Estimating the burden of market loss due to aflatoxins in maize: methods and estimates for Thailand

2015 ◽  
Vol 8 (4) ◽  
pp. 459-464 ◽  
Author(s):  
G.A.S. Lubulwa ◽  
P. Siriacha ◽  
P.J. Markwell ◽  
J.I. Pitt
Keyword(s):  
Gross Domestic Product ◽  
Agricultural Sector ◽  
Agricultural Research ◽  
Economic Loss ◽  
Aflatoxin Contamination ◽  
Poultry Meat ◽  
Domestic Product ◽  
Fresh Water Fish ◽  
Data Set ◽  
Aflatoxin Level

This communication updates and enhances earlier estimates of the burden of market loss associated with aflatoxin contamination of maize in Thailand using two unrelated data sets. The first, supplied by Mars Petcare (Thailand) Ltd. was compiled in 2010 from two sets of 295 random samples of maize, one collected at harvest and the second after drying, from two regions that produce more than 70% of the commercially grown maize in Thailand. The second data set was compiled between 1989 and 1993 under a collaborative research project funded by the Australian Centre for International Agricultural Research, the Commonwealth Scientific and Industrial Research Organisation and project partner countries in South East Asia. It provides aflatoxin concentrations in 108 maize samples randomly selected from retail markets in Thailand. This study shows that, even with the low aflatoxin levels found in the first data set, a burden of economic loss in Thailand exists, estimated here at about US$ 6.9 million per annum (about 0.05% of agricultural sector gross domestic product in Thailand in 2009). If the higher aflatoxin levels in the second data set are representative, then the burden of economic loss in Thailand could exceed US$ 100 million per annum (about 0.85% of agricultural sector gross domestic product), based on 2009 production and prices data. Most of the losses are borne by producers of chicken meat, eggs, pig meat, duck meat, freshwater fish, milk and maize, in descending order of magnitude of loss. This communication enhances earlier estimates by broadening the scope to include milk and fresh water fish, by disaggregating poultry meat into chicken and duck meat, and by extending the analysis to cover the impacts of aflatoxins under a low aflatoxin level scenario.

scholarly journals The Role of Technological Advancement in Agriculture Sector and Economy of Pakistan

2019 ◽  
Vol 1 (1) ◽  
pp. 21-31
Author(s):  
Afsheen Hashmat ◽  
Ghulam Ghouse
Keyword(s):  
Gross Domestic Product ◽  
Granger Causality ◽  
Agricultural Sector ◽  
Positive Impact ◽  
Domestic Product ◽  
Technological Advancement ◽  
Data Set ◽  
Long Run ◽  
Short Run

This study is designed to explore the role of technological advancement in agricultural sector in the context of economy of Pakistan. The data set are based on the period from 1972 to 2019. The Autoregressive Distributed Lag (ARDL) bound testing employed to identify the short run and long run relationships between the technological advancement in agricultural sector and gross domestic product. The Granger causality also applied to find out the direction of causal relationships. The ARDL cointegration results indicates that there is positive relationship between technological advancement in agricultural sector and gross domestic product in short run and long run. The Granger causality results also indicate that the GDP growth also Granger cause the agriculture technology. And stability tests show that the model CUSUM and CUSUM of squares indicate that models are valid. The results suggest that technological advancement in agricultural sector has positive impact on GDP in case of Pakistan economy. Technological advancement in agricultural sector strengthens the Pakistan’s economy. There is need to pay more attention by the government administration to provide technological facilities in agricultural sector, properly and efficiently to get more benefits which ultimately enhance the agriculture growth and development of Pakistan’s economy.

scholarly journals Geographical Indication and Regional Development: Cause or Consequence

2019 ◽  
Vol 11 (3) ◽  
pp. 535
Author(s):  
Alan Malacarne ◽  
Liaria Nunes da Silva ◽  
Camila Souza Vieira ◽  
Ricardo Fontes Macedo ◽  
Andreia Malacarne ◽  
...  
Keyword(s):  
Gross Domestic Product ◽  
Regional Development ◽  
Agricultural Sector ◽  
Intrinsic Value ◽  
Domestic Product ◽  
Geographical Indication ◽  
Starting Point ◽  
Per Capita

The Geographical Indication is an instrument of protection to products and services that have intrinsic value. The cities of Bento Gonçalves, Flores da Cunha, Monte Belo do Sul, Farroupilha, Paraty, Urussanga, Salinas and Abaíra are highlights in the Brazilian agricultural sector. These regions have territorial demarcations with a Geographical Indication certification, where the producers live in the same region and can sell their own products with this seal of quality. An analysis has as a starting point the following study problem: Is the success of the implementation of a Geographical Indication linked to the development of the region? The results showed that only the Gross Domestic Product per capita is not sufficient to prove a record of Geographic Indication was actually implemented successfully in a certain region or not, however it can be observed that in the developed regions the trend is much higher.

scholarly journals Toolkit for Strategic Analysis of the Agricultural Sector Development in a Market Environment

