Long-term cost-effectiveness of interventions for loss of electricity/industry compared to artificial general intelligence safety

The Fragile State of Industrial Agriculture: Estimating Crop Yield Reductions in a Global Catastrophic Infrastructure Loss Scenario

Modern civilization relies on a complex, globally interconnected industrial agriculture system to produce food. Its unprecedented yields hinge on external inputs like machinery, fertilizers, and pesticides, rendering it vulnerable to disruptions in production and international trade. Such a disruption could be caused by large-scale damage to the electrical grid. Solar storms, nuclear detonations in the upper atmosphere, pandemics, or cyber-attacks, could cause this severe damage to electrical infrastructure. To assess the impact of such a global catastrophic infrastructure loss on major food crops (corn, rice, soybean, wheat), we employ a generalized linear model. The predictions show a crop-specific yield reduction between 15% and 37% in phase 1, the year after the catastrophe, assuming rationed use of fertilizers, pesticides, and fuel stocks. In phase 2, when all stocks are depleted, yields decrease by 35%-48%. Soybean is less affected in phase 1, while all crops experience strong declines in phase 2. Europe, North and South America, and parts of India, China, and Indonesia face major yield reductions, potentially up to 75%, while most African countries are less affected. These findings underscore the necessity for preparation by highlighting the vulnerability of the food system.

Methane Single Cell Protein: Potential to Secure a Global Protein Supply Against Catastrophic Food Shocks

Global catastrophes such as a supervolcanic eruption, asteroid impact, or nuclear winter could cause global agricultural collapse due to reduced sunlight reaching the Earth’s surface. The human civilization’s food production system is unprepared to respond to such events, but methane single cell protein (SCP) could be a key part of the solution. Current preparedness centers around food stockpiling, an excessively expensive solution given that an abrupt sunlight reduction scenario (ASRS) could hamper conventional agriculture for 5–10 years. Instead, it is more cost-effective to consider resilient food production techniques requiring little to no sunlight. This study analyses the potential of SCP produced from methane (natural gas and biogas) as a resilient food source for global catastrophic food shocks from ASRS. The following are quantified: global production potential of methane SCP, capital costs, material and energy requirements, ramp-up rates, and retail prices. In addition, potential bottlenecks for fast deployment are considered. While providing a more valuable, protein-rich product than its alternatives, the production capacity could be slower to ramp up. Based on 24/7 construction of facilities, 7%–11% of the global protein requirements could be fulfilled at the end of the first year. Despite significant remaining uncertainties, methane SCP shows significant potential to prevent global protein starvation during an ASRS at an affordable price—US$3–5/kg dry.

Open source software toolchain for automated non-targeted screening for toxins in alternative foods

Previous published methods for non-targeted screening of toxins in alternative foods such as leaf concentrate, agricultural residues or plastic fed to biological consortia are time consuming and expensive and thus present accessibility, as well as, time-constraint issues for scientists from under resourced settings to identify safe alternative foods. The novel methodology presented here, utilizes a completely free and open source software toolchain for automatically screening unknown alternative foods for toxicity using experimental data from ultra-high-pressure liquid chromatography and mass spectrometry. The process uses three distinct tools (mass spectrometry analysis with MZmine 2, formula assignment with MFAssignR, and data filtering with ToxAssign) enabling it to be modular and easily upgradable in the future. MZmine 2 and MFAssignR have been previously described, while ToxAssign was developed here to match the formulas output by formula assignment to potentially toxic compounds in a local table, then look up toxic data on the Open Food Tox Database for the matched compounds. This process is designed to fill the gap between food safety analysis techniques and developing alternative food production techniques to allow for new methods of food production to be preliminarily tested before animal testing. The methodology was validated against a previous method using proprietary commercial software. The new process identifies all of the toxic elements the previous process identified with more detailed information than the previous process was able to provide automatically.•Efficient analysis to find potentially toxic compounds in alternative foods and resilient foods.•Identification of potentially unsafe products without the use of live animal testing.•Modular free and open source design to allow for upgrading or fitting of user needs.