Abstract
An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO 2 concentration [CO 2 ] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO 2 ]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO 2 ] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO 2 ] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO 2 ] and temperature is needed to reduce the uncertainty from the individual models.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 11
Claim · model-written summary
Models varied considerably in simulated soybean yield responses to temperature and CO2.
- Taxon
- soybean
Glycine maxt0 - Outcome
- simulated yield responsese0
- Comparator
- modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“considerable variability among models in simulated yield responses to increasing temperature and [CO 2 ]”
t0“soybean”
Claim · model-written summary
At the coolest Argentine location, models simulated a 24% yield reduction under 3 °C warming.
- Taxon
- soybeanGlycine maxt0
- Treatment
- + 3 °C temperature risee0
- Outcome
- yielde0
- Direction
- reducee0
- Value
- 24%e0
- Setting
- Argentinae0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%”
t0“soybean”
Claim · model-written summary
At the coolest Argentine location, other models simulated yield increases up to 29%.
- Taxon
- soybeanGlycine maxt0
- Outcome
- yielde0
- Direction
- increasese0
- Value
- 29%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“others simulated yield increases up to 29%”
t0“soybean”
Claim · model-written summary
At the warmest Brazilian location, simulated yield losses reached 38% under 3 °C warming.
- Taxon
- soybeanGlycine maxt0
- Treatment
- + 3 °C temperature risee0
- Outcome
- yielde0
- Direction
- decreasee0
- Value
- 38%e0
- Setting
- Brazile0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield”
t0“soybean”
Claim · model-written summary
At the warmest Brazilian location, some models simulated no yield effect.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield”
t0“soybean”
Claim · model-written summary
Raising CO2 from 360 to 540 ppm produced simulated yield increases of 6% to 31%.
- Taxon
- soybeanGlycine maxt0
- Treatment
- increasing [CO 2 ] from 360 to 540 ppme0
- Outcome
- yielde0
- Direction
- increasee0
- Value
- 6% to 31%e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“when increasing [CO 2 ] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%”
t0“soybean”
Claim · model-written summary
Calibration did not reduce variation across models.
- Taxon
- soybeanGlycine maxt0
- Outcome
- variability across modelse0
- Direction
- did not reducee0
- Comparator
- modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Model calibration did not reduce variability across models”
t0“soybean”
Claim · model-written summary
Calibration altered temperature yield responses in some models.
- Taxon
- soybeanGlycine maxt0
- Outcome
- yield responses to temperaturee0
- Comparator
- modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“modifying yield responses to temperature for some of the models”
t0“soybean”
Claim · model-written summary
Ensemble means reduced uncertainty in soybean yield simulations.
- Taxon
- soybeanGlycine maxe0
- Outcome
- uncertainty in soybean yield simulationse0
- Direction
- reducee0
- Comparator
- individual modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization”
Claim · model-written summary
Ensemble mean temperature and CO2 responses resembled literature results.
- Taxon
- soybeanGlycine maxt0
- Outcome
- yield responses to temperature and [CO 2 ]e0
- Comparator
- those reported from the literaturee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Ensemble mean yield responses to temperature and [CO 2 ] were similar to those reported from the literature”
t0“soybean”
Claim · model-written summary
Further model development using elevated CO2 and temperature experiments was recommended.
- Taxon
- soybeanGlycine maxe0
- Outcome
- model development with experiments under elevated [CO 2 ] and temperaturee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“further soybean model development with experiments under elevated [CO 2 ] and temperature is needed to reduce the uncertainty from the individual models”