تعیین مدل مناسب در تجزیه و تحلیل خلأ عملکرد برنج (Oryza sativa L.) در استان گیلان با روش آنالیز خط مرزی

نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشگاه علوم کشاورزی و منابع طبیعی ساری

2 دانشگاه گیلان

3 پردیس کشاورزی و منابع طبیعی دانشگاه تهران

4 علوم کشاورزی و منابع طبیعی ساری

چکیده

یکی از روش­های توانمند در جهت ارزیابی پتانسیل عملکرد و دلایل خلأ عملکرد، آنالیز خط مرزی می­باشد. پژوهش حاضر به­منظور بررسی تعیین عملکرد بهینه و تأثیر احتمالی اجزای وابسته به عملکرد در شالیزارهای برنج (Oryza sativa L.) دشت فومنات استان گیلان (رقم طارم هاشمی) اجرا شد. جهت توصیف رابطه بین عملکرد و اجزای عملکرد از مدل­های دو­تکه­ای، دندان­مانند و درجه دوم استفاده گردید. برای انتخاب مدل برتر از چهار معیار میانگین قدر مطلق خطا، ضریب تبیین، ضرایب رگرسیون خطی ساده و ضریب تغییرات استفاده و پس از انتخاب مدل برتر، خلأ عملکرد، عملکرد بهینه و مقادیر بهینه اجزای عملکرد با استفاده از روش آنالیز خط مرزی محاسبه شدند. در بین مدل­های برازش­یافته، مدل دوتکه­ای برای دو ویژگی تعداد خوشه در متر­مربع و وزن صد دانه دارای کمترین RMSE و ضریب تغییرات بوده و به­خوبی توانسته به توصیف روند تغییرات بپردازد. علاوه براین، تابع دندان­مانند با کمترین RMSE و ضریب تغییرات برای توصیف روند تغییرات ویژگی تعداد دانه پر مورد استفاده قرار گرفت. با توجه به مدل­ها، خلأ عملکرد در دشت فومنات برابر با 63/3 تن در هکتار با میانگین عملکرد بهینه و عملکرد کشاورز به­ترتیب برابر با 44/8 و 81/4 تن در هکتار برآورد شد. همچنین، مقادیر بهینه اجزای عملکرد شامل تعداد خوشه در متر مربع، تعداد دانه پر در خوشه و وزن صد دانه (گرم) به­ترتیب برابر با 560، 9/83-47 و 18/2 به­دست آمد.

کلیدواژه‌ها


عنوان مقاله [English]

Determination of Appropriate Model for Yield Gap Analysis of Rice in Guilan Province using Boundary Line Analysis Method

نویسندگان [English]

  • Niloofar Aghaeipour 1
  • Hemmatollah Pirdashti 1
  • Mohsen Zavareh 2
  • Hossein Asadi 3
  • Mohammad Ali Bahmanyar 4
1 Sari Agricultural Sciences and Natural Resources University
2 Guilan University
3 Tehran University
4 Sari Agricultural Sciences and Natural Resources University
چکیده [English]

Introduction[1]
Nowadays, identification of the yield limiting factors in the field particularly the various yield components including number of panicle per unit area, number of seeds per panicle and seed weight) is one of the most important methods to increase the production of rice. The yield gap (YG) analysis can be performed by measuring the yield related characteristics. Yield gap was estimated as the difference between actual and potential yield that has been used in various studies as an important indicator to increase the yield in crops and different areas. One of the most powerful methods to evaluate the reasons of yield potential and yield gap is boundary line analysis. The purpose of this research was to select an appropriate function for describing the relationship between yield and yield components in the Fumann plain of Guilan province. Furthermore, after selecting the superior function, the parameters of the yield and yield components were estimated  to calculate the yield gap in the region.
 
