Prediction accuracy and limitations of the world food prices due to crop, energy, and economic model based on the seasonal ... View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2016-2019

FUNDING AMOUNT

18460000 JPY

ABSTRACT

① Creation of crop model: Based on weather condition and agricultural relation database, statistical analysis is applied to estimate parameters of crop model. Using the estimated model, a simulation is conducted to reproduce and estimate the yield of each crop by using the results of the seasonal prediction of GCM over the past 20 years as input values. Together, based on hydrological data, we examine the predictability of the impact of flood damage. ② Creation of energy model: Based on the database of energy relations, parameters of the mathematical model and the input-output model based on the nonlinear programming method for the world are determined, and the impact prediction from the energy resource utilization side with respect to the food price (season Prediction) possible energy model. In addition, by using a model that estimates the impact on crude oil · bioethanol price from food prices, it is necessary to clarify the prediction accuracy of food prices and to analyze the relation between energy resource usage and food price by time series analysis and econometric model analysis To start developing the model to predict the impact of changes in energy resource usage on food prices. ③ Creation of the world macroeconomic model: For the econometric model, the coefficient of the structural equation is estimated using time series economic data, the final test of the model is performed using only the initial value and the exogenous variable, and the reproducibility of the model . For the world CGE model, we model the parameters from the social accounting matrix of GTAP by calibrating the parameters and verify the reproducibility of the model by performing the sensitivity analysis on the parameter change. Furthermore, for the RBC model considering the expected utility, based on the Forward-looking type CGE model, model prototypes are created based on estimates of deep parameters of previous research and social accounting matrix data of GTAP. More... »

URL

https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16KT0036

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2201", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2214", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2214", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "amount": {
      "currency": "JPY", 
      "type": "MonetaryAmount", 
      "value": "18460000"
    }, 
    "description": "\u2460 Creation of crop model: Based on weather condition and agricultural relation database, statistical analysis is applied to estimate parameters of crop model. Using the estimated model, a simulation is conducted to reproduce and estimate the yield of each crop by using the results of the seasonal prediction of GCM over the past 20 years as input values. Together, based on hydrological data, we examine the predictability of the impact of flood damage. \u2461 Creation of energy model: Based on the database of energy relations, parameters of the mathematical model and the input-output model based on the nonlinear programming method for the world are determined, and the impact prediction from the energy resource utilization side with respect to the food price (season Prediction) possible energy model. In addition, by using a model that estimates the impact on crude oil \u00b7 bioethanol price from food prices, it is necessary to clarify the prediction accuracy of food prices and to analyze the relation between energy resource usage and food price by time series analysis and econometric model analysis To start developing the model to predict the impact of changes in energy resource usage on food prices. \u2462 Creation of the world macroeconomic model: For the econometric model, the coefficient of the structural equation is estimated using time series economic data, the final test of the model is performed using only the initial value and the exogenous variable, and the reproducibility of the model . For the world CGE model, we model the parameters from the social accounting matrix of GTAP by calibrating the parameters and verify the reproducibility of the model by performing the sensitivity analysis on the parameter change. Furthermore, for the RBC model considering the expected utility, based on the Forward-looking type CGE model, model prototypes are created based on estimates of deep parameters of previous research and social accounting matrix data of GTAP.", 
    "endDate": "2019-03-31T00:00:00Z", 
    "funder": {
      "id": "https://www.grid.ac/institutes/grid.54432.34", 
      "type": "Organization"
    }, 
    "id": "sg:grant.6536557", 
    "identifier": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "6536557"
        ]
      }, 
      {
        "name": "kaken_id", 
        "type": "PropertyValue", 
        "value": [
          "16KT0036"
        ]
      }
    ], 
    "inLanguage": [
      "ja"
    ], 
    "keywords": [
      "world food prices", 
      "exogenous variables", 
      "simulation", 
      "nonlinear programming method", 
      "coefficient", 
      "input values", 
      "addition", 
      "relation", 
      "prediction accuracy", 
      "food prices", 
      "social accounting matrix", 
      "energy resource usage", 
      "previous research", 
      "GCM", 
      "yield", 
      "time series", 
      "respect", 
      "seasonal prediction", 
      "season prediction", 
      "world", 
      "parameters", 
      "creation", 
      "database", 
      "energy", 
      "estimates", 
      "impact", 
      "world CGE model", 
      "initial value", 
      "RBC model", 
      "agricultural relation database", 
      "GTAP", 
      "bioethanol price", 
      "results", 
      "econometric model", 
      "type CGE model", 
      "hydrological data", 
      "social accounting matrix data", 
      "utility", 
      "structural equation", 
      "Econometric Model Analysis", 
      "sensitivity analysis", 
      "predictability", 
      "deep parameters", 
      "energy resource utilization side", 
      "final test", 
      "possible energy model", 
      "input-output model", 
      "time series analysis", 
      "model prototype", 
      "limitations", 
      "economic model", 
      "crop models", 
      "changes", 
      "weather conditions", 
      "seasonal forecasts", 
      "years", 
      "impact prediction", 
      "energy model", 
      "model", 
      "crude oil", 
      "economic data", 
      "parameter changes", 
      "mathematical model", 
      "reproducibility", 
      "statistical analysis", 
      "macroeconomic model", 
      "flood damage", 
      "crops", 
      "Forward", 
      "energy relations"
    ], 
    "name": "Prediction accuracy and limitations of the world food prices due to crop, energy, and economic model based on the seasonal forecast", 
    "recipient": [
      {
        "id": "https://www.grid.ac/institutes/grid.416835.d", 
        "type": "Organization"
      }
    ], 
    "sameAs": [
      "https://app.dimensions.ai/details/grant/grant.6536557"
    ], 
    "sdDataset": "grants", 
    "sdDatePublished": "2021-01-20T02:21", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com.uberresearch.data.processor/core_data/20181219_192338/projects/base/kaken_projects_18.xml.gz", 
    "startDate": "2016-07-19T00:00:00Z", 
    "type": "MonetaryGrant", 
    "url": "https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16KT0036"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/grant.6536557'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.6536557'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.6536557'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.6536557'


 

This table displays all metadata directly associated to this object as RDF triples.

