مقایسه روش‎های درون‎یابی زمین‎آماری (کریجینگ) برای تخمین شوری خاک و عملکرد گندم در مزرعه نمونه ارتش آق ‏قلا

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی ثابق کارشناسی ارشد کشاورزی اکولوژیک

2 دانشیار گروه زراعت، دانشکده علوم گیاهی، دانشگاه علوم کشاورزی و منابع طبیعی گرگان

3 کارشناس GIS و RS، اداره منابع و آبخیزداری گرگان

چکیده

سابقه و هدف: در دهه اخیر داده‎های به دست آمده از سامانه اطلاعات جغرافیایی (GIS) و سامانه موقعیت‏یاب جهانی (GPS) و زمین‎آمار نقش مهمی در مطالعه توزیع مکانی ویژگی‎های خاک داشته‎اند و نتایج به دست آمده اغلب حاکی از این است که تغییرات ویژگی‎های خاک می‎تواند در فواصل بسیار کوچک (در حد چند میلی‎متر) تا فواصل طولانی (چندین کیلومتر) صورت گیرد. این پژوهش با هدف مقایسه روش‏های مختلف درون‏یابی (کریجینگ) برای تعیین بهترین مدل در تهیه نقشه متغیرهای شوری خاک و عملکرد گندم در مزرعه نمونه ارتش آق‏قلا اجرا گردید.
مواد و روش‏ها: به منظور مطالعه اثرات شوری خاک و تغییرات آن در طول فصل رشد گندم، با روش نمونه‏برداری سیستماتیک از 101 نقطه در مزارع نمونه ارتش آق‏قلا نمونه‏ خاک تهیه شد و مقادیر EC و pH در دو مرحله رشد گندم و عملکرد متناظر آن‏ها در مرحله برداشت اندازه‏گیری شد. به منظور درون‏یابی مقادیر شوری، کریجینگ‏های معمولی، جهانی و فصلی در تلفیق با پنج مدل واریوگرامی مورد آزمون قرار گرفتند. به این منظور مزرعه به چهار قسمت جداگانه تقسیم و مدل‏ها به شکل جداگانه آزمون شدند.
یافته‏ها: نتایج حاصل از روش‎های درون‎یابی کریجینگ نشان داد که از بین سه روش و پنج مدل انتخابی، روش کریجینگ معمولی‎ با مدل نمایی در برآورد شوری خاک و کریجینگ جهانی با مدل نمایی در برآورد عملکرد گندم از دقت بالاتری برخوردار هستند. در این مطالعه رابطه معنی‏داری بین اختلاف شوری دو مرحله با عملکرد گندم مشاهده شد، به نحوی که به ازای هر واحد افزایش شوری، عملکرد به میزان 5/4 گرم کاهش یافت. همچنین نتایج آزمون خاک نشان داد که مقدار EC برای هر کدام از 4 واحد A، B، C و D، بسته به توپوگرافی و مساحت واحد متفاوت بود، به نحوی که در زمینه شوری خاک واحد A با طیف شوری پایین‎تر در دو مرحله نمونه‎برداری، بیشترین عملکرد را نشان داد. طبق نتایج روش‎های درون‎یابی در زمینه پیش‎بینی عملکرد گندم برای 4 واحد A، B، C و D، واحد A طیف عملکرد بیشتر و واحد B طیف عملکرد کم‎تری نسبت به واحد‏‏های دیگر از خود نشان دادند‎.‎ در واحد A شوری کمتر بود و صرفا رقم کوهدشت کشت می‏شد، در حالی که در سایر واحدها سایر ارقام نیز (لاین 17، مروارید، کوهدشت و 8019 N) کشت می‏شدند.
نتیجه‏گیری: در کل نتایج نشان داد که سامانه اطلاعات جغرافیایی در کنار سایر اطلاعات می‏تواند ابزار قدرتمندی برای تشخیص اثرات عوامل غیر زنده (نظیر شوری) بر کارکرد بوم‏ نظام‏های کشاورزی باشد. همچنین نتایج بر این واقعیت تأکید دارند که مزارع از نظر عوامل مختلف، در معرض نوسان‏های مکانی گسترده‏ای قرار دارند و به همین دلیل مدیریت‏های متفاوتی را می‏ طلبند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison of geostatistical interpolation models (kriging) to estimate soil salinity and wheat yield (a case study: army field of Aq qala)

نویسندگان [English]

  • rahim azhirabi 1
  • behnam kamkar 2
  • omid abdi 3
چکیده [English]

Background and objective: In the past decade, data acquired from the geographical information system (GIS), Global Positioning System (GPS) and geostatistics have an important role in the study of spatial distribution of soil properties and the results often show that changes in soil properties could occur from very small distances (a few mm) to long distances (several kilometers). This study compared different methods of interpolation (kriging) to determine the best model to map soil salinity and yield variables in the field of military, AQ-Qala.
Material and methods: In order to investigate the effects of soil salinity and its changes during wheat growing season, 101 ground control points were taken in the field of military, AQ-Qala, based on systematic sampling selection, and EC and pH levels were measured in two stages along with corresponded yield in harvesting stage. In order to interpolate salinity levels, ordinary, universal and disjunctive kriging in combination with five models of semivarograms were tested. For this, the field was divided to four separate sections and the models were tested separately.
Results: Final results showed that among three kriging methods and five applied models, ordinary kriging with an exponential model and the universal kriging with exponential models were the best models to estimate soil salinity and wheat yield, respectively . In this study, a significant relation was found between salinity differences for two samplings and wheat yield, as 4.5 gr yield reduction was demonstrated per salinity unit increase. Also, results of soil testing revealed that EC value for each parcel is different than others which is related to topography and parcel area, as the parcel A with the least difference between ECs had the highest yield. Results showed that EC difference had a pronounced variation which could be used to interpret yield differences among four parcels. According to estimates by interpolation methods used for predicting wheat yield in 4 studied units (A, B, C and D), unit A had the most, while unit B the least yield range. Unit A had the lowest salinity and on the other hand, only one cultivar (Koohdasht) was grown in unit A, while in the other units more than one cultivar (Line 17, Morvarid, Koohdasht and N8019) was planted.
Conclusion: Overall results revealed that GIS along with available information could be used as a powerful tool for detecting the effects of abiotic factors effects (including salinity) on the agroecosystems performance. Also, these results emphasizes on this reality that the fields are faced by widespread spatial variations of different factors which need different management options.

کلیدواژه‌ها [English]

  • Kriging
  • RMSE
  • Semivariogram
  • soil Salinity
  • Wheat
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