Assessment of Water Stress in Rice Fields Incorporating Environmental Parameters

Muhammad Kamran; Sajid Rasheed Ahmad; Khurram Chohan; Azeem Akhtar; Amna Hassan; Rao Mansoor Ali Khan

Authors

Muhammad Kamran University of the Punjab PU · College of Earth and Environment sciences
Sajid Rasheed Ahmad University of the Punjab PU · College of Earth and Environment Sciences
Khurram Chohan Government College University, Lahore
Azeem Akhtar Department of Space Science University of the Punjab Lahore
Amna Hassan The Islamia University of Bahawalpur
Rao Mansoor Ali Khan University of Minesota USA

Keywords:

Shortwave radiations, CASA Model, Net radiations, Sensible Heat Flux, Ground heat flux.

Abstract

Rice is considered as a major crop due to its demand globally. Pakistan is famous throughout the world to produce export quality rice which have healthy contribution in boosting the regional economy. Rice plant require plenty of water for its proper growth and development therefore, water conservation is significant to maintain water reserves for a sustainable future. The main objective of this study was to identify day-to-day availability of water in rice fields from Germination to Ripening (GTR) using Carnegie Ames Stanford Approach (CASA) model. CASA model incorporates real-time parameter e.g., temperature, pressure, extraterrestrial radiations, Leaf Area Index (LAI), vapor pressure and sunshine hours to compute net-shortwave radiations (R_ns), net-longwave radiations (R_nl), net-radiations (R_n), actual incoming radiations (R_so), sensible heat flux (H), ground heat flux (G_o) and finally the water stress (W). The averaged values of R_n, R_so, R_ns, R_nland H were computed as 206, 319, 178, 34 and 124 (wm^-2) respectively for GTR. Total expected sunshine hours were 1584h but we could receive only 874 h during GTR due to “off and on” cloud activity. LAI and G_o were observed in inverse relation to each other.

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References

M. Saifullah et al., “Estimation of Water Stress on Rice Crop Using Ecological Parameters.,” Int. J. Agric. Sustain. Dev., vol. 01, no. 01, pp. 17–29, 2019, doi: 10.33411/ijasd/20190103.

J. Yang and J. Zhang, “Crop management techniques to enhance harvest index in rice,” J. Exp. Bot., vol. 61, no. 12, pp. 3177–3189, 2010, doi: 10.1093/jxb/erq112.

A. K. Thakur, S. Rath, D. U. Patil, and A. Kumar, “Effects on rice plant morphology and physiology of water and associated management practices of the system of rice intensification and their implications for crop performance,” Paddy Water Environ., vol. 9, no. 1, pp. 13–24, 2011, doi: 10.1007/s10333-010-0236-0.

B. A. M. Bouman, “A conceptual framework for the improvement of crop water productivity at different spatial scales,” Agric. Syst., vol. 93, no. 1–3, pp. 43–60, 2007, doi: 10.1016/j.agsy.2006.04.004.

T. Chapagain, A. Riseman, and E. Yamaji, “Assessment of System of Rice Intensification (SRI) and Conventional Practices under Organic and Inorganic Management in Japan,” Rice Sci., vol. 18, no. 4, pp. 311–320, 2011, doi: 10.1016/S1672-6308(12)60010-9.

S. D. Khepar, A. K. Yadav, S. K. Sondhi, and M. Siag, “Water balance model for paddy fields under intermittent irrigation practices,” Irrig. Sci., vol. 19, no. 4, pp. 199–208, 2000, doi: 10.1007/PL00006713.

H. J. Nesbitt, “Rice Production in,” World, vol. 12, no. 2, pp. 186–191, 1997.

V. J. Pascual and Y. M. Wang, “Utilizing rainfall and alternate wetting and drying irrigation for high water productivity in irrigated lowland paddy rice in Southern Taiwan,” Plant Prod. Sci., vol. 20, no. 1, pp. 24–35, 2017, doi: 10.1080/1343943X.2016.1242373.

