Leveraging temporal variability in global sensitivity analysis of the Daisy soil-plant-atmosphere model

[en] Dynamic crop models, such as the Daisy soil-plant-atmosphere model, simulate many processes and encompass a large number of parameters. Global sensitivity analysis (GSA) aims to identify the most influential parameters and understand model structure and behaviour. However, little attention has been paid to the temporal dynamics of parameter sensitivity in crop models, even though it can provide greater insight into model structure. This study performs a comprehensive GSA on the Daisy model, including the soil-vegetation-atmosphere transfer (SVAT) module, focusing on crop yield as well as CO2, N2O and energy fluxes. The Sobol’ method was applied to two types of outputs: (i) outputs aggregated into a scalar with an objective function (RMSE or cumulative) and (ii) vector outputs analysed at each time step. The main objectives of this paper were to compare the temporal and aggregated applications of GSA and to identify influential parameters of Daisy under different environmental conditions. Both aggregated and temporal methods identified the same main parameters. Nevertheless, temporal analysis provided deeper insight into model behaviour and calibration guidelines, revealing dynamic changes in parameter sensitivity at weekly and hourly resolutions and identifying critical periods for calibration. Aggregated analysis was less time-consuming and focused on specific aspects due to the definition of the objective function. Finally, we discussed the risks and solutions for Daisy over-parameterisation as well as methods for parameter estimation based on information provided by the GSA.

Disciplines :

Environmental sciences & ecology

Author, co-author :

Delhez, Laura ; Université de Liège - ULiège > TERRA Research Centre

Dumont, Benjamin ; Université de Liège - ULiège > Département GxABT > Plant Sciences

Longdoz, Bernard ; Université de Liège - ULiège > TERRA Research Centre > Biosystems Dynamics and Exchanges (BIODYNE)

Language :

English

Title :

Leveraging temporal variability in global sensitivity analysis of the Daisy soil-plant-atmosphere model

Publication date :

April 2025

Journal title :

European Journal of Agronomy

ISSN :

1161-0301

Publisher :

Elsevier BV

Volume :

165

Pages :

127533

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

https://api.elsevier.com/content/article/PII:S1161030125000292?httpAccept=text/xml

Available on ORBi :

