اثر عوامل اقليمي و مديريتي بر پتانسيل و خلاء عملكرد گندم در ايران با استفاده از مدل WOFOST

Effect of Climate and Management Factors on Potential and Gap of Wheat Yield in Iran with Using WOFOST Model

در اكثر موارد عملكرد واقعي به دليل ضعف هاي مديريتي و فناوري كمتر از عملكرد پتانسيل مي باشد، كه اختلاف بين اين دو به عنوان خلاء عملكرد در نظر گرفته مي شود. درك سهم نسبي هر يك از متغيرهاي مديريتي در خلاء عملكرد مي تواند به عنوان يكي از مهمترين گام هاي كاهش آن و نزديك شدن عملكرد واقعي به عملكرد پتانسيل باشد. به منظور ارزيابي ميزان خلاء عملكرد گندم و همچنين سهم نسبي متغيرهاي مديريتي و فناوري در ميزان اين خلاء در ايران، از تابع حد مرزي كه يك رگرسيون چند متغيره مي باشد، استفاده شد. براي اين منظور داده هاي اقليمي استان هاي مختلف كشور از سازمان هاي هواشناسي كشور و عملكرد گندم در استان هاي مختلف از سازمان هاي جهاد كشاورزي كشور استخراج شد. عملكرد پتانسيل گندم در استان هاي مختلف نيز با استفاده از مدل WOFOST شبيه سازي شد. از اختلاف بين عملكرد پتانسيل و عملكرد واقعي، خلاء عملكرد گندم براي استان هاي مختلف برآورد شد. سپس با استفاده از تابع حد مرزي سهم نسبي متغيرهاي اقليمي مؤثر در عملكرد پتانسيل و همچنين سهم نسبي هر يك از متغيرهاي مديريتي مؤثر شامل آبياري، كاربرد كود، مكانيزاسيون، كاربرد آفت كش و كود دامي بر خلاء عملكرد گندم محاسبه شد. نتايج نشان داد كه به طور كلي در بين عوامل اقليمي اثر بارندگي و تابش بر عملكرد پتانسيل گندم مثبت و اثر دما منفي بود. در بين اين متغيرها نيز بارندگي بيشترين تأثير را بر عملكرد پتانسيل گندم دارا بود. محدوده خلاء عملكرد گندم در ايران بين 1646 تا 4470 كيلوگرم در هكتار و 29 تا 58 درصد متغير بود. به طور كلي تأثير كليه عوامل مديريتي بر ميزان خلاء عملكرد گندم كشور منفي بود كه بدين ترتيب با بهبود يا افزايش هر كدام از اين فاكتورها، ميزان خلاء عملكرد گندم در كشور كاهش نشان داد. از بين عوامل مديريتي مورد بررسي، عامل آبياري بويژه در استان هاي واقع در اقليم گرم و خشك كشور و بعد از آن نيز عامل كاربرد كود شيميايي بيشترين تأثير را بر كاهش خلاء عملكرد گندم نشان دادند. بنابراين، به نظر مي رسد براي كاهش خلاء عملكرد گندم در ايران بايد در وهله اول مديريت آبياري و كاربرد كود را مدنظر قرار داد.

Introduction: Human diets strongly rely on wheat (Triticum aestivum L.). Its production has increased dramatically during the past 50 years, partly due to area extension and new varieties but mainly as a consequence of intensified land management and introduction of new technologies. For the future, a continuous strong increase in the demand for agricultural products is expected. It is highly unlikely that this increasing demand will be satisfied by area expansion because productive land is scarce and also increasingly demanded by nonagricultural uses. The role of agricultural intensification as key to increasing actual crop yields and food supply has been discussed in several studies. However, in many regions, increases in grain yields have been declining Inefficient management of agricultural land may cause deviations of actual from potential crop yields: the yield gap. At the global scale little information is available on the spatial distribution of agricultural yield gaps and the potential for agricultural intensification. Actual yield is mostly lower than potential yield due to inefficient management and technological that difference between these yields is considered as yield gap. Understanding of relative share of every management factors in yield gap could be as one of the important keys to reduce gap and close actual yield to potential yield. Materials and Methods: In order to evaluate the amount of wheat yield gap and also relative share of management and technological variables in yield gap, frontier production function was used which is a multivariable regression. The frontier production function to be estimated is a Cobb-Douglas function as proposed by Coelli et al. (2005). Cobb-Douglas functions are extensively used in agricultural production studies to explain returns to scale. We propose a methodology to explain the spatial variation of the potential for intensification and identifying the nature of the constraints for further intensification. We estimated a stochastic frontier production function to calculate global datasets of maximum attainable grain yields, yield gaps, and efficiencies of grain production at. Applying a stochastic frontier production function facilitates estimating the yield gap based on the actual grain yield data only, instead of using actual and potential grain yield data from different sources. Therefore, the method allows for a robust and consistent analysis of the yield gap. The factors determining the yield gap are quantified at both global and regional scales. For this purpose, climatic information and wheat yield of different provinces were obtained from Iran meteorological organization and Agriculture Jahade organization, respectively. Wheat potential yield in different provinces was simulated by WOFOST model. Wheat gap was gained by difference between actual and potential yield in different provinces. Relative share of climatic variables in potential yield and also relative share of management variables included irrigation, fertilizer application, mechanization, pesticide application and manure in wheat yield gap was calculated by frontier production function. Results and Discussion: The results showed that the effect of precipitation and radiation on wheat potential yield was positive and the impact of temperature was negative. Precipitation had the highest impact on wheat potential yield among other climatic variables. The range of wheat yield gap was from 1646 to 4470 kg ha-1 and 29 to 58% in Iran. Generally, the effect of all management variables on wheat yield gap was negative so that wheat yield gap was reduced by improving of these variables. Among studied management variables, irrigation had the highest effect on yield gap reduction, especially in dry-warm climate and fertilizer application was the second factor which had high effect on yield gap reduction. Therefore, to reduce wheat yield gap in Iran,irrigation management and fertilizer application should be considered. Conclusions: Between studied climate variables, the relative contribution of temperature and rainfall was higher on wheat yield potential compared with radiation in all provinces except the province of Zanjan, Golestan, Gilan and Mazandaran. The highest gap yield (4470 kg ha-1) was assigned to Ilam and Mazandaran provinces. Irrigation and fertilizer application were the more affective variables in yield gap induction.