نوع مقاله : پژوهشی
نویسندگان
1 دانشجو/دانشگاه علوم کشاورزی و منابع طبیعی گرگان
2 استاد دانشگاه علوم کشاورزی و منابع طبیعی گرگان
3 عضو هیئت علمی مرکز تحقیقات گرگان
4 دانشجوی دانشگاه علوم کشاورزی و منابع طبیعی گرگان
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
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.
While there are many simulation models for soybean, SSM-iLegume model was preferred for the following reasons:
(1) Other models are not adequately transparent. A suitable model should have specific and obvious parameters, figures and codes.
(2) Many models use many equations and parameters for crop key process. In some cases, models are so complex that aspects of their structure and performance are not clear even to members of the modeling team. Adding complexity within a model does not necessarily move the model closer to reality. In fact, it is quite likely that including hypotheses without extensive experimental justification can easily increase the imperfection of the model. Users have to use these models as ‘black-boxes’ without a clear understanding of the model structures and limitations. Complex models need considerable input data that may not be easily available.
(3) Sometimes software of the model is not flexible, simple and obvious, and in many cases, expertise person should help for running the model.
(4) Many models include one or more parameters with opaque meaning. We believe all crop and cultivar parameters should have a clear meaning and should be directly measureable.
The objectives of this study were to describe a soybean model (SSM-iLegume), determine the genetic coefficients of soybean cultivar in Gorgan and finally report results of the model evaluation.
SSM-iLegume 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.
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-2 d-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). 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, can be downloaded from https://sites.google.com/site/afshinsoltani/-9-down. The model needs daily weather data, i.e. maximum and minimum temperatures, rainfall and solar radiation. The model can be run for multiple scenarios/treatments over many years.
Field data were used for coefficient estimation and model evaluation. After estimation of genetic parameters, the model was tested using independent data and indicated an acceptable performance and predictions for important crop variables as compared to observed data including days to flowering (RMSE=5.8, CV=11%) and maturity (RMSE=8.7, CV=6%), main stem node number (RMSE=1.7, CV=13%) and grain yield (RMSE=48, CV=15%).
The results indicate that an acceptable estimate for different variables was obtained. So, the model can be used in simulation studies of soybean yield and its limitations in response to environmental conditions, management inputs and genetic factors.
کلیدواژه‌ها [English]
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