conservative projection), and assuming world capture fisheries production would remain at the current level, the world fish price would have to increase to reduce fish demand in order to clear the market (i.e. no demand-supply gap). Results gen erated by the short-term projection model are useful for policymaking, development aids, business or investment planning, and other decision-making by various stakeholders in aquaculture and fisheries. They are a complement to and can potentially enhance the understanding of the results of more sophisticated forecasting models such as the OECD-FAO Fish Model and the World Bank-IFPRI-FAO Fish to 2030 model.
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