The yield penalty happens in almost every crop, in almost every study.
The gap could narrow with more research/varieties, but it won’t go away.
This has big, bad climate implications.
Let’s talk about ORGANIC.
Robert Paarlberg’s new book on the food system is out today, and here’s an excerpt.
He’s not a fan of organic, and he makes some good points but misses others.
Here we go.
The yield penalty happens in almost every crop, in almost every study.
The gap could narrow with more research/varieties, but it won’t go away.
This has big, bad climate implications.
Natural doesn’t mean safe, or even safer. A farming standard based on it forces farmers to sometimes use more dangerous, less effective tools.
https://t.co/Kh8TCgPEBp
What I don’t want is for folks who can’t afford organic to worry about feeding their kids conventional produce.
This is why I buy organic animal products.
Many in the organic world would like more regs ensuring animals have decent lives.
https://t.co/s1VfzlOmgK
It enables growers to find like-minded consumers willing to pay a premium, and organic farmers are more profitable. This is good for a couple of reasons.
If they all could, maybe we wouldn’t have to subsidize them to the tune of $20 billion a year.
But profitable farmers also have the cushion to experiment with new ways to farm better.
There has been, in the conventional ag community, a tendency toward sneering superciliousness about organic. Some call to boycott it!
Why? Because SCIENCE!
More from Health
You gotta think about this one carefully!
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
