مدلی برای برنامه‌ریزی آبیاری با استفاده از اختلاف دمای هوا و سطح برگ گیاه ذرت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری گروه علوم و مهندسی آب دانشکده علوم کشاورزی و صنایع غذایی دانشگاه آزاد اسلامی واحد علوم تحقیقات. تهران. ایران.

2 استاد گروه مهندسی آب دانشکده علوم کشاورزی و صنایع غذایی دانشگاه آزاد اسلامی واحد علوم تحقیقات تهران.

3 استاد گروه مهندسی آب دانشکده علوم کشاورزی دانشگاه آزاد اسلامی واحد خوراسگان اصفهان.

4 استاد دانشگاه علوم و تحقیقات

چکیده

تعیین سریع و دقیق زمان آبیاری به‌منظور جلوگیری از تنش آبی گیاه، از مهم‌ترین مسائل مدیریت پایدار آب در مزرعه است. اندازه‌گیری رطوبت خاک و درجه حرارت سطح برگ، دو روش تعیین زمان آبیاری است. در این تحقیق با ترکیب این دو روش مدلی برای برنامه‌ریزی و مدیریت آبیاری ذرت علوفه‌ای (SC-701) ارائه شده است. رطوبت نسبی (RH)، دمای هوا (Ta)، دمای سطح برگ (TL) و رطوبت خاک (SM) در سال1392 اندازه‌گیری شد و با استفاده از مدل شبکه عصبی مصنوعی ورگرسیون خطی چندگانهآزمون (stepwise Method)، مدلی ارائه شد. در سال 1393 پنج تیمار شامل 35،% 65%، 75%، 85% و 100% کل آب قابل دسترس با چهار تکرار تعریف شد، زمانی که رطوبت خاک به رطوبت‌های مزبور می‌رسید آبیاری صورت می‌گرفت. اندازه‌گیری‌های سال قبل تکرار و مدل واسنجی شد. نتایج سال اول، همبستگی بین پارامترهای RH، Ta، TL، Ta-TLبه‌عنوان متغیر مستقل و SMبه‌عنوان متغیر وابستهR2=0.87را نشان داد. ضریب تبیین مدل رطوبت خاک با سه پارامتر ورودی دمای هوا، دمای سطح برگ و رطوبت نسبی، R2=0.92به دست آمد. در این مدل، رطوبت خاک رابطه معکوس با متغیرهای (Ta) و (TL-Ta) و رابطه مستقیم با RH دارد. رطوبت خاک با استفاده از مدل برای تیمارهای سال دوم به‌کار رفت و با مقادیر اندازه‌گیری شده مقایسه شد. اختلاف میانگین رطوبت خاک اندازه‌گیری و برآورد شده با مدل در زمان اوج تشعشع خورشید (هنگام ظهر) کمتر از 10± درصد بود. مدل مزبور داده‌های تیمار 75% کل آب قابل دسترس را به‌خوبی و با اختلاف بسیارکم تخمین زد.

کلیدواژه‌ها

عنوان مقاله [English]

A Model for Irrigation Scheduling Using the Difference between Air and Leaf Temperature of Corn

نویسندگان [English]

  • khadije fattahi dolatabadi 1
  • hosin babazadeh 2
  • payam najafi 3
  • hossin sedghi 4
1 PhD student, Department of Water Science and Research branch, Islamic Azad University, Tehran, Iran.
2 Professor, Department of Water Science and Research branch, Islamic Azad University, Tehran.
3 Professor, Department of Water Engineering, Khorasgan Branch, Islamic Azad University, Isfahan.
4 Professor, Department of Water Science and Research branch, Islamic Azad University, Tehran.
چکیده [English]

To prevent water stress in plants and have sustainable water management in the field, fast and accurate determination of irrigation time is one of the most important issues. Measuring soil moisture and leaf surface temperature are two methods of determining time of irrigation. In this research, by combination of these two methods, a model for planning and management of forage maize irrigation (cultivar SC-701) is presented. The air relative humidity (RH) and temperature (Ta), leaf surface temperature (TL), and soil moisture content (SM) were measured in 2013 and, by using artificial neural network model and multiple stepwise method, a regression model was developed. Experiments were carried out in 2014 with five treatments including 100%, 85%, 75%, 65%, and 35% total available water (TAW), with four replications, Irrigation was carried out when soil moisture content reached the treatments moisture level. Measurements of the previous year were repeated and the model was calibrated. The results of the first year showed a correlation (R2=0.87) between the parameters RH, Ta, TL, Ta-TL as independent variable and SM as the dependent variable. Then, using three input parameters of air temperature, leaf surface temperature, and relative humidity, Determination Coefficient  of soil moisture content model was calculated as R2= 0.92. In this model, soil moisture has an inverse relation with (Ta) and (TL-Ta) variables, but a direct relation with RH. Soil moisture content was compared using the model for the second year treatments and compared with the measured values. The difference in soil moisture content measured and estimated by the model at the peak solar radiation time (at noon) was less than ±10%. The model estimated 75% TAW treatment data well, with very small difference compared to the measured value.
 

کلیدواژه‌ها [English]

  • Soil Moisture
  • Water stress
  • Relative humidity
  • Artificial Neural Network
  • Multiple Linear Regression

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پژوهش آب در کشاورزی
دوره 32، شماره 2
تیر 1397
صفحه 305-320

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