Articles | Volume 25, issue 4
https://doi.org/10.5194/hess-25-2027-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-2027-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Apr 2021
Research article |
| 16 Apr 2021
| 16 Apr 2021
Global cotton production under climate change – Implications for yield and water consumption
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Department of Geography, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
Werner von Bloh
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Sibyll Schaphoff
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Christoph Müller
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Related authors
No articles found.
Jamir Priesner, Boris Sakschewski, Maik Billing, Werner von Bloh, Sebastian Fiedler, Sarah Bereswill, Kirsten Thonicke, and Britta Tietjen
Nat. Hazards Earth Syst. Sci., 25, 3309–3331, https://doi.org/10.5194/nhess-25-3309-2025, https://doi.org/10.5194/nhess-25-3309-2025, 2025
Short summary
Short summary
In our simulations increased drought frequencies lead to a drastic reduction in biomass in temperate pine monoculture and mixed forests. Mixed forests eventually recovered as long as drought frequency was not too high. The higher resilience of mixed forests was due to higher adaptive capacity. After adaptation mixed forests were mainly composed of smaller, broadleaved trees with higher wood density and slower growth. This would have strong implications for forestry and other ecosystem services.
Heindriken Dahlmann, Lauren S. Andersen, Sibyll Schaphoff, Fabian Stenzel, Johanna Braun, Christoph Müller, and Dieter Gerten
EGUsphere, https://doi.org/10.5194/egusphere-2025-3817, https://doi.org/10.5194/egusphere-2025-3817, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Green water stress can negatively affect agricultural production and is often alleviated through irrigation. In this global modelling study, we investigate where and to what extent the implementation of irrigation helps to decrease green water stress but in the same time leads to an increase in blue water scarcity. Our findings highlight the need to consider both water stresses together, along with their dynamic interactions for sustainable water management.
Lily-belle Sweet, Christoph Müller, Jonas Jägermeyr, and Jakob Zscheischler
EGUsphere, https://doi.org/10.5194/egusphere-2025-3006, https://doi.org/10.5194/egusphere-2025-3006, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
This study presents a method to identify climate drivers of an impact, such as agricultural yield failure, from high-resolution weather data. The approach systematically generates, selects and combines predictors that generalise across different environments. Tested on crop model simulations, the identified drivers are used to create parsimonious models that achieve high predictive performance over long time horizons, offering a more interpretable alternative to black-box models.
Edna Johanna Molina Bacca, Miodrag Stevanović, Benjamin Leon Bodirsky, Jonathan Cornelis Doelman, Louise Parsons Chini, Jan Volkholz, Katja Frieler, Christopher Paul Oliver Reyer, George Hurtt, Florian Humpenöder, Kristine Karstens, Jens Heinke, Christoph Müller, Jan Philipp Dietrich, Hermann Lotze-Campen, Elke Stehfest, and Alexander Popp
Earth Syst. Dynam., 16, 753–801, https://doi.org/10.5194/esd-16-753-2025, https://doi.org/10.5194/esd-16-753-2025, 2025
Short summary
Short summary
Land-use change projections are vital for impact studies. This study compares updated land-use model projections, including CO2 fertilization among other upgrades, from the MAgPIE and IMAGE models under three scenarios, highlighting differences, uncertainty hotspots, and harmonization effects. Key findings include reduced bioenergy crop demand projections and differences in grassland area allocation and sizes, with socioeconomic–climate scenarios' largest effect on variance starting in 2030.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Elena Xoplaki, Florian Ellsäßer, Jens Grieger, Katrin M. Nissen, Joaquim G. Pinto, Markus Augenstein, Ting-Chen Chen, Hendrik Feldmann, Petra Friederichs, Daniel Gliksman, Laura Goulier, Karsten Haustein, Jens Heinke, Lisa Jach, Florian Knutzen, Stefan Kollet, Jürg Luterbacher, Niklas Luther, Susanna Mohr, Christoph Mudersbach, Christoph Müller, Efi Rousi, Felix Simon, Laura Suarez-Gutierrez, Svenja Szemkus, Sara M. Vallejo-Bernal, Odysseas Vlachopoulos, and Frederik Wolf
Nat. Hazards Earth Syst. Sci., 25, 541–564, https://doi.org/10.5194/nhess-25-541-2025, https://doi.org/10.5194/nhess-25-541-2025, 2025
Short summary
Short summary
Europe frequently experiences compound events, with major impacts. We investigate these events’ interactions, characteristics, and changes over time, focusing on socio-economic impacts in Germany and central Europe. Highlighting 2018’s extreme events, this study reveals impacts on water, agriculture, and forests and stresses the need for impact-focused definitions and better future risk quantification to support adaptation planning.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
Felix Jäger, Jonas Schwaab, Yann Quilcaille, Michael Windisch, Jonathan Doelman, Stefan Frank, Mykola Gusti, Petr Havlik, Florian Humpenöder, Andrey Lessa Derci Augustynczik, Christoph Müller, Kanishka Balu Narayan, Ryan Sebastian Padrón, Alexander Popp, Detlef van Vuuren, Michael Wögerer, and Sonia Isabelle Seneviratne
Earth Syst. Dynam., 15, 1055–1071, https://doi.org/10.5194/esd-15-1055-2024, https://doi.org/10.5194/esd-15-1055-2024, 2024
Short summary
Short summary
Climate change mitigation strategies developed with socioeconomic models rely on the widespread (re)planting of trees to limit global warming below 2°. However, most of these models neglect climate-driven shifts in forest damage like fires. By assessing existing mitigation scenarios, we show the exposure of projected forestation areas to fire-promoting weather conditions. Our study highlights the problem of ignoring climate-driven shifts in forest damage and ways to address it.
Markus Drüke, Wolfgang Lucht, Werner von Bloh, Stefan Petri, Boris Sakschewski, Arne Tobian, Sina Loriani, Sibyll Schaphoff, Georg Feulner, and Kirsten Thonicke
Earth Syst. Dynam., 15, 467–483, https://doi.org/10.5194/esd-15-467-2024, https://doi.org/10.5194/esd-15-467-2024, 2024
Short summary
Short summary
The planetary boundary framework characterizes major risks of destabilization of the Earth system. We use the comprehensive Earth system model POEM to study the impact of the interacting boundaries for climate change and land system change. Our study shows the importance of long-term effects on carbon dynamics and climate, as well as the need to investigate both boundaries simultaneously and to generally keep both boundaries within acceptable ranges to avoid a catastrophic scenario for humanity.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
Short summary
Short summary
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Weihang Liu, Tao Ye, Christoph Müller, Jonas Jägermeyr, James A. Franke, Haynes Stephens, and Shuo Chen
Geosci. Model Dev., 16, 7203–7221, https://doi.org/10.5194/gmd-16-7203-2023, https://doi.org/10.5194/gmd-16-7203-2023, 2023
Short summary
Short summary
We develop a machine-learning-based crop model emulator with the inputs and outputs of multiple global gridded crop model ensemble simulations to capture the year-to-year variation of crop yield under future climate change. The emulator can reproduce the year-to-year variation of simulated yield given by the crop models under CO2, temperature, water, and nitrogen perturbations. Developing this emulator can provide a tool to project future climate change impact in a simple way.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
Short summary
Short summary
We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Jens Heinke, Susanne Rolinski, and Christoph Müller
Geosci. Model Dev., 16, 2455–2475, https://doi.org/10.5194/gmd-16-2455-2023, https://doi.org/10.5194/gmd-16-2455-2023, 2023
Short summary
Short summary
We develop a livestock module for the global vegetation model LPJmL5.0 to simulate the impact of grazing dairy cattle on carbon and nitrogen cycles in grasslands. A novelty of the approach is that it accounts for the effect of feed quality on feed uptake and feed utilization by animals. The portioning of dietary nitrogen into milk, feces, and urine shows very good agreement with estimates obtained from animal trials.
Jenny Niebsch, Werner von Bloh, Kirsten Thonicke, and Ronny Ramlau
Geosci. Model Dev., 16, 17–33, https://doi.org/10.5194/gmd-16-17-2023, https://doi.org/10.5194/gmd-16-17-2023, 2023
Short summary
Short summary
The impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are used to study the effects of multiple processes in the biosphere under climate change. There is a demand for a better computational performance of the models. In this paper, the photosynthesis model in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We found a better numerical solution of a nonlinear equation. A significant run time reduction was possible.
Kristine Karstens, Benjamin Leon Bodirsky, Jan Philipp Dietrich, Marta Dondini, Jens Heinke, Matthias Kuhnert, Christoph Müller, Susanne Rolinski, Pete Smith, Isabelle Weindl, Hermann Lotze-Campen, and Alexander Popp
Biogeosciences, 19, 5125–5149, https://doi.org/10.5194/bg-19-5125-2022, https://doi.org/10.5194/bg-19-5125-2022, 2022
Short summary
Short summary
Soil organic carbon (SOC) has been depleted by anthropogenic land cover change and agricultural management. While SOC models often simulate detailed biochemical processes, the management decisions are still little investigated at the global scale. We estimate that soils have lost around 26 GtC relative to a counterfactual natural state in 1975. Yet, since 1975, SOC has been increasing again by 4 GtC due to a higher productivity, recycling of crop residues and manure, and no-tillage practices.
Vera Porwollik, Susanne Rolinski, Jens Heinke, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Biogeosciences, 19, 957–977, https://doi.org/10.5194/bg-19-957-2022, https://doi.org/10.5194/bg-19-957-2022, 2022
Short summary
Short summary
The study assesses impacts of grass cover crop cultivation on cropland during main-crop off-season periods applying the global vegetation model LPJmL (V.5.0-tillage-cc). Compared to simulated bare-soil fallowing practices, cover crops led to increased soil carbon content and reduced nitrogen leaching rates on the majority of global cropland. Yield responses of main crops following cover crops vary with location, duration of altered management, crop type, water regime, and tillage practice.
Tobias Herzfeld, Jens Heinke, Susanne Rolinski, and Christoph Müller
Earth Syst. Dynam., 12, 1037–1055, https://doi.org/10.5194/esd-12-1037-2021, https://doi.org/10.5194/esd-12-1037-2021, 2021
Short summary
Short summary
Soil organic carbon sequestration on cropland has been proposed as a climate change mitigation strategy. We simulate different agricultural management practices under climate change scenarios using a global biophysical model. We find that at the global aggregated level, agricultural management practices are not capable of enhancing total carbon storage in the soil, yet for some climate regions, we find that there is potential to enhance the carbon content in cropland soils.
Boris Sakschewski, Werner von Bloh, Markus Drüke, Anna Amelia Sörensson, Romina Ruscica, Fanny Langerwisch, Maik Billing, Sarah Bereswill, Marina Hirota, Rafael Silva Oliveira, Jens Heinke, and Kirsten Thonicke
Biogeosciences, 18, 4091–4116, https://doi.org/10.5194/bg-18-4091-2021, https://doi.org/10.5194/bg-18-4091-2021, 2021
Short summary
Short summary
This study shows how local adaptations of tree roots across tropical and sub-tropical South America explain patterns of biome distribution, productivity and evapotranspiration on this continent. By allowing for high diversity of tree rooting strategies in a dynamic global vegetation model (DGVM), we are able to mechanistically explain patterns of mean rooting depth and the effects on ecosystem functions. The approach can advance DGVMs and Earth system models.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
Short summary
Short summary
In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Bruno Ringeval, Christoph Müller, Thomas A. M. Pugh, Nathaniel D. Mueller, Philippe Ciais, Christian Folberth, Wenfeng Liu, Philippe Debaeke, and Sylvain Pellerin
Geosci. Model Dev., 14, 1639–1656, https://doi.org/10.5194/gmd-14-1639-2021, https://doi.org/10.5194/gmd-14-1639-2021, 2021
Short summary
Short summary
We assess how and why global gridded crop models (GGCMs) differ in their simulation of potential yield. We build a GCCM emulator based on generic formalism and fit its parameters against aboveground biomass and yield at harvest simulated by eight GGCMs. Despite huge differences between GGCMs, we show that the calibration of a few key parameters allows the emulator to reproduce the GGCM simulations. Our simple but mechanistic model could help to improve the global simulation of potential yield.
Cited articles
Abdullaev, I., Giordano, M., and Rasulov, A.: Cotton in Uzbekistan: water and welfare, in: The Cotton Sector in Central Asia – Economic Policy and Development Challenges, The School of Oriental and African Studies, London, UK, 112–128, 2007. a
Akhtar, M., Cheema, M. S., Jamil, M., Farooq, M. R., and Aslam, M.: Effect of
plant density on four short statured cotton varieties, Asian Journal of Plant Sciences, 1, 644–645, 2002. a
Allan, J. A.: “Virtual water”: a long term solution for water short Middle Eastern economies?, School of Oriental and African Studies, University of London, London, UK, 1997. a
Allan, J. A.: Virtual water: A strategic resource global solutions to
regional deficits, Groundwater, 36, 545–546, 1998. a
Asseng, S., Ewert, F., Martre, P., et al.: Rising temperatures reduce global wheat production, Nat. Clim. Change, 5, 143–147, https://doi.org/10.1038/nclimate2470, 2015. a
Aujla, M., Thind, H., and Buttar, G.: Response of normally sown and paired sown cotton to various quantities of water applied through drip system, Irrigation Sci., 26, 357–366, 2008. a
Bange, M. and Milroy, S. P.: Effect of temperature on the rate of early
fruiting developmental processes of cotton, in: Proceedings 10th Australian
agronomy conference, Australian Agronomy Society, Hobart, Australia, 29 January–1 February 2001, available at:
http://www.regional.org.au/au/asa/2001/1/d/bange.htm (last access: 12 April 2021), 2001. a
Bange, M., Baker, J. T., Bauer, P. J., Broughton, K. J., Constable, G. A., Luo, Q., Oosterhuis, D. M., Osanai, Y., Payton, P., Tissue, D. T., Reddy, K. R., and Singh, B. K.: Climate Change and Cotton Production in Modern Farming Systems, ICAC review articles on cotton production research, CAB International, Boston, MA, 61 pp., available at:
https://books.google.de/books?id=KUJFjwEACAAJ (last access: 12 April 2021), 2016. a, b, c
Bange, M. P. and Milroy, S. P.: Growth and dry matter partitioning of diverse
cotton genotypes, Field Crop. Res., 87, 73–87, https://doi.org/10.1016/j.fcr.2003.09.007, 2004. a, b
Bange, M. P., Constable, G. A., McRae, D., and Roth, G.: Cotton, in: Adapting Agriculture to Climate Change: Preparing Australian Agriculture, Forestry and Fisheries for the Future, edited by: Stokes, C. and Howden, M., CSIRO Publishing, Melbourne, Australia, 49–66, 2010. a
Bednarz, C. W., Nichols, R. L., and Brown, S. M.: Plant density modifies
within-canopy cotton fiber quality, Crop Sci., 46, 950–956, 2006. a
Bentsen, M., Bethke, I., Debernard, J. B., Iversen, T., Kirkevåg, A., Seland, Ø., Drange, H., Roelandt, C., Seierstad, I. A., Hoose, C., and Kristjánsson, J. E.: The Norwegian Earth System Model, NorESM1-M – Part 1: Description and basic evaluation of the physical climate, Geosci. Model Dev., 6, 687–720, https://doi.org/10.5194/gmd-6-687-2013, 2013. a
Beringer, T., Lucht, W., and Schaphoff, S.: Bioenergy production potential of
global biomass plantations under environmental and agricultural constraints,
GCB Bioenergy, 3, 299–312, https://doi.org/10.1111/j.1757-1707.2010.01088.x, 2011. a
Bhattacharya, N., Radin, J., Kimball, B., Mauney, J., Hendrey, G., Nagy, J.,
Lewin, K., and Ponce, D.: Leaf water relations of cotton in a free-air
CO2-enriched environment, Agr. Forest Meteorol., 70,
171–182, 1994. a
Bibi, A., Oosterhuis, D., and Gonias, E.: Photosynthesis, quantum yield of
photosystem II and membrane leakage as affected by high temperatures in
cotton genotypes, Journal of Cotton Science, 12, 150–159, 2008a. a
Bibi, A. C., Oosterhuis, D. M., and Gonias, E. D.: Changes in the antioxidant
enzymes activity of cotton genotypes during high temperature stress,
Life Sci. Int. J., 2, 621–627, 2008b. a
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W.,
Gerten, D., Lotze-Campen, H., Müller, C., Reichstein, M., and Smith, B.:
Modelling the role of agriculture for the 20th century global terrestrial
carbon balance, Global Change Biol., 13, 679–706,
https://doi.org/10.1111/j.1365-2486.2006.01305.x, 2007. a, b, c
Bozbek, T., Sezener, V., and Unay, A.: The effect of sowing date and plant
density on cotton yield, J. Agronomy, 5, 122–125, 2006. a
Burke, J. J. and Wanjura, D. F.: Plant Responses to Temperature Extremes,
in: Physiology of Cotton, edited by: Stewart, J. M., Oosterhuis, D. M.,
Heitholt, J. J., and Mauney, J. R., Springer, Dordrecht, The Netherlands, 123–128, https://doi.org/10.1007/978-90-481-3195-2_12, 2010. a
Challinor, A. J., Watson, J., Lobell, D. B., Howden, S. M., Smith, D. R., and
Chhetri, N.: A meta-analysis of crop yield under climate change and
adaptation, Nat. Clim. Change, 4, 287–291, https://doi.org/10.1038/nclimate2153, 2014. a
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and
environmental regulation of stomatal conductance, photosynthesis and
transpiration: a model that includes a laminar boundary layer,
Agr. Forest Meteorol., 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991. a
Committee, I. C. A.: ICAC World Cotton Calendar, available at:
http://worldcottoncalendar.icac.org/ (27 December 2018), 2014. a
Constable, G. and Bange, M.: The yield potential of cotton (Gossypium hirsutum L.), Field Crop. Res., 182, 98–106, 2015. a
Cure, J. D. and Acock, B.: Crop responses to carbon dioxide doubling: a
