scholarly journals On Prediction and Simulation of Wheat Yield in Bengbu City by Using G (1, 1) Model Based on Sine Function Transformation Type

2016 ◽  
Vol 11 (3) ◽  
pp. 232-235
Author(s):  
Huaxi Chen
2009 ◽  
Vol 60 (1) ◽  
pp. 60 ◽  
Author(s):  
A. G. T. Schut ◽  
D. J. Stephens ◽  
R. G. H. Stovold ◽  
M. Adams ◽  
R. L. Craig

The objective of this study was to improve the current wheat yield and production forecasting system for Western Australia on a LGA basis. PLS regression models including temporal NDVI data from AVHRR and/or MODIS, CR, and/or SI, calculated with the STIN, were developed. Census and survey wheat yield data from the Australian Bureau of Statistics were combined with questionnaire data to construct a full time-series for the years 1991–2005. The accuracy of fortnightly in-season forecasts was evaluated with a leave-year-out procedure from Week 32 onwards. The best model had a mean relative prediction error per LGA (RE) of 10% for yield and 15% for production, compared with RE of 13% for yield and 18% for production for the model based on SI only. For yield there was a decrease in RMSE from below 0.5 t/ha to below 0.3 t/ha in all years. The best multivariate model also had the added feature of being more robust than the model based on SI only, especially in drought years. In-season forecasts were accurate (RE of 10–12% and 15–18% for yield and production, respectively) from Week 34 onwards. Models including AVHRR and MODIS NDVI had comparable errors, providing means for predictions based on MODIS. It is concluded that the multivariate model is a major improvement over the current DAFWA wheat yield forecasting system, providing for accurate in-season wheat yield and production forecasts from the end of August onwards.


Author(s):  
A. Kolotii ◽  
N. Kussul ◽  
A. Shelestov ◽  
S. Skakun ◽  
B. Yailymov ◽  
...  

Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.


2020 ◽  
Author(s):  
Lixun Xian ◽  
Guangjun Li ◽  
Qing Xiao ◽  
Zhibin Li ◽  
Xiangbin Zhang ◽  
...  

Abstract Background : In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring; However, the ability of geometric indices to identify the error of contouring results is limited, and there is lack of clinical background. Based on the reference contouring, we systematically introduced geometric errors to study the relationship between geometric and dosimetric indices and evaluated the clinical feasibility of assessing the accuracy of contouring based on geometric indices alone. Materials and Methods: A C-shaped target, organ at risk (Core), and intensity-modulated radiotherapy (IMRT) plan outlined in the American Association of Physicists in Medicine (AAPM) TG-119 report (The report of Task Group 119 of the AAPM) were used as references. Translation, scaling, rotation (except for the Core), and sine function transformation were performed to simulate the contouring results. The corresponding dosimetric indices were obtained from the original dose distribution of the radiotherapy plan, and correlations (R²) between geometric indices and dosimetric indices were quantified through linear regression. The clinical applicability of the threshold for geometric indices was analyzed by combining the geometric indices and dose difference diagram. Results: The correlations between the geometric and dosimetric indices were different and inconsistent for the contouring of the target and Core after the geometric transformation simulation. Except for the sine function transformation (R²: 0.04–0.023, p > 0.05), the other three geometric indices of the planning target volume (PTV) had strong correlations with the dosimetric indices D98% and D mean (R²: 0.689–0.988), 80% of which were strongly correlated with a p < 0.001. The correlation results for the other geometric transformations in the Core were similar to those in the PTV except for the down shift transformation. Conclusions: The dosimetric indices are heavily influenced by the contour differences, thus highlighting their importance in the evaluation process. Clinically, an assessment of the contour accuracy of the region of interest is not feasible based on geometric indices alone, and should be combined with dosimetric indices. Keywords: Contour evaluation, Geometric indices, Dosimetric indices, Geometric transformation simulation


Sign in / Sign up

Export Citation Format

Share Document