Hazards SEES: Understanding Cross-Scale Interactions of Trade and Food Policy to Improve Resilience to Drought Risk View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2015-2018

FUNDING AMOUNT

2519689 USD

ABSTRACT

Food security in regions affected by drought is influenced by a complex set of interactions between hydrological, agricultural, and social systems. Previous models examining the impact of drought on food security have not incorporated food trade and food movements at fine spatial scales, yet these components are critical parts of regional food systems. In sub-Saharan Africa droughts and floods account for approximately 80% of fatalities and 70% of the economic losses that are due to natural hazards. Zambia is particularly vulnerable to droughts, having high levels of malnutrition, poverty, income inequality, exposure to HIV/AIDS and malaria, and low levels of educational attainment. Zambia's agricultural production is rain-fed, which further increases vulnerability in the region. With the extreme vulnerability of the region, Zambia serves as an ideal place to study how the interactions between drought risk, crop production, trade, and policy affect food security. By incorporating the effects of trade and policy into predictive hydrological and agricultural models, this project is improving existing early warning systems for famine which rarely assess the capacity for a region to ameliorate drought via food transfers and trade. This project's goal is to understand the effect of drought hazards in subsistence agriculture using a novel integrative framework that merges data, models, and knowledge of drought risk and crop production; their interactions with the dynamics of trade-based and aid-based responses; and their effect on household food security and consumption. We are addressing three questions: 1) What are the spatio-temporal scales of drought risk across Zambia and how does risk transfer into agricultural impacts? 2) What is the role of trade and domestic food policy on food security at local to national levels? 3) Can drought impacts be more effectively reduced by integrating an understanding of policy and food transfers into an agricultural drought early warning system? To answer these questions, we are collecting biophysical data to characterize historical droughts and their impacts on regional agriculture; examining household and market level data to characterize food security outcomes, market prices, and food sourcing; using complex network analysis to characterize food trade and flows; assessing market integration associated with price fluctuations and infrastructure to determine economic exposure and resilience at the household, community and district levels; examining how policies at the national scale constrain decisions at the local scale; and developing computational models for high resolution predictions and to explore probabilistic solutions for resource allocation and risk management. This project is the first to create an integrated model of food trade, household consumption and crop production at such fine spatial scales built on an empirical foundation in each dimension. More... »

