Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Towards a multiscale crop modelling framework for climate change adaptation assessment

Abstract

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Crop modelling plays a central role in assessing agricultural CC adaptation for food security and environmental sustainability.
Fig. 2: A conceptual illustration of the multiscale crop modelling framework.
Fig. 3: Temperature free-air controlled enhancement experiment for soybean in Illinois, USA.

References

  1. 1.

    Long, S. P., Ainsworth, E. A., Leakey, A. D., Nösberger, J. & Ort, D. R. Food for thought: lower-than-expected crop yield stimulation with rising CO2 concentrations. Science 312, 1918–1921 (2006).

    CAS  Google Scholar 

  2. 2.

    Asseng, S. et al. Climate change impact and adaptation for wheat protein. Glob. Change Biol. 25, 155–173 (2019).

    Google Scholar 

  3. 3.

    Cooper, M., Gho, C., Leafgren, R., Tang, T. & Messina, C. Breeding drought-tolerant maize hybrids for the US corn-belt: discovery to product. J. Exp. Bot. 65, 6191–6204 (2014).

    CAS  Google Scholar 

  4. 4.

    Głowacka, K. et al. Photosystem II Subunit S overexpression increases the efficiency of water use in a field-grown crop. Nat. Commun. 9, 868 (2018).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Kromdijk, J. et al. Improving photosynthesis and crop productivity by accelerating recovery from photoprotection. Science 354, 857–861 (2016).

    CAS  Google Scholar 

  6. 6.

    Hammer, G. L. et al. Crop design for specific adaptation in variable dryland production environments. Crop Pasture Sci. 65, 614–626 (2014).

    Google Scholar 

  7. 7.

    Zhao, G. et al. The implication of irrigation in climate change impact assessment: a European‐wide study. Glob. Change Biol. 21, 4031–4048 (2015).

    Google Scholar 

  8. 8.

    Lobell, D. B. et al. Prioritizing climate change adaptation needs for food security in 2030. Science 319, 607–610 (2008).

    CAS  Google Scholar 

  9. 9.

    Chapman, S. C., Hammer, G. L., Butler, D. G. & Cooper, M. Genotype by environment interactions affecting grain sorghum. III. Temporal sequences and spatial patterns in the target population of environments. Aust. J. Agr. Res. 51, 223–234 (2000).

    Google Scholar 

  10. 10.

    Wang, E. et al. Improving process-based crop models to better capture genotype×environment×management interactions. J. Exp. Bot. 70, 2389–2401 (2019).

    CAS  Google Scholar 

  11. 11.

    Chenu, K. et al. Contribution of crop models to adaptation in wheat. Trends Plant Sci. 22, 472–490 (2017).

    CAS  Google Scholar 

  12. 12.

    Hammer, G., McLean, G., Doherty, A., van Oosterom, E. & Chapman, S. in Sorghum: State of the Art and Future Perspectives Agronomy Monographs Ch. 17 (American Society of Agronomy and Crop Science Society of America, 2016).

  13. 13.

    Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W. & Mortensen, D. A. Agriculture in 2050: recalibrating targets for sustainable intensification. BioScience 67, 386–391 (2017).

    Google Scholar 

  14. 14.

    Jones, J. W. et al. Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science. Agr. Syst. 155, 269–288 (2017).

    Google Scholar 

  15. 15.

    Challinor, A. J., Ewert, F., Arnold, S., Simelton, E. & Fraser, E. Crops and climate change: progress, trends and challenges in simulating impacts and informing adaptation. J. Exp. Bot. 60, 2775–2789 (2009).

    CAS  Google Scholar 

  16. 16.

    Hernandez-Ochoa, I. M. et al. Adapting irrigated and rainfed wheat to climate change in semi-arid environments: management, breeding options and land use change. Eur. J. Agron. 109, 125915 (2019).

    Google Scholar 

  17. 17.

    Wu, A., Hammer, G. L., Doherty, A., von Caemmerer, S. & Farquhar, G. D. Quantifying impacts of enhancing photosynthesis on crop yield. Nat. Plants 5, 380–388 (2019).

    Google Scholar 

  18. 18.

    Yin, X., van der Linden, C. G. & Struik, P. C. Bringing genetics and biochemistry to crop modelling, and vice versa. Eur. J. Agron. 100, 132–140 (2018).

    Google Scholar 

  19. 19.

    Rötter, R. P., Tao, F., Höhn, J. G. & Palosuo, T. Use of crop simulation modelling to aid ideotype design of future cereal cultivars. J. Exp. Bot. 66, 3463–3476 (2015).

    Google Scholar 

  20. 20.

    Messina, C. D. et al. On the dynamic determinants of reproductive failure under drought in maize. in silico. Plants 1, diz003 (2019).

