Document Type : Research Paper
Authors
- Anahita Hadighanavat 1
- Abdolrahim hooshmand 2
- Parvaneh Tishehzan 3
- Naser Alemzadeh ansari, 4
- Kazem Rangzan 5
1 Ph.D. student, Department of Irrigation and Drainage, Faculty of Water and Environmental, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Professor, Department of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
3 Assistant Professor, Department of Environmental, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
4 Associate Professor, Department of Horticulture and Crop Science, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
5 Professor of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Abstract
Introduction: Nowadays, the management of water resources has become one of the major challenges in the world due to recent droughts and water shortages. Therefore, timely and non-destructive monitoring of Water use efficiency (WUE) and yield of plants to screen cultivars with high water use performance and efficiency and rational allocation of water has become one of the important goals in agriculture. Compared with traditional crop Yield and WUE monitoring and diagnostic tools, hyperspectral remote sensing technology has made it possible to obtain water use efficiency and Yield by taking advantage of large amounts of continuous data on a large scale. Therefore, this study aimed to create a model capable of estimating the WUE and Yield of tomato plants based on hyperspectral remote sensing data by establishing a relationship between common spectral indices and WUE and Yield in greenhouse conditions.
Materials and Methods: The research was carried out during the growing season of 2021 from January to June at the research greenhouse of the Department of Water and Environmental Engineering, Shahid Chamran University, Ahvaz, Khuzestan Province, The main factor was irrigation treatments in three levels including full irrigation, 20%, and 40% deficit irrigation, and the sub-main factor was silica nanoparticles with concentrations of 0 and 100 mg/lit. The pot experiment plot was laid out in a split plot in a randomized complete design (RCD) with four replications (120 pots in total). Then Tomato yield and WUE under the various treatments, were calculated. Canopy hyperspectral reflectance was measured using a portable spectrometer (ASD FieldSpec 3) operated in the spectral range of 350-2500 nm. The spectral data acquisition was conducted in four stages of plant growth during the growing season and the data were used to calculate spectral indices NWI5, WI3, WI4, NDVI, NDVI, and OSAVI. Then the ability of spectral indices to evaluate the water use efficiency and yield of tomato plants in different irrigation regimes and nanoparticles was investigated. Analysis of variance (ANOVA) was performed on spectral indices and WUE and yield of tomato plants using a split plot design. The PROC GLM method of SAS software (version 9.4, SAS Institute, Inc., Cary, NC, USA) was used for this analysis. Then, in order to compare the averages and whether they have a significant difference with each other at the 0.01 and 0.05 levels, the least significant difference (LSD) test was used.
Results and Discussion: The results showed that the Water use efficiency (WUE) under deficit irrigation treatments is increased with increasing water stress but the yield of tomato decreases with increase of water stress. In addition, the WUE and yield of tomato increases with increasing the concentration of silica nanoparticles (from 0 to 100 mg/liter). The value of NDVI and OSAVI indices decreased with the increase of water stress, while the value of RDVI, WI3, WI4 and NWI5 indices increased. The amount of NDVI and OSAVI spectral indices in the treatment containing 100 mg/liter nanoparticles was higher than the treatment without nanoparticles. Also, the amount of spectral indices RDVI, WI3, WI4 and NWI5 in nanoparticles with a concentration of 100 mg/liter was lower than the control treatment (zero concentration). The results also showed that the coefficient of determination between the different spectral indices and the WUE and Yield index was 0.55**
Furthermore, among the six spectral indices, three spectral indices (NWI5, WI3, and WI4) jointly met most of the criteria used to determine the accuracy of the models for predicting yield and WUE
Conclusion: Maintaining existing water resources through improving irrigation management and increasing water use efficiency and Yield of plants is the main goal for the sustainable development of water agriculture. As a result, rapid and non-destructive monitoring of water use efficiency and Yield is of great importance in improving irrigation management of crops and saving water consumption. significant relationship with yield and WUE index of tomato plants in greenhouse conditions. In conclusion, Spectral indices studied in this research could be useful and non-destructive assessments of the water use efficiency and yield of tomato in greenhouse conditions.
