Beschreibung:
Salinity in surface waters is on the rise throughout much of the world. Many factors contribute to this change, including increased water extraction, poor irrigation management, and sea-level rise. To date no study has attempted to quantify the impacts on global food production. This paper develops a plausibly causal model to test the sensitivity of global and regional agricultural productivity to changes in water salinity. To do so, it utilizes several local and global data sets on water quality and agricultural productivity and a model that isolates the impact of exogenous changes in water salinity on yields. The analysis trains a machine-learning model to predict salinity globally, to simulate average global food losses over 2000-13. These losses are found to be high, in the range of the equivalent of 124 trillion kilocalories, or enough to feed more than 170 million people every day, each year. Global maps building on these results show that pockets of high losses occur on all continents, but the losses can be expected to be particularly problematic in regions already experiencing malnutrition challenges