URL

http://www.nsf.gov/awardsearch/showAward?AWD_ID=1534544&HistoricalAwards=false

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/2008/2214", 
        "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": "USD", 
      "type": "MonetaryAmount", 
      "value": "2519689"
    }, 
    "description": "Food security in regions affected by drought is influenced by a complex set of interactions between hydrological, agricultural, and social systems. Previous models examining the impact of drought on food security have not incorporated food trade and food movements at fine spatial scales, yet these components are critical parts of regional food systems. In sub-Saharan Africa droughts and floods account for approximately 80% of fatalities and 70% of the economic losses that are due to natural hazards. Zambia is particularly vulnerable to droughts, having high levels of malnutrition, poverty, income inequality, exposure to HIV/AIDS and malaria, and low levels of educational attainment. Zambia's agricultural production is rain-fed, which further increases vulnerability in the region. With the extreme vulnerability of the region, Zambia serves as an ideal place to study how the interactions between drought risk, crop production, trade, and policy affect food security. By incorporating the effects of trade and policy into predictive hydrological and agricultural models, this project is improving existing early warning systems for famine which rarely assess the capacity for a region to ameliorate drought via food transfers and trade. This project's goal is to understand the effect of drought hazards in subsistence agriculture using a novel integrative framework that merges data, models, and knowledge of drought risk and crop production; their interactions with the dynamics of trade-based and aid-based responses; and their effect on household food security and consumption. We are addressing three questions: 1) What are the spatio-temporal scales of drought risk across Zambia and how does risk transfer into agricultural impacts? 2) What is the role of trade and domestic food policy on food security at local to national levels? 3) Can drought impacts be more effectively reduced by integrating an understanding of policy and food transfers into an agricultural drought early warning system? To answer these questions, we are collecting biophysical data to characterize historical droughts and their impacts on regional agriculture; examining household and market level data to characterize food security outcomes, market prices, and food sourcing; using complex network analysis to characterize food trade and flows; assessing market integration associated with price fluctuations and infrastructure to determine economic exposure and resilience at the household, community and district levels; examining how policies at the national scale constrain decisions at the local scale; and developing computational models for high resolution predictions and to explore probabilistic solutions for resource allocation and risk management. This project is the first to create an integrated model of food trade, household consumption and crop production at such fine spatial scales built on an empirical foundation in each dimension.", 
    "endDate": "2018-05-31T00:00:00Z", 
    "funder": {
      "id": "https://www.grid.ac/institutes/grid.457916.8", 
      "type": "Organization"
    }, 
    "id": "sg:grant.4314652", 
    "identifier": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "4314652"
        ]
      }, 
      {
        "name": "nsf_id", 
        "type": "PropertyValue", 
        "value": [
          "1534544"
        ]
      }
    ], 
    "inLanguage": [
      "en"
    ], 
    "keywords": [
      "agricultural drought", 
      "role", 
      "trade", 
      "exposure", 
      "Zambia's agricultural production", 
      "food movement", 
      "Cross-Scale Interactions", 
      "dynamics", 
      "consumption", 
      "educational attainment", 
      "market level data", 
      "project goals", 
      "food transfers", 
      "social systems", 
      "regional food systems", 
      "households", 
      "income inequality", 
      "complex network analysis", 
      "food policy", 
      "understanding", 
      "malaria", 
      "aid", 
      "integrative framework", 
      "district level", 
      "food trade", 
      "price fluctuations", 
      "effect", 
      "novel", 
      "complex set", 
      "economic losses", 
      "drought risk", 
      "probabilistic solution", 
      "economic exposure", 
      "food security", 
      "local scale", 
      "market prices", 
      "previous models", 
      "hazards", 
      "risk management", 
      "policy affect food security", 
      "critical part", 
      "HIV/AIDS", 
      "spatio-temporal scales", 
      "impact", 
      "market integration", 
      "empirical foundation", 
      "national scale constrain decisions", 
      "drought", 
      "household food security", 
      "warning system", 
      "drought hazard", 
      "resource allocation", 
      "question", 
      "resilience", 
      "such fine spatial scales", 
      "data", 
      "biophysical data", 
      "household consumption", 
      "rain-fed", 
      "historical droughts", 
      "malnutrition", 
      "region", 
      "ideal place", 
      "fatalities", 
      "food sourcing", 
      "computational model", 
      "sub-Saharan Africa droughts", 
      "dimensions", 
      "agricultural models", 
      "response", 
      "policy", 
      "domestic food policy", 
      "capacity", 
      "regional agriculture", 
      "knowledge", 
      "drought impacts", 
      "natural hazards", 
      "flood", 
      "extreme vulnerability", 
      "poverty", 
      "food security outcomes", 
      "crop production", 
      "fine spatial scales", 
      "subsistence agriculture", 
      "infrastructure", 
      "high resolution predictions", 
      "high levels", 
      "Zambia", 
      "community", 
      "agricultural impacts", 
      "vulnerability", 
      "model", 
      "components", 
      "risk", 
      "project", 
      "low levels", 
      "early warning system", 
      "interaction", 
      "famine", 
      "national level"
    ], 
    "name": "Hazards SEES: Understanding Cross-Scale Interactions of Trade and Food Policy to Improve Resilience to Drought Risk", 
    "recipient": [
      {
        "id": "https://www.grid.ac/institutes/grid.16750.35", 
        "type": "Organization"
      }, 
      {
        "affiliation": {
          "id": "https://www.grid.ac/institutes/grid.16750.35", 
          "name": "Princeton University", 
          "type": "Organization"
        }, 
        "familyName": "Sheffield", 
        "givenName": "Justin", 
        "id": "sg:person.013067246157.47", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.013067246157.47", 
        "roleName": "PI", 
        "type": "Role"
      }, 
      {
        "affiliation": {
          "id": "https://www.grid.ac/institutes/grid.16750.35", 
          "name": "Princeton University", 
          "type": "Organization"
        }, 
        "familyName": "Estes", 
        "givenName": "Lyndon", 
        "id": "sg:person.01121022322.88", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.01121022322.88", 
        "roleName": "PI", 
        "type": "Role"
      }, 
      {
        "affiliation": {
          "id": "https://www.grid.ac/institutes/grid.16750.35", 
          "name": "Princeton University", 
          "type": "Organization"
        }, 
        "familyName": "Caylor", 
        "givenName": "Kelly", 
        "id": "sg:person.0771003261.98", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.0771003261.98", 
        "roleName": "Co-PI", 
        "type": "Role"
      }
    ], 
    "sameAs": [
      "https://app.dimensions.ai/details/grant/grant.4314652"
    ], 
    "sdDataset": "grants", 
    "sdDatePublished": "2019-03-07T12:37", 
    "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/nsf_projects_8.xml.gz", 
    "startDate": "2015-08-15T00:00:00Z", 
    "type": "MonetaryGrant", 
    "url": "http://www.nsf.gov/awardsearch/showAward?AWD_ID=1534544&HistoricalAwards=false"
  }
]
 

