Impact of coupled input data source-resolution and aggregation on contributions of high-yielding traits to simulated wheat yield.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
05 Oct 2024
05 Oct 2024
Historique:
received:
19
06
2024
accepted:
25
09
2024
medline:
6
10
2024
pubmed:
6
10
2024
entrez:
5
10
2024
Statut:
epublish
Résumé
High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implications of this practice. The SIMPLACE modeling platform was applied using field, 1 km, 25 km, and 50 km input data resolution and sources, with 1881 combinations of three traits [radiation use efficiency (RUE), light extinction coefficient (K), and fruiting efficiency (FE)] for the period 2001-2010 across Germany. Simulations at the grid level were aggregated to the administrative units, enabling the quantification of the aggregation effect. The simulated yield increased by between 1.4 and 3.1 t ha
Identifiants
pubmed: 39369136
doi: 10.1038/s41598-024-74309-4
pii: 10.1038/s41598-024-74309-4
doi:
Substances chimiques
Soil
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23172Informations de copyright
© 2024. The Author(s).
Références
van Dijk, M., Morley, T., Rau, M. L. & Saghai, Y. A meta-analysis of projected global food demand and population at risk of hunger for the period 2010–2050. Nat. Food. 2, 494–501. https://doi.org/10.1038/s43016-021-00322-9 (2021).
doi: 10.1038/s43016-021-00322-9
Tian, X. et al. Will reaching the maximum achievable yield potential meet future global food demand? J. Clean. Prod. 294, 126285. https://doi.org/10.1016/j.jclepro.2021.126285 (2021).
doi: 10.1016/j.jclepro.2021.126285
Beltran-Peña, A., Rosa, L. & D’Odorico, P. Global food self-sufficiency in the 21st century under sustainable intensification of agriculture. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/ab9388 (2020).
Lesk, C. et al. Stronger temperature–moisture couplings exacerbate the impact of climate warming on global crop yields. Nat. Food. 2, 683–691. https://doi.org/10.1038/s43016-021-00341-6 (2021).
doi: 10.1038/s43016-021-00341-6
Wang, X. et al. Emergent constraint on crop yield response to warmer temperature from field experiments. Nat. Sustain. 3, 908–916. https://doi.org/10.1038/s41893-020-0569-7 (2020).
doi: 10.1038/s41893-020-0569-7
Jägermeyr, J. et al. Climate impacts on global agriculture emerge earlier in new generation of climate and crop models. Nat. Food. 2, 873–885. https://doi.org/10.1038/s43016-021-00400-y (2021).
doi: 10.1038/s43016-021-00400-y
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293. https://doi.org/10.1038/ncomms2296 (2012).
doi: 10.1038/ncomms2296
Sharma, R. C. et al. Genetic gains for Grain Yield in CIMMYT Spring Bread Wheat across International environments. Crop Sci. 52, 1522–1533. https://doi.org/10.2135/cropsci2011.12.0634 (2012).
doi: 10.2135/cropsci2011.12.0634
Tshikunde, N. M., Mashilo, J., Shimelis, H. & Odindo, A. Agronomic and physiological traits, and Associated Quantitative Trait Loci (QTL) affecting yield response in wheat (Triticum aestivum L.): A review. Front. Plant Sci. 10, 1428. https://doi.org/10.3389/fpls.2019.01428 (2019).
doi: 10.3389/fpls.2019.01428
Bailey-Serres, J., Parker, J. E., Ainsworth, E. A., Oldroyd, G. E. D. & Schroeder J. I. Genetic strategies for improving crop yields. Nature. 575, 109–118. https://doi.org/10.1038/s41586-019-1679-0 (2019).
doi: 10.1038/s41586-019-1679-0
Reynolds, M. et al. Raising yield potential of wheat. I. Overview of a consortium approach and breeding strategies. J. Exp. Bot. 62, 439–452. https://doi.org/10.1093/jxb/erq311 (2011).
doi: 10.1093/jxb/erq311
Foulkes, M. J. et al. Raising yield potential of wheat. III. Optimizing partitioning to grain while maintaining lodging resistance. J. Exp. Bot. 62, 469–486. https://doi.org/10.1093/jxb/erq300 (2011).
doi: 10.1093/jxb/erq300
Brancourt-Hulmel, M. et al. Genetic improvement of agronomic traits of Winter Wheat cultivars Released in France from 1946 to 1992. Crop Sci. 43, 37. https://doi.org/10.2135/cropsci2003.3700 (2003).
doi: 10.2135/cropsci2003.3700
Molero, G. et al. Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential. Plant Biotechnol. J. 17, 1276–1288. https://doi.org/10.1111/pbi.13052 (2019).
doi: 10.1111/pbi.13052
Acreche, M. M. & Slafer, G. A. Grain weight, radiation interception and use efficiency as affected by sink-strength in Mediterranean wheats released from 1940 to 2005. Field Crops Res. 110, 98–105. https://doi.org/10.1016/j.fcr.2008.07.006 (2009).
doi: 10.1016/j.fcr.2008.07.006
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. https://doi.org/10.2135/cropsci2018.09.0562 (2019).
doi: 10.2135/cropsci2018.09.0562
Álvaro, F., Royo, C., García Moral, D., Villegas, D. & L. F. & Grain Filling and Dry Matter translocation responses to Source-Sink modifications in a historical series of Durum Wheat. Crop Sci. 48, 1523–1531. https://doi.org/10.2135/cropsci2007.10.0545 (2008).
doi: 10.2135/cropsci2007.10.0545
Reynolds, M., Calderini, D., Condon, A. & Vargas, M. Association of source/sink traits with yield, biomass and radiation use efficiency among random sister lines from three wheat crosses in a high-yield environment. J. Agric. Sci. 145, 3–16. https://doi.org/10.1017/S0021859607006831 (2007).
doi: 10.1017/S0021859607006831
Slafer, G. A. et al. Fruiting efficiency: An alternative trait to further rise wheat yield. Food Energy Secur. 4, 92–109. https://doi.org/10.1002/fes3.59 (2015).
doi: 10.1002/fes3.59
Acreche, M. M., Briceño-Félix, G., Martín Sánchez, J. A. & Slafer, G. A. Radiation interception and use efficiency as affected by breeding in Mediterranean wheat. Field Crops Res. 110, 91–97. https://doi.org/10.1016/j.fcr.2008.07.005 (2009).
doi: 10.1016/j.fcr.2008.07.005
Zhang, L., Hu, Z., Fan, J., Zhou, D. & Tang, F. A meta-analysis of the canopy light extinction coefficient in terrestrial ecosystems. Front. Earth Sci. 8, 599–609. https://doi.org/10.1007/s11707-014-0446-7 (2014).
doi: 10.1007/s11707-014-0446-7
Furbank, R. T., Quick, W. P. & Sirault, X. R. Improving photosynthesis and yield potential in cereal crops by targeted genetic manipulation: Prospects, progress and challenges. Field Crops Res. 182, 19–29. https://doi.org/10.1016/j.fcr.2015.04.009 (2015).
doi: 10.1016/j.fcr.2015.04.009
de Leon, N., Jannink, J. L., Edwards, J. W. & Kaeppler, S. M. Introduction to a special issue on genotype by Environment Interaction. Crop Sci. 56, 2081–2089. https://doi.org/10.2135/cropsci2016.07.0002in (2016).
doi: 10.2135/cropsci2016.07.0002in
Stella, T. et al. Wheat crop traits conferring high yield potential may also improve yield stability under climate change. In Silico Plants. https://doi.org/10.1093/insilicoplants/diad013 (2023).
Senapati, N. et al. Global wheat production could benefit from closing the genetic yield gap. Nat. Food. 3, 532–541. https://doi.org/10.1038/s43016-022-00540-9 (2022).
doi: 10.1038/s43016-022-00540-9
Manivasagam, V. S. & Rozenstein, O. Practices for upscaling crop simulation models from field scale to large regions. Comput. Electron. Agric. 175, 105554. https://doi.org/10.1016/j.compag.2020.105554 (2020).
doi: 10.1016/j.compag.2020.105554
Grassini, P. et al. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Res. 177, 49–63. https://doi.org/10.1016/j.fcr.2015.03.004 (2015).
doi: 10.1016/j.fcr.2015.03.004
Ojeda, J. J. et al. Implications of data aggregation method on crop model outputs—The case of irrigated potato systems in Tasmania, Australia. Eur. J. Agron. 126, 126276. https://doi.org/10.1016/j.eja.2021.126276 (2021).
doi: 10.1016/j.eja.2021.126276
Zhao, G. et al. Demand for multi-scale weather data for regional crop modeling. Agric. For. Meteorol. 200, 156–171. https://doi.org/10.1016/j.agrformet.2014.09.026 (2015).
doi: 10.1016/j.agrformet.2014.09.026
Rezaei, E. E., Siebert, S. & Ewert, F. Impact of data resolution on heat and drought stress simulated for winter wheat in Germany. Eur. J. Agron. 65, 69–82. https://doi.org/10.1016/j.eja.2015.02.003 (2015).
doi: 10.1016/j.eja.2015.02.003
Grosz, B. et al. The implication of input data aggregation on up-scaling soil organic carbon changes. Environ. Model. Softw. 96, 361–377. https://doi.org/10.1016/j.envsoft.2017.06.046 (2017).
doi: 10.1016/j.envsoft.2017.06.046
Ojeda, J. J. et al. Impact of crop management and environment on the spatio-temporal variance of potato yield at regional scale. Field Crops Res. 270, 108213. https://doi.org/10.1016/j.fcr.2021.108213 (2021).
doi: 10.1016/j.fcr.2021.108213
van Wart, J., Grassini, P. & Cassman, K. G. Impact of derived global weather data on simulated crop yields. Glob. Change Biol. 19, 3822–3834. https://doi.org/10.1111/gcb.12302 (2013).
doi: 10.1111/gcb.12302
Kværnø, S. H. & Stolte, J. Effects of soil physical data sources on discharge and soil loss simulated by the LISEM model. CATENA. 97, 137–149. https://doi.org/10.1016/j.catena.2012.05.001 (2012).
doi: 10.1016/j.catena.2012.05.001
Cooper, M., Voss-Fels, K. P., Messina, C. D., Tang, T. & Hammer, G. L. Tackling G × E × M interactions to close on-farm yield-gaps: Creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity. TAG. Theor. Appl. Genet. Theoretische Und Angewandte Genetik. 134, 1625–1644. https://doi.org/10.1007/s00122-021-03812-3 (2021).
doi: 10.1007/s00122-021-03812-3
Enders, A. et al. SIMPLACE—A versatile modelling and simulation framework for sustainable crops and agroecosystems. In Silico Plants. https://doi.org/10.1093/insilicoplants/diad006 (2023).
Bustos, D. V., Hasan, A. K., Reynolds, M. P. & Calderini, D. F. Combining high grain number and weight through a DH-population to improve grain yield potential of wheat in high-yielding environments. Field Crops Res. 145, 106–115. https://doi.org/10.1016/j.fcr.2013.01.015 (2013).
doi: 10.1016/j.fcr.2013.01.015
Webber, H. et al. No perfect storm for crop yield failure in Germany. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aba2a4 (2020).
Webber, H. et al. Diverging importance of drought stress for maize and winter wheat in Europe. Nat. Commun. https://doi.org/10.1038/s41467-018-06525-2 (2018).
CORINE. CORINE Land Cover. (2006). https://land.copernicus.eu/pan-european/corine-land-cover/clc-2006
García, G. A. et al. Wheat grain number: identification of favourable physiological traits in an elite doubled-haploid population. Field Crops Res. 168, 126–134. https://doi.org/10.1016/j.fcr.2014.07.018 (2014).
doi: 10.1016/j.fcr.2014.07.018
Guarin, J. R. et al. Evidence for increasing global wheat yield potential. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aca77c (2022).
Gabaldón-Leal, C. et al. Modelling the impact of heat stress on maize yield formation. Field Crops Res. 198, 226–237. https://doi.org/10.1016/j.fcr.2016.08.013 (2016).
doi: 10.1016/j.fcr.2016.08.013
Asseng, S. et al. Rising temperatures reduce global wheat production. Nat. Clim. Change. 5, 143–147. https://doi.org/10.1038/nclimate2470 (2015).
doi: 10.1038/nclimate2470
Rezaei, E. E., Siebert, S. & Ewert, F. in Temperature Routines in SIMPLACE < LINTUL2-CC-HEAT>. (eds Alderman, P., Quilligan, E., Asseng, S., Ewert, F. & Reynolds, M. P.) (El Batan, 2013).
Kaspar, F., Zimmermann, K. & Polte-Rudolf, C. An overview of the phenological observation network and the phenological database of Germany’s national meteorological service (Deutscher Wetterdienst). Adv. Sci. Res. 11, 93–99. https://doi.org/10.5194/asr-11-93-2014 (2014).
doi: 10.5194/asr-11-93-2014
Muggeo, V., Atkins, D. C., Gallop, R. J. & Dimidjian, S. Segmented mixed models with random changepoints: A maximum likelihood approach with application to treatment for depression study. Stat. Modelling. 14, 293–313. https://doi.org/10.1177/1471082X13504721 (2014).
doi: 10.1177/1471082X13504721
Rezaei, E. E., Siebert, S. & Ewert, F. Climate and management interaction cause diverse crop phenology trends. Agric. For. Meteorol. 233, 55–70. https://doi.org/10.1016/j.agrformet.2016.11.003 (2017).
doi: 10.1016/j.agrformet.2016.11.003
Reynolds, M. P. et al. Strategic crossing of biomass and harvest index—Source and sink—Achieves genetic gains in wheat. Euphytica. https://doi.org/10.1007/s10681-017-2040-z (2017).
Li, P. F. et al. Dryland wheat domestication changed the development of aboveground architecture for a well-structured canopy. PloS One. 9, e95825. https://doi.org/10.1371/journal.pone.0095825 (2014).
doi: 10.1371/journal.pone.0095825
Maharjan, G. R. et al. Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates. Eur. J. Agron. 103, 32–46. https://doi.org/10.1016/j.eja.2018.11.001 (2019).
doi: 10.1016/j.eja.2018.11.001
Eyshi Rezaei, E., Gaiser, T., Siebert, S., Sultan, B. & Ewert, F. Combined impacts of climate and nutrient fertilization on yields of pearl millet in Niger. Eur. J. Agron. 55, 77–88. https://doi.org/10.1016/j.eja.2014.02.001 (2014).
doi: 10.1016/j.eja.2014.02.001
Folberth, C. et al. Uncertainty in soil data can outweigh climate impact signals in global crop yield simulations. Nat. Commun. 7, 11872. https://doi.org/10.1038/ncomms11872 (2016).
doi: 10.1038/ncomms11872
Addiscott, T. M. & Whitmore, A. P. Simulation of solute leaching in soils of differing permeabilities. Soil Use Manag. 7, 94–102. https://doi.org/10.1111/j.1475-2743.1991.tb00856.x (1991).
doi: 10.1111/j.1475-2743.1991.tb00856.x
Duden, C., Nacke, C. & Offermann, F. German yield and area data for 11 crops from 1979 to 2021 at a harmonized spatial resolution of 397 districts. Sci. data. 11, 95. https://doi.org/10.1038/s41597-024-02951-8 (2024).
doi: 10.1038/s41597-024-02951-8
Dias de Oliveira, E. A., Siddique, K. H. M., Bramley, H., Stefanova, K. & Palta, J. A. Response of wheat restricted-tillering and vigorous growth traits to variables of climate change. Glob. Change Biol. 21, 857–873. https://doi.org/10.1111/gcb.12769 (2015).
doi: 10.1111/gcb.12769
Dodig, D., Zoric, M., Knezevic, D., King, S. R. & Surlan-Momirovic, G. Genotype×environment interaction for wheat yield in different drought stress conditions and agronomic traits suitable for selection. Aust J. Agric. Res. 59, 536. https://doi.org/10.1071/AR07281 (2008).
doi: 10.1071/AR07281
Ojeda, J. J. et al. Effects of soil- and climate data aggregation on simulated potato yield and irrigation water requirement. Sci. Total Environ. 710, 135589. https://doi.org/10.1016/j.scitotenv.2019.135589 (2020).
doi: 10.1016/j.scitotenv.2019.135589
Angulo, C. et al. Characteristic ‘fingerprints’ of crop model responses to weather input data at different spatial resolutions. Eur. J. Agron. 49, 104–114. https://doi.org/10.1016/j.eja.2013.04.003 (2013).
doi: 10.1016/j.eja.2013.04.003
Reynolds, M. P. et al. A wiring diagram to integrate physiological traits of wheat yield potential. Nat. Food. 3, 318–324. https://doi.org/10.1038/s43016-022-00512-z (2022).
doi: 10.1038/s43016-022-00512-z