Keywords: Deficit Irrigation, Spectral indices, spectroscopy, Tomato Plant, Water use efficiency
Keywords
Main Subjects
germination, growth and yield of faba bean (Vicia faba L.) under salt stress conditions. American
Journal of Experimental Agriculture, 5(6): 509- 524. https://doi.org/10.9734/AJEA/2015/14109
2. Ashourloo, D., Salehi Shahrabi, H and Nematollahi, H. 2021. Estimating Yield of Canola Based on Time Series of Remote Sensing Data. Journal of Iranian Remote Sensing and GIS, 13(3): 59-72. (In Persian with English abstract)
3.Azizi, K., Nabiollahi, K., and Davari, M. 2018. Evaluation of the capability of spectroscopic methods in estimating some properties of salt-affected soil. Agricultural Engineering (Scientific Journal of Agriculture. 41(3). (In Persian)
4. Ban, H. Y., Kim, K.S., Park, N. W., and Lee, B. W. 2016. Using MODIS Data to Predict Regional Corn Yields. Journal of Remote Sensing, 9: 1-16. https://doi.org/10.3390/rs9010016
5. Campos, J., Llop, J., Gallart, M., García-Ruiz, F., Gras, A., Salcedo, R ., and Gil, E. 2019. Development of canopy vigor maps using UAV for site-specific management during the vineyard spraying process. Precis, Agric, Precision Agric, 20 (6):1136-1156. https://doi.org/10.1007/s11119-019-09643-z
6. Chen, F., Li, Y., Irshad, M.A., Hussain, A., Nawaz, R., Qayyum, M.F., Ma, J., Zia-ur-Rehman, M., Rizwan, M. and Ali, S. 2023. Effect of titanium dioxide nanoparticles and co-composted biochar on growth and Cd uptake by wheat plants: A field study, Environmental Research, 231(19):116057. doi: 10.1016/j.envres.2023.116057.
7. da Silva, V.S., Salami, G., da Silva, M.I.O., Silva, E.A., Monteiro Junior, J.J., and Alba, E. 2020. Methodological evaluation of vegetation indexes in land use and land cover (LULC) classificationGeol. Journal of Geology, Ecology, and Landscape, 4 (2): 159–169.
8. Dong, B., Shi, L., Shi, C. H., Qiao, Y. Z., Liu, M. Y., and Zhang, Z. B. 2011. Grain yield and water use efficiency of two winter wheat cultivars under different water regimes. Journal of Agricultural and Water Management, 99: 103–110. https://doi.org/10.1016/j. agwat.2011.07.013
9. Ebrahimi, H. 2020. Evaluation yield and water productivity of tomato in deficit irrigation conditions and application of silicon nanoparticle, Thesis, Shahid Chamran University of Ahvaz, Iran. 124p. (In Persian with English abstract).
10. Elazab, A., Bort, J., Zhou, B., Serret, M.D., Nieto-Taladriz, M.T., and Araus, J.L. 2015. The combined use of vegetation indices and stable isotopes to predict durum wheat grain yield under contrasting water conditions. Agric, Water Management, 158: 196–208. https://doi.org/10.1016/j.agwat.2015.05.003
