fattahi dolatabadi, K., babazadeh, H., najafi, P., sedghi, H. (2018). A Model for Irrigation Scheduling Using the Difference between Air and Leaf Temperature of Corn. , 32(2), 305-320. doi: 10.22092/jwra.2018.116972
khadije fattahi dolatabadi; hosin babazadeh; payam najafi; hossin sedghi. "A Model for Irrigation Scheduling Using the Difference between Air and Leaf Temperature of Corn". , 32, 2, 2018, 305-320. doi: 10.22092/jwra.2018.116972
fattahi dolatabadi, K., babazadeh, H., najafi, P., sedghi, H. (2018). 'A Model for Irrigation Scheduling Using the Difference between Air and Leaf Temperature of Corn', , 32(2), pp. 305-320. doi: 10.22092/jwra.2018.116972
fattahi dolatabadi, K., babazadeh, H., najafi, P., sedghi, H. A Model for Irrigation Scheduling Using the Difference between Air and Leaf Temperature of Corn. , 2018; 32(2): 305-320. doi: 10.22092/jwra.2018.116972

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

پژوهش آب در کشاورزی
Article 11, Volume 32, Issue 2, August 2018, Pages 305-320    PDF (1.5 M)
Document Type: Research Paper
DOI: 10.22092/jwra.2018.116972
Authors
khadije fattahi dolatabadi1; hosin babazadeh* 2; payam najafi3; hossin sedghi2
1PhD student, Department of Water Science and Research branch, Islamic Azad University, Tehran, Iran.
2Professor, Department of Water Science and Research branch, Islamic Azad University, Tehran.
3Professor, Department of Water Engineering, Khorasgan Branch, Islamic Azad University, Isfahan.
Abstract
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.
 
