پیش‌بینی برخی از خصوصیات شیمیایی خاک مراتع تحت شرایط قرق و چرا شده با استفاده از سیستم استنتاج فازی- عصبی تطبیقی در مراتع قوشچی ارومیه

سوری, مهشید; افتخاری, علیرضا; قربانی, ژیلا; کمالی, نادیا

doi:10.22059/jrwm.2022.342436.1658

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ استادیار پژوهشی، بخش تحقیقات مرتع، موسسه تحقیقات جنگلها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران

² استادیار پژوهشی، بخش تحقیقات مرتع، موسسه تحقیقات جنگلها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران.

³ دانشجوی دکتری مرتعداری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ساری، ایران

10.22059/jrwm.2022.342436.1658

چکیده

خاک مهمترین جزء تشکیل‌دهنده زیست‌بوم‌های مرتعی بوده و به واسطه حفظ آن و خصوصیاتش می‌توان با سهولت بیشتری به احیاء پوشش گیاهی با صرف کمترین هزینه و زمان اقدام نموده و از کاهش توان تولید مراتع پیشگیری نمود. در تحقیق حاضر به بررسی میزان پتاسیم و فسفر موجود در خاک مراتع قوشچی ارومیه واقع در استان آذربایجان غربی از سال 1397 الی 1400 تحت تأثیر شرایط قرق و چرا شده پرداخته شد. به ‌علاوه، توسعه و ارزیابی مدل استنتاج فازی- عصبی تطبیقی (انفیس) به منظور پیش‌بینی میزان پتاسیم و فسفر خاک و مقایسه نتایج آن با مدل رگرسیونی ارائه گردید. برای ارزیابی مدل‌های رگرسیونی و انفیس از مجذور میانگین مربعات خطا (RMSE) و ضریب تبیین (R2) استفاده شد. نتایج تجزیه واریانس داده‌ها نشان داد که سال‌های متفاوت و شرایط تحت قرق و تحت چرا اثر معنی‌داری بر میزان پتاسیم و فسفر موجود در خاک داشته اما اثر متقابل آنها بی‌معنی بود. بیشترین میزان پتاسیم خاک مربوط به سال 1400 و شرایط تحت چرا می‌باشد. درحالیکه بیش‌ترین میزان فسفر خاک مربوط به سال 1398و شرایط قرق بود. در بخش مدل‌سازی فاکتور فسفر، مدل انفیس با دقت بالاتر (59/0R2=) و خطای کمتر (0187/0RMSE=) نسبت به مدل کم دقت‌تر رگرسیونی (38/0R2=) با خطای بیشتر (089/0RMSE=) توانست مقدار فسفر را پیش بینی نماید. در مورد فاکتور پتاسیم نیز، مدل انفیس با دقت بالاتر (62/0R2= و خطای کمتر (017/0RMSE=) نسبت به مدل کم دقت‌تر رگرسیونی (42/0R2=) با خطای بیشتر (097/0 RMSE=) توانست میزان پتاسیم خاک را پیش‌بینی نماید.

کلیدواژه‌ها

20.1001.1.50442008.1401.75.3.9.7

عنوان مقاله [English]

Prediction of some chemical properties of rangeland soils under exclosure and grazed conditions using adaptive fuzzy-neural inference system in Ghoshchi rangelands of Urmia

نویسندگان [English]

mahshid souri ¹
alireza eftekhari ²
Zhila Ghorbani ³
nadia kamali ¹

¹ Assistant Professor, Rangeland Research Division, Research Institute of Forests and Rangelands, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.

² Assistant professor, range research Division Research Institute of Forests and Rangelands, Agricultural and Natural Resources Research and Education Center AREEO, Tehran, Iran

³ PhD student of rangeland, Faculty of Natural Resources, Sari Agricultural and Natural Resources University, Sari, Iran

چکیده [English]

Soil is the most important component of rangeland ecosystems and by preserving it and its characteristics, In the present study, the amount of potassium and phosphorus in the soil of Ghoshchi rangelands of Urmia located in West Azerbaijan province from 2019 to 2021 under the influence of grazing and grazing conditions was investigated. In addition, the development and evaluation of an adaptive fuzzy-neural inference model (ANFIS) was presented in order to predict the amount of potassium and phosphorus in the soil and compare its results with the regression model. The mean squared error (RMSE) and the coefficient of explanation (R2) were used to evaluate the regression and inference models. The results of analysis of variance showed that different years and conditions under confinement and grazing had a significant effect on the amount of potassium and phosphorus in the soil, but their interaction was meaningless. The highest amount of soil potassium is related to the year 2021 and the conditions under grazing. While the highest amount of soil phosphorus was related to 2020. In the phosphorus factor modeling section, the ANFIS model with higher accuracy (R2 = 59.5) and less error (RMSE = 0.087) than the regression model (R2=0.38) with more error (RMSE = 0.089) was able to determine the amount of P to predict. Regarding potassium factor, ANFIS model with higher accuracy (R2 = 0.62 and less error (RMSE = 0.017) than regression model (R2 = 0.42) with more error (RMSE = 0.097) was able to measure soil potassium.

کلیدواژه‌ها [English]

modeling
ANFIS
regression
potassium
phosphorus

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نشریه علمی - پژوهشی مرتع و آبخیزداری

پیش‌بینی برخی از خصوصیات شیمیایی خاک مراتع تحت شرایط قرق و چرا شده با استفاده از سیستم استنتاج فازی- عصبی تطبیقی در مراتع قوشچی ارومیه

مراجع

مراجع

دوره 75، شماره 3
آذر 1401
صفحه 471-484

پیش‌بینی برخی از خصوصیات شیمیایی خاک مراتع تحت شرایط قرق و چرا شده با استفاده از سیستم استنتاج فازی- عصبی تطبیقی در مراتع قوشچی ارومیه

مراجع

مراجع

دوره 75، شماره 3آذر 1401صفحه 471-484

دوره 75، شماره 3
آذر 1401
صفحه 471-484