Jonas Kathage reviews “A Meta-Analysis of the Impacts of Genetically Modified Crops”

The work was funded exclusively with public money, in part from the German Federal Ministry of Economic Cooperation and Development (BMZ) and the European Union’s Seventh Framework Programme (FP7/2007-2011). The authors are Wilhelm Klümper, a PhD student at the Department of Agricultural Economics and Rural Development at the University of Göttingen, Germany, and Matin Qaim*, a professor at the same institution and well-published researcher on the economics of GMOs.

A new meta-analysis on the farm-level impacts of GMOs – Biology Fortified, Inc. This is the breed of meta-analysis that we need – in that the authors have no conflict of interest issues of any kind [full disclosure: reviewer Jonas Kathage is a former graduate student of coauthor Matin Qaim].

As far as I can tell the authors made every reasonable effort to extract well-supported conclusions from the 147 studies. This is a big challenge — in choosing your population of studies you want to avoid cherry-picking while excluding studies that are either unreliable or do not report in ways that are consistent with the design of the meta-analysis. In the subject PLOS paper A Meta-Analysis of the Impacts of Genetically Modified Crops the authors screened 24,079 studies down to 147 that met all their criteria.


Assuming it holds up, a key result was the greater benefits experienced by developing country farmers:

Furthermore, yield gains of GM crops are 14 percentage points higher in developing countries than in developed countries. Especially smallholder farmers in the tropics and subtropics suffer from considerable pest damage that can be reduced through GM crop adoption.

Dr. Kathage agreed with the authors’ finding that industry funding did not bias the base study results towards higher yields.

Apart from the type of GM trait (IR/HT) and the type of country (developing/developed) the paper also sheds light on several other reasons why some yield results are different from others. For example, it looks at whether funding from industry is associated with higher yield estimates. It is not.

This graphic summarizes the differences between GM and non-GM crops:


Is there a superior meta-analysis that we can cite for the big picture on the results of applying GM crops?