Dataset Information

Economic impact of GM crops: the global income and production effects 1996-2012.

ABSTRACT: A key part of any assessment of the global value of crop biotechnology in agriculture is an examination of its economic impact at the farm level. This paper follows earlier annual studies which examined economic impacts on yields, key costs of production, direct farm income and effects, and impacts on the production base of the four main crops of soybeans, corn, cotton and canola. The commercialization of genetically modified (GM) crops has continued to occur at a rapid rate, with important changes in both the overall level of adoption and impact occurring in 2012. This annual updated analysis shows that there have been very significant net economic benefits at the farm level amounting to $18.8 billion in 2012 and $116.6 billion for the 17-year period (in nominal terms). These economic gains have been divided roughly 50% each to farmers in developed and developing countries. GM technology have also made important contributions to increasing global production levels of the four main crops, having added 122 million tonnes and 230 million tonnes respectively, to the global production of soybeans and maize since the introduction of the technology in the mid-1990s.

SUBMITTER: Brookes G

PROVIDER: S-EPMC5033197 | biostudies-literature | 2014 Jan-Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Economic impact of GM crops: the global income and production effects 1996-2012.

Brookes Graham G Barfoot Peter P

GM crops & food 20140101 1

A key part of any assessment of the global value of crop biotechnology in agriculture is an examination of its economic impact at the farm level. This paper follows earlier annual studies which examined economic impacts on yields, key costs of production, direct farm income and effects, and impacts on the production base of the four main crops of soybeans, corn, cotton and canola. The commercialization of genetically modified (GM) crops has continued to occur at a rapid rate, with important chan ...[more]

PMID: 24637520

Similar Datasets

Project description:BackgroundCigarette smoking is a leading risk factor for morbidity and premature mortality in the United States, yet information about smoking prevalence and trends is not routinely available below the state level, impeding local-level action.MethodsWe used data on 4.7 million adults age 18 and older from the Behavioral Risk Factor Surveillance System (BRFSS) from 1996 to 2012. We derived cigarette smoking status from self-reported data in the BRFSS and applied validated small area estimation methods to generate estimates of current total cigarette smoking prevalence and current daily cigarette smoking prevalence for 3,127 counties and county equivalents annually from 1996 to 2012. We applied a novel method to correct for bias resulting from the exclusion of the wireless-only population in the BRFSS prior to 2011.ResultsTotal cigarette smoking prevalence varies dramatically between counties, even within states, ranging from 9.9% to 41.5% for males and from 5.8% to 40.8% for females in 2012. Counties in the South, particularly in Kentucky, Tennessee, and West Virginia, as well as those with large Native American populations, have the highest rates of total cigarette smoking, while counties in Utah and other Western states have the lowest. Overall, total cigarette smoking prevalence declined between 1996 and 2012 with a median decline across counties of 0.9% per year for males and 0.6% per year for females, and rates of decline for males and females in some counties exceeded 3% per year. Statistically significant declines were concentrated in a relatively small number of counties, however, and more counties saw statistically significant declines in male cigarette smoking prevalence (39.8% of counties) than in female cigarette smoking prevalence (16.2%). Rates of decline varied by income level: counties in the top quintile in terms of income experienced noticeably faster declines than those in the bottom quintile.ConclusionsCounty-level estimates of cigarette smoking prevalence provide a unique opportunity to assess where prevalence remains high and where progress has been slow. These estimates provide the data needed to better develop and implement strategies at a local and at a state level to further reduce the burden imposed by cigarette smoking.

Project description:IntroductionHealth-income inequality has been the focus of many studies. The relationship between economic conditions and health has also been widely studied. However, not much is known about how changes in aggregate economic conditions relate to health-income inequality. Nevertheless, such knowledge would have both scientific and practical value as substantial public expenditures are used to decrease such inequalities and opportunities to do so may differ over the business cycle. For this reason we examine the effect of the Icelandic economic collapse in 2008 on health-income inequality.MethodsThe data used come from a health and lifestyle survey carried out by the Public Health Institute of Iceland in 2007 and 2009. A stratified random sample of 9,807 individuals 18-79 years old received questionnaires and a total of 42.1% answered in both years. As measures of health-income inequality, health-income concentration indices are calculated and decomposed into individual-level determinants. Self-assessed health is used as the health measure in the analyses, but three different measures of income are used: individual income, household income, and equivalized household income.ResultsIn both years there is evidence of health-income inequality favoring the better off. However, changes are apparent between years. For males health-income inequality increases after the crisis while it remains fairly stable for females or slightly decreases. The decomposition analyses show that income itself and disability constitute the most substantial determinants of inequality. The largest increases in contributions between years for males come from being a student, having low education and being obese, as well as age and income but those changes are sensitive to the income measure used.ConclusionsChanges in health and income over the business cycle can differ across socioeconomic strata, resulting in cyclicality of income-related health distributions. As substantial fiscal expenditures go to limiting the relationship between income and health, the business-cycle effect on equality, which has up until now not received much attention, needs to be considered.

Dataset Information

Economic impact of GM crops: the global income and production effects 1996-2012.

Publications

Economic impact of GM crops: the global income and production effects 1996-2012.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets