Impact of Field Characteristics on Pneumatic Seed Drill Performance: A Case Study in Eritrea

Articles in Press

Document Type : Research Article-en

Authors

• T. A. Medhn ^{1, 2}
• A. G. Levshin ²
• S. G. Teklay ¹

¹ Department of Agricultural Engineering, Mai-Nefhi College of Engineering and Technology, Eritrea

² Russian State Agrarian University, Timiryazev Moscow Agricultural Academy, Moscow

https://doi.org/10.22067/jam.2024.89995.1286

Abstract

The efficient use of agricultural machinery significantly improves both the quantity and quality of field operations; therefore, it is essential to optimize operational speed and field time. Factors such as field shape complexities and soil surface roughness (SSR) significantly impact seeding performance. The objective of this research was thus to evaluate how these key factors affect seeder performance: (1) field size and shape, and (2) the interaction of seeder speed and SSR. The performance metrics, effective field capacity (F_eff), efficiency (η), and average working speed (v_a), were analyzed using SAS software. The convexity (I_con) and rectangularity (I_R) indices for each plot were calculated using the ArcGIS minimal bounding geometry Data Management tool, while the elevation standard deviation (σ_e) was computed using Python. The resulting values for F_eff, η, and v_a varied widely, with values ranging from 10.2 to 3.1 ha h^-1, 30% to 65.7%, and 5.2 to 17 km h^-1, respectively. A v_a process capability index (C_pk) of 0.22 indicates a significant challenge in meeting the established limits. As the plot run-length increased, the F_eff also increased (R² = 42%), while it decreased with a rising perimeter to area ratio (P/A) (R² = 51%). Additionally, F_eff exhibited an upward trend as the I_con and I_R indices rose, while it experienced a decline with greater compactness (I_com) and square perimeter (I_sp) indices; albeit these relationships were not statistically significant. Higher roughness levels generally resulted in a decline in η. Furthermore, operating the planter at higher speed on uneven terrain led to a significant decrease in efficiency. Hence, redesigning the plots to minimize border complexities, eliminating topographic abnormalities, and implementing tailored plot-specific pre-sowing procedures, will significantly enhance planter performance.

Keywords

• Effective field capacity
• Plots
• Shape and size index
• Soil surface roughness