since 13 February 2025

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Bibliography

Abrahamsen, P., Hansen, S., Daisy: an open soil-crop-atmosphere system model. Environ. Model. Softw. 15 (2000), 313–330, 10.1016/S1364-8152(00)00003-7.
Ahmadi, S.H., Andersen, M.N., Poulsen, R.T., Plauborg, F., Hansen, S., A quantitative approach to developing more mechanistic gas exchange models for field grown potato: A new insight into chemical and hydraulic signalling. Agric. For. Meteorol. 149 (2009), 1541–1551, 10.1016/j.agrformet.2009.04.009.
Allen, R.G., Pereira, L.S., Raes, D., and Smith, M.: Crop evapotranspiration: Guidelines for computing crop water requirements, edited by: Food and Agriculture Organization of the United Nations, Food and Agriculture Organization of the United Nations, Rome, 300 pp., 1998.
Allen, R.G., Pruitt, W.O., Wright, J.L., Howell, T.A., Ventura, F., Snyder, R., Itenfisu, D., Steduto, P., Berengena, J., Yrisarry, J.B., Smith, M., Pereira, L.S., Raes, D., Perrier, A., Alves, I., Walter, I., Elliott, R., A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 penman-monteith method. Agric. Water Manag. 81 (2006), 1–22, 10.1016/j.agwat.2005.03.007.
Asseng, S., Zhu, Y., Basso, B., Wilson, T., Cammarano, D., Simulation modeling: applications in cropping systems. Encyclopedia of Agriculture and Food Systems, 2014, Elsevier, 102–112, 10.1016/B978-0-444-52512-3.00233-3.
Asseng, S., Zhu, Y., Wang, E., Zhang, W., Crop modeling for climate change impact and adaptation. Crop Physiology, 2015, Elsevier, 505–546, 10.1016/B978-0-12-417104-6.00020-0.
Aubinet, M., Moureaux, C., Bodson, B., Dufranne, D., Heinesch, B., Suleau, M., Vancutsem, F., Vilret, A., Carbon sequestration by a crop over a 4-year sugar beet/winter wheat/seed potato/winter wheat rotation cycle. Agric. For. Meteorol. 149 (2009), 407–418, 10.1016/j.agrformet.2008.09.003.
Baddoo, T.D., Li, Z., Guan, Y., Boni, K.R.C., Nooni, I.K., Data-driven modeling and the influence of objective function selection on model performance in limited data regions. IJERPH, 17, 2020, 4132, 10.3390/ijerph17114132.
Beauclaire, Q., Heinesch, B., Longdoz, B., Non-stomatal processes are responsible for the decrease in gross primary production of a potato crop during edaphic drought. Agric. For. Meteorol., 343, 2023, 109782, 10.1016/j.agrformet.2023.109782.
Beauclaire, Q., De Cannière, S., Jonard, F., Pezzetti, N., Delhez, L., Longdoz, B., Modeling gross primary production and transpiration from sun-induced chlorophyll fluorescence using a mechanistic light-response approach. Remote Sens. Environ., 307, 2024, 114150, 10.1016/j.rse.2024.114150.
Bittner, D., Engel, M., Wohlmuth, B., Labat, D., Chiogna, G., Temporal scale-dependent sensitivity analysis for hydrological model parameters using the discrete wavelet transform and active subspaces. e2020WR028511 Water Resour. Res., 57, 2021, 10.1029/2020WR028511.
Borgonovo, E., A new uncertainty importance measure. Reliab. Eng. Syst. Saf. 92 (2007), 771–784, 10.1016/j.ress.2006.04.015.
Brestic, M., Zivcak, M., PSII fluorescence techniques for measurement of drought and high temperature stress signal in crop plants: protocols and applications. Rout, G.R., Das, A.B., (eds.) Molecular Stress Physiology of Plants, 2013, Springer, India, 87–131, 10.1007/978-81-322-0807-5_4.
Brisson, N., Launay, M., Mary, B., and Beaudoin, N.: Conceptual Basis, Formalisations and Parameterization of the STICS Crop Model, Quæ, Versailles, 1 pp., 2009.
Brunt, D., Notes on radiation in the atmosphere. Quart. J. R. Meteor. Soc. 58 (1932), 389–420, 10.1002/qj.49705824704.
Buysse, P., Bodson, B., Debacq, A., De Ligne, A., Heinesch, B., Manise, T., Moureaux, C., Aubinet, M., Carbon budget measurement over 12 years at a crop production site in the silty-loam region in Belgium. Agric. For. Meteorol. 246 (2017), 241–255, 10.1016/j.agrformet.2017.07.004.
Casadebaig, P., Zheng, B., Chapman, S., Huth, N., Faivre, R., Chenu, K., Assessment of the potential impacts of wheat plant traits across environments by combining crop modeling and global sensitivity analysis. PLoS One, 11, 2016, e0146385, 10.1371/journal.pone.0146385.
