Deciding sowing-window for maize-based cropping system in arid and semiarid environments in Punjab, Pakistan
文献类型: 外文期刊
作者: Abbas, Ghulam 1 ; Rehman, Atique Ur 1 ; Sarwar, Naeem 1 ; Fatima, Zartash 1 ; Hussain, Sajjad 2 ; Ahmed, Mukhtar 3 ; Raza, Muhammad Ali 4 ; Kan, Mustafa 8 ; Dogan, Hulya 9 ; Khan, Muhammad Azam 10 ; Ahmad, Shakeel 1 ;
作者机构: 1.Bahauddin Zakariya Univ, Dept Agron, Multan, Pakistan
2.Bahauddin Zakariya Univ, Dept Hort, Multan, Pakistan
3.Peer Mehar Ali Shah Arid Agr Univ, Peer Mehar Ali Shah, Rawalpindi, Pakistan
4.Islamia Univ Bahawalpur, Natl Res Ctr Intercropping, Bahawalpur, Pakistan
5.Sichuan Agr Univ, Coll Agron, Chengdu, Peoples R China
6.Sichuan Agr Univ, Sichuan Engn Res Ctr Crop Strip Intercropping Syst, Key Lab Crop Ecophysiol & Farming Syst Southwest C, Chengdu, Peoples R China
7.Gansu Acad Agr Sci, Lanzhou, Peoples R China
8.Kirsehir Ahi Evran Univ, Agr Fac, Dept Agr Econ, Kirsehir, Turkiye
9.Yozgat Bozok Univ, Hemp Res Inst, Dept Agr & Food, Yozgat, Turkiye
10.Inserv Agr Training Inst Sargodha, Sargodha, Pakistan
关键词: Cropping system model; Zea mays L.; DSSAT; hybrid; maize; spring; autumn; grain yield
期刊名称:TURKISH JOURNAL OF AGRICULTURE AND FORESTRY ( 影响因子:2.9; 五年影响因子:2.3 )
ISSN: 1300-011X
年卷期: 2023 年 47 卷 6 期
页码:
收录情况: SCI
摘要: Crop growth models can be valuable tools for researchers, academia, extension educators, and policy makers/planners for the evaluation of sustainable and long-term husbandry practices. Determining the optimum sowing window, which can be determined using crop growth models, is imperative under changing climate conditions. Thus, the main objectives herein were to 1) assess the performance of the cropping system model-crop environment resource synthesis-maize model for hybrids and sowing dates in the spring and autumn, and 2) determine the optimum sowing window in 15 districts of Punjab, Pakistan. In the spring experiment, 3 hybrids (P-33M15, M-DK6525, and S-NK8441) were planted in the main plots and then on 5 different sowing dates (January 15th, February 5th, February 25th, March 15th, and April 5th), they were planted in the subplots. In the autumn experiment, 3 hybrids (P-30R50, M-DK6714, and S-NK6621) were planted in the main plots and then on 5 different sowing dates (June 15th, July 5th, July 25th, August 15th, and September 5th), they were planted in the subplots. Model calibration and evaluation results were better in the spring and autumn. Performance of the model was good for the grain yield in the autumn (mean percentage difference (MPD): 7.47% to 8.90%) compared to the spring (MPD: 9.42% to 11.72%). Model evaluation was good for the early sowing dates (January 15th and February 5th) (error range: 6.26% to 9.65%) compared to the delayed dates (February 25th to April 5th) (error range: 9.34 to 14.91%). In the autumn, the model showed better performance for the delayed sowing dates (February 25th and August 15th) (error range: 5.22% to 9.43%) compared to the early dates (June 15th and July 5th) (error range: 8.56% to 11.27%). The model simulated good growth, development, grain yield, and yield components in the spring and autumn and of both 2016 and 2017. For the model application simulation of data over the long-term (1980 to 2017), the optimum sowing window in the spring was January 15th to March 5th and for the autumn it was July 23rd to August 27th for the 15 districts in Punjab, Pakistan. Simulation of the sowing dates for the whole year indicated that the spring was better compared to the autumn for obtaining the maximum grain yield. The results of the model were in line with the recommendations of the agricultural extension department for the sowing window for spring and autumn maize. It is therefore suggested that farmers should complete the sowing of spring and autumn maize within the sowing window to attain a higher yield of maize in arid and semiarid areas of Punjab, Pakistan.
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