@article{KothariBattistiBooteetal.2022,
  author    = {Kothari, Kritika and Battisti, Rafael and Boote, Kenneth J. and Archontoulis, Sotirios and Confalone, Adriana and Constantin, Julie and Cuadra, Santiago and Debaeke, Philippe and Faye, Babacar and Grant, Brian and Hoogenboom, Gerrit and Jing, Qi and van der Laan, Michael and Macena da Silva, Fernando Antonio and Marin, Fabio R. and Nehbandani, Alireza and Nendel, Claas and Purcell, Larry C. and Qian, Budong and Ruane, Alex C. and Schoving, Celine and Silva, Evandro H. F. M. and Smith, Ward and Soltani, Afshin and Srivastava, Amit and Vieira, Nilson A. and Slone, Stacey and Salmeron, Montserrat},
  title     = {Are soybean models ready for climate change food impact assessments?},
  series = {European journal of agronomy : the official journal of the European Society for Agronomy},
  volume    = {135},
  journal   = {European journal of agronomy : the official journal of the European Society for Agronomy},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {1161-0301},
  doi       = {10.1016/j.eja.2022.126482},
  pages     = {15},
  year      = {2022},
  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 CO2 concentration [CO2] 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 [CO2]. For example, under a + 3 degrees 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 degrees C temperature rise to no effect on yield. Similarly, when increasing [CO2] 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 [CO2] 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 [CO2] and temperature is needed to reduce the uncertainty from the individual models.},
  language  = {en}
}
@article{NendelRecklingDebaekeetal.2023,
  author    = {Nendel, Claas and Reckling, Moritz and Debaeke, Philippe and Schulz, Susanne and Berg-Mohnicke, Michael and Constantin, Julie and Fronzek, Stefan and Hoffmann, Munir and Jakšić, Snežana and Kersebaum, Kurt-Christian and Klimek-Kopyra, Agnieszka and Raynal, H{\´e}l{\`e}ne and Schoving, C{\´e}line and Stella, Tommaso and Battisti, Rafael},
  title     = {Future area expansion outweighs increasing drought risk for soybean in Europe},
  series = {Global change biology},
  volume    = {29},
  journal   = {Global change biology},
  number    = {5},
  publisher = {Wiley-Blackwell},
  address   = {Ocford [u.a]},
  issn      = {1354-1013},
  doi       = {10.1111/gcb.16562},
  pages     = {1340 -- 1358},
  year      = {2023},
  abstract  = {The European Union is highly dependent on soybean imports from overseas to meet its protein demands. Individual Member States have been quick to declare self-sufficiency targets for plant-based proteins, but detailed strategies are still lacking. Rising global temperatures have painted an image of a bright future for soybean production in Europe, but emerging climatic risks such as drought have so far not been included in any of those outlooks. Here, we present simulations of future soybean production and the most prominent risk factors across Europe using an ensemble of climate and soybean growth models. Projections suggest a substantial increase in potential soybean production area and productivity in Central Europe, while southern European production would become increasingly dependent on supplementary irrigation. Average productivity would rise by 8.3\% (RCP 4.5) to 8.7\% (RCP 8.5) as a result of improved growing conditions (plant physiology benefiting from rising temperature and CO2 levels) and farmers adapting to them by using cultivars with longer phenological cycles. Suitable production area would rise by 31.4\% (RCP 4.5) to 37.7\% (RCP 8.5) by the mid-century, contributing considerably more than productivity increase to the production potential for closing the protein gap in Europe. While wet conditions at harvest and incidental cold spells are the current key challenges for extending soybean production, the models and climate data analysis anticipate that drought and heat will become the dominant limitations in the future. Breeding for heat-tolerant and water-efficient genotypes is needed to further improve soybean adaptation to changing climatic conditions.},
  language  = {en}
}