پژوهشهای زراعی ایران (Mar 2023)
Quinoa Phenological Development Modeling Based on Field Data
Abstract
IntroductionClimate change is rapidly degrading the conditions of crop production. For instance, increasing salinization and aridity is forecasted to increase in most parts of the world. As a consequence, new stress-tolerant species and genotypes must be identified and used for future agriculture. Stress-tolerant species exist but are actually underutilized and neglected. Quinoa, scientifically known as Chenopodium quinoa Willd. is a member of the Amaranthaceae family. Promoting the cultivation and nutrition of quinoa will diversify food products in the country, sustainable production, increase farmers' incomes and provide part of the community's food needs. Crop simulation models have been used for various studies such as selecting the appropriate cultivar, determining the best planting date, predicting the effect of diversity and climate change on growth. Field research requires a lot of time and money, while computer simulation models can save time and money by conducting extensive experimental simulations.Materials and MethodsThis research was conducted in two regions of Yazd province with 10 separate experiments in the form of a randomized complete block design with 3 replications. Experimental factors included 5 promising modified lines in Yazd Salinity Research Center with Titicaca cultivar. The lines consisted of four intermediate maturity lines, numbered 1 (NSRCQE), 2 (NSRCQC), 3 (NSRCQD), and 6 (NSRCQA), one late maturity line numbered 4 (NSRCQB), and the early maturity cultivar Titicaca numbered 5. Sampling and note-taking were performed regularly, once every three days, in proportion to the progress of the phenological stages of each line. A model based on degree-day-growth was prepared in FST language. In preparing the length table, due to the short day of Quinoa, for all lines in the model up to 12.5 hours, the development rate was one, and after 13.8 hours, the development rate was zero. The base temperature in the model was 2 °C. Then, the model was calibrated and evaluated with data taken from the field.Results and DiscussionRMSE (CV) coefficient of variation between 7 to 12%, root mean square error (RMSE) between 4.4 to 6.4 days, Wilmot agreement index (d) between 0.99 to 1, model efficiency (ME) between 0.96 to 0.98, the mean deviation from the model (MB) was between 0.05 to 0.08 and the coefficient of determination (R2) was between 92 % to 98%. These values indicated a good estimate of the day to flowering of quinoa with the model written in FST language, and the values of day to flowering simulated gained the necessary validity. The coefficient of variation of nRMSE (CV) is between 6.8 to 8.6%, the root mean square error (RMSE) is between 6.2 and 8.7 days, the Wilmot agreement index (d) is between 0.75 and 0.92, The mean deviation from the model (MB) was between 0.05 to 0.08 and the coefficient of determination (R2) was between 92% and 98%. These values indicated a good estimate of the day to physiological maturity with the model written in the FST language, and the day values to the simulated physiological maturity gained the necessary validity. Calibration and evaluation of model efficiency using root mean square error (RMSE), coefficient of variation or nRMSE percentage (CV), Wilmot agreement index (d), model efficiency (ME), and mean model deviation (MB), coefficient of explanation (R2), line test 1: 1 day until germination, flowering and good physiological maturity was estimated.ConclusionThe results of this study indicated that, the quinoa model prepared for quinoa in terms of degree-day-growth well predicts the developmental stages (emergence, flowering and maturation) of this plant in terms of maturity (early, medium and late) and can be its help determined the appropriate planting date in different areas. This calibrated sub-model can now be used to evaluate different temperature and photoperiod effects for decision making in a wide range of growth environments in quinoa cultivation systems in current and future climatic conditions. Therefore, this sub-model can be used in educational-research and applied work in the field.
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