Simulating the Effects of Drought Stress Timing and the Amount Irrigation on Cotton Yield Using the CSM-CROPGRO-Cotton Model

Author:

Wang Lei1,Lin Meiwei2,Han Zhenxiang1,Han Lianjin1,He Liang134,Sun Weihong2

Affiliation:

1. School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China

2. School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

3. Department of Electronic Engineering, and Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China

4. Xinjiang Key Laboratory of Signal Detection and Processing, Urumqi 830017, China

Abstract

Drought stress disrupts the molecular-level water balance in plants, and severe water deficiency can be fatal for cotton plants. However, mild water deficits or short-term drought stress may enhance crop resilience, increasing yields. The present study aims to determine the optimal watering time and irrigation amount to induce drought tolerance in cotton seedlings during drought training. Specifically, the investigation focuses on identifying the ideal day for watering and the corresponding irrigation volume that effectively triggers the transition of cotton plants into a state of enhanced resistance to drought stress during the seedling stage. In this study, the CSM-CROPGRO-Cotton model was utilized, and our objectives were to (i) evaluate the predictive capability of CSM-CROPGRO-Cotton for yield estimation in field experiments in Xinjiang and (ii) simulate and assess the range of time during the seedling stage when cotton plants can withstand drought stress without reducing yields, identifying irrigation strategies that induce drought training while maintaining yield under mild water deficiency. The model was validated using yield data from field experiments conducted in 2023. The validation criteria included a normalized root mean square error (nRMSE)>10% and a coefficient of determination (r2)>85% for yield; for the leaf area index (LAI), the criterion was (r2)>90%, with a degree of agreement of (d)>75%. The results demonstrated the accuracy of the CSM-CROPGRO-Cotton model in predicting cotton yield. Based on the validated CSM-CROPGRO-Cotton model, this study employed the LINUX crop model batch-processing technique to efficiently simulate 357 different irrigation strategies by adjusting the amount of “first irrigation” and timing. The findings revealed that in the irrigation scheme for cotton during the seedling stage, when the amount of first irrigation was in the lower range of 10 mm to 15 mm, the cotton plants underwent drought training during the early growth stage, and their yields did not exhibit drastic fluctuations due to reduced amounts of first irrigation. The suitable period for first irrigation for drought training was from 25 June to 6 July, and the amount of first irrigation could save approximately 57.14% in irrigation water. This implies that subjecting cotton plants to a certain level of drought training can enhance their stress tolerance and increase yields. This finding holds great significance for cotton cultivation in drought-prone regions.

Funder

National Key R&D Program of China

Publisher

MDPI AG

Subject

Agronomy and Crop Science

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