CONSERVING OF THE RESOURCES

The impact of education on the utilization and comprehension of chemical fertilizers and pesticides in relation to the environment. A case study in the ohrid lake catchment area

  • 1 Academia „Nehemiah Gateway“, Buçimas, Pogradec, Albania

Abstract

Farmers use commercially available chemical fertilizers and pesticides to increase crop yields, though this can potentially harm their health and the environment. This research aims to scrutinize the correlation between farmers’ educational attainment and their awareness of the ecological ramifications associated with using these chemical inputs. Structured interviews with farmers from seven villages along the shores of Lake Ohrid in Albania provided empirical data to elucidate the relationship between farmers’ educational backgrounds and their propensity to use environmentally harmful chemicals. This premise constitutes the central hypothesis underpinning the study. Employing rigorous non-parametric statistical methodologies, the amassed data underwent comprehensive analysis. The findings reveal a substantive association between farmers’ educational levels and their utilization patterns of chemical agents within agricultural contexts, thus accentuating the imperative for educational interventions and broader policy measures.

Keywords

References

  1. Chhun, B., Sehdev, D., Prentice, A. C., Rodríguez, M. C., & Haščič, I. (2024). Environmental domain tagging in the OECD PINE database (OECD Environment Working Papers 232; OECD Environment Working Papers, Vol. 232). https://doi.org/10.1787/be984b0a-en
  2. Hazell, P. (2001). Agriculture and the environment. Environment and Development Economics, 6(4), 503–531. https://doi.org/10.1017/S1355770X01250281
  3. OECD. (2004). Agriculture and the Environment: Lessons Learned from a Decade of OECD Work. OECD.
  4. Zilberman, D., Templeton, S. R., & Khanna, M. (1999). Agriculture and the environment: An economic perspective with implications for nutrition. Food Policy, 24, 211–229.
  5. Kullaj, E. (2005). Environmental Implications of Agriculture Activities in Albania and Sustainable Development Policy Objectives. Revue d’études comparatives Est-Ouest.
  6. Hashemi, S. M., Rostami, R., Hashemi, M. K., & Damalas, C. A. (2012). Pesticide Use and Risk Perceptions among Farmers in Southwest Iran. Human and Ecological Risk Assessment: An International Journal, 18(2), 456–470. https://doi.org/10.1080/10807039.2012.652472
  7. Kalıpcı, E., Ozdemir, C., & Oztas, H. (2011). Investigation of the level of education and knowledge about pesticide use of farmers and their environmental sensitivities. TUBAV Science Journal, 4(3), 179–187.
  8. Öztaş, D., Kurt, B., Koç, A., Akbaba, M., & İlter, H. (2018). Knowledge Level, Attitude, and Behaviors of Farmers in Çukurova Region regarding the Use of Pesticides. BioMed Research International, 2018, 1–7. https://doi.org/10.1155/2018/6146509
  9. Tijani, A. A., & Nurudeen, S. (2012). Assessment of Farm Level Pesticide Use among Maize Farmers in Oyo State, Nigeria. Food Science and Quality Management, 3, 1–8.
  10. Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
  11. Sarma, P. K. (2022). Farmer behavior towards pesticide use for reduction production risk: A Theory of Planned Behavior. Cleaner and Circular Bioeconomy, 1, 100002. https://doi.org/10.1016/j.clcb.2021.100002
  12. Skreli, E., Imami, D., Chan-Halbrendt, C., Canavari, M., Zhllima, E., & Pire, E. (2017). Assessing consumer preferences and willingness to pay for organic tomatoes in Albania: A conjoint choice experiment study. Spanish Journal of Agricultural Research, 15(3), e0114. https://doi.org/10.5424/sjar/2017153-9889