    Google Scholar 

  21. 21.

    Messina, C. D. et al. Leveraging biological insight and environmental variation to improve phenotypic prediction: integrating crop growth models (CGM) with whole genome prediction (WGP). Eur. J. Agron. 100, 151–162 (2018).

    Google Scholar 

  22. 22.

    Cooper, M., Technow, F., Messina, C., Gho, C. & Totir, L. R. Use of crop growth models with whole-genome prediction: application to a maize multienvironment trial. Crop Sci. 56, 2141–2156 (2016).

  23. 23.

    Sinclair, T. R., Soltani, A., Marrou, H., Ghanem, M. & Vadez, V. Geospatial assessment for crop physiological and management improvements with examples using the simple simulation model. Crop Sci. 59, 1–9 (2019).

    Google Scholar 

  24. 24.

    Chang, T.-G., Chang, S., Song, Q.-F., Perveen, S. & Zhu, X.-G. Systems models, phenomics and genomics: three pillars for developing high-yielding photosynthetically efficient crops. in silico Plants 1, diy003 (2019).

    Google Scholar 

  25. 25.

    Hammer, G. et al. Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci. 11, 587–593 (2006).

    CAS  Google Scholar 

  26. 26.

    Minorsky, P. V. Achieving the in silico plant. Systems biology and the future of plant biological research. Plant Physiol. 13, 404–409 (2003).

    Google Scholar 

  27. 27.

    Hammer, G. L., Sinclair, T. R., Chapman, S. C. & van Oosterom, E. On systems thinking, systems biology, and the in silico plant. Plant Physiol. 134, 909–911 (2004).

    CAS  Google Scholar 

  28. 28.

    Yin, X. & Struik, P. C. Modelling the crop: from system dynamics to systems biology. J. Exp. Bot. 61, 2171–2183 (2010).

    CAS  Google Scholar 

  29. 29.

    Hammer, G. L. et al. Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J. Exp. Bot. 61, 2185–2202 (2010).

    CAS  Google Scholar 

  30. 30.

    de Wit, C. T. & Penning de Vries, F. W. T. Crop growth models without hormones. Neth. J. Agr. Sci. 31, 313–323 (1983).

    Google Scholar 

  31. 31.

    Parent, B. & Tardieu, F. Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature? J. Exp. Bot. 65, 6179–6189 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Messina, C. D., Jones, J. W., Boote, K. J. & Vallejos, C. E. A gene-based model to simulate soybean development and yield responses to environment Florida agricultural experiment station, journal series no. R-11017. Crop Sci. 46, 456–466 (2006).

    CAS  Google Scholar 

  33. 33.

    Chenu, K. et al. Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a ‘gene-to-phenotype’ modeling approach. Genetics 183, 1507 (2009).

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Reymond, M., Muller, B., Leonardi, A., Charcosset, A. & Tardieu, F. Combining quantitative trait loci analysis and an ecophysiological model to analyze the genetic variability of the responses of maize leaf growth to temperature and water deficit. Plant Physiol. 131, 664 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Gu, J., Yin, X., Zhang, C., Wang, H. & Struik, P. C. Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress. Annal. Bot. 114, 499–511 (2014).

    Google Scholar 

  36. 36.

    Kadam, N. N., Krishna Jagadish, S., Struik, P. C., der Linden, C. & Yin, X. Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields. J. Exp. Bot. 70, 2575–2586 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Bhakta, M. S. et al. A predictive model for time-to-flowering in the common bean based on QTL and environmental variables. G3-Genes Genom. Genet. 7, 3901–3912 (2017).

    CAS  Google Scholar 

  38. 38.

    Marshall-Colon, A. et al. Crops in silico: generating virtual crops using an integrative and multi-scale modeling platform. Front. Plant Sci. 8, 786 (2017).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Zhu, X.-G. et al. Plants in silico: why, why now and what?—an integrative platform for plant systems biology research. Plant Cell Environ. 39, 1049–1057 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Zhu, X.-G., Wang, Y. U., Ort, D. R. & Long, S. P. e-photosynthesis: a comprehensive dynamic mechanistic model of C3 photosynthesis: from light capture to sucrose synthesis. Plant Cell Environ. 36, 1711–1727 (2013).

    CAS  Google Scholar 

  41. 41.

    Kannan, K. et al. Combining gene network, metabolic and leaf-level models shows means to future-proof soybean photosynthesis under rising CO2. in silico. Plants 1, diz008 (2019).

    Google Scholar 

  42. 42.

    Chew, Y. H. et al. Multiscale digital Arabidopsis predicts individual organ and whole-organism growth. Proc. Natl Acad. Sci. USA 111, E4127–E4136 (2014).

    CAS  Google Scholar 

  43. 43.

