Have a personal or library account? Click to login

Potential of Optical Sensors for Predicting Winter Wheat Yield Through Variable-Rate Nitrogen Application

Open Access
|Dec 2024

References

  1. Ameen A., Tang C., Liu J., Han L., Xie G.H. (2019): Switchgrass as forage and biofuel feedstock: Effect of nitrogen fertilization rate on the quality of biomass harvested in late summer and early fall. Field Crops Research, 235: 154-162. https://doi.org/10.1016/j.fcr.2019.03.009Getrightsandcontent
  2. Babar M.A., Reynolds M.P., Van Ginkel M., Klatt A.R., Raun W.R., Stone M.L. (2006): Spectral reflectance indices as a potential indirect selection criteria for wheat yield under irrigation. Crop Science, 46: 578-588. https://doi.org/10.2135/cropsci2005.0059
  3. Berger K., Verrelst J., Féret J.B., Wang Z., Wocher M., Strathmann M., Hank T. (2020): Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions. Remote Sensing of Environment, 242: 111758. https://doi.org/10.1016/j.rse.2020.111758
  4. BioSense (2023): Plant-O-Meter – low-cost portable multispectral optical device for precise plant status assessment. Available at: https://biosens.rs/en/themes/plant-o-meter (accessed 21.12.2023.).
  5. Cai C., Yang Z., Liu L., La, Y., Lei J., Fan S. (2021): Consistent effects of canopy vs. understory nitrogen addition on soil respiration and net ecosystem production in moso bamboo forests. Forests, 12: 1427. https://doi.org/10.3390/f12101427
  6. Cartelle J., Pedró A., Savin R., Slafer G.A. (2006): Grain weight responses to post-anthesis spikelet-trimming in an old and a modern wheat under Mediterranean conditions. European Journal of Agronomy, 25(4): 365-371. https://doi.org/10.1016/j.eja.2006.07.004
  7. Duan T., Chapman S.C., Guo Y., Yheng B. (2017): Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Research, 210: 71-80. https://doi.org/10.1016/j.fcr.2017.05.025
  8. Erenstein O. (2009): Zero tillage in the rice-wheat systems of the Indo-Gangetic plains: A review of impacts and sustainability implications. International Food Policy Research Institute, Washington.
  9. European Parliamentary Research Service (2016): Precision agriculture and the future of farming in Europe. Scientific Foresight Study. IP/G/STOA/FWC/2013-1/Lot 7/SC5, December 2016.
  10. Fageria N.K. (2009): The use of nutrients in crop plants. CRC Press, Taylor & Francis Group, LLC, Boca Raton, USA, Florida.
  11. Francesconi S., Harfouche A., Maesano M., Balestra G.M. (2021): UAV-based thermal, RGB imaging and gene expression analysis allowed detection of fusarium head blight and gave new insights into the physiological responses to the disease in durum wheat. Frontiers in Plant Science, 12: 628575. https://doi.org/10.3389/fpls.2021.628575
  12. Goodwin A.W., Lindsey L.E., Harrison S.K., Paul P.A. (2018): Estimating wheat yield with normalized difference vegetation index and fractional green canopy cover. Crop, forage & turfgrass management, 4(1): 1-6. https://doi.org/10.2134/cftm2018.04.0026
  13. Gracia-Romero A., Kefauver S.C., Fernandez-Gallego J.A., Vergara-Diaz O., Nieto-Taladriz M.T., Araus J.L. (2019): UAV and ground image-based phenotyping: a proof of concept with durum wheat. Remote Sensing, 11(10): 1244. https://doi.org/10.3390/rs11101244
  14. Guo Y., Chen Y., Searchinger T.D., Zhou M., Pan D., Yang J., Wu L., Cui Z., Zhang W., Zhang F., Ma L., Sun Y., Zondlo M.A., Zhang L., Mauzerall D.L. (2020): Air quality, nitrogen use efficiency and food security in China are improved by cost-effective agricultural nitrogen management. Nature Food, 1: 648-658. https://doi.org/10.1038/s43016-020-00162-z
  15. Hernández V., Hellin P., Fenoll J., Flores P. (2019): Interaction of nitrogen and shading on tomato yield and quality. Scientia Horticulturae, 255: 255-259. https://doi.org/10.1016/j.scienta.2019.05.040
  16. Huang S., Tang L., Hupy J.P., Wang Y., Shao G. (2021): A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1): 1-6. https://doi.org/10.1007/s11676-020-01155-1
  17. Kitić G., Tagarakis A., Cselyuszka N., Panić M., Birgermajer S., Sakulski D., Matović J. (2019): A new low-cost portable multispectral optical device for precise plant status assessment. Computers and Electronics in Agriculture, 162: 300-308. https://doi.org/10.1016/j.compag.2019.04.021
  18. Kostić M. (2021): Precizna polјoprivreda. Faculty of Agriculture, University of Novi Sad, Novi Sad.
  19. Kovačević V., Kovačević D., Pepo P., Marković M. (2013): Climate change in Croatia, Serbia, Hungary and Bosnia and Herzegovina: Comparison the 2010 and 2012 maize growing seasons. Poljoprivreda, 19(2): 16-22.