  13. Daziano, R. A., & Bolduc, D. (2013). Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model. Transportmetrica A: Transport Science, 9(1), 74–106. https://doi.org/10.1080/18128602.2010.524173
  14. Kaplan, S., Gruber, J., Reinthaler, M., & Klauenberg, J. (2016). Intentions to introduce electric vehicles in the commercial sector: A model based on the theory of planned behaviour. Research in Transportation Economics, 55, 12–19. https://doi.org/10.1016/j.retrec.2016.04.006
  15. Padel, S., & Foster, C. (2005). Exploring the gap between attitudes and behaviour: Understanding why consumers buy or do not buy organic food. British Food Journal, 107(8), 606–625. https://doi.org/10.1108/00070700510611002
  16. Zhen, H., Qiao, Y., Zhao, H., Ju, X., Zanoli, R., Waqas, M. A., Lun, F., & Knudsen, M. T. (2022). Developing a conceptual model to quantify eco-compensation based on environmental and economic cost-benefit analysis for promoting the ecologically intensified agriculture. Ecosystem Services, 56, 101442. https://doi.org/10.1016/j.ecoser.2022.101442
  17. Damalas, C. A., Georgiou, E. B., & Teodorou, M. G. (2006). Pesticide use and safety practices among Greek tobacco farmers: A survey. International Journal of Environmental Health Research, 16(5), 339–348.
  18. Hansson, H., Ferguson, R., & Olofsson, C. (2012). Psychological Constructs Underlying Farmers’ Decisions to Diversify or Specialise their Businesses – An Application of Theory of Planned Behaviour. Journal of Agricultural Economics, 63(2), 465–482. https://doi.org/10.1111/j.1477- 9552.2012.00344.x
  19. Martin, P., Bouty, C., Barbottin, A., & Lebail, M. (2014). Diversifying strategies of agricultural cooperatives towards agro-ecological transition. Farming Systems Facing Global Challenges: Capacities and Strategies., 2.
  20. Kulak, M., Nemecek, T., Frossard, E., & Gaillard, G. (2014). Improving resource efficiency of low-input farming systems through integrative design – two case studies from France. Farming Systems Facing Global Challenges: Capacities and Strategies., 2.
  21. European Commission. Directorate General for Agriculture and Rural Development. (2024). Agricultural policy developments in the EU pre-accession countries: Final report. Publications Office. https://data.europa.eu/doi/10.2762/638991
  22. Clay, J. W. (2003). Agriculture and the Environment Volume I: Introduction and Commodities A WWF HANDBOOK ON AGRICULTURAL IMPACTS AND BETTER PRACTICES.
  23. Raheem, S., Rasul, H., & Harun, R. (2020). Farmer’s Behavior and Attitude in Using Chemical Fertilizers and Pesticide in Rural Areas. Journal of Plant Production, 11(11), 1077–1081. https://doi.org/10.21608/jpp.2020.130915
  24. Zheng, S., Yin, K., & Yu, L. (2022). Factors influencing the farmer’s chemical fertilizer reduction behavior from the perspective of farmer differentiation. Heliyon, 8(12), e11918. https://doi.org/10.1016/j.heliyon.2022.e11918
  25. Huang, J., Hu, R., Cao, J., & Rozelle, S. (2008). Training programs and in-the-field guidance to reduce China’s overuse of fertilizer without hurting profitability. Journal of Soil and Water Conservation, 63(5), 165A-167A. https://doi.org/10.2489/jswc.63.5.165A
  26. Hameed, T. S., & Sawicka, B. (2017). FARMERS’ KNOWLEDGE OF FERTILIZERS’ PRACTICES ON THEIR FARMS. Farm Machinery and Processes Management in Sustainable Agriculture, IX International Scientific Symposium, 119–124. https://doi.org/10.24326/fmpmsa.2017.22
  27. Oluwole, O., & Cheke, R. A. (2009). Health and environmental impacts of pesticide use practices: A case study of farmers in Ekiti State, Nigeria. International Journal of Agricultural Sustainability, 7(3), 153–163. https://doi.org/10.3763/ijas.2009.0431
