Validation of the EPIC model using a long-term experimental data on the semi-arid Loess Plateau of China

Wang, Xue Chun; Li, Jun; Tahir, Muhammad Naveed; Ming, Hao

doi:10.1016/j.mcm.2010.11.025

Cited by 26 publications

(13 citation statements)

References 21 publications

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“…1). Mean annual precipitation at Changwu is 578.5 mm, mean annual air temperature is 9.1°C, and the annual frost‐free period is 171 d. The soil texture is predominantly a silty clay loam (Wang et al, 2011). Data from the field experiment were used to calibrate and validate the APSIM model.…”

Section: Methodsmentioning

confidence: 99%

Diverse Responses of Winter Wheat Yield and Water Use to Climate Change and Variability on the Semiarid Loess Plateau in China

Cleverly

Chen

et al. 2014

Agronomy Journal

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Crop production and water use in rainfed cropland are vulnerable to climate change. This study was to quantify diverse responses of winter wheat (Triticum aestivum L.) yield and water use to climate change on the Loess Plateau (LP) under different combinations of climatic variables. The crop model APSIM was validated against field experimental data and applied to calculate yield and water use at 18 sites on the LP during 1961 to 2010. The coefficient of variation of yield ranged from 12 to 66%, in which the vulnerability of yield increased from the southeast (12%) to the northwest (66%). This change was attributed to the gradual increase in precipitation variation from the southeast to the northwest. An obvious warming trend during 1961 to 2010 resulted in a significant decrease in the growth duration by 1 to 5 d decade -1 . The yield at 12 sites was significantly reduced by 120 to 720 kg ha -1 decade -1 . Evapotranspiration was significantly decreased by 1 to 26 mm decade -1 ; however, water use efficiency at most sites showed no significant trend. Eighteen sites were classified into three climatic zones by cluster analysis: high temperature-high precipitation-low radiation (HHL), medium temperature-medium precipitation-medium radiation (MMM), and low temperature-low precipitation-high radiation (LLH). The trend of decreasing yield was smallest in the HHL cluster because of a minimal reduction in precipitation, while decreasing trends in yield and evapotranspiration were larger in the LLH and MMM because of larger reductions in precipitation. The results imply that among strategies such as breeding for long duration or drought tolerance, modification of the planting date will be necessary to avoid high temperatures associated with climate change. Abbreviations: ET, evapotranspiration; HHL, high temperature-high precipitation-low radiation cluster; LAI, leaf area index; LLH, low temperature-low precipitation-high radiation cluster; LP, Loess Plateau; MMM, medium temperature-medium precipitation-medium radiation cluster.Climatic factors such as solar radiation, precipitation, and temperature are major determinants of crop production and water use. Climate change is characterized by increased surface temperature and modified precipitation patterns (Intergovernmental Panel on Climate Change, 2007). Thus, crop production and water use are influenced by climate change. Consequently, a better understanding of the interactions between climatic factors and their impacts on crop production is essential for optimizing crop management, improving water use efficiency, and adopting reasonable strategies to mitigate climate change (Yu et al., 2014).The Loess Plateau (LP, Fig. 1) is located in northern China, where dryland agriculture is the primary economic activity. The LP has a typical continental monsoon climate, which means that it is cold in the winter and warm and humid in the summer. The average temperature ranges from 3.6°C in the north to 14.3°C in the south (Sun et al., 2010). Agriculture on the LP is vulner...

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Section: Methodsmentioning

confidence: 99%

Diverse Responses of Winter Wheat Yield and Water Use to Climate Change and Variability on the Semiarid Loess Plateau in China

Cleverly

Chen

et al. 2014

Agronomy Journal

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“…The default value of 40 kg ha −1 MJ −1 m 2 was therefore adopted as the final calibration value. This value is the same as that used by [67] and [19]. WA increases yield through biomass changes and should be adjusted last based on experimental data as it can significantly change the rate of crop growth and final crop yield [18].…”

Section: Discussionmentioning

confidence: 99%

Calibration and Validation of the EPIC Model for Maize Production in the Eastern Cape, South Africa

2019

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Crop models are useful tools to evaluate the effects of agricultural management on ecosystem services. However, before they can be applied with confidence, it is important to calibrate and validate crop models in the region of interest. In this study, the Environmental Policy Integrated Climate (EPIC) model was evaluated for its potential to simulate maize yield using limited data from field trials on two maize cultivars. Two independent fields at the Cradock Research Farm were used, one for calibration and one for validation. Before calibration, mean simulated yield was 8 t ha −1 while mean observed yield was 11.26 t ha −1 . Model calibration improved mean simulated yield to 11.23 t ha −1 with a coefficient of determination, (r 2 ) = 0.76 and a model efficiency (NSE) = 0.56. Validation with grain yield was satisfactory with r 2 = 0.85 and NSE = 0.61. Calibration of potential heat units (PHUs) and soil-carbon related parameters improved model simulations. Although the study only used grain yield to calibrate and evaluate the model, results show that the calibrated model can provide reasonably accurate simulations. It can be concluded that limited data sets from field trials on maize can be used to calibrate the EPIC model when comprehensive experimental data are not available.Agronomy 2019, 9, 494 2 of 16 used to test the effectiveness of alternative agricultural land management practices under varying climate change scenarios. However, to yield meaningful results, it is prudent to calibrate crop models in the region of intended use before their application [11].Model calibration is the procedure where model parameters are fine-tuned to increase the agreement between model simulations and real-world observations [12]. Calibration is important to increase model accurateness and decrease model prediction uncertainty [13]. Calibration is done by judiciously choosing model parameter values, adjusting them within recommended ranges, for example, from literature or expert opinions, and comparing the simulated outputs with observed data for a given set of conditions [14]. A successful calibration would be when the model reproduces observed data within a satisfactory degree of accuracy and precision for the intended model use [15,16]. Once calibrated and validated, the model can be reasonably applied in the area of interest. Calibrated crop growth models can, therefore, be useful tools to complement field experiments and support decision making for sustainable agricultural land management.The Environmental Policy Integrated Climate (EPIC) model [17], originally developed in the United States of America (USA), is a process-based, field-scale model with a daily time scale. It simulates the chemical processes occurring in the soil-water-plant interaction under different agricultural management regimes [18]. The main components of EPIC are weather simulation, crop growth, carbon and nutrient cycling, tillage, soil erosion, and hydrology [19]. Globally, the model has been applied to study crop yield responses to nutrients and...

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“…38 , Wang, et al . 230 , Wang, et al . 231 EPIC-IIASASite-basedSite-specific (EPIC 0810) & Global e F, HIpot (ric, mai)F (others)Yield (FE & FAO)NationalAround 2000various, e.g.…”

Section: Methodsmentioning

confidence: 99%

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

et al. 2019

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The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives.

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Validation of the EPIC model using a long-term experimental data on the semi-arid Loess Plateau of China

Cited by 26 publications

References 21 publications

Diverse Responses of Winter Wheat Yield and Water Use to Climate Change and Variability on the Semiarid Loess Plateau in China

Diverse Responses of Winter Wheat Yield and Water Use to Climate Change and Variability on the Semiarid Loess Plateau in China

Calibration and Validation of the EPIC Model for Maize Production in the Eastern Cape, South Africa

The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

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