Take 2: what I wish I'd put in my recent book chapter

I asked my wife Sarah to take a look at my recent chapter in the book "Effective Conservation Science: Data Not Dogma" and she made an excellent point: what should the take-away be? Here are the things I was hoping to convey but wasn't sufficiently clear about.

The key fact I wanted to convey is that the global agriculture situation is complex: the global land used for agriculture hit its peak in 1998 (so it's not true agricultural land is rapidly expanding around the world), BUT in some places there is a lot of agricultural expansion and/or reliance on unsustainable levels of water and nutrients. So there is good news and bad news.

Some other important facts I didn't go into in much detail:

• In the last two decades we have been able to meet increasing demand for food through intensification (producing more food on existing lands). But going forward projected demand rises faster than what we're likely to be able to produce through intensification. So between 2030-2050 we can expect conversion to agriculture to speed up and lead to a net expansion of land used for agriculture.
• Agriculture in many regions currently relies on unsustainable irrigation. As groundwater is depleted and crops get thirsty we can expect yields to eventually fall substantially on agricultural lands in water-scarce areas. The California drought gives us a taste of what that could look like. Note that changes in irrigation technology are likely insufficient to solve this on their own (see the summary of Richter 2017).
• Other agricultural inputs may start to run out or at least limit improved crop yields. That could include rock phosphate for fertilizer or even nitrogen fertilizer if actions to limit climate change make it more expensive.

The key message of the piece for other scientists is: don't assume that things that seem obvious (like ag land rapidly expanding) are true, and don't assume that global data sets are reliable enough to inform policy or other action. Dig deeper! Ask questions, and look for more local data to corroborate your suspicions.

Let me know if you have other questions or suggestions!