Using SSM-iCrop Model to Predict Phenology, Yield, and Water Productivity of Canola (Brassica napus L.) in Iran Condition

Document Type : Scientific - Research

Authors

• Samaneh Rahban ¹
• Benjamin Torabi ²
• Afshin Soltani ³
• Ebrahim Zeinali ³

¹ Department of Agronomy, Gorgan University of Agricultural Sciences and Natural Resources, Iran.

² Department of Agronomy, Gorgan University of Agricultural Sciences and Natural Resources, Golestan, Iran.

³ Department of Agronomy, Gorgan University of Agricultural Sciences and Natural Resources, Golestan, Iran

10.22067/jag.v13i2.84057

Abstract

Introduction
Due to limitation of water and soil resources resulted from geological and climatic conditions of Iran as well as necessity of self-reliance in infrastructural issues, efficient usage of water and soil resources available in Iran is inevitable. Climatic changes, reduced biodiversity in the region and concerns about food security are regarded as important issues; hence, evaluation of conditions to attain improved crop production seems essential. To investigate yield improvement methods, yield potential and yield limiting factors (climate, soil, water, and genetic factors) should be determined and evaluated in the first step. Simulation models may be used as scaled-up designs of field experiments to overcome limitations such as time and costs. Crop  simulation  models  are  mathematical representations  of  plant  growth  processes  as  influenced  by  interactions  among genotype,  environment and crop  management. Using crop simulation models can be an efficient complement to experimental research.  Models are being used to understand the response of crops to possible changes in crop, cultural management, and environmental variables. Crop models use various plant and environmental parameters to simulate crop growth and should be calibrated and evaluated before usage.
Materials and Methods
 SSM-iCrop model predicts phenological stages as a function of temperature, day length.  Calculation of phenological development in the model is based on the biological day concept. A biological day is a day with optimal temperature, photoperiod, and moisture conditions for plant development. Leaf  area  development  and  senescence  is  a  function  of  temperature,  provide nitrogen  for  leaf  growth,  plant  density  and  nitrogen  remobilization.  To  simulate leaf area expansion, the first step is to determine on each day the  increase in leaf number  on  the  main  stem  using  the  phyllochron  (temperature  unit between emergences of successive leaves) concept. In this model biomass is estimated as a function of the received radiation and temperature.  Daily  increase  of  crop  mass  is  estimated  as  the  product  of  incident photosynthetic  active  radiation  (PAR,  MJ  m^-2d^-1),  the  fraction  of  that  radiation
intercepted  by  the  crop  (FINT)  and  efficiency  with  which  the intercepted  PAR  is used to produce crop dry mass, i.e., radiation use efficiency (RUE, g MJ^-1).  Yield formation  in  the  model is simply  simulated  as  total  dry matter  production  during seed  filling  period  plus  a  fraction  of  crop  dry  mass at  BSG  (as  mobilized  dry matter).  Modeling  seed  growth  rate  and  yield  formation  in  the  current  model  is based on a modified linear increase in harvest index concept as described by Soltani and Sinclair (2011).
The model needs daily weather data, i.e. maximum and minimum temperatures, rainfall, and solar radiation. The model can be run under multiple scenarios/treatments over many years.
 Results
 As a result of the SSM-iCrop model parameterization, three early, medium and late maturing cultivars were determined for canola, which their cumulative degree days (GDD) for growth period completion were estimated as 2000, 2500 and 2700 °C days. After determination of the required parameters, the model was run based on sowing date, management, and meteorological statistics of the region using the data from the papers which were not used for parameterization, so as to validate the model. The average of the simulated data for days to maturity and yield were 222 (days) and 383 (g.m^-2), respectively, whereas observed values for this traits were 223 (days) and 359 (g.m^-2).
Conclusion:
 Based on the 1:1 line and statistics of r=0.87, CV=18% and RMSE=67.04 (g.m^-2) for grain yield and r=0.97, CV=5% and RMSE=10.68 (days) for days to maturity, it may be concluded that simulation canola growth using SSM-iCrop model has been satisfactory and indicates accurate estimation of the model parameters, as well as serving as a verification of the model efficiency in prediction of canola yield under climatic conditions of major canola production regions of Iran.

Keywords

• Evaluation
• Parameterization
• Plant models
• Simulation

20.1001.1.20087713.1400.13.1.10.9

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