A. S. Kima, W. G. Chung, Y. M. Wang, and S. Traoré, “Evaluating water depths for high water productivity in irrigated lowland rice field by employing alternate wetting and drying technique under tropical climate conditions, Southern Taiwan,” Paddy Water Environ., vol. 13, no. 4, pp. 379–389, Oct. 2015, doi: 10.1007/S10333-014-0458-7.

S. M. H. Raza, S. A. Mahmood, A. A. Khan, and V. Liesenberg, “Delineation of Potential Sites for Rice Cultivation Through Multi-Criteria Evaluation (MCE) Using Remote Sensing and GIS,” Int. J. Plant Prod., vol. 12, no. 1, pp. 1–11, 2018, doi: 10.1007/s42106-017-0001-z.

S. M. H. Raza and S. A. Mahmood, “Estimation of net rice production through improved CASA model by addition of soil suitability constant (hα),” Sustain., vol. 10, no. 6, 2018, doi: 10.3390/su10061788.

M. M. Waqar, F. Rehman, and M. Ikram, “Land suitability assessment for rice crop using geospatial techniques,” Int. Geosci. Remote Sens. Symp., no. July 2013, pp. 2844–2847, 2013, doi: 10.1109/IGARSS.2013.6723417.

H. Wang, X. Li, H. Long, and W. Zhu, “A study of the seasonal dynamics of grassland growth rates in Inner Mongolia based on AVHRR data and a light-use efficiency model,” Int. J. Remote Sens., vol. 30, no. 14, pp. 3799–3815, 2009, doi: 10.1080/01431160802552702.

J. B. Bradford, J. A. Hicke, and W. K. Lauenroth, “The relative importance of light-use efficiency modifications from environmental conditions and cultivation for estimation of large-scale net primary productivity,” Remote Sens. Environ., vol. 96, no. 2, pp. 246–255, May 2005, doi: 10.1016/J.RSE.2005.02.013.

D. E. Ahl, S. T. Gower, D. S. Mackay, S. N. Burrows, J. M. Norman, and G. R. Diak, “Heterogeneity of light use efficiency in a northern Wisconsin forest: Implications for modeling net primary production with remote sensing,” Remote Sens. Environ., vol. 93, no. 1–2, pp. 168–178, Oct. 2004, doi: 10.1016/J.RSE.2004.07.003.

W. Zhu, Y. Pan, H. He, D. Yu, and H. Hu, “Simulation of maximum light use efficiency for some typical vegetation types in China,” Chinese Sci. Bull., vol. 51, no. 4, pp. 457–463, 2006, doi: 10.1007/s11434-006-0457-1.

X. Xiao et al., “Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest,” Remote Sens. Environ., vol. 94, no. 1, pp. 105–122, Jan. 2005, doi: 10.1016/J.RSE.2004.08.015.

Pei, B.; Yuan, Y.; Jia, Y.; Wang, W.; Josef, E 2000. A study on light utilization of poplar crop intercropping system. Sci. Silvae Sin., 36, 13–18, doi: 10.11707/J.1001-7488.20000303.

F. G. Hall et al., “Multi-angle remote sensing of forest light use efficiency by observing PRI variation with canopy shadow fraction,” Remote Sens. Environ., vol. 112, no. 7, pp. 3201–3211, Jul. 2008, doi: 10.1016/J.RSE.2008.03.015.

J. L. Monteith, “Solar Radiation and Productivity in Tropical Ecosystems,” J. Appl. Ecol., vol. 9, no. 3, p. 747, Dec. 1972, doi: 10.2307/2401901.

Zhu, W.; Pan, Y.; He, J.; Yu, D.; Hu, H. 2006 Simulation of maximum light use efficiency for some typical vegetation types in China. Chin. Sci. Bull., 51, 457–463. .

B. Wu, S. Liu, W. Zhu, N. Yan, Q. Xing, and S. Tan, “An Improved Approach for Estimating Daily Net Radiation over the Heihe River Basin,” Sensors (Basel)., vol. 17, no. 1, Jan. 2017, doi: 10.3390/S17010086.

