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Produce certification and income risk management strategies of cocoa farming households in South-West Nigeria
75-79Views:217Agricultural produce certification is synonymous to farm assurance of which cocoa certification is an example; dealing with issues of Good Agricultural, Environmental and Social Practices (GAP, GEP and GSP) in cocoa production. Essentially, GAP, GEP and GSP packages had in-built mechanism that can aid farmers mitigate factors that could lead to farm income risks in cocoa production. Consequently, this study examined the influence of cocoa certification on income risks of cocoa farming households in South-west Nigeria. A multistage sampling technique was used to select 180 cocoa farming households from whose heads data were obtained with interview schedule in Southwest Nigeria. Data were analyzed with Chi-square Statistic, Income Risk Management Diversification Index (IRD) and Mann-Whitney-U Test Statistic. Chi-square analysis shows that (52.3%) certified cocoa farming households employed more risk management strategies than (94.2%) uncertified cocoa farming households (p<0.01). The Mann-Whitney-U test revealed a significant difference (p>0.05) between the income risk management practices of certified and uncertified cocoa farming households. Therefore, produce certification has been helping cocoa farming households in mitigating farm income risk in cocoa production through the employment of diverse (risk) management strategies. Hence, stakeholders should intensify efforts in encouraging farming households to embrace (cocoa) produce certification.
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Diversification strategies and their impact on farm performance
57-61Views:161The objective of this study is to identify factors determining the economic performance of agricultural holdings in Italy, with specific attention to the impact of the adoption of on-farm diversification strategies, namely income diversification and product differentiation. The adoption of these kinds of strategies has been increasingly recognised as a viable business option in agriculture as they allow better allocation of farm resources and an increase in the quota of value added retained on farms and therefore not passed on to other agents operating at the end of the food supply chain. By using a panel of professional Italian farms over the time period of 2003-2009, we estimate random effect, ordinary least square and quantile regression models to estimate the impact of income diversification and product differentiation strategies on the levels of farm income per unit of labour income. Our findings show that scale economies are important positive determinants of farm economic performance. On the contrary, when the family play an important role in the farm business, economic performance is worse. Finally, we do not find evidence of a statistically significant impact of the adoption of income diversification and product differentiation strategies. This latter result may be interpreted as a signal that farms use these strategies as risk management tools rather than as income increasing ones.
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Climate change impact on crop production in Central Asian Countries
75-82Views:210Increased risk due to global warming has already become embedded in agricultural decision making in Central Asia and uncertainties are projected to increase even further. Agro-ecology and economies of Central Asia are heterogeneous and very little is known about the impact of climate change at the subnational levels. The bio-economic farm model is used for ex-ante assessment of climate change impacts at sub-national levels in Central Asia. The bio-economic farm model is calibrated to ten farming systems in Central Asia based on the household survey and crop growth experiment data. The production uncertainties and the adaptation options of agricultural producers to changing environments are considered paramount in the simulations. Very large differences in climate change impacts across the studied farming systems are found. The positive income gains in large-scale commercial farms in the northern regions of Kazakhstan and negative impact in small-scale farms in arid zones of Tajikistan are likely to happen. Producers in Kyrgyzstan may expect higher revenues but also higher income volatilities in the future. Agricultural producers in Uzbekistan may benefit in the near future but may lose their income in the distant future. The negative impacts could be further aggravated in arid zones of Central Asia if irrigation water availability decline due to climate change and water demand increase in upstream regions. The scenario simulations show that market liberalization and improved commodity exchange between the countries have very good potential to cope with the negative consequences of climate change.
JEL classification: Q11, Q18
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Some thoughts on the repayment methods of Hungarian household forex loans
17-23Views:171Nowadays the volatility of exchange rates and the macroeconomic changes strongly affect the monthly instalments of the debtors. The growth of delayed forex loans - mostly denominated in Swiss Franc and Euro - can include a high risk, which as a part of a vicious circle can ruin Hungary’s economic situation and even the country’s external judgement. Steps were taken to handle the problem of the forex loans but their result is questionable. In this paper different repayment methods are compared in different economic scenarios. More precisely, the third edition of exchange-rate barrier and income based repayment are analysed in an optimistic realistic and pessimistic scenario. This article is aiming to quantify and interpret the difference between each repayment methods regarding different scenarios. Based on the results suggestions are made how to eliminate efficiently currency exposure from the continuously deteriorating portfolio.
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Investment analysis of a piglet producer farm – a Hungarian case study
141-152Views:282The pig population in Hungary was about 8 million in 1990, while this number dropped to only 2.8 million by 2018. The previously so successful integrated domestic pig farming has almost completely disappeared and most of the smaller farms still operating in the 1990s are no longer functioning. At present, a process of concentration can be observed, which was accompanied by the further specialization of pig farming. The main profile of most pig farms is fattening, but there is a smaller number of farms in Hungary today specialized for piglet production, the successful operation of which requires significantly more expertise and more complex technology.
The main aim of this study is to present the production and economic indicators of a pig farm specialized in piglet production in Hungary as a result of a greenfield investment in the current economic environment, on a case study basis. For this purpose, an economic simulation was prepared based on primary data collection, operating on a deterministic basis, modelling the production and economic processes of the farm. The performed calculation does not derive the economic indicators of the activity from accounting records, but assigns the prices of natural inputs used on the basis of technological data. Primary data and information collection (e.g. technological data, input and output prices, unit cost items, etc.) took place between 2018-2019.
At the purchase prices of pigs in the last two years, which have increased significantly due to the African Swine Fever (ASF), the majority of pig farms in Hungary have an outstanding profit-making capacity. The physical efficiency indicators of the analysed pig farm are almost identical to the average data of such farms in the Netherlands, which has one of the most developed pig industry. The income of the examined pig farm at farm level is about 734 thousand EUR, i.e. 232 EUR per sow. Moreover, this activity is profitable even without subsidies. As a result, the greenfield investment pays off in the 8th year by default (average scenario). The investment has a Net Present Value (NPVr=3%) of EUR 2,609 thousand for 10 years, an Internal Rate of Return of 8.5%, and a Profitability Index (PIr=3%) of 1.3. At the same time, risk factors such as sales prices, output and capacity utilization, and feed costs should be taken into consideration as in extreme cases the return on investment may be unfavourable (pessimistic scenario).
JEL code: D24, M11, Q12
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