Is it useful to combine measurements taken during the growing season with a dynamic model to predict the nitrogen status of winter wheat?

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

doi:10.1016/j.eja.2007.08.005

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…Crop growth models can be used to assess optimal N rates; however, the predictions are fairly imprecise and vary substantially among these models (Kyveryga et al, 2007; Naud et al, 2008). Under site‐specific N fertilization strategies, some researchers have recommended applying more N to historically high‐yielding areas and less to low‐yielding areas, whereas others advocate the opposite approach (James and Godwin, 2003).…”

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Corn Response to Nitrogen is Influenced by Soil Texture and Weather

et al. 2012

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Soil properties and weather conditions are known to affect soil N availability and plant N uptake; however, studies examining N response as affected by soil and weather sometimes give conflicting results. Meta‐analysis is a statistical method for estimating treatment effects in a series of experiments to explain the sources of heterogeneity. In this study, the technique was used to examine the influence of soil and weather parameters on N response of corn (Zea mays L.) across 51 studies involving the same N rate treatments that were performed in a diversity of North American locations between 2006 and 2009. Results showed that corn response to added N was significantly greater in fine‐textured soils than in medium‐textured soils. Abundant and well‐distributed rainfall and, to a lesser extent, accumulated corn heat units enhanced N response. Corn yields increased by a factor of 1.6 (over the unfertilized control) in medium‐textured soils and 2.7 in fine‐textured soils at high N rates. Subgroup analyses were performed on the fine‐textured soil class based on weather parameters. Rainfall patterns had an important effect on N response in this soil texture class, with yields being increased 4.5‐fold by in‐season N fertilization under conditions of “abundant and well‐distributed rainfall.” These findings could be useful for developing N fertilization algorithms that would prescribe N application at optimal rates taking into account rainfall pattern and soil texture, which would lead to improved crop profitability and reduced environmental impacts.

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Corn Response to Nitrogen is Influenced by Soil Texture and Weather

et al. 2012

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“…However, in the context of plant growth models, traditional approaches are commonly not able to cope with the model complexity combined to the scarcity of experimental data and their general lack of accuracy. This explains why despite the various data assimilation approaches and their proven benefits for uncertainty analysis and model prediction, only few applications to plant growth model and crop yield prediction have been published ( [22], [19]). …”

Section: Resultsmentioning

confidence: 99%

Iterative convolution particle filtering for nonlinear parameter estimation and data assimilation with application to crop yield prediction

Chen¹,

Trevezas²,

Cournède³

2013

2013 Proceedings of the Conference on Control and Its Applications

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The complexity of plant growth models and the scarcity of experimental data make the application of conventional data assimilation techniques rather difficult. In this paper, we use the Convolution Particle Filter (CPF) and an iterative adaptation, the Iterative Convolution Particle Filter (ICPF) for nonlinear parameter estimation. Both methods provide prior distributions in the Bayesian framework for data assimilation. CPF is sequentially used to update state and parameter estimates in order to improve model prediction and to assess the predictive uncertainty. The predictive performances of the two methods are evaluated by an application to the LNAS sugar beet growth model with three sets of real measurements, one used for parameter estimation and the two others used to test the model predictive capacity, both with and without data assimilation. Despite the low accuracy and the scarcity of the early data used for assimilation, the CPFbased data assimilation approach with the prior distribution based on ICPF estimations showed promising predictive capacities and provided robust confidence intervals. The method can therefore be considered as a potential candidate for yield prediction applications in agriculture.

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“…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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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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Is it useful to combine measurements taken during the growing season with a dynamic model to predict the nitrogen status of winter wheat?

Cited by 14 publications

References 25 publications

Corn Response to Nitrogen is Influenced by Soil Texture and Weather

Corn Response to Nitrogen is Influenced by Soil Texture and Weather

Iterative convolution particle filtering for nonlinear parameter estimation and data assimilation with application to crop yield prediction

Improving a weed population model using a sequential Monte Carlo method

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