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Bandyopadhyay, K. K.
- Characterization and Crop Planning of Rabi Fallows Using Remote Sensing and GIS
Abstract Views :397 |
PDF Views:171
Authors
K. K. Bandyopadhyay
1,
R. N. Sahoo
1,
Ravender Singh
1,
S. Pradhan
1,
S. Singh
1,
Gopal Krishna
1,
S. Pargal
1,
S. K. Mahapatra
2
Affiliations
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
2 National Bureau of Soil Survey and Land Use Planning, Delhi Centre, New Delhi 110 012, IN
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
2 National Bureau of Soil Survey and Land Use Planning, Delhi Centre, New Delhi 110 012, IN
Source
Current Science, Vol 108, No 11 (2015), Pagination: 2051-2062Abstract
Rice is the principal crop during kharif (rainy) season in eastern India, which occupies 26.8 M ha accounting for 63.3% of the total rice-growing areas of the country. However, this area is not fully utilized for crop production in the subsequent rabi (post-rainy) season and kept fallow due to a number of biotic, abiotic and socio-economic constraints. If this rabi fallow area can be effectively utilized, it will help in improving the economy of this region, which is yet to be benefited from the green revolution. The objectives of the present study include: (i) delineation of rabi fallow areas of eastern India using remote sensing and GIS technique; (ii) characterization of soil resources of the rabi fallow regions, and (iii) suggesting site-specific crop planning for this region. It was estimated that about 12.54 M ha area in the rabi season is left fallow in eastern India. The soil properties like soil texture, soil moisture retention at field capacity and permanent wilting point, saturated hydraulic conductivity, soil pH, electrical conductivity, soil organic carbon, etc. were determined at the representative profiles distributed in different agro-ecological sub-regions (AESRs) of this region and mapped in a GIS environment. Using water balance studies, site-specific crop planning based on available residual soil moisture has been suggested. In most of the AESRs, pulses and oilseeds like green gram, black gram, Sesamum and mustard can be grown successfully on residual soil moisture content. Crops which suffer from water deficit during maturity stages can also be grown during rabi season with one or two supplemental irrigations, wherever possible. If the site-specific constraints to crop production can be alleviated and these fallow lands can be brought under cultivation through proper crop planning as suggested, poverty in this resourceful region can be eradicated to a great extent.Keywords
Crop Planning, Rabi Fallow, Remote Sensing and GIS, Water Balance.- Prediction of Wheat Yield Using Spectral Reflectance Indices Under Different Tillage, Residue and Nitrogen Management Practices
Abstract Views :443 |
PDF Views:159
Authors
Sujan Adak
1,
K. K. Bandyopadhyay
1,
R. N. Sahoo
1,
N. Mridha
2,
M. Shrivastava
3,
T. J. Purakayastha
4
Affiliations
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
2 National Institute of Research on Jute and Allied Fibre Technology, Kolkata 700 040, IN
3 Centre for Environment Sciences and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi 110 012, IN
4 Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute, New Delhi 110 012, IN
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
2 National Institute of Research on Jute and Allied Fibre Technology, Kolkata 700 040, IN
3 Centre for Environment Sciences and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi 110 012, IN
4 Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 121, No 3 (2021), Pagination: 402-413Abstract
Effect of tillage, residue mulch and nitrogen manage-ment on canopy spectral reflectance indices and their potential to predict the grain and biomass yield of wheat in advance were studied in a field experiment conducted at the Indian Agricultural Research Institute, New Delhi during 2016–17 and 2017–18. The canopy reflectance was measured using a hand-held ASD FieldSpec spectroradiometer at booting, milking and dough stage of wheat. Then 38 hyperspectral structural indices were recorded using the spectral reflectance data and correlated with wheat yield. It was observed that correlation of these indices with wheat grain and biomass yield was maximum for the booting stage. Among the 38 indices recorded at the booting stage, 13 showed significantly higher correlation with grain yield and 10 indices with biomass yield of wheat (r³0.8). Regression models were developed between grain and biomass yield of wheat with these identified spectral indices recorded at booting stage for 2016–17. Validation of these regression models during 2017–18 showed that normalized difference red edge index (NDREI)-based model performed best for grain and biomass prediction. It could account for maximum 76.4% and 84.3% variation in the observed grain and biomass yield of wheat with ischolar_main mean square error of 37.8% and 50.5% of the corresponding mean values respectively. Thus the regression models based on NDREI recorded at booting stage can be successfully used for the prediction of grain and biomass yield of wheat in advance.Keywords
Canopy Reflectance, Regression Models, Spectral Indices, Wheat, Yield Prediction.References
- Rashidi, M. and Keshavarzpour, F., Effect of different tillage methods on soil physical properties and crop yield of watermelon (Citrullus vulgaris). ARPN J. Agric. Biol. Sci., 2007, 2(6), 1–16.
- Kumar, S. et al., Long-term tillage and drainage influences on soil organic carbon dynamics, aggregate stability and corn yield. J. Soil Sci. Plant Nutr., 2014, 60(1), 108–118.
- Ogban, P. I., Ogunewe, W. N., Dike, R. I., Ajaelo, A. C., Ikeata, N. I., Achumba, U. E. and Nyong, E. E., Effect of tillage and mulching practices on soil properties and growth and yield of cowpea (Vigna unguiculata (L), Walp) in Southeastern Nigeria. J. Trop. Agric., Food, Environ. Extension, 2008, 7(2), 118–128.
- Anikwe, M. A. N. and Ubochi, J. N., Short-term changes in soil properties under tillage systems and their effect on sweet potato (Ipomea batatas L.) growth and yield in an Ultisol in southeastern Nigeria. Soil Res., 2007, 45(5), 351–358.
- Boone, F. R. and Veen, B. W., Mechanisms of crop responses to soil compaction. Developments in Agricultural Engineering, Elsevier, 1994, vol. 11, pp. 237–264.
- Davis, J. G., Managing plant nutrients for optimum water use efficiency and water conservation. Adv. Agro, 1994, 53, 85–121.
- Lal, R., Tillage effects on soil degradation, soil resilience, soil quality, and sustainability. Soil Tillage Res., 1993, 27(1–4), 1–8.
- Acharya, C. L., Hati, K. M. and Bandyopadhyay, K. K., Mulches. In Encyclopedia of Soils in the Environment (eds Hillel, D. et al.), Elsevier Publication, 2005, pp. 521–532.
- Chandel, N. S., Tiwari, P. S., Singh, K. P., Jat, D., Gaikwad, B. B., Tripathi, H. and Golhani, K., Yield prediction in wheat (Triticum aestivum L.) using spectral reflectance indices. Curr. Sci., 2019, 116(2), 272.
- Royo, C., Aparicio, N., Villegas, D., Casadesus, J., Monneveux, P. and Araus, J. L., Usefulness of spectral reflectance indices as durum wheat yield predictors under contrasting Mediterranean conditions. Int. J. Remote Sensing, 2003, 24(22), 4403–4419.
- Babar, M. A., Reynolds, M. P., Van Ginkel, M., Klatt, A. R., Raun, W. R. and Stone, M. L., Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Sci., 2006, 46(2), 578–588.
- Prasad, B., Carver, B. F., Stone, M. L., Babar, M. A., Raun, W. R. and Klatt, A. R., Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under Great Plains conditions. Crop Sci., 2007, 47(4), 1426–1440.
- Li-Hong, X. U. E., Wei-Xing, C. A. O. and Lin-Zhang, Y. A. N. G., Predicting grain yield and protein content in winter wheat at different N supply levels using canopy reflectance spectra. Pedosphere, 2007, 17(5), 646–653.
- Araus, J., Slafer, G. A., Reynolds, M. P. and Royo, C., Plant breeding and drought in C3 cereals: what should we breed for? Ann. Bot., 2002, 89(7), 925–940.
- Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E. and Schepers, J. S., In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J., 2001, 93(1), 131–138.
- Serrano, L., Filella, I. and Penuelas, J., Remote sensing of biomass and yield of winter wheat under different nitrogen supplies. Crop Sci., 2000, 40(3), 723–731.
- Aparicio, N., Villegas, D., Casadesus, J., Araus, J. L. and Royo, C., Spectral vegetation indices as nondestructive tools for determining durum wheat yield. Agron. J., 2000, 92(1), 83–91.
- Dempewolf, J., Becker-Reshef, I., Adusei, B. and Barker, B., Performance of vegetation indices for wheat yield forecasting for Punjab, Pakistan. In AGU Fall Meeting Abstracts, 2013.
- Pradhan, S., Bandyopadhyay, K. K., Sahoo, R. N., Sehgal, V. K., Singh, R., Gupta, V. K. and Joshi, D. K., Predicting wheat grain and biomass yield using canopy reflectance of booting stage. J. Indian Soc. Remote. Sensing, 2014, 42(4), 711–718.
- Bandyopadhyay, K. K., Pradhan, S., Sahoo, R. N., Singh, R., Gupta, V. K., Joshi, D. K. and Sutradhar, A. K., Characterization of water stress and prediction of yield of wheat using spectral indices under varied water and nitrogen management practices. Agric. Water Manage., 2014, 146, 115–123.
- Lawrence, R. L. and Ripple, W. J., Comparisons among vegetation indices and band wise regression in a highly disturbed, heterogeneous landscape: Mount St. Helens, Washington. Remote Sensing Environ, 1998, 64(1), 91–102.
- Shibayama, M. and Akiyama, T., Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sensing Environ., 1991, 36, 45–53.
- Daughtry, C. S. T., Discriminating crop residues from soil by shortwave infrared reflectance. Agron. J., 2001, 93, 125–131.
- Daughtry, C. and Hunt Jr, E., Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover. Remote Sensing Environ., 2008, 112, 1647–1657.
- Pearson, R. L. and Miller, L. D., Remote mapping of standing crop biomass for estimation of the productivity of the short-grass Prairie, Pawnee National Grasslands, Colorado. In Proceedings of the 8th International Symposium on Remote Sensing of Environment, ERIM, Ann Arbor, MI, 1972, pp. 1357–1381.
- Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W. and Harlan, J. C., Monitoring the vernal advancement of retrogradation of natural vegetation. NASA/GSFC, Type III, Final Report, Greenbelt, MD, USA, 1974, pp. 1–371.
- Baret, F., Guyot, G. and Major, D. J., TASVI: a vegetation index which minimizes soil brightness effects on LAI and APAR estimation. In Proceedings of IGARSS’ 89 and 12th Canadian Symposium on Remote Sensing, Vancouver, Canada, 10–14 July 1989, pp. 1355–1358.
- Major, D., Baret, F. and Guyot, G., A ratio vegetation index adjusted for soil brightness. Int. J. Remote Sensing, 1990, 11, 727–740.
- Zhao, D., Huang, L., Li, J. and Qi, J., A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy. ISPRS J. Photogramm. Remote Sensing, 2007, 62, 25–33.
- Yao, X., Zhu, Y., Tian, Y. C., Feng, W. and Cao, W. X., Exploring hyperspectral bands and estimation indices for leaf nitrogen accumulation in wheat. Int. J. Appl. Earth Obs. Geoinf., 2010, 12, 89–100.
- Zhao, D., Yang, T. and An, S., Effects of crop residue cover resulting from tillage practices on LAI estimation of wheat canopies using remote sensing. Int. J. Appl. Earth Obs. Geoinf., 2011, 14(1), 169–177.
- Eskandari, I., Navid, H. and Rangzan, K., Evaluating spectral indices for determining conservation and conventional tillage systems in a vetch-wheat rotation. Int. Soil Water Conserv. Res., 2016, 4(2), 93–98.
- Savitzky, A. and Golay, M. J. E., Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem., 1964, 36, 1627–1638.
- Gomez, K. A. and Gomez, A. A., Statistical Procedures for Agricultural Research, John Wiley & Sons, 1984.
- Rani, A., Bandyopadhyay, K. K., Krishnan, P., Sarangi, A. and Datta, S., Effect of tillage, residue and nitrogen management on soil physical properties, soil temperature dynamics and yield of wheat in an inceptisol. J. Agric. Phys., 2017, 17(1), 31–44.
- Mohammad, W., Shah, S. M., Shehzadi, S. and Shah, S. A., Effect of tillage, rotation and crop residues on wheat crop productivity, fertilizer nitrogen and water use efficiency and soil organic carbon status in dry area (rainfed) of north-west Pakistan. J. Soil Sci. Plant Nutr., 2012, 12(4), 715–727.
- Ullah, I. et al., Effect of different nitrogen levels on growth, yield and yield contributing attributes of wheat. Int. J. Sci. Eng. Res., 2018, 9, 595–602.
