A new approach for variable rate fertilization based on direct read of soil map image
Abstract
Aim of study: To develop a methodology for variable rate (VR) fertilization with less complexity in practice for variable rate fertilization.
Area of study: Northwest of Iran.
Materials and methods: A software was developed to read a soil map image pixel-by-pixel to provide the required information to tailor the fertilizer rate, regardless of which software was used for map generation. A total of 78 soil samples were collected and analyzed for soil potassium, and the results were used to generate an actual map including zones ranging from 70 to 190 kg/ha. The application rates were evaluated based on 50 deposition pans and compared with those calculated from the actual map. Based on the lag time in fertilization, three applied maps were also generated.
Main results: The correlation coefficients found between the application rates computed based on the original soil samples and posted the locations of the sample points on the applied maps were 0.95, 0.95, and 0.94, over the ravel speeds of 6, 7, and 8 km/h, respectively. The results showed there is a correlation coefficient of 0.96 with an RMSE of 1.88 kg/ha, where the application rates computed from deposition pans compared with the corresponding location on the actual map. All applied maps were identical to the actual map. The results showed that the VR fertilization based on a direct read of a map image operated as expected.
Research highlights: Fertilizer application was based on the direct read of map image. This study highlights also the need of new approaches in programing for simplicity of precision agriculture.
Downloads
References
Albornoz EM, Kemerer AC, Galarza R, Mastaglia N, Melchiori R, Martínez CE, 2017. Development and evaluation of an automatic software for management zone delineation. Precis Agric 19(3): 463-476. https://doi.org/10.1007/s11119-017-9530-9
Bernacki H, Haman J, Kanafojski CZ, 1972. Agricultural machines, theory and construction. Sci Publ Foreign Coop Centre, Centr Inst for Sci Tech Econ Inform, Vol. 1, 883 pp, Warsaw, Poland.
Betzek NM, de Souza EG, Bazzi CL, Schenatto K, Gavioli A, Graziano Magalhaes PS, 2019. Computational routines for the automatic selection of the best parameters used by interpolation methods to create thematic maps. Comput Electron Agric 157: 49-62. https://doi.org/10.1016/j.compag.2018.12.004
Bogrekci I, Lee WS, 2005. Spectral phosphorus mapping using diffuse reflectance of soils and grass. Biosyst Eng 91(3): 305-312. https://doi.org/10.1016/j.biosystemseng.2005.04.015
Boucneau G, van Meirvenne M, Thas O, Hofman G, 1998. Integrating properties of soil map delineations into ordinary kriging. Eur J Soil Sci 49: 213-229. https://doi.org/10.1046/j.1365-2389.1998.00157.x
Chan CW, Schueller JK, Miller WM, Whitnet JD, Cornell JA, 2004. Error sources affecting variable rate application of nitrogen fertilizer. Precis Agric 5: 601-616. https://doi.org/10.1007/s11119-004-6345-2
Dusadeerungsikul PO, Nof SY, Bechar A, Tao Y, 2019. Collaborative control protocol for agricultural cyberphysical system. In: Procedia Manufacturing; Proc 25th Int Conf on Production Research, August, 2019. https://doi.org/10.1016/j.promfg.2020.01.330
Faber BA, Downer AJ, Holstege D, Mochizuki MJ, 2007. Accuracy varies for commercially available soil test kits analyzing nitrate-nitrogen, phosphorus, potassium, and pH. Hort Technol 17(3): 355-362. https://doi.org/10.21273/HORTTECH.17.3.358
Ferguson R, Rundquist D, 2017. Remote sensing for site-specific plant management. In: Shannon DK et al., 2019. Precision Agriculture Basics, ACSESS Publications, 265 pp. https://doi.org/10.2134/precisionagbasics.2016.0092
Fulton JP, Scott AS, Timothy SS, Stephen FH, 2002. Simulation of variable-rate application of granular materials. ASAE Meeting Paper No. 021186. St. Joseph, MI, USA.
Fulton JP, Shearer SA, Stombaugh TS, Anderson ME, Burks TF, Higgins SF, 2003. Simulation of fixed- and variable-rate application of granular materials. T ASAE 46(5): 1311-1321. https://doi.org/10.13031/2013.15440
Fulton JP, Scott AS, Timothy SS, Stephen FH, 2005. Rate response asessment from various granular VRT applicators. T ASAE 48(6): 2095-2103. https://doi.org/10.13031/2013.20086
Gallagher PA, Herlihy M, 1963. An evaluation of errors associated with soil testing, Irish J Agric Res 2(2): 149-167.