Keywords
Soil Moisture; Water stress; Relative humidity; Artificial Neural Network; Multiple Linear Regression
References
  1. وردی نژاد و. ر. بشارت، س. عبقری، ه؛ و احمدی، ح. 1390. برآورد حداکثر تخلیه مجاز رطوبتی ذرت علوفه‌ای در مراحل مختلف رشد بااستفاده از اختلاف دمای پوشش سبز گیاه و هوا. نشریه آب‌وخاک (علوم و صنایع کشاورزی)، 25 (6):1352-1344.
    1. Ballester, C. Jimenez-Bello, M.A. Castel, J.R. and Intrigliolo, D.S. 2013. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agricultural and forest Meteorology. 168:120–129.
    2. Blonquist Jr, J.M. Norman, J.M. and Bugbee, B. 2009. Automated measurement of canopy stomata conductance based on infrared temperature. Agricultural and forest Meteorology. 149:2183-2197.
    3. Cohen, Y. Alchanatis, V. Sela, E. Saranga, Y. Cohen, S. Meron, M. Bosak, A. Tsipris, J. Ostrovsky, V. Orolov, V. Levi, A. and Brikman, R. 2014. Crop water status estimation using thermography: multi-year model development using ground base thermal images. Precision Agriculture. http://dx.doi.org/10.1007/s11119-014- 9378-1.
    4. Gonzalez-Dugo, V. Zarco-Tejada, P. Nicolas, E. Nortes, P.A. Alarcon, J.J. Intrigliolo, D.S. and Fereres, E. 2013. Using high resolution UAV thermal imagery to assess the variability in the water status of five fruit tree species within a commercial orchard. Precision Agriculture. 14:660–678. http://dx.doi.org/10.1007/s11119-013- 9322-9.
    5. Herwitz, S.R. Johnson, L.F. Dunagan, S.E. Higgins, R.G. Sullivan, D.V. Zheng, J. Lobitz, B.M. Leung, J.G. Gall Meyer, B.A. Aoyagi, M. Slye, R.E. Brass, J. A. 2004. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Computers and Electronics in Agriculture. 44: 49–61.
    6. Idso, S.B. Jackson, R.D. and Reginato, R.J. 1977. Remote sensing of crop yields. Science. 196: 19–25.
    7. Irmak, S. Haman, D.Z. and Bastug, R. 2000. Determination of crop water stress index for irrigation timing and yield estimation of corn. Agronomy Journal. 92: 1221–1227.
    8. Jones, H.G. and Demmers, D. 1999. Use of thermograph for quantitative studies of spatial and temporal variation of stomatal conductance over leaf surfaces. Plant Cell and Environment. 22: 1043–1055.
    9. Kim, Y. Still, Ch. J. Hanson, Ch. V. Kwon, H. Greer, B.T. and Law, B.E. 2016. Canopy skin temperature variations in relation to climate, soil temperature, and carbon flux at a ponderosa pine forest in central Oregon. Agricultural and Forest Meteorology. 226–227:161–173
    10. Landeras, G. Ortiz-Barredo, A. and López, J.J. 2009. Forecasting weekly evapotranspiration with ARIMA and artificial neural network models. Irrigation and Drainage Engineering, ASCE.135: 323-334.
    11. Lu, Z. Radin, J.W. Turcotte, E.L. Percy, R. and Zeiger, E. 1994. High yields in advanced lines of Pima cotton are associated with higher stomata conductance, reduced leaf area and lower leaf temperature. Physiologia Plantarum. 92: 266–272.
    12. Mangus, D.L. Sharda, A. and Zhang, N. 2016. Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Computers and Electronics in Agriculture. 121:149–159.
    13. Marquardt, D.W. 1963. An algorithm for least- squares estimation of nonlinear parameters. J. Sco. Ind Appl. Math. 11:431-441.
    14. Möller, M. Alchanatis, V. Cohen, Y. Meron, M. Tsipris, J. Naor, A. Ostrovsky, V. Sprintsin, M. Cohen, S. 2007. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine. Experimental Botany. 58:827–838.
    15. Monteith, J.L. and Unsworth, M.H. 2013. Principles of Environmental Physics: Plants, Animals, and the Atmosphere, fourth ed. Elsevier Ltd, Oxford, UK
    16. Norouzi, M. Ayoubi, S. Jalalian, A. Khademi, H. and Dehghani, A.A. 2010. Predicting rainfed wheat quality and quantity by artificial neural network using terrain and soil characteristics. Acta Agriculture Scandinavica, Section B- Soil Plant Science. 60: 341-352.
    17. Nouri azhar, J. and Ehsanzedeh, P. 2007. Study of relationship of some growth indices and yield of five corn hybrids at two irrigation regime in Esfahan region. Science and Technology. 41: 261-272
    18. Orta, A. H. I. Baser, S. Sehirali, T. and Erdem, Y. 2004. Use of infrared thermometry for developing baseline equation and scheduling irrigation in wheat, Cereal Research Communications. 32(3):363-370.
    19. Pandey, R.K. Maranville, J.W. and Chetima, M.M. 2000. Deficit irrigation and nitrogen effects on maize in a Sahelian environment. II. Shoot growth. Agricultural Water Management. 46: 15–27.
    20. Scherrer, D. Karl-Friedrich Bader, M. and, Korner, Ch. 2011. Drought-sensitivity ranking of deciduous tree species based on thermal imaging of forest canopies. Agricultural and Forest Meteorology. 151: 1632–1640.
    21. Sdoodee, S. and Kaewkong, P. 2006. Use of an infrared thermometer for assessment of plant water stress in neck orange (Citrus reticulate Blanco), Songklanakarin J. Science and Technology. 28(6):1161-1167.
    22. Smith, B.A. Hoogenboom, G. and McClendon, R.W. 2009. Artificial neural networks for automated year round temperature prediction. Computers and Electronics in Agriculture. 68: 52-61.
    23. Taghvaeian, S. Chávez, J. and Hansen, N. 2012. Infrared thermometry to estimate crop water stress index and water use of irrigated maize in northeastern Colorado. Remote Sensing. 4: 3619–3637.
    24. Taghvaeian, S. Chávez, J. L. 2013. Remote sensing for evaluating crop water stress at field scale using infrared thermography: potential and limitations. Hydrology Days. 73–83.
    25. Taghvaeian, S. Chávez, J. L. Bausch, W.C. DeJonge, K.C. and Trout, T.J. 2014a. Minimizing instrumentation requirement for estimating crop water stress index and transpiration of maize. Irrigation Science. 32: 53–65.
    26.  Mangus, D.L. Sharda, A. and Zhang, N. 2016. Development and evaluation of thermal infrared imaging system for high spatial and temporal resolution crop water stress monitoring of corn within a greenhouse. Computers and Electronics in Agriculture. 121: 149–159
    27. Zia, S. Romano, G. Spreer, W. Sanchez, C. Cairns, J. Araus, J.L. Muller, J. 2013. Infrared thermal imaging as a rapid tool for identifying water stress tolerant maize genotypes of different phenology. Agronomy and Crop Science. 75–84.
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