- López-Bellido, L., Fuentes, M., Castillo, J. E. and López-Garrido, F. J., Effects of tillage, crop rotation and nitrogen fertilization on wheat-grain quality grown under rainfed Mediterranean conditions. Field Crops Res., 1998, 57(3), 265–276.
- Bandyopadhyay, P. K., Singh, K. C., Mondal, K., Nath, R., Kumar, N. and Singh, S. S., Effect of balanced fertilization in puddled rice on the productivity of lentil in rice-fallow system under zero tillage. Bangladesh Agron. J., 2016, 19(1), 67–79.
- Lee, Y. J., Yang, C. M. and Chang, A. H., Changes of nitrogen and chlorophyll contents and reflectance spectral characteristics to the application of nitrogen fertilizer in rice plants. J. Agric. Res. China, 2002, 51(1), 1–14.
- Chang, K. W., Shen, Y. and Lo, J. C., Predicting rice yield using canopy reflectance measured at booting stage. Agron. J., 2005, 97(3), 872–878.
- Joseph, G., Fundamentals of Remote Sensing, Universities Press (India) Private Limited, Hyderabad, AP, India, 2005.
- Guyot G., Optical properties of vegetation canopies. Optical Properties of Vegetation Canopies, 1990, 19–43.
- Asner, G. P., Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing Environ., 1998, 64(3), 234– 253.
- Ranjan, R., Chopra, U. K., Sahoo, R. N., Singh, A. K. and Pradhan, S., Assessment of plant nitrogen stress in wheat (Triticum aestivum L.) through hyperspectral indices. Int. J. Remote Sensing, 2012, 33(20), 6342–6360.
- Pu, R., Gong, P., Biging, G. S. and Larrieu, M. R., Extraction of red edge optical parameters from hyperion data for estimation of forest leaf area index. IEEE Trans. Geosci. Remote Sensing, 2003, 41, 916–921.
- Cho, M. A., Skidmore, A. K. and Atzberger, C., Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optiquespectrales des feuilles (PROSPECT) and scattering by arbitratily inclined leaves (SAILH) simulated data. Int. J. Remote Sensing, 2008, 29, 2241– 2255.
- Sharabian, V. R., Noguchi, N. and Ishi, K., Significant wavelengths for prediction of winter wheat growth status and grain yield using multivariate analysis. Eng. Agric., Environ. Food, 2014, 7(1), 14–21.
- Kanke, Y., Tubana, B., Dalen, M. and Harrell, D., Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precis. Agric., 2016, 17(5), 507–530.
- Carter, G. A., Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Remote Sensing, 1994, 15(3), 697–703.
- Zarco-Tejada, P. J. et al., Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sensing Environ., 2005, 99(3), 271–287.
- Gitelson, A. A. and Merzlyak, M. N., Remote estimation of chlorophyll content in higher plant leaves. Int. J. Remote Sensing, 1997, 18(12), 2691–2697.
- Kauth, R. J. and Thomas, G. S., The tasselled cap a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In LARS Symposia, 1976, p. 159.
- Lichtenthaler, H. K., Gitelson, A. and Lang, M., Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements. J. Plant Physiol., 1996, 148(3–4), 483–493.
- Sims, D. A. and Gamon, J. A., Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing Environ., 2002, 81(2–3), 337–354.
- Datt, B., Visible/near infrared reflectance and chlorophyll content in Eucalyptus leaves. Int. J. Remote Sensing, 1999, 20(14), 2741–2759.
- Chen, J. M., Evaluation of vegetation indices and a modified simple ratio for boreal applications. Can. J. Remote Sensing, 1996, 22(3), 229–242.
- Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H. and Sorooshian, S., A modified soil adjusted vegetation index. Remote Sensing Environ., 1994, 48(2), 119–126.
- Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J. and Strachan, I. B., Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing Environ., 2004, 90(3), 337-352.
- Rodriguez, D., Fitzgerald, G. J., Belford, R. and Christensen, L. K., Detection of nitrogen deficiency in wheat from spectral reflectance indices and basic crop eco-physiological concepts. Aust. J. Agric. Res., 2006, 57(7), 781–789.
- Rouse Jr, J. W., Haas, R. H., Deering, D. W., Schell, J. A. and Harlan, J. C., Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation, Technical Report, Texas A&M University, College Station, TX, USA, 1974.