Main Subjects

• The relationship between machine and soil

©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

References

1. Ale, M. O., Manuwa, S. I., & Olukunle. (2023). Effect of Forward Speed and Drive Wheel on the Performance of a Semi-Automatic Cassava Planter. Achievers Journal of Scientific Research, 4(2), 85-94.
2. AMIA. (2021). Nardi Dora Air Drill| AMIA. Retrieved 15 December 2023, from AMIA Online Shop website: https://www.agrimarketia.com/product/nardi-dora-air-drill
3. Badua, S. A., Sharda, A., Strasser, R., & Ciampitti, I. (2021). Ground speed and planter downforce influence on corn seed spacing and depth. Precision Agriculture, 22(4), 1154-1170. https://doi.org/10.1007/s11119-020-09775-7
4. Benos, L., Tsaopoulos, D., & Bochtis, D. (2020). A Review on Ergonomics in Agriculture. Part I: Manual Operations. Applied Sciences, 10(6), 1905. https://doi.org/10.3390/APP10061905
5. Biocca, M., Fanigliulo, R., Grilli, R., Benigni, S., Fornaciari, L., & Pochi, D. (2022). Effect of sowing speed and width on spacing uniformity of precision seed drills effect of sowing speed and width on spacing uniformity of precision seed drills. INMATEH - Agricultural Engineering, 66(1), 9-18. https://doi.org/10.35633/inmateh-66-01
6. Brandelero, E. M., Adami, P. F., Modolo, A. J., Baesso, M. M., & Fabian, A. J. (2015). Seeder performance under different speeds and its relation to soybean cultivars yield. Journal of Agronomy, 14(3), 139-145. https://doi.org/10.3923/ja.2015.139.145
7. Demetriou, D., See, L., & Stillwell, J. (2013). A parcel shape index for use in land consolidation planning. Transactions in GIS, 17(6), 861-882. https://doi.org/10.1111/j.1467-9671.2012.01371.x
8. Diao, X., Takeshima, H., & Zhang, X. (2020). An Evolving Agricultural Paradigm of Mechanization Development: How Much Can Africa Learn from Asia?
9. Griffel, L. M., Vazhnik, V., Hartley, D., Hansen, J. K., & Richard, T. L. (2018). Machinery maneuvering efficiency and perennial crops: Field shape complexity defines the efficiency. ASABE 2018 Annual International Meeting. American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/aim.201800440
10. Herodowicz-Mleczak, K., Piekarczyk, J., Kaźmierowski, C., Nowosad, J., & Mleczak, M. (2022). Estimating soil surface roughness with models based on the information about tillage practises and soil parameters. Journal of Advances in Modeling Earth Systems, 14(3). https://doi.org/10.1029/2021MS002578
11. Ivančan, S., Sito, S., & Fabijanić, G. (2004). Effect of precision drill operating speed on the intra-row seed distribution for parsley. Biosystems Engineering, 89(3), 373-376. https://doi.org/10.1016/j.biosystemseng.2004.07.007
12. Janulevičius, A., Šarauskis, E., Čiplienė, A., & Juostas, A. (2019). Estimation of farm tractor performance as a function of time efficiency during ploughing in fields of different sizes. Biosystems Engineering, 179, 80-93. https://doi.org/10.1016/j.biosystemseng.2019.01.004
13. Khater, M. M. I. (2017). Desiging a software to calculate the field capacity for full mechanized agriculture practices in light soils. Misr Journal of Agricultural Engineering, 34(3), 1143-1154.
14. Kirkegaard Nielsen, S., Munkholm, L. J., Lamandé, M., Nørremark, M., Edwards, G. T. C., & Green, O. (2018). Seed drill depth control system for precision seeding. Computers and Electronics in Agriculture, 144, 174-180. https://doi.org/10.1016/j.compag.2017.12.008
15. Matsuura, S. (2023). Bayes estimator of process capability index Cpk with a specified prior mean. Communications in Statistics - Theory and Methods, 52(7), 2215-2227. https://doi.org/10.1080/03610926.2021.1947508
16. Montgomery, D. C. (2013). Introduction to Statistical Quality Control (Seventh Edition). New York City: Wiley.
17. Oksanen, T. (2013). Shape-describing indices for agricultural field plots and their relationship to operational efficiency. Computers and Electronics in Agriculture, 98, 252-259. https://doi.org/10.1016/j.compag.2013.08.014
18. Shinde, G. U., Mandal, S., Ghosh, P. K., Bhalerao, S., Kakade, O., Motapalukula, J., & Das, A. (2023). Farm Mechanization. In P. K. Ghosh, A. Das, R. Saxena, K. Banerjee, G. Kar, & D. Vijay (Eds.), Trajectory of 75 years of Indian Agriculture after Independence (pp. 475–496). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-7997-2_18
19. Srivastava, A., Goering, C., & Rohrbach, R. (2006). Engineering principles of agricultural machines (second; Peg McCann, Ed.). American Society of Agricultural and Biological Engineers.
20. Toba, A. L., Griffel, L. M., & Hartley, D. S. (2020). Devs based modeling and simulation of agricultural machinery movement. Computers and Electronics in Agriculture, 177(August). https://doi.org/10.1016/j.compag.2020.105669
21. Toscano, P., Cutini, M., Filisetti, A., Premoli, E., Porcu, M., Catalano, N., …, & Brambilla, M. (2022). Workability Assessment of Different Stony Soils by Soil–Planter Interface Noise and Acceleration Measurement. AgriEngineering, 4(4), 1139-1152. https://doi.org/10.3390/agriengineering4040070
22. Vereshchagin, N. I., Levshin, A. G., Skorokhodov, A. N., Kiselyov, S. N., Kosyrev, V. P., Zubkov, V. V., & Gorshkov, M. I. (2018). Organization and technology of mechanized work in crop production (12^th). Moscow.
23. Wang, Y., Zhang, W., Luo, X., Zang, Y., Ma, L., Zhang, W., …, & Zeng, S. (2024). Effect of Vibration Conditions on the Seed Suction Performance of an Air-Suction Precision Seeder for Small Seeds. Agriculture, 14(4), 559. https://doi.org/10.3390/AGRICULTURE14040559
24. Wu, C. W., Pearn, W. L., & Kotz, S. (2009). An overview of theory and practice on process capability indices for quality assurance. International Journal of Production Economics, 117(2), 338-359. https://doi.org/10.1016/j.ijpe.2008.11.008
25. Zangiev, A. A., & Skorokhodov, A. N. (2018). Practical on the operation of the machine and tractor fleet [Electronic resource]: textbook. handbook. St. Petersburg: Lan, Electron. dan.