Abstract
Global gridded crop models (GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national- and provincial-scale evaluation of the simulations by 13 GGCMs [12 from the GGCM Intercomparison (GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops (wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national- and provincial-scale crop yield prediction in China.
摘 要
全球格点作物模式(GGCMs)已广泛应用于气候与环境变化对农业生产的影响及适应相关研究. 作为全球主要粮食生产国, 目前较少有研究评估全球格点作物模式在中国的表现, 且相关研究也仅评估了其模拟全国和区域产量均值和年际变化的能力. 本文利用收集整理的中国国家统计局统计资料, 系统性地评估了13个全球格点作物模式(包括CLM5-crop和12个来自全球格点作物模式比较计划(GGCMI)第1阶段的模式)模拟1980–2009年4种作物(小麦、玉米、水稻和大豆)的全国和省级产量的能力. 结果表明: GGCMI模式普遍低估了水稻而高估了小麦、 玉米和大豆的全国平均产量; 而CLM5-crop则可较好地模拟小麦、玉米和水稻的全国平均产量. 此外, 多数模式对产量空间分布特征的模拟能力较差. 对于时间变化, 部分GGCMI模式可较好地模拟作物产量的年际变化, 但多数模式未能模拟出产量的显著增加趋势. CLM5-crop可较好地模拟小麦、玉米和水稻产量的显著增加趋势, 但其对年际变化特征的模拟能力则相对较差. 多数情况下, 至少有一个模式可以较好地模拟作物总产量前10省份的产量时间变化特征. 本文系统性评估了全球格点作物模式对中国作物产量的模拟能力, 对发展全球格点作物模式及预测中国粮食产量有重要意义.
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References
Balkovič, J., and Coauthors, 2014: Global wheat production potentials and management flexibility under the representative concentration pathways. Global and Planetary Change, 122, 107–121, https://doi.org/10.1016/j.gloplacha.2014.08.010.
Barlow, K. M., B. P. Christy, G. J. O’Leary, P. A. Riffkin, and J. G. Nuttall, 2015: Simulating the impact of extreme heat and frost events on wheat crop production: A review. Field Crops Research, 171, 109–119, https://doi.org/10.1016/j.fcr.2014.11.010.
Bondeau, A., and Coauthors, 2007: Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Global Change Biology, 13, 679–706, https://doi.org/10.1111/j.1365-2486.2006.01305.x.
de Wit, A. J. W., and C. A. Van Diepen, 2008: Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs. International Journal of Applied Earth Observation and Geoinformation, 10, 414–425, https://doi.org/10.1016/j.jag.2007.10.004.
Deryng, D., W. J. Sacks, C. C. Barford, and N. Ramankutty, 2011: Simulating the effects of climate and agricultural management practices on global crop yield. Global Biogeochemical Cycles, 25, GB2006, https://doi.org/10.1029/2009GB003765.
Deryng, D., D. Conway, N. Ramankutty, J. Price, and R. Warren, 2014: Global crop yield response to extreme heat stress under multiple climate change futures. Environmental Research Letters, 9, 034011, https://doi.org/10.1088/1748-9326/9/3/034011.
Elliott, J., and Coauthors, 2014: The parallel system for integrating impact models and sectors (pSIMS). Environmental Modelling & Software, 62, 509–516, https://doi.org/10.1016/j.envsoft.2014.04.008.
Elliott, J., and Coauthors, 2015: The global gridded crop model intercomparison: Data and modeling protocols for phase 1 (v1.0). Geoscientific Model Development, 8, 261–277, https://doi.org/10.5194/gmd-8-261-2015.
FAO, 2021: World Food and Agriculture—Statistical Yearbook 2020. Food and Agriculture Organization of the United Nations, Rome, 353 pp, https://doi.org/10.4060/cb1329en. https://doi.org/10.4060/cb1329en.