literature survey, Agr. Forest Meteorol., 38, 127–145, 1986. a
Dai, J. and Dong, H.: Intensive cotton farming technologies in China:
Achievements, challenges and countermeasures, Field Crop. Res., 155,
99–110, https://doi.org/10.1016/j.fcr.2013.09.017, 2014. a
Dong, H., Li, Z., Tang, W., and Zhang, D.: Evaluation of a production system in China that uses reduced plant densities and retention of vegetation branches, Journal of Cotton Science, 1, 1–9, 2005. a
Dong, H. Z., Li, W. J., Tang, W., Li, Z. H., and Zhang, D. M.: Effects of
genotypes and plant density on yield, yield components and photosynthesis in
Bt transgenic cotton, J. Agron. Crop Sci., 192, 132–139, 2006. a
Dufresne, J.-L., Foujols, M.-A., Denvil, S., Caubel, A., Marti, O., Aumont, O., Balkanski, Y., Bekki, S., Bellenger, H., and Benshila, R.: Climate change
projections using the IPSL-CM5 Earth System Model: from CMIP3 to
CMIP5, Clim. Dynam., 40, 2123–2165, 2013. a
Dugas, W., Heuer, M., Hunsaker, D., Kimball, B., Lewin, K., Nagy, J., and
Johnson, M.: Sap flow measurements of transpiration from cotton grown under
ambient and enriched CO2 concentrations, Agr. Forest Meteorol., 70, 231–245, 1994. a
Dunne, J. P., John, J. G., Adcroft, A. J., Griffies, S. M., Hallberg, R. W.,
Shevliakova, E., Stouffer, R. J., Cooke, W., Dunne, K. A., and Harrison,
M. J.: GFDL’s ESM2 global coupled climate-carbon earth system models,
Part I: Physical formulation and baseline simulation characteristics,
J. Climate, 25, 6646–6665, 2012. a
Dunne, J. P., John, J. G., Shevliakova, E., Stouffer, R. J., Krasting, J. P.,
Malyshev, S. L., Milly, P. C. D., Sentman, L. T., Adcroft, A. J., and Cooke,
W.: GFDL’s ESM2 global coupled climate-carbon earth system models,
Part II: carbon system formulation and baseline simulation
characteristics, J. Climate, 26, 2247–2267, 2013. a
Echer, F. R. and Rosolem, C. A.: Cotton yield and fiber quality affected by row spacing and shading at different growth stages,
Eur. J. Agron., 65, 18–26, https://doi.org/10.1016/j.eja.2015.01.001, 2015. a
Elliott, J., Deryng, D., Müller, C., Frieler, K., Konzmann, M., Gerten, D.,
Glotter, M., Flörke, M., Wada, Y., Best, N., Eisner, S., Fekete, B. M.,
Folberth, C., Foster, I., Gosling, S. N., Haddeland, I., Khabarov, N.,
Ludwig, F., Masaki, Y., Olin, S., Rosenzweig, C., Ruane, A. C., Satoh, Y.,
Schmid, E., Stacke, T., Tang, Q., and Wisser, D.: Constraints and potentials
of future irrigation water availability on agricultural production under
climate change, P. Natl. Acad. Sci. USA, 111, 3239–3244, https://doi.org/10.1073/pnas.1222474110, 00193, 2014. a
Ephrath, J., Timlin, D., Reddy, V., and Baker, J.: Irrigation and elevated
carbon dioxide effects on whole canopy photosynthesis and water use
efficiency in cotton (Gossypium hirsutum L.), Plant Biosyst., 145,
202–215, 2011. a
Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migliavacca, M., Thurner, M., and Thonicke, K.: Identifying environmental controls on vegetation greenness phenology through model–data integration, Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, 2014. a
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski, L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K., Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R., Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K., Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F., Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova, V., Marcé, R., Müller Schmied, H., Mouratiadou, I., Pierson, D., Tittensor, D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A., Bodirsky, B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K., Lotze-Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.: Assessing the impacts of 1.5 ∘C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b), Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, 2017. a, b
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., and Sitch, S.:
Terrestrial vegetation and water balance – hydrological evaluation of a
dynamic global vegetation model, J. Hydrol., 286, 249–270,
https://doi.org/10.1016/j.jhydrol.2003.09.029, 2004. a
Gerten, D., Schaphoff, S., and Lucht, W.: Potential future changes in water
limitations of the terrestrial biosphere, Climatic Change, 80, 277–299,
https://doi.org/10.1007/s10584-006-9104-8, 2007. a
Glantz, M.: Creeping environmental problems and sustainable development in the Aral Sea basin, Cambridge University Press, Cambridge, UK, 1999. a
Gleick, P. H.: Global freshwater resources: soft-path solutions for the 21st
century, Science, 302, 1524–1528, 2003. a
Hall, A. E.: Crop Responses to Environment, CRC Press, Boca Raton, Florida, 248 pp., 2000. a
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642,
https://doi.org/10.1002/joc.3711, 2014. a
Haxeltine, A. and Prentice, I. C.: A General Model for the Light-Use
Efficiency of Primary Production, Funct. Ecol., 10, 551–561,
https://doi.org/10.2307/2390165, 1996. a
Hearn, A. B. and Constable, G. A.: Irrigation for crops in a sub-humid
environment VII, Evaluation of irrigation strategies for cotton,
Irrigation Sci., 5, 75–94, 1984. a
Heitholt, J. and Sassenrath-Cole, G.: Inter-Plant Competition: Growth
Responses to Plant Density and Row Spacing, in: Physiology of