- McFeeters, S. K., The use of the normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sensing, 1996, 17(7), 1425–1432.
- Rondeaux, G., Steven, M. and Baret, F., Optimization of soiladjusted vegetation indices. Remote Sensing Environ., 1996, 55(2), 95–107.
- Richardson, A. J. and Wiegand, C. L., Distinguishing vegetation from soil background information. Photogramm. Eng. Remote Sensing, 1977, 43(12), 1541–1552.
- Garbulsky, M. F., Peñuelas, J., Gamon, J., Inoue, Y. and Filella, I., The photochemical reflectance index (PRI) and the remote sensing of leaf, canopy and ecosystem radiation use efficiencies: a review and meta-analysis. Remote Sensing Environ., 2011, 115(2), 281– 297.
- Merzlyak, M. N., Gitelson, A. A., Chivkunova, O. B. and Rakitin, V. Y., Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiol. Plant, 1999, 106(1), 135–141.
- Gupta, R. K., Vijayan, D. and Prasad, T. S., Comparative analysis of red-edge hyperspectral indices. Adv. Space Res., 2003, 32(11), 2217–2222.
- Guyot, G. and Baret, F., Utilisation de la haute resolution spectrale pour suivrel’etat des couverts vegetaux. In Spectral Signatures of Objects in Remote Sensing, 1988, vol. 287, p. 279.
- Gitelson, A. and Merzlyak, M. N., Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. J. Plant Physiol., 1994, 143(3), 286– 292.
- Roujean, J. L. and Breon, F. M., Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing Environ., 1995, 51(3), 375–384.
- Major, D. J., Baret, F. and Guyot, G., A ratio vegetation index adjusted for soil brightness. Int. J. Remote Sensing, 1990, 11(5), 727–740.
- Huete, A. R., A soil-adjusted vegetation index (SAVI). Remote Sensing Environ., 1988, 25(3), 295–309.
- Broge, N. H. and Leblanc, E., Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing Environ., 2001, 76(2), 156–172.
- Vogelman, J. E., Rock, B. N. V. and Moss, D. M., Red edge spectral measurements from sugar maple leaves. Remote Sensing, 1993, 14(8), 1563–1575.
- Peñuelas, J., Filella, I., Biel, C., Serrano, L. and Save, R. The reflectance at the 950–970 nm region as an indicator of plant water status. Int. J. Remote Sensing, 1993, 14(10), 1887–1905.
- Zarco-Tejada, P. J., Miller, J. R., Noland, T. L., Mohammed, G. H. and Sampson, P. H., Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Trans. Geosci. Remote Sensing, 2001, 39(7), 1491–1507.
- Comparative Evaluation of Reference Evapotranspiration Estimation Models In New Bhupania Minor Command, Jhajjar, Haryana, India
Abstract Views :244 |
PDF Views:134
Authors
Venkatesh Gaddikeri
1,
A. Sarangi
2,
D. K. Singh
1,
K. K. Bandyopadhyay
3,
Bidisha Chakrabarti
4,
S. K. Sarkar
5
Affiliations
1 Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
2 Water Science and Technology, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
3 Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
4 Centre for Environment Science and Climate Resilient Agriculture, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
5 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India., IN
1 Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
2 Water Science and Technology, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
3 Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
4 Centre for Environment Science and Climate Resilient Agriculture, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, India., IN
5 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, India., IN
Source
Current Science, Vol 124, No 10 (2023), Pagination: 1181-1187Abstract
Accurate quantification of reference crop evapotran-spiration (ETo) plays a significant role in determining crop water requirements in irrigated agriculture. A plethora of methods for the estimation of ETo are available. However, the regional suitability of these methods needs to be assessed given the limited availa-bility of meteorological data. In this study, daily estimates of 11 ETo models were selected and compared with the FAO-Penman–Monteith equation (FAO-PM). The select-ed methods were Blaney–Criddle (BC), Jaisen–Haise (JH), Hargreaves method (HM), McGuinness–Borndne (MB), Chapman (CM), Abtew model (AM), Turc method (TM), FAO-PM equation, Penman equation (PM), Prie-stley–Taylor (PT) and Matt–Shuttleworth (MS). Evalua-tion of these models was carried out during 2016–20 in the New Bhupania Minor Command of the Dulhera dis-tributary, Western Yamuna Canal Command (WYCC), Haryana, India. The selected models were evaluated to find a substitute for the FAO-PM equation based on different statistical indices. It was observed that the PT method performed best and was in line with the FAO-PM equation with correlation coefficient, root mean square error, mean absolute error, Nash–Sutcliffe co-efficient and mean bias error as 0.92, 0.74, 0.48, 0.83, 0.171 respectively. Based on this study and statistical error indices values, the models can be ranked as PT > CM > TM > JH > AM > PM > MS > HM > BC > MB. Thus, we recommend using the PT model for the esti-mation of ETo in the study area with available meteoro-logical parameters for irrigation scheduling.Keywords
Canal Command, Climatological Data, Comparative Evaluation, Evapotranspiration Estimation Models, Irrigated Agriculture.References
- Pandey, P. K., Dabral, P. P. and Pandey, V., Evaluation of reference evapotranspiration methods for the northeastern region of India. Int. Soil Water Conserv. Res., 2016, 4, 52–63.