FAOSTAT, 2022: Food and agriculture data. [Available online from https://www.fao.org/faostat/en/#home]
Folberth, C., T. Gaiser, K. C. Abbaspour, R. Schulin, and H. Yang, 2012: Regionalization of a large-scale crop growth model for sub-Saharan Africa: Model setup, evaluation, and estimation of maize yields. Agriculture, Ecosystems & Environment, 151, 21–33, https://doi.org/10.1016/j.agee.2012.01.026.
Franke, J. A., and Coauthors, 2020: The GGCMI Phase 2 experiment: Global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0). Geoscientific Model Development, 13, 2315–2336, https://doi.org/10.5194/gmd-13-2315-2020.
Ghose, B., 2014: Food security and food self-sufficiency in China: From past to 2050. Food and Energy Security, 3, 86–95, https://doi.org/10.1002/fes3.48.
Hantson, S., and Coauthors, 2020: Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. Geoscientific Model Development, 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020.
Heinicke, S., K. Frieler, J. Jägermeyr, and M. Mengel, 2022: Global gridded crop models underestimate yield responses to droughts and heatwaves. Environmental Research Letters, 17, 044026, https://doi.org/10.1088/1748-9326/ac592e.
Hurtt, G. C., and Coauthors, 2011: Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change, 109, 117–161, https://doi.org/10.1007/s10584-011-0153-2.
Jägermeyr, J., and Coauthors, 2020: A regional nuclear conflict would compromise global food security. Proceedings of the National Academy of Sciences of the United States of America, 117, 7071–7081, https://doi.org/10.1073/pnas.1919049117.
Jones, J. W., and Coauthors, 2003: The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265, https://doi.org/10.1016/S1161-0301(02)00107-7.
Keating, B. A., and Coauthors, 2003: An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18, 267–288, https://doi.org/10.1016/S1161-0301(02)00108-9.
Kiniry, J. R., J. R. Williams, D. J. Major, R. C. Izaurralde, P. W. Gassman, M. Morrison, R. Bergentine, and R. P. Zentner, 1995: EPIC model parameters for cereal, oilseed, and forage crops in the northern Great Plains region. Canadian Journal of Plant Science, 75, 679–688, https://doi.org/10.4141/cjps95-114.
Kukal, M. S., and S. Irmak, 2018: Climate-driven crop yield and yield variability and climate change impacts on the U.S. Great Plains agricultural production. Scientific Reports, 8, 3450, https://doi.org/10.1038/s41598-018-21848-2.
Lawrence, D. M., and Coauthors, 2019: The Community Land Model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. Journal of Advances in Modeling Earth Systems, 11, 4245–4287, https://doi.org/10.1029/2018MS001583.
Levis, S., A. Badger, B. Drewniak, C. Nevison, and X. L. Ren, 2018: CLMcrop yields and water requirements: Avoided impacts by choosing RCP 4.5 over 8.5. Climatic Change, 146, 501–515, https://doi.org/10.1007/s10584-016-1654-9.
Levis, S., G. B. Bonan, E. Kluzek, P. E. Thornton, A. Jones, W. J. Sacks, and C. J. Kucharik, 2012: Interactive crop management in the Community Earth System Model (CESM1): Seasonal influences on land–atmosphere fluxes. J. Climate, 25, 4839–4859, https://doi.org/10.1175/JCLI-D-11-00446.1.
Li, F., 2011: Probabilistic seasonal prediction of summer rainfall over East China based on multi-model ensemble schemes. Acta Meteorologica Sinica, 25, 283–292, https://doi.org/10.1007/s13351-011-0304-4.
Li, F., and Coauthors, 2019: Historical (1700–2012) global multi-model estimates of the fire emissions from the Fire Modeling Intercomparison Project (FireMIP). Atmospheric Chemistry and Physics, 19, 12 545–12 567, https://doi.org/10.5194/acp-19-12545-2019.
Li, Z. H., C. S. Zhan, S. Hu, L. K. Ning, L. F. Wu, and H. Guo, 2022: Evaluation of global gridded crop models (GGCMs) for the simulation of major grain crop yields in China. Hydrology Research, 53, 353–369, https://doi.org/10.2166/nh.2022.087.
