بوم شناسی کشاورزی (May 2021)
Using SSM-iCrop Model to Predict Phenology, Yield, and Water Productivity of Canola (Brassica napus L.) in Iran Condition
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.
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