پیش بینی رقومی مکانی عملکرد گندم دیم (مطالعه موردی: حوضه آبخیز بدر، شهرستان قروه، استان کردستان)

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهرکرد، شهرکرد، ایران

2 دانشیار گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه شهرکرد، شهرکرد، ایران

3 استادیار رشته علوم خاک، دانشکده کشاورزی و منابع طبیعی دانشگاه اردکان، اردکان، ایران

چکیده

گندم یکی از غلات کلیدی است که منبع تغذیه برای میلیون­ها نفر از مردم جهان را فراهم می­کند. با توجه به جمعیت رو به افزایش کشور و نیاز روز افزون به این محصول استراتژیک، در تحقیق حاضر، عوامل مؤثر بر عملکرد گندم دیم به ترتیب اهمیت و کارایی روش­های مختلف برآورد تغییرات مکانی این عملکرد مورد بررسی قرار گرفت و نقشه تغییرات مکانی آن ترسیم شد. تعداد 125 عملکرد اندازه­گیری­شده در حوضه آبخیز بدر شهرستان قروه در استان کردستان، با استفاده از مدل‌های شبکه عصبی مصنوعی، تحلیل درخت تصمیم، آنالیز تشخیصی و مدل میانگین­گیری نزدیک­ترین همسایه K به داده­های کمکی (مستخرج از مدل رقومی ارتفاع، تصویر ماهواره لندست و نقشه ژئومرفولوژی) ارتباط داده شد. سپس با استفاده از معادله به‌دست آمده برای نقاط فاقد مشاهده، میزان عملکرد برای آن نقاط برآورد و نقشه تغییرات مکانی عملکرد پیش­بینی­شده ترسیم گردید. الگوریتم ReliefAttributeEval در نرم افزار وکا، به ترتیب کاهش اهمیت، ژئومورفولوژی، موقعیت نسبی شیب، انحنای طولی، شاخص همواری قله برآمدگی با درجه تفکیک بالا، شیب، شاخص گیاهی تفاضلی نرمال شده و شاخص گیاهی تعدیل کننده اثر خاک را به عنوان مهم­ترین عوامل مؤثر در تولید، شناسایی کرد. نتایج بررسی­ها نشان داد که مدل میانگین­گیری نزدیک­ترین همسایه K که یک روش جدید در برآورد مکانی عملکرد می­باشد، با بالاترین ضریب تبیین، یعنی 998/0= R2 و کم‌ترین میانگین ریشه مربعات خطا (408/31 = RMSE) بهتر از سایر مدل­های به کار رفته در این پژوهش، مقادیر عملکرد گندم دیم را پیش­بینی نمود.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Digital Spatial Prediction of Rainfed Wheat Yield (Case Study: Badr Watershed, Qorveh, Kurdistan Province)

نویسندگان [English]

  • Moslem Zarrini Bahador 1
  • Javad Givi 2
  • Ruhollah Taghizadeh Mehrjerdi 3
1 Ph.D. Student, Orientation of Soil Genesis and Classification and Land Suitability Evaluation, Soil Sciences and Engineering Department, College of Agriculture, Shahrekord University, Shahrekord, Iran
2 A member of scientific staff, Soil Science and Engineering Department, College of Agriculture, Shahrekord University, Shahrekord, Iran
3 Associate Professor, Orientation of Soil Genesis and Classification, Field of Soil Sciences, Ardakan University of Agriculture and Natural Resources, Ardakan, Iran
چکیده [English]