Cloke, H.L., Pappenberger, F., Renaud, J. -P., Multi-method global sensitivity analysis (MMGSA) for modelling floodplain hydrological processes. Hydrol. Process. 22 (2008), 1660–1674, 10.1002/hyp.6734.
Confalonieri, R., Bregaglio, S., Stella, T., Negrini, G., Acutis, M., Donatelli, M., An extensible, multi-model software library for simulating crop growth and development. Environ. Model. Softw., 2012.
Corbeels, M., Chirat, G., Messad, S., Thierfelder, C., Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture. Eur. J. Agron. 76 (2016), 41–53, 10.1016/j.eja.2016.02.001.
Currit, N., Inductive regression: overcoming OLS limitations with the general regression neural network. Comput., Environ. Urban Syst. 26 (2002), 335–353, 10.1016/S0198-9715(01)00045-X.
DeJonge, K.C., Ascough, J.C., Ahmadi, M., Andales, A.A., Arabi, M., Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments. Ecol. Model. 231 (2012), 113–125, 10.1016/j.ecolmodel.2012.01.024.
Dinh, T.L.A., Aires, F., Nested leave-two-out cross-validation for the optimal crop yield model selection. Geosci. Model Dev. 15 (2022), 3519–3535, 10.5194/gmd-15-3519-2022.
Duan, Q., Sorooshian, S., Gupta, V.K., Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 158 (1994), 265–284, 10.1016/0022-1694(94)90057-4.
Dumont, B., Basso, B., Bodson, B., Destain, J.-P., Destain, M.-F., Climatic risk assessment to improve nitrogen fertilisation recommendations: a strategic crop model-based approach. Eur. J. Agron. 65 (2015), 10–17, 10.1016/j.eja.2015.01.003.
Dumont, B., Basso, B., Leemans, V., Bodson, B., Destain, J.-P., Destain, M.-F., Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions. Precis. Agric. 16 (2015), 361–384, 10.1007/s11119-014-9380-7.
Dumont, B., Heinesch, B., Bodson, B., Bogaerts, G., Chopin, H., De Ligne, A., Demoulin, L., Douxfils, B., Engelmann, T., Faurès, A., Longdoz, B., Manise, T., Orgun, A., Piret, A., Thyrion, T., ETC L2 Fluxnet (half-hourly). 2017-12-31–2023-09-30 Lonzee, 2023 2017-12-31–2023-09-30.
Ehdaie, B., Alloush, G.A., Waines, J.G., Genotypic variation in linear rate of grain growth and contribution of stem reserves to grain yield in wheat. Field Crops Res. 106 (2008), 34–43, 10.1016/j.fcr.2007.10.012.
Fang, L., Martre, P., Jin, K., Du, X., Van Der Putten, P.E.L., Yin, X., Struik, P.C., Neglecting acclimation of photosynthesis under drought can cause significant errors in predicting leaf photosynthesis in wheat. Glob. Change Biol. 29 (2023), 505–521, 10.1111/gcb.16488.
Franks, S.W., Beven, K.J., Quinn, P.F., Wright, I.R., On the sensitivity of soil-vegetation-atmosphere transfer (SVAT) schemes: equifinality and the problem of robust calibration. Agric. For. Meteorol. 86 (1997), 63–75, 10.1016/S0168-1923(96)02421-5.
Gad, A.G., Particle swarm optimization algorithm and its applications: a systematic review. Arch. Comput. Methods Eng. 29 (2022), 2531–2561, 10.1007/s11831-021-09694-4.
van Genuchten, M.Th, A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44 (1980), 892–898, 10.2136/sssaj1980.03615995004400050002x.
Gourlez de la Motte, L., Beauclaire, Q., Heinesch, B., Cuntz, M., Foltýnová, L., Šigut, L., Kowalska, N., Manca, G., Ballarin, I.G., Vincke, C., Roland, M., Ibrom, A., Lousteau, D., Siebicke, L., Neiryink, J., Longdoz, B., Non-stomatal processes reduce gross primary productivity in temperate forest ecosystems during severe edaphic drought. Philos. Trans. R. Soc. B, 375, 2020, 20190527, 10.1098/rstb.2019.0527.
Grassi, G., Magnani, F., Stomatal, mesophyll conductance and biochemical limitations to photosynthesis as affected by drought and leaf ontogeny in ash and oak trees. Plant Cell Environ. 28 (2005), 834–849, 10.1111/j.1365-3040.2005.01333.x.
Guan, X. -K., Song, L., Wang, T. -C., Turner, N.C., Li, F. -M., Effect of drought on the gas exchange, chlorophyll fluorescence and yield of six different-era spring wheat cultivars. J. Agron. Crop Sci. 201 (2015), 253–266, 10.1111/jac.12103.
Gupta, H.V., Razavi, S., Revisiting the basis of sensitivity analysis for dynamical earth system models. Water Resour. Res. 54 (2018), 8692–8717, 10.1029/2018WR022668.
