Abstract:
بهینهسازی مقدار و تقسیط کود نیتروژن میتواند سبب افزایش زمان دسترسی گیاه به این منبع غذایی پرمصرف شده و عملکرد و اجزای عملکرد گیاهان زراعی را افزایش دهد. در این پژوهش بهمنظور تعیین مصرف بهینه کود نیتروژن برای گیاه ذرت با استفاده از روش سطح-پاسخ، از دادههای مستخرج از طرح تحقیقاتی اجرا شده در مزرعه 500 هکتاری موسسه تحقیقات اصلاح و تهیه نهال و بذر در دو سال زراعی استفاده شد. تیمارهای مورد بررسی در طرح مذکور شامل مقدار کود نیتروژن در سه سطح (N1: 100 درصد نیاز کودی و N2:60 درصد نیاز کودی و N3: 50 درصد توصیه کودی)، زمان تقسیط کود به سه صورت (T1: دو تقسیط؛ T2: سه تقسیط و T3: چهار تقسیط) و روش آبیاری مزرعه، جویچهای بود. شاخصههای آماری مورد استفاده شامل جذر میانگین مربعات خطا (RMSE)، جذر میانگین مربعات نرمال شده (NRMSE)، میانگین خطای اریب (MBE)، کارایی مدل (EF)، شاخص توافق (d) و ضریب تبیین (R2) میباشد. نتایج نشان داد که مدل رگرسیونی مورد استفاده قابلیت پیشبینی صفات عملکرد، وزن هزار دانه، تعداد دانه در ردیف، تعداد ردیف در بلال، طول بلال و میزان روی دانه را داشت. لیکن این مدل برای همهی صفات دچار خطای کمبرآوردی (0 ≥ MBE) شد. دقت مدل رگرسیونی برای میزان روی دانه در دسته خوب (2/0≥ NRMSE≥1/0) و برای سایر صفات در دسته عالی (0.1≥ NRMSE≥0) قرار داشت. به جز تعداد ردیف در بلال، سایر صفات با افزایش مقدار کود و تقسیط آن افزایش یافتند. نتایج بهینهسازی کلیه صفات نشان داد که اگر نیاز کودی به صورت کامل (N1) و تعداد تقسیم به پنج نوبت افزایش یابد؛ مقدار عملکرد، وزن هزار دانه، تعداد دانه در ردیف، طول بلال و مقدار روی دانه بهترتیب 6، 9، 12، 5/18 و 6/19 درصد نسبت به مقادیر حداکثر این پارامترها افزایش خواهد یافت. بنابراین، اعمال این سناریو در مزرعه برای بهبود عملکرد و شاخصهای عملکردی مانند غلظت روی دانه ذرت پیشنهاد میگردد.
IntroductionCorn is one of the most widely consumed cereals in the world, which is highly compatible with many climates. For this reason, corn has been cultivated in most regions of the world since ancient times. The effect of nitrogen fertilizer, as an agricultural solution, on the growth and yield of corn has caused it to be splitted to increase the plant's access time to this nitrogen source. In fact, due to leaching of nitrogen fertilizer, it is usually not applied in one step. For this reason, based on the prevailing conditions of the field, the operators divide it into two or more divisions and perform nitrogen fertilization during the growth period. In each division, it is necessary to determine and apply the optimal amount of nitrogen fertilizer in order to minimize environmental pollution in addition to being economical. It requires many field experiments, which require a lot of time and spend money. To solve this problem, the use of simulation and optimization models, such as response surface modeling, is suggested. The response surface method is one of the suitable optimization tools that has been considered in various sciences for many years. The statistical basis of this method is very complex and uses a multi-objective nonlinear model for optimization and modeling. The response surface method first provides a suitable combination of treatments, and by considering them, a statistical model is created that has the best fit compared to other models. Next, the most optimal value is determined for the independent variables so that the value of the dependent variables reaches their maximum or minimum. Materials and MethodsFor this purpose, the data collected from a research project in a research project, which was carried out in the 500-hectare farm of the Seedling and Seed Research Institute in two years (2011-2012), were used. Two factors consisted of fertilizer in three levels (N1: 100 and N2: 60% and N3: 50% of fertilizer requirement) and the time of splitting into three methods (T1: the farmer's application with two splitting; T2: three equal divisions and T3: four equal divisions) was considered. The response surface method was used to optimize yield and yield components. In response surface method, the code of -1, zero and +1 for nitrogen indicates 50, 60 and 100 kg/ha of nitrogen fertilizer, respectively. The code of -1, zero and +1 for fertilizer spliting indicates the number of 2, 3 and 4 nitrogen fertilizer splitting during the growing season, respectively. In this method, to fit the data, multivariate regression was used by adding linear terms, quadratic and interaction between factors. Then, regression was evaluated based on analysis of variance. Results and DiscussionThe results of ANOVA showed that the linear and quadratic regression model for seed yield and the linear regression model for fertilizer efficiency were significant at the 5% probability level (P-value ≤0.05). For water productivity, the splitting factor had a greater effect on the regression than the amount of fertilizer, although both factors did not show a significant effect. The regression model had a significant effect on the 1000 seed weight, number of seeds in a row, number of rows in a cob, cob length and seed size. The regression of other parameters was not statistically significant. Therefore, the response surface method can be used to predict and optimize parameters with significant regression. The results showed that the regression model was capable of predicting parameters include: 1000 seed weight, number of seeds in a row, number of rows in a cob, corn length and seed zinc content. But this model had an underestimation error (MBE ≤ 0.0) for all parameters. The accuracy of the regression model for grain zinc content was in good category (0.1Conclusion In general, The optimization results of all parameters showed that if the fertilizer requirement is applied as N1 and in five splitting; the amount of yield, 1000 seed weight, the number of seeds in a row, the length of the cob and the amount of seed will increase by 6, 9, 12, 18.5 and 19.6% respectively compared to the maximum values of these parameters.Therefore, it is suggested to apply this scenario in the field to improve yield and yield criteria such as zinc concentration in corn seeds.