A technique to develop and validate simulation models

Jones, Peter N.; Carberry, P. S.

doi:10.1016/0308-521x(94)90105-o

Cited by 31 publications

(31 citation statements)

References 35 publications

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“…The four methods were also compared for their ability to estimate RII dynamics for a different year using a cross‐validation technique (Jones & Carberry, 1994). Five regression equations were calculated using the pooled data of four years, excluding one year in turn; afterwards, these equations were used to estimate the data for the year not included.…”

Section: Methodsmentioning

confidence: 99%

Estimating the germination dynamics of Plasmopara viticola oospores using hydro‐thermal time

Rossi

Caffi

Bugiani³

et al. 2008

Plant Pathology

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The effects of environmental conditions on the variability in germination dynamics of Plasmopara viticola oospores were studied from 1999 to 2003. The germination course was determined indirectly as the relative infection incidence (RII) occurring on grape leaf discs kept in contact with oospores sampled from a vineyard between March and July. The time elapsed between 1 January and the infection occurrence was expressed as physiological time, using four methods: (i) sums of daily temperatures > 8 ° C; (ii) hourly temperatures > 10 ° C; (iii) sums of hourly rates from a temperature-dependent function; or (iv) sums of these rates in hours with a rain or vapour pressure deficit ≤ 4·5 hPa (hydro-thermal time, HT). An equation of Gompertz in the form RII = exp[ − a · exp( − b · HT)] produced an accurate fit for both separate years ( R 2 = 0·97 to 0·99) and pooled data ( R 2 = 0·89), as well as a good accuracy in cross-estimating new data ( r between observed and cross-estimated data were between 0·93 and 0·99, P < 0·0001). It also accounted for a great part of the variability in oospore germination between years and both between and within sampling periods. Therefore, the equation of Gompertz (with a = 15·9 ± 2·63 and b = 0·653 ± 0·034) calculated over hydro-thermal time, a physiological time accounting for the effects of both temperature and moisture, produced a consistent modelling of the general relationships between the germination dynamics of a population of P. viticola oospores and weather conditions. It represents the relative density of the seasonal oospores that should have produced sporangia when they have experienced favourable conditions for germination.

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Section: Methodsmentioning

confidence: 99%

Estimating the germination dynamics of Plasmopara viticola oospores using hydro‐thermal time

Rossi

Caffi

Bugiani³

et al. 2008

Plant Pathology

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“…In this procedure, the data is randomly divided into K-fold (in this paper K = 5) datasets of approximately equal size ( Jones and Carberry, 1994;Kohavi, 1995). In this procedure, the data is randomly divided into K-fold (in this paper K = 5) datasets of approximately equal size ( Jones and Carberry, 1994;Kohavi, 1995).…”

Section: Model Optimization and Evaluationmentioning

confidence: 99%

“…We used a fivefold cross-validation statistical procedure to assess the prediction accuracy of optimized model parameters in DD10, DD_Opt, DD_2S, and DD_3S for all developmental stages. In this procedure, the data is randomly divided into K-fold (in this paper K = 5) datasets of approximately equal size ( Jones and Carberry, 1994;Kohavi, 1995). Model results are presented as the mean of the resulting five sample RMSEs ( James et al, 2013).…”

Section: Model Optimization and Evaluationmentioning

confidence: 99%

Using Stage‐Dependent Temperature Parameters to Improve Phenological Model Prediction Accuracy in Rice Models

Sharifi

Hijmans²,

Hill

2017

Crop Science

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Crop phenology models that use constant temperature parameters across developmental stages may be less accurate and have temperature‐dependent systematic prediction error (bias). Using the DD10 model, we evaluated default and optimized (DD_Opt) temperature parameters using data from seven California rice (Oryza sativa L.) cultivars grown in six locations over 3 yr (2012–2014). Furthermore, we evaluated the effect of using stage‐dependent temperature parameters on model performance using two‐ and three‐stage optimization approaches. Optimized temperature parameters, or DD_Opt (RMSE: 2.3–5.4 d), performed better than DD10 (RMSE: 2.9–7.3 d). A temperature sensitivity analysis indicated that the time from planting to panicle initiation was most sensitive to temperature (every 1°C increase decreased days to panicle initiation by 1.8 d) while time from heading to R7 (marked by the appearance of one yellow hull on the main stem panicle) was not affected by temperature. Optimized temperature parameters varied between stages, with base temperature decreasing and optimum temperature increasing with plant development. Compared to the DD_Opt, two‐stage optimization (planting–heading and heading–R7) reduced the RMSE by 0.8 d and the systematic error by 0.6 d °C−1. Three‐stage optimization (planting–panicle initiation, panicle initiation–heading, and heading–R7) further reduced RMSE by 1.1 d and systematic error by 1.4 d °C−1 for preheading. These results demonstrate the importance of using stage‐dependent parameters to improve accuracy of phenological models, which may be important when models are used to study the crop response to climate change, field management options, ecosystem productivity, breeding, and yield gap analysis.

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“…A variety of techniques have been explored for implementing calibration procedures to estimate model input parameters. In general, most techniques involve an optimization algorithm that solves for the parameter set that maximizes or minimizes an objective function (Jones and Carberry, 1994;Calmon et al, 1999;Irmak et al, 2001). Typically, the root mean square error (RMSE), or related error statistics, between simulated output and measured values is the objective function to be minimized in the calibration of crop models Gauch et al, 2003 (Jones and Carberry, 1994).…”

Section: Introductionmentioning

confidence: 99%

“…With the LOO cross validation technique, observations in the measured dataset are iteratively and exhaustively used for both model calibration and model testing, resulting in an estimate of model predictive performance that is more reliable than estimates from the two-group partition method and less biased than estimates derived from calibration-dependent datasets (Jones and Carberry, 1994). Given a measured dataset having (1994) and Irmak et al (2000); however, these techniques have not been used within a precision agriculture framework.…”

Section: Introductionmentioning

confidence: 99%

Integration of remote sensing and crop growth modeling for nitrogen management decision support in corn

Thorp

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A technique to develop and validate simulation models

Cited by 31 publications

References 35 publications

Estimating the germination dynamics of Plasmopara viticola oospores using hydro‐thermal time

Estimating the germination dynamics of Plasmopara viticola oospores using hydro‐thermal time

Using Stage‐Dependent Temperature Parameters to Improve Phenological Model Prediction Accuracy in Rice Models

Integration of remote sensing and crop growth modeling for nitrogen management decision support in corn

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