Materials and Methods
The present study was carried out during two cropping seasons: 2012-13 and 2013-14 in Foumanat plain (cv. ‘Tarom Hashemi’). We recorded the geographic coordinates of 53 fields. At the end of growing season (harvesting time), paddy yield and yield components (panicle number, filled grain number and 100- grain weight) were calculated in each field. The correlation coefficients between yield components and yield were studied. Segmented, quadratic and dent-like models were applied to describe the relationship between yield and yield components. Root mean square error (RMSE), determination coefficient (R2), regression simple coefficients (a & b) and coefficient of variation (CV) were used to identify the appropriate model. After selecting a superior model, the boundary line method was used to calculate yield gap and its percentage, optimum yield and optimum amount of yield components for each field.
 
Results and Discussion
According to the results, a positive and significant correlation was existed between paddy yield with panicle number and filled grain number with 100- grain weight and a negative and significant correlation was existed between 100- grain weights with panicle number. Linear regression simple coefficients for all traits studied in the quadratic function and for two traits of panicles number per square meter and of filled grains number in the panicle in the segmented model were significant. Among the fitted models, segmented model has the lowest RMSE (respectively equal to 0.082 and 0.472) and coefficient of variation (equal to 1.26 and 6.39, respectively) in terms of two characteristics of panicle number and 100- grain weight and was able to describe the trend of the experimental data. In addition, dent-like model with the lowest RMSE (equal to 0.484) and coefficient of variation (equal to 6.60) used to describe the changes of filled grain number. In Foumanat plain, YG was recorded 3.63 t.ha-1with the average optimum yield and actual yield of 8.44 and 4.81 t.ha-1, respectively (40% reduction in yield). Also, the optimum amount of panicle number, filled grain number and 100- grain weight were 560, 47-83.9, and 2.18 g, respectively.
 
Conclusion
Although, the area of Foumanat plain in the west of Guilan province has low actual yield, there is a good potential to increase the current yield. In this study, two segmented and dent-like models were identified as superior models. The highest YG in this study was related to the number of panicles per square meter followed by the number of filled grains and the 100- grain weight. Therefore, proper crop management for improving the yield components could be an important step towards reducing the YG and increasing the yield potential in the studied area.
 

کلیدواژه‌ها [English]

