discussion / Community Base  / 8 April 2019

Help requested—verifiable stories where low-tech solutions beats out high-tech

Hi all,

I know this forum is focused on using technology to aid in conservation, but I also know that technology isn't a silver bullet and in some cases low-tech solutions can be more effective, and even cheaper. One that comes to mind is painting eyes on the rumps of cattle to trick predators into thinking the animal is looking at them. I can't find any follow up on the effectiveness of this strategy, so if you have an update, definitely please share!

Another (potentially apocryphal) story I'd love to have specific data to back up: to combat poaching a group sets up a high tech array of cameras and sensors to detect poachers and deploy response teams to catch them. But the high tech gear just ends up getting stolen by the locals and sold for cash. So they pivot from the tech solution; they take the money they would otherwise have used on surveillance tech and use it to establish a bounty program which pays locals rewards for information that leads to arrests and convictions of poachers. And this turns out to be a more effective strategy.

Does anyone know if this happened and if so where and ideally links to articles about it?

Also, if you can think of any other "low-tech more effective than high-tech" stores, please do share those as well.

Thanks!




I've covered an array of these topics for Deutsche Welle Global Ideas. Some of the low- tech solutions that impress me take the community-based conservation approach, which provide market incentives that can improve the lives of resource dependent villagers, as well.

https://www.dw.com/en/fishing-for-success-in-madagascar/a-18685270

https://www.dw.com/en/indigenous-groups-fighting-for-the-planet/a-19262246

 

Now, I'm researching enforcement efforts designed to prevent rare and valuable timber from entering the global supply chain and why it matters. 

Regards,

 

Enrique Gili
Freelance Writer
Twitter: gili92107

 

Hi. I'm new to wildlife conservation but have many stories about my work with World Bank in developing countries. 

1) We were auditing water reservoirs in the deserts of La Guajira, Colombia near Venezuela. The area is harsh and is controlled by FARC and drug lords. Without running water, people have to travel 8 hours a day with a donkey to get water. They were using solar panels to power the water pumps to move the water from the reservoir to the water towers, but the problem is the solar panels kept on getting stolen. There were discussions about how to improve security on the solar panels including remote cameras, GPS trackers, etc. Instead, the locals installed a hammock, a sound system, and some lighting underneath the solar panels which would create shade. It turned into a gathering area for the community to hang out in the hot desert sun. Nobody dared to steal those solar panels because it benefited the whole community and they would come down on whoever would try to take them.

2) We were checking out a water management storage tank in Dharamsala, India in the Himalayas that was supplying water to Tibetan Childrens' Village, Tibetan monks, and the Dalai Lama. We were discussing setting up a sonar-based water level sensor in the tank and whether or not the area had a nearby WiFi connection so the water levels could be monitored. The Tibetan monk in charge of the tank smiled and pointed at a long stick. He sticks it in the tank in the morning and once in the evening to check the water level. That way, he always knows if it's too low or too high. I think we all smiled after that. Then he asked us to continue the pilot because he was interested in learning something new. He also asked us if we knew how to fix his Android tablet which I thought was hilarious. 

3) I was teaching a workshop on agriculture technology and then somebody said that farmers could just use machine learning and trained algorithms to judge when the right time to pick vegetables were. I told him that harvesting is not just about the vegetables, but about weather conditions, market prices, storage space, available labor, etc. A farmer doesn't need machine learning to know when to pick a vegetable.