نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشجوی دکتری دانشگاه گنبد
2 استادیار گروه کشاورزی دانشگاه گنبد
3 استاد گروه زراعت، دانشگاه فردوسی مشهد
4 استادیار گروه امور زراعی دانشکده کشاورزی و منابع طبیعی دانشگاه گنبد کاووس
5 عضو هیات علمی موسسه تحقیقات پنبه کشور
6 استادیار گروه تولیدات گیاهی دانشگاه گنبد کاووس
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Background and Objective: One of the primary challenges contributing to low cotton yields in Golestan Province is the substantial yield gap the discrepancy between farmers' actual yields and the achievable potential yield. Estimating this gap and identifying its contributing factors require the application of robust analytical methods. This study aimed to document the production processes and estimate the yield gap associated with cotton agricultural management practices in the cotton-growing regions along the Caspian Sea coast, specifically in the cities of Kurdkoy and Bandarga.
Materials and Methods: To investigate the management factors limiting cotton yield and to estimate the yield gap, a comprehensive study was conducted during the 2021-2022 growing season in the western Golestan Province. Data on agricultural management practices, from planting to boll harvesting, were collected through direct interviews with farmers. Comparative performance analysis (CPA) was employed to identify yield gap-inducing factors. The collected data were analyzed using stepwise regression and boundary line analysis in SAS software. Quantitative variables were further evaluated through simple and boundary linear regression. The contributions of individual limiting factors to the yield gap were determined using the derived production equation and model component values.
Results: The analysis of agricultural management variables using the production model revealed that the average and maximum potential yields were 2165 and 5358 kg/ha, respectively. Observed average and maximum yields in the field were 1988.5 and 4500 kg/ha, respectively. The total estimated yield gap, calculated using stepwise regression, was 3220 kg/ha. Among yield-limiting factors, the contributions to the yield gap were as follows: nitrogen fertilizer (1000 kg/ha; 31%), phosphorus fertilizer (277 kg/ha; 9%), and complementary element spraying (688 kg/ha; 21%). Yield increases were attributed to irrigation volume (490 kg/ha; 15%) and pest damage reduction (150 kg/ha; 5%). Irrigation during the budding and boll formation stages contributed an additional 149 kg/ha (5%) and 326 kg/ha (10%) to yield increases, respectively. Conversely, planting date and nitrogen top-dressing during flowering negatively impacted yields, with contributions of 69 kg/ha (2%) and 72 kg/ha (6%), respectively. When influential yield-related variables were included in the model, the estimated average and maximum yields were 2165 and 5358 kg/ha, closely matching observed field values of 1988.4 kg/ha and 4500 kg/ha, respectively.
Conclusion: The observed yield variations among farms underscore the disparities in agricultural management practices. The use of boundary line analysis, alongside regression and performance comparison methods, proved effective in identifying production-limiting factors. The results indicate that optimizing key factors, as identified through stepwise regression and boundary line analysis, could significantly reduce the yield gap in farmers' cotton fields.
کلیدواژه‌ها [English]
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