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disaggregation_of_crop_areas [2020/03/28 09:26] – matszdisaggregation_of_crop_areas [2022/11/07 10:23] (current) – external edit 127.0.0.1
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 Primacy of land stability means that if there is no indication (i. e. new observation, policy restricting previous land distributions, …) it is more likely that the spatial pattern remains similar to the previous (prior) pattern. Therefore, once a likely distribution of land and livestock has been determined on the basis of high-resolution FSS statistics, the model tries to stay as close as possible to this distribution. This is achieved with penalty factors that are activated as soon as the estimated land use area deviated from the prior values, assigning a higher penalty for deviations of permanent crops and forests, and very high penalties of a land use is estimated in a spatial unit where it didn’t exist in the prior’s data base.  Primacy of land stability means that if there is no indication (i. e. new observation, policy restricting previous land distributions, …) it is more likely that the spatial pattern remains similar to the previous (prior) pattern. Therefore, once a likely distribution of land and livestock has been determined on the basis of high-resolution FSS statistics, the model tries to stay as close as possible to this distribution. This is achieved with penalty factors that are activated as soon as the estimated land use area deviated from the prior values, assigning a higher penalty for deviations of permanent crops and forests, and very high penalties of a land use is estimated in a spatial unit where it didn’t exist in the prior’s data base. 
  
-The disaggregation model m_hpdCropSpat is described in [[https://www.capri-model.org/dokuwiki_help/doku.php?id=disaggregation_of_crop_areas#simulation_model_m_hpdcropspat|Section simulation_model_m_hpdcropspat]]. FIXME Section 7.4.2 describes the required input data, and Section 7.4.3 describes the preparation of the input data for their use in hpdCropSpat.+The disaggregation model m_hpdCropSpat is described in [[disaggregation_of_crop_areas#simulation_model_m_hpdcropspat|Section Simulation model m_hpdCropSpat]].[[disaggregation_of_crop_areas#data_sets|Section Data sets]] describes the required input data, and [[disaggregation_of_crop_areas#data_preparation|Section Data preparation]] describes the preparation of the input data for their use in hpdCropSpat.
  
 ====Simulation model m_hpdCropSpat==== ====Simulation model m_hpdCropSpat====
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 This is done on the basis of ‘similarity’ assuming that similar crops have similar preferences for natural conditions (or available infrastructure) and a similar spatial heterogeneity. This is done on the basis of ‘similarity’ assuming that similar crops have similar preferences for natural conditions (or available infrastructure) and a similar spatial heterogeneity.
 +
 This ‘gap-filling’ is done in three hierarchical steps: This ‘gap-filling’ is done in three hierarchical steps:
-  - Average crop area of similar crops in the same spatial unit as defined in the set mactgroups: + 
 +1. Average crop area of similar crops in the same spatial unit as defined in the set mactgroups: 
  
  
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     TREE.(APPL,CITR,OFRU,NURS,NUTS,LOLIV,LVINY)/;     TREE.(APPL,CITR,OFRU,NURS,NUTS,LOLIV,LVINY)/;
  
-  - If there are no ‘similar’ crops in the region or grid cell, the average area of all available crops is used. +2. If there are no ‘similar’ crops in the region or grid cell, the average area of all available crops is used. \\ 
-  - If there is still no prediction of the in the spatial unit, the same crop area is given to the prior estimates in all spatial units in the region/grid cell.+3. If there is still no prediction of the in the spatial unit, the same crop area is given to the prior estimates in all spatial units in the region/grid cell.
  
 ===Checking availability of standard deviations=== ===Checking availability of standard deviations===
disaggregation_of_crop_areas.1585387598.txt.gz · Last modified: 2022/11/07 10:23 (external edit)
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