Gupta, H.V., Bastidas, L.A., Vrugt, J.A., Sorooshian, S., Multiple criteria global optimization for watershed model calibration. Calibration of Watershed Models, 2003, American Geophysical Union (AGU), 125–132, 10.1029/WS006p0125.
Gyldengren, J.G., Abrahamsen, P., Olesen, J.E., Styczen, M., Hansen, S., Gislum, R., Effects of winter wheat N status on assimilate and N partitioning in the mechanistic agroecosystem model DAISY. J. Agron. Crop Sci. 206 (2020), 784–805, 10.1111/jac.12412.
Hansen, S., Abrahamsen, P., Modeling water and nitrogen uptake using a single-root concept: exemplified by the use in the daisy model. Ma, L., Ahuja, L., Bruulsema, T.W., (eds.) Quantifying and understanding plant nitrogen uptake for systems modeling, 2009, CRC Press, 169–195.
Hansen, S., Abrahamsen, P., Petersen, C.T., Styczen, M., Daisy: model use, calibration, and validation. Trans. ASABE 55 (2012), 1317–1335, 10.13031/2013.42244.
Heinesch, B., De Ligne, A., Manise, T., and Longdoz, B.: Warm winter 2020 ecosystem eddy covariance flux product from Lonzee, https://doi.org/10.18160/46P3-WT1D, 2022.
Herman, J.D., Reed, P.M., Wagener, T., Time-varying sensitivity analysis clarifies the effects of watershed model formulation on model behavior. Water Resour. Res. 49 (2013), 1400–1414, 10.1002/wrcr.20124.
Herman, J., Usher, W., SALib: an open-source python library for sensitivity analysis. JOSS, 2, 2017, 97, 10.21105/joss.00097.
Hodson, T.O., Over, T.M., Smith, T.J., Marshall, L.M., How to select an objective function using information theory. e2023WR035803 Water Resour. Res., 60, 2024, 10.1029/2023WR035803.
Iooss, B., Lemaître, P., A Review on Global Sensitivity Analysis Methods. Dellino, G., Meloni, C., (eds.) Uncertainty Management in Simulation-Optimization of Complex Systems, 59, 2015, Springer US, Boston, MA, 101–122, 10.1007/978-1-4899-7547-8_5.
IPCC, Climate Change 2022 - Mitigation of Climate Change: Working Group III Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st ed., 2023, Cambridge University Press, 10.1017/9781009157926.
Jabloun, M., Li, X., Zhang, X., Tao, F., Hu, C., Olesen, J.E., Sensitivity of simulated crop yield and nitrate leaching of the wheat-maize cropping system in the North China Plain to model parameters. Agric. For. Meteorol. 263 (2018), 25–40, 10.1016/j.agrformet.2018.08.002.
Jones, J.W., Antle, J.M., Basso, B., Boote, K.J., Conant, R.T., Foster, I., Godfray, H.C.J., Herrero, M., Howitt, R.E., Janssen, S., Keating, B.A., Munoz-Carpena, R., Porter, C.H., Rosenzweig, C., Wheeler, T.R., Brief history of agricultural systems modeling. Agric. Syst. 155 (2017), 240–254, 10.1016/j.agsy.2016.05.014.
Kavetski, D., Qin, Y., Kuczera, G., The fast and the robust: trade-offs between optimization robustness and cost in the calibration of environmental models. Water Resour. Res. 54 (2018), 9432–9455, 10.1029/2017WR022051.
Kelleher, C., Wagener, T., McGlynn, B., Ward, A.S., Gooseff, M.N., Payn, R.A., Identifiability of transient storage model parameters along a mountain stream. Water Resour. Res. 49 (2013), 5290–5306, 10.1002/wrcr.20413.
Krause, P., Boyle, D.P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, in: Advances in Geosciences, Proceedings of the 8th Workshop for Large Scale Hydrological Modelling - Oppurg 2004 - 8th workshop for Large-scale hydrological modelling, Oppurg, Germany, 11–13 November 2004, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005.
Laloy, E., Bielders, C.L., Modelling intercrop management impact on runoff and erosion in a continuous maize cropping system: Part I. Model description, global sensitivity analysis and Bayesian estimation of parameter identifiability. Eur. J. Soil Sci. 60 (2009), 1005–1021, 10.1111/j.1365-2389.2009.01187.x.
Lamboni, M., Makowski, D., Lehuger, S., Gabrielle, B., Monod, H., Multivariate global sensitivity analysis for dynamic crop models. Field Crops Res. 113 (2009), 312–320, 10.1016/j.fcr.2009.06.007.
Léonard, J., 2016. Nitrification, denitrification and N2O emissions in STICS, 2016.
Leuning, R., A critical appraisal of a combined stomatal-photosynthesis model for C3 plants. Plant Cell Environ. 18 (1995), 339–355, 10.1111/j.1365-3040.1995.tb00370.x.