Guerrero A, Mouazen AM, 2021. Evaluation of variable rate nitrogen fertilization scenarios in cereal crops from economic, environmental and technical perspective. Soil Till Res 213: 105110. https://doi.org/10.1016/j.still.2021.105110
Maleki MR, Mouazen AM, Ramon H, De Baerdemaeker J, 2007. Optimisation of soil VIS-NIR sensor-based variable rate application system of soil phosphorus. Soil Till Res 94: 239-250. https://doi.org/10.1016/j.still.2006.07.016
Maleki MR, Mouazen AM, De Ketelaere B, Ramon H, De Baerdemaeker J, 2008a. On-the-go variable rate phosphorus fertilization based on a visible and near-infrared soil sensor. Biosyst Eng 99(1): 35-46. https://doi.org/10.1016/j.biosystemseng.2007.09.007
Maleki MR, Ramon H, De Baerdemaeker J, Mouazen AM, 2008b. A study on the time response of a soil sensor-based variable rate granular fertiliser applicator. Biosyst Eng 100(2): 160-166. https://doi.org/10.1016/j.biosystemseng.2008.03.007
Mouazen AM, Maleki MR, De Baerdemaeker J, Ramon H, 2007. On-line measurement of some selected soil properties using a VIS-NIR sensor. Soil Till Res 93(1): 13-27. https://doi.org/10.1016/j.still.2006.03.009
Oberthür T, Goovaerts P, Dobermann A, 1999. Mapping soil texture classes using field texturing, particle size distribution and local knowledge by both conventional and geostatistical methods. Eur J Soil Sci 50: 457-479. https://doi.org/10.1046/j.1365-2389.1999.00255.x
Pathak HS, Brown P, Best T, 2019. A systematic literature review of the factors affecting the precision agriculture adoption process. Precis Agric 20(6): 1-25. https://doi.org/10.1007/s11119-019-09653-x
Raun WR, Soile JB, Johnson GV, Stone ML, Whitney RW, Lees HL, et al., 1998. Microvariability in soil test, plant nutrient and yield parameters in Bermudagras. Soil Sci Soc Am J 62: 683-690. https://doi.org/10.2136/sssaj1998.03615995006200030020x
Robert PC, 2002. Precision agriculture: a challenge for crop nutrition management. Plant Soil 247 (1): 143-149. https://doi.org/10.1023/A:1021171514148
Shannon DK, David E, Clay DE, Kitchen NR, 2019. Precision agriculture basics, ACSESS Publications, 265 pp.
Stein A, Hoogerwerf M, Bouma J, 1988. Use of map-delineation to improve co-kriging of point data on moisture deficits. Geoderma 43: 311-325. https://doi.org/10.1016/0016-7061(88)90041-9
Thorp KR, Batchelor WD, Paz JO, Kaleita AL, DeJonge KC, 2007. Using cross-validation to evaluate CERES-maize yield simulations within a decision support system for precision agriculture. T ASAE 50(4): 1467-1479. https://doi.org/10.13031/2013.23605
Tiwari A, Kumar Jaga P, 2012. Precision farming in India-A review. Outlook Agric 41(2): 139-143. https://doi.org/10.5367/oa.2012.0082
Warrick AW, 1998. Spatial variability in environmental soil physics. In: Environmental Soil Physics; Hillel D (Ed). Academic Press, USA. pp: 655-675. https://doi.org/10.1016/B978-012348525-0/50026-4
Copyright (c) 2022 INIA-CSIC
This work is licensed under a Creative Commons Attribution 4.0 International License.
© CSIC. Manuscripts published in both the print and online versions of this journal are the property of the Consejo Superior de Investigaciones Científicas, and quoting this source is a requirement for any partial or full reproduction.
All contents of this electronic edition, except where otherwise noted, are distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. You may read the basic information and the legal text of the licence. The indication of the CC BY 4.0 licence must be expressly stated in this way when necessary.
Self-archiving in repositories, personal webpages or similar, of any version other than the final version of the work produced by the publisher, is not allowed.
