EDP Sciences logo
Search
Menu
All issues Agronomie, 22 2 (2002) 191-203 Article references

Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

A minimal mathematical model to study B-cell receptor signalling mediated calcium dynamics

Poulomi Chatterjee, Garhima Arora, Jayendrajyoti Kundu, Suvankar Halder and Samrat Chatterjee
Applied Mathematical Modelling 162 117174 (2027)
https://doi.org/10.1016/j.apm.2026.117174

Decomposing total uncertainty in rice yield prediction: A quantification of relative contributions of model structure‐driven and parameter value‐driven uncertainties

Jin Yu, Xiaojun Liu, Guoqing Lei and Baokun Xu
Agronomy Journal 118 (3) (2026)
https://doi.org/10.1002/agj2.70412

Model structure and data available for estimation affect uncertainty of genotype-specific parameter estimates

Muhammad Zeeshan Mehmood, Phillip D. Alderman, Romulo P. Lollato and Amanda de Oliveira Silva
European Journal of Agronomy 175 127968 (2026)
https://doi.org/10.1016/j.eja.2025.127968

Estimation of multi-depth soil water storage properties by inversion of crop model: Effect of type, frequency, and timing of observations

V.K. Aswathi, K. Sreelash, S. Amrutha, K. Shenu Jose and Sarmistha Singh
Journal of Hydrology 669 135178 (2026)
https://doi.org/10.1016/j.jhydrol.2026.135178

Calibration of a climate suitability model using a generalized likelihood uncertainty estimation (GLUE): a global case study of orange production

Shinwoo Hyun, Kwang Soo Kim and Robert M. Beresford
Scientific Reports 16 (1) (2026)
https://doi.org/10.1038/s41598-026-44664-5

Spatiotemporal optimization of irrigation practices for winter wheat in China: Rationale, implications, and solutions

Luchen Zhang, Yuan Cao, Weihao Qian, Junning Tian, Shengshi Huang, Xiaolei Qiu, Bing Liu, Liang Tang, Liujun Xiao, Weixing Cao, Yan Zhu and Leilei Liu
Agricultural Water Management 308 109297 (2025)
https://doi.org/10.1016/j.agwat.2025.109297

Crop choice advisory for the West African Sudan Savanna based on soil type and presowing rainfall forecasts: A machine learning residual model approach

Toshichika Iizumi, Kohtaro Iseki, Kenta Ikazaki, Toru Sakai, Shintaro Kobayashi and Benoit Joseph Batieno
Climate Services 40 100605 (2025)
https://doi.org/10.1016/j.cliser.2025.100605

A Bayesian framework for crop model calibration: A case study in the US Corn Belt

Matteo G. Ziliani, Muhammad U. Altaf, Trenton E. Franz, Bangyou Zheng, Scott Chapman, Justin Sheffield, Ibrahim Hoteit and Matthew F. McCabe
European Journal of Agronomy 168 127650 (2025)
https://doi.org/10.1016/j.eja.2025.127650

Causal analysis methods for estimating the impact of drought and cold events on crop yields in France

Cláudia Mendes, Korgan Aldebert, François Brun and David Makowski
European Journal of Agronomy 170 127743 (2025)
https://doi.org/10.1016/j.eja.2025.127743

Aqua-MC as a simple open access code for uncountable runs of AquaCrop

Vahid Adabi, Hadi Ramezani Etedali, Asghar Azizian, Faraz Gorginpaveh, Ali Salem and Ahmed Elbeltagi
Scientific Reports 15 (1) (2025)
https://doi.org/10.1038/s41598-025-08995-z

Predicting rice phenology across China by integrating crop phenology model and machine learning

Jinhan Zhang, Xiaomao Lin, Chongya Jiang, Xuntao Hu, Bing Liu, Leilei Liu, Liujun Xiao, Yan Zhu, Weixing Cao and Liang Tang
Science of The Total Environment 951 175585 (2024)
https://doi.org/10.1016/j.scitotenv.2024.175585