  20. Kumar L., Schmidt K.S., Dury S., Skidmore A.K. (2001): Review of hyperspectral remote sensing and vegetation science. In: Imaging spectrometry: Basic principles and prospective applications, 111-155. Kluwer Academic Publishers, Dordrecht.
  21. Lepore M. & Delfino I. (2022): Optical sensors technology and applications. Sensors, 22(20): 7905. https://doi.org/10.3390/s22207905
  22. Loffler C.M., Rauch T.L., Busch R.H. (1985): Grain and plant protein relationships in hard red spring wheat 1. Crop science, 25(3): 521-524. https://doi.org/10.2135/cropsci1985.0011183X002500030021x
  23. LV R.J., Shang Q.Y., Chen L., Zeng Y.J., Hu S.X., Yang X.X. (2018): Study on diagnosis of nitrogen nutrition in rice based on critical nitrogen concentration. Journal of Plant Nutrition and Fertilizers, 24(5): 1396-1405.
  24. Ljubičić N., Kostić M., Marko O., Panić M., Brdar S., Lugonja P., Knežević M., Minić V., Ivošević B., Jevtić R., Crnojević V. (2018): Estimation of aboveground biomass and grain yield of winter wheat using NDVI measurements. Proceedings of the 9th International Agricultural Symposium “Agrosym 2018”, 390-397.
  25. Ljubičić N., Popović V., Kostić M., Pajić M., Buđen M., Gligorević K., Dražić M., Bižić M., Crnojević V. (2023): Multivariate interaction analysis of Zea mays L. genotypes growth productivity in different environmental conditions. Plants, 12(11): 2165. https://doi.org/10.3390/plants12112165
  26. Marti J., Bort J., Slafer G.A., Araus J.L. (2007): Can wheat yield be assessed by early measurements of normalized difference vegetation index? Annals of Applied Biology, 150: 253-257. https://doi.org/10.1111/j.1744-7348.2007.00126.x
  27. Naser M.A., Khosla R., Longchamps L., Dahal S. (2020): Using NDVI to differentiate wheat genotypes productivity under dryland and irrigated conditions. Remote Sensing, 12(5): 824. https://doi.org/10.3390/rs12050824
  28. Nduku L., Munghemezulu C., Mashaba-Munghemezulu Z., Kalumba A.M., Chirima G.J., Masiza W., De Villiers C. (2023): Global research trends for unmanned aerial vehicle remote sensing application in wheat crop monitoring. Geomatics, 3(1): 115-136. https://doi.org/10.3390/geomatics3010006
  29. Nicoletto C., Galvao A., Maucieri C., Borin M., Sambo P. (2017): Distillery anaerobic digestion residues: A new opportunity for sweet potato fertilization. Scientia Horticulturae, 225: 38-47. https://doi.org/10.1016/j.scienta.2017.06.048
  30. Ohta K. & Makino, R. (2019): Stem direction affects the fruit yield, plant growth, and physiological characteristics of a determinate-type processing tomato (Solanum lycopersicum L.). Scientia Horticulturae, 244: 102-108. https://doi.org/10.1016/j.scienta.2018.09.008
  31. Pandžić M., Tagarakis A.T., Radonić V., Marko O., Kitić G., Panić M., Ljubičić N., Crnojević V. (2022): Potential of sentinel-2 satellite and novel proximal sensor data fusion for agricultural applications. In: Bochtis, D.D., Lampridi M., Petropoulos G.P., Ampatzidis Y., Pardalos P. (2022): Information and communication technologies for agriculture - theme I: sensors. Springer, Cham. https://doi.org/10.1007/978-3-030-84144-7_7
  32. Pavuluri K., Chim B.K., Griffey C.A., Reiter M.S., Balota M., Thomason W.E. (2015): Canopy spectral reflectance can predict grain nitrogen use efficiency in soft red winter wheat. Precision Agriculture, 16(4): 405-424.
  33. Prasad B., Carver B.F., Stone M.L., Babar M.A., Raun W.R., Klatt A.R. (2007): Potential use of spectral reflectance indices as a selection tool for grain yield in winter wheat under great plains conditions. Crop Science, 47: 1426-1440. https://doi.org/10.2135/cropsci2006.07.0492
  34. Reynolds M.P., Pask A.J.D., Mullan D.M. (2012): Physiological breeding I: Interdisciplinary approaches to improve crop adaptation. CIMMYT, International maize and wheat improvement center, Mexico.
