A bit of op-ed.
As every schoolchild learns, one of the keys to the scientific method, and one of the reasons it allows us to know things rather than merely believe them is that the description of a method allows another scientist to go out and reproduce the results, either by using the same method or by using a slightly different one: the latter method provides consilience, whereby different approaches confirm results. For example, when Michael Mann published his hockey stick warming research (1) other researchers were able to confirm the result using slightly different methods but reaching the same conclusions, thus improving on Mann's work (2). In this case, the system worked and worked well.
It's becoming increasingly widely known that not all research can be properly reproduced. In pharmacology only one “positive” result is required to gain approval from the US Food and Drug Administration, and the regulatory bodies of many other countries simply follow the FDA's lead. There is good evidence that there are many trials (which we know of because of the trials' participants, if nothing else) locked in file drawers. The big biotech companies specifically ban the use of their seed for research purposes, making attempts to replicate impossible: anyone publishing would find herself in court.
This is a problem that affects some fields more than others. This recently
led Nature, one of the world's top scientific journals, to ask over 1500 scientists about the problem of reproducibility. You can find an article on the survey here:
http://www.nature.com/news/1-500-scientists-lift-the-lid-on-reproducibility-1.19970
“More than 70% of researchers have tried and failed to reproduce another scientist's experiments, and more than half have failed to reproduce their own experiments.”
Does this mean that all, even most, science is unreliable?
Not necessarily. For one thing, the participants in this study were self-selecting. The researchers who took part are more likely to be those who, for one reason or another, are more concerned about the issue. There may well be many thousands more who don't consider it a big issue, or who don't consider it a big issue in their field. Of dozens of studies conducted in a professional scientist's career, at least one of those conducted by the minority who took part in this study could not be reproduced. That doesn't stop the majority of research being sound.
It's also worth bearing in mind that Nature is a commercial concern: it has to make a
profit, and headlines such as this draw in readers, and in this they have just been successful!
Nevertheless, the majority of researchers who took part in this study think there is some sort of reproducibility crisis. On the other hand, they are probably biased. As studies go, this is not a good one. I suspect there is a problem and lessons to be learned. I don't think we need to end up having some sort of hysterical reaction to the problem.
I think the first lesson it's worth being aware of is that the fact that something worked for one person doesn't mean it will work for someone else. Not only that, but we need to be aware that's okay. When we are dealing with a squishy science like agronomy there are so many uncontrollable variables that replication isn't always going to work. Biological systems are incredibly complicated. Even if half a dozen people try to replicate results and half of those fail to replicate that does not mean something interesting didn't happen the other four times: they key question then becomes why. It doesn't mean accusing someone of lying (fraud is often a possibility, and does happen, but it's not usually the most likely possibility).
For example, let's say five people undertake an otherwise identical protocol involving a
compost tea on five different plots of
land.
1) Predominantly sand: This may already have a simple microbiota, but the minerals required for plant growth simply aren't there to begin with. In this case the compost tea condition might show a slight but not statistically significant improvement over the control plot. (What might be needed is a load of organic matter.)
2) Loam with a history of organic cultivation: in this case the microbes are already there. The application of more does nothing by comparison with the control plot.
3) A patch of
lawn treated until recently with the usual agrochemicals: no improvement over the control is seen because the remaining chemicals wipe out the bacteria.
4) A similar patch of lawn treated until a couple of years ago with the usual agrochemicals. Still no improvement is seen against the control condition, because bacteria and other microorganisms have recolonised the soil naturally.
5) Yet another similar patch of lawn just after the agrochemicals have broken down into inert substances. “Miraculous” results are seen: miraculous results that would have happened anyway by allowing the birds to forage on it.
That doesn't mean the fifth trial was an aberration, or even that the person reporting the first set of results was “lying”, but that she had unique conditions that facilitated a “positive” result – but it's no more than a useful case study, not to be overgeneralised. I'm not saying this is what would happen: merely that it seems plausible at 2am when I wrote this.
Soil testing and checks with a microscope would explain the lot.
Equally, if there are persistent failures to replicate, it seems to remain important to figure out why.
The thing is, we are going to be out at the cutting edge of
polyculture agronomy, at least in temperate zones. Not all results are going to be robust. Not all explanations are necessarily likely to be good ones, although understanding relevant aspects of plant biology and of ecology is going to be important. If we don't understand the basics of what is known about these systems we are more likely to make mistakes.
One lesson I think it's important to draw is that people need to be willing to come forward and say that they tried something and then something happened – and be willing to have others check their work and query their methods and conclusions, but not to make overblown claims until they've been checked. That's how we avoid pseudoscience. Checking under multiple conditions is what allows us to develop good practice.
Part of the problem is that humans are masters of self deception. Unfortunately, instincts that worked well in Olduvai Gorge may be less useful when trying to figure out how to
feed ten billion humans while addressing the threat of climate disruption and avoiding the worst extinction event since the Chixculub Impact. “It worked last time” may well be of survival benefit when working out that the proto-impala are likely to be near the watering hole, but may be less useful when trying to establish a forest garden. That's not a criticism of anyone in particular: we all make these mistakes,
except me me included. See also:
http://www.nature.com/news/how-scientists-fool-themselves-and-how-they-can-stop-1.18517
We all have a tendency to jump to conclusions. I do it too. I know there is a rule that you are not allowed to imply someone is less than perfect: I'm going to stick my neck out and say that we are all less than perfect. I'm less than perfect.
Even when one of us tries to conduct unbiased research, we're likely to have a stake in the outcome, and this applies as much to us as it does to a postgrad in a lab, if for different reasons. We all want to prove that
permaculture works, by some standard of “works”. I want to prove that a multi-strata agroforest is the best (by several measures) way to produce a balanced animal-free diet. A rancher will want to prove that it's possible to produce meat while sequestering
carbon. One solution to this is to declare an experimental design and invite those who might disagree to comment on that design. I might plan to measure net primary productivity, biodiversity change, total yield and protein yield (see me look for something that will prove what I want it to prove!).
If I don't have a decent control plot and report all the results and not just the ones that suit me I'm doing nobody any favours.
This article
http://www.nature.com/news/how-scientists-fool-themselves-and-how-they-can-stop-1.18517 discusses problems ranging from the failure to consider other explanations, p-hacking (where you look for numbers to confirm your pet hypothesis), to disconfirmation bias (checking unlikely results but giving expected ones a free pass), among others. All these hit the accelerator, leading to potentially interesting, but also potentially wrong, conclusions that lead us to waste time and
energy on practices that are not as helpful as we want to think they are.
One thing that I think would benefit most of us (anyone without a lot of
experience in agroecological research) is decent mentoring. The
Permaculture Association has a list of researchers who might be interested in some degree of collaboration:
https://www.permaculture.org.uk/research/list-permaculture-friendly-academics With luck this also means help with design. This probably also wildly improves your chances of publication.
In conclusion, I don't think science as a means of understanding the universe is necessarily in crisis. I think there is a problem that I think the scientific community will be able to overcome. I think there are simple but important lessons that we can learn from the process that will help permaculturalists improve our own practices. We're still going to make mistakes, go down unhelpful paths and so on. We're never going to achieve some pristine “truth”. We can learn to avoid some pitfalls.
1) doi:10.1029/1999GL900070
2) see for example doi:10.1177/0959683608098952