This post presents a new tool for deciding which countries to prioritise in preventing or limiting the growth of industrial animal agriculture in countries in Sub Saharan Africa.
Introduction
The Prevention of Intensification of Factory Farming (PIFF) country scoring model (Sub Saharan Africa) is a geographical weighted factor model used to assess countries for their promisingness as targets for interventions to prevent or limit the extent of the intensification of factory farming in Sub Saharan Africa. A previous version of this model has been developed by Moritz Stumpe for Animal Advocacy Africa's research project with Bryant Research, and was further developed by Aashish during the AIM Research Training Program.
Model Usage
This model can serve as the basis for various geographic assessments. Whilst the model in its current state serves as a tool to assess appropriateness for a general intervention, it can be modified for specific purposes by weighting each category and its constituent criteria as is desired, and factors may be added or removed from the model. The model can also be applied to other geographic areas, by pulling the respective data from the listed sources and plugging it into the same or a similar structure.
The model in its current form calculates scores for each of the following categories: scale, projected intensification, current intensification, tractability, and movement support, and combines these into a weighted sum to give an overall score. Weighted multiplication is another calculation method that is used to provide an additional perspective. Further details on each category, its constituent criteria, and their weights can be found in the “Summary Sheet” of the model.
A shared tool for the movement
We encourage advocates to edit and extend this tool, and share further iterations, particularly if adapting it for considering particular intervention strategies, as this may provide a useful resource for the community.
Hi. Thanks for sharing the model. I’d like to question you putting 32.5% weight on the scale, which you define as “Number of land animals projected to be farmed in 2050 under business-as-usual conditions”. The value of this variable depends on:
I think that the 2, 3, and 4 are relevant and should be in the metric. But of these four, I’d bet it depends on the 1 (current human population) by far the most. No matter the growth, Djibouti (pop 1.1 million) will not farm more animals than Nigeria (pop 213 million). But I’m unsure if that provides evidence that the marginal dollar would go further in supporting animal advocacy in Nigeria.
Uh, I know embarrassingly little about the geopolitical situation in Africa. I’ll just say that I saw this type of metric being used to prioritise animal advocacy work in the U.S. over the work in Poland because U.S. is bigger. But federal legislation in the U.S. is very unlikely, so any work in the U.S. will focus on a particular state, which might be smaller than Poland. If that state somehow becomes an independent country (or EU becomes one country), suddenly the comparisons shift to favour Poland. But it’s unclear whether such administrative changes would actually impact how many animals would be helped by charities in either country.
There is an implicit assumption that we would achieve more if we spent $10 million on a big country like Nigeria, rather than if we dispersed this $10 million among 10 smaller countries which combined might be as big as Nigeria. It’s unclear whether this is the case even for country-wide interventions. It’s possible that lobbying bigger government bodies is more cost-effective. But is it? I just honestly don’t know. And also Nigeria likely has bigger corporations than Djibouti, and corporate campaigns against bigger corporations could be more cost-effective. But I haven’t seen this shown anywhere either. It’s also possible that campaigns against medium-sized corporations and governments are more cost-effective. AFAIK, we just don’t know. And for some other interventions, like working with farmers to achieve win-win reforms (in the style of Fish Welfare Initiative), it might not matter whether the farmer is in a big or small country.
Sorry for the longwinded comment, I didn’t want to spend much time tidying it up, and thanks for your work :)
Nice ^_^ One final thought. I mentioned that scale depends on multiple parameters:
You account for 2,3, and 4 with a separate variable “expected growth in animal production” which would be something like “projected number of farmed animals in 2050 divided by the current number of farmed animals”. And then also have a variable “Current human population”. I think it makes sense to split because these two variables matter for different reasons, and someone may put weight on one but not the other.