Application of an interacting particle filter to improve nitrogen nutrition index predictions for winter wheat

Naud, Cédric; Makowski, David; Jeuffroy, Marie‐Hélène

doi:10.1016/j.ecolmodel.2007.05.003

Cited by 20 publications

(10 citation statements)

References 32 publications

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“…The algorithm was implemented for the seven experimental plots for 6 years, to approximate the posterior distribution of the state variable for each plot‐year. The number of Monte Carlo simulations was set equal to N = 10 000, as recommended by Naud et al . (2007).…”

Section: Methodsmentioning

confidence: 99%

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Improving a weed population model using a sequential Monte Carlo method

2010

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Field measurements and weed population model predictions have been proposed as the basis for recommendations on the need for chemical or mechanical treatment, but both approaches have some limitations. This study shows how a Sequential Monte Carlo (SMC) method can be used to combine weed count measurements and model predictions, to derive a better estimate of weed population characteristics. SMC was applied to a dynamic model simulating weed densities, seed production and seedbank densities for Alopecurus myosuroides (blackgrass). The benefit resulting from SMC was quantified for several types of weed count data, using experiments carried out in seven plots during 6 years. Compared with the initial model predictions, SMC reduced the root mean squared error (RMSE) by 33.5–81.5%. Compared with the weed densities derived from the weed counts alone, SMC reduced the RMSE by 1.2–10%. SMC should be preferred to the single use of model or of weed count data, because it can improve weed density predictions and because the probability distributions computed by SMC can be used to analyse the uncertainty about the state of the system

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Section: Methodsmentioning

confidence: 99%

“…SMC has become very popular over the past few years in statistics and related fields. It has been recently applied to improve the predictions of a dynamic winter wheat crop model (Naud et al ., 2007; 2008).…”

Section: Introductionmentioning

confidence: 99%

Improving a weed population model using a sequential Monte Carlo method

2010

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“…Although GlnLux glutamine was demonstrated in this study to reflect N status at various points in the growing season, integration with other measures/models [34][35][36][37][38][39][40][41][42] is required to generate an estimation of N requirement, and prescribe a mid-season application rate. The results of this study displayed consistent correlation of GlnLux glutamine with end-season yield, but in the future it may be interesting to test the leaf lamina instead of the midrib.…”

Section: Potential Of Leaf Gln and Glnlux As Tools For Field Researchmentioning

confidence: 99%

Mid-Season Leaf Glutamine Predicts End-Season Maize Grain Yield and Nitrogen Content in Response to Nitrogen Fertilization under Field Conditions

et al. 2017

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Abstract:After uptake in cereal crops, nitrogen (N) is rapidly assimilated into glutamine (Gln) and other amino acids for transport to sinks. Therefore Gln has potential as an improved indicator of soil N availability compared to plant N demand. Gln has primarily been assayed to understand basic plant physiology, rather than to measure plant/soil-N under field conditions. It was hypothesized that leaf Gln at early-to-mid season could report the N application rate and predict end-season grain yield in field-grown maize. A three-year maize field experiment was conducted with N application rates ranging from 30 to 218 kg ha −1 . Relative leaf Gln was assayed from leaf disk tissue using a whole-cell biosensor for Gln (GlnLux) at the V3-V14 growth stages. SPAD (Soil Plant Analysis Development) and NDVI (Normalized Difference Vegetation Index) measurements were also performed. When sampled at V6 or later, GlnLux glutamine output consistently correlated with the N application rate, end-season yield, and grain N content. Yield correlation outperformed GreenSeeker TM NDVI, and was equivalent to SPAD chlorophyll, indicating the potential for yield prediction. Additionally, depleting soil N via overplanting increased GlnLux resolution to the earlier V5 stage. The results of the study are discussed in the context of luxury N consumption, leaf N remobilization, senescence, and grain fill. The potential and challenges of leaf Gln and GlnLux for the study of crop N physiology, and future N management are also discussed.

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“…One way to smooth these curves is the data assimilation technique [3,17], which has been used to integrate observed data with models in hydrology [18], crop science [19], oceanography [20], morphodynamics [21], and air quality modeling [22,23]. The data assimilation technique requires a dynamic model.…”

Section: Introductionmentioning

confidence: 99%

An Extended Fourier Approach to Improve the Retrieved Leaf Area Index (LAI) in a Time Series from an Alpine Wetland

Quan

Wang

et al. 2014

Remote Sensing

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An extended Fourier approach was presented to improve the retrieved leaf area index (LAIr) of herbaceous vegetation in a time series from an alpine wetland. The retrieval was performed from the Aqua MODIS 8-day composite surface reflectance product (MYD09Q1) from day of year (DOY) 97 to 297 using a look-up table (LUT) based inversion of a two-layer canopy reflectance model (ACRM). To reduce the uncertainty (the ACRM inversion is ill-posed), we used NDVI and NIR images to reduce the influence of the soil background and the priori information to constrain the range of sensitive ACRM parameters determined using the Sobol's method. Even so the uncertainty caused the LAIr versus time curve to oscillate. To further reduce the uncertainty, a Fourier model was fitted using the periodically LAIr results, obtaining LAIF. We note that the level of precision of the LAIF potentially may increase through removing singular points or decrease if the LAIr data were too noisy. To further improve the precision level of the LAIr, the Fourier model was extended by considering the LAIr uncertainty. The LAIr, the LAI simulated using the Fourier model, and the LAI simulated using the extended Fourier approach (LAIeF) were validated through comparisons with the field measured LAI. The R 2 values were 0.68, 0.67 and 0.72, the residual sums of squares (RSS) were 3.47, 3.42 and 3.15, and the root-mean-square errors (RMSE) were 0. 31, 0.30 and 0.29, respectively, on DOY 177 (early July 2011

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Application of an interacting particle filter to improve nitrogen nutrition index predictions for winter wheat

Cited by 20 publications

References 32 publications

Improving a weed population model using a sequential Monte Carlo method

Improving a weed population model using a sequential Monte Carlo method

Mid-Season Leaf Glutamine Predicts End-Season Maize Grain Yield and Nitrogen Content in Response to Nitrogen Fertilization under Field Conditions

An Extended Fourier Approach to Improve the Retrieved Leaf Area Index (LAI) in a Time Series from an Alpine Wetland

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