Applications of Crop Modeling in Cotton Production

Abbas, Ghulam; Fatima, Zartash; Tariq, Muhammad; Ahmed, Mukhtar; Habib-ur-Rahman, Muhammad; Nasim, Wajid; Rasul, Ghulam; Ahmad, Shakeel

doi:10.1007/978-981-15-1472-2_21

Cited by 6 publications

(1 citation statement)

References 49 publications

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“…Although there are some differences in the simulated methods, details, and yield components of existing cotton models, the main processes include phenology, light energy interception, carbon (C) assimilation, respiration, organ formation, biomass accumulation and distribution, and stress factor simulation [13]. These models have been used in irrigation management [14][15][16][17][18][19][20][21] and water use efficiency evaluation [22][23][24], assessment of effects of deficit irrigation on cotton growth and yield [25][26][27][28], evaluation of saline water irrigation on cotton growth and yield [29], nitrogen and phosphorus dynamics and fertilization management [14,30,31], fiber quality, embryo oil and protein accumulation simulation [32][33][34], and topping management [35].…”

Section: Introductionmentioning

confidence: 99%

Simulating Growth and Evaluating the Regional Adaptability of Cotton Fields with Non-Film Mulching in Xinjiang

Wang

et al. 2022

Agriculture

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Planting with non-film mulching is the fundamental means to eliminate the pollution of residual film in cotton fields. However, this planting approach should have regional adaptability. Therefore, the calibrated WOFOST model and an early mature cultivar CRI619 (Gossypium hirsutum Linn) were employed to simulate the cotton growth, and regions were then evaluated for planting in Xinjiang. A field experiment was conducted in 2019–2020 at the experimental irrigation station of Alar City, and the data were used to calibrate and validate the WOFOST model. The field validation results showed that the errors of the WOFOST simulation for emergence, flowering, and maturity were +1 day, +2 days, and +1 day, respectively, with good simulation accuracy of phenological development time. The simulated WLV, WST, WSO, and TAGP agreed well with measured values, with R2 = 0.96, 0.97, 0.99, and 0.99, respectively. The RMSE values of simulated versus measured WLV, WST, WSO, and TAGP were 175, 210, 199, and 251 kg ha−1, and showed high accuracy. The simulated soil moisture (SM) agreed with the measured value, with R2 = 0.87. The calibration model also showed high SM simulation accuracy, with RMSE = 0.022 (cm3 cm−3). Under all treatments, the simulated TAGP and yield agreed well with the measured results, with R2 of 0.76 and 0.70, respectively. RMSE of simulated TAGP and yield was 465 and 200 kg ha−1, and showed high accuracy. The percentage RMSE values (ratio of RMSE to the average measured value, NRMSE) of ETa and WUE were 9.8% and 11.7%, indicating extremely high precision (NRMSE < 10%) and high precision (10% < NRMSE ≤ 20%), respectively. The simulated results for phenology length at the regional scales showed that the effective accumulation temperature in counties such as Yingjisha and Luntai was not enough for the phenological maturity of the studied cotton cultivar. The southern area of Xinjiang had a generally higher yield than the northern area but required more irrigation. This research can provide a method for evaluating the adaptability of filmless cultivation techniques for cotton in different counties.

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