By Francesca Gugino
My research thesis is to conduct a meta-analysis of 58 articles that account for economic data on agroecology for climate change mitigation and adaptation. The articles are from a larger data set previously used to create a rapid evidence review of agroecology by CGIAR/CCAFS. The rapid evidence review explores the effectiveness of agroecological practices in LMICs, particularly focusing on pest and disease management and nutrient management. Agroecology is defined in this review by using the FAOs 10 elements of agroecology and the Gliessman (2016) framework. These were both utilized to identify which practices would be considered agroecological, helping to narrow the dataset.
My objective is to formulate which, if any, agroecological practices from the 58 articles are cost effective for famers and donors/investors, to calculate possible return on investment, to consider and disaggregate all existing economic data, such as inputs, yield data, labor data etc., and to hopefully create a comprehensive scale for levels of agroecological input and the impact that it has at the farm scale. Initially looking at the articles my hope was to create a scale from 0.0 to 3.3, where the first number represents the level of intensity of agroecological inputs (0= none, 1=low, 2=moderate, 3=high), while the second number represents the benefits that its implementation has at the farm. This will be explored through an analysis after all presentation data is considered and hopefully proves to be beneficial for farmers and donors/investors as a way to sustainably intensify agriculture and mitigate and adapt to climate change.
After reading and re-reading the economic data from each of the 58 articles, I have found that in many cases yield has improved for farmers after the implementation of agroecological practices and that in many scenarios pest and disease issues have diminished, while soil nutrients have benefit. Much relevant economic data exists within the articles, including but not limited to: return on investment, internal return data, gross and net income, output data, yield data, input data, gender data and market data. It seems that there is plenty of information to work with, and I will not utilize any data strings of my own until it is necessary to supplement what I already have found within the existing data set, as of now this does not seem to pose an issue.
Over the next week I will continue to disaggregate all economic data into presentation data, group this data in clusters based on location, practices, farm type etc., create a presentation for my supervisors and begin to work on the analysis.