IntroductionWheat is one of the key cereals that provides a nutrition source to millions of people around the world. By conducting applied studies, the limitations of soil and climate that reduce the yield per unit area must be understood and solutions should be provided to address these limitations. One of these strategies is a detailed study and spatial prediction of yield at points with different soil and climate characteristics. Models that predict crop yield can estimate the yield regarding climate, landscape, soil and management constraints. Considering the arid and semi-arid climate of Iran, the shortage of yield per unit area and the growing population, the country needs new research and strategies to increase yield per unit area. For this purpose, the first step is to examine the spatial variations of the yield. In the present study, the factors affecting the rainfed wheat yield in order of importance and efficiency of different methods of estimating spatial variations were investigated and the predicted yield of this crop was mapped digitally.
Materials and MethodsThe study area, with an area of 6700 hectares is located in Badr watershed, around Ghorveh city, Kurdistan province, west of Iran. The mean annual air temperature is 12.1oC and the average annual precipitation is 345.8 mm. The soils of the area were classified in the orders of Entisols, Inceptisols and Mollisols and in 32 soil families, according to the last version of Keys to Soil Taxonomy. Based on hypercube technique, 125 observation points were selected, soil profiles were dug and described at these points and soil samples were collected from horizons of the profiles. Some physical and chemical characteristics of the soils were determined according to the standard laboratory methods. Rainfed wheat yield was measured at each side of one soil profile in a 1m×1m quadrangle. In the present study, in addition to geomorphological data, different types of auxiliary variables such as some of the primary and secondary derivatives of digital elevation model (DEM) and Landsat satellite image data were used. To find out the affecting auxiliary topographic and plantcover data on rainfed wheat yield prediction in order of importance,ReliefAttributeEval algorithm of WEKA software was used. Artificial neural network, decision tree Analysis, discriminant analysis, and averaging k-nearest neighbors are the models that were used in this research for prediction of rainfed wheat yield.
Results and Discussion Calcium carbonate, organic carbon and coarse fragments, respectively with variability coefficients of 174.4, 62.4 and 61.3%, had the highest variation and pH, CEC and sand, respectively with 3.6, 16.9 and 20.3% variability coefficients showed the least variability in the soils of the studied area. In addition to geomorphological data, the parameters that were taken from the digital elevation model include elevation, slope percentage, slope aspect, slope curvature, slope surface curvature, longitudinal curvature, slope relative position, wetness index, multiresolution valley bottom flatness index, multiresolution ridge top flatness index, valley depth, channel network base level, modified catchment area, catchment slope, catchment slope aspect and catchment height. The environmental parameters that were taken from the Landsat 8 satellite imagery, include the normalized differential vegetation and the soil-adjusted vegetation indices. The ReliefAttributeEval algorithm in Weka software, in order of decreasing importance, identified geomorphology, relative slope position, longitudinal curvature, multi-resolution ridge top flatness index, slope, normalized differential vegetation index and soil-adjusted vegetation index as the most important factors affecting rainfed wheat production in the studied area. The amount of rainfed wheat yield was predicted by the models of artificial neural network, decision tree analysis, discriminant analysis, and averaging k-nearest neighbors. The error criteria for this prediction and a significant correlation between measured and estimated values of the rainfed wheat yield, indicate a higher accuracy for the averaging k-nearest neighbors model, compared to other models. The spatial distribution of the rainfed wheat yield, predicted by the averaging k-nearest neighbors model, was mapped. In the Badr watershed, the yields are continuously reduced towards the mountains. In this landscape, as the slope increases, depth and water storage capacity of the soil decrease mainly in the presence of Entisols. These soils are seen in the eastern, southern and western parts of the watershed. At lower elevations, the soils are deeper and are mainly Inceptisols. Rainfed wheat yield increases in the piedmont landscape, including hill, glacie and alluvial fan.
Conclusion In order of decreasing importance, geomorphology, relative slope position, longitudinal curvature, multi-resolution ridge top flatness index, slope, normalized differential vegetation index and soil-adjusted vegetation index are the most important factors affecting rainfed wheat production in the studied area. The averaging k-nearest neighbors model has a higher accuracy for rainfed wheat yield prediction, compared to other models. In the Badr watershed, the rainfed wheat yield is continuously reduced towards the mountains in the eastern, southern and western parts, where mainly Entisols are present. The yield increases in the Inceptisols, located on the piedmont landscape.

کلیدواژه‌ها [English]

  • Digital mapping
  • Spatial yield prediction
  • KNN averaging model
  • Rainfed wheat
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