Quantitative assessment of maize yield gap in Shush county using comparative performance and boundary line analysis

Yarahmadi, Rahim; Fateh, Esfandiar; Aynehband, Amir

doi:10.22034/aes.2026.519532.1102

Quantitative assessment of maize yield gap in Shush county using comparative performance and boundary line analysis

Document Type : Original research article

Authors

Rahim Yarahmadi ¹

Esfandiar Fateh ²

Amir Aynehband ²

¹ M.Sc. Student, Department of Production Engineering and Plant Genetic, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

² Department of Plant Production and Genetics Engineering, Faculty of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz,Iran

10.22034/aes.2026.519532.1102

Abstract

Grain maize (Zea mays) is a strategic crop with a vital role in global food security. Due to a significant yield gap observed in maize farms of Khuzestan Province, this study aimed to assess the yield gap and identify the key agronomic factors contributing to it in Shush County. The research was conducted across 100 maize fields, and data were collected through structured questionnaires and face-to-face interviews. Yield performance was evaluated using Comparative Performance Analysis (CPA) and Boundary Line Analysis (BLA). The average predicted yield was estimated at 8,685 kg ha⁻¹, whereas the potential yield was estimated at 11,557 kg ha⁻¹, indicating a yield gap of 2,872 kg ha⁻¹. The main factors contributing to this gap included insufficient application of poultry manure (29%), inadequate use of insecticides (18%), sulfur fertilizer deficiency (17%), suboptimal disking frequency (22%), and insufficient irrigation frequency (14%). According to the BLA results, optimizing these management variables could substantially improve maize yields. Overall, enhancing farm management practices—particularly the efficient use of organic and chemical fertilizers, improved pest control, and proper scheduling of irrigation and tillage operations—can significantly reduce the maize yield gap and increase productivity in the region.

Highlights

· The maize yield gap in Shush County was quantified as 2,872 kg ha⁻¹ using CPA and BLA methods.

· Five key agronomic factors; poultry manure, insecticide use, sulfur fertilizer, disking, and irrigation, explained most of the yield gap.

· Poultry manure deficiency alone accounted for 29% of the yield gap, highlighting the importance of organic nutrient management.

· Boundary Line Analysis revealed clear yield response patterns to input levels and management frequency.

· Improving fertilizer use, pest control, and irrigation scheduling can substantially increase maize productivity in the region.

Keywords

Agronomic management

Boundary Line Analysis (BLA)

Comparative Performance Analysis (CPA)

Grain maize

Optimal yield

Yield gap

Abarvan, P., Soltani, A., Majidian, M., & Mohsenabadi, G. R. (2017). Evaluation of management factors limiting canola yield in the eastern region of Golestan province using the CPA method. Agroecology, 7(2), 46–60. (In Persian).

Bhatia, V., Singh, P., Wani, S., Chauhan, G., Rao, A. K., Mishra, A., & Srinivas, K. (2008). Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model. Agricultural and Forest Meteorology, 148(8), 1252–1265. https://doi.org/10.1016/j.agrformet.2008.03.004

Binder, J., Graeff, S., Link, J., Claupein, W., Liu, M., Dai, M., & Wang, P. (2008). Model-based approach to quantify production potentials of summer maize and spring maize in the North China Plain. Agronomy Journal, 100, 862–873. https://doi.org/10.2134/agronj2007.0106

De Wrachien, D., Schultz, B., & Goli, M. B. (2021). Impacts of population growth and climate change on food production and irrigation and drainage needs: A world‐wide view. Irrigation and Drainage, 70, 981–995. https://doi.org/10.1002/ird.2597

Goodwin, B. K., Featherstone, A. M., & Zeuli, K. (2002). Producer experience, learning by doing, and yield performance. American Journal of Agricultural Economics, 84(3), 660–678. https://doi.org/10.1111/1467-8276.00326

Gorjizad, A., Dastan, S., Soltani, A., & Ajam Norouzi, H. (2019). Large scale assessment of the production process and rice yield gap analysis by Comparative Performance Analysis and Boundary-Line Analysis methods. Italian Journal of Agronomy, 14, 123–131. https://doi.org/10.4081/ija.2019.1174

Habibi, E., Niknejad, Y., Fallah, H., Dastan, S., & Barari Tari, D. (2019). Estimation of rice yield gap using Comparative Performance Analysis (CPA) method in Amol and Rasht. Plant Production (Scientific Journal of Agriculture), 42(4), 551–562.