    Xiao, Y. et al. ePlant for quantitative and predictive plant science research in the big data era—Lay the foundation for the future model guided crop breeding, engineering and agronomy. Quant. Biol. 5, 260–271 (2017).

    CAS  Google Scholar 

  44. 44.

    Earles, J. M. et al. Embracing 3D complexity in leaf carbon–water exchange. Trends Plant Sci. 24, 15–24 (2018).

    Google Scholar 

  45. 45.

    Hansen, J. W. & Jones, J. W. Scaling-up crop models for climate variability applications. Agr. Syst. 65, 43–72 (2000).

    Google Scholar 

  46. 46.

    Lawrence, D. M. et al. The Community Land Model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Sy. 11, 4245–4287 (2019).

    Google Scholar 

  47. 47.

    Peng, B. et al. Improving maize growth processes in the community land model: implementation and evaluation. Agr. Forest Meteorol. 250–251, 64–89 (2018).

    Google Scholar 

  48. 48.

    Scanlon, B. R. et al. The food–energy–water nexus: transforming science for society. Water Resour. Res. 53, 3550–3556 (2017).

    Google Scholar 

  49. 49.

    Levis, S. et al. Interactive crop management in the Community Earth System Model (CESM1): seasonal influences on land–atmosphere fluxes. J. Climate 25, 4839–4859 (2012).

    Google Scholar 

  50. 50.

    Osborne, T. et al. JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator. Geosci. Model Dev. 8, 1139–1155 (2015).

    Google Scholar 

  51. 51.

    Wu, X. et al. ORCHIDEE-CROP (v0), a new process-based agro-land surface model: model description and evaluation over Europe. Geosci. Model Dev. 9, 857–873 (2016).

    Google Scholar 

  52. 52.

    Drewniak, B., Song, J., Prell, J., Kotamarthi, V. R. & Jacob, R. Modeling agriculture in the Community Land Model. Geosci. Model Dev. 6, 495–515 (2013).

    Google Scholar 

  53. 53.

    Dunbabin, V. M. et al. Modelling root–soil interactions using three–dimensional models of root growth, architecture and function. Plant Soil 372, 93–124 (2013).

    CAS  Google Scholar 

  54. 54.

    Wang, Y. et al. Development of a three-dimensional ray-tracing model of sugarcane canopy photosynthesis and its application in assessing impacts of varied row spacing. BioEnerg. Res. 10, 626–634 (2017).

    Google Scholar 

  55. 55.

    Vos, J. et al. Functional–structural plant modelling: a new versatile tool in crop science. J. Exp. Bot. 61, 2101–2115 (2009).

    Google Scholar 

  56. 56.

    Bonan, G. B. et al. Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0). Geosci. Model Dev. 11, 1467–1496 (2018).

    CAS  Google Scholar 

  57. 57.

    Ewert, F. et al. Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agr. Ecosyst. Environ. 142, 6–17 (2011).

    Google Scholar 

  58. 58.

    Müller, C. et al. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).

    Google Scholar 

  59. 59.

    Elliott, J. et al. The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0). Geosci. Model Dev. 8, 261–277 (2015).

    Google Scholar 

  60. 60.

    Rosenzweig, C. et al. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agr. Forest Meteorol. 170, 166–182 (2013).

    Google Scholar 

  61. 61.

    Hoffmann, H. et al. Impact of spatial soil and climate input data aggregation on regional yield simulations. PLoS ONE 11, e0151782 (2016).

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Chaney, N. W. et al. Harnessing big data to rethink land heterogeneity in Earth system models. Hydrol. Earth Syst. Sci. 22, 3311–3330 (2018).

    Google Scholar 

  63. 63.

    Webber, H. et al. Climate change impacts on European crop yields: do we need to consider nitrogen limitation? Eur. J. Agron. 71, 123–134 (2015).

    Google Scholar 

  64. 64.

    Zimmermann, A. et al. Climate change impacts on crop yields, land use and environment in response to crop sowing dates and thermal time requirements. Agr. Syst. 157, 81–92 (2017).

    Google Scholar 

  65. 65.

    Boote, K. J., Jones, J. W., White, J. W., Asseng, S. & Lizaso, J. I. Putting mechanisms into crop production models. Plant Cell Environ. 36, 1658–1672 (2013).

    CAS  Google Scholar 

  66. 66.

    Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change 3, 827–832 (2013).

    CAS  Google Scholar 

  67. 67.

    Bassu, S. et al. How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol. 20, 2301–2320 (2014).

    Google Scholar 

  68. 68.

    Fleisher, D. H. et al. A potato model intercomparison across varying climates and productivity levels. Glob. Change Biol. 23, 1258–1281 (2017).

    Google Scholar 

  69. 69.

