An EPIC model-based vulnerability assessment of wheat subject to drought

Abstract

This paper presents a regionalized vulnerability curve-building approach to vulnerability and risk assessment of wheat subjected to drought that uses the Environmental Policy Integrated Climate (EPIC) model and statistical analysis. We defined wheat vulnerability as the degree to which a wheat production system is likely to experience yield loss due to a perturbation or drought hazard. Wheat vulnerability in a given region is thus the yield loss divided by the drought hazard index (DHI yield losses and associated DHIs, wheat drought vulnerability curves can be developed. We propose that agricultural systems be considered uniform within each wheat-planting region and different in different regions, according to territorial differentiation, when regionalized vulnerability curves are built. Based on this principle, a detailed regional crop calendar was improved, and optimized wheat varieties were refined that can differentiate agricultural systems within wheat-planting regions. The crop calendar was improved based on the assumption that local farmers have perfect knowledge in selecting sowing and harvesting dates. The wheat varieties were optimized by adjusting the genetic parameters of wheat in the EPIC model using the Shuffled Complex Evolution algorithm-University of Arizona (SCE-UA) method. Based on these improvements and innovations, the precision of most vulnerability curves was improved, and the curves were compared favorably to those observed in previous studies related to differences in the genetic character of wheat, the crop calendar, environmental conditions, and other relevant factors. Differences within each region were smaller than differences between regions. More detailed wheat vulnerability curves allow for the assessment of expected wheat yield loss and also allow for a high level of precision in an evaluation, at a variety of scales, of risk of wheat subject to drought. The proposed approach to building regionalized vulnerability curves has the potential to be the basis for crop drought vulnerability curves in different geographical areas at multiple scales.

Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model

“…We argue that the vulnerability of plants plays a key role in frost risk formation [32][33][34][35], which we should take into account in frost risk assessment. Vulnerability is recognized as a propensity to suffer adverse consequence of when crops are threatened [36][37][38][39].…”

“…As a fine quantitative assessment method, a vulnerability curve expresses the quantitative relationship between the hazard intensity and the vulnerability of the hazard-affected body. Practically, a vulnerability curve is constructed by using historical disaster data and statistical analysis [35]. However, historical records of disaster losses are often insufficient and incompatible for using traditional statistical methods to build vulnerability curves [58].…”

Establishment and characteristics analysis of a crop-drought vulnerability curve: a case study of European winter wheat

“…1) . 15 In different environments, the drought vulnerability curve presents different shapes (Yue et al, 2015;Guo et al, 2016;Wang et al, 2013) and the core lies in the difference in loss extent and loss variation (Wang et al, 2013;Hu et al, 2012;Gottschalk and Dunn, 2005). Therefore, the key points of the curve-the turning point of the stages (the third order is 0) and the turning point of the increasing speed (the second order is 0) are used to describe the loss variation characteristics, the cumulative loss to the loss extent 20 characteristics, and the morphological classification to the integrated description.…”

Evaluation of Long-Term SOC and Crop Productivity within Conservation Systems Using GFDL CM2.1 and EPIC

“…A top dressing of urea (46% N) and potassium chloride (KCl; 60% K 2 O) was applied to the main crops. The EPIC model is widely used by researchers and government agencies to optimize crop management, maximize crop yield and profit, and identify impacts of agricultural practices on soil erosion, SOC and water quality [38,[51][52][53][54][55][56][57][58][59][60][61][62].…”