A methodology for model-based greenhouse design: Part 3, sensitivity analysis of a combined greenhouse climate-crop yield model

2011 ◽  
Vol 110 (4) ◽  
pp. 396-412 ◽  
Author(s):  
B.H.E. Vanthoor ◽  
E.J. van Henten ◽  
C. Stanghellini ◽  
P.H.B. de Visser
Keyword(s):  
Sensitivity Analysis ◽  
Crop Yield ◽  
Yield Model ◽  
Greenhouse Climate ◽  
Model Based

Dynamic Validation and Sensitivity Analysis of a NOx Estimation Model Based on In-Cylinder Pressure Measurement

2017 ◽  
Author(s):  
Sergio Mario Camporeale ◽  
Patrizia D. Ciliberti ◽  
Antonio Carlucci ◽  
Daniela Ingrosso
Keyword(s):  
Sensitivity Analysis ◽  
Pressure Measurement ◽  
Cylinder Pressure ◽  
Estimation Model ◽  
Model Based

Techniques to Predict Agricultural Droughts

2005 ◽  
Author(s):  
Zekai Şen
Keyword(s):  
Crop Yield ◽  
Satellite Data ◽  
Wheat Yield ◽  
Weather Variables ◽  
Short Term ◽  
Yield Model ◽  
Regression Techniques ◽  
The Difference ◽  
Effects Of Drought

In general, the techniques to predict drought include statistical regression, time series, stochastic (or probabilistic), and, lately, pattern recognition techniques. All of these techniques require that a quantitative variable be identified to define drought, with which to begin the process of prediction. In the case of agricultural drought, such a variable can be the yield (production per unit area) of the major crop in a region (Kumar, 1998; Boken, 2000). The crop yield in a year can be compared with its long-term average, and drought intensity can be classified as nil, mild, moderate, severe, or disastrous, based on the difference between the current yield and the average yield. Regression techniques estimate crop yields using yield-affecting variables. A comprehensive list of possible variables that affect yield is provided in chapter 1. Usually, the weather variables routinely available for a historical period that significantly affect the yield are included in a regression analysis. Regression techniques using weather data during a growing season produce short-term estimates (e.g., Sakamoto, 1978; Idso et al., 1979; Slabbers and Dunin, 1981; Diaz et al., 1983; Cordery and Graham, 1989; Walker, 1989; Toure et al., 1995; Kumar, 1998). Various researchers in different parts of the world (see other chapters) have developed drought indices that can also be included along with the weather variables to estimate crop yield. For example, Boken and Shaykewich (2002) modifed the Western Canada Wheat Yield Model (Walker, 1989) drought index using daily temperature and precipitation data and advanced very high resolution radiometer (AVHRR) satellite data. The modified model improved the predictive power of the wheat yield model significantly. Some satellite data-based variables that can be used to predict crop yield are described in chapters 5, 6, 9, 13, 19, and 28. The short-term estimates are available just before or around harvest time. But many times long-term estimates are required to predict drought for next year, so that long-term planning for dealing with the effects of drought can be initiated in time.

scholarly journals Simplified Identification Strategy of Load Model Based on Global Sensitivity Analysis

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Xing. Tian ◽  
Xueliang. Li ◽  
Long. Zhao ◽  
Zuoyun. Tan ◽  
Shuchen. Luo ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Load Model ◽  
Global Sensitivity ◽  
Model Based ◽  
Identification Strategy
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