2020 ◽  
pp. 107-113
Author(s):  
Nataliia Sirenko ◽  
◽  
Kateryna Mikulyak ◽  
Keyword(s):  
Gross Domestic Product ◽  
Balanced Scorecard ◽  
Agricultural Sector ◽  
Positive Impact ◽  
Innovation Policy ◽  
Fund Raising ◽  
Domestic Product ◽  
Strategic Analysis ◽  
Market Environment ◽  
State Support

The rapid deployment of global globalization processes, the intensification of competition, the active advancement of Ukraine on the path of European integration have a decisive influence on the economic and social development of the agricultural sector. The purpose of the article is to substantiate the toolkit for strategic analysis of the of Ukraine's agricultural sector development in a market environment. Strategic analysis tools with the use of balanced scorecard, PEST analysis and economic and mathematical modeling have been defined. The tools included in the system of balanced indices (investment return, fund return, fund-raising and productivity in agriculture) were analyzed and the state of development of the agricultural sector was assessed. Opportunities and threats to the development of the agrarian sector are identified by means of PEST analysis (political, economic, social and technological factors) with the use of expert assessments and the model of influence of factors (volume of capital investments, amount of expenditures of general and special fund and indirect state support) on the key indicator of development is the volume of agrarian gross domestic product. It was established that the volume of agrarian gross domestic product is most influenced by the amount of indirect state support (due to the special VAT regime of activity in the field of agriculture, forestry and fisheries, as well as fisheries and at the expense of a fixed agricultural tax (of the fourth group single tax)). The strategic guidelines for the development of the agricultural sector in the market environment are regulations that successfully combine the key principles of financial and innovation policy for material support and modernization of agricultural production. Adoption of such documents will have a positive impact on agricultural GDP growth as a strategic development goal of agricultural sector.

scholarly journals Economic Crisis in Europe: Panel Analysis of Inflation, Unemployment and Gross Domestic Product Growth Rates

2017 ◽  
Vol 9 (10) ◽  
pp. 145 ◽  
Author(s):  
Bibi Rouksar-Dussoyea ◽  
Ho Ming-Kang ◽  
Raja Rajeswari ◽  
Benjamin Chan Yin-Fah
Keyword(s):  
Gross Domestic Product ◽  
Growth Rates ◽  
Fixed Effects ◽  
Domestic Product ◽  
Panel Analysis ◽  
Causality Test ◽  
Data Set ◽  
Gross Domestic Product Growth ◽  
European Crisis ◽  
The Relationship

This panel analysis study is conducted to examine the relationship between inflation rates (CPI) and unemployment rates (HUR) with the Gross Domestic Product growth rates (GDP), before and after the 2008 European crisis. Quarterly data for 18 consecutive years and six sample countries from Europe (Austria, France, Germany, Greece, Hungary and United Kingdom) have been considered in the panel. In order to get a more profound understanding of the impacts of the European crisis on the relationship between the variables, the panel data set has been classified into 3 separate panels, such that Panel 1 (1999Q1-2007Q4) represents before-crisis panel, Panel 2 (2008Q1-2016Q4) represents the during/after crisis panel and lastly, Panel 3 (1999Q1-2016Q4) represents the long-run panel. Panel 1 is subject to the Fixed Effects with LSDV model, whereby four out of the six countries are significant, and CPI and HUR are insignificant predictors of the GDP. Both Panel 2 and Panel 3 are subject to the Two-way Random Effects model, whereby both CPI and HUR have negative significant effect on GDP. Granger Causality test has also been carried out to determine whether causality is present among variables, based on each panel.

War Making and the Building of State Capacity: Expanding the Bivariate Relationship

2017 ◽  
Author(s):  
Karen A. Rasler ◽  
William R. Thompson
Keyword(s):  
Gross Domestic Product ◽  
Empirical Analysis ◽  
State Capacity ◽  
Point Of View ◽  
Regime Type ◽  
Domestic Product ◽  
Data Set ◽  
Representative Data ◽  
The Relationship ◽  
Do So

A central cleavage in the war making-state making literature is between advocates of the notion that warfare has been the principal path to developing stronger states and critics who argue that the relationship no longer holds, especially in non-European contexts. It is suggested that the problem is simply one of theoretical specification. Increasingly intensive warfare, as manifested in European combat, made states stronger. Less intensive warfare, particularly common after 1945, is less likely to do so. Empirical analysis of a more representative data set on state capacity (revenues as a proportion of gross domestic product [GDP]), focusing on cases since 1870, strongly supports this point of view. The intensiveness of war is not the only factor at work—regime type and win/loss outcomes matter as well—but the relationship does not appear to be constrained by the level of development.

scholarly journals Dampak output GDP sektor pertanian terhadap masalah pembangunan ekonomi di Indonesia (kemiskinan dan pengangguran)

2018 ◽  
Vol 13 (1) ◽  
pp. 22-28
Author(s):  
Candra Mustika ◽  
Emilia Emilia
Keyword(s):  
Gross Domestic Product ◽  
Agricultural Sector ◽  
Domestic Product ◽  
Unemployed People ◽  
Average Value