Lindeskog, M., A. Arneth, A. Bondeau, K. Waha, J. Seaquist, S. Olin, and B. Smith, 2013: Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa. Earth System Dynamics, 4, 385–407, https://doi.org/10.5194/esd-4-385-2013.
Liu, L. L., Y. Zhu, L. Tang, W. X. Cao, and E. L. Wang, 2013: Impacts of climate changes, soil nutrients, variety types and management practices on rice yield in East China: A case study in the Taihu region. Field Crops Research, 149, 40–48, https://doi.org/10.1016/j.fcr.2013.04.022.
Liu, W. F., H. Yang, C. Folberth, X. Y. Wang, Q. Y. Luo, and R. Schulin, 2016: Global investigation of impacts of PET methods on simulating crop-water relations for maize. Agricultural and Forest Meteorology, 221, 164–175, https://doi.org/10.1016/j.agrformet.2016.02.017.
Lobell, D. B., G. Bala, and P. B. Duffy, 2006: Biogeophysical impacts of cropland management changes on climate. Geophys. Res. Lett., 33, L06708, https://doi.org/10.1029/2005GL025492.
Lombardozzi, D. L., Y. Q. Lu, P. J. Lawrence, D. M. Lawrence, S. Swenson, K. W. Oleson, W. R. Wieder, and E. A. Ainsworth, 2020: Simulating agriculture in the Community Land Model version 5. J. Geophys. Res.: Biogeosci., 125, e2019JG005529, https://doi.org/10.1029/2019JG005529.
Luo, Y. C., Z. Zhang, Y. Chen, Z. Y. Li, and F. L. Tao, 2020: ChinaCropPhen1km: A high-resolution crop phenological dataset for three staple crops in China during 2000–2015 based on leaf area index (LAI) products. Earth System Science Data, 12, 197–214, https://doi.org/10.6084/m9.figshare.8313530.
Martre, P., and Coauthors, 2015: Multimodel ensembles of wheat growth: Many models are better than one. Global Change Biology, 21, 911–925, https://doi.org/10.1111/gcb.12768.
Müller, C., and Coauthors, 2017: Global gridded crop model evaluation: Benchmarking, skills, deficiencies and implications. Geoscientific Model Development, 10, 1403–1422, https://doi.org/10.5194/gmd-10-1403-2017.
Müller, C., and Coauthors, 2019: The global gridded crop model intercomparison phase 1 simulation dataset. Scientific Data, 6, 50, https://doi.org/10.1038/s41597-019-0023-8.
Portmann, F. T., S. Siebert, and P. Döll, 2010: MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling. Global Biogeochemical Cycles, 24, GB1011, https://doi.org/10.1029/2008GB003435.
Ray, D. K., J. S. Gerber, G. K. MacDonald, and P. C. West, 2015: Climate variation explains a third of global crop yield variability. Nature Communications, 6, 5989, https://doi.org/10.1038/ncomms6989.
Rosenzweig, C., and Coauthors, 2014: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences of the United States of America, 111, 3268–3273, https://doi.org/10.1073/pnas.1222463110.
Rötter, R. P., M. Appiah, E. Fichtler, K. C. Kersebaum, M. Trnka, and M. P. Hoffmann, 2018: Linking modelling and experimentation to better capture crop impacts of agroclimatic extremes—A review. Field Crops Research, 221, 142–156, https://doi.org/10.1016/j.fcr.2018.02.023.
Ruane, A. C., R. Goldberg, and J. Chryssanthacopoulos, 2015: Climate forcing datasets for agricultural modeling: Merged products for gap-filling and historical climate series estimation. Agricultural and Forest Meteorology, 200, 233–248, https://doi.org/10.1016/j.agrformet.2014.09.016.
Sinclair, T. R., and T. W. Rufty, 2012: Nitrogen and water resources commonly limit crop yield increases, not necessarily plant genetics. Global Food Security, 1, 94–98, https://doi.org/10.1016/j.gfs.2012.07.001.
