Abstract
High-quality measured weather data (MWD) are essential for long-term and in-season crop model applications. When MWD is not available, one alternative for crop simulations is to employ gridded weather data (GWD), which needs to be evaluated a priori. Therefore, this study aimed to evaluate the impact of weather data from two GWD sources (NASA and XAVIER), in the way that they are available for end users, on simulating sugarcane crop performance within the APSIM-Sugar model at traditional sites where sugarcane is grown in Center-South Brazil, compared to simulations with MWD. Besides, this study also evaluated the impact of replacing GWD rainfall by the site-specific measured data on such simulations. A common sugarcane cropping system was repeatedly simulated between 1997 and 2015 for different combinations of climate input. Both NASA and XAVIER appear to be interesting for applications that only require temperature and solar radiation for predictions, such as crop phenology and potential yield. Nonetheless, GWD should be used with caution for crop model applications that rely on accurate estimation of crop water balance, canopy development, and biomass accumulation, at least with crop models that run at a daily time-step. The replacement of gridded rainfall with measured rainfall was pivotal for improving sugarcane simulations, as observed for cane yield, by increasing both agreement (NASA d index from 0.67 to 0.90; XAVIER d from 0.73 to 0.93) and R2 (NASA from 0.35 to 0.76; XAVIER from 0.43 to 0.79) and reducing root mean square errors (RMSE) from 32.8 to 16.3 t/ha when simulated with other variables of NASA data and from 27.9 to 12.7 t/ha when having XAVIER data as input. Therefore, while using both GWD sets without any correction, it is recommended to replace gridded rainfall by measured values, whenever possible, to improve sugarcane simulations in Center-South Brazil.
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References
Aggarwal PK (1995) Uncertainties in crop, soil and weather inputs used in growth models: implications for simulated outputs and their applications. Agric Syst 48:361–384. https://doi.org/10.1016/0308-521X(94)00018-M
Alvares CA, Stape JL, Sentelhas PC, de Moraes Gonçalves JL, Sparovek G (2013) Köppen’s climate classification map for Brazil. Meteorol Z 22:711–728. https://doi.org/10.1127/0941-2948/2013/0507
ANA (2020) HidroWeb v3.1.1 - National Water Resources Information System. In: Natl. Water Agency. http://www.snirh.gov.br/hidroweb/apresentacao. Accessed 14 Feb 2020
Bai J, Chen X, Dobermann A, Yang H, Cassman KG, Zhang F (2010) Evaluation of NASA satellite- and model-derived weather data for simulation of maize yield potential in China. Agron J 102:9–16. https://doi.org/10.2134/agronj2009.0085
Basnayake J, Jackson PA, Inman-Bamber NG, Lakshmanan P (2012) Sugarcane for water-limited environments: genetic variation in cane yield and sugar content in response to water stress. J Exp Bot 63:6023–6033. https://doi.org/10.1093/jxb/ers251
Basso B, Hyndman DW, Kendall AD, Grace PR, Robertson GP (2015) Can impacts of climate change and agricultural adaptation strategies be accurately quantified if crop models are annually re-initialized? PlosOne June 4:1–12. https://doi.org/10.1371/journal.pone.0127333
Battisti R, Bender FD, Sentelhas PC (2019) Assessment of different gridded weather data for soybean yield simulations in Brazil. Theor Appl Climatol 135:237–247. https://doi.org/10.1007/s00704-018-2383-y