11. Elfarkh, J ., Johansen, K., El Hajj, M.M., Almashharawi, S.K., and McCabe, M. 2023.
Evapotranspiration, gross primary productivity, and water use efficiency over a high-density olive orchard using ground and satellite-based data. Journal of Agricultural Water Management, 287: 108423. https://doi.org/10.1016/j.agwat.2023.108423
12. El-Hendawy, SE., Abd El-Lattief, E.A., Ahmed, AS., and Schmidhalter, U. 2008. Irrigation rate and plant density effects on yield and water use efficiency of drip-irrigated corn. Journal of Agricultural and Water Management, 95(7):836–844. https://doi.org/10.1016/j.agwat.2008.02.008
13. El-Hendawy, SE., Al-Suhaibani, N., Hassan, W., Dewir, YH., El-Sayed, S., and Al-Ashkar, I. 2019. Evaluation of wavelengths and spectral reflectance indices for high throughput assessment of growth, water relations, and ion contents of wheat irrigated with saline water. Journal of Agricultural Water Management, 212: 358–377. https://doi.org/10.1016/j.agwat.2018.09.009
14. El-Hendawy, S. E., Al-Suhaibania, N. A., Elsayed, S, Hassand, W. E., Dewira Y. H., Refaya, Y. and Abdella, K. A. 2019. Potential of the existing and novel spectral reflectance indices for estimating the leaf water status and grain yield of spring wheat exposed to different irrigation rates. Agricultural Water Management, 217: 356-373. https://doi.org/ 10.1016/j.agwat.2019.03.006 15. El-Hendawy, S., Al-Suhaibani, N., Elsayed, S., Refay, Y., Alotaibi, M., Hassan, Y., HassanI.W., and Schmidhalter, U. 2019. Combining biophysical parameters, spectral indices, and multivariate hyperspectral models for estimating yield and water productivity of spring wheat across different agronomic practices. Plos One Journal,14(11): 1-26. https://doi.org/10.1371/journal.pone.0212294 16. El-Hendawy, S.E., Alotaibi, M., Al-Suhaibani, N., Al-Gaadi, K., Hassan, W., Dewir, Y.H., Emam, M.A.E.-G., Elsayed, S., Schmidhalter, U. 2019. Comparative performance of spectral reflectance indices and multivariate modeling for assessing agronomic parameters in advanced spring wheat lines under two contrasting irrigation regimes.Front, Journal of Plant Sci, 10: 1537. 17. Elsayed, S., and Darwish, W.B. 2017. Hyperspectral remote sensing to assess the water status, biomass, and yield of maize cultivars under salinity and water stress. Journal of crop production and management, 76(1): 62-72. https://doi.org/10.1590/1678-4499.018
18. Elsayed, S., El-Hendawy, S., Dewir, Y., Schmidhalter, U., Ibrahim, H., Ibrahim, M., Elsherbiny, O., and Farouk, M. 2021. Estimating the Leaf Water Status and Grain Yield of Wheat under Different Irrigation Regimes Using Optimized Two- and Three-Band Hyperspectral Indices and Multivariate Regression Models. Journal of Water, 13(19): 1-22. https://doi.org/10.3390/w13192666
19. Elsayed, S., El-Hendawy, S., Khadr, M., Elsherbiny, O., Al-Suhaibani, N., Dewir, Y.H.,
Tahir, M.U. Mubushar, M., and Darwish, W. (2021). Integration of spectral reflectance indices and
adaptive neuro-fuzzy inference system for assessing the growth performance and yield of potato under
different drip irrigation regimes. Chemosensors, 9(3): 55.
20. Elsayed, S., Mistele, B., and Schmidhalter, U. 2011. Can changes in leaf water potential be assessed spectrally Funct. Journal of Functional Plant Biology, 38(6): 523–533 https://doi.org/10.1071/FP11021
21.Etesami, H., and Jeong, B.R.2018.Silicon (Si): Review and future prospects on the action mechanisms in alleviating biotic and abiotic stresses in plants. Ecotoxicology and Environmental Safety, 147:881-896. https://doi.org/10.1016/j.ecoenv.2017.09.063.
22. Ghazali, S., Mirzaei, A., and Soltani, Gh.R. (2014). Investigating the physical and economic productivity of agricultural water, Case study of Fars province. International Journal of Analytical
Resources for Water Development Quarterly, 2(3):125-134. (In Persian with English abstract)
23.Gitelson, A., and Merzlyak, M. N. 1994. Spectral reflectance changes associated with autumn senescence of Aesculus hippocastanum L. and Acer platanoides L. leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology, 143: 286-292. https://doi.org/10.1016/S0176-1617(11)81633-0.