  • Coefficient of variation
  • dent-like model
  • Grain weight
  • non linear regression
  • panicle number
  • segmented model
Adibnia, M., Torabi, B., Rahimi, A., and Azari, A. 2015. Quantifying response of safflower seedling emergence to temperature. Electronic Journal of Crop Production 8: 161-177. (In Persian with English Summary)
Ahmadi, M., Kamkar, B., Soltani, A., and Zeinali, E. 2010. Evaluation of non-linear regression models to predict stem elongation rate of wheat ((Tajan cultivar) in response to temperature and Photoperiod. Electronic Journal of Crop Production 2: 39-54. (In Persian with English Summary)
Ajam Norouzi, H., Soltani, A., Majidi, E., and Homaei, M. 2007. Modelling response of emergence to temperature in faba bean under field condition. Journal of Agricultural Sciences and Natural Resources 14: 100-111. (In Persian with English Summary)
Amiri Deh Ahmadi, S.R., Parsa, M., Bannayan Aval, M., and Nassiri Mahallati, M. 2015. Yield gap analysis of chickpea under semi-arid conditions: A simulation study. Journal of Agroecology 7: 84-98. (In Persian with English Summary)
Banneheka, B.M.S.G., Dhanushika, M.P., Wijesuriya, W., and Herath, K. 2013. A linear programming approach to fitting an upper quadratic boundary line to natural rubber data. Journal of the National Science Foundation of Sri Lanka 41.
Bouman, B.A.M., Peng, S., Castaneda, A.R., and Visperas, R.M. 2005. Yield and water use of irrigated tropical aerobic rice systems. Agricultural Water Management 74: 87-105.
Brancourt-Hulmel, M., Lecomte, C., and Meynard, J.M. 1999. A diagnosis of yield-limiting factors on Probe genotypes for characterizing environments in winter wheat trials. Crop Science 39: 1798-1808.
Casanova, D., Goudriaan, J., Forner, M.M.C., and Withagen, J.C.M. 2002. Rice yield prediction from yield components and limiting factors. European Journal of Agronomy 17: 41-61.
Dore, T., Meynard, J.M., and Sebillotte, M. 1998. The role of grain number, nitrogen nutrition and stem number in limiting pea crop (Pisum sativum) yields under agricultural conditions. European Journal of Agronomy 8: 29-37.
Espe, M.B., Cassman, K.G., Yang, H.W., Guilpart, N., Grassini, P., Van Wart, J., Anders, M., Beighley, D., Harrell, D., Linscombe, S., McKenzie, K., Mutters, R., Wilson, L.T., and Linquist, B.A. 2016. Yield gap analysis of US rice production systems shows opportunities for improvement. Field Crops Research 196: 276-283.
Gharavi Baigi, M., Pirdashti, H., Abbasian, A., and Aghajaniye Mazandarani, G. 2014. Response of yield and yield components of rice (Oryza sativa L. cv. Tarom Hashemi) in rice, duck and Azolla (Azolla sp.) farming. Journal of Agroecology 6: 477-487. (In Persian with English Summary)
Hajarpoor, A., Soltani, A., and Torabi, B. 2015. Using boundary line analysis in yield gap studies: Case study of wheat in Gorgan. Scientific Journal Management System 8: 183-201. (In Persian with English Summary)
Hatami, H., Mohsenabadi, G., Esfahani, M., Amiri Garijani, B., and Aalami, A. 2016. Effect of transplanting time on grain yield and physiological traits in grain filling period in rice cultivars. Journal of Crops Improvement 18: 655-671. (In Persian with English Summary)
Huang, M., Zou, Y.b., Jiang, P., Xia, B., Md, I., and Ao, H.J. 2011. Relationship Between Grain Yield and Yield Components in Super Hybrid Rice. Agricultural Sciences in China 10: 1537-1544.
Inusah, B.I.Y., Dogbe, W., Abdulai, A.L., Yirzagla, J., Mawunya, M., and Issahak, A.S. 2015. Yield gap survey in sudanno-guinea savanna agro-ecological zones of ghana. Sustainable Agriculture Research 4: 127-137.
Kazemi Poshtmassari, H., Pirdashti, H., Bahmanyar, M.A., and Nassiri, M. 2007. Study the effects of nitrogen fertilizer rates and split application on yield and yield components of different rice (Oryza sativa L.) cultivars. Pajouhesh and Sazandegi 75: 68-77. (In Persian with English Summary)
Khalili, N., Kamkar, B., and Khodabakhshi, A.H. 2015. Quantifying and analysis of germination responses of annual savory (Satureja hortensis L.) to temperature and salinity stress. Environmental Stresses in Crop Sciences 8: 83-92. (In Persian with English Summary)
Kundu, S., and Kundagrami, S. 2015. Estimation of path coefficient analysis to identify the yield contributing traits in rice (Oryza sativa L.) under saline and non-saline coastal regions of West Bengal. Journal of Advances in Biology 8: 1433-1438.