Bender FD, Sentelhas PC (2018) Solar radiation models and gridded databases to fill gaps in weather series and to project climate change in Brazil. Adv Meteorol 2018:1–15. https://doi.org/10.1155/2018/6204382
Boote KJ, Jones JW, Pickering NB (1996) Potential uses and limitations of crop models. Agron J 88:704–716. https://doi.org/10.2134/agronj1996.00021962008800050005x
Börjesson P (2009) Good or bad bioethanol from a greenhouse gas perspective - what determines this? Appl Energy 86:589–594. https://doi.org/10.1016/j.apenergy.2008.11.025
Bouman BAA, van Keulen H, van Laar HH, Rabbinge R (1996) The ‘School of de Wit’ crop growth simulation models: a pedigree and historical overview. Agric Syst 52:171–198. https://doi.org/10.1016/0308-521X(96)00011-X
Brazil (2015) Intended nationally determined contribution towards achieving the objective of the United Nations Framework Convention on Climate Change. In: Fed. Repub. Brazil. http://www.itamaraty.gov.br/images/ed_desenvsust/BRAZILiNDC-%0Aenglish.pdf. Accessed 30 May 2018
Cardoso TF, Watanabe MDB, Souza A, Chagas MF, Cavalett O, Morais ER, Nogueira LAH, Leal MRLV, Braunbeck OA, Cortez LAB, Bonomi A (2019) A regional approach to determine economic, environmental and social impacts of different sugarcane production systems in Brazil. Biomass Bioenergy 120:9–20. https://doi.org/10.1016/j.biombioe.2018.10.018
Cardozo NP, Sentelhas PC (2013) Climatic effects on sugarcane ripening under the influence of cultivars and crop age. Sci Agric 70:250. https://doi.org/10.1590/S0103-90162013000600011
Cardozo NP, Sentelhas PC, Panosso AR, Palhares AL, Ide BY (2015) Modeling sugarcane ripening as a function of accumulated rainfall in Southern Brazil. Int J Biometeorol 59:1913–1925. https://doi.org/10.1007/s00484-015-0998-6
Costa LG (2017) Crescimento, desenvolvimento e consumo hídrico de cana-de-açúcar sob dois sistemas de manejo da palha. Universidade de São Paulo, Escola Superior de Agricultura “Luiz de Queiroz”. Piracicaba, Brasil [in Portuguese]
de Oliveira APP, Thorburn PJ, Biggs JS et al (2016) The response of sugarcane to trash retention and nitrogen in the Brazilian coastal tablelands: a simulation study. Exp Agric 52:69–86. https://doi.org/10.1017/S0014479714000568
Dias HB, Sentelhas PC (2017) Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields. F Crop Res 213:174–185. https://doi.org/10.1016/j.fcr.2017.07.022
Dias HB, Sentelhas PC (2018) Sugarcane yield gap analysis in Brazil – a multi-model approach for determining magnitudes and causes. Sci Total Environ 637–368:1127–1136. https://doi.org/10.1016/j.scitotenv.2018.05.017
Dias HB, Inman-Bamber G, Bermejo R, Sentelhas PC, Christodoulou D (2019) New APSIM-Sugar features and parameters required to account for high sugarcane yields in tropical environments. F Crop Res 235:38–53. https://doi.org/10.1016/j.fcr.2019.02.002
Dias HB, Inman-Bamber G, Everingham Y, Sentelhas PC, Bermejo R, Christodoulou D (2020) Traits for canopy development and light interception by twenty-seven Brazilian sugarcane varieties. F Crop Res 249:107716. https://doi.org/10.1016/j.fcr.2020.107716
Duarte YCN, Sentelhas PC (2020) NASA/POWER and DailyGridded weather datasets—how good they are for estimating maize yields in Brazil? Int J Biometeorol 64:319–329. https://doi.org/10.1007/s00484-019-01810-1
Fageria NK, Baligar VC, Jones CA (2010) Growth and mineral nutrition of field crops, 3rd. CRC Press, Boca Raton, FL
Galdos MV, Cerri CC, Cerri CEP, Paustian K, van Antwerpen R (2010) Simulation of sugarcane residue decomposition and aboveground growth. Plant Soil 326:243–259. https://doi.org/10.1007/s11104-009-0004-3