24. Haboudane, D., Miller, J. R., Tremblay, N., Zarco-Tejada, P. J., and Dextraze, L. 2002. Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Journal Remote Sensing of Environment, 81(2-3): 416-426. https://doi.org/10.1016/S0034-4257(02)00018-4
25.Haghighi, M., and Pessarakli, M. 2013. Influence of silicon and nano-silicon on salinity tolerance of cherrytomatoes (Solanum lycopersicum L.) at early growth stage. Scientia Horticulturae, 161(1): 111–117. (In Persian with English abstract)
26.Hatfield, J. L., Gitelson, A. A., Schepers, J. S., and Walthall, C. L. 2008. Application of spectral remote sensing for argonomic decisions. Journal of Agronomy, 100: 117-131. https://doi.org/10.2134/agronj2006.0370c
27. Kaya, C., Tuna, L., and Higgs, D. 2006. Effect of silicon on plant growth and mineral nutrition of
maize grown under water stress conditions. Journal of Plant Nutrition, 29: 1469-1480
https://doi.org/10.1080/01904160600837238
28. Kiafar, H., Mousavi, M., Ebadi, A., Moallemi, N., and Fattahi Moghadam, M.R. 2020. The effect of Ca nanoparticles on peach cultivars (Valad Abadi and Elberta). Agricultural Engineering (Scientific Journal of Agriculture). 43(1): 1-14. (In Persian with English abstract)
29. Kiani, A. 2015. Guidelines for water productivity in farms. Technical and Engineering
Research Division, Golestan Agricultural and Natural Resources Research Center, Agricultural Research, Training and Promotion Organization. pp. 1-16. (In Persian with English abstract)
30. Ihuoma, S.O., and Madramootoo, C. A. 2019. Crop reflectance indices for mapping water stress in greenhouse-grown bell pepper. Journal of Agricultural Water Management, 219: 49-58.
https://doi.org/10.1016/j.agwat.2019.04.001
31.Ihuoma, S. O., and Madramootoo, C. A. 2020. Narrow-band reflectance indices for mapping the combined effects of water and nitrogen stress in field-grown tomato crops. Journal Biosystem Engineering, 192(1): 133-143. https://doi.org/10.1016/j.biosystemseng.2020.01.017
32. Li, H. 2010. Spectral response of winter wheat to crop development and various agronomic limitations. International Conference on Multimedia Technology, Oct. Ningbo, China, 29–31.
http://dx.doi.org/10.1007/s11119-022-09976-2
33. Lima, M.C.F., Krus, A., Valero, C., Barrientos, A., Del Cerro, J., and Roldan-Gómez, J.J. 2020. Monitoring plant status and fertilization strategy through multispectral images. MDPI Journal, 20(2): 435. https://doi.org/10.3390/s20020435
34. Mazraee, M.A. 2020. The effect of different irrigation treatments on water use efficiency and crop yield Tomatoes in Kavar plain. Journal of water science and engineering, Islamic Azad University, Ahvaz. Branch, 8(22): 21-28. (In Persian with English abstract)
35. M’hamed, HC., Rezig, M., and Mbarek, BN. 2015. Water use efficiency of durum wheat (Triticum
durum Desf) under deficit irrigation. Journal of Agricultural Science, 7(8): 238-249.
https://doi.org/10.5539/jas.v7n8p238
36. Mistele, B., and Schmidhalter, U. 2008. Estimating the nitrogen nutrition index using spectral
canopy reflectance meassurements. Journal of Agronomy, 29: 184-190.
https://doi.org/10.1016/j.eja.2008.05.007
37. Naderi, M., and Abedi, A. 2012. Application of nanotechnology in agriculture and purification of environmental pollutants. Journal of Nanotechnology, 1: 19-26. (In Persian with English abstract)
38.Nair, S. H., Varghese, B. G., Nair, T., Maekawa, Y., Yoshida, D., and Kumar, S. 2010. Nano particulate material delivery to plants. Journal of Plant Science, 179: 154-163.
http://dx.doi.org/10.1016/j.plantsci.2010.04.012
39.Nangare, DD., Singh, Y., Kumar, PS., and Minhas, PS .2016. Growth, fruit yield and quality of tomato (Lycopersicon esculentum Mill.) As afected by defcit irrigation regulated on phenologicalbasis. Journal of Agricaltural Water Management, 171:73–79. https://doi.org/10.1016/j.agwat.2016.03.016
40. Prasad, A.M., Iverson, L.R., and Liaw, A. 2006. Newer classification and regression tree techniques:
Bagging and random forests for ecological prediction. Journal of Ecosystems, 9: 181–199.
41. Parveen, N., and Ashraf, M. 2010. Role of silicon in mitigating the adverse effects of salt stress on growth and photosynthetic attributes of two maize (Zea mays L.) cultivars grown hydroponically. Pakistan. Journal of Botany, 42(3): 1675-1684.
