Estimation of Maize Yield at Spatial Level Using DSSAT Crop Simulation Model

2018 ◽  
Vol 105 (10-12) ◽  
Author(s):  
M. Venkatesan ◽  
S. Pazhanivelan
Keyword(s):  
Simulation Model ◽  
Maize Yield ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Spatial Level

scholarly journals Evaluating maize yield variability and gaps in two agroecologies in northern Ghana using a crop simulation model

2017 ◽  
Vol 35 (2) ◽  
pp. 137-147 ◽  
Author(s):  
Dilys S MacCarthy ◽  
Samuel G Adiku ◽  
Bright S Freduah ◽  
Alpha Y Kamara ◽  
Stephen Narh ◽  
...  
Keyword(s):  
Simulation Model ◽  
Maize Yield ◽  
Northern Ghana ◽  
Yield Variability ◽  
Crop Simulation Model ◽  
Crop Simulation

Optimizing Sugarcane Planting Windows Using a Crop Simulation Model at the State Level

2021 ◽  
Author(s):  
Jéssica Sousa Paixão ◽  
Derblai Casaroli ◽  
João Carlos Rocha dos Anjos ◽  
José Alves Júnior ◽  
Adão Wagner Pêgo Evangelista ◽  
...  
Keyword(s):  
Simulation Model ◽  
State Level ◽  
The State ◽  
Crop Simulation Model ◽  
Crop Simulation

scholarly journals Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS

2020 ◽  
Vol 12 (13) ◽  
pp. 2099
Author(s):  
Mongkol Raksapatcharawong ◽  
Watcharee Veerakachen ◽  
Koki Homma ◽  
Masayasu Maki ◽  
Kazuo Oki
Keyword(s):  
Remote Sensing ◽  
Simulation Model ◽  
Rice Yield ◽  
Drought Monitoring ◽  
Weather Data ◽  
Area Index ◽  
Drought Impact ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Rice Yields

Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change.

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