    Li, T. et al. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions. Glob. Change Biol. 21, 1328–1341 (2015).

    CAS  Google Scholar 

  70. 70.

    Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).

    CAS  Google Scholar 

  71. 71.

    Martre, P. et al. Multimodel ensembles of wheat growth: many models are better than one. Glob. Change Biol. 21, 911–925 (2015).

    Google Scholar 

  72. 72.

    Ainsworth, E. A., Leakey, A. D. B., Ort, D. R. & Long, S. P. FACE-ing the facts: inconsistencies and interdependence among field, chamber and modeling studies of elevated [CO2] impacts on crop yield and food supply. New Phytol. 179, 5–9 (2008).

    CAS  Google Scholar 

  73. 73.

    Tao, F. et al. Why do crop models diverge substantially in climate impact projections? A comprehensive analysis based on eight barley crop models. Agr. Forest Meteorol. 281, 107851 (2020).

    Google Scholar 

  74. 74.

    Wang, E. et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants 3, 17102 (2017).

    Google Scholar 

  75. 75.

    Wallach, D. et al. Multimodel ensembles improve predictions of crop–environment–management interactions. Glob. Change Biol. 24, 5072–5083 (2018).

    Google Scholar 

  76. 76.

    Rötter, R. P., Carter, T. R., Olesen, J. E. & Porter, J. R. Crop-climate models need an overhaul. Nat. Clim. Change 1, 175–177 (2011).

    Google Scholar 

  77. 77.

    Manderscheid, R., Erbs, M. & Weigel, H.-J. Interactive effects of free-air CO2 enrichment and drought stress on maize growth. Eur. J. Agr. 52, 11–21 (2014).

    CAS  Google Scholar 

  78. 78.

    Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free‐air CO2 enrichment (FACE)? A meta‐analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2005).

    Google Scholar 

  79. 79.

    Kimball, B. A. Lessons from FACE: CO2 effects and interactions with water, nitrogen, and temperature. Curr. Opin. Plant Biol. 31, 36–43 (2010).

    Google Scholar 

  80. 80.

    Kimball, B. A. Crop responses to elevated CO2 and interactions with H2O, N, and temperature. Curr. Opin. Plant Biol. 31, 36–43 (2016).

    CAS  Google Scholar 

  81. 81.

    Bernacchi, C. J., Kimball, B. A., Quarles, D. R., Long, S. P. & Ort, D. R. Decreases in stomatal conductance of soybean under open-air elevation of [CO2] are closely coupled with decreases in ecosystem evapotranspiration. Plant Physiol. 143, 134–144 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Gray, S. B. et al. Intensifying drought eliminates the expected benefits of elevated carbon dioxide for soybean. Nat. Plants 2, 16132 (2016).

    CAS  Google Scholar 

  83. 83.

    Jin, Z., Ainsworth, E. A., Leakey, A. D. B. & Lobell, D. B. Increasing drought and diminishing benefits of elevated carbon dioxide for soybean yields across the US Midwest. Glob. Change Biol. 24, e522–e533 (2018).

    Google Scholar 

  84. 84.

    Sanz-Sáez, Á. et al. Leaf and canopy scale drivers of genotypic variation in soybean response to elevated carbon dioxide concentration. Glob. Change Biol. 23, 3908–3920 (2017).

    Google Scholar 

  85. 85.

    Bishop, K. A., Betzelberger, A. M., Long, S. P. & Ainsworth, E. A. Is there potential to adapt soybean (Glycine max Merr.) to future [CO2]? An analysis of the yield response of 18 genotypes in free-air CO2 enrichment. Plant Cell Environ. 38, 1765–1774 (2015).

    Google Scholar 

  86. 86.

    Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant Cell Environ. 30, 258–270 (2007).

    CAS  Google Scholar 

  87. 87.

    Cai, C. et al. Responses of wheat and rice to factorial combinations of ambient and elevated CO2 and temperature in FACE experiments. Glob. Change Biol. 22, 856–874 (2016).

    Google Scholar 

  88. 88.

    Ruiz-Vera, U. M., Siebers, M. H., Drag, D. W., Ort, D. R. & Bernacchi, C. J. Canopy warming caused photosynthetic acclimation and reduced seed yield in maize grown at ambient and elevated [CO2]. Glob. Change Biol. 21, 4237–4249 (2015).

    Google Scholar 

  89. 89.

    Sinclair, T. R. & Muchow, R. C. in Advances in Agronomy Vol. 65 (Ed. Sparks, D. L.) 215–265 (Academic Press, 1999).

  90. 90.