P. G. Oguntunde, O. J. Olukunle, O. A. Ijatuyi, and A. A. Olufayo, “A semi-empirical model for estimating surface albedo of wetland rice field,” Agric. Eng. Int. CIGR J., no. May 2014, 2007.

J. L. Tsai, B. J. Tsuang, P. S. Lu, M. H. Yao, and Y. Shen, “Surface energy components and land characteristics of a rice paddy,” J. Appl. Meteorol. Climatol., vol. 46, no. 11, pp. 1879–1900, 2007, doi: 10.1175/2007JAMC1568.1.

T. W. Giambelluca, D. Hölscher, T. X. Bastos, R. R. Frazão, M. A. Nullet, and A. D. Ziegler, “Observations of albedo and radiation balance over postforest land surfaces in the eastern Amazon Basin,” J. Clim., vol. 10, no. 5, pp. 919–928, 1997, doi: 10.1175/1520-0442(1997)010<0919:OOAARB>2.0.CO;2.

M. Irfan, Z.-Y. Zhao, M. Ahmad, and M. Mukeshimana, “Solar Energy Development in Pakistan: Barriers and Policy Recommendations,” Sustainability, vol. 11, no. 4, p. 1206, 2019, doi: 10.3390/su11041206.

W. Munley and L. Hipps, 1991 Estimation of regional evaporation for a tallgrass prairie from measurements of properties of the atmospheric layer., Water Resour. Res., vol. 27, p. 225–230.,.

Angstrom, 1924 Solar and terrestrial radiation., Q. J. R. Meteorol. Soc. , vol. 50, pp. 121-125,.

Tasumi, M. 2003; Progress in Operational Estimation of Regional Evapotranspiration Using Satellite Imagery. Ph.D. Thesis, University of Idaho, Moscow, ID, USA, p. 357.

Sauer, T.J.; Horton, R. Soil Heat Flux; 2005, University of Nebraska Lincolon: Lincoln, NE, USA, Volume 47, pp. 131–154. Available online: http://digitalcommons.unl.edu/usdaarsfacpub

Bowen, I.S. 1926 The ratio of heat losses by conduction and by evaporation from any water surface. Phys. Rev.J. Achive, 27, 779–787

Zotarelli, L.; Dukes, M.D.; Romero, C.C.; Migliaccio, K.W.; Morgan, K.T. 2015, Step by Step Calculation of the Penman-Monteith Evapotranspiration (FAO-56 Method); IFAS University of Florida: Gainesville, FL, USA

Tanner, C.B 1960, Energy balance approach to evapotranspiration from crops. Soil Sci. Soc. Am. Proc. 24, 1–9

Sajid. M, Mohsin. M, Jamal. T, Mobeen. M, Rehman. A, Rafique A, “Impact of Land-use Change on Agricultural Production & Accuracy Assessment through Confusion Matrix”. International Journal of Innovations in Science & Technology, Vol 04, Issue 01: pp; 233- 245, 2022.

Akram. F, Fatimah. T, Jamal. T, Saleem. M. U, Javed. H, Sharif. S, Yousaf. K, Khan. N. I, Karamat. A, “Salinity and Fertility Status of Irrigated soils in District Nankana Sahib, Punjab, Pakistan”. International Journal of Innovations in Science & Technology. Vol 4, Issue 1, pp: 213-221, 2022.

Farhan. S. M, Arif. H, Ahmad. H. H, “Estimation of relation between moisture content of soil and reflectivity index using GPS signals”. International Journal of Innovations in Science & Technology, Vol 03 Issue 04: pp 218-227, 2021.

Younis. H, Arshed. M. A, Hassan. F, Khurshid. M, Ghassan. H, “Tomato Disease Classification using Fine-Tuned Convolutional Neural Network”. International Journal of Innovations in Science & Technology. Vol 4, issue 1, pp: 123-134, 2022.

Khan. R. M. A, Naqvi. S. B, Rana. Q. S, Mahmood. S. A, Rasheed. M, “Detecting Aphid Concentration in Wheat Leaf Using Remote Sensing and GIS”. International Journal of Innovations in Science & Technology, Vol 4, Issue 2, 2022, pp: 336-347