This study aims to analyze the development of agricultural GDP output and poverty and unemployment during the period 1993 – 2014 and analyze the effect of the GDP Output of the agricultural sector on the level of poverty and unemployment in Indonesia in that period. The results showed that during the period of 1993-2014 the data on gross domestic product (GDP) originating from the agricultural sector continued to fluctuate in the increase and decrease, the average GDP value of the agricultural sector is 496.9 trillion with an average value of 17%. The regression results in the first model show that agricultural sector GDP does not have a significant effect on poverty and the regression results in the second model show that agricultural GDP does not have a significant effect on the number of unemployed people in Indonesia  

scholarly journals Selected Macroeconomic Variables and Agricultural Sector in Nigeria

2021 ◽  
Vol 7 (06) ◽  
Author(s):  
James Ese Ighoroje ◽  
Catherine, Ogheneovo Orife
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Gross Domestic Product ◽  
Money Supply ◽  
Agricultural Sector ◽  
Least Square ◽  
Domestic Product ◽  
Agricultural Output ◽  
Macroeconomic Variables ◽  
Ex Post

The study investigated effect of selected macroeconomic variables on agricultural sector output in Nigeria from 1987 - 2019. Annual Agricultural Output (AAO) represented the dependent variable for the study while gross domestic product, interest rate, money supply, and exchange rate represented the explanatory variables. Ex-post factor research design was employed for the study. Augmented Dickey Fuller Unit Roots test and Ordinary Least Square (OLS) Regression techniques were used to analyze data collected. The empirical investigation showed that gross domestic product as well as money supply has a positive and significant effect on agricultural output, while interest rate and exchange rate exerted a negative and insignificant effect on agricultural output. From the study, selected macroeconomic variables have positive effect on agricultural output in Nigeria and this has tremendously contributed to the country's growth and development. The study recommends amongst other; that government should accelerate the rate of economic growth by investing heavily on the agricultural sector so as to boost domestic production and enhance exportation in order to stabilize exchange rate while curbing inflation; give incentives to banks extending agricultural loans by lowering the lending rate on agricultural loans to ease access to funds for agricultural investment.

scholarly journals Potensi dan Prospek Industri Kecil

The Winners ◽  
2006 ◽  
Vol 7 (2) ◽  
pp. 164
Author(s):  
Dedi Walujadi
Keyword(s):  
Gross Domestic Product ◽  
Empirical Evidence ◽  
Agricultural Sector ◽  
Manufacturing Sector ◽  
Value Added ◽  
Manufacturing Industries ◽  
Domestic Product ◽  
Small Scale ◽  
Main Contributor ◽  
Economic Contribution

The manufacturing sector has retained its importance in the Indonesian Economy. Since 1990 it has surpassed the agricultural sector as the main contributor to the Gross Domestic Product (GDP). Article analyses strenght and weaknesses of the small-scale manufacturing industries (SSIs). By ussing the economic contribution approach and the framework proposed by Pyke, based on 2003 data provided by BPS statistics Indonesiathe study investigates the SSIs performance in relation to their economic contribution, the collective efficiency, constant innovation and economic ofscope strategy. It is conluded that Pyke’s framework was not apply since SSIs facing lack of social infrastructures and knowledge, and mostly less educated compared with the larger one. The empirical evidence also shows that in terms of value added and labor absorption, its share less than 1 % and 16 % respectively of the whole of industrialsectors. 

scholarly journals ANALISIS PERTUMBUHAN EKONOMI DAN KEUNGGULAN SEKTOR PERTANIAN INDONESIA

Agro Ekonomi ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 63
Author(s):  
Nur Fitriana ◽  
Dwidjono Hadi Darwanto ◽  
Slamet Hartono
Keyword(s):  
Growth Rate ◽  
Gross Domestic Product ◽  
Agricultural Sector ◽  
Domestic Product ◽  
Economic Sector ◽  
Business Sector ◽  
Location Quotient ◽  
Linear Pattern ◽  
East Kalimantan ◽  
Table Analysis

The study aims: (1) to measure the growth rate of economic sector based on Gross Domestic Product; (2) to determine contribution of economic sector to GDP; (3) to measure contribution trend of economic sector of GDP and to determine basic sectors inprovinces of Indonesia. The data used is secondary datafrom Badan Pusat Statistik, i.e. Gross Regional Domestic Product and Gross Domestic Product according with 1993 constant price. Data included overall provinces in Indonesia among 19842003. Table analysis, Location Quotient, shift-share, and Klassen Typology were used to analyze. The results are: (1) growth rate o/GDP is positively fluctuated, except at 1998 due to economic crisis; (2) contribution ofagricultural sector; mining and quarrying sector; and services sector weredecreased Contribution of manufacturing industries sector; electricity, gasand water supply sector; trade, restaurant and hotel sector; and transportation and communication sector were increased. Contribution of construction sector and finance and rent of building and business sector werefluctuated; (3) overall sector follow quadratic pattern, exceptfinance, building rent, and business sector that follow linear pattern; (4) agricultural sector was the basic sector in ·26 provinces, except in East Kalimantan, Banten, WestJava and Jakarta.

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