Salt of the Earth
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.
Main Authors: | , , , , , |
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Format: | Working Paper biblioteca |
Language: | English |
Published: |
World Bank, Washington, DC
2020-02
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Subjects: | SALINITY, AGRICULTURAL PRODUCTIVITY, WATER QUALITY, FOOD SECURITY, CROP YIELD, |
Online Access: | http://documents.worldbank.org/curated/en/284971581348972217/Salt-of-the-Earth-Quantifying-the-Impact-of-Water-Salinity-on-Global-Agricultural-Productivity https://hdl.handle.net/10986/33320 |
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Summary: | 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. |
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