Grassini P, van Bussel LGJ, van Wart J, Wolf J, Claessens L, Yang H, Boogaard H, de Groot H, van Ittersum MK, Cassman KG (2015) How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. F Crop Res 177:49–63. https://doi.org/10.1016/j.fcr.2015.03.004
Hoffmann H, Zhao G, Asseng S, Bindi M, Biernath C, Constantin J, Coucheney E, Dechow R, Doro L, Eckersten H, Gaiser T, Grosz B, Heinlein F, Kassie BT, Kersebaum KC, Klein C, Kuhnert M, Lewan E, Moriondo M, Nendel C, Priesack E, Raynal H, Roggero PP, Rötter RP, Siebert S, Specka X, Tao F, Teixeira E, Trombi G, Wallach D, Weihermüller L, Yeluripati J, Ewert F (2016) Impact of spatial soil and climate input data aggregation on regional Yield Simulations. PLoS One 11:1–23. https://doi.org/10.1371/journal.pone.0151782
Holzworth DP, Huth NI, deVoil PG et al (2014) APSIM - evolution towards a new generation of agricultural systems simulation. Environ Model Softw 62:327–350. https://doi.org/10.1016/j.envsoft.2014.07.009
Hoogenboom G (2000) Contribution of agrometeorology to the simulation of crop production and its applications. Agric For Meteorol 103:137–157. https://doi.org/10.1016/s0168-1923(00)00108-8
IBGE (2020) Sistema de recuperação automática (SIDRA). In: Inst. Bras. Geogr. e Estatística. http://www.sidra.ibge.gov.br/. Accessed 12 Feb 2020
Inman-Bamber NG (2004) Sugarcane water stress criteria for irrigation and drying off. F Crop Res 89:107–122. https://doi.org/10.1016/j.fcr.2004.01.018
Inman-Bamber NG (2014) Sugarcane yields and yield-limiting processes. In: Moore PH, Botha FC (eds) Sugarcane: Physiology, Biochemistry, and Functional Biology. John Wiley & Sons, Inc., Chichester, UK, pp 579–600
Inman-Bamber NG, Lakshmanan P, Park S (2012) Sugarcane for water-limited environments: theoretical assessment of suitable traits. F Crop Res 134:95–104. https://doi.org/10.1016/j.fcr.2012.05.004
Inman-Bamber NG, Jackson PA, Stokes CJ, Verrall S, Lakshmanan P, Basnayake J (2016) Sugarcane for water-limited environments: enhanced capability of the APSIM sugarcane model for assessing traits for transpiration efficiency and root water supply. F Crop Res 196:112–123. https://doi.org/10.1016/j.fcr.2016.06.013
Jaiswal D, De Souza AP, Larsen S et al (2017) Brazilian sugarcane ethanol as an expandable green alternative to crude oil use. Nat Clim Chang 7:788–792. https://doi.org/10.1038/nclimate3410
Keating BA, Robertson MJ, Muchow RC, Huth NI (1999) Modelling sugarcane production systems I. Development and performance of the sugarcane module. F Crop Res 61:253–271. https://doi.org/10.1016/S0378-4290(98)00167-1
Kersebaum KC, Boote KJ, Jorgenson JS, Nendel C, Bindi M, Frühauf C, Gaiser T, Hoogenboom G, Kollas C, Olesen JE, Rötter RP, Ruget F, Thorburn PJ, Trnka M, Wegehenkel M (2015) Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Environ Model Softw 72:402–417. https://doi.org/10.1016/j.envsoft.2015.05.009
Kingston G (2002) Recognising the impact of climate on CCS of sugarcane across tropical and sub-tropical regions of the Australian sugar industry. Proc Aust Soc Sugar Cane Technol 24 (CD-ROM:
Marin FR, Martha GB, Cassman KG, Grassini P (2016) Prospects for increasing sugarcane and bioethanol production on existing crop area in Brazil. Bioscience 66:307–316. https://doi.org/10.1093/biosci/biw009
Monod H, Naud C, Makowski D (2006) Uncertainty and sensitivity analysis for crop models. In: Wallach D, Makowski D, Jones JW (eds) Working with Dynamic Crop Models, 1st edn. Elsevier, Amsterdam, The Netherlands, pp 55–100
Monteiro LA, Sentelhas PC (2017) Sugarcane yield gap: can it be determined at national level with a simple agrometeorological model? Crop Pasture Sci 68:272–284. https://doi.org/10.1071/CP16334
