Abstract

In an agricultural drought risk system, crop drought loss sensitivity evaluation is a fundamental link for quantitative agricultural drought loss risk assessment. Summer maize growth processes under various drought patterns were simulated using the Cropping System Model (CSM)-CERES-maize, which was calibrated and validated based on pit experiments conducted in the Huaibei Plain during 2016 and 2017 seasons. Then S-shaped maize drought loss sensitivity curve was built for fitting the relationship between drought hazard index intensity at a given stage and the corresponding dry matter accumulation and grain yield loss rate, respectively. Drought stress reduced summer maize evapotranspiration, dry matter, and yield accumulation, and the reductions increased with the drought intensity at each stage. Moreover, the losses caused by drought at different stages were significantly different. When maize plants were exposed to a severe water deficit at the jointing stage, the dry matter and grain yield formation were greatly affected. Therefore, maize growth was more sensitive to drought stress at the jointing stage when the stress was serious. Furthermore, when plants encountered a relatively slight drought during the seedling or jointing stage, which represented as a lower soil water deficit intensity, the grain yield loss rates approached the maximum for the sensitivity curves of these two stages. Therefore, summer maize tolerance to water deficit at the seedling and jointing stages were weak, and yield formation was more sensitive to water deficit during these two stages when the deficit was relatively slight.

Highlights

  • Natural disasters are occurring frequently, and are affected by global climate change and warming tendencies around the world

  • The calibration of Cropping System Model (CSM)-CERES-maize model mainly focused on determining the variety and genetic parameters of summer maize cultivated in the experiments

  • These parameters were adjusted using the generalized likelihood uncertainty estimation (GLUE) procedure of Decision Support System for Agrotechnology Transfer (DSSAT) set to 10,000 iterations

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Summary

Introduction

Natural disasters are occurring frequently, and are affected by global climate change and warming tendencies around the world. Drought is a typical natural disaster with wide effects that causes great losses in worldwide agriculture [1]. The average annual economic losses in agricultural production due to drought have exceeded $ 6 billion, and still increasing [2]. A drought disaster-prone country located in the East Asian monsoon region, reports over 25 billion kg of annual grain losses due to drought [3], directly affecting agricultural production and directly threatening food security [4]. In order to effectively cope with drought events and reduce grain losses, it is necessary to assess agricultural drought risk quantitatively [6]. Agricultural drought risk can be regarded as a complex system which is composed of drought disaster-inducing factor hazards and crop drought loss vulnerability [7,8]. Studying sensitivity can provide scientific guidance for agricultural drought warning [10]

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