تخمین مکانی ماده آلی خاک با داده‌های نامطمئن و کمکی خاکی و روش آنتروپی حداکثر اریب

نوع مقاله: مقاله پژوهشی

نویسندگان

1 پیداش رده بندی و تخمین مکانی

2 دانشگاه مراغه

چکیده

ماده آلی خاک یکی از مهمترین فاکتورهای کیفیت خاک می­باشد و آگاهی از وضعیت آن در خاک، از مهمترین اقدامات در جهت مدیریت منابع اراضی است. با توجه به هزینه و زمان زیادی که در جهت پایش ماده آلی خاک نیاز است، هر روشی که بتواند با حداقل نمونه استفاده از هر نوع داده­های خاکی، بهترین نقشه­های ماده آلی را تولید کند، قدمی در راستای کشاورزی پایدار خواهد بود. هدف از انجام این تحقیق، تخمین مکانی ماده آلی خاک با استفاده از اندازه­گیری­های با دقت پایین ماده آلی، داده­های کمکی خاکی و روش آنتروپی حداکثر اریب (BME) بود. نمونه­برداری از 122 نقطه و از عمق 20-0 سانتی­متری از دشت بناب و میاندوآب صورت گرفت. سپس، ماده آلی خاک به روش والکلی و بلاک و نیز به شکل تسهیل شده آن، تعیین گردید. همچنین، برخی از پارامترهای خاکی از جمله بافت، پایداری خاکدانه­ها و آهک نیز در نمونه­ها اندازه­گیری شدند. تخمین مکانی ماده آلی با داده­های نامطمئن، مدل خطی توسعه یافته با استفاده از داده­های کمکی خاکی و روش BME انجام شد. براساس نتایج، بالاترین R، کمترین RMSE و nRMSE با مقادیر به ترتیب 97/0،  07/0 درصد و 06/0 متعلق به تخمین مکانی ماده آلی با داده­های نامطمئن ماده آلی با درنظر گرفتن خطا بود. همچنین استفاده از مدل خطی توسعه یافته با داده­های کمکی خاکی و وارد کردن خطای این داده­ها در معادلات تخمین، منجر به بهبود تخمین نسبت به زمانی گردید که از خطا استفاده نشده بود (R، RMSE و nRMSE به ترتیب از 65/0، 58/0 و 55/0 به 85/0، 31/0 و 29/0 بهبود یافت). با توجه به نتایج حاصل از این تحقیق، روش BME امکان استفاده از طیف وسیعی از اطلاعات خاکی را در تخمین فراهم می­آورد و با  ادغام خطای ناشی از استفاده از داده­های نامطمئن در تخمین، منجر به بهبود تخمین مکانی ماده آلی گردید.

کلیدواژه‌ها


عنوان مقاله [English]

Spatial Prediction of Soil Organic Matter with Soft and Axillary Data Using Bayesian Maximum Entropy Method

نویسندگان [English]

  • nikou Hamzehpour 1
  • sara mola ali abasiyan 2
1 هیئت غلمی
چکیده [English]

Soil organic matter (SOM) is one of the important soil quality factors and knowledge of its condition in soil, is one of the most important steps in the management of land resources and controlling soil losses. As SOM monitoring is an expensive and time-consuming task, any method which can produce high quality maps of SOM with available axillary soil data and less samples, would be a step forward in reaching the goals of sustainable agriculture. The aim of this research is to predict SOM using soft data, auxiliary data and Bayesian maximum entropy method (BME). Soil samples were gathered from the Bonab-Miandoab plain, and almost 122 samples were collected from 0-20 cm depth of surface soil. SOM and some other soil properties including soil texture, aggregate stability, and calcium carbonate equivalent were measured. Later spatial prediction of SOM was done using SOM soft data, auxiliary data and generalized linear model (GLM) using BME method. Results showed that the highest R, lowest RMSE and nRMSE with values of 0.97, 0.07 and 0.12 respectively, belonged to spatial prediction of SOM with soft data and error. Results also revealed that the developed GLM model with calculated error, resulted in better R, RMSE and nRMSE in comparison to predictions with GLM model without error (R, RMSE and nRMSE improved from 0.65, 0.58 and 0.55 to 0.85, 0.31 and 0.29 respectively). As a conclusion, BME method has provided the possibility of merging error resulted from the use of soft data, in spatial prediction equations and through that, has helped to improve spatial prediction of SOM.

کلیدواژه‌ها [English]

  • Auxiliary data
  • error
  • modified Walkley and Black method
  • Soil texture
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