GEPIC-V-R model: A GIS-based tool for regional crop drought risk assessment

Yin, Yuanyuan; Zhang, Xingming; Lin, Degen; Han, Yu; Wang, Jing’ai; Shi, Peijun

doi:10.1016/j.agwat.2014.05.017

Cited by 59 publications

(44 citation statements)

References 25 publications

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“…In addition to agricultural management factors, factors such as the sowing and harvesting date (Wu et al 2009), potential radiation use efficiency (WA), harvest index (HI), the point in the growing season at which leaf area begins to decline due to leaf senescence (DLAI), the normal fraction of N in crop biomass at mid-season (BN2), potential heat unit (PHU), and crop parameter control of leaf area growth in a crop under a non-stressed condition (DLP2) (Wang et al 2005;Wu et al 2009;Liu 2009), have significant impacts on wheat yield, whether in reality or in model processing, due to the complexity of the wheat production system. It is not only time-consuming, but also very difficult to obtain satisfactory accuracy by calibrating and validating the EPIC model while adjusting parameters manually Yin et al 2014). We therefore suggest that the calibration and validation of the EPIC model should seek overall optimization, not relying on one or a few of the parameters, for obtaining satisfactory wheat yield simulation results.…”

Section: Regional Wheat Yield Simulationmentioning

confidence: 99%

“…We therefore suggest that the calibration and validation of the EPIC model should seek overall optimization, not relying on one or a few of the parameters, for obtaining satisfactory wheat yield simulation results. We thus propose partition parameter optimization according to the law of regional differentiation in wheat-planting areas (Jia 2010;Jia et al 2012;Wang et al 2012Wang et al , 2013Yin et al 2014). From a technical perspective, the overall optimization goal was achieved using a detailed, regional wheat-planting calendar and optimized genetic parameters of wheat for the EPIC model, with the support of the SCE- UA parameter optimization method.…”

Section: Regional Wheat Yield Simulationmentioning

confidence: 99%

“…Because of its advantage in simulating crop growth processes based on daily step (Williams et al 1989;Sharpley and Williams 1990), the EPIC model has been widely used in crop drought research, such as building wheat drought vulnerability curves , maize drought risk assessment (Jia 2010;Jia et al 2012;Yin et al 2014), and crop irrigation management (Rinaldi 2001). For simulating the wheat growth process, the model inputs include meteorological data, soil data, and agricultural data such as crop yield used for model calibration and validation (Table 3).…”

Section: Epic Model and Inputsmentioning

confidence: 99%

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

Yue

et al. 2015

Nat Hazards

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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.

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Section: Regional Wheat Yield Simulationmentioning

confidence: 99%

Section: Regional Wheat Yield Simulationmentioning

confidence: 99%

Section: Epic Model and Inputsmentioning

confidence: 99%

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

Yue

et al. 2015

Nat Hazards

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“…In a similar study, researchers calculated the global drought risk for maize by fitting vulnerability curves for different areas [30]. However, two-dimensional vulnerability curves can only express the relationship between hazard intensity and yield loss, without considering the environmental dimension.…”

Section: Significance Of Vulnerability Surfacesmentioning

confidence: 99%

Drought Risk Assessment Based on Vulnerability Surfaces: A Case Study of Maize

et al. 2016

Self Cite

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Abstract:Agriculture is a sector easily affected by meteorological conditions. Crop yield reduction, even regional conflicts, may occur during a drought. It is extremely important to improve the state of our knowledge on agricultural drought risk. This study has proposed a new method (vulnerability surfaces) for assessing vulnerability quantitatively and continuously by including the environmental variable as an additional perspective on exposure and assessed global maize drought risk based on these surfaces. In this research, based on the Environmental Policy Impact Climate (EPIC) model, irrigation scenarios were adopted to fit "Loss rate-Drought index-Environmental indicator (L-D-E)" vulnerability surfaces by constructing a database suitable for risk assessment on a large scale. Global maize drought risk was quantitatively assessed based on its optimal vulnerability surface. The results showed an R 2 for the optimal vulnerability surface of 0.9934, with coarse fragment content as the environmental indicator. The expected global average annual yield loss rate due to drought was 19.18%. The global average yield loss rate due to drought with different return periods (10a, 20a, 50a, and 100a) was 29.18%, 32.76%, 36.89%, and 38.26%, respectively. From a global perspective, Central Asia, the Iberian Peninsula, Eastern Africa, the Midwestern United States, Chile, and Brazil are the areas with the highest maize drought risk. The vulnerability surface is a further development of the vulnerability curve as a continuous expression of vulnerability and considers differences in environmental factors. It can reflect the spatial heterogeneity of crop vulnerability and can be applied in large-scale risk assessment research.

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“…These models simulate biomass yield, nutrient cycling, water requirements, carbon flux, and other key parameters under different crop management practices. Recent efforts have been made to integrate these models with Geographic Information System (GIS) to create spatially explicit large-scale crop models [85]. Conversion of biomass to fuel can be modeled using conventional process simulators such as Aspen Plus, ChemCAD, or SuperPro [86].…”

Section: Process Scalementioning

confidence: 99%

Design of Sustainable Biofuel Processes and Supply Chains: Challenges and Opportunities

et al. 2015

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Abstract:The current methodological approach for developing sustainable biofuel processes and supply chains is flawed. Life cycle principles are often retrospectively incorporated in the design phase resulting in incremental environmental improvement rather than selection of fuel pathways that minimize environmental impacts across the life cycle. Further, designing sustainable biofuel supply chains requires joint consideration of economic, environmental, and social factors that span multiple spatial and temporal scales. However, traditional life cycle assessment (LCA) ignores economic aspects and the role of ecological goods and services in supply chains, and hence is limited in its ability for guiding decisionmaking among alternatives-often resulting in sub-optimal solutions. Simultaneously incorporating economic and environment objectives in the design and optimization of emerging biofuel supply chains requires a radical new paradigm. This work discusses key research opportunities and challenges in the design of emerging biofuel supply chains and provides a high-level overview of the current "state of the art" in environmental sustainability assessment of biofuel production. Additionally, a bibliometric analysis of over 20,000 biofuel research articles from 2000-to-present is performed to identify active topical areas of research in the biofuel literature, quantify the relative strength of connections between various biofuels research domains, and determine any potential research gaps. OPEN ACCESSProcesses 2015, 3 635

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GEPIC-V-R model: A GIS-based tool for regional crop drought risk assessment

Cited by 59 publications

References 25 publications

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

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

Drought Risk Assessment Based on Vulnerability Surfaces: A Case Study of Maize

Design of Sustainable Biofuel Processes and Supply Chains: Challenges and Opportunities

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