بررسی روابط بین عملکرد گل محمدی و ویژگی‌های خاک و توپوگرافی با روش‌های رگرسیون چندمتغیره، شبکه عصبی مصنوعی و مدل‌سازی روابط ساختاری

نوع مقاله: مقاله پژوهشی

نویسندگان

1 گروه خاک‌شناسی، دانشگاه شهرکرد

2 عضو هیئت علمی گروه خاک‌شناسی، دانشگاه شهرکرد

3 پیدایش و رده بندی خاک، پدومتری گروه خاک‌شناسی، دانشگاه ولی عصر (عج) رفسنجان

4 هیئت علمی گروه خاک‌شناسی، دانشگاه شهرکرد

چکیده

چکیده
با توجه به ارتباط بین عملکرد محصول و ویژگی­های خاک و توپوگرافی زمین، شـناخت و آگاهی از ویژگی­ها برای دستیابی به توسعه پایدار در کشاورزی، ضروری است. پژوهش حاضر با هدف ارزیابی و تعیین رابطه بین عملکرد گل محمدی و ویژگی‌های خاک و توپوگرافی زمین با استفاده از مدل‌های رگرسیون خطی چندمتغیره، شبکه عصبی مصنوعی و روش مدل‌سازی معادلات ساختاری در شهرستان بردسیر، استان کرمان اجرا گردید. برای این منظور، نمونه­برداری از خاک و عملکرد محصول، در قالب یک الگوی شبکه­ای منظم صورت گرفت. همچنین، با تهیه مدل رقومی ارتفاع منطقه، برخی ویژگی‌های توپوگرافی زمین محاسبه گردید و برای اجرای مدل روابط ساختاری، سه مدل نظری طراحی و مورد آزمون قرار گرفت. نتایج نشان داد که مدل­های رگرسیون خطی چندمتغیره و شبکه عصبی مصنوعی به‌ترتیب، 68 و 87 درصد از تغییرپذیری عملکرد را توجیه می­کنند که نشان‌دهنده دقت بالاتر مدل شبکه عصبی مصنوعی نسبت به رگرسیون خطی چندمتغیره در تخمین عملکرد می­باشد. نتایج مدل‌سازی معادلات ساختاری نشان داد که کنترل عملکرد گل محمدی در این منطقه، بیشتر در اختیار ویژگی­های شیمیایی خاک، سپس ویژگی­های توپوگرافی زمین و ویژگی­های فیزیکی خاک قرار دارد. سناریوهای مختلف برای انجام مدل‌سازی معادلات ساختاری نشان داد که هر چقدر مدل طراحی‌شده ساده‌تر و دارای سازه‌های پنهان کمتری باشد، می‌تواند برازش مطلوب‌تری داشته باشد. بنابراین، اولین مدل مفهومی این روش با دارا بودن مقادیر جذر میانگین مربعات خطا، شاخص نیکویی برازش و شاخص برازش تطبیقی به‌ترتیب 033/0، 88/0 و 94/0، به­عنوان بهترین مدل انتخاب شد. نتایج کلی نشان داد که مدل شبکه عصبی مصنوعی به‌دلیل لحاظ کردن روابط غیرخطی بین عملکرد و عوامل تأثیرگذار بر آن، کارایی بهتری نسبت به رگرسیون چندمتغیره در تخمین عملکرد داشت. علاوه بر توانایی مدل شبکه عصبی در برآورد عملکرد محصول، مدل‌سازی روابط ساختاری نشان داد که روش اخیر نیز می­تواند توضیحات بیشتری در مورد روابط و تعامل­های همزمان بین متغیرها ارائه کند. به‌طور کلی، کاربرد روش مدل‌سازی روابط ساختاری، با تکیه بر توانایی‌های این روش می‌تواند زمینه ارتقای عملکرد محصولات مختلف را فراهم کند.

کلیدواژه‌ها


عنوان مقاله [English]

Assessment of Relationships between Rose Yield and Soil and Topography Properties Using Multivariate Regression, Artificial Neural Network and Structure Equation Modeling

نویسندگان [English]

  • Morteza Bahmani 1
  • Jahangard Mohammadi 2
  • Isa Esfandiarpour Boroujeni 3
  • Hamidreza Mottaghian 4
1 Dept. of Soil Science, Shahrekord University
2 Professor, Dept. of Soil Science, Shahrekord University
3 Associate Prof, Dept. of Soil Science, Vali-e-Asr Univ. of Rafsanjan
4 Assistant Prof, Dept. of Soil Science, Shahrekord University
چکیده [English]

Abstract
Due to the relationship between crop yield, and soil characteristics and land topography, knowledge and awareness of these characteristics is necessary to achieve sustainable development in agriculture. This study was performed to evaluate and determine the relationships between Rose yield (Rosa Damasceneea Mill) with soil properties and land topography by using Multivariate Linear Regression models (MLR), Artificial Neural Network (ANN) and Structural Equation Modeling (SEM) in Bardsir City, Kerman Province. For this purpose, soil sampling and crop yield were performed in the form of a regular grid pattern. Besides, some topographic features of the land were calculated using digital elevation model (DEM) of the region, and to implement the conceptual models, three theoretical models were designed and tested. The results showed that MLR and ANN models were able to justify 68 and 87 % of the yield variability, respectively, which indicates the higher accuracy of ANN model than MLR in yield estimation. The results of SEM illustrated that Rose yield is mainly controlled by soil chemical properties, topographic features, and soil physical properties, respectively. Different scenarios for SEM showed that simpler models with fewer hidden structures could have a better fitting. Therefore, the first conceptual model of this method with the values of root mean square error, goodness of fit index and comparative fit index of 0.033, 0.88 and 0.94, respectively, was selected as the best model. The overall results showed that the ANN model was more efficient than MLR in yield prediction due to consideration of the nonlinear relationship between crop yield and the factors affecting it. In addition to the ability of the ANN model to estimate crop yield, the SEM also showed that the latter method can provide more explanations about the relationships and simultaneous interactions between variables. In general, the application of SEM method, relying on the capabilities of this method, can improve the yield of various crops.

کلیدواژه‌ها [English]

  • Bardsir of Kerman
  • Comparative fit index
  • digital elevation model
  • Goodness fit index
  • Sustainable agriculture
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