Monteiro LA, Sentelhas PC, Pedra GU (2018) Assessment of NASA/POWER satellite-based weather system for Brazilian conditions and its impact on sugarcane yield simulation. Int J Climatol 38:1571–1581. https://doi.org/10.1002/joc.5282
Mourtzinis S, Rattalino Edreira JI, Conley SP, Grassini P (2017) From grid to field: assessing quality of gridded weather data for agricultural applications. Eur J Agron 82:163–172. https://doi.org/10.1016/j.eja.2016.10.013
Muchow RC, Robertson MJ, Keating BA (1997) Limits to Australian sugar industry: climate and biological factors. In: Keating BA, Wilson J (eds) Intensive Sugarcane Production: Meeting the Challenges beyond 2000. CAB International, Wallingford, UK, pp 37–54
Ojeda JJ, Volenec JJ, Brouder SM, Caviglia OP, Agnusdei MG (2017) Evaluation of agricultural production systems simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments. GCB Bioenergy 9:796–816. https://doi.org/10.1111/gcbb.12384
Ojeda JJ, Rezaei EE, Remenyi TA, Webb MA, Webber HA, Kamali B, Harris RMB, Brown JN, Kidd DB, Mohammed CL, Siebert S, Ewert F, Meinke H (2020) 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
R CORE TEAM (2018) R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.r-project.org/
Ramburan S, Wettergreen T, Berry SD, Shongwe B (2013) Genetic, environmental and management contributions to ratoon decline in sugarcane. F Crop Res 146:105–112. https://doi.org/10.1016/j.fcr.2013.03.011
Ramirez-Villegas J, Challinor A (2012) Assessing relevant climate data for agricultural applications. Agric For Meteorol 161:26–45. https://doi.org/10.1016/j.agrformet.2012.03.015
Ray DK, Gerber JS, Macdonald GK, West PC (2015) Climate variation explains a third of global crop yield variability. Nat Commun 6:1–9. https://doi.org/10.1038/ncomms6989
Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Boote KJ, Thorburn P, Antle JM, Nelson GC, Porter C, Janssen S, Asseng S, Basso B, Ewert F, Wallach D, Baigorria G, Winter JM (2013) The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agric For Meteorol 170:166–182. https://doi.org/10.1016/j.agrformet.2012.09.011
Ruane AC, McDermid SP (2017) Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment. Earth Perspect 4:1–20. https://doi.org/10.1186/s40322-017-0036-4
Ruane AC, Goldberg R, Chryssanthacopoulos J (2015) Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation. Agric For Meteorol 200:233–248. https://doi.org/10.1016/j.agrformet.2014.09.016
Scarpari MS, de Beauclair EGF (2004) Sugarcane maturity estimation through edaphic-climatic parameters. Sci Agric 61:486–491. https://doi.org/10.1590/S0103-90162004000500004
Sexton J, Everingham YL, Inman-Bamber NG (2017) A global sensitivity analysis of cultivar trait parameters in a sugarcane growth model for contrasting production environments in Queensland, Australia. Eur J Agron 88:96–105. https://doi.org/10.1016/j.eja.2015.11.009
Silva-Olaya AM, Cerri CEP, Williams S, Cerri CC, Davies CA, Paustian K (2017) Modelling SOC response to land use change and management practices in sugarcane cultivation in South-Central Brazil. Plant Soil 410:483–498. https://doi.org/10.1007/s11104-016-3030-y
Sivakumar MVK (2006) Dissemination and communication of agrometeorological information—global perspectives. Meteorol Appl 13:21–30. https://doi.org/10.1017/S1350482706002520
Stackhouse PW, Zhang T, Westber D, et al (2018) POWER Release 8.0.1 (with GIS applications) methodology (data parameters, sources, & validation)
