عنوان مقاله [English]
Background and Objectives
Rice (Oryza sativa L.) is the staple food of more than half of the world’s population and has an important role in feeding, income and job creation in the world, especially Iran. Yield gap analysis provides little estimation of increased production capacity which is an important component in designing food-providing strategy in regional, national and global scales. The existing heated discussions regarding food security have increased the number of necessary studies that estimate the quantity of yield gap and the reasons behind it through appropriate statistical methods in Iran and the rest of the world. It seems that by defining the effectiveness of each management parameter regarding the amount of presented yield gap and, consequently, the farmer’s knowledge on that matter the distance between actual yield and attainable yield can be reduced. In this research, estimation of potential yield, yield gap, and determining yield restricting factors and each of their portions in creating rice yield gap have been investigated.
Materials and Methods
Research was done in 100 paddy fields in Amol and Rasht regions in 2016 and 2017. All managerial practices from nursery preparation to harvest for local rice cultivars were recorded through fields monitoring. Field selection was done in a way that included all main production procedures with variation in management viewpoint. To define yield model, the relationship between all measured variables and the final model was designed by controlled trial and error method, which could quantify the effect of yield limitations. The average yield was calculated by placing the observed average variables (Xs) in the fields under study in the yield model. Thereafter, by putting the best observed value of the variables in the yield model the maximum obtainable yield was calculated. The difference between these two has been considered yield gap. Different procedures of the software SAS version 9.1 were used for analysis.
Data analysis revealed that from 155 variables under study, the final model in Amol and Rasht regions with seven and six independent variables was chosen. In Amol yield model, maximum and average yield were 4798 and 6505 kg ha-1, respectively that estimated yield gap was 1707 kg ha-1. In Rasht yield model, maximum and average yield were 4443 and 6377 kg ha-1, respectively, which estimated that yield gap was 1934 kg ha-1. In Amol region, the amount of increased yield related to transplanting date, top-dressing and nitrogen after flowering variable was 364, 292 and 416 kg ha-1 which equals to 21, 17 and 24 percent of total yield increase. In Rasht region, the yield increase related to the effect of potassium and nitrogen before transplanting, and N after flowering was 644, 325 and 730 kg ha-1 which equals to 33, 17 and 38 percent.
According to the findings, we suggest that the model precision is appropriate and can be applied for both estimating the quantity of yield gap and determining the portion of each constraints yield variables. Furthermore, considering the fact that the calculated yield potential is reached through actual data in each paddy field, it is suggested that this yield potential is attainable.
Beza, E., Vasco Silva, J., Kooistra, L. and Reidsma, P. (2017). Review of yield gap explaining factors and opportunities for alternative data collection approaches. European Journal of Agronomy, 82(b), 206-222.
Dastan, S. (2012). Evaluation on agronomic and eco-physiological indices of lowland rice genotypes in modified agronomical methods. Ph.D. Thesis, Islamic Azad University, Science and Research Branch, Tehran. [In Farsi]
Dastan, S., Noormohamadi, Gh., Madani, H. and Soltani, A. (2015). Analysis of energy indices in rice production systems in the Neka region. Journal of Environmental Sciences, 13(1), 53-66. [In Farsi]
De Bie, C. A. J. M. (2000). Yield gap studies through comparative performance analysis of agro-ecosystems. International Institute for Aerospace and Earth Science (ITC), Enschede, The Netherlands, Retrieved fromhttps://edepot.wur.nl/121245.
FAO. (2016). Faostat-trade/crops and livestock products. Retrieved fromhttp://faostat3.fao.org/browse/T/TP/E.
Gorjizad, A., Dastan, S., Soltani, A. and Ajam Norouzi, H. (2019). Evaluation of potential yield and yield gap associated with crop management in improved rice cultivars in Neka region. Agroecology Journal, 11(1), 277-294. [In Farsi]
Halalkhor, S., Dastan, S., Soltani, A. and Ajam Norouzi, H. (2018). Documenting the process of rice production and yield gap associated with crop management in local cultivars of rice production (case study: Mazandaran province, Babol region). Agricultural Crop Management, 20(2), 397-414. [In Farsi]
Hochman, Z., Gobbett, D., Holzworth, D., McClelland, T., Van Rees, H., Marinoni, O., Garcia, K. N. and Horan, H. (2013). Reprint of quantifying yield gaps in rain-fed cropping systems: A case study of wheat in Australia. Field Crops Research, 143(1), 65-67.
Kayiranga D. (2006). The effects of land factors and management practices on rice yields. International Institute for Geo-information Science and Earth Observation Enscheda (ITC), The Netherlands. Retrieved from https://webapps.itc.utwente.nl/librarywww/papers_2006/msc/nrm/kayiranga.pdf.
Liu, Z., Yang, X., Lin, X., Hubbard, K. G., Lv, S. and Wang, J. (2016). Narrowing the agronomic yield gaps of maize by improved soil, cultivar, and agricultural management practices in different climate zones of northeast China. Earth Interactions, 20(12), 1-18.
Lobell, D.B., Cassman, K.G. and Field, C.B. (2009). Crop yield gaps: their importance, magnitudes, and causes. Annual Review of Environment and Resources, 34(1), 179-204.
Ministry of Jihad-e-Agriculture of Iran. (2016). Annual Agricultural Statics. Retrieved from www.maj.ir.
Reidsma, P. and Jeuffroy, H. (2017). Farming systems analysis and design for sustainable intensification: new methods and assessments. European Journal of Agronomy, 82(1), 203-205.
Rezaei, A. and Soltani, A. (1998). An introduction to applied regression analysis. 4th. Isfahan University of Technology, Esfahan. [In Farsi]
Soltani, A., Galeshi, S. and Zeinali, E. )2000(. Analysis of limitations contained in wheat production in Golestan province (Research Report). Management and Planning Organization of Golestan province. Retrieved from [In Farsi]
Soltani, A., Hajjarpoor, A. and Vadez, V. (2016). Analysis of chickpea yield gap and water-limited potential yield in Iran. Field Crops Research, 185(3), 21-30.
Torabi, B., Soltani, A., Galeshi, S., Zeinali, E. and Kazemi Korgehei, M. (2013). Ranking factors causing the wheat yield gap in Gorgan. Electronic Journal of Crop Production, 6(1), 171-189.
Van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P. and Hochman, Z. (2013). Yield gap analysis with local to global relevance-A review. Field Crops Research, 143(2), 4-17.
Van Wart, J., Kersebaum, K. C., Peng, S., Milner, M. and Cassman, K. G. (2013). Estimating crop yield potential at regional to national scales. Field Crops Research, 143(2), 34-43.
Yousefian, M., Dastan, S., Soltani, A. and Ajam Norouzi, H. (2018). Estimation of yield gap in local rice cultivars by using CPA and BLF methods (Case study: Mazandaran province, Sari region). Journal of Crop Management, 10(3), 265-288. [In Farsi]
© 2019 by the authors. Licensee SCU, Ahvaz, Iran. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-Non Commercial 4.0 International (CC BY-NC 4.0 license) (http://creativecommons.org/licenses/by-nc/4.0/)