Using pattern recognition for estimating cultivar coefficients of a crop simulation model

Aval, Mohammad Bannayan; Hoogenboom, Gerrit

doi:10.1016/j.fcr.2009.01.007

Cited by 101 publications

(38 citation statements)

References 43 publications

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“…8a); the area-specific parameters for Koshihikari were not transferable to different environments. The results of Bannayan and Hoogenboom (2009) were similar in that the parameters they estimated depended on the location of cultivation. We also attempted to estimate parameters for the 5 cultivars for which trial records covered only a small number of areas, but failed to obtain reasonable parameter values (Fig.…”

Section: Discussionsupporting

confidence: 63%

A methodology for estimating phenological parameters of rice cultivars utilizing data from common variety trials

Fukui

Ishigooka

Kuwagata

et al. 2015

J. Agric. Meteorol.

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Crop phenology models play a pivotal role in predicting yields under climate change. Cultivar-specific model parameters are essential for accurate prediction, but their estimation generally requires elaborate and laborious experiments, and such parameter sets have therefore been available only for a small number of cultivars. We propose methodology for estimating phenological parameters, combining a stochastic parameter estimation method (genetic algorithm) with the use of a database comprising 30 years of records from variety trials conducted at experimental stations across Japan. Optimal parameter sets were selected based on the results of cross-validation tests. This methodology allowed us to estimate phenological parameters objectively. We estimated phenological parameters for the 10 leading cultivars currently planted in Japan, and showed that these parameters reflect the cultivars' sensitivity to temperature and/or photoperiod. The proposed methodology can be used to provide quantitative evaluations of the environmental responses of rice cultivars, without relying on elaborate and laborious experiments, and substantially improves the efficiency of phenological trait phenotyping.

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Section: Discussionsupporting

confidence: 63%

A methodology for estimating phenological parameters of rice cultivars utilizing data from common variety trials

Fukui

Ishigooka

Kuwagata

et al. 2015

J. Agric. Meteorol.

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“…The simulation was labeled (1) excellent, (2) good, (3) fair, or (4) poor for NRMSE values smaller than 10%, between 10% and 20%, between 20% and 30%, or >30%, respectively [34].…”

Section: Model Performance Assessmentmentioning

confidence: 99%

Evaluation of AquaCrop Model for Foxtail Millet (Setaria italica) Growth and Water Use with Plastic Film Mulching and No Mulching under Different Weather Conditions

Guo

Chen

Guo

et al. 2018

Water

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Abstract:The water-driven AquaCrop model is used extensively for simulating crop growth and water use. A three-year field experiment (2015-2017) of foxtail millet (Setaria italica) that was grown using plastic film mulching (PM) and no mulching (NM) was conducted in a rain-fed region of China to simulate canopy cover (CC), biomass, soil water content (SWC), yield, evapotranspiration (ETc), and water use efficiency (WUE). The year 2015 was much drier and warmer than the two other years. The model was calibrated using field data from 2016 and validated using the data from 2015 and 2017. Simulations of CC, biomass, and yield achieved favorable performance for both PM and NM in all years, as indicated by the high determination coefficient (R 2 ), model efficiency (EF), small root mean square error (RMSE), normalized root mean square error (NRMSE), and deviations < 10%. Simulations of SWC, ETc, and WUE gave acceptable results for both PM and NM in the normal year (2017). However, low R 2 and EF, and large NRMSE, RMSE, and deviations were observed in the predictions of PM and NM for SWC, ETc, and WUE in the dry year (2015) with a severe drought stress, indicating that the model performed unsatisfactorily under severe drought stress condition that was caused by the adverse weather. In addition, the simulation performance of NM was more favorable than that of PM for most crop growth and water use indexes under no drought stress condition.

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“…The minimum data set necessary to run DSSAT consists of daily weather data of maximum (Tmax) and minimum (Tmin) temperature, rainfall and solar radiation, soil chemical and physical parameters for each layer of the soil profile, genetic coefficients for each cultivar with information about development and biomass accumulation, and management information, such as soil preparation, planting dates, plant density, fertilization amounts and timing, or other agricultural practices. Regarding crop parameters, the introduction of a new cultivar in a process-based crop simulation model requires the estimation of cultivar coefficients that define its growth and development characteristics (Bannayan and Hoogenboom 2009). Experimental data like plant phenology, biomass partitioning, and other morphological components like leaf area index are mandatory to calibrate the genetic coefficients and to test the overall reliability of the model simulation.…”

Section: Introductionmentioning

confidence: 99%

Yield stability and lower susceptibility to abiotic stresses of improved open-pollinated and hybrid maize cultivars

Lana

Eulenstein

Schlindwein

et al. 2017

Agron. Sustain. Dev.

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Maize is one of the world's most important cereals, cultivated in a wide range of environments. Besides the importance of maize and the gains in yield from selection schemes, commercial breeding drastically reduced the number of cultivars of this crop. Current common sense states that hybrids, when compared to open-pollinated cultivars, are a better adaptation strategy to cope with the impacts of climate change. However, the performance and resilience of cultivars with different levels of improvement are still not explored in this context. Four cultivars-a commercial hybrid, one commercially improved open-pollinated, one improved openpollinated derived from participatory breeding, and one from a farmer's selection-were tested using the CERES-Maize crop model. Field experiments conducted in Brazil were used for calibration and evaluation. Synthetic scenarios of climate change resulted from the application of the incremental method on historical series of observations (30 years), with temperature increments ranging from +0.5 up to +3.0°C and precipitation changes from −30 up to +30%. Planting dates consisted in nine dates (August 1-December 1, each 15 days).Results demonstrate that the model could mimic the phenology and yield of two improved open-pollinated cultivars (MPA01 and Fortuna) and the hybrid (AS1548). One openpollinated cultivar could not be validated due to its high phenotypic variability. Yield response surfaces showed distinct impacts among cultivars, with improved open-pollinated cultivar MPA01 having a higher yield stability when compared to the hybrid. Early planting dates produced lower yields with higher risk of crop failure for all cultivars. Late planting dates produced higher yields with higher failure risk. Considering risk and yield, the best planting window for all cultivars and scenarios is between September and October. Our results demonstrate, for the first time, that improved open-pollinated cultivars are equivalent or more resilient than hybrids to yield changes under different scenarios of abiotic stresses.

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Using pattern recognition for estimating cultivar coefficients of a crop simulation model

Cited by 101 publications

References 43 publications

A methodology for estimating phenological parameters of rice cultivars utilizing data from common variety trials

A methodology for estimating phenological parameters of rice cultivars utilizing data from common variety trials

Evaluation of AquaCrop Model for Foxtail Millet (Setaria italica) Growth and Water Use with Plastic Film Mulching and No Mulching under Different Weather Conditions

Yield stability and lower susceptibility to abiotic stresses of improved open-pollinated and hybrid maize cultivars

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