- Shirmohammadi-Aliakbarkhani, Z. and Saberali, S. F., Evaluating of eight evapotranspiration estimation methods in arid regions of Iran. Agric. Water Manage., 2020, 239, 106243.
- Djaman, K., Koudahe, K., Akinbile, C. O. and Irmak, S., Evaluation of eleven reference evapotranspiration models in semiarid conditions. J. Water Resour. Prot., 2017, 9, 1469–1490.
- Saggi, M. K. and Jain, S., Reference evapotranspiration estimation and modeling of the Punjab, Northern India using deep learning. Comput. Electron. Agric., 2019, 156, 387–398.
- Song, X., Lu, F., Xiao, W., Zhu, K., Zhou, Y. and Xie, Z., Performance of 12 reference evapotranspiration estimation methods compared with the Penman–Monteith method and the potential influences in Northeast China. Meteorol. Appl., 2019, 26, 83–96.
- Allen, R. G. et al., Crop evapotranspiration – guidelines for compu-ting crop water requirements FAO Irrigation and Drainage paper 56. FAO, Rome, Italy, 1998, vol. 300(9), pp. 65–79.
- Zhao, L. B., Zhao, Y. L. and Jiang, Z. D., Design and fabrication of a piezoresistive pressure sensor for ultra high temperature environ-ment. J. Phys. Conf. Ser., 2006, 48, 178–183.
- Lang, D. et al., A comparative study of potential evapotranspiration estimation by eight methods with FAO Penman–Monteith method in southwestern China. Water, 2017, 9, 734.
- Todorovic, M., Karic, B. and Pereira, L. S., Reference evapo-transpiration estimate with limited weather data across a range of Mediterranean climates. J. Hydrol., 2013, 481, 166–176.
- Gupta, R. and Misra, A. K., Groundwater quality analysis of qua-ternary aquifers in Jhajjar district, Haryana, India: focus on ground-water fluoride and health implications. Alexandria Eng. J., 2018, 57, 375–381.
- NABARD, District Project Plan. PLP – 2016–17, Jhajjar district, Haryana, 2016; https://www.nabard.org/demo/auth/writereaddata/ tender/2110161208Haryana-StateFocusPaper-2016-17.split-and-mer-ged.pdf
- Doorenbos, J. and Pruitt, W. O., Guidelines for predicting crop water requirements. FAO Irrigation Drainage Paper, 1977, no. 24, pp. 1–144.
- Abtew, W., Evapotranspiration measurements and modeling for three wetland systems in South Florida. J. Am. Water Resour. Assoc., 1996, 32, 465–473; https://doi.org/10.1111/k.1752-1688.1996.tb0-4044.x.
- Jensen, M. E. and Haise, H. R., Estimating evapotranspiration from solar radiation. J. Irrig. Drain. Div., 1963, 89, 15–41.
- Hargreaves, G. H. and Samani, Z. A., Reference crop evapotrans-piration from temperature. Appl. Eng. Agric., 1985, 1, 96–99.
- Turc, L., Water requirements assessment of irrigation, potential evapotranspiration: simplified and updated climatic formula. Ann. Agronom., 1961, 12, 13–49.
- Penman, H. L., Natural evaporation from open water, bare soil and grass. Proc. R. Soc. London, Ser. A, 1948, 193, 120–145.
