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Map-based site-specific seeding of seed potato production by fusion of proximal and remote sensing data

Muhammad Abdul Munnaf (UGent) , Geert Haesaert (UGent) and Abdul Mouazen (UGent)
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Abstract
Uniform rate seeding (URS), adopted by the majority of farmers worldwide, is an inappropriate approach particularly in fields with large variability to optimize seeding rate that is crucial to manage farm resources sustainably. The objective of this study was to examine the potential of map-based site-specific seeding (SSS) of seed potato for improving crop yield and economic return. A 6-ha-field was scanned using an on-line visible and near infrared (vis-NIR) spectroscopy and an electromagnetic induction (EMI) sensor. Two SSS treatments were adopted based-on two types of management zone (MZ) maps, delineated with K-means clustering algorithm, and these were compared with the URS. The first treatment was based on a MZ map developed using EMI data (EMI-SSS), whereas the second treatment was based on fusion of vis-NIR measured soil data with Sentinel-2 derived normalized difference vegetation index (visNIRsen-SSS). Seed potatoes (Solanum tuberosum L. cv. Hermes) were planted at 11, 13, 15, 17 and 19 cm seed-to-seed spaces according to “Kings” principle i.e., sowing more seeds to the most fertile zones and vice-versa. Results showed that both SSS approaches have resulted in increased tuber yields (EMI-SSS: 32.42 t/ha, visNIRsen-SSS: 31.89 t/ha) and higher economic return, compared to URS . Although visNIRSen-SSS provided lower gross yield than EMI-SSS, the larger yield of small size (28−45 mm) tubers, having higher market price than other size categories has resulted in actual market price for the former being higher than that of the latter approach. The visNIRsen-SSS and EMI-SSS produced net returns of 4995 €/ha and 4947 €/ha, respectively, which were more than that of the URS of 4528 €/ha, by 467 €/ha and 419 €/ha, respectively. In addition, visNIRsen-SSS approach has saved seeding costs by 14 €/ha though EMI-SSS consumed 5 €/ha more compared to URS. Therefore, it is recommended to adopt SSS (particularly visNIRsen-SSS) for seed potato production, as a means of increasing crop yield, reducing input cost and thus maximizing profitability compared to URS.
Keywords
Earth-Surface Processes, Agronomy and Crop Science, Soil Science, Precision agriculture, Seed potato, Data fusion, Map-based site-specific seeding, Economic analysis, Key soil fertility indicators

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MLA
Munnaf, Muhammad Abdul, et al. “Map-Based Site-Specific Seeding of Seed Potato Production by Fusion of Proximal and Remote Sensing Data.” SOIL & TILLAGE RESEARCH, vol. 206, 2021, doi:10.1016/j.still.2020.104801.
APA
Munnaf, M. A., Haesaert, G., & Mouazen, A. (2021). Map-based site-specific seeding of seed potato production by fusion of proximal and remote sensing data. SOIL & TILLAGE RESEARCH, 206. https://doi.org/10.1016/j.still.2020.104801
Chicago author-date
Munnaf, Muhammad Abdul, Geert Haesaert, and Abdul Mouazen. 2021. “Map-Based Site-Specific Seeding of Seed Potato Production by Fusion of Proximal and Remote Sensing Data.” SOIL & TILLAGE RESEARCH 206. https://doi.org/10.1016/j.still.2020.104801.
Chicago author-date (all authors)
Munnaf, Muhammad Abdul, Geert Haesaert, and Abdul Mouazen. 2021. “Map-Based Site-Specific Seeding of Seed Potato Production by Fusion of Proximal and Remote Sensing Data.” SOIL & TILLAGE RESEARCH 206. doi:10.1016/j.still.2020.104801.
Vancouver
1.
Munnaf MA, Haesaert G, Mouazen A. Map-based site-specific seeding of seed potato production by fusion of proximal and remote sensing data. SOIL & TILLAGE RESEARCH. 2021;206.
IEEE
[1]
M. A. Munnaf, G. Haesaert, and A. Mouazen, “Map-based site-specific seeding of seed potato production by fusion of proximal and remote sensing data,” SOIL & TILLAGE RESEARCH, vol. 206, 2021.
@article{8680478,
  abstract     = {{Uniform rate seeding (URS), adopted by the majority of farmers worldwide, is an inappropriate approach particularly in fields with large variability to optimize seeding rate that is crucial to manage farm resources sustainably. The objective of this study was to examine the potential of map-based site-specific seeding (SSS) of seed potato for improving crop yield and economic return. A 6-ha-field was scanned using an on-line visible and near infrared (vis-NIR) spectroscopy and an electromagnetic induction (EMI) sensor. Two SSS treatments were adopted based-on two types of management zone (MZ) maps, delineated with K-means clustering algorithm, and these were compared with the URS. The first treatment was based on a MZ map developed using EMI data (EMI-SSS), whereas the second treatment was based on fusion of vis-NIR measured soil data with Sentinel-2 derived normalized difference vegetation index (visNIRsen-SSS). Seed potatoes (Solanum tuberosum L. cv. Hermes) were planted at 11, 13, 15, 17 and 19 cm seed-to-seed spaces according to “Kings” principle i.e., sowing more seeds to the most fertile zones and vice-versa. Results showed that both SSS approaches have resulted in increased tuber yields (EMI-SSS: 32.42 t/ha, visNIRsen-SSS: 31.89 t/ha) and higher economic return, compared to URS . Although visNIRSen-SSS provided lower gross yield than EMI-SSS, the larger yield of small size (28−45 mm) tubers, having higher market price than other size categories has resulted in actual market price for the former being higher than that of the latter approach. The visNIRsen-SSS and EMI-SSS produced net returns of 4995 €/ha and 4947 €/ha, respectively, which were more than that of the URS of 4528 €/ha, by 467 €/ha and 419 €/ha, respectively. In addition, visNIRsen-SSS approach has saved seeding costs by 14 €/ha though EMI-SSS consumed 5 €/ha more compared to URS. Therefore, it is recommended to adopt SSS (particularly visNIRsen-SSS) for seed potato production, as a means of increasing crop yield, reducing input cost and thus maximizing profitability compared to URS.}},
  articleno    = {{104801}},
  author       = {{Munnaf, Muhammad Abdul and Haesaert, Geert and Mouazen, Abdul}},
  issn         = {{0167-1987}},
  journal      = {{SOIL & TILLAGE RESEARCH}},
  keywords     = {{Earth-Surface Processes,Agronomy and Crop Science,Soil Science,Precision agriculture,Seed potato,Data fusion,Map-based site-specific seeding,Economic analysis,Key soil fertility indicators}},
  language     = {{eng}},
  pages        = {{12}},
  title        = {{Map-based site-specific seeding of seed potato production by fusion of proximal and remote sensing data}},
  url          = {{http://dx.doi.org/10.1016/j.still.2020.104801}},
  volume       = {{206}},
  year         = {{2021}},
}

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