Document Type : Research Paper - Agronomy

Authors

• Ghazban Anafjeh ¹
• Elham Elahifard ²
• Abolfazl Derakhshan ³
• Ahmad Zare ⁴

¹ M.Sc. Graduated in Weed Science, Department of Plant Production and Genetics, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Bavi, Mollasani, Iran

² Associate Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Bavi, Mollasani, Iran

³ Researcher, Sugarcane and By Products Development Company, Ahvaz, Iran

⁴ Associate Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Bavi, Mollasani, Iran.

Abstract

Introduction
One of the main issues of crop production in Iran and many countries of the world is the difference between the farmers’ yields (actual yield) and the achievable yields (potential yield). This distance and yield difference is named the yield gap. Therefore, research was conducted with the aim of determining the rice yield gap in Shoaybiyeh region of Khuzestan province.
Materials and Methods
This research was conducted in 2020 growing season as a survey in 100 rice fields in Shoaybiyeh region of Shushtar county. Comparative performance analysis method was used to determine rice grain yield gap. The modeling of grain production in rice fields was done by examining the relationship between all the investigated variables with the grain yield observed in the fields using progressive stepwise regression method. The production model obtained with this method was able to justify 89% of the grain yield changes of the studied rice fields with the studied variables.
Results and Discussion
The results showed that there was a gap of about 1495 kg ha^-1 between the average yield of farmers (5044.47 kg ha^-1) and the achievable yields in the region (6902.61 kg ha^-1). From this value of the expected yield gap in the region, the contribution of factors such as the age of the farmer, the intensity of the weed infestation in the fields, the presence of weed in the fields, the use of adjuvants during the spraying operation, the intensity of the infestation of the field with the striped rice stemborer, and the intensity of the infestation of the fields with the blast disease were equivalent to 3.29, 62.29, 2.53, 6.99, 20.22 and 4.68%, respectively. The analysis of rice yield gap showed that the weed infestation of the fields alone accounted for about 62.3% of the yield reduction compared to the achievable yields. In all the studied fields, the weeds of purple nutsedge, junglerice, broadleaf cattail, and johnsongrass were observed and recorded. Due to the lack of diversity in the fields, the model was not able to distinguish the amount of yield loss caused by these weeds. Intensity of weed contamination of rice fields was also observed in only 6% of the fields, and in the remaining 94%, moderate to very high contamination was observed. Some used herbicides such as bensulfuron-methyl and tribenuron-methyl showed a significant negative relationship with grain yields observed in rice fields. Therefore, it seems that farmers should reconsider the use of these herbicides in rice fields. Also, the delay in sowing and laxuary consumption of nitrogenous fertilizers was closely related to the severity of weed infestation and yield reduction. Investigating the relationship between the yields obtained in rice fields and the date of herbicide spraying (31 to 77 days after planting), a significant negative relationship was clearly observed; That is, with the delay in the spraying time and the consequent increase in the competitive ability of weeds, which may be associated with the decrease in the effectiveness of herbicides, yield have decreased linearly. 
Conclusion
In general, among the variables that had a significant impact on the observed yields in the region, except for the age variable of the farmer, all of them were related to yield-reducing factors, including pests, diseases, and weeds. Based on the results, the yield reducing factors, explained about 90% of the yield gap observed in the region.

Keywords

• Achievable yield
• Actual yield
• Comparative performance analysis
• Weed management
• Yield model

Main Subjects

• A: Yield Gap