Hajjarpoor, A., Soltani, A., Zeinali, E., Kashiri, H., Aynehband, A., & Vadez, V. (2018). Using boundary line analysis to assess the on-farm crop yield gap of wheat. Field Crops Research, 225, 64–73. https://doi.org/10.1016/j.fcr.2018.06.003

Lobell, D. B., Cassman, K. G., & Field, C. B. (2009). Crop yield gaps: Their importance, magnitudes, and causes. Annual Review of Environment and Resources, 34, 179–204. https://doi.org/10.1146/annurev.environ.041008.093740

Mahrokh, A., Golzardi, F., Azizi, F., Mofidian, S. M. A., Zamanian, M., Rahjou, V., Torabi, M., & Soltani, A. (2021). Meta-analysis of agronomic factors affecting maize yield reduction in Iran. Journal of Crop Improvement, 23(1), 73–86. (In Persian). https://doi.org/10.22059/jci.2020.292889.2299

Muhumed, M. A., Jusop, S., Sung, C. T. B., Megat Wahab, P. E., & Panhwar, Q. A. (2014). Effects of drip irrigation frequency, fertilizer sources and their interaction on the dry matter and yield components of sweet corn. Australian Journal of Crop Science, 8(2), 223–231.

Nuss, E. T., & Tanumihardjo, S. A. (2010). Maize: A paramount staple crop in the context of global nutrition. Comprehensive Reviews in Food Science and Food Safety, 9(4), 417–436.
https://doi.org/10.1111/j.1541-4337.2010.00117.x

Oyeyemi, A., & Nwagbo, C. U. (2025). Interactive effects of dichlorvos, dimethoate, and cypermethrin on growth and oxidative homeostasis in maize (Zea mays). Research and Analysis Journals, 8(1), 11–28. https://doi.org/10.18535/raj.v8i01.496

Rostami, M. A., Sharifi, A., & Raoufat, M. H. (2012). Comparison of different tillage methods in a maize-wheat rotation. Iranian Journal of Agricultural Sciences, 14(2), 123–135.

Singh, A., & Prasad, A. (2024). Effects of fertilizer nitrogen rates on broiler litter-fertilized corn: Grain yield and nutrient composition. Agronomy Journal. https://doi.org/10.1002/agj2.21718

Sohrabi, Y., Ahmadi, A., Heydari, G., & Siosehmarde, A. (2019). The effect of sulfur and zinc fertilizers on physiological characteristics and grain yield of maize (Zea mays L.) under drought stress. Environmental Stresses in Crop Sciences, 12(3), 763–779. https://doi.org/10.22077/escs.2019.1398.1302

Soltani, A. (2015). Application of SAS statistical software in agricultural statistical analyses. Mashhad: Jihad Daneshgahi Press, Mashhad University. 184 pp. (In Persian).

Soltani, S., Nakhzari Moghaddam, A., Bonyanian, M., Rahimi Karizaki, A., Keshiri, H., & Naeimi, M. (2023). Assessment of managerial factors affecting the performance gap of cotton under semi-arid temperate climate conditions using the comparative performance analysis (CPA) method: A case study of western Golestan Province. Iranian Cotton Research Journal, 11(1), 35–53. (In Persian). https://doi.org/10.22092/ijcr.2024.363702.1203

Torabi, B. (2011). Analysis of the yield limitations in Gorgan using simulation model and hierarchical process (AHP) (Ph.D. thesis, Gorgan University of Agricultural Science and Natural Resources). 223 pp. (In Persian).

Torabi, B., Soltani, A., Galeshi, S., & Zeinali, E. (2011). Analysis of yield-limiting factors of wheat in Gorgan region. Journal of Crop Production, 4(4), 1–17. (In Persian).

Van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013). Yield gap analysis with local to global relevance: A review. Field Crops Research, 143, 4–17. https://doi.org/10.1016/j.fcr.2012.09.009