Liang, H., Gao, S., Hu, K., Global sensitivity and uncertainty analysis of the dynamic simulation of crop N uptake by using various N dilution curve approaches. Eur. J. Agron., 116, 2020, 126044, 10.1016/j.eja.2020.126044.
Liu, M., Qi, H., Zhang, Z.P., Song, Z.W., Kou, T.J., Zhang, W.J., Yu, J.L., Response of photosynthesis and chlorophyll fluorescence to drought stress in two maize cultivars. Afr. J. Agric. Res., 7, 2012, 10.5897/AJAR12.082.
Lognoul, M., Debacq, A., De Ligne, A., Dumont, B., Manise, T., Bodson, B., Heinesch, B., Aubinet, M., N2O flux short-term response to temperature and topsoil disturbance in a fertilized crop: an eddy covariance campaign. Agric. For. Meteorol. 271 (2019), 193–206, 10.1016/j.agrformet.2019.02.033.
Long, S.P., Bernacchi, C.J., Gas exchange measurements, what can they tell us about the underlying limitations to photosynthesis? Procedures and sources of error. J. Exp. Bot. 54 (2003), 2393–2401, 10.1093/jxb/erg262.
López-Cruz, I.L., Rojano-Aguilar, A., Salazar-Moreno, R., Ruiz-García, A., Goddard, J., A comparison of local and global sensitivity analyses for greenhouse crop models. Acta Hortic., 2012, 267–273, 10.17660/ActaHortic.2012.957.30.
Luo, Y., Weng, E., Wu, X., Gao, C., Zhou, X., Zhang, L., Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models. Ecol. Appl. 19 (2009), 571–574.
Manevski, K., Børgesen, C.D., Li, X., Andersen, M.N., Abrahamsen, P., Hu, C., Hansen, S., Integrated modelling of crop production and nitrate leaching with the Daisy model. MethodsX 3 (2016), 350–363, 10.1016/j.mex.2016.04.008.
Manevski, K., Børgesen, C.D., Li, X., Andersen, M.N., Zhang, X., Abrahamsen, P., Hu, C., Hansen, S., Optimising crop production and nitrate leaching in China: Measured and simulated effects of straw incorporation and nitrogen fertilisation. Eur. J. Agron. 80 (2016), 32–44, 10.1016/j.eja.2016.06.009.
Massmann, C., Wagener, T., Holzmann, H., A new approach to visualizing time-varying sensitivity indices for environmental model diagnostics across evaluation time-scales. Environ. Model. Softw. 51 (2014), 190–194, 10.1016/j.envsoft.2013.09.033.
Maxwell, K., Johnson, G.N., Chlorophyll fluorescence—a practical guide. J. Exp. Bot. 51 (2000), 659–668, 10.1093/jexbot/51.345.659.
Maydup, M.L., Antonietta, M., Guiamet, J.J., Graciano, C., López, J.R., Tambussi, E.A., The contribution of ear photosynthesis to grain filling in bread wheat (Triticum aestivum L.). Field Crops Res. 119 (2010), 48–58, 10.1016/j.fcr.2010.06.014.
Medina, Y., Muñoz, E., A simple time-varying sensitivity analysis (TVSA) for assessment of temporal variability of hydrological processes. Water, 12, 2020, 2463, 10.3390/w12092463.
Meza, R., Jacquemin, G., Dumont, B., Bacchetta, R., Heens, B., Mahieu, O., Blanchard, R., Monfort, B., Chavalle, S., De Proft, M., Gofflot, S., Van Remoortel, V., Sinnaeve, G., and Bodson, B.: Variétés, in: Live Blanc “Céréales,” 2018.
Meza, R., Crevits, C., Eylenbosch, D., Mahieu, O., Bonnave, M., Blanchard, R., Van der Verren, B., Godin, B., Faux, A.-M., Legrand, J., and Heens, B.: Variétés en froment d'hiver, in: Livre Blanc “Céréales,” 2023.
Mollerup, M., Abrahamsen, P., Petersen, C.T., Hansen, S., Comparison of simulated water, nitrate, and bromide transport using a Hooghoudt-based and a dynamic drainage model: MODELING OF TILE DRAINS. Water Resour. Res. 50 (2014), 1080–1094, 10.1002/2012WR013318.
Morris, M.D., Factorial sampling plans for preliminary computational experiments. Technometrics 33 (1991), 161–174, 10.1080/00401706.1991.10484804.
Motarjemi, S.K., Rosenbom, A.E., Iversen, B.V., Plauborg, F., Important factors when simulating the water and nitrogen balance in a tile-drained agricultural field under long-term monitoring. Sci. Total Environ., 787, 2021, 147610, 10.1016/j.scitotenv.2021.147610.
Moureaux, C., Debacq, A., Bodson, B., Heinesch, B., Aubinet, M., Annual net ecosystem carbon exchange by a sugar beet crop. Agric. For. Meteorol. 139 (2006), 25–39, 10.1016/j.agrformet.2006.05.009.
Moureaux, C., Debacq, A., Hoyaux, J., Suleau, M., Tourneur, D., Vancutsem, F., Bodson, B., Aubinet, M., Carbon balance assessment of a Belgian winter wheat crop ( Triticum aestivum L.). Glob. Change Biol. 14 (2008), 1353–1366, 10.1111/j.1365-2486.2008.01560.x.
