scholarly journals The effect of up-scaling soil properties and model parameters on predictive accuracy of DSSAT crop simulation model under variable weather conditions

Geoderma ◽  
2017 ◽  
Vol 287 ◽  
pp. 105-115 ◽  
Author(s):  
Jessica Fry ◽  
Andrey K. Guber ◽  
Moslem Ladoni ◽  
Juan D. Munoz ◽  
Alexandra N. Kravchenko
Keyword(s):  
Simulation Model ◽  
Soil Properties ◽  
Predictive Accuracy ◽  
Weather Conditions ◽  
Model Parameters ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Variable Weather ◽  
Up Scaling

scholarly journals Heliaphen, an outdoor high-throughput phenotyping platform designed to integrate genetics and crop modeling

2018 ◽  
Author(s):  
Florie Gosseau ◽  
Nicolas Blanchet ◽  
Didier Varès ◽  
Philippe Burger ◽  
Didier Campergue ◽  
...  
Keyword(s):  
Simulation Model ◽  
High Throughput ◽  
Simulation Study ◽  
Large Population ◽  
Model Parameters ◽  
Crop Simulation Model ◽  
Continental Scale ◽  
Crop Simulation ◽  
High Throughput Phenotyping ◽  
The Impact

AbstractHeliaphen is an outdoor pot platform designed for high-throughput phenotyping. It allows automated management of drought scenarios and plant monitoring during the whole plant cycle. A robot moving between plants growing in 15L pots monitors plant water status and phenotypes plant or leaf morphology, from which we can compute more complex traits such as the response of leaf expansion (LE) or plant transpiration (TR) to water deficit. Here, we illustrate the platform capabilities for sunflower on two practical cases: a genetic and genomics study for the response to drought of yield-related traits and a simulation study, where we use measured parameters as inputs for a crop simulation model. For the genetic study, classical measurements of thousand-kernel weight (TKW) were done on a sunflower bi-parental population under water stress and control conditions managed automatically. The association study using the TKW drought-response highlighted five genetic markers. A complementary transcriptomic experiment identified closeby candidate genes differentially expressed in the parental backgrounds in drought conditions. For the simulation study, we used the SUNFLO crop simulation model to assess the impact of two traits measured on the platform (LE and TR) on crop yield in a large population of environments. We conducted simulations in 42 contrasted locations across Europe and 21 years of climate data. We defined the pattern of abiotic stresses occurring at this continental scale and identified ideotypes (i.e. genotypes with specific traits values) that are more adapted to specific environment types. This study exemplifies how phenotyping platforms can help with the identification of the genetic architecture of complex response traits and the estimation of eco-physiological model parameters in order to define ideotypes adapted to different environmental conditions.

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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