S. Noori, N., H. Hameed, B., Kh. Mohammed, M. (2024). AN ECONOMIC EVALUATION OF THE PERFORMANCE EFFICIENCY OF CONSERVATION AGRICULTURE AND FOOD SECURITY PROJECTS USING LOGISTIC REGRESSION IN IRAQ FOR THE 2022-2023 SEASON. , 22(2), 1033-1049. doi: 10.32649/ajas.2024.184466
N. S. Noori; B. H. Hameed; M. Kh. Mohammed. "AN ECONOMIC EVALUATION OF THE PERFORMANCE EFFICIENCY OF CONSERVATION AGRICULTURE AND FOOD SECURITY PROJECTS USING LOGISTIC REGRESSION IN IRAQ FOR THE 2022-2023 SEASON". , 22, 2, 2024, 1033-1049. doi: 10.32649/ajas.2024.184466
S. Noori, N., H. Hameed, B., Kh. Mohammed, M. (2024). 'AN ECONOMIC EVALUATION OF THE PERFORMANCE EFFICIENCY OF CONSERVATION AGRICULTURE AND FOOD SECURITY PROJECTS USING LOGISTIC REGRESSION IN IRAQ FOR THE 2022-2023 SEASON', , 22(2), pp. 1033-1049. doi: 10.32649/ajas.2024.184466
S. Noori, N., H. Hameed, B., Kh. Mohammed, M. AN ECONOMIC EVALUATION OF THE PERFORMANCE EFFICIENCY OF CONSERVATION AGRICULTURE AND FOOD SECURITY PROJECTS USING LOGISTIC REGRESSION IN IRAQ FOR THE 2022-2023 SEASON. , 2024; 22(2): 1033-1049. doi: 10.32649/ajas.2024.184466

AN ECONOMIC EVALUATION OF THE PERFORMANCE EFFICIENCY OF CONSERVATION AGRICULTURE AND FOOD SECURITY PROJECTS USING LOGISTIC REGRESSION IN IRAQ FOR THE 2022-2023 SEASON