Cotton, edited by: Stewart, J. M., Oosterhuis, D. M., Heitholt, J. J., and
Mauney, J. R., Springer, Dordrecht, The Netherlands, 179–186,
https://doi.org/10.1007/978-90-481-3195-2_17, 2010. a
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013. a, b
Hendrix, D., Mauney, J., Kimball, B., Lewin, K., Nagy, J., and Hendrey, G.:
Influence of elevated CO2 and mild water stress on nonstructural
carbohydrates in field-grown cotton tissues, Agr. Forest Meteorol., 70, 153–162, 1994. a
Hileman, D., Huluka, G., Kenjige, P., Sinha, N., Bhattacharya, N., Biswas, P., Lewin, K., Nagy, J., and Hendrey, G.: Canopy photosynthesis and transpiration of field-grown cotton exposed to free-air CO2 enrichment (FACE) and differential irrigation, Agr. Forest Meteorol., 70, 189–207, 1994. a, b, c, d, e
Hodges, H. F., Reddy, K., McKinion, J., and Reddy, V.: Temperature effects on
cotton, Bulletin, Mississippi State University, Starkville, MS, USA, available at: https://www.mafes.msstate.edu/publications/bulletins/b0990.pdf (last access: 13 April 2021), 1993. a
Hoekstra, A. Y.: Virtual water: An introduction, in: Virtual water trade, in:
Proceedings of the international expert meeting on virtual water trade,
Value of water research report series (11), IHE Delft, Delft, The Netherlands, 12–13 December 2002, 13–23, 2003. a
Hoekstra, A. Y. and Mekonnen, M. M.: The water footprint of humanity,
P. Natl. Acad. Sci. USA, 109, 3232–3237,
https://doi.org/10.1073/pnas.1109936109, 2012. a
Hunsaker, D., Hendrey, G., Kimball, B., Lewin, K., Mauney, J., and Nagy, J.:
Cotton evapotranspiration under field conditions with CO2 enrichment and
variable soil moisture regimes, Agr. Forest Meteorol., 70,
247–258, 1994. a
Hussein, K., Perret, C., and Hitimana, L.: Economic and social importance of
cotton in West Africa: Role of cotton in regional development, trade
and livelihoods, Tech. Rep., Sahel and West Africa Club/OECD, Paris, France, 45 pp., 2005. a
Iqbal, M., Ahmad, S., Nazeer, W., Muhammad, T., Khan, M. B., Hussain, M.,
Mehmood, A., Tauseef, M., Hameed, A., and Karim, A.: High plant density by
narrow plant spacing ensures cotton productivity in elite cotton (Gossypium
hirsutum L.) genotypes under severe cotton leaf curl virus (CLCV)
infestation, Afr. J. Biotechnol., 11, 2869, https://doi.org/10.5897/AJB11.3259, 2012. a
ITC: Trade Map – List of exported products for the selected product
(Cotton), available at:
https://www.trademap.org/tradestat/Product_SelProduct_TS.aspx?nvpm=17c7c7c7c7c527c7c7c47c17c17c27c27c17c17c37c1, last access: 11 September 2019. a
Jägermeyr, J., Gerten, D., Schaphoff, S., Heinke, J., Lucht, W., and
Rockström, J.: Integrated crop water management might sustainably halve the
global food gap, Environ. Res. Lett., 11, 025002, https://doi.org/10.1088/1748-9326/11/2/025002, 2016. a
Jans (Ed.), Y., von Bloh, W., Schaphoff, S., and Müller, C.: LPJmL4 model code and model output for: Global cotton production under
climate change–Implications for yield and water consumption, GFZ Data Services, https://doi.org/10.5880/Pik.2020.001, 2021.
Jones, C. D., Hughes, J. K., Bellouin, N., Hardiman, S. C., Jones, G. S., Knight, J., Liddicoat, S., O'Connor, F. M., Andres, R. J., Bell, C., Boo, K.-O., Bozzo, A., Butchart, N., Cadule, P., Corbin, K. D., Doutriaux-Boucher, M., Friedlingstein, P., Gornall, J., Gray, L., Halloran, P. R., Hurtt, G., Ingram, W. J., Lamarque, J.-F., Law, R. M., Meinshausen, M., Osprey, S., Palin, E. J., Parsons Chini, L., Raddatz, T., Sanderson, M. G., Sellar, A. A., Schurer, A., Valdes, P., Wood, N., Woodward, S., Yoshioka, M., and Zerroukat, M.: The HadGEM2-ES implementation of CMIP5 centennial simulations, Geosci. Model Dev., 4, 543–570, https://doi.org/10.5194/gmd-4-543-2011, 2011. a
Khan, A., Najeeb, U., Wang, L., Tan, D. K. Y., Yang, G., Munsif, F., Ali, S.,
and Hafeez, A.: Planting density and sowing date strongly influence growth
and lint yield of cotton crops, Field Crop. Res., 209, 129–135,
https://doi.org/10.1016/j.fcr.2017.04.019, 2017. a
Kimball, B. A.: Carbon dioxide and agricultural yield: An assemblage and
analysis of 430 prior observations 1, Agron. J., 75, 779–788, 1983. a
Kimball, B. A.: Crop responses to elevated CO2 and interactions with
H2O, N, and temperature, Curr. Opin. Plant Biol., 31, 36–43, https://doi.org/10.1016/j.pbi.2016.03.006, 2016. a, b, c, d
Kimball, B. A., Mauney, J., La Morte, R., Guinn, G., Nakayama, F., Radin, J.,
Lakatos, E., Michell, S., Parker, L., Peresta, G., Nixon III, P., Savoy, B.,
Harris, S., MacDonald, R., Pros, H., and Martinez, J.: Carbon Dioxide
Enrichment: Data on the Response of Cotton to Varying CO2
Irrigation, and Nitrogen [Dataset], Tech. Rep., Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, Tennessee, USA, https://doi.org/10.3334/CDIAC/vrc.ndp037, 1992. a
Kimball, B. A., LaMorte, R. L., Seay, R. S., Pinter Jr., P. J., Rokey, R. R., Hunsaker, D. J., Dugas,W. A., Heuer, M. L., Mauney, J. R., Hendrey, G. R., Lewin, K. F., and Nagy, J.: Effects of free-air
CO2 enrichment on energy balance and evapotranspiration of cotton,
Agr. Forest Meteorol., 70, 259–278, 1994. a
Ko, J. and Piccinni, G.: Characterizing leaf gas exchange responses of cotton
