Use of crop growth model to simulate the impact of climate change on yield of various wheat cultivars under different agro-environmental conditions in Khyber Pakhtunkhwa, Pakistan

2020 ◽  
Vol 13 (3) ◽  
Author(s):  
Farhana Gul ◽  
Ishfaq Ahmed ◽  
Muhammad Ashfaq ◽  
Dawood Jan ◽  
Shah Fahad ◽  
...  
Keyword(s):  
Climate Change ◽  
Growth Model ◽  
Environmental Conditions ◽  
Crop Growth ◽  
Wheat Cultivars ◽  
Crop Growth Model ◽  
Khyber Pakhtunkhwa ◽  
Impact Of Climate Change ◽  
The Impact

scholarly journals Coupling a land-surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

2014 ◽  
Vol 18 (10) ◽  
pp. 4223-4238 ◽  
Author(s):  
G. M. Tsarouchi ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Coupled Model ◽  
Crop Growth ◽  
Original Model ◽  
Surface Model ◽  
Strong Impact ◽  
Crop Growth Model ◽  
Mean Error ◽  
The Impact

Abstract. Land-Surface Models (LSMs) are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land-surface–atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the LSM JULES with the crop growth model InfoCrop. JULES in its current version (v3.4) does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parametrized it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges River basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm month−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 5.4–11.6 mm month−1. For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm month−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 2.2–3.4 mm month−1. The new modelling scheme, by offering increased accuracy of evapotranspiration estimations, is an important step towards a better understanding of the two-way crops–atmosphere interactions.

Climate Change, Economic Growth and Growth Determinants: Insights From Vietnam’s Coastal South Central Region

2020 ◽  
pp. 002190962094034
Author(s):  
Hong Hiep Hoang ◽  
Cong Minh Huynh
Keyword(s):  
Climate Change ◽  
Economic Growth ◽  
Growth Model ◽  
Central Region ◽  
Negative Impact ◽  
Generalized Least Squares ◽  
Policy Implications ◽  
Impact Of Climate Change ◽  
South Central ◽  
The Impact

Using the Feasible Generalized Least Squares econometric method, the paper analyzes the impact of climate change on economic growth in Vietnam’s coastal South Central region over the period of 2006–2015. The results indicate that, after controlling for the main determinants in the growth model, the climate change with various proxies has a significantly negative impact on provinces’ economic growth in the region. In particular, local institutions not only increase economic growth, but also reduce the negative impact of climate change on economic growth as well. These results suggest some policy implications aimed at boosting the process of transforming the economic growth model for the coastal region adapting to climate change. JEL codes: F21, F23, E22

scholarly journals Assessing the Impact of Climate Change on Wheat Productivity in Khyber Pakhtunkhwa, Pakistan

2019 ◽  
Vol 35 (1) ◽  
Author(s):  
Sonia Sonia ◽  
Khuram Nawaz Sadozai ◽  
Noor Paio Khan ◽  
Abbas Ullah Jan ◽  
Gulnaz Hameed
Keyword(s):  
Climate Change ◽  
Khyber Pakhtunkhwa ◽  
Impact Of Climate Change ◽  
The Impact

scholarly journals Coupling a land surface model with a crop growth model to improve ET flux estimations in the Upper Ganges basin, India

2014 ◽  
Vol 11 (6) ◽  
pp. 6843-6880
Author(s):  
G. M. Tsarouchi ◽  
W. Buytaert ◽  
A. Mijic
Keyword(s):  
Growth Model ◽  
Land Surface ◽  
Land Surface Model ◽  
Coupled Model ◽  
Crop Growth ◽  
Original Model ◽  
Surface Model ◽  
Crop Growth Model ◽  
Mean Error ◽  
The Impact

Abstract. Land surface models are tools that represent energy and water flux exchanges between land and the atmosphere. Although much progress has been made in adding detailed physical processes into these models, there is much room left for improved estimates of evapotranspiration fluxes, by including a more reasonable and accurate representation of crop dynamics. Recent studies suggest a strong land surface–atmosphere coupling over India and since this is one of the most intensively cultivated areas in the world, the strong impact of crops on the evaporative flux cannot be neglected. In this study we dynamically couple the land surface model JULES with the crop growth model InfoCrop. JULES in its current version does not simulate crop growth. Instead, it treats crops as natural grass, while using prescribed vegetation parameters. Such simplification might lead to modelling errors. Therefore we developed a coupled modelling scheme that simulates dynamically crop development and parameterised it for the two main crops of the study area, wheat and rice. This setup is used to examine the impact of inter-seasonal land cover changes in evapotranspiration fluxes of the Upper Ganges river basin (India). The sensitivity of JULES with regard to the dynamics of the vegetation cover is evaluated. Our results show that the model is sensitive to the changes introduced after coupling it with the crop model. Evapotranspiration fluxes, which are significantly different between the original and the coupled model, are giving an approximation of the magnitude of error to be expected in LSMs that do not include dynamic crop growth. For the wet season, in the original model, the monthly Mean Error ranges from 7.5 to 24.4 mm m−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 7–14 mm m−1. For the dry season, in the original model, the monthly Mean Error ranges from 10 to 17 mm m−1, depending on different precipitation forcing. For the same season, in the coupled model, the monthly Mean Error's range is reduced to 1–2 mm m−1. The new modelling scheme, by offering increased accuracy of evapotranspiration estimations, is an important step towards a better understanding of the two-way crops–atmosphere interactions.

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