42. Patanèa, P., Tringali , S., and Sortino, O. 2011. Effects of deficit irrigation on biomass, yield, water productivity and fruit quality of processing tomato under semi-arid Mediterranean climate conditions. Journal of Scientia Horticulturae, 129 (4): 590–596. https://doi.org/10.1016/j.scienta.2011.04.030
43. Pelesco, VA., and Alagao, FB. 2014. Evapotranspiration Rate of Lettuce (Lactuca sativa L., Asteraceae) in a Non-Circulating Hydroponics System. Journal of Society and Technology, 4(1):1-6. https://doi.org/ 10.5281/jst.v4i1.18
44. Peñuelas, J., Pinol, J., Ogaya, R., and Filella, I. 1997. Estimation of plant water concentration by the
reflectance water index WI (R900/R970). Journal of Remote Sensing, 18: 2869- 2875.
https://doi.org/ 10.1080/014311697217396 45. Raskar, S.V., and Laware, S.L. 2014. Effect of zinc oxide nanoparticles on cytology and seed germination in onion. International Journal of Current Microbiology and Applied Science, 3(2): 467-473. 46. Ripoll, J., Urban L, Brunel B., and Bertin, N. 2016. Water defcit efects on tomato quality depend on fruit developmental stage and genotype. Journal of Plant Physical, 190: 26-35. https://doi.org/10.1016/j.jplph.2015.10.006
47. Roujean, J. L., and Breon, F. M. 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements Remote Sensing of Environment. Journal Remote Sensing of Environment, 51(3): 375-384. https://doi.org/10.1016/0034-4257(94)00114-3
48.Sadak, M. S. 2019. Impact of silver nanoparticles on plant growth, some biochemical aspects, and yield of fenugreek plant (Trigonella foenumgraecum). Journal of the National Research center, 38-43.
https://doi.org/10.1186/s42269-019-0077-y
49. Safi, C., Pareeth, S., Yalew, S., Zaag, P., and Mul, W. 2024. Estimating agricultural water productivity using remote sensing derived data. Journal of Modeling Earth Systems and Environment, 10(1):11–13. https://doi.org/0.1007/s40808-023-01841-z
50. Salehi, S., Goldani, M., and Nabati, J. 2020. Effect of Partial Root-Zone Drying on Yield, Yield Components and Water Use Efficiency of Tomato (Solanum lycopersicum L.) under Conditions Deficit Irrigation. Iranian Journal of Irrigation and Drainage,14(2): 426-435. (In Persian with English abstract)
51. Saydi, Z., Abbasi, N., Zarea, M. J., and Zarei, B. 2022. Effects of nitroxin biofertilizer on morpho-physiological characteristics of blackseed (Nigella sativa L.) ecotypes under drought stress. Journal of Agroecology, 14(3): 485-507 (In Persian with English abstract)
52. Sharabian, VR., Noguchi, N., and Ishi, K. 2014. Significant wavelengths for prediction of winter wheat growth status and grain yield using multivariate analysis. Journal of Engineering Agriculture Environment Food, 7: 14–21. https://doi.org/10.1016/j.eaef.2013.12.003
53. SHokri, S., Hooshmand, A., Golabi, M., Alemzadeansari, N., and Struve, D. 2021. Effect of Silica Nanoparticles on Yield of Cucumber (Cucumis sativus L.) in Ahvaz region. Journal of Sustainable Agriculture and Production Sciences. 22(1): 279-292. (In Persian with English abstract)
54. Ullah, I., Mao, H., Rasool, GH., Gao, H., Javed, Q., Abid Sarwar, A., and Muhammad Imran Khan, M. 2021. Effect of Deficit Irrigation and Reduced N Fertilization on Plant Growth, Root Morphology and Water Use Efficiency of Tomato Grown in Soilless Culture. Journal of Agronomy,11:228. https://doi.org/10.3390/agronomy11020228
55. Zhang, H. Z., Liu, M. R., Feng, Z. H., Song, L., Li, X., Liu, W. D., Wang, CH. Y., and Feng, W. 2021. Estimation of Water Use Efficiency in Winter Wheat Based on Multi-Angle Remote Sensing. on Multi-Angle. Journal of Remote Sensing, 12:614417. https://doi.org/ 10.3389/fpls.2021.614417
)