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.4314652'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.4314652'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.4314652'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.4314652'


 

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

162 TRIPLES      19 PREDICATES      126 URIs      116 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:grant.4314652 schema:about anzsrc-for:2214
2 schema:amount N4b04f925661c40168df5f8e57c53d685
3 schema:description Food security in regions affected by drought is influenced by a complex set of interactions between hydrological, agricultural, and social systems. Previous models examining the impact of drought on food security have not incorporated food trade and food movements at fine spatial scales, yet these components are critical parts of regional food systems. In sub-Saharan Africa droughts and floods account for approximately 80% of fatalities and 70% of the economic losses that are due to natural hazards. Zambia is particularly vulnerable to droughts, having high levels of malnutrition, poverty, income inequality, exposure to HIV/AIDS and malaria, and low levels of educational attainment. Zambia's agricultural production is rain-fed, which further increases vulnerability in the region. With the extreme vulnerability of the region, Zambia serves as an ideal place to study how the interactions between drought risk, crop production, trade, and policy affect food security. By incorporating the effects of trade and policy into predictive hydrological and agricultural models, this project is improving existing early warning systems for famine which rarely assess the capacity for a region to ameliorate drought via food transfers and trade. This project's goal is to understand the effect of drought hazards in subsistence agriculture using a novel integrative framework that merges data, models, and knowledge of drought risk and crop production; their interactions with the dynamics of trade-based and aid-based responses; and their effect on household food security and consumption. We are addressing three questions: 1) What are the spatio-temporal scales of drought risk across Zambia and how does risk transfer into agricultural impacts? 2) What is the role of trade and domestic food policy on food security at local to national levels? 3) Can drought impacts be more effectively reduced by integrating an understanding of policy and food transfers into an agricultural drought early warning system? To answer these questions, we are collecting biophysical data to characterize historical droughts and their impacts on regional agriculture; examining household and market level data to characterize food security outcomes, market prices, and food sourcing; using complex network analysis to characterize food trade and flows; assessing market integration associated with price fluctuations and infrastructure to determine economic exposure and resilience at the household, community and district levels; examining how policies at the national scale constrain decisions at the local scale; and developing computational models for high resolution predictions and to explore probabilistic solutions for resource allocation and risk management. This project is the first to create an integrated model of food trade, household consumption and crop production at such fine spatial scales built on an empirical foundation in each dimension.
4 schema:endDate 2018-05-31T00:00:00Z
5 schema:funder https://www.grid.ac/institutes/grid.457916.8
6 schema:identifier N0f6ffe62e1e640f781ef0bd4cbdbb97f
7 Ne57cf85b2e74435489f670035552cb84
8 schema:inLanguage en
9 schema:keywords Cross-Scale Interactions
10 HIV/AIDS
11 Zambia
12 Zambia's agricultural production
13 agricultural drought
14 agricultural impacts
15 agricultural models
16 aid
17 biophysical data
18 capacity
19 community
20 complex network analysis
21 complex set
22 components
23 computational model
24 consumption
25 critical part
26 crop production
27 data