    Yin, X. & Struik, P. C. Can increased leaf photosynthesis be converted into higher crop mass production? A simulation study for rice using the crop model GECROS. J. Exp. Bot. 68, 2345–2360 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Vanuytrecht, E. & Thorburn, P. J. Responses to atmospheric CO2 concentrations in crop simulation models: a review of current simple and semicomplex representations and options for model development. Glob. Change Biol. 23, 1806–1820 (2017).

    Google Scholar 

  92. 92.

    Huntingford, C. et al. Implications of improved representations of plant respiration in a changing climate. Nat. Commun. 8, 1602 (2017).

    PubMed  PubMed Central  Google Scholar 

  93. 93.

    Yin, X. Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity. Annal. Bot. 112, 465–475 (2013).

    CAS  Google Scholar 

  94. 94.

    Asseng, S., Kassie, B. T., Labra, M. H., Amador, C. & Calderini, D. F. Simulating the impact of source-sink manipulations in wheat. Field Crop. Res. 202, 47–56 (2017).

    Google Scholar 

  95. 95.

    Emberson, L. D. et al. Ozone effects on crops and consideration in crop models. Eur. J. Agr. 100, 19–34 (2018).

    CAS  Google Scholar 

  96. 96.

    Ewert, F. & Porter, J. R. Ozone effects on wheat in relation to CO2: modelling short-term and long-term responses of leaf photosynthesis and leaf duration. Glob. Change Biol. 6, 735–750 (2000).

    Google Scholar 

  97. 97.

    Guarin, J. R., Kassie, B., Mashaheet, A. M., Burkey, K. & Asseng, S. Modeling the effects of tropospheric ozone on wheat growth and yield. Eur. J. Agr. 105, 13–23 (2019).

    CAS  Google Scholar 

  98. 98.

    van Oijen, M., Dreccer, M. F., Firsching, K. H. & Schnieders, B. J. Simple equations for dynamic models of the effects of CO2 and O3 on light-use efficiency and growth of crops. Ecol. Model. 179, 39–60 (2004).

    Google Scholar 

  99. 99.

    Ainsworth, E. A., Yendrek, C. R., Sitch, S., Collins, W. J. & Emberson, L. D. The effects of tropospheric ozone on net primary productivity and implications for climate change. Annual Rev. Plant Biol. 63, 637–661 (2012).

    CAS  Google Scholar 

  100. 100.

    Tao, F., Feng, Z., Tang, H., Chen, Y. & Kobayashi, K. Effects of climate change, CO2 and O3 on wheat productivity in Eastern China, singly and in combination. Atmos. Environ. 153, 182–193 (2017).

    CAS  Google Scholar 

  101. 101.

    Field, C. B., Barros, V., Stocker, T. F. & Dahe, Q. Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. (Cambridge Univ. Press, 2012).

  102. 102.

    Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).

    CAS  Google Scholar 

  103. 103.

    Barnabás, B., Jäger, K. & Fehér, A. The effect of drought and heat stress on reproductive processes in cereals. Plant Cell Environ. 31, 11–38 (2008).

    Google Scholar 

  104. 104.

    Eyshi Rezaei, E., Webber, H., Gaiser, T., Naab, J. & Ewert, F. Heat stress in cereals: mechanisms and modelling. Eur. J. Agr. 64, 98–113 (2015).

    Google Scholar 

  105. 105.

    Prasad, P. V. V., Bheemanahalli, R. & Jagadish, S. V. K. Field crops and the fear of heat stress—Opportunities, challenges and future directions. Field Crop. Res. 200, 114–121 (2017).

    Google Scholar 

  106. 106.

    Shi, W. et al. High day- and night-time temperatures affect grain growth dynamics in contrasting rice genotypes. J. Exp. Bot. 68, 5233–5245 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Peng, S. et al. Rice yields decline with higher night temperature from global warming. Proc. Natl Acad. Sci. USA 101, 9971–9975 (2004).

    CAS  Google Scholar 

  108. 108.

    Saini, H. S. & Westgate, M. E. in Advances in Agronomy Vol. 68 (Ed. Sparks, D. L.) 59–96 (Academic Press, 1999).

  109. 109.

    Mazdiyasni, O. & AghaKouchak, A. Substantial increase in concurrent droughts and heatwaves in the United States. Proc. Natl Acad. Sci. USA 112, 11484–11489 (2015).

    CAS  Google Scholar 

  110. 110.

    Lobell, D. B. et al. The shifting influence of drought and heat stress for crops in northeast Australia. Glob. Change Biol. 21, 4115–4127 (2015).

    Google Scholar 

  111. 111.

    Liu, B. et al. Testing the responses of four wheat crop models to heat stress at anthesis and grain filling. Glob. Change Biol. 22, 1890–1903 (2016).

    Google Scholar 

  112. 112.