  28. Pan, D., & Zhang, N. (2018). The Role of Agricultural Training on Fertilizer Use Knowledge: A Randomized Controlled Experiment. Ecological Economics, 148, 77–91. https://doi.org/10.1016/j.ecolecon.2018.02.004
  29. Likert, R. (1932). A Technique for the Measurement of Attitudes (Vol. 140).
  30. Yaddanapudi, S., & Yaddanapudi, L. (2019). How to design a questionnaire. Indian Journal of Anaesthesia, 63(5), 335. https://doi.org/10.4103/ija.IJA_334_19
  31. Wacker, J. G. (2008). A CONCEPTUAL UNDERSTANDING OF REQUIREMENTS FOR THEORY‐ BUILDING RESEARCH: GUIDELINES FOR SCIENTIFIC THEORY BUILDING *. Journal of Supply Chain Management, 44(3), 5–15. https://doi.org/10.1111/j.1745-493X.2008.00062.x
  32. Henley, S. (1981). Nonparametric geostatistics. Applied Science Publishers ; Wiley.
  33. Nahm, F. S. (2016). Nonparametric statistical tests for the continuous data: The basic concept and the practical use. Korean Journal of Anesthesiology, 69(1), 8. https://doi.org/10.4097/kjae.2016.69.1.8
  34. Scheff, S. W. (2016). Nonparametric Statistics. In Fundamental Statistical Principles for the Neurobiologist (pp. 157–182). Elsevier. https://doi.org/10.1016/B978-0-12- 804753-8.00008-7
  35. Henderson, A., Comiskey, J., Dallmeier, F., & Alonso, A. (2007). Framework for Assessment and Monitoring of Biodiversity. In Encyclopedia of Biodiversity (pp. 1–11). Elsevier. https://doi.org/10.1016/B0-12-226865-2/00129-2
  36. Dallmeier, F., Szaro, R. C., Alonso, A., Comiskey, J., & Henderson, A. (2013). Framework for Assessment and Monitoring of Biodiversity. In Encyclopedia of Biodiversity (pp. 545–559). Elsevier. https://doi.org/10.1016/B978-0-12- 384719-5.00316-6
  37. Haldar, S. K. (2018). Statistical and Geostatistical Applications in Geology. In Mineral Exploration (pp. 167– 194). Elsevier. https://doi.org/10.1016/B978-0-12-814022- 2.00009-5
  38. Gerald, B., & Frank Patson, T. (2021). Parametric and Nonparametric Tests: A Brief Review. International Journal of Statistical Distributions and Applications, 7(3), 78. https://doi.org/10.11648/j.ijsd.20210703.12
  39. Verma, J. P., & G. Abdel‐ Salam, A. (2019). Testing Statistical Assumptions in Research (1st ed.). Wiley. https://doi.org/10.1002/9781119528388
  40. Aliev, F., Özbek, L., Kaya, M. F., Kuş, C., Ng, H. K. T., & Nagaraja, H. N. (2024). A nonparametric test for the two-sample problem based on order statistics. Communications in Statistics - Theory and Methods, 53(10), 3688–3712. https://doi.org/10.1080/03610926.2022.2161310
  41. Warrens, M. J. (2008). On Association Coefficients for 2×2 Tables and Properties That Do Not Depend on the Marginal Distributions. Psychometrika, 73(4), 777–789. https://doi.org/10.1007/s11336-008-9070-3
  42. Albatineh, A. N., Niewiadomska-Bugaj, M., & Mihalko, D. (2006). On Similarity Indices and Correction for Chance Agreement. Journal of Classification, 23(2), 301–313. https://doi.org/10.1007/s00357-006-0017-z
  43. Baulieu, F. B. (1997). Two Variant Axiom Systems for Presence/Absence Based Dissimilarity Coefficients. Journal of Classification, 14(1), 159–170. https://doi.org/10.1007/s003579900009
  44. Altman, D. G. (1999). Practical statistics for medical research. Chapman & Hall/CRC.
  45. Yule, G. U. (1912). On the Methods of Measuring Association Between Two Attributes. Journal of the Royal Statistical Society, 75(6), 579. https://doi.org/10.2307/2340126
  46. Fernando, J. (2024). The Correlation Coefficient: What It Is and What It Tells Investors?
  47. Stommel, M., & Dontje, K. J. (2014). Statistics for advanced practice nurses and health professionals. Springer Publishing Company.
  48. Black, K. (2010). Business statistics: For contemporary decision making (6th ed). Wiley.

Article full text

Download PDF