Enhancing crop model parameter estimation across computing environments: Utilizing the GLUE method and parallel computing for determining genetic coefficients

Thiago Berton Ferreira, Vakhtang Shelia, Cheryl Porter, Patricia Moreno Cadena, Montserrat Salmeron Cortasa, Muhammad Sohail Khan, Willingthon Pavan and Gerrit Hoogenboom
Computers and Electronics in Agriculture 227 109513 (2024)
https://doi.org/10.1016/j.compag.2024.109513

Why make inverse modeling and which methods to use in agriculture? A review

Yulin Zhang, Léo Pichon, Sébastien Roux, Anne Pellegrino, Thierry Simonneau and Bruno Tisseyre
Computers and Electronics in Agriculture 217 108624 (2024)
https://doi.org/10.1016/j.compag.2024.108624

Sample size, range of parameters and time-dependent effects on global sensitivity analysis in sugarcane modelling

R. A. A. Pereira, M. S. Vianna and F. R. Marin
The Journal of Agricultural Science 1 (2024)
https://doi.org/10.1017/S0021859624000030

Nonparametric Inference for the Reproductive Rate in Generalized Compartmental Models

Imelda Trejo, Yen Ting Lin, Amanda Patrick and Nicolas Hengartner
SIAM Journal on Applied Mathematics 84 (3) S297 (2024)
https://doi.org/10.1137/22M1505499

Non-stationary evaluation of runoff peaks in response to climate variability and land use change in Ferson Creek, Illinois, USA

Nasim Sadra, Mohammad Reza Nikoo and Nasser Talebbeydokhti
Environmental Monitoring and Assessment 195 (6) (2023)
https://doi.org/10.1007/s10661-023-11238-1

The parametric uncertainty estimation of water and nitrogen transport simulation in a paddy field experiment using HYDRUS‐1D

Jianmin Bian, Juanjuan Wu, Siyu Nie, Yu Wang and Xiaosheng Lin
Irrigation and Drainage 72 (2) 451 (2023)
https://doi.org/10.1002/ird.2787

Comparison of statistical methods to fit critical nitrogen dilution curves

Josefina Lacasa, David Makowski, Trevor Hefley, Javier Fernandez, Emmanuela van Versendaal, Gilles Lemaire and Ignacio Ciampitti
European Journal of Agronomy 145 126770 (2023)
https://doi.org/10.1016/j.eja.2023.126770

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

Hamze Dokoohaki, Teerath Rai, Marissa Kivi, Philip Lewis, Jose L. Gómez-Dans and Feng Yin
Remote Sensing 14 (21) 5389 (2022)
https://doi.org/10.3390/rs14215389

Bayesian Calibration and Uncertainty Assessment of HYDRUS-1D Model Using GLUE Algorithm for Simulating Corn Root Zone Salinity under Linear Move Sprinkle Irrigation System

Farzam Moghbel, Abolfazl Mosaedi, Jonathan Aguilar, Bijan Ghahraman, Hossein Ansari and Maria C. Gonçalves
Water 14 (24) 4003 (2022)
https://doi.org/10.3390/w14244003

The sensitivity of C and N mineralization to soil water potential varies with soil characteristics: Experimental evidences to fine-tune models

Daniel Plaza-Bonilla, Bruno Mary, Matthieu Valé and Eric Justes
Geoderma 409 115644 (2022)
https://doi.org/10.1016/j.geoderma.2021.115644

A comparison of multiple calibration and ensembling methods for estimating genetic coefficients of CERES-Rice to simulate phenology and yields

Prakash Kumar Jha, Amor V.M. Ines, Eunjin Han, Rolando Cruz and P.V. Vara Prasad
Field Crops Research 284 108560 (2022)
https://doi.org/10.1016/j.fcr.2022.108560

A modified Susceptible-Infected-Recovered model for observed under-reported incidence data

Imelda Trejo, Nicolas W. Hengartner and Alberto d’Onofrio
PLOS ONE 17 (2) e0263047 (2022)
https://doi.org/10.1371/journal.pone.0263047

Identifying and modelling key physiological traits that confer tolerance or sensitivity to ozone in winter wheat

Yanru Feng, Thuy Huu Nguyen, Muhammad Shahedul Alam, Lisa Emberson, Thomas Gaiser, Frank Ewert and Michael Frei
Environmental Pollution 304 119251 (2022)
https://doi.org/10.1016/j.envpol.2022.119251

GLUEOS: A high performance computing system based on the orchestration of containers for the GLUE parameter calibration of a crop growth model

Shinwoo Hyun, Jin Yu Park, Junhwan Kim, David H. Fleisher and Kwang Soo Kim
Computers and Electronics in Agriculture 197 106906 (2022)
https://doi.org/10.1016/j.compag.2022.106906

Bayesian multi-level calibration of a process-based maize phenology model

Michelle Viswanathan, Andreas Scheidegger, Thilo Streck, Sebastian Gayler and Tobias K.D. Weber
Ecological Modelling 474 110154 (2022)
https://doi.org/10.1016/j.ecolmodel.2022.110154

Bayesian Estimation of Adsorption and Desorption Parameters for Pore Scale Transport

Vasiliy V. Grigoriev and Petr N. Vabishchevich
Mathematics 9 (16) 1974 (2021)
https://doi.org/10.3390/math9161974

Assessing parametric and nitrogen fertilizer input uncertainties in the ORYZA_V3 model predictions

Jin Yu, Liangsheng Shi, Jingye Han, Qi Yang, Jiesheng Huang and Ming Ye
Agronomy Journal 113 (6) 4965 (2021)
https://doi.org/10.1002/agj2.20905

Ecological Intensification of Natural Resources for Sustainable Agriculture

Dyhia Boukirat and Mohamed Maatoug
Ecological Intensification of Natural Resources for Sustainable Agriculture 341 (2021)
https://doi.org/10.1007/978-981-33-4203-3_10

Computational modeling of behavioral tasks: An illustration on a classic reinforcement learning paradigm

Praveen Suthaharan, Philip R. Corlett and Yuen-Siang Ang
The Quantitative Methods for Psychology 17 (2) 105 (2021)
https://doi.org/10.20982/tqmp.17.2.p105

A time-dependent parameter estimation framework for crop modeling

Faezeh Akhavizadegan, Javad Ansarifar, Lizhi Wang, Isaiah Huber and Sotirios V. Archontoulis
Scientific Reports 11 (1) (2021)
https://doi.org/10.1038/s41598-021-90835-x

Estimation of Crop Yield From Combined Optical and SAR Imagery Using Gaussian Kernel Regression

Yeshanbele Alebele, Wenhui Wang, Weiguo Yu, Xue Zhang, Xia Yao, Yongchao Tian, Yan Zhu, Weixing Cao and Tao Cheng
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 10520 (2021)
https://doi.org/10.1109/JSTARS.2021.3118707

Uncertainty analysis of the HORTSYST model applied to fertigated tomatoes cultivated in a hydroponic greenhouse system

Antonio Martinez-Ruiz, Irineo L. López-Cruz, Agustín Ruiz-García, et al.
Spanish Journal of Agricultural Research 19 (3) e0802 (2021)
https://doi.org/10.5424/sjar/2021193-17842

Comparison of three calibration methods for modeling rice phenology

Yujing Gao, Daniel Wallach, Bing Liu, Michael Dingkuhn, Kenneth J. Boote, Upendra Singh, Senthold Asseng, Tamer Kahveci, Jianqiang He, Ruoyang Zhang, Roberto Confalonieri and Gerrit Hoogenboom
Agricultural and Forest Meteorology 280 107785 (2020)
https://doi.org/10.1016/j.agrformet.2019.107785

CheKiPEUQ Intro 1: Bayesian Parameter Estimation Considering Uncertainty or Error from both Experiments and Theory**

Aditya Savara and Eric A. Walker
ChemCatChem 12 (21) 5385 (2020)
https://doi.org/10.1002/cctc.202000953

Runoff Losses on Urban Surfaces during Frequent Rainfall Events: A Review of Observations and Modeling Attempts

Mohamad Rammal and Emmanuel Berthier
Water 12 (10) 2777 (2020)
https://doi.org/10.3390/w12102777

Assessment of MC&MCMC uncertainty analysis frameworks on SWAT model by focusing on future runoff prediction in a mountainous watershed via CMIP5 models

Armin Ahmadi, Amirhosein Aghakhani Afshar, Vahid Nourani, Mohsen Pourreza-Bilondi and A. A. Besalatpour
Journal of Water and Climate Change 11 (4) 1811 (2020)
https://doi.org/10.2166/wcc.2019.122

Assessment of sowing dates and plant densities using CSM-CROPGRO-Soybean for soybean maturity groups in low latitude

L. S. Sampaio, R. Battisti, M. A. Lana and K. J. Boote
The Journal of Agricultural Science 158 (10) 819 (2020)
https://doi.org/10.1017/S0021859621000204

Uncertainty Analysis of a Continuous Hydrological Model Using DREAM-ZS Algorithm

Amirhosein Aghakhani Afshar, Yousef Hassanzadeh, Mohsen Pourreza-Bilondi and Hadi Memarian
Iranian Journal of Science and Technology, Transactions of Civil Engineering (2019)
https://doi.org/10.1007/s40996-019-00287-7

Progress in Accurate Chemical Kinetic Modeling, Simulations, and Parameter Estimation for Heterogeneous Catalysis

Sebastian Matera, William F. Schneider, Andreas Heyden and Aditya Savara
ACS Catalysis 9 (8) 6624 (2019)
https://doi.org/10.1021/acscatal.9b01234

Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL

Huidae Cho, Jeongha Park and Dongkyun Kim
Water 11 (3) 447 (2019)
https://doi.org/10.3390/w11030447

Uncertainty analysis of a pollutant-hydrograph model in assessing inflow and infiltration of sanitary sewer systems

Moran Wang, Mingkai Zhang, Hanchang Shi, Xia Huang and Yanchen Liu
Journal of Hydrology 574 64 (2019)
https://doi.org/10.1016/j.jhydrol.2019.04.011

Comparison of the Generalized Likelihood Uncertainty Estimation and Markov Chain Monte Carlo Methods for Uncertainty Analysis of the ORYZA_V3 Model

Junwei Tan, Jingjing Cao, Yuanlai Cui, Qingyun Duan and Wei Gong
Agronomy Journal 111 (2) 555 (2019)
https://doi.org/10.2134/agronj2018.05.0336

Comparison of GLUE and DREAM for the estimation of cultivar parameters in the APSIM-maize model

Meiling Sheng, Junzhi Liu, A-Xing Zhu, David G. Rossiter, Haoran Liu, Zhangcong Liu and Liming Zhu
Agricultural and Forest Meteorology 278 107659 (2019)
https://doi.org/10.1016/j.agrformet.2019.107659

From marginal to conditional probability functions of parameters in a conceptual rainfall–runoff model: an event-based approach

Santiago Sandoval and Jean-Luc Bertrand-Krajewski
Hydrological Sciences Journal 64 (11) 1340 (2019)
https://doi.org/10.1080/02626667.2019.1635696

Assimilation of remote sensing into crop growth models: Current status and perspectives

Jianxi Huang, Jose L. Gómez-Dans, Hai Huang, Hongyuan Ma, Qingling Wu, Philip E. Lewis, Shunlin Liang, Zhongxin Chen, Jing-Hao Xue, Yantong Wu, Feng Zhao, Jing Wang and Xianhong Xie
Agricultural and Forest Meteorology 276-277 107609 (2019)
https://doi.org/10.1016/j.agrformet.2019.06.008

Generalized Likelihood Uncertainty Estimation and Markov Chain Monte Carlo Simulation to Prioritize TMDL Pollutant Allocations

Anurag Mishra, Ebrahim Ahmadisharaf, Brian L. Benham, Mary Leigh Wolfe, Scotland C. Leman, Daniel L. Gallagher, Kenneth H. Reckhow and Eric P. Smith
Journal of Hydrologic Engineering 23 (12) (2018)
https://doi.org/10.1061/(ASCE)HE.1943-5584.0001720

Towards improved calibration of crop models – Where are we now and where should we go?

S.J. Seidel, T. Palosuo, P. Thorburn and D. Wallach
European Journal of Agronomy 94 25 (2018)
https://doi.org/10.1016/j.eja.2018.01.006

Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis

Phillip D. Alderman and Bryan Stanfill
European Journal of Agronomy 88 1 (2017)
https://doi.org/10.1016/j.eja.2016.09.016

Optimization of multi-environment trials for genomic selection based on crop models

R. Rincent, E. Kuhn, H. Monod, et al.
Theoretical and Applied Genetics 130 (8) 1735 (2017)
https://doi.org/10.1007/s00122-017-2922-4

A Model-Based Estimate of Regional Wheat Yield Gaps and Water Use Efficiency in Main Winter Wheat Production Regions of China

Zunfu Lv, Xiaojun Liu, Weixing Cao and Yan Zhu
Scientific Reports 7 (1) (2017)
https://doi.org/10.1038/s41598-017-06312-x

Assessment of uncertainty and sensitivity analyses for ORYZA model under different ranges of parameter variation

Junwei Tan, Yuanlai Cui and Yufeng Luo
European Journal of Agronomy 91 54 (2017)
https://doi.org/10.1016/j.eja.2017.09.001

Assessment of soybean yield with altered water-related genetic improvement traits under climate change in Southern Brazil

Rafael Battisti, Paulo C. Sentelhas, Kenneth J. Boote, et al.
European Journal of Agronomy 83 1 (2017)
https://doi.org/10.1016/j.eja.2016.11.004

Probabilistic development of shear strength model for reinforced concrete squat walls

Chao‐Lie Ning and Bing Li
Earthquake Engineering & Structural Dynamics 46 (6) 877 (2017)
https://doi.org/10.1002/eqe.2834

A comparison of Bayesian and classical methods for parameter estimation in greenhouse crop models

I.L. López-Cruz, A. Ruiz-García, E. Fitz-Rodríguez, R. Salazar-Moreno and A. Rojano-Aguilar
Acta Horticulturae (1182) 241 (2017)
https://doi.org/10.17660/ActaHortic.2017.1182.29

Estimation of available water capacity components of two-layered soils using crop model inversion: Effect of crop type and water regime

K. Sreelash, Samuel Buis, M. Sekhar, Laurent Ruiz, Sat Kumar Tomer and Martine Guérif
Journal of Hydrology 546 166 (2017)
https://doi.org/10.1016/j.jhydrol.2016.12.049

Uncertainty analysis of modified VegSyst model applied to a soilless culture tomato crop

A. Martinez-Ruiz, I.L. López-Cruz, A. Ruiz-García, J. Pineda-Pineda and A. Ramírez-Arias
Acta Horticulturae (1182) 249 (2017)
https://doi.org/10.17660/ActaHortic.2017.1182.30

Estimation of ecotype-specific cultivar parameters in a wheat phenology model and uncertainty analysis

Zunfu Lv, Xiaojun Liu, Liang Tang, et al.
Agricultural and Forest Meteorology 221 219 (2016)
https://doi.org/10.1016/j.agrformet.2016.02.016

A theoretical and real world evaluation of two Bayesian techniques for the calibration of variety parameters in a sugarcane crop model

J. Sexton, Y. Everingham and G. Inman-Bamber
Environmental Modelling & Software 83 126 (2016)
https://doi.org/10.1016/j.envsoft.2016.05.014

Effect of formal and informal likelihood functions on uncertainty assessment in a single event rainfall-runoff model

Mahrouz Nourali, Bijan Ghahraman, Mohsen Pourreza-Bilondi and Kamran Davary
Journal of Hydrology 540 549 (2016)
https://doi.org/10.1016/j.jhydrol.2016.06.022

Quantifying uncertainty in crop model predictions due to the uncertainty in the observations used for calibration

Roberto Confalonieri, Simone Bregaglio and Marco Acutis
Ecological Modelling 328 72 (2016)
https://doi.org/10.1016/j.ecolmodel.2016.02.013

Uncertainty and sensitivity assessments of an agricultural–hydrological model (RZWQM2) using the GLUE method

Mei Sun, Xiaolin Zhang, Zailin Huo, et al.
Journal of Hydrology 534 19 (2016)
https://doi.org/10.1016/j.jhydrol.2015.12.045

Benoit Bayol, Paul-Henry Cournede, Julien Sainte-Marie, Gautier Viaud, Faustino Chi, Winfried Kurth, Qinqin Long, Johannes Merklein, Katarina Streit, Evelyne Costes, Vincent Migault, Benoit Pallas, Gerhard Buck-Sorlin, Magalie Poirier-Pocovi and Christophe Pradal
1 (2016)
https://doi.org/10.1109/FSPMA.2016.7818281

Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

N Pirttioja, TR Carter, S Fronzek, et al.
Climate Research 65 87 (2015)
https://doi.org/10.3354/cr01322

A generalised likelihood uncertainty estimation mixed-integer programming model: Application to a water resource distribution network

Godfrey Chagwiza, Brian C. Jones, Senelani D. Hove-Musekwa and Sobona Mtisi
Cogent Mathematics 2 (1) 1048076 (2015)
https://doi.org/10.1080/23311835.2015.1048076

On the assessment of ground water parameter uncertainty over an arid aquifer

BaharehSadat Hamraz, Abolfazl Akbarpour, Mohsen Pourreza Bilondi and Sadegh Sadeghi Tabas
Arabian Journal of Geosciences 8 (12) 10759 (2015)
https://doi.org/10.1007/s12517-015-1935-z

Uncertainty assessment of the agro-hydrological SWAP model application at field scale: A case study in a dry region

Mojtaba Shafiei, Bijan Ghahraman, Bahram Saghafian, Kamran Davary, Saket Pande and Majid Vazifedoust
Agricultural Water Management 146 324 (2014)
https://doi.org/10.1016/j.agwat.2014.09.008

Reductive soil disinfestation (RSD) alters gross N transformation rates and reduces NO and N2O emissions in degraded vegetable soils

Tongbin Zhu, Qi Dang, Jinbo Zhang, Christoph Müller and Zucong Cai
Plant and Soil 382 (1-2) 269 (2014)
https://doi.org/10.1007/s11104-014-2160-3

Application of Multimodal Optimization for Uncertainty Estimation of Computationally Expensive Hydrologic Models

Huidae Cho and Francisco Olivera
Journal of Water Resources Planning and Management 140 (3) 313 (2014)
https://doi.org/10.1061/(ASCE)WR.1943-5452.0000330

Parameter identification of the STICS crop model, using an accelerated formal MCMC approach

B. Dumont, V. Leemans, M. Mansouri, et al.
Environmental Modelling & Software 52 121 (2014)
https://doi.org/10.1016/j.envsoft.2013.10.022

Past acidification and recovery of surface waters, soils and ecology in the United Kingdom: Prospects for the future under current deposition and land use protocols

R.C. Helliwell, J. Aherne, G. MacDougall, et al.
Ecological Indicators 37 381 (2014)
https://doi.org/10.1016/j.ecolind.2013.02.005

Improving performance of Agro-Ecological Zone (AEZ) modeling by cross-scale model coupling: An application to japonica rice production in Northeast China

Zhan Tian, Honglin Zhong, Laixiang Sun, Günther Fischer, Harrij van Velthuizen and Zhuoran Liang
Ecological Modelling 290 155 (2014)
https://doi.org/10.1016/j.ecolmodel.2013.11.020

Probabilistic parameter estimation of activated sludge processes using Markov Chain Monte Carlo

Soroosh Sharifi, Sudhir Murthy, Imre Takács and Arash Massoudieh
Water Research 50 254 (2014)
https://doi.org/10.1016/j.watres.2013.12.010

Contributions of historical changes in sowing date and climate to U.S. maize yield trend: An evaluation using large-area crop modeling and data assimilation

Toshichika IIZUMI, Gen SAKURAI and Masayuki YOKOZAWA
Journal of Agricultural Meteorology 70 (2) 73 (2014)
https://doi.org/10.2480/agrmet.D-13-00023

Dependency of parameter values of a crop model on the spatial scale of simulation

Toshichika Iizumi, Yukiko Tanaka, Gen Sakurai, Yasushi Ishigooka and Masayuki Yokozawa
Journal of Advances in Modeling Earth Systems 6 (3) 527 (2014)
https://doi.org/10.1002/2014MS000311

Nitrous oxide emissions from soils: how well do we understand the processes and their controls?

Klaus Butterbach-Bahl, Elizabeth M. Baggs, Michael Dannenmann, Ralf Kiese and Sophie Zechmeister-Boltenstern
Philosophical Transactions of the Royal Society B: Biological Sciences 368 (1621) 20130122 (2013)
https://doi.org/10.1098/rstb.2013.0122

Separately accounting for uncertainties in rainfall and runoff: Calibration of event-based conceptual hydrological models in small urban catchments using Bayesian method

Siao Sun and Jean-Luc Bertrand-Krajewski
Water Resources Research 49 (9) 5381 (2013)
https://doi.org/10.1002/wrcr.20444

Parameter and predictive outcomes of model simplification

Ty A. Watson, John E. Doherty and Steen Christensen
Water Resources Research 49 (7) 3952 (2013)
https://doi.org/10.1002/wrcr.20145

An ensemble approach to the representation of subgrid-scale heterogeneity of crop phenology and yield in coarse-resolution large-area crop models

Toshichika IIZUMI, Gen SAKURAI and Masayuki YOKOZAWA
Journal of Agricultural Meteorology 69 (4) 243 (2013)
https://doi.org/10.2480/agrmet.69.4.2

Predicting Growth of Panicum maximum: An Adaptation of the CROPGRO–Perennial Forage Model

Márcio A.S. Lara, Carlos G.S. Pedreira, Kenneth J. Boote, Bruno C. Pedreira, Leonardo S.B. Moreno and Phillip D. Alderman
Agronomy Journal 104 (3) 600 (2012)
https://doi.org/10.2134/agronj2011.0272

Comparison of different uncertainty techniques in urban stormwater quantity and quality modelling

Cintia B.S. Dotto, Giorgio Mannina, Manfred Kleidorfer, et al.
Water Research 46 (8) 2545 (2012)
https://doi.org/10.1016/j.watres.2012.02.009

Estimating potential yield of wheat production in China based on cross-scale data-model fusion

Zhan Tian, Honglin Zhong, Runhe Shi, et al.
Frontiers of Earth Science 6 (4) 364 (2012)
https://doi.org/10.1007/s11707-012-0332-0

Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments

Kendall C. DeJonge, James C. Ascough, Mehdi Ahmadi, Allan A. Andales and Mazdak Arabi
Ecological Modelling 231 113 (2012)
https://doi.org/10.1016/j.ecolmodel.2012.01.024

Uncertainty in sewer sediment deposit modelling: Detailed vs simplified modelling approaches

Giorgio Mannina, A.N.A. Schellart, Simon Tait and Gaspare Viviani
Physics and Chemistry of the Earth, Parts A/B/C 42-44 11 (2012)
https://doi.org/10.1016/j.pce.2011.04.003