Assessment of the Impact of Wheat Cleaning Processes on Weed Dissemination in the Green Mountain Region, Libya
DOI:
https://doi.org/10.58916/jhas.v10i2.772Keywords:
weed diversity, wheat cultivation, Green Mountain, seed screening, Traditional MethodsAbstract
Wheat screening residues represent a significant and diverse source of weed seeds, indicating the potential transmission of various weed species during the seed cleaning process. This contamination can result in substantial reductions in grain yield. Therefore, weed management programs should place greater emphasis on the efficiency of seed cleaning operations prior to sowing.
In this study, five wheat seed samples (each weighing approximately 250 g) were collected from trusted farms in the Green Mountain region during the 2023/2024 growing season. The samples were screened using a 2 mm sieve at the Crop Science Laboratory, and the screening residues were subsequently sown in sterilized soil-filled containers (60 × 90 cm surface area and 30 cm depth). The residues were applied at different weights (0, 2.5, 5, 7.5, and 10 g per container) under controlled laboratory conditions. A randomized complete block design (RCBD) with three replicates was employed to evaluate the density and diversity of weed seeds transferred via wheat seed lots. Diversity indices such as Shannon’s Index and Pielou’s Evenness Index were calculated, and data were analyzed using SAS software.
The results revealed a high diversity of weed species, which increased with the amount of screening residues. Convolvulus arvensis was the dominant species across all containers, followed by Brassica campestris, Lecanora escylenta, and Lathyrus sativus, while Fumaria densiflora had the lowest frequency. These findings underscore the importance of improving seed screening protocols to minimize the spread of weed seeds through wheat seed lots The study also highlights the socio-economic and environmental benefits of advanced screening, emphasizing the need for integrating smart technologies like GIS to optimize weed management.
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References
Amare, A., Tesfaye, K., & Lemma, B. (2014). Weed flora and their interference in wheat (Triticum aestivum L.) at Adet area, Amhara Region, Ethiopia. Journal of Agricultural Science, 6(1), 1–10. https://doi.org/10.5539/jas.v6n1p1
Burkov, V. I., Tkachuk, O. V., & Kovalchuk, S. I. (2018). Weed seed contamination in harvested grain and its impact on crop yield. Ukrainian Journal of Ecology, 8(3), 234–242.
Chicouene, P. (2020). Integrated weed management in wheat: A review. Journal of Agricultural Science, 12(2), 1–15. https://doi.org/10.5539/jas.v12n2p1
Das, A. K. (2008). Weed management in wheat: A review. Indian Journal of Weed Science, 40(1), 1–10.
Asmaa Al-Mabrouk Abdel-Sayed, Fatima Faraj Mohamed, Najwa Mohamed, & Fatima Khamis. (2023). Performance evaluation of some genotypes of double-row barley (Hordeum vulgare L.) under rainfed conditions in the Green Mountain region of Libya. Bani Waleed University Journal for Humanities and Applied Sciences, 8(2), 453-473.
Food and Agriculture Organization of the United Nations. (2025). FAO homepage. https://www.fao.org
Gharde, S. S., Patil, S. S., & Patil, P. S. (2018). Weed flora and their interference in wheat (Triticum aestivum L.) in Vidarbha region of Maharashtra. Journal of Pharmacology and Phytochemistry, 7(1), 1–5.
Hossain, M. A. (2015). Integrated weed management in wheat: A sustainable approach. Journal of Agronomy, 14(3), 121–128.
ISWS (International Survey of Weed Seeds). (2018). Weed seed contamination in crop seeds: A global perspective (Vol. 15, pp. 1–100). International Survey of Weed Seeds.
Kumar, V. (2014). Weed seed contamination in harvested grain: A review. Journal of Crop Science and Biotechnology, 17(2), 101–110.
Lollato, R. P., Holman, J. D., & Jhala, A. J. (2020). Weed seed return to the soil and its implications for wheat production. Crop Science, 60(2), 651–662. https://doi.org/10.1002/csc2.20051
Norsworthy, J. K., Oliver, S. R., & Bond, J. D. (2012). Integrated weed management: Principles and practices. Weed Science, 60(2), 167–178. https://doi.org/10.1614/WS-D-11-00061.1
Oerke, E. C. (2006). Crop losses to pests. Journal of Agricultural Science, 144, 31–43. https://doi.org/10.1017/S0021859606006043
Jamal Saeed Dariaq, Abdul Qader Mohammed Abu Jadida, & Mohammed Fathallah Al-Hassi. (2024). Pollution of some groundwater wells in the city of Al-Marj with some heavy elements and nitrates. Journal of Bani Waleed University for Humanities and Applied Sciences, 9(5), 457-464.
Owen, M. D. K., & Powles, S. B. (2020). The global threat of herbicide-resistant weeds. Trends in Plant Science, 25(10), 1003–1015. https://doi.org/10.1016/j.tplants.2020.07.003
Pisal, V. S., & Sagarka, D. D. (2013). Effectiveness of seed cleaning methods in reducing weed seed contamination in wheat (Triticum aestivum L.). Indian Journal of Weed Science, 45(1), 1–6.
Pradhan, P., & Chakraborti, A. (2010). Integrated weed management in wheat: A review. Indian Journal of Agronomy, 55(2), 101–108.
SAS Institute Inc. (2021). SAS/STAT® 15.2 user’s guide. SAS Institute Inc.
Walker, S. (1995). Weed seed contamination in harvested grain: Implications for crop production. Weed Research, 35(3), 201–210. https://doi.org/10.1111/j.1365-3180.1995.tb01823.x
Sabah Musa Abdul Majeed, Zakia Fadel Mansour, & Fatima Muhammad Younis. (2024). A study of the effect of fertilization with different rates of nitrogen and phosphorus on the percentage and quantity of volatile oil yield and the total chlorophyll content of leaves of marjoram plant Majorana hortensis moench growing under the conditions of the Green Mountain region. Journal of Bani Waleed University for Humanities and Applied Sciences, 490-504.
Zhang, L., et al. (2023). Deep learning-based weed detection in wheat fields. Nature Agriculture, 7(2), 112–125. https://doi.org/10.1038/s44100-023-00142-w
Wang, H., et al. (2022). UAVs for weed mapping. Remote Sensing, 14(3), 550. https://doi.org/10.3390/rs14030550
Food and Agriculture Organization of the United Nations. (2024). Climate-smart weed management. https://www.fao.org
Smith, J., et al. (2023). AI-based seed sorting: A revolution in weed management. Nature Agriculture, 7(2), 45–60. https://doi.org/10.1038/s44100-023-00120-2