28 dimensions
29 district level
30 domestic food policy
31 drought
32 drought hazard
33 drought impacts
34 drought risk
35 dynamics
36 early warning system
37 economic exposure
38 economic losses
39 educational attainment
40 effect
41 empirical foundation
42 exposure
43 extreme vulnerability
44 famine
45 fatalities
46 fine spatial scales
47 flood
48 food movement
49 food policy
50 food security
51 food security outcomes
52 food sourcing
53 food trade
54 food transfers
55 hazards
56 high levels
57 high resolution predictions
58 historical droughts
59 household consumption
60 household food security
61 households
62 ideal place
63 impact
64 income inequality
65 infrastructure
66 integrative framework
67 interaction
68 knowledge
69 local scale
70 low levels
71 malaria
72 malnutrition
73 market integration
74 market level data
75 market prices
76 model
77 national level
78 national scale constrain decisions
79 natural hazards
80 novel
81 policy
82 policy affect food security
83 poverty
84 previous models
85 price fluctuations
86 probabilistic solution
87 project
88 project goals
89 question
90 rain-fed
91 region
92 regional agriculture
93 regional food systems
94 resilience
95 resource allocation
96 response
97 risk
98 risk management
99 role
100 social systems
101 spatio-temporal scales
102 sub-Saharan Africa droughts
103 subsistence agriculture
104 such fine spatial scales
105 trade
106 understanding
107 vulnerability
108 warning system
109 schema:name Hazards SEES: Understanding Cross-Scale Interactions of Trade and Food Policy to Improve Resilience to Drought Risk
110 schema:recipient N60f91c38f78d43f7877d8c7515f19296
111 Nf8d88bae25ef49958954424e1c7c5fbb
112 Nfeaccf3a661041f59b348b43be54f106
113 sg:person.01121022322.88
114 sg:person.013067246157.47
115 sg:person.0771003261.98
116 https://www.grid.ac/institutes/grid.16750.35
117 schema:sameAs https://app.dimensions.ai/details/grant/grant.4314652
118 schema:sdDatePublished 2019-03-07T12:37
119 schema:sdLicense https://scigraph.springernature.com/explorer/license/
120 schema:sdPublisher N7b002365e98d48f0bb5efe93d83277de
121 schema:startDate 2015-08-15T00:00:00Z
122 schema:url http://www.nsf.gov/awardsearch/showAward?AWD_ID=1534544&HistoricalAwards=false
123 sgo:license sg:explorer/license/
124 sgo:sdDataset grants
125 rdf:type schema:MonetaryGrant
126 N0f6ffe62e1e640f781ef0bd4cbdbb97f schema:name dimensions_id
127 schema:value 4314652
128 rdf:type schema:PropertyValue
129 N4b04f925661c40168df5f8e57c53d685 schema:currency USD
130 schema:value 2519689
131 rdf:type schema:MonetaryAmount
132 N60f91c38f78d43f7877d8c7515f19296 schema:member sg:person.01121022322.88
133 schema:roleName PI
134 rdf:type schema:Role
135 N7b002365e98d48f0bb5efe93d83277de schema:name Springer Nature - SN SciGraph project
136 rdf:type schema:Organization
137 Ne57cf85b2e74435489f670035552cb84 schema:name nsf_id
138 schema:value 1534544
139 rdf:type schema:PropertyValue
140 Nf8d88bae25ef49958954424e1c7c5fbb schema:member sg:person.0771003261.98
141 schema:roleName Co-PI
142 rdf:type schema:Role
143 Nfeaccf3a661041f59b348b43be54f106 schema:member sg:person.013067246157.47
144 schema:roleName PI
145 rdf:type schema:Role
146 anzsrc-for:2214 schema:inDefinedTermSet anzsrc-for:
147 rdf:type schema:DefinedTerm
148 sg:person.01121022322.88 schema:affiliation https://www.grid.ac/institutes/grid.16750.35
149 schema:familyName Estes
150 schema:givenName Lyndon
151 rdf:type schema:Person
152 sg:person.013067246157.47 schema:affiliation https://www.grid.ac/institutes/grid.16750.35
153 schema:familyName Sheffield
154 schema:givenName Justin
155 rdf:type schema:Person
156 sg:person.0771003261.98 schema:affiliation https://www.grid.ac/institutes/grid.16750.35
157 schema:familyName Caylor
158 schema:givenName Kelly
159 rdf:type schema:Person
160 https://www.grid.ac/institutes/grid.16750.35 schema:name Princeton University
161 rdf:type schema:Organization
162 https://www.grid.ac/institutes/grid.457916.8 schema:Organization
 




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


...