Mualem, Y., A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12 (1976), 513–522, 10.1029/WR012i003p00513.
Nossent, J., Bauwens, W., Optimising the convergence of a Sobol’ sensitivity analysis for an environmental model: application of an appropriate estimate for the square of the expectation value and the total variance. Int. Congr. Environ. Model. Softw., 2012.
Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y.-W., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Isaac, P., Polidori, D., Reichstein, M., Ribeca, A., van Ingen, C., Vuichard, N., Zhang, L., Amiro, B., Ammann, C., Arain, M.A., Ardö, J., Arkebauer, T., Arndt, S.K., Arriga, N., Aubinet, M., Aurela, M., Baldocchi, D., Barr, A., Beamesderfer, E., Marchesini, L.B., Bergeron, O., Beringer, J., Bernhofer, C., Berveiller, D., Billesbach, D., Black, T.A., Blanken, P.D., Bohrer, G., Boike, J., Bolstad, P.V., Bonal, D., Bonnefond, J.-M., Bowling, D.R., Bracho, R., Brodeur, J., Brümmer, C., Buchmann, N., Burban, B., Burns, S.P., Buysse, P., Cale, P., Cavagna, M., Cellier, P., Chen, S., Chini, I., Christensen, T.R., Cleverly, J., Collalti, A., Consalvo, C., Cook, B.D., Cook, D., Coursolle, C., Cremonese, E., Curtis, P.S., D'Andrea, E., da Rocha, H., Dai, X., Davis, K.J., Cinti, B.D., Grandcourt, A. de, Ligne, A.D., De Oliveira, R.C., Delpierre, N., Desai, A.R., Di Bella, C.M., Tommasi, P. di, Dolman, H., Domingo, F., Dong, G., Dore, S., Duce, P., Dufrêne, E., Dunn, A., Dušek, J., Eamus, D., Eichelmann, U., ElKhidir, H.A.M., Eugster, W., Ewenz, C.M., Ewers, B., Famulari, D., Fares, S., Feigenwinter, I., Feitz, A., Fensholt, R., Filippa, G., Fischer, M., Frank, J., Galvagno, M., 2020. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225. 10.1038/s41597-020-0534-3.
Petropoulos, G.P., Griffiths, H.M., Tarantola, S., A sensitivity analysis of the SimSphere SVAT model in the context of EO-based operational products development. Environ. Model. Softw. 49 (2013), 166–179, 10.1016/j.envsoft.2013.07.010.
Pianosi, F., Beven, K., Freer, J., Hall, J.W., Rougier, J., Stephenson, D.B., Wagener, T., Sensitivity analysis of environmental models: a systematic review with practical workflow. Environ. Model. Softw. 79 (2016), 214–232, 10.1016/j.envsoft.2016.02.008.
Pianosi, F., Iwema, J., Rosolem, R., Wagener, T., Chapter 7 - a multimethod global sensitivity analysis approach to support the calibration and evaluation of land surface Models. Petropoulos, G.P., Srivastava, P.K., (eds.) Sensitivity Analysis in Earth Observation Modelling, 2017, Elsevier, 125–144, 10.1016/B978-0-12-803011-0.00007-0.
Pianosi, F., Wagener, T., Understanding the time-varying importance of different uncertainty sources in hydrological modelling using global sensitivity analysis. Hydrol. Process. 30 (2016), 3991–4003, 10.1002/hyp.10968.
Pianosi, F., Wagener, T., Distribution-based sensitivity analysis from a generic input-output sample. Environ. Model. Softw. 108 (2018), 197–207, 10.1016/j.envsoft.2018.07.019.
Plauborg, F., Abrahamsen, P., Gjettermann, B., Mollerup, M., Iversen, B.V., Liu, F., Andersen, M.N., Hansen, S., Modelling of root ABA synthesis, stomatal conductance, transpiration and potato production under water saving irrigation regimes. Agric. Water Manag. 98 (2010), 425–439, 10.1016/j.agwat.2010.10.006.
de Pury, D.G.G., Farquhar, G.D., Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant Cell Environ. 20 (1997), 537–557, 10.1111/j.1365-3040.1997.00094.x.
Raj Deepak, S.N., Seiler, C., Monahan, A.H., A global sensitivity analysis of parameter uncertainty in the CLASSIC model. Atmosph.Ocean 0 (2024), 1–13, 10.1080/07055900.2024.2396426.
Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J.H.A., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., Smith, S., Sheikholeslami, R., Hosseini, N., Asadzadeh, M., Puy, A., Kucherenko, S., Maier, H.R., The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. Environ. Model. Softw., 137, 2021, 104954, 10.1016/j.envsoft.2020.104954.
Reusser, D.E., Zehe, E., Inferring model structural deficits by analyzing temporal dynamics of model performance and parameter sensitivity. Water Resour. Res., 47, 2011, 10.1029/2010WR009946.
Saltelli, A., Making best use of model evaluations to compute sensitivity indices. Comput. Phys. Commun. 145 (2002), 280–297, 10.1016/S0010-4655(02)00280-1.
Saltelli, A., Aleksankina, K., Becker, W., Fennell, P., Ferretti, F., Holst, N., Li, S., Wu, Q., Why so many published sensitivity analyses are false: a systematic review of sensitivity analysis practices. Environ. Model. Softw. 114 (2019), 29–39, 10.1016/j.envsoft.2019.01.012.
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., Tarantola, S., Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Comput. Phys. Commun. 181 (2010), 259–270, 10.1016/j.cpc.2009.09.018.
Sarrazin, F., Pianosi, F., Wagener, T., Global Sensitivity Analysis of environmental models: convergence and validation. Environ. Model. Softw. 79 (2016), 135–152, 10.1016/j.envsoft.2016.02.005.
Seidel, S.J., Palosuo, T., Thorburn, P., Wallach, D., Towards improved calibration of crop models – where are we now and where should we go?. Eur. J. Agron. 94 (2018), 25–35, 10.1016/j.eja.2018.01.006.
Shin, M.-J., Guillaume, J.H.A., Croke, B.F.W., Jakeman, A.J., Addressing ten questions about conceptual rainfall–runoff models with global sensitivity analyses in R. J. Hydrol. 503 (2013), 135–152, 10.1016/j.jhydrol.2013.08.047.
Sinclair, T.R., Seligman, N., Criteria for publishing papers on crop modeling. Field Crops Res. 68 (2000), 165–172, 10.1016/S0378-4290(00)00105-2.
Sobol, I.M., Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55 (2001), 271–280, 10.1016/S0378-4754(00)00270-6.
Sommer, S.G., Han, E., Li, X., Rosenqvist, E., Liu, F., The chlorophyll fluorescence parameter Fv/Fm correlates with loss of grain yield after severe drought in three wheat genotypes grown at two CO2 concentrations. Plants, 12, 2023, 436, 10.3390/plants12030436.
Song, X., Bryan, B.A., Almeida, A.C., Paul, K.I., Zhao, G., Ren, Y., Time-dependent sensitivity of a process-based ecological model. Ecol. Model. 265 (2013), 114–123, 10.1016/j.ecolmodel.2013.06.013.
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., Xu, C., Global sensitivity analysis in hydrological modeling: review of concepts, methods, theoretical framework, and applications. J. Hydrol. 523 (2015), 739–757, 10.1016/j.jhydrol.2015.02.013.
Stein, B.V., Raponi, E., Sadeghi, Z., Bouman, N., Van Ham, R.C.H.J., Back, T., A comparison of global sensitivity analysis methods for explainable AI with an application in genomic prediction. IEEE Access 10 (2022), 103364–103381, 10.1109/ACCESS.2022.3210175.
Stella, T., Frasso, N., Negrini, G., Bregaglio, S., Cappelli, G., Acutis, M., Confalonieri, R., Model simplification and development via reuse, sensitivity analysis and composition: A case study in crop modelling. Environ. Model. Softw. 59 (2014), 44–58, 10.1016/j.envsoft.2014.05.007.
Stephens, G.L., Li, J., Wild, M., Clayson, C.A., Loeb, N., Kato, S., L'Ecuyer, T., Stackhouse, P.W., Lebsock, M., Andrews, T., An update on Earth's energy balance in light of the latest global observations. Nat. Geosci. 5 (2012), 691–696, 10.1038/ngeo1580.
Thorp, K.R., DeJonge, K.C., Marek, G.W., Evett, S.R., Comparison of evapotranspiration methods in the DSSAT Cropping System Model: I. Global sensitivity analysis. Comput. Electron. Agric., 177, 2020, 105658, 10.1016/j.compag.2020.105658.
Timlin, D., Paff, K., Han, E., The role of crop simulation modeling in assessing potential climate change impacts. Agrosystems Geosci. amp; Env, 7, 2024, e20453, 10.1002/agg2.20453.
Trueba, S., Pan, R., Scoffoni, C., John, G.P., Davis, S.D., Sack, L., Thresholds for leaf damage due to dehydration: declines of hydraulic function, stomatal conductance and cellular integrity precede those for photochemistry. N. Phytol. 223 (2019), 134–149, 10.1111/nph.15779.
Vanuytrecht, E., Raes, D., Willems, P., Global sensitivity analysis of yield output from the water productivity model. Environ. Model. Softw. 51 (2014), 323–332, 10.1016/j.envsoft.2013.10.017.
Varella, H., Buis, S., Launay, M., Guérif, M., Global sensitivity analysis for choosing the main soil parameters of a crop model to be determined. AS 03 (2012), 949–961, 10.4236/as.2012.37116.
Vrugt, J.A., Markov chain Monte Carlo simulation using the DREAM software package: theory, concepts, and MATLAB implementation. Environ. Model. Softw. 75 (2016), 273–316, 10.1016/j.envsoft.2015.08.013.
Wagener, T., McIntyre, N., Lees, M.J., Wheater, H.S., Gupta, H.V., Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic identifiability analysis. Hydrol. Process. 17 (2003), 455–476, 10.1002/hyp.1135.
Wallach, D., Makowski, D., Jones, J.W., Brun, F., Working with dynamic crop models: Methods, tools and examples for agriculture and environment, 3rd ed., 2018, Academic press, London.
Wallach, D., Palosuo, T., Thorburn, P., Hochman, Z., Gourdain, E., Andrianasolo, F., Asseng, S., Basso, B., Buis, S., Crout, N., Dibari, C., Dumont, B., Ferrise, R., Gaiser, T., Garcia, C., Gayler, S., Ghahramani, A., Hiremath, S., Hoek, S., Horan, H., Hoogenboom, G., Huang, M., Jabloun, M., Jansson, P.-E., Jing, Q., Justes, E., Kersebaum, K.C., Klosterhalfen, A., Launay, M., Lewan, E., Luo, Q., Maestrini, B., Mielenz, H., Moriondo, M., Nariman Zadeh, H., Padovan, G., Olesen, J.E., Poyda, A., Priesack, E., Pullens, J.W.M., Qian, B., Schütze, N., Shelia, V., Souissi, A., Specka, X., Srivastava, A.K., Stella, T., Streck, T., Trombi, G., Wallor, E., Wang, J., Weber, T.K.D., Weihermüller, L., De Wit, A., Wöhling, T., Xiao, L., Zhao, C., Zhu, Y., Seidel, S.J., The chaos in calibrating crop models: lessons learned from a multi-model calibration exercise. Environ. Model. Softw., 145, 2021, 105206, 10.1016/j.envsoft.2021.105206.
Wang, A., Solomatine, D.P., Practical experience of sensitivity analysis: comparing six methods, on three hydrological models, with three performance criteria. Water, 11(1062), 2019, 10.3390/w11051062.
Wang, S., Flipo, N., Romary, T., Time-dependent global sensitivity analysis of the C-RIVE biogeochemical model in contrasted hydrological and trophic contexts. Water Res. 144 (2018), 341–355, 10.1016/j.watres.2018.07.033.
Wang, J., Li, X., Lu, L., Fang, F., Parameter sensitivity analysis of crop growth models based on the extended Fourier Amplitude Sensitivity Test method. Environ. Model. Softw. 48 (2013), 171–182, 10.1016/j.envsoft.2013.06.007.
Wang, D., Tan, D., Liu, L., Particle swarm optimization algorithm: an overview. Soft Comput. 22 (2018), 387–408, 10.1007/s00500-016-2474-6.
van Werkhoven, K., Wagener, T., Reed, P., Tang, Y., Characterization of watershed model behavior across a hydroclimatic gradient. 2007WR006271 Water Resour. Res., 44, 2008, 10.1029/2007WR006271.
Whittaker, G., Confesor, R.B., Di Luzio, M., and Arnold, J.G.: Detection of Overparameterization and Overfitting in an Automatic Calibration of SWAT, https://doi.org/10.13031/2013.34909, 2010.
Wu, Q., Zhang, Y., Xie, M., Zhao, Z., Yang, L., Liu, J., Hou, D., Estimation of Fv/Fm in spring wheat using UAV-based multispectral and RGB imagery with multiple machine learning methods. Agronomy, 13, 2023, 1003, 10.3390/agronomy13041003.
Yang, Z., Tian, J., Wang, Z., Feng, K., Ouyang, Z., Zhang, L., Yan, X., Coupled soil water stress and environmental effects on changing photosynthetic traits in wheat and maize. Agric. Water Manag., 282, 2023, 108246, 10.1016/j.agwat.2023.108246.
Yates, L.A., Aandahl, Z., Richards, S.A., Brook, B.W., Cross validation for model selection: a review with examples from ecology. Ecol. Monogr., 93, 2023, e1557, 10.1002/ecm.1557.
Zhang, Y., Xue, Q., Li, J., Huang, J., Yao, D., Wang, Z., Dry matter and nitrogen accumulation and remobilization in wheat as affected by genotype and irrigation. J. Plant Nutr. 40 (2017), 2279–2289, 10.1080/01904167.2016.1264593.
Zhang, B., Zhou, M., Zhu, B., Kemmann, B., Pfülb, L., Burkart, S., Liu, H., Butterbach-Bahl, K., Well, R., Threshold-like effect of soil NO3− concentrations on denitrification product N2O/(N2O+N2) ratio is mediated by soil pH. Soil Biol. Biochem., 187, 2023, 109213, 10.1016/j.soilbio.2023.109213.
Zhao, G., Bryan, B.A., Song, X., Sensitivity and uncertainty analysis of the APSIM-wheat model: interactions between cultivar, environmental, and management parameters. Ecol. Model. 279 (2014), 1–11, 10.1016/j.ecolmodel.2014.02.003.