Thanks for posting :) I’m coming at this as someone who spent a lot of time running on-farm research at Fish Welfare Initiative (and planning to do more through my new charity). I broadly agree, but I’d add a few caveats:
The core issue with farms as “welfare labs” is heterogeneity (variability). Especially in LMICs, farms are messy, uncontrolled environments where confounding variables easily creep in. That creates a lot of statistical noise. If you’re aiming for high certainty, farms can make that difficult.
Relatedly, on-farm research makes it harder to isolate specific effects. You mention the benefit of insights collected “under real commercial conditions”, and I agree that ultimately effectiveness in the real world is what matters. But there’s also strong value in isolating variables to understand mechanisms. If we’re testing whether pigs prefer straw or wood shavings, we may not want to simultaneously capture differences in how farmers manage those materials. Otherwise, when results don’t support our hypothesis, we don’t know whether we’re observing animal preference or management differences. That’s why the difference between efficacy (does it work in controlled conditions?) and effectiveness (does it work in real-world conditions?) is useful.
So in sum: on-farm research is a valuable tool, but it can’t replace controlled research, and I’d hesitate to frame it as more valuable.
I also think two ideas may be getting conflated: monitoring existing farm conditions vs running experiments on farms. Both can be useful, but both need a clear use-case for the data. I agree, though, that there’s strong potential on both fronts.
Aaron!
Thanks for posting :) I’m coming at this as someone who spent a lot of time running on-farm research at Fish Welfare Initiative (and planning to do more through my new charity). I broadly agree, but I’d add a few caveats:
The core issue with farms as “welfare labs” is heterogeneity (variability). Especially in LMICs, farms are messy, uncontrolled environments where confounding variables easily creep in. That creates a lot of statistical noise. If you’re aiming for high certainty, farms can make that difficult.
Relatedly, on-farm research makes it harder to isolate specific effects. You mention the benefit of insights collected “under real commercial conditions”, and I agree that ultimately effectiveness in the real world is what matters. But there’s also strong value in isolating variables to understand mechanisms. If we’re testing whether pigs prefer straw or wood shavings, we may not want to simultaneously capture differences in how farmers manage those materials. Otherwise, when results don’t support our hypothesis, we don’t know whether we’re observing animal preference or management differences. That’s why the difference between efficacy (does it work in controlled conditions?) and effectiveness (does it work in real-world conditions?) is useful.
So in sum: on-farm research is a valuable tool, but it can’t replace controlled research, and I’d hesitate to frame it as more valuable.
I also think two ideas may be getting conflated: monitoring existing farm conditions vs running experiments on farms. Both can be useful, but both need a clear use-case for the data. I agree, though, that there’s strong potential on both fronts.