Sperber, K. R., H. Annamalai, I. S. Kang, A. Kitoh, A. Moise, A. Turner, B. Wang, and T. Zhou, 2013: The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Climate Dyn., 41, 2711–2744, https://doi.org/10.1007/s00382-012-1607-6.
Sun, H. Y., X. Y. Zhang, S. Y. Chen, D. Pei, and C. M. Liu, 2007: Effects of harvest and sowing time on the performance of the rotation of winter wheat–summer maize in the North China Plain. Industrial Crops and Products, 25, 239–247, https://doi.org/10.1016/j.indcrop.2006.12.003.
Tian, S. Z., X. X. Dong, H. H. Guo, L. Dong, Y. F. Zhang, S. L. Liu, and J. F. Luo, 2019: Key soil nutrient requirements for different yield levels in North China. Soil Science and Plant Nutrition, 65, 519–524, https://doi.org/10.1080/00380768.2019.1639215.
Wallach, D., and Coauthors, 2018: Multimodel ensembles improve predictions of crop–environment–management interactions. Global Change Biology, 24, 5072–5083, https://doi.org/10.1111/gcb.14411.
Wang, E. L., Q. Yu, D. R. Wu, and J. Xia, 2008: Climate, agricultural production and hydrological balance in the North China Plain. International Journal of Climatology, 28, 1959–1970, https://doi.org/10.1002/joc.1677.
Wu, J., and X.-J. Gao, 2013: A gridded daily observation dataset over China region and comparison with the other datasets. Chinese Journal of Geophysics, 56, 1102–1111, https://doi.org/10.6038/cjg20130406. (in Chinese with English abstract)
Wu, X., and Coauthors, 2016: ORCHIDEE-CROP (v0), a new process-based agro-land surface model: Model description and evaluation over Europe. Geoscientific Model Development, 9, 857–873, https://doi.org/10.5194/gmd-9-857-2016.
Xiao, D. P., and F. L. Tao, 2014: Contributions of cultivars, management and climate change to winter wheat yield in the North China Plain in the past three decades. European Journal of Agronomy, 52, 112–122, https://doi.org/10.1016/j.eja.2013.09.020.
Yin, Y., Q. Tang, and X. Liu, 2015: A multi-model analysis of change in potential yield of major crops in China under climate change. Earth System Dynamics, 6, 45–59, https://doi.org/10.5194/esd-6-45-2015.
Yu, Y. Q., Y. Huang, and W. Zhang, 2012: Changes in rice yields in China since 1980 associated with cultivar improvement, climate and crop management. Field Crops Research, 136, 65–75, https://doi.org/10.1016/j.fcr.2012.07.021.
Zhao, H., and Coauthors, 2021: China’s future food demand and its implications for trade and environment. Nature Sustainability, 4, 1042–1051, https://doi.org/10.1038/s41893-021-00784-6.
Acknowledgements
This study was co-supported by the Guangdong Major Project of Basic and Applied Basic Research (Grant No. 2021B0301030007), the National Key Research and Development Program of China (Grant Nos. 2017YFA0604302 and 2017YFA0604804), the National Natural Science Foundation of China (Grant No. 41875137), and the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (EarthLab). We thank Christoph Müller for his help in answering our question about GGCMI phase 1 simulation data, the two anonymous reviewers for their valuable comments and suggestions, and the Editor for handling our paper.
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Article Highlights
• GGCMs generally underestimate rice yield but overestimate wheat, maize, and soybean yield.
• GGCMs fail to capture the spatial patterns of observed crop yields in China.
• GGCMI models are more skillful in reproducing the interannual variability, while CLM5-crop is better at simulating long-term trends.
• At least one model can skillfully simulate the temporal variability of yield in the top-10 producing provinces, with a few exceptions.
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Yin, D., Li, F., Lu, Y. et al. Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models. Adv. Atmos. Sci. 41, 420–434 (2024). https://doi.org/10.1007/s00376-023-2234-3
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DOI: https://doi.org/10.1007/s00376-023-2234-3