Assessment of the Potential Climate Change on Rice Yield in Lower Ayeyarwady Delta of Myanmar Using EPIC Model

Lar, Naw Mar; Arunrat, Noppol; Tint, Soe; Pumijumnong, Nathsuda

doi:10.14456/ennrj.2018.14

Cited by 3 publications

(3 citation statements)

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“…Candradijaya et al [15] stated that rice yield is highly vulnerable to the planting period. It was also noted that a delay in planting of approximately 20 days from the actual planting time would enhance rice yields in future climate scenarios [49]. Similar results were also reported by Kassie et al [62] in Ethiopia, indicating that a delay in the planting period increased the crop yields.…”

Section: Discussionsupporting

confidence: 75%

“…Although there were variations in overestimations and underestimations in comparison, there was a good statistical agreement between the simulated and observed data. The ability of the EPIC model to simulate rice yields under various management options is well documented [31,49,52]. Worou [57] and Le et al [31] found that EPIC was able to simulate rice productivity and, generally, the results were overestimated.…”

Section: Discussionmentioning

confidence: 99%

“…The EPIC model was developed by USDA-ARS to estimate soil productivity in the 1980s in the United Sates [47,48]. It simulates approximately more than one hundred crops using unique attributes of each crop [49]. EPIC has also been applied in rice research, including simulating yield and biomass [31], global yield estimations [50], water, and nutrients [51][52][53].…”

Section: Model Descriptionmentioning

confidence: 99%

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Assessment and Application of EPIC in Simulating Upland Rice Productivity, Soil Water, and Nitrogen Dynamics under Different Nitrogen Applications and Planting Windows

Hussain,

Gollany,

Mulla

et al. 2023

Agronomy

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A suitable nitrogen (N) application rate (NAR) and ideal planting period could improve upland rice productivity, enhance the soil water utilization, and reduce N losses. This study was conducted for the assessment and application of the EPIC model to simulate upland rice productivity, soil water, and N dynamics under different NARs and planting windows (PWs). The nitrogen treatments were 30 (N30), 60 (N60), and 90 (N90) kg N ha−1 with a control (no N applied −N0). Planting was performed as early (PW1), moderately delayed (PW2), and delayed (PW3) between September and December of each growing season. The NAR and PW impacted upland rice productivity and the EPIC model predicted grain yield, aboveground biomass, and harvest index for all NARs in all PWs with a normalized good–excellent root mean square error (RMSEn) of 7.4–9.4%, 9.9–12.2%, and 2.3–12.4% and d-index range of 0.90–0.98, 0.87–0.94, and 0.89–0.91 for the grain yield, aboveground biomass, and harvest index, respectively. For grain and total plant N uptake, RMSEn ranged fair to excellent with values ranging from 10.3 to 22.8% and from 6.9 to 28.1%, and a d-index of 0.87–0.97 and 0.73–0.99, respectively. Evapotranspiration was slightly underestimated for all NARs at all PWs in both seasons with excellent RMSEn ranging from 2.0 to 3.1% and a d-index ranging from 0.65 to 0.97. A comparison of N and water balance components indicated that PW was the major factor impacting N and water losses as compared to NAR. There was a good agreement between simulated and observed soil water contents, and the model was able to estimate fluctuations in soil water contents. An adjustment in the planting window would be necessary for improved upland rice productivity, enhanced N, and soil water utilization to reduce N and soil water losses. Our results indicated that a well-calibrated EPIC model has the potential to identify suitable N and seasonal planting management options.

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Section: Discussionsupporting

confidence: 75%

Section: Discussionmentioning

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

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Assessment and Application of EPIC in Simulating Upland Rice Productivity, Soil Water, and Nitrogen Dynamics under Different Nitrogen Applications and Planting Windows

Hussain,

Gollany,

Mulla

et al. 2023

Agronomy

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Simulating resilience of rainfed wheat–based cropping systems of Iran under future climate change

Koocheki

Mahallati

Aval

et al. 2022

Mitig Adapt Strateg Glob Change

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Identifying Land-Use Related Potential Disaster Risk Drivers in the Ayeyarwady Delta (Myanmar) during the Last 50 Years (1974–2021) Using a Hybrid Ensemble Learning Model

et al. 2022

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Land-use and land-cover change (LULCC) dynamics significantly impact deltas, which are among the world’s most valuable but also vulnerable habitats. Non-risk-oriented LULCCs can act as disaster risk drivers by increasing levels of exposure or vulnerability or by reducing capacity. Making thematically detailed long-term LULCC data available is crucial to improving understanding of those dynamics interlinked at different spatiotemporal scales. For the Ayeyarwady Delta, one of the least studied mega-deltas, such comprehensive information is still lacking. This study used 50 Landsat and Sentinel-1A images spanning five decades from 1974 to 2021 in five-year intervals. A hybrid ensemble model consisting of six machine-learning classifiers was employed to generate land-cover maps from the images, achieving accuracies of about 90%. The major identified potential risk-relevant LULCC dynamics include urban growth towards low-lying areas, mangrove deforestation, and the expansion of irrigated agricultural areas and cultivated aquatic surfaces. The novel area-wide LULCC products achieved through the analyses provide a basis to support future risk-sensitive development decisions and can be used for regionally adapted disaster risk management plans and models. Developed with freely available data and open-source software, they hold great potential to increase research activity in the Ayeyarwady Delta and will be shared upon request.

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Assessment of the Potential Climate Change on Rice Yield in Lower Ayeyarwady Delta of Myanmar Using EPIC Model

Cited by 3 publications

References 0 publications

Assessment and Application of EPIC in Simulating Upland Rice Productivity, Soil Water, and Nitrogen Dynamics under Different Nitrogen Applications and Planting Windows

Assessment and Application of EPIC in Simulating Upland Rice Productivity, Soil Water, and Nitrogen Dynamics under Different Nitrogen Applications and Planting Windows

Simulating resilience of rainfed wheat–based cropping systems of Iran under future climate change

Identifying Land-Use Related Potential Disaster Risk Drivers in the Ayeyarwady Delta (Myanmar) during the Last 50 Years (1974–2021) Using a Hybrid Ensemble Learning Model

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