to full and limited irrigation conditions, Field Crop. Res., 112, 77–89,
2009. a
Lapola, D. M., Schaldach, R., Alcamo, J., Bondeau, A., Koch, J., Koelking, C., and Priess, J. A.: Indirect land-use changes can overcome carbon savings from biofuels in Brazil, P. Natl. Acad. Sci. USA, 107, 3388–3393, https://doi.org/10.1073/pnas.0907318107, 2010. a, b
Le Houérou, H. N.: Climate change, drought and desertification,
J. Arid Environ., 34, 133–185, 1996. a
Mauney, J.: Carbon Allocation in Cotton Grown in CO2 Enriched Environments, Journal of Cotton Science, 20, 232–236, 2016. a
Minoli, S., Egli, D. B., Rolinski, S., and Müller, C.: Modelling cropping
periods of grain crops at the global scale, Global Planet. Change, 174,
35–46, https://doi.org/10.1016/j.gloplacha.2018.12.013, 2019. a
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next
generation of scenarios for climate change research and assessment, Nature,
463, 747–756, https://doi.org/10.1038/nature08823, 2010. a, b
Müller, C., Elliott, J., Chryssanthacopoulos, J., Deryng, D., Folberth, C.,
Pugh, T. A. M., and Schmid, E.: Implications of climate mitigation for future
agricultural production, Environ. Res. Lett., 10, 125004,
https://doi.org/10.1088/1748-9326/10/12/125004, 2015. a
Müller, C., Elliott, J., Chryssanthacopoulos, J., Arneth, A., Balkovic, J., Ciais, P., Deryng, D., Folberth, C., Glotter, M., Hoek, S., Iizumi, T., Izaurralde, R. C., Jones, C., Khabarov, N., Lawrence, P., Liu, W., Olin, S., Pugh, T. A. M., Ray, D. K., Reddy, A., Rosenzweig, C., Ruane, A. C., Sakurai, G., Schmid, E., Skalsky, R., Song, C. X., Wang, X., de Wit, A., and Yang, H.: Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications, Geosci. Model Dev., 10, 1403–1422, https://doi.org/10.5194/gmd-10-1403-2017, 2017. a, b, c
Nelson, G. C., Valin, H., Sands, R. D., Havlík, P., Ahammad, H., Deryng, D.,
Elliott, J., Fujimori, S., Hasegawa, T., and Heyhoe, E.: Climate change
effects on agriculture: Economic responses to biophysical shocks,
P. Natl. Acad. Sci. USA, 111, 3274–3279, 2014. a
Oosterhuis, D. M. and Snider, J. L.: High temperature stress on floral development and yield of cotton, in: Stress Physiology in Cotton,
edited by: Oosterhuis, D. M. and Robertson, W. C., 1–24, The Cotton Foundation Cordova, TN, USA, available at: https://www.journal.cotton.org/foundation/upload/Stress-Physiology-in-Cotton.pdf#page=12 (last access: 12 April 2021), 2011. a
Oosterhuis, D. M., Bourland, F. M., and Tugwell, N. P.: Physiological Basis for the Nodes-Above-White-Flower Cotton Monitoring System, in: 1993 Proceedings
Beltwide Cotton Conferences, 10–14 January, 1181–1183, National Cotton Council, Memphis, TN, USA, 1993. a
Ottman, M. J., Kimball, B., White, J., and Wall, G.: Wheat growth response to
increased temperature from varied planting dates and supplemental infrared
heating, Agron. J., 104, 7–16, 2012. a
Pereira, L. S., Cordery, I., and Iacovides, I.: Coping with water scarcity:
Addressing the challenges, Springer Science & Business Media, Paris, France, 272 pp., 2009. a
Perret, C. and Bossard, L.: Atlas on Regional Integration in West
Africa: Cotton, Tech. Rep., Sahel and West Africa Club/OECD, Paris, France, 20 pp., 2006. a
Perry, C.: Efficient irrigation; inefficient communication; flawed
recommendations, Irrig. Drain., 56, 367–378, 2007. a
Perry, C., Steduto, P., Allen, R. G., and Burt, C. M.: Increasing productivity
in irrigated agriculture: Agronomic constraints and hydrological realities,
Agr. Water Manage., 96, 1517–1524, 2009. a
Porwollik, V., Müller, C., Elliott, J., Chryssanthacopoulos, J., Iizumi, T., Ray, D. K., Ruane, A. C., Arneth, A., Balkoviˇc, J., Ciais, P., Deryng,
D., Folberth, C., Izaurralde, R. C., Jones, C. D., Khabarov, N., Lawrence, P. J., Liu, W., Pugh, T. A. M., Reddy, A., Sakurai, G., Schmid, E., Wang, X., de Wit, A., and Wu, X.: Spatial and temporal uncertainty of crop yield aggregations, Eur. J. Agron., 88, 10–21, 2017. a
Pugh, T. A. M., Müller, C., Elliott, J., Deryng, D., Folberth, C., Olin,
S., Schmid, E., and Arneth, A.: Climate analogues suggest limited potential
for intensification of production on current croplands under climate change,
Nat. Commun., 7, 12608, https://doi.org/10.1038/ncomms12608, 2016. a
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C., and Foley, J. A.:
Recent patterns of crop yield growth and stagnation, Nat. Commun.,
3, 1293–1299, https://doi.org/10.1038/ncomms2296, 2012. a
Reddy, A. R., Reddy, K., and Hodges, H.: Interactive effects of elevated carbon dioxide and growth temperature on photosynthesis in cotton
leaves, Plant Growth Regul., 26, 33–40, 1998. a
Reddy, K. R., Davidonis, G. H., Johnson, A. S., and Vinyard, B. T.: Temperature
regime and carbon dioxide enrichment alter cotton boll development and fiber
properties, Agron. J., 91, 851–858, 1999. a
Reddy, K. R., Vara Prasad, P., and Kakani, V. G.: Crop responses to elevated
carbon dioxide and interactions with temperature: cotton,
Journal of Crop Improvement, 13, 157–191, 2005a. a
Reddy, K. R., Vara Prasad, P. V., and Kakani, V. G.: Crop responses to elevated carbon dioxide and interactions with temperature: cotton, Journal of Crop Improvement, 13, 157–191, 2005b. a
Reddy, V., Baker, D., and Hodges, H.: Temperature effects on cotton canopy