  35. Reynolds M.P., Skovmand B., Trethowan R., Singh R.P., van Ginkel M. (2001): Research highlights of the CIMMYT wheat program, 1999–2000. CIMMYT, Mexico City, Mexico.
  36. Shahrokhnia M.H. & Sepaskhah A.R. (2018): Water and nitrate dynamics in safflower field lysimeters under different irrigation strategies, planting methods, and nitrogen fertilization and application of HYDRUS-1D model. Environmental Science and Pollution Research, 25(9): 8563-8580. https://doi.org/10.1007/s11356-017-1184-7
  37. Shewry P.R. (2009): Wheat. Journal of experimental botany, 60(6): 1537-1553. https://doi.org/10.1093/jxb/erp058
  38. Shiferaw B., Smale M., Braun H.J., Duveiller E., Reynolds M., Muricho G. (2013): Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Security, 5: 291-317. https://doi.org/10.1007/s12571-013-0263-y
  39. Simović I., Šikoparija B., Panić M., Radulović M., Lugonja P. (2022): Remote sensing of poplar phenophase and leaf miner attack in urban forests. Remote Sensing, 14(24): 6331. https://doi.org/10.3390/rs14246331
  40. Talal M., Raza A., Safdar M., Al Ansari M.S., Ali S.K., Sattar J. (2024): Optical sensing for precision agriculture. In: A. Khang (Ed.), Agriculture and aquaculture applications of biosensors and bioelectronics, IGI Global, pp. 213-240. https://doi.org/10.4018/979-8-3693-2069-3.ch011
  41. Tremblay N., Wang Z., Ma B.L., Belec C., Vigneault P. (2009): A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precision Agriculture, 10: 145-161. https://doi.org/10.1007/s11119-008-9080-2
  42. Trimble (2024): GreenSeeker handheld crop sensor. Available at: https://ptxtrimble.com/en/products/hardware/flow-application-control/greenseeker-handheld-crop-sensor (accessed 10.04.2024.).
  43. Tubaña B., Harrell D., Walker T., Teboh J., Lofton J., Kanke Y., Phillips S. (2011): Relationships of spectral vegetation indices with rice biomass and grain yield at different sensor view angles. Agronomy Journal, 103: 1405-1413. https://doi.org/10.2134/agronj2011.0061
  44. Wang H. (2017): Crop assessment and monitoring using optical sensors. PhD dissertation. Department of Agronomy College of Agriculture, Kansas State University, Manhattan, Kansas.
  45. Weiss M., Jacob F.G., Duveiller G. (2020): Remote sensing for agricultural applications: A meta-review. Remote Sensing of Environment, 236: 111402. https://doi.org/10.1016/j.rse.2019.111402
  46. Welsh J.P., Wood G.A., Godwin R.J., Taylor J.C., Earl R., Blackmore S., Knight S.M. (2003): Developing strategies for spatially variable nitrogen application in cereals, part II: wheat. Biosystems engineering, 84(4): 495-511. https://doi.org/10.1016/S1537-5110(03)00003-5
  47. Wiethölter S. (2011): Fertilidade do solo e a cultura do trigo no Brasil. Trigo no Brasil, 6: 135-184.
  48. Yao Z., Pelster D.E., Liu C., Zheng X. (2020): Soil N intensity as a measure to estimate annual N2O and NO fluxes from natural and managed ecosystems. Current Opinion in Environmental Sustainability, 47: 1-6. https://doi.org/10.1016/j.cosust.2020.03.008
  49. Yule I. & Pullanagari R. (2012): Optical sensors to assist agricultural crop and pasture management. In: Mukhopadhyay, S. (eds) Smart sensing technology for agriculture and environmental monitoring. Lecture Notes in Electrical Engineering. Vol. 146, Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27638-5_2
  50. Zhao W., Rong Y., Zhou Y., Zhang Y., Li S., Liu L. (2024): The relationship of gross primary productivity with NDVI rather than solar-induced chlorophyll fluorescence is weakened under the stress of drought. Remote Sensing, 16(3): 555. https://doi.org/10.3390/rs16030555
DOI: https://doi.org/10.2478/contagri-2024-0022 | Journal eISSN: 2466-4774 | Journal ISSN: 0350-1205
Language: English
Page range: 181 - 191
Submitted on: Feb 19, 2024
Accepted on: May 13, 2024
Published on: Dec 12, 2024
Published by: University of Novi Sad
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2024 Nikola Stanković, Marko Kostić, Nataša Ljubičić, Goran Kitić, Nevena Stevanović, Maša Buđen, published by University of Novi Sad
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.