مدل‌سازی توزیع عمودی کربن آلی خاک و برآورد آن با رویکرد تلفیقی در گزینش بهینه متغیرها

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران.

2 گروه علوم و مهندسی آب، دانشکده کشاورزی، دانشگاه تبریز، تبریز، ایران.

چکیده

کربن آلی خاک (SOC) یکی از اجزای اصلی تعیین‌کننده حاصلخیزی خاک و چرخه جهانی کربن است و نقش مهمی در ترسیب کربن و کاهش نشر گازهای گلخانه‌ای دارد. با وجود تمرکز بیشتر مطالعات بر افق یا لایه سطحی خاک، سهم قابل‌توجه کربن در افق یا لایه زیرسطحی، بررسی توزیع عمودی SOC را ضروری می‌سازد. این پژوهش با هدف مدل‌سازی توزیع عمودی SOC در پروفیل‌های سه کاربری جنگل طبیعی، مرتع و زراعت در استان آذربایجان شرقی، با استفاده از رویکرد تلفیقی شامل آزمون گاما و مدل‌های هوش مصنوعی شامل شبکه عصبی مصنوعی (ANN-1 و ANN-2) و سامانه استنتاج عصبی–فازی تطبیقی (ANFIS) انجام شد؛ به‌طوری‌که 60 درصد داده‌ها به آموزش، 20 درصد به اعتبارسنجی و 20 درصد به آزمون اختصاص یافت. در این مطالعه، ویژگی‌های مورفولوژیکی و فیزیکی-شیمیایی ۶۰ پروفیل خاک انتخاب شده به‌طور تصادفی از سه کاربری مختلف به‌همراه متغیرهای اقلیمی (بارش و دما) بررسی گردید. نتایج نشان داد که میانگین SOC پروفیل‌ها در کاربری جنگل طبیعی (1.66 درصد) به‌طور معنی‌داری بیشتر از مرتع (0.43 درصد) و زراعت (0.35 درصد) بود. همچنین، مدل‌های ریاضی لگاریتمی و توانی با دقت بسیار بالا (0.93<r2) توانستند تغییرات عمودی SOC را در تمامی کاربری‌ها توصیف کنند. مدل توانی در کاربری جنگل‌ طبیعی و مدل لگاریتمی در کاربری مرتع برازش بهتری داشتند. بهترین متغیرهای ورودی برای برآورد SOC در آزمون گاما شامل بارش، رس، ظرفیت تبادل کاتیونی، عمق و کاربری اراضی بودند. در میان مدل‌های هوش مصنوعی، مدل ANN-2 مبتنی بر متغیرهای بهینه منتخب بهترین کارایی را داشت (0.867=r2 و 0.157=RMSE) که نسبت به مدل ANN-1 با ۱۱ متغیر ورودی، بهبود معناداری نشان داد (0.701= r2 و 0.228= RMSE) که بیانگر نقش مؤثر انتخاب بهینه متغیرها در افزایش دقت پیش‌بینی بود. مدل ANFIS نیز با وجود دقت برآورد کمتر (0.773= r2) نسبت به ANN-2، از دیدگاه مدیریتی می‌تواند ارزشمند باشد. در مجموع، الگوی توزیع عمودی SOC در کاربری‌ها و اقلیم‌های مختلف متفاوت بود. همچنین، به‌کارگیری آزمون گاما در انتخاب متغیرهای ورودی، دقت مدل‌های هوش مصنوعی را در برآورد SOC به‌طور قابل‌توجهی افزایش داد.

کلیدواژه‌ها

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