growth, photosynthesis, and respiration, Agron. J., 83, 699–704,
1991. a
Reddy, V., Reddy, K., and Hodges, H.: Carbon dioxide enrichment and temperature
effects on cotton canopy photosynthesis, transpiration, and water-use
efficiency, Field Crop. Res., 41, 13–23, 1995. a
Rolinski, S., Müller, C., Heinke, J., Weindl, I., Biewald, A., Bodirsky, B. L., Bondeau, A., Boons-Prins, E. R., Bouwman, A. F., Leffelaar, P. A., te Roller, J. A., Schaphoff, S., and Thonicke, K.: Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6, Geosci. Model Dev., 11, 429–451, https://doi.org/10.5194/gmd-11-429-2018, 2018. a
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., and Khabarov, N.: Assessing
agricultural risks of climate change in the 21st century in a global gridded
crop model intercomparison, P. Natl. Acad. Sci. USA,
111, 3268–3273, 2014. a
Rossi, J., Novick, G., Murray, J., Landivar, J., Zhang, S., Baxevanos, D.,
Mateos, A., Kerby, T., Hake, K., and Krieg, D.: Ultra narrow row cotton:
global perspective, in: Proceedings of the Technical Seminar of the 63rd
Plenary Meeting of the ICAC: How to Improve Yields and Reduce Pesticide Use,
Mumbai, India, 28 November–3 December 2004, 7–11, 2004. a
Rost, S., Gerten, D., Bondeau, A., Lucht, W., Rohwer, J., and Schaphoff, S.:
Agricultural green and blue water consumption and its influence on the global
water system, Water Resour. Res., 44, W09405,
https://doi.org/10.1029/2007WR006331, 2008a. a
Rost, S., Gerten, D., and Heyder, U.: Human alterations of the terrestrial water cycle through land management, Adv. Geosci., 18, 43–50, https://doi.org/10.5194/adgeo-18-43-2008, 2008b. a, b
Rudolf, B., Becker, A., Schneider, U., Meyer-Christoffer, A., and Ziese, M.:
New GPCC full data reanalysis version 5 provides high-quality gridded
monthly precipitation data, Gewex News, available at:
https://www.researchgate.net/profile/Udo_Schneider2/publication/268383243_New_GPCC_Full_Data_Reanalysis_Version_5_Provides_High-Quality_Gridded_Monthly_Precipitation_Data/links/553fc36c0cf29680de9da43f.pdf (last access: 9 March 2016), 2011. a
Samarakoon, A. and Gifford, R.: Soil water content under plants at high
CO2 concentration and interactions with the direct CO2 effects: a
species comparison, J. Biogeogr., 22, 193–202, https://doi.org/10.2307/2845910, 1995. a
Samarakoon, A. and Gifford, R.: Elevated CO2 effects on water use and
growth of maize in wet and drying soil, Funct. Plant Biol., 23, 53–62,
1996. a
Schaphoff, S., Heyder, U., Ostberg, S., Gerten, D., Heinke, J., and Lucht, W.: Contribution of permafrost soils to the global carbon budget, Environ. Res. Lett., 8, 014026, https://doi.org/10.1088/1748-9326/8/1/014026, 2013. a, b
Schaphoff, S., Forkel, M., Müller, C., Knauer, J., von Bloh, W., Gerten, D., Jägermeyr, J., Lucht, W., Rammig, A., Thonicke, K., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation, Geosci. Model Dev., 11, 1377–1403, https://doi.org/10.5194/gmd-11-1377-2018, 2018a. a, b
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J., Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description, Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, 2018b. a, b, c, d, e, f, g, h, i, j
Schaphoff (Ed.), S., von Bloh, W., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Müller, C., Rolinski, S., Waha, K., Stehfest, E., de Waal, L., Heyder, U., Gumpenberger, M., and Beringer, T.: LPJmL4 Model Code. V. 4.0, GFZ Data Services, https://doi.org/10.5880/pik.2018.002, 2018.
Schauberger, B., Rolinski, S., and Müller, C.: A network-based approach for semi-quantitative knowledge mining and its application to yield variability, Environ. Res. Lett., 11, 123001, https://doi.org/10.1088/1748-9326/11/12/123001, 2016. a
Schauberger, B., Archontoulis, S., Arneth, A., Balkovic, J., Ciais, P., Deryng, D., Elliott, J., Folberth, C., Khabarov, N., Müller, C., Pugh, T. A. M., Rolinski, S., Schaphoff, S., Schmid, E.,Wang, X., Schlenker, W., and Frieler, K.: Consistent negative response of US crops to high temperatures in observations and crop models, Nat. Commun., 8, 1–9, 2017. a
Schauberger, B., Rolinski, S., Schaphoff, S., and Müller, C.: Global
historical soybean and wheat yield loss estimates from ozone pollution
considering water and temperature as modifying effects, Agr. Forest Meteorol., 265, 1–15, https://doi.org/10.1016/j.agrformet.2018.11.004, 2019. a
Schlenker, W. and Roberts, M. J.: Nonlinear temperature effects indicate severe damages to US crop yields under climate change, P. Natl. Acad. Sci. USA, 106, 15594–15598, 2009. a
Schleussner, C.-F., Deryng, D., Müller, C., Elliott, J., Saeed, F.,
Folberth, C., Liu, W., Wang, X., Pugh, T. A. M., Thiery, W., Seneviratne,
S. I., and Rogelj, J.: Crop productivity changes in
1.5 ∘C and 2 ∘C
worlds under climate sensitivity uncertainty, Environ. Res. Lett.,
13, 064007, https://doi.org/10.1088/1748-9326/aab63b, 2018. a
Sheth, K.: Top cotton producing countries in the world, available at:
https://www.worldatlas.com/articles/top-cotton-producing-countries-in-the-world.html (last access: 11 September 2019), 2017. a
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic global vegetation model, Global Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x,