Anbar Journal of Agricultural Sciences
Volume 22, Issue 2, December 2024, Pages 1033-1049    PDF (524.31 K)
Document Type: Research Paper
DOI: 10.32649/ajas.2024.184466
Authors
N. S. Noori1; B. H. Hameed2; M. Kh. Mohammed1
1Ministry of Agriculture
2University of Baghdad
Abstract
This research involved an economic evaluation of the performance efficiency of conservation agriculture and food security projects using logistic regression in Iraq for the 2022-2023 agricultural season. Using quantitative methodology and the logistics regression method (logit), it analyzed the main factors influencing the adoption of conservation agriculture and food security projects. Data was collected through questionnaires with 74 responses each received from farmers in the food security enhancement project and in traditional agriculture. Also, 45 forms each were received for the conservation and traditional agriculture projects. Regression model coefficients were estimated using the maximum likelihood method, explaining the dependent variable and independent variables. The dependent variable is a binary variable with a value of 1 for farmers adopting the technology and 0 for those who did not. The independent variables included age, experience, educational level, source of household income, productivity, water source, land tenure, field records, and use of agricultural technologies. The study produced several results, the most important of which is the significance of most of the variables for the two projects. The goodness-of-fit coefficient of the model was also shown using the Hosmer and Lemeshow statistic with degrees of freedom 8 d.f. The P value for the conservation agriculture and enhancing food security projects was greater than 0.05% indicating acceptance of the null hypothesis (H0) which states no difference between the observed and estimated values of the dependent variable and that the model estimates fit the data very well. The study recommended increasing government support for the dissemination of modern technologies to facilitate farmers’ access to them and intensifying training programs on the use of these technologies appropriate to the conditions of the region. This also applies to the conservation agriculture and food security projects. There is also a need to transfer the experience of the applied regions to other areas in Iraq.
Keywords
Logit; Hosmer and Lemeshow; Likelihood
References
  1. Abbas, A. K. (2012). Using the logistic regression model in predicting functions with qualitative dependent economic variables. Kirkuk University Journal of Administrative and Economic Sciences, 2(2).
  2. Abd, M. N. (2023). An economic study of the determinants of rural women’s contribution to farm work and its impact on improving the level of farm income (Baghdad Governorate as a model). doctoral thesis, College of Agricultural Engineering Sciences, Baghdad University, Department of Agricultural Economics.
  3. Al-Badri, B. H., Mohammad, M. K., and Khalid, J. O. (2023). The Water Footprint and Virtual Water and Their Effect on Food Security in Iraq. In IOP Conference Series: Earth and Environmental Science, 1222(1): p. 012023. DOI: 10.1088/1755-1315/1222/1/012023.
  4. Al-Bahloul, R. (2021). The Impact of Agriculture and Viewpoints on Specific Production and Some Soil Fertility Indicators. Al-Baath University Journal, 43(20).
  5. Al-Dulaimi, A. J. (2022). Conservation agriculture and its applications for growing wheat and barley crops in a number of Arab countries. Journal of Geographical Research, (25).
  6. Al-Ghannam, A. (2020). The varying effects of some demographic, social, economic, and agricultural factors on enhancing Arab food security in light of the Corona pandemic. Journal of Agricultural Economics and Science Social Studies, 11(12): 1027-1037. https://dx.doi.org/10.21608/jaess.2020.161880.
  7. Al-Hadithi, B. N. (2013). Economic analysis of conservation agriculture for wheat in Nineveh Governorate for the agricultural season (2010-2011), Master’s thesis, College of Agricultural Engineering Sciences, Baghdad University, Department of Agricultural Economics.
  8. Al-Jumaili, A. A (2020). An economic study of the reality of food security in Iraq for the period (1980-2017), Master’s thesis, Agricultural Economics and Extension - Agricultural Economics, College of Agriculture, Tikrit University.
  9. ACSAD, Arab Center for Studies on Dry Zones and Dry Lands. (2012). Conservation Agriculture, System for Encouraging Sustainable Agricultural Adaptation for Agricultural Production.
  10. Al-Moola, A. H. (2020), Forecasting of wheat production in iraq using box- jenkins model for period 2020-2024, Anbar Journal of Agricultural Sciences, 18(1): 109-118. https://doi.org/10.32649/ajas.2020.170516.
  11. Al-Obaidi, A. R. (2021). Economic analysis of the impact of modern technologies on wheat production for farms operating within the program to enhance food security in Iraq for the agricultural season, Master’s thesis, College of Agricultural Engineering Sciences, Baghdad University, Department of Agricultural Economics.
  12. Alsaho, N. A., A. D. Kassar, and M. K. Mohammed. (2022). The impact of some agricultural legislation and wheat farmers' commitment to it in wasit governorate. Anbar Journal of Agricultural Sciences, 20(1)‏: 90-103. https://doi.org/10.32649/ajas.2022.175490.
  13. Azeez, R. A., and khalaf, M. A. (2017). Effect of Agricultural Policy on Marketing Wheat Crop in Wasit province. Anbar Journal of Agricultural Sciences, 15(2): 625-635.
  14. Babtain, A. A. (2008). Logistic regression and how to use it in building prediction models for data with two-valued dependent variables, doctoral thesis, Umm Al-Qura University - College of Education, Kingdom of Saudi Arabia.
  15. Dallal, G. E. (2001). Logistic Regression.
  16. Ghanem, A., and Al-Jaouni, F. (2011). Using the binary-response logistic regression technique in studying the most important economic and social determinants of family income adequacy, An applied study on a random sample of families in Damascus Governorate. Damascus University Journal of Economic and Legal Sciences, (27)1.
  17. Hamdi, M., N., & F. Ahmed, A. (2024). An Economic Analysis Of The Impact Of Government Lending Agricultural And The Agricultural Initiative On The Agricultural Domestic Product In Iraq For The Period (1990-2020). Anbar Journal Of Agricultural Sciences, 22(1), 665–679. https://doi.org/10.32649/ajas.2024.183785.  
  18. Ismail, M. A. (2001). Linear Regression Analysis. Institute of Public Administration, Kingdom of Saudi Arabia - Riyadh.
  19. Jasim, S. A., and Al-Snbll, I. A. (2017). Measurement of technical and economic efficiency and determine the economic size of wheat farms in Erbil using the data envelope analysis method (DEA) in agricultural season 2014-2015. Anbar Journal of Agricultural Sciences, 15(1) :244-256.
  20. Kadhim, R., Z., T. Abd Al-majeed, A., & T. Jassam, Q. (2024). Estimation Of Marketing Efficiency And Production Quantity At The Break-Even Point For Cotton Farmers In Iraq (Baghdad Province: A Case Study). Anbar Journal Of Agricultural Sciences, 22(1), 1–15. https://doi.org/10.32649/ajas.2024.183691.
  21. Lea, S. (1997). Multivariate Analysis II: Manifest variables analysis. Topic 4: Logistic Regression and Discriminant Analysis. University of EXETER, Department of Psychology.
  22. Mahboub, A. A. G. (1998). Principles of econometrics: Theory and application. Al-Mansour University College.
  23. Mahyoub, E. J. (2018). The effect of the conservation agriculture system on some hydro physical soil properties and the productivity of some forage crops in the Salmiya Research Center area, Master’s thesis, College of Agriculture, Soil Sciences Department, University of Damascus.
  24. Namek, F. N. (2015). Using the logistic regression technique and the multiple linear regression technique to diagnose the factors affecting the rise in fee rates in higher education. Journal of the Baghdad College of Economic Sciences, (43).
  25. Newsom, I. (2015). Data analysis II: logistic regression.
  26. Pampel, F. C. (2020). Logistic regression: A primer (No. 132). Sage publications.
  27. Porter, S. R. (1999). Viewing One-Year Retention as a Continuum: The Use of Dichotomous Logistic Regression, Ordered Logit and Multinomial Logit.
  28. Qamra, S., Jamila, A., and Hassan, G. H. (2020). Measuring the impact of losses on agricultural economic resources and food security for the most important grain crops in Egypt. Agricultural Economics Research Institute, Research Center, pp. 181-193.
  29. Saleh, M. M., and Jbara, O. K. (2022). Measuring economic efficiency of wheat crop producers in desert areas who adopt pivot irrigation technology and who do not adopt for the season 2020-2021. Anbar Journal of Agricultural Sciences, 20(1): 120-137. https://doi.org/10.32649/ajas.2022.175493.
  30. Woldbeck, T. (1998). A Primer on Logistic Regression.
  31. Wolfe, R. A. (2002). Logistic Regression, Unpublished Classic Lectures in Statistics.
Statistics
Article View: 982
PDF Download: 267


Journal Management System. Powered by iJournalPro.com