- Feddes, R. A. and Lenselink, K. J., Evaporation from Open Water: The Penman Method. Drainage Principles and Application (ed. Ritzema, H. P.), International Institute for Land Reclamation and Improvement, The Netherlands, pp. 145–172.
- Bapuji Rao, B., Sandeep, V. M., Rao, V. U. M. and Venkateswarlu, B., Potential Evapotranspiration estimation for Indian conditions: Improving accuracy through calibration coefficients. Tech. Bull. No 1/2012. All India Co-ordinated Research Project on Agromete-orology, Central Research Institute for Dryland Agriculture, Hy-derabad, 2012, p. 60.
- Allen, R. G. and Pruitt, W. O., FAO-24 reference evapotranspira-tion factors. J. Irrig. Drain. Eng., 1991, 117, 758–773.
- Chapman, T. G., Estimation of evaporation in rainfall-runoff mod-els. In Proceedings MODSIM 2003. International Congress on Modelling and Simulation, Modelling and Simulation Society of Australia, 2003, vol. 1, pp. 148–153.
- Shuttleworth, W. J. and Wallace, J. S., Calculating the water requi-rements of irrigated crops in Australia using the Matt–Shuttleworth approach. Trans. ASABE, 2009, 52(6), 1895–1906.
- Khan, R., Ali, I., Asif Suryani, M., Ahmad, M. and Zakarya, M., Wireless sensor network based irrigation management system for container grown crops in Pakistan. World Appl. Sci. J., 2013, 24, 1111–1118.
- Lhomme, J. P., Boudhina, N. and Masmoudi, M. M., Technical note: on the Matt–Shuttleworth approach to estimate crop water requi-rements. Hydrol. Earth Syst. Sci., 2014, 18, 4341–4348.
- Oudin, L. et al., Which potential evapotranspiration input for a lumped rainfall-runoff model? Part 2 – towards a simple and effi-cient potential evapotranspiration model for rainfall-runoff modelling. J. Hydrol., 2005, 303, 290–306.
- McGuinness, J. L. and Bordne, E. F., A Comparison of Lysimeter-derived Potential Evapotranspiration with Computed Values, US Department of Agriculture, 1972.
- Singh, P., Sarangi, A., Singh, D. K., Sehegal, V. K., Dash, S. and Chakrabarti, B., Performance evaluation of evapotranspiration esti-mation methods in Sultanpur, Uttar Pradesh, India. Indian J. Agric. Sci., 2021, 91, 421–425.
- Borah, R. S. M. K., Comparative evaluation of different reference evapotranspiration estimation methods for Lakhimpur district of Assam, India. Int. J. Sci. Res., 2017, 6, 2162–2168.
- Heydari, M. M., Aghamajidi, R., Beygipoor, G. and Heydari, M., Comparison and evaluation of 38 equations for estimating reference evapotranspiration in an arid region. Fresenius Environ. Bull., 2014, 23, 1985–1996.
- Xu, C. Y. and Chen, D., Comparison of seven models for estimation of evapotranspiration and groundwater recharge using lysimeter measurement data in Germany. Hydrol. Process., 2005, 19, 3717– 3734.
- Patle, G. T. and Singh, D. K., Sensitivity of annual and seasonal reference crop evapotranspiration to principal climatic variables. J. Earth Syst. Sci., 2015, 124(4), 819–828.
- Efthimiou, N., Alexandris, S., Karavitis, C. and Mamassis, N., Comparative analysis of reference evapotranspiration estimation between various methods and the FAO-56 Penman–Monteith procedure. Eur. Water, 2013, 42, 19–34.
- Chowdhury, A., Gupta, D., Das, D. P. and Bhowmick, A., Compari-son of different evapotranspiration estimation techniques for Mohan-pur, Nadia District, West Bengal. Int. J. Comput. Eng. Res., 2017, 7(4), 33–39.
- Zheng, H. et al., Assessing the ability of potential evapotranspi-ration models in capturing dynamics of evaporative demand across various biomes and climatic regimes with ChinaFLUX measure-ments. J. Hydrol., 2017, 551, 70–80.
- Liu, X., Xu, C., Zhong, X., Li, Y., Yuan, X. and Cao, J., Comparison of 16 models for reference crop evapotranspiration against weighing lysimeter measurement. Agric. Water Manage., 2017, 184, 145–155.