Regional yield estimation using a crop simulation model: Concepts, methods, and validation

1994 ◽  
Vol 46 (1) ◽  
pp. 79-92 ◽  
Author(s):  
Thomas N. Moen ◽  
Harry M. Kaiser ◽  
Susan J. Riha
Keyword(s):  
Simulation Model ◽  
Yield Estimation ◽  
Crop Simulation Model ◽  
Crop Simulation

scholarly journals RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL

2016 ◽  
Vol XLI-B8 ◽  
pp. 993-996
Author(s):  
N. T. Son ◽  
C. F. Chen ◽  
C. R. Chen ◽  
L. Y. Chang ◽  
S. H. Chiang
Keyword(s):  
Data Assimilation ◽  
Simulation Model ◽  
Cost Function ◽  
Crop Yield ◽  
Satellite Data ◽  
Rice Yield ◽  
Yield Estimation ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
The Cost

Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

scholarly journals RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL

2016 ◽  
Vol XLI-B8 ◽  
pp. 993-996 ◽  
Author(s):  
N. T. Son ◽  
C. F. Chen ◽  
C. R. Chen ◽  
L. Y. Chang ◽  
S. H. Chiang
Keyword(s):  
Data Assimilation ◽  
Simulation Model ◽  
Cost Function ◽  
Crop Yield ◽  
Satellite Data ◽  
Rice Yield ◽  
Yield Estimation ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
The Cost

Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

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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