Comparison of parameter estimation methods for crop models

Tremblay, Marie; Wallach, Daniel

doi:10.1051/agro:2004033

Cited by 54 publications

(26 citation statements)

References 39 publications

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“…At the field level, only agronomic predictive models using the appropriate biogical and environmental parameters [210] should be able to take into account interactions between plants and their environment to obtain an integrated view of the various inputs or outputs, influencing crop NUE [211,212]. One of the main challenges in the future will be to develop reliable decision support systems with the help of sensors [213,214] and biological diagnostic tools in precision agriculture, in order to optimize the application of N under organic or conventional conditions in a more sustainable manner.…”

Section: Discussionmentioning

confidence: 99%

Improving Nitrogen Use Efficiency in Crops for Sustainable Agriculture

et al. 2011

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Abstract:In this review, we present the recent developments and future prospects of improving nitrogen use efficiency (NUE) in crops using various complementary approaches. These include conventional breeding and molecular genetics, in addition to alternative farming techniques based on no-till continuous cover cropping cultures and/or organic nitrogen (N) nutrition. Whatever the mode of N fertilization, an increased knowledge of the mechanisms controlling plant N economy is essential for improving NUE and for reducing excessive input of fertilizers, while maintaining an acceptable yield and sufficient profit margin for the farmers. Using plants grown under agronomic conditions, with different tillage conditions, in pure or associated cultures, at low and high N mineral fertilizer input, or using organic fertilization, it is now possible to develop further whole plant agronomic and physiological studies. These can be combined with gene, protein and metabolite profiling to build up a comprehensive picture depicting the different steps of N uptake, assimilation and recycling to produce either biomass in vegetative organs or proteins in storage organs. We provide a critical overview as to how our understanding of the agro-ecophysiological, physiological and molecular controls of N assimilation in crops, under varying environmental conditions, has been improved. We have used combined approaches, based on agronomic studies, whole plant physiology, quantitative genetics, forward and reverse genetics and the emerging systems biology. Long-term sustainability may require a gradual transition from synthetic N inputs to legume-based crop rotation, including continuous cover cropping systems, where these may be possible in certain areas of the world, depending on climatic conditions. Current knowledge and prospects for future agronomic development and application for breeding crops adapted to lower mineral fertilizer input and to alternative farming techniques are explored, whilst taking into account the constraints of both the current world economic situation and the environment.

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Section: Discussionmentioning

confidence: 99%

Improving Nitrogen Use Efficiency in Crops for Sustainable Agriculture

et al. 2011

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“…It must be noticed that for a given number of parameters , the penalization term is constant regardless of which parameters are estimated. Tremblay and Wallach (2004) have attested that BICC performs better than classic BIC both in terms of mean squared error of the parameter estimates and in terms of prediction error.…”

Section: Modification Of the Bayesian Information Criterion For Smallmentioning

confidence: 99%

Phonemic segmentation using the generalised Gamma distribution and small sample Bayesian information criterion

Almpanidis

Kotropoulos

2008

Speech Communication

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“…Note that there are many choices in this model selection step. Other advanced model selection methodologies, e.g., the least absolute shrinkage and selection operator (lasso) (Tibshirani, 1996), the smoothly clipped absolute deviation (SCAD) (Fan, 1997), the adaptive lasso (Zou, 2006), the least-angle regression algorithm (LARS) (Efron, Hastie, Johnstone & Tibshirani, 2004), the Dantzig selector (Candes & Tao, 2007), or another kind of selection criteria, e.g., the Akaike information criterion (AIC) (Akaike, 1974), the Corrected Akaike information criterion (AICc) (Hurvich & Tsai, 1993), the Corrected Bayesian information criterion (BICc) (Tremblay & Wallach, 2004), the significance of each individual variable criterion; can be applied here. Note that each association rule can be converted into only one interaction.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Model Selection for Poisson Regression via Association Rules Analysis

Changpetch¹,

Lin²

2015

IJSP

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This study integrates association rules analysis, a methodology for selecting potential interactions, with Poisson regression modeling. Though typically ignored in conventional Poisson regression, interactions are very common in practice. However, selecting a Poisson regression model when many main effects and interactions are involved is problematic. In this study, we develop a model selection framework to address this problem. Specifically, we focus on building an optimal Poisson regression model by (1) discretizing the response and quantitative attributes into levels; (2) exploring via association rules analysis combinations of input variables that have a significant impact on response; (3) selecting potential (low-and high-order) interactions; (4) converting these potential interactions into new variables; and (5) selecting variables from all the input variables and the newly created variables (interactions) to build the optimal Poisson regression model. Our model selection procedure is the first approach to enable a global search for potential interactions and the first to establish the optimal combination of main effects and interaction effects in the Poisson regression model. A real-life example is given for illustration. It is shown that the proposed method finds the optimal model including important interactions that cannot be found by other existing methods.

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Comparison of parameter estimation methods for crop models

Cited by 54 publications

References 39 publications

Improving Nitrogen Use Efficiency in Crops for Sustainable Agriculture

Improving Nitrogen Use Efficiency in Crops for Sustainable Agriculture

Phonemic segmentation using the generalised Gamma distribution and small sample Bayesian information criterion

Model Selection for Poisson Regression via Association Rules Analysis

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