    Barlow, K. M., Christy, B. P., O’Leary, G. J., Riffkin, P. A. & Nuttall, J. G. Simulating the impact of extreme heat and frost events on wheat crop production: a review. Field Crop. Res. 171, 109–119 (2015).

    Google Scholar 

  113. 113.

    Siebert, S., Webber, H., Zhao, G. & Ewert, F. Heat stress is overestimated in climate impact studies for irrigated agriculture. Environ. Res. Lett. 12, 054023 (2017).

    Google Scholar 

  114. 114.

    Webber, H. et al. Diverging importance of drought stress for maize and winter wheat in Europe. Nat. Commun. 9, 4249 (2018).

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Siebert, S., Ewert, F., Rezaei, E. E., Kage, H. & Graβ, R. Impact of heat stress on crop yield—on the importance of considering canopy temperature. Environ. Res. Lett. 9, 044012 (2014).

    Google Scholar 

  116. 116.

    Webber, H. et al. Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions. Field Crop. Res. 216, 75–88 (2018).

    Google Scholar 

  117. 117.

    Rosenzweig, C., Tubiello, F. N., Goldberg, R., Mills, E. & Bloomfield, J. Increased crop damage in the US from excess precipitation under climate change. Glob. Environ. Change 12, 197–202 (2002).

    Google Scholar 

  118. 118.

    Ebrahimi-Mollabashi, E. et al. Enhancing APSIM to simulate excessive moisture effects on root growth. Field Crop. Res. 236, 58–67 (2019).

    Google Scholar 

  119. 119.

    Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E. & Peng, B. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Glob. Change Biol. 25, 2325–2337 (2019).

    Google Scholar 

  120. 120.

    Constantin, J. et al. Management and spatial resolution effects on yield and water balance at regional scale in crop models. Agr. Forest Meteorol. 275, 184–195 (2019).

    Google Scholar 

  121. 121.

    Brilli, L. et al. Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes. Sci. Total Environ. 598, 445–470 (2017).

    CAS  Google Scholar 

  122. 122.

    Luo, Y. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Global Biogeochem. Cy. 30, 40–56 (2015).

    Google Scholar 

  123. 123.

    Koven, C. D. et al. The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4. Biogeosciences 10, 7109–7131 (2013).

    CAS  Google Scholar 

  124. 124.

    Tang, J. Y., Riley, W. J., Koven, C. D. & Subin, Z. M. CLM4-BeTR, a generic biogeochemical transport and reaction module for CLM4: model development, evaluation, and application. Geosci. Model Dev. 6, 127–140 (2013).

    CAS  Google Scholar 

  125. 125.

    Niu, S. et al. Global patterns and substrate-based mechanisms of the terrestrial nitrogen cycle. Ecol. Lett. 19, 697–709 (2016).

    Google Scholar 

  126. 126.

    Rötter, R. P. et al. Simulation of spring barley yield in different climatic zones of Northern and Central Europe: a comparison of nine crop models. Field Crop. Res. 133, 23–36 (2012).

    Google Scholar 

  127. 127.

    Palosuo, T. et al. Simulation of winter wheat yield and its variability in different climates of Europe: a comparison of eight crop growth models. Eur. J. Agr. 35, 103–114 (2011).

    Google Scholar 

  128. 128.

    Ehrhardt, F. et al. Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2O emissions. Glob. Change Biol. 24, e603–e616 (2018).

    Google Scholar 

  129. 129.

    Basso, B. et al. Soil organic carbon and nitrogen feedbacks on crop yields under climate change. Agricultural & Environmental Letters 3, 180026 (2018).

    Google Scholar 

  130. 130.

    Basso, B., Hyndman, D. W., Kendall, A. D., Grace, P. R. & Robertson, G. P. Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized? PLoS ONE 10, e0127333 (2015).

    PubMed  PubMed Central  Google Scholar 

  131. 131.

    Kollas, C. et al. Crop rotation modelling—a European model intercomparison. Eur. J. Agr. 70, 98–111 (2015).

    Google Scholar 

  132. 132.

    McDermid, S., Mearns, L. & Ruane, A. Representing agriculture in Earth system models: approaches and priorities for development. J. Adv. Model. Earth Sy. 9, 2230–2265 (2017).

    CAS  Google Scholar 

  133. 133.

    Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).

    CAS  Google Scholar 

  134. 134.

    Savary, S. et al. Crop health and its global impacts on the components of food security. Food Secur. 9, 311–327 (2017).

    Google Scholar 

  135. 135.

    Porter, J. H., Parry, M. L. & Carter, T. R. The potential effects of climatic change on agricultural insect pests. Agr. Forest Meteorol. 57, 221–240 (1991).

    Google Scholar 

  136. 136.

    Donatelli, M. et al. Modelling the impacts of pests and diseases on agricultural systems. Agr. Syst. 155, 213–224 (2017).

