Towards a multiscale crop modelling framework for climate change adaptation assessment (original) (raw)
References
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. Science312, 1918–1921 (2006). ArticleCASPubMed Google Scholar
Asseng, S. et al. Climate change impact and adaptation for wheat protein. Glob. Change Biol.25, 155–173 (2019). Article Google Scholar
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). ArticleCASPubMed Google Scholar
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). ArticlePubMedPubMed CentralCAS Google Scholar
Kromdijk, J. et al. Improving photosynthesis and crop productivity by accelerating recovery from photoprotection. Science354, 857–861 (2016). ArticleCASPubMed Google Scholar
Hammer, G. L. et al. Crop design for specific adaptation in variable dryland production environments. Crop Pasture Sci.65, 614–626 (2014). Article Google Scholar
Zhao, G. et al. The implication of irrigation in climate change impact assessment: a European‐wide study. Glob. Change Biol.21, 4031–4048 (2015). Article Google Scholar
Lobell, D. B. et al. Prioritizing climate change adaptation needs for food security in 2030. Science319, 607–610 (2008). ArticleCASPubMed Google Scholar
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). Article Google Scholar
Wang, E. et al. Improving process-based crop models to better capture genotype×environment×management interactions. J. Exp. Bot.70, 2389–2401 (2019). ArticleCASPubMed Google Scholar
Chenu, K. et al. Contribution of crop models to adaptation in wheat. Trends Plant Sci.22, 472–490 (2017). ArticleCASPubMed Google Scholar
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).
Hunter, M. C., Smith, R. G., Schipanski, M. E., Atwood, L. W. & Mortensen, D. A. Agriculture in 2050: recalibrating targets for sustainable intensification. BioScience67, 386–391 (2017). Article Google Scholar
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). Article Google Scholar
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). ArticleCASPubMed Google Scholar
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). Article Google Scholar
Wu, A., Hammer, G. L., Doherty, A., von Caemmerer, S. & Farquhar, G. D. Quantifying impacts of enhancing photosynthesis on crop yield. Nat. Plants5, 380–388 (2019). ArticlePubMed Google Scholar
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). Article Google Scholar
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). ArticleCASPubMed Google Scholar
Messina, C. D. et al. On the dynamic determinants of reproductive failure under drought in maize. in silico. Plants1, diz003 (2019). Google Scholar
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). Article Google Scholar
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).
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). Article Google Scholar
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 Plants1, diy003 (2019). Article Google Scholar
Hammer, G. et al. Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci.11, 587–593 (2006). ArticleCASPubMed Google Scholar
Minorsky, P. V. Achieving the in silico plant. Systems biology and the future of plant biological research. Plant Physiol.13, 404–409 (2003). ArticleCAS Google Scholar
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). ArticleCASPubMed Google Scholar
Yin, X. & Struik, P. C. Modelling the crop: from system dynamics to systems biology. J. Exp. Bot.61, 2171–2183 (2010). ArticleCASPubMed Google Scholar
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). ArticleCASPubMed Google Scholar
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
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). ArticleCASPubMed Google Scholar
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). ArticleCAS Google Scholar
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. Genetics183, 1507 (2009). ArticlePubMedPubMed Central Google Scholar
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). ArticleCASPubMedPubMed Central Google Scholar
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). Article Google Scholar
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). ArticleCASPubMedPubMed Central Google Scholar
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
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). ArticlePubMedPubMed Central Google Scholar
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). ArticleCASPubMed Google Scholar
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). ArticleCASPubMed Google Scholar
Kannan, K. et al. Combining gene network, metabolic and leaf-level models shows means to future-proof soybean photosynthesis under rising CO2. in silico. Plants1, diz008 (2019). Google Scholar
Chew, Y. H. et al. Multiscale digital Arabidopsis predicts individual organ and whole-organism growth. Proc. Natl Acad. Sci. USA111, E4127–E4136 (2014). ArticleCASPubMedPubMed Central Google Scholar
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). ArticleCAS Google Scholar
Earles, J. M. et al. Embracing 3D complexity in leaf carbon–water exchange. Trends Plant Sci.24, 15–24 (2018). ArticleCASPubMed Google Scholar
Hansen, J. W. & Jones, J. W. Scaling-up crop models for climate variability applications. Agr. Syst.65, 43–72 (2000). Article Google Scholar
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). Article Google Scholar
Peng, B. et al. Improving maize growth processes in the community land model: implementation and evaluation. Agr. Forest Meteorol.250–251, 64–89 (2018). Article Google Scholar