Mahdavi, F., Esmaeili, M.A., Fallah, A., and Pirdashti, H. 2006. Study of morphological characteristics, physiological indices, grain yield and its components in rice (Oryza sativa L.) Landraces and Improved Cultivars 27: 280-297. (In Persian with English Summary)
Makowski, D., Dore, T., and Monod, H. 2007. A new method to analyse relationships between yield components with boundary lines. Agronomy for Sustainable Development 27: 119-128.
Meier, U. 1997. Growth stages of mono-and dicotyledonous plants: BBCH-Monograph. Blackwell wissenschafts-verlag, Berlin and Braunschweig.
Milne, A.E., Ferguson, R.B., and Lark, R.M. 2006a. Estimating a boundary line model for a biological response by maximum likelihood. Annals of Applied Biology 149: 223-234.
Mohandass, S., Natarajaratnam, N., and Kailasam, C. 1988. A new hybrid model for panicle growth in rice (Oryza sativa L.). Journal of Agronomy and Crop Science 161: 207-209.
Mojtabaie Zamani, M., Esfahany, M., Honarnejad, R., and Alahgholipour, M. 2007. Relationship between grain filling rate, grain filling duration, yield components and other physiological traits in rice (Oryza sativa L.). Journal of Water and Soil Science 10: 213-225. (In Persian with English Summary)
Mustafavi Rad, M., and Tahmasbi Sarvestani, Z.A.A. 2003. Evaluation of nitrogen fertilizer effects on yield, yield components and dry matter remobilization of three rice genotype. Journal of Agricultural Sciences and Natural Resources 2: 21-31. (In Persian with English Summary)
Nassiri Mahallati, M., and Koocheki, A.R. 2014. Long term evaluation of yield stability trend for cereal crops in Iran. Agroecology 6: 607-621. (In Persian with English Summary)
Nassiri Mahallati, M., Koocheki, A., and Jahani, M. 2016. Estimating Within Field Variability of Wheat Yield Using Spatial Variables: An Approach to Precision Agriculture. Journal of Agroecology 8: 329-345. (In Persian with English Summary)
Nhamo, N., Rodenburg, J., Zenna, N., Makombe, G., and Luzi-Kihupi, A. 2014. Narrowing the rice yield gap in East and Southern Africa: Using and adapting existing technologies. Agricultural Systems 131: 45-55.
Rajeshwari, S., and Nadarajan, N. 2004. Correlation between yield and yield components in rice (Oryza sativa L.). Agricultural Science Digest 24: 280-282.
Sabouri, H., Sabouri, A., and Dadras, A.R. 2012. Modeling the response of germination rate of different rice genotypes to temperature. Cereal Research 2: 123-135. (In Persian with English Summary)
Safae Chaykar, S., Samie zade, H., Esfahani, M., and Rabiei, B. 2009. Correlation of agronomic traits under favorable irrigation and water stress conditions in rice (Oryza sativa L.). Journal of Water and Soil Science 13: 91-105. (In Persian with English Summary)
Selvaraj, C.I., Nagarajan, P., Thiyagarajan, K., Bharathi, M., and Rabindran, R. 2011. Genetic parameters of variability, correlation and path coefficient studies for grain yield and other yield attributes among rice blast disease resistant genotypes of rice (Oryza sativa L.). African Journal of Biotechnology 10: 3322-3334.
Shokri, S., Siadat, S.A., Fathi, G., Abdali Mashhadi, A.R., Gilani, A.A., and Maadi, B. 2012. Evalution of nitrogen fertilizer effects on paddy yield, yield components and dry matter remobilization of three rice genotype. Electronic Journal of Crop Production 3: 73-87. (In Persian with English Summary)
Soltani, A., Hajjarpour, A., and Vadez, V. 2016. Analysis of chickpea yield gap and water-limited potential yield in Iran. Field Crops Research 185: 21-30.
Soltani, A., Hammer, G.L., Torabi, B., Robertson, M.J., and Zeinali, E. 2006a. Modeling chickpea growth and development: Phenological development. Field Crops Research 99: 1-13.
Souroush, H.R., Mesbah, M., and Hossian Zadeh, A.H. 2004. A study of relationship between grain yield and yield components in rice. Iranian Journal of Agricultural Sciences 35: 983-993.
Tarang, A., Hossieni Chaleshtary, M., Tolghilani, A., and Esfahani, M. 2013. Evaluation of grain yield stability of pure lines of rice in Guilan province. Iranian Journal of Crop Sciences 2: 24-34. (In Persian with English Summary)
Xu, X., He, P., Zhao, S., Qiu, S., Johnstond, A.M., and Zhou, W. 2016. Quantification of yield gap and nutrient use efficiency of irrigated rice in China. Field Crops Research 186: 58-65.