2003. a, b
Soliz, L. M. A., Oosterhuis, D. M., Coker, D. L., and Brown, R. S.:
Physiological response of cotton to high night temperature,
Am. J. Plant Sci. Biotechnol., 2, 63–68, 2008. a
Stiller, W. N., Read, J. J., Constable, G. A., and Reid, P. E.: Selection for
water use efficiency traits in a cotton breeding program, Crop Sci., 45,
1107–1113, 2005. a
Tans, P. and Keeling, R.: Trends in Atmospheric Carbon Dioxide, National Oceanic & Atmospheric Administration, Earth System Research Laboratory (NOAA/ESRL), Boulder, CO, USA, available at: http://www.esrl.noaa.gov/gmd/ccgg/trends (last access: 30 August 2019), 2015. a
Thind, H., Aujla, M., and Buttar, G.: Response of cotton to various levels of
nitrogen and water applied to normal and paired sown cotton under drip
irrigation in relation to check-basin, Agr. Water Manage., 95, 25–34, 2008. a
Thind, H. S., Buttar, G. S., and Aujla, M. S.: Yield and water use efficiency
of wheat and cotton under alternate furrow and check-basin irrigation with
canal and tube well water in Punjab, India, Irrigation Sci., 28,
489–496, https://doi.org/10.1007/s00271-010-0208-6, 2010. a
Thonicke, K., Spessa, A., Prentice, I. C., Harrison, S. P., Dong, L., and Carmona-Moreno, C.: The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model, Biogeosciences, 7, 1991–2011, https://doi.org/10.5194/bg-7-1991-2010, 2010. a
Turner, N. C., Hearn, A. B., Begg, J. E., and Constable, G. A.: Cotton
(Gossypium hirsutum L.): Physiological and morphological responses to
water deficits and their relationship to yield, Field Crop. Res., 14,
153–170, 1986. a
Vaughan, A. M.: Factors affecting plant density and cotton yields in
Turkmenistan, PhD thesis, Universty of Western Sydney, Sydney, Australia,
available at: http://researchdirect.westernsydney.edu.au/islandora/object/uws3A3605/ (last access: 13 April 2018), 2005. a
von Bloh, W., Schaphoff, S., Müller, C., Rolinski, S., Waha, K., and Zaehle, S.: Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0), Geosci. Model Dev., 11, 2789–2812, https://doi.org/10.5194/gmd-11-2789-2018, 2018. a
Waha, K., van Bussel, L. G. J., Müller, C., and Bondeau, A.: Climate-driven
simulation of global crop sowing dates, Global Ecol. Biogeogr., 21,
247–259, https://doi.org/10.1111/j.1466-8238.2011.00678.x, 2012. a
Wang, H., Gan, Y., Wang, R., Niu, J., Zhao, H., Yang, Q., and Li, G.:
Phenological trends in winter wheat and spring cotton in response to climate
changes in northwest China, Agr. Forest Meteorol., 148,
1242–1251, 2008. a
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and Schewe, J.: The Inter-Sectoral Impact Model Intercomparison Project
(ISI-MIP): Project framework, P. Natl. Acad. Sci. USA, 111, 3228–3232, https://doi.org/10.1073/pnas.1312330110, 2014.
a
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, https://doi.org/10.5194/gmd-4-845-2011, 2011. a
Welch, R. M. and Graham, R. D.: Breeding for micronutrients in staple food
crops from a human nutrition perspective, J. Exp. Bot., 55,
353–364, 2004. a
Whitaker, J., Culpepper, S., Freeman, M., Harris, G., Kemerait, B., Perry, C., Porter, W., Roberts, P., Shurley, D., and Smith, A.: 2018 Georgia Cotton Production Guide, Tech. Rep., Georgia Cotton Commision, Tifton, USA, available at: http://www.ugacotton.com/production-guide/ (last access: 26 March 2019), 2018. a, b, c
Wullschleger, S. D. and Oosterhuis, D. M.: Photosynthetic Carbon Production
and Use by Developing Cotton Leaves and Bolls, Crop Sci., 30,
1259–1264, https://doi.org/10.2135/cropsci1990.0011183X003000060021x, 1990. a
Yahia, E. M., García-Solís, P., and Celis, M. E. M.: Contribution of Fruits and Vegetables to Human Nutrition and
Health, in: Postharvest Physiology and Biochemistry of Fruits and
Vegetables, edited by: Yahia, E. M., Woodhead Publishing, Sawston, Cambridge, UK, 19–45,
https://doi.org/10.1016/B978-0-12-813278-4.00002-6, 2019. a
Zhao, D., Reddy, K. R., Kakani, V. G., Mohammed, A. R., Read, J. J., and Gao,
W.: Leaf and canopy photosynthetic characteristics of cotton (Gossypium
hirsutum) under elevated CO2 concentration and UV-B radiation,
J. Plant Physiol., 161, 581–590, 2004. a
Zhao, D., Reddy, K. R., Kakani, V. G., Koti, S., and Gao, W.: Physiological
causes of cotton fruit abscission under conditions of high temperature and
enhanced ultraviolet-B radiation, Physiologia Plantarum, 124, 189–199, 2005. a
Zhi, X.-Y., Han, Y.-C., Li, Y.-B., Wang, G.-P., Du, W.-L., Li, X.-X., Mao,
S.-C., and Feng, L.: Effects of plant density on cotton yield components and
quality, J. Integr. Agr., 15, 1469–1479,
https://doi.org/10.1016/S2095-3119(15)61174-1, 2016. a
Short summary
Growth of and irrigation water demand on cotton may be challenged by future climate change. To analyze the global cotton production and irrigation water consumption under spatially varying present and future climatic conditions, we use the global terrestrial biosphere model LPJmL. Our simulation results suggest that the beneficial effects of elevated [CO2] on cotton yields overcompensate yield losses from direct climate change impacts, i.e., without the beneficial effect of [CO2] fertilization.
Growth of and irrigation water demand on cotton may be challenged by future climate change. To...