    CAS  Google Scholar 

  137. 137.

    Lammoglia, S.-K. et al. Modelling pesticides leaching in cropping systems: effect of uncertainties in climate, agricultural practices, soil and pesticide properties. Environ. Modell. Softw. 109, 342–352 (2018).

    Google Scholar 

  138. 138.

    Wang, R. et al. A review of pesticide fate and transport simulation at watershed level using SWAT: current status and research concerns. Sci. Total Environ. 669, 512–526 (2019).

    CAS  Google Scholar 

  139. 139.

    Ruane, A. C. et al. An AgMIP framework for improved agricultural representation in IAMs. Environ. Res. Lett. 12, 125003 (2017).

    PubMed  PubMed Central  Google Scholar 

  140. 140.

    Rötter, R. P. et al. Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes—a review. Field Crop. Res. 221, 142–156 (2018).

    Google Scholar 

  141. 141.

    Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U. S. crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).

    CAS  Google Scholar 

  142. 142.

    Grunwald, S., Thompson, J. & Boettinger, J. Digital soil mapping and modeling at continental scales: finding solutions for global issues. Soil Sci. Soc. Am. J. 75, 1201–1213 (2011).

    Google Scholar 

  143. 143.

    Hengl, T. et al. SoilGrids1km—global soil information based on automated mapping. PLoS ONE 9, e105992 (2014).

    PubMed  PubMed Central  Google Scholar 

  144. 144.

    Chaney, N. W. et al. POLARIS soil properties: 30-meter probabilistic maps of soil properties over the contiguous United States. Water Resour. Res. 55, 2916–2938 (2019).

    Google Scholar 

  145. 145.

    Han, E., Ines, A. V. M. & Koo, J. Development of a 10-km resolution global soil profile dataset for crop modeling applications. Environ. Modell. Softw. 119, 70–83 (2019).

    Google Scholar 

  146. 146.

    Coucheney, E. et al. Key functional soil types explain data aggregation effects on simulated yield, soil carbon, drainage and nitrogen leaching at a regional scale. Geoderma 318, 167–181 (2018).

    CAS  Google Scholar 

  147. 147.

    Pongratz, J. et al. Models meet data: challenges and opportunities in implementing land management in Earth system models. Glob. Change Biol. 24, 1470–1487 (2018).

    Google Scholar 

  148. 148.

    Gbegbelegbe, S. et al. Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars. Field Crop. Res. 202, 122–135 (2017).

    Google Scholar 

  149. 149.

    Woodard, J. D. et al. The power of agricultural data. Science 362, 410–411 (2018).

    CAS  Google Scholar 

  150. 150.

    Minet, J. et al. Crowdsourcing for agricultural applications: a review of uses and opportunities for a farmsourcing approach. Comput. Electron. Agr. 142, 126–138 (2017).

    Google Scholar 

  151. 151.

    van Bussel, L. G. J., Ewert, F. & Leffelaar, P. A. Effects of data aggregation on simulations of crop phenology. Agr. Ecosyst. Environ. 142, 75–84 (2011).

    Google Scholar 

  152. 152.

    Boryan, C., Yang, Z., Mueller, R. & Craig, M. Monitoring US agriculture: the US department of agriculture, national agricultural statistics service, cropland data layer program. Geocarto Int. 26, 341–358 (2011).

    Google Scholar 

  153. 153.

    Xie, Y., Lark, T. J., Brown, J. F. & Gibbs, H. K. Mapping irrigated cropland extent across the conterminous United States at 30 m resolution using a semi-automatic training approach on Google Earth Engine. ISPRS J. Photogramm. 155, 136–149 (2019).

    Google Scholar 

  154. 154.

    Azzari, G. et al. Satellite mapping of tillage practices in the North Central US region from 2005 to 2016. Remote Sens. Environ. 221, 417–429 (2019).

    Google Scholar 

  155. 155.

    Seifert, C. A., Azzari, G. & Lobell, D. B. Satellite detection of cover crops and their effects on crop yield in the Midwestern United States. Environ. Res. Lett. 13, 064033 (2018).

    Google Scholar 

  156. 156.

    Urban, D., Guan, K. & Jain, M. Estimating sowing dates from satellite data over the U. S. Midwest: a comparison of multiple sensors and metrics. Remote Sens. Environ. 211, 400–412 (2018).

    Google Scholar 

  157. 157.

    Lobell, D. B., Sibley, A. & Ortiz-Monasterio, J. I. Extreme heat effects on wheat senescence in India. Nat. Clim. Change 2, 186–189 (2012).

    Google Scholar 

  158. 158.

    Sakamoto, T. et al. A two-step filtering approach for detecting maize and soybean phenology with time-series MODIS data. Remote Sens. Environ. 114, 2146–2159 (2010).

