Raed Hamed, S. Vijverberg, A. V. van Loon, J. Aerts, D. Coumou
{"title":"Persistent La Niñas drive joint soybean harvest failures in North and South America","authors":"Raed Hamed, S. Vijverberg, A. V. van Loon, J. Aerts, D. Coumou","doi":"10.5194/esd-14-255-2023","DOIUrl":null,"url":null,"abstract":"Abstract. Around 80 % of global soybean supply is produced in southeast\nSouth America (SESA), central Brazil (CB) and the United States (US) alone.\nThis concentration of production in few regions makes global soybean supply\nsensitive to spatially compounding harvest failures. Weather variability is\na key driver of soybean variability, with soybeans being especially vulnerable to\nhot and dry conditions during the reproductive growth stage in summer. El\nNiño–Southern Oscillation (ENSO) teleconnections can influence summer\nweather conditions across the Americas, presenting potential risks for\nspatially compounding harvest failures. Here, we develop causal structural\nmodels to quantify the influence of ENSO on soybean yields via mediating\nvariables like local weather conditions and extratropical sea surface\ntemperatures (SSTs). We show that soybean yields are predominately driven by\nsoil moisture conditions in summer, explaining ∼50 %, 18 %\nand 40 % of yield variability in SESA, CB and the US respectively. Summer soil\nmoisture is strongly driven by spring soil moisture, as well as by remote\nextratropical SST patterns in both hemispheres. Both of these soil moisture\ndrivers are again influenced by ENSO. Our causal models show that persistent\nnegative ENSO anomalies of −1.5 standard deviation (SD) lead to a −0.4 SD\nsoybean reduction in the US and SESA. When spring soil moisture and\nextratropical SST precursors are pronouncedly negative (−1.5 SD), then\nestimated soybean losses increase to −0.9 SD for the US and SESA. Thus, by\ninfluencing extratropical SSTs and spring soil moisture, persistent La\nNiñas can trigger substantial soybean losses in both the US and SESA,\nwith only minor potential gains in CB. Our findings highlight the physical\npathways by which ENSO conditions can drive spatially compounding events.\nSuch information may increase preparedness against climate-related global\nsoybean supply shocks.","PeriodicalId":92775,"journal":{"name":"Earth system dynamics : ESD","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth system dynamics : ESD","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5194/esd-14-255-2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Abstract. Around 80 % of global soybean supply is produced in southeast
South America (SESA), central Brazil (CB) and the United States (US) alone.
This concentration of production in few regions makes global soybean supply
sensitive to spatially compounding harvest failures. Weather variability is
a key driver of soybean variability, with soybeans being especially vulnerable to
hot and dry conditions during the reproductive growth stage in summer. El
Niño–Southern Oscillation (ENSO) teleconnections can influence summer
weather conditions across the Americas, presenting potential risks for
spatially compounding harvest failures. Here, we develop causal structural
models to quantify the influence of ENSO on soybean yields via mediating
variables like local weather conditions and extratropical sea surface
temperatures (SSTs). We show that soybean yields are predominately driven by
soil moisture conditions in summer, explaining ∼50 %, 18 %
and 40 % of yield variability in SESA, CB and the US respectively. Summer soil
moisture is strongly driven by spring soil moisture, as well as by remote
extratropical SST patterns in both hemispheres. Both of these soil moisture
drivers are again influenced by ENSO. Our causal models show that persistent
negative ENSO anomalies of −1.5 standard deviation (SD) lead to a −0.4 SD
soybean reduction in the US and SESA. When spring soil moisture and
extratropical SST precursors are pronouncedly negative (−1.5 SD), then
estimated soybean losses increase to −0.9 SD for the US and SESA. Thus, by
influencing extratropical SSTs and spring soil moisture, persistent La
Niñas can trigger substantial soybean losses in both the US and SESA,
with only minor potential gains in CB. Our findings highlight the physical
pathways by which ENSO conditions can drive spatially compounding events.
Such information may increase preparedness against climate-related global
soybean supply shocks.