Scanlon, B. R. et al. The food–energy–water nexus: transforming science for society. Water Resour. Res.53, 3550–3556 (2017). Article Google Scholar
Levis, S. et al. Interactive crop management in the Community Earth System Model (CESM1): seasonal influences on land–atmosphere fluxes. J. Climate25, 4839–4859 (2012). Article Google Scholar
Osborne, T. et al. JULES-crop: a parametrisation of crops in the Joint UK Land Environment Simulator. Geosci. Model Dev.8, 1139–1155 (2015). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
Dunbabin, V. M. et al. Modelling root–soil interactions using three–dimensional models of root growth, architecture and function. Plant Soil372, 93–124 (2013). ArticleCAS Google Scholar
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). ArticleCAS Google Scholar
Vos, J. et al. Functional–structural plant modelling: a new versatile tool in crop science. J. Exp. Bot.61, 2101–2115 (2009). ArticleCASPubMed Google Scholar
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). ArticleCAS Google Scholar
Ewert, F. et al. Scale changes and model linking methods for integrated assessment of agri-environmental systems. Agr. Ecosyst. Environ.142, 6–17 (2011). Article Google Scholar
Müller, C. et al. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev.10, 1403–1422 (2017). Article Google Scholar
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). Article Google Scholar
Rosenzweig, C. et al. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agr. Forest Meteorol.170, 166–182 (2013). Article Google Scholar
Hoffmann, H. et al. Impact of spatial soil and climate input data aggregation on regional yield simulations. PLoS ONE11, e0151782 (2016). ArticlePubMedPubMed CentralCAS Google Scholar
Chaney, N. W. et al. Harnessing big data to rethink land heterogeneity in Earth system models. Hydrol. Earth Syst. Sci.22, 3311–3330 (2018). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). ArticleCASPubMed Google Scholar
Asseng, S. et al. Uncertainty in simulating wheat yields under climate change. Nat. Clim. Change3, 827–832 (2013). ArticleCAS Google Scholar
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). Article Google Scholar
Fleisher, D. H. et al. A potato model intercomparison across varying climates and productivity levels. Glob. Change Biol.23, 1258–1281 (2017). Article Google Scholar
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). ArticleCAS Google Scholar
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. USA111, 3268–3273 (2014). ArticleCASPubMed Google Scholar
Martre, P. et al. Multimodel ensembles of wheat growth: many models are better than one. Glob. Change Biol.21, 911–925 (2015). Article Google Scholar
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). ArticleCASPubMed Google Scholar
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). Article Google Scholar
Wang, E. et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat. Plants3, 17102 (2017). ArticlePubMed Google Scholar
Wallach, D. et al. Multimodel ensembles improve predictions of crop–environment–management interactions. Glob. Change Biol.24, 5072–5083 (2018). Article Google Scholar
Rötter, R. P., Carter, T. R., Olesen, J. E. & Porter, J. R. Crop-climate models need an overhaul. Nat. Clim. Change1, 175–177 (2011). Article Google Scholar
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). ArticleCAS Google Scholar
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). ArticlePubMed Google Scholar
Kimball, B. A. Lessons from FACE: CO2 effects and interactions with water, nitrogen, and temperature. Curr. Opin. Plant Biol.31, 36–43 (2010). ArticleCAS Google Scholar
Kimball, B. A. Crop responses to elevated CO2 and interactions with H2O, N, and temperature. Curr. Opin. Plant Biol.31, 36–43 (2016). ArticleCASPubMed Google Scholar
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). ArticleCASPubMedPubMed Central Google Scholar
Gray, S. B. et al. Intensifying drought eliminates the expected benefits of elevated carbon dioxide for soybean. Nat. Plants2, 16132 (2016). ArticleCASPubMed Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). ArticlePubMed Google Scholar
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). ArticleCASPubMed Google Scholar
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). Article Google Scholar
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). Article Google Scholar
Sinclair, T. R. & Muchow, R. C. in Advances in Agronomy Vol. 65 (Ed. Sparks, D. L.) 215–265 (Academic Press, 1999).
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). ArticleCASPubMedPubMed Central Google Scholar
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). Article Google Scholar
Huntingford, C. et al. Implications of improved representations of plant respiration in a changing climate. Nat. Commun.8, 1602 (2017). ArticlePubMedPubMed CentralCAS Google Scholar
Yin, X. Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity. Annal. Bot.112, 465–475 (2013). ArticleCAS Google Scholar
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). Article Google Scholar
Emberson, L. D. et al. Ozone effects on crops and consideration in crop models. Eur. J. Agr.100, 19–34 (2018). ArticleCAS Google Scholar
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). Article Google Scholar
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). ArticleCAS Google Scholar
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). ArticleCAS Google Scholar
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). ArticleCAS Google Scholar
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). ArticleCAS Google Scholar
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).
Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature529, 84–87 (2016). ArticleCASPubMed Google Scholar
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). PubMed Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). ArticleCASPubMedPubMed Central Google Scholar
Peng, S. et al. Rice yields decline with higher night temperature from global warming. Proc. Natl Acad. Sci. USA101, 9971–9975 (2004). ArticleCASPubMedPubMed Central Google Scholar
Saini, H. S. & Westgate, M. E. in Advances in Agronomy Vol. 68 (Ed. Sparks, D. L.) 59–96 (Academic Press, 1999).
Mazdiyasni, O. & AghaKouchak, A. Substantial increase in concurrent droughts and heatwaves in the United States. Proc. Natl Acad. Sci. USA112, 11484–11489 (2015). ArticleCASPubMedPubMed Central Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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. Change12, 197–202 (2002). Article Google Scholar
Ebrahimi-Mollabashi, E. et al. Enhancing APSIM to simulate excessive moisture effects on root growth. Field Crop. Res.236, 58–67 (2019). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). ArticleCASPubMed Google Scholar
Luo, Y. et al. Toward more realistic projections of soil carbon dynamics by Earth system models. Global Biogeochem. Cy.30, 40–56 (2015). ArticleCAS Google Scholar
Koven, C. D. et al. The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4. Biogeosciences10, 7109–7131 (2013). ArticleCAS Google Scholar
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). ArticleCAS Google Scholar
Niu, S. et al. Global patterns and substrate-based mechanisms of the terrestrial nitrogen cycle. Ecol. Lett.19, 697–709 (2016). ArticlePubMed Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
Basso, B. et al. Soil organic carbon and nitrogen feedbacks on crop yields under climate change. Agricultural & Environmental Letters3, 180026 (2018). ArticleCAS Google Scholar
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 ONE10, e0127333 (2015). ArticlePubMedPubMed CentralCAS Google Scholar
Kollas, C. et al. Crop rotation modelling—a European model intercomparison. Eur. J. Agr.70, 98–111 (2015). Article Google Scholar
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). ArticleCAS Google Scholar
Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science361, 916–919 (2018). ArticleCASPubMed Google Scholar
Savary, S. et al. Crop health and its global impacts on the components of food security. Food Secur.9, 311–327 (2017). Article Google Scholar
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). Article Google Scholar
Donatelli, M. et al. Modelling the impacts of pests and diseases on agricultural systems. Agr. Syst.155, 213–224 (2017). ArticleCAS Google Scholar
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). Article Google Scholar
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). ArticleCASPubMed Google Scholar
Ruane, A. C. et al. An AgMIP framework for improved agricultural representation in IAMs. Environ. Res. Lett.12, 125003 (2017). ArticlePubMedPubMed Central Google Scholar
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). Article Google Scholar
Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U. S. crop yields under climate change. Proc. Natl Acad. Sci. USA106, 15594–15598 (2009). ArticleCASPubMedPubMed Central Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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. Geoderma318, 167–181 (2018). ArticleCAS Google Scholar
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). Article Google Scholar
Gbegbelegbe, S. et al. Baseline simulation for global wheat production with CIMMYT mega-environment specific cultivars. Field Crop. Res.202, 122–135 (2017). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
Lobell, D. B., Sibley, A. & Ortiz-Monasterio, J. I. Extreme heat effects on wheat senescence in India. Nat. Clim. Change2, 186–189 (2012). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
Kimball, B. A. et al. Simulation of maize evapotranspiration: an inter-comparison among 29 maize models. Agr. Forest Meteorol.271, 264–284 (2019). Article Google Scholar
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). Article Google Scholar
Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase in the U. S. Midwest. Science344, 516–519 (2014). ArticleCASPubMed Google Scholar
Lobell, D. B. & Asseng, S. Comparing estimates of climate change impacts from process-based and statistical crop models. Environ. Res. Lett.12, 015001 (2017). ArticleCAS Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar
Huang, J. et al. Assimilation of remote sensing into crop growth models: current status and perspectives. Agr. Forest Meteorol.276–277, 107609 (2019). Article Google Scholar
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). Article Google Scholar
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). Article Google Scholar