    Google Scholar 

  159. 159.

    Baldocchi, D. et al. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem–scale aarbon dioxide, water vapor, and energy flux densities. B. Am. Meteorol. Soc. 82, 2415–2434 (2001).

    Google Scholar 

  160. 160.

    Kimball, B. A. et al. Simulation of maize evapotranspiration: an inter-comparison among 29 maize models. Agr. Forest Meteorol. 271, 264–284 (2019).

    Google Scholar 

  161. 161.

    Boote, K. J., Prasad, V., Allen, L. H. Jr, Singh, P. & Jones, J. W. Modeling sensitivity of grain yield to elevated temperature in the DSSAT crop models for peanut, soybean, dry bean, chickpea, sorghum, and millet. Eur. J. Agr. 100, 99–109 (2017).

    Google Scholar 

  162. 162.

    Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase in the U. S. Midwest. Science 344, 516–519 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  163. 163.

    Lobell, D. B. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett. 12, 015001 (2017).

    Google Scholar 

  164. 164.

    Roberts, M. J., Braun, N. O., Sinclair, T. R., Lobell, D. B. & Schlenker, W. Comparing and combining process-based crop models and statistical models with some implications for climate change. Environ. Res. Lett. 12, 095010 (2017).

    Google Scholar 

  165. 165.

    Guan, K. et al. The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields. Remote Sens. Environ. 199, 333–349 (2017).

    Google Scholar 

  166. 166.

    Luo, Y., Guan, K. & Peng, J. STAIR: a generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product. Remote Sens. Environ. 214, 87–99 (2018).

    Google Scholar 

  167. 167.

    Viña, A., Gitelson, A. A., Nguy-Robertson, A. L. & Peng, Y. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops. Remote Sens. Environ. 115, 3468–3478 (2011).

    Google Scholar 

  168. 168.

    Anderson, M. et al. Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery. Hydrol. Earth Syst. Sc. 15, 223–239 (2011).

    Google Scholar 

  169. 169.

    Cai, Y. et al. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agr. Forest Meteorol. 274, 144–159 (2019).

    Google Scholar 

  170. 170.

    Huang, J. et al. Assimilation of remote sensing into crop growth models: current status and perspectives. Agr. Forest Meteorol. 276–277, 107609 (2019).

    Google Scholar 

  171. 171.

    Asseng, S. et al. Model-driven multidisciplinary global research to meet future needs: the case for “improving radiation use efficiency to increase yield”. Crop Sci. 59, 843–849 (2019).

    Google Scholar 

  172. 172.

    Vermeulen, S. et al. Climate change, agriculture and food security: a global partnership to link research and action for low-income agricultural producers and consumers. Curr. Opin. Env. Sust. 4, 128–133 (2012).

    Google Scholar 

Download references

Acknowledgements

B.P., K.G., H.K. and W.Z. are supported by the United States National Science Foundation (NSF) Career Award (grant no. 1847334), National Aeronautics and Space Administration (NASA) Carbon Monitoring System managed by NASA Terrestrial Ecology Program (grant no. 80NSSC18K0170), United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Program (grant no. 2017-67013-26253) to K.G. B.P., K.G., S.A. and D.I.G acknowledge support by USDA NIFA (grant no. 2017-68002-26789). B.P., K.G., W.Z., A.M.C. and J.C.S. acknowledge support by the Foundation for Food and Agriculture Research (FFAR) (grant no. 602757). B.P., K.G., D.M.L. and D.L.L acknowledge support by the National Center for Atmospheric Research, which is a major facility sponsored by the NSF under cooperative agreement no. 1852977. The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the FFAR. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the USDA. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. Due to space constrictions, we could not cite all relevant literature. We apologize to the authors whose important work was not cited in this Perspective.

Author information

Affiliations

Authors

Contributions

B.P. and K.G. conceived the research. B.P., K.G. and J.T. wrote the paper; B.P. and K.G. designed the figures. B.P and H.K. produced the figures. B.P. designed and produced the table. E.A.A., S.A., C.J.B., M.C., E.H.D., J.W.E, F.E., R.F.G., D.I.G, G.L.H., J.W.J., Z.J., H.K., D.M.L., Y.L., D.L.L., A.M.C., C.D.M., D.R.O., J.C.S., C.E.V., A.W., X.Y. and W.Z. all contributed to the text.

Corresponding authors

Correspondence to Bin Peng or Kaiyu Guan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Peng, B., Guan, K., Tang, J. et al. Towards a multiscale crop modelling framework for climate change adaptation assessment. Nat. Plants 6, 338–348 (2020). https://doi.org/10.1038/s41477-020-0625-3

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing