Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice

Confalonieri, Roberto; Acutis, Marco; Bellocchi, Gianni; Donatelli, Marcello

doi:10.1016/j.ecolmodel.2009.02.017

Cited by 107 publications

(80 citation statements)

References 43 publications

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“…Existem vários modelos de simulação do rendimento de grãos de arroz, alguns mais complexos como o CERES-Rice (TIMSINA & HUMPHREYS, 2006) e o ORYZA (BOUMAN et al, 2004), ambos calibrados para as condições Asiáticas, e outros com algumas simplificações, principalmente na maneira de calcular a produção de matéria seca na planta, que facilitam sua utilização, como o InfoCrop (AGGARWAL et al, 2006a), desenvolvido e avaliado na Índia, e o WARM (CONFALONIERI et al, 2009), avaliado para as condições da Europa. Destes, o modelo InfoCrop é um modelo mecanístico, genérico e que permite simular os efeitos do clima, do solo, de manejo da cultura, de danos das principais pragas e doenças, além de possibilitar a simulação do impacto ambiental de agroecossistemas.…”

Section: Introductionunclassified

Adaptação e avaliação do modelo infocrop para simulação do rendimento de grãos da cultura do arroz irrigado

et al. 2012

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Este trabalho teve como objetivo adaptar e avaliar o modelo InfoCrop para simulação do rendimento de grãos da cultura do arroz irrigado, em Santa Maria - RS. O rendimento de grãos da cultura do arroz irrigado foi simulado, considerando-se três versões do modelo InfoCrop com adaptações para cultivares locais. Os rendimentos simulados foram comparados com os rendimentos observados no município de Santa Maria, no período 1996/1997 a 2008/2009, disponibilizados pelo Instituto Riograndense do Arroz (IRGA), e com rendimentos de experimentos realizados na Universidade Federal de Santa Maria (UFSM), entre os anos agrícolas de 1998/1999 e 2005/2006. O modelo InfoCrop capturou a variação interanual do rendimento de grãos de arroz irrigado para o município de Santa Maria e dos experimentos realizados na UFSM. A raiz do erro quadrático médio da simulação de rendimento de grãos com a Versão 3 do modelo InfoCrop (melhor desempenho), considerando todo o conjunto de dados observados, foi de 0,850 Mg ha-1, com tendência de superestimativa, que pode ter ocorrido pelo fato de o modelo considerar uma situação de rendimento potencial, o que nem sempre acontece em lavouras comerciais.

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Section: Introductionunclassified

Adaptação e avaliação do modelo infocrop para simulação do rendimento de grãos da cultura do arroz irrigado

et al. 2012

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“…Model users perform evaluation of model adequacy (6), which is preliminary to the use of model outputs by third parties (7) much interest in the use of model estimates for decision support and strategic planning). This step should integrate different components of model quality, not only the agreement between model outputs and actual data (based on metrics and test statistics), but also elements of model complexity (essentially related to the number and relevance of parameters in a model, which is pertinent for model comparison, Confalonieri et al 2009), as well as an assessment of the stability of model performance over a variety of conditions (Confalonieri et al 2010). The deliberative process consists for a group of actors (stakeholders) to receive and exchange information, to critically examine an issue, and to come to an agreement which will inform decision making (after Fearon 1998).…”

Section: Deliberative Processes For Comprehensive Model Evaluationmentioning

confidence: 99%

Deliberative processes for comprehensive evaluation of agroecological models. A review

Bellocchi

Rivington

Matthews

et al. 2014

Agron. Sustain. Dev.

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The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.

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“…Crop simulation models vary in complexity from simple empirical statistic-based models (Pedro Júnior et al, 1995;Klering et al, 2008) to very complex process-based models (Hasegawa & Horie, 1996;Mall & Aggarwal, 2002;Bouman & Laar, 2006;Confalonieri et al, 2009). Models with intermediate levels of complexity combine advantages from the former (simplicity) and from the latter (mechanisms) by using general and robust response functions for describing plant processes on a canopy level, which requires less and more easily-measured coefficients and inputs.…”

Section: Introductionmentioning

confidence: 99%

CO2-response function of radiation use efficiency in rice for climate change scenarios

Streck

Rosa

Walter

et al. 2012

Pesq. agropec. bras.

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-The objective of this work was to evaluate a generalized response function to the atmospheric CO 2 concentration [f(CO 2 )] by the radiation use efficiency (RUE) in rice. Experimental data on RUE at different CO 2 concentrations were collected from rice trials performed in several locations around the world. RUE data were then normalized, so that all RUE at current CO 2 concentration were equal to 1. The response function was obtained by fitting normalized RUE versus CO 2 concentration to a Morgan-Mercer-Flodin (MMF) function, and by using Marquardt's method to estimate the model coefficients. Goodness of fit was measured by the standard deviation of the estimated coefficients, the coefficient of determination (R 2 ), and the root mean square error (RMSE). The f(CO 2 ) describes a nonlinear sigmoidal response of RUE in rice, in function of the atmospheric CO 2 concentration, which has an ecophysiological background, and, therefore, renders a robust function that can be easily coupled to rice simulation models, besides covering the range of CO 2 emissions for the next generation of climate scenarios for the 21 st century.Index terms: Oryza sativa, global warming, modeling, photosynthesis. Função de resposta a CO 2 na eficiência de uso da radiação em arroz para cenários de mudanças climáticasResumo -O objetivo deste trabalho foi avaliar uma função de resposta generalizada à concentração de CO 2 atmosférico [f (CO 2 )] pela eficiência de uso de radiação (RUE) em arroz. Os dados experimentais de RUE, em diferentes concentrações de CO 2 , foram coletados de ensaios sobre o arroz realizados em diferentes locais do mundo. Os dados de RUE foram normalizados de modo que todos os valores de RUE, na atual concentração de CO 2 , foram iguais a 1. A função de resposta foi obtida pelos valores de RUE normalizados versus a concentração de CO 2 , ajustados à função de Morgan-Mercer-Flodin (MMF), com uso do método de Marquardt para estimar os coeficientes do modelo. O ajuste da função foi medido pelo desvio-padrão dos coeficientes estimados, o coeficiente de determinação (R²) e a raiz do quadrado médio do erro (RQME). A f(CO 2 ) descreve uma resposta não linear sigmoidal da RUE em arroz, em função da concentração do CO 2 atmosférico, o que tem significado ecofisiológico e, portanto, é uma função robusta que pode ser facilmente acoplada a modelos de simulação de arroz, além de abranger as emissões de CO 2 para a próxima geração de cenários climáticos para o século 21.Termos para indexação: Oryza sativa, aquecimento global, modelagem, fotossíntese.

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Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice

Cited by 107 publications

References 43 publications

Adaptação e avaliação do modelo infocrop para simulação do rendimento de grãos da cultura do arroz irrigado

Adaptação e avaliação do modelo infocrop para simulação do rendimento de grãos da cultura do arroz irrigado

Deliberative processes for comprehensive evaluation of agroecological models. A review

CO2-response function of radiation use efficiency in rice for climate change scenarios

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