Thorburn PJ, Meier EA, Probert ME (2005) Modelling nitrogen dynamics in sugarcane systems: recent advances and applications. F Crop Res 92:337–351. https://doi.org/10.1016/j.fcr.2005.01.016
Valeriano TTB, Rolim GDS, Bispo RC, et al (2019) Evaluation of air temperature and rainfall from ECMWF and NASA gridded data for southeastern Brazil. Theor Appl Climatol 137:1925–1938. https://doi.org/10.1007/s00704-018-2706-z
van Bussel LGJ, Müller C, van Keulen H, Ewert F, Leffelaar PA (2011) The effect of temporal aggregation of weather input data on crop growth models’ results. Agric For Meteorol 151:607–619. https://doi.org/10.1016/j.agrformet.2011.01.007
van Ittersum MK, Cassman KG, Grassini P, Wolf J, Tittonell P, Hochman Z (2013) Yield gap analysis with local to global relevance — a review. F Crop Res 143:4–17. https://doi.org/10.1016/j.fcr.2012.09.009
Van Wart J, Grassini P, Cassman KG (2013a) Impact of derived global weather data on simulated crop yields. Glob Chang Biol 19:3822–3834. https://doi.org/10.1111/gcb.12302
Van Wart J, Kersebaum KC, Peng S et al (2013b) Estimating crop yield potential at regional to national scales. F Crop Res 143:34–43. https://doi.org/10.1016/j.fcr.2012.11.018
Wallach D (2006) Evaluating crop models. In: Wallach D, Makowski D, Jones JW (eds) Working with Dynamic Crop Models, 1st edn. Elsevier, Amsterdam, The Netherlands, pp 11–54
White JW, Hoogenboom G, Wilkens PW, Stackhouse PW Jr, Hoel JM (2011) Evaluation of satellite-based, modeled-derived daily solar radiation data for the continental United States. Agron J 103:1242–1251. https://doi.org/10.2134/agronj2011.0038
White JW, Hunt LA, Boote KJ, Jones JW, Koo J, Kim S, Porter CH, Wilkens PW, Hoogenboom G (2013) Integrated description of agricultural field experiments and production: the ICASA Version 2.0 data standards. Comput Electron Agric 96:1–12. https://doi.org/10.1016/j.compag.2013.04.003
Wickham H (2016) ggplot2: Elegant graphics for data analysis. Springer-Verlag New York
Xavier AC, King CW, Scanlon BR (2016) Daily gridded meteorological variables in Brazil (1980–2013). Int J Climatol 36:2644–2659. https://doi.org/10.1002/joc.4518
Acknowledgements
The authors are grateful to the São Paulo Research Foundation (FAPESP, grant no. 2016/11170-2) and the Coordination for the Improvement of Higher Education Personnel (CAPES). The second author is thankful to the Brazilian Research and Development Council (CNPq) for his fellowship (Level 1A). Dr. Glauco Rolim (UNESP, Brazil) is gratefully acknowledged for providing climate data of Jaboticabal site. NASA gridded data were obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program. We are in debt with Professor Yvette Everingham (JCU, Australia) who kindly revised the first version of this paper in terms of English language. We also would like to express our gratitude to the two anonymous reviewers for helpful comments, suggestions, and insights that improved the earlier version.
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Henrique Dias: Conceptualisation, methodology, formal analysis, investigation, writing—original draft preparation, writing—reviewing and editing, visualisation, funding acquisition
Paulo Sentelhas: Conceptualisation, writing—reviewing and editing, supervision, funding acquisition
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Dias, H.B., Sentelhas, P.C. Assessing the performance of two gridded weather data for sugarcane crop simulations with a process-based model in Center-South Brazil. Int J Biometeorol 65, 1881–1893 (2021). https://doi.org/10.1007/s00484-021-02145-6
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DOI: https://doi.org/10.1007/s00484-021-02145-6