107 TRIPLES      19 PREDICATES      91 URIs      83 LITERALS      4 BLANK NODES

Subject Predicate Object
1 sg:grant.6536557 schema:about anzsrc-for:2201
2 anzsrc-for:2214
3 schema:amount Ne668df22f352446bb9386f031431e839
4 schema:description ① Creation of crop model: Based on weather condition and agricultural relation database, statistical analysis is applied to estimate parameters of crop model. Using the estimated model, a simulation is conducted to reproduce and estimate the yield of each crop by using the results of the seasonal prediction of GCM over the past 20 years as input values. Together, based on hydrological data, we examine the predictability of the impact of flood damage. ② Creation of energy model: Based on the database of energy relations, parameters of the mathematical model and the input-output model based on the nonlinear programming method for the world are determined, and the impact prediction from the energy resource utilization side with respect to the food price (season Prediction) possible energy model. In addition, by using a model that estimates the impact on crude oil · bioethanol price from food prices, it is necessary to clarify the prediction accuracy of food prices and to analyze the relation between energy resource usage and food price by time series analysis and econometric model analysis To start developing the model to predict the impact of changes in energy resource usage on food prices. ③ Creation of the world macroeconomic model: For the econometric model, the coefficient of the structural equation is estimated using time series economic data, the final test of the model is performed using only the initial value and the exogenous variable, and the reproducibility of the model . For the world CGE model, we model the parameters from the social accounting matrix of GTAP by calibrating the parameters and verify the reproducibility of the model by performing the sensitivity analysis on the parameter change. Furthermore, for the RBC model considering the expected utility, based on the Forward-looking type CGE model, model prototypes are created based on estimates of deep parameters of previous research and social accounting matrix data of GTAP.
5 schema:endDate 2019-03-31T00:00:00Z
6 schema:funder https://www.grid.ac/institutes/grid.54432.34
7 schema:identifier N43ac8196dcb4401ca1c2f645b5f0389f
8 N7ddb7fdf9d1c4bf395514252266e19a1
9 schema:inLanguage ja
10 schema:keywords Econometric Model Analysis
11 Forward
12 GCM
13 GTAP
14 RBC model
15 addition
16 agricultural relation database
17 bioethanol price
18 changes
19 coefficient
20 creation
21 crop models
22 crops
23 crude oil
24 database
25 deep parameters
26 econometric model
27 economic data
28 economic model
29 energy
30 energy model
31 energy relations
32 energy resource usage
33 energy resource utilization side
34 estimates
35 exogenous variables
36 final test
37 flood damage
38 food prices
39 hydrological data
40 impact
41 impact prediction
42 initial value
43 input values
44 input-output model
45 limitations
46 macroeconomic model
47 mathematical model
48 model
49 model prototype
50 nonlinear programming method
51 parameter changes
52 parameters
53 possible energy model
54 predictability
55 prediction accuracy
56 previous research
57 relation
58 reproducibility
59 respect
60 results
61 season prediction
62 seasonal forecasts
63 seasonal prediction
64 sensitivity analysis
65 simulation
66 social accounting matrix
67 social accounting matrix data
68 statistical analysis
69 structural equation
70 time series
71 time series analysis
72 type CGE model
73 utility
74 weather conditions
75 world
76 world CGE model
77 world food prices
78 years
79 yield
80 schema:name Prediction accuracy and limitations of the world food prices due to crop, energy, and economic model based on the seasonal forecast
81 schema:recipient https://www.grid.ac/institutes/grid.416835.d
82 schema:sameAs https://app.dimensions.ai/details/grant/grant.6536557
83 schema:sdDatePublished 2021-01-20T02:21
84 schema:sdLicense https://scigraph.springernature.com/explorer/license/
85 schema:sdPublisher N45eed1e7847f4d92a8055f04ebe49c3e
86 schema:startDate 2016-07-19T00:00:00Z
87 schema:url https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-16KT0036
88 sgo:license sg:explorer/license/
89 sgo:sdDataset grants
90 rdf:type schema:MonetaryGrant
91 N43ac8196dcb4401ca1c2f645b5f0389f schema:name dimensions_id
92 schema:value 6536557
93 rdf:type schema:PropertyValue
94 N45eed1e7847f4d92a8055f04ebe49c3e schema:name Springer Nature - SN SciGraph project
95 rdf:type schema:Organization
96 N7ddb7fdf9d1c4bf395514252266e19a1 schema:name kaken_id
97 schema:value 16KT0036
98 rdf:type schema:PropertyValue
99 Ne668df22f352446bb9386f031431e839 schema:currency JPY
100 schema:value 18460000
101 rdf:type schema:MonetaryAmount
102 anzsrc-for:2201 schema:inDefinedTermSet anzsrc-for:
103 rdf:type schema:DefinedTerm
104 anzsrc-for:2214 schema:inDefinedTermSet anzsrc-for:
105 rdf:type schema:DefinedTerm
106 https://www.grid.ac/institutes/grid.416835.d schema:Organization
107 https://www.grid.ac/institutes/grid.54432.34 schema:Organization
 




Preview window. Press ESC to close (or click here)


...