Instead of using single-crop models, the project includes multiple models ( Figure 4), scenarios, locations, crops, and participants to standardize climate change impact studies and define the uncertainty of impact assessments ( Rosenzweig et al., 2013).Capsim simulations are a more engaging way to learn. AgMIP incorporates state-of-the-art climate products as well as crop and agricultural trade model improvements in coordinated regional and global assessments of future climate impacts. The recently founded Agricultural Model Intercomparison and Improvement Project (AgMIP is a major international collaborative effort to assess climate impacts on the agricultural sector. Including uncertainty ensures that crop models are used in a similar way to the multimodel approach in climate change science ( Meehl et al., 2007). Experts suggest including the uncertainty in crop models in such climate change assessments and multimodel applications.
Building a model is a well recog-nised way of understanding the world it is a simplification of some structure or a system.Xin Li. Key words: simulation model, computer simulation, computer assisted learn-ing, lifelong education, building scheme Introduction Simulation is a particular type of modelling. Simulations engage learners and help them develop.simulation model in the curriculum.
Example Simulation Models. Relative values of models in different phases of development (From Penning de Vries, 1982) Use of simulation modelsV. One may note that the evaluation process and its ambitions will depend on the type of model.
Crop modeling, which incorporates all (if possible) parameters affecting a trait into a model, can combine both genetic and environmental elements to predict phenotype ( Hoogenboom et al., 2004 Messina et al., 2006 White and Hoogenboom, 2003 Yin et al., 2003, 2004). Researchers now dissect model parameters into genetic factors to make cultivar-specific models. Both directions are increasingly coupled with molecular genetics to facilitate crop modeling.Crop model parameters are usually determined by iterative parameter adjustment and comparison with observed data from field trials however, parameters estimated in this way are inaccurate because of the inherent experimental errors associated with field observation.
Third, under the influence of pleiotropy, selecting for one trait may cause an adverse effect if that trait is coupled to another trait. Second, because of GEI, some QTLs identified as beneficial to the desired trait might have the opposite effect in another environment ( Chenu et al., 2009). When we examine the underlying traits (leaf elongation rate, leaf angle, biomass allocation, etc.) one by one, more QTLs might be discovered. Some QTLs may have a small effect statistically, but that does not mean the QTL is unimportant to the target trait, even though we neglect these QTLs in almost all circumstances and turn to others that can explain more variance. In this situation, the ecophysiological model is like a gene network linking all of the relevant genes into a complete picture. First, by separating the underlying QTLs into different parts (parameters) of the ecophysiological model, some QTLs otherwise have only small effects if using the target trait (e.g., yield or resistance to drought) could be detected by QTL mapping ( Letort et al., 2008).
The model accounted for 72% of the observed variation among 94 RILs and 94% of the variation among the two parents across eight environments however, because some QTLs have only small effects on the input traits, the ecophysiological model is less accurate in predicting phenotypes of the RILs than the original phenotypic input values. CIM was conducted to identify QTLs controlling four input traits. (2005) investigated flowering time of a recombinant inbred line population of spring barley ( Hordeum vulgare L.). QTL information and ecophysiological models theoretically can be combined to predict a physiological trait of any cultivar under study in any climate scenario ( Reymond et al., 2003).Yin et al. Moreover, in multienvironmental trials (METs), the values of the parameters are not necessarily the same because of GEI ( Chapman, 2008) thus, crop models are like bridges over the genotype–phenotype gap. QTL mapping is carried out according to the parameters of the ecophysiological model that is, each parameter of the ecophysiological model is computed as the sum of QTL effects ( Yin and Struik, 2010).
On loamy soils, the increased SLA trait actually reduced grain yields.Using sorghum as an example, Chapman et al. Simulation results showed that increasing SLA could increase yield only on the sandy, low water-holding soils in a Mediterranean environment when the N supply was unlimited. (2003) used a derivative of APSIM, APSIM-Nwheat, to investigate the relationship between specific leaf area (SLA) and yield under the environmental conditions of Australia. Further, QTLs cannot explain all the variation this means the ecophysiological model, like other genetic models, cannot totally predict the phenotype of a cultivar with a specific genetic background.APSIM (The Agricultural Production Systems sIMulator) is a widely used simulation tool for cropping systems that was designed to combine accurate predictions of economic products (e.g., grain, biomass, or sugar yield) for many crop species in response to climate and management conditions ( Keating et al., 2003 McCown et al., 1996). This is also an intrinsic problem with QTL-based crop modeling ( Messina et al., 2006). The results showed that the latter method accounted for 85.5% of overall variation, higher than the variation explained by the QTL-based model (72.6%), mainly because the prediction using the QTL-based model is independent of the greenhouse experiments calibrating the parameters of the model.
Simulation results showed that increasing the level of trait expression (by increasing the number of favorable alleles for a trait) led to higher grain yield, except for trait PH in the Severe-Terminal drought environment type. The introduction of crop models has modified the relative yield value of the different genes in the environment types and thus has influenced the rate and timing of fixation of the favorable alleles for these four traits. Without the biophysical crop simulation model, and assuming that all the 15 genes have equal additive effects on yield in different environments, each of these genes would be fixed at a similar rate. This study demonstrated the advantages of combining a crop physiology model with a breeding simulation program: to investigate selection response in different environments.
Gene information and expression states in TPE are input into the QU-GENE engine to generate a simulated breeding population and the corresponding trait values (e.g., flowering time) of each genotype. Link QU-GENE with Agricultural Production Systems sIMulator (APSIM). This is a kind of epistasis effect, which has important implications for MAS programs for the sequence of gene fixation that can result in the highest selection response.Figure 5. On the other hand, higher values of TE and OA had more effect on yield in the Severe-Terminal drought environment type.
With the breeding population and yield value for each individual, the breeding process can be simulated with QU-GENE simulation modules (e.g., QU-Line).
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