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85 results for “Food security”
Data and Code for "Does Organic Farming Jeopardize Food Security of Farm Households in Benin?"
<p>This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "<a href="https://doi.org/10.1016/j.foodpol.2024.102622" target="_blank" rel="noopener">Does Organic Farming Jeopardize Food Security of Farm Households in Benin?</a>" authored by Ghislain B.D. Aïhounton and Arne Henningsen and published in the journal Food Policy (Volume 124, April 2024, 102622, DOI: 10.1016/j.foodpol.2024.102622).</p> <p>We conducted the empirical analysis with the "R" statistical software (version 4.3.3) using the add-on packages "AER" (version 1.2.12), "DescTools" (version 0.99.54), "lmtest" (version 0.9.40), "moments" (version 0.14.1), "sandwich" (version 3.1.0), "stargazer" (version 5.2.3), and "xtable" (version 1.8.4) that are all available at CRAN.</p> <p>This replication package contains the following files:</p> <p>* README<br>This file.</p> <p>* R/dataBenin.csv<br>A CSV file that contains the (unprepared) data set. The variables in this file are described in file R/Variables.csv. This CSV file is imported by R script PrepareDataFoodNutrition.R.</p> <p>* R/Variables.csv<br>A CSV file that describes the variables in the (unprepared) data set (file R/dataBenin.csv).</p> <p>* R/PrepareData.R<br>An R script that imports the (unprepared) data set (file R/dataBenin.csv), calculates additional variables and add theses variables to the data set, removes observations that should not be used in the empirical analysis, and saves the prepared data set as CSV file (R/dataFoodNutrition.csv).</p> <p>* R/dataPrepared.csv<br>A CSV file that contains the (prepared) data set used in the empirical analysis. This CSV file is created by the R script R/PrepareDataFoodNutrition.R. It is imported by the R scripts R/DescriptiveTab.R, FoodNutritionImpact.R, and GridSearchFoodSecurity.R.</p> <p>* R/DescriptiveTab.R<br>An R script that imports the prepared data set (file R/dataFoodNutrition.R) and creates Table 1 of the paper ("Descriptive statistics", file paper/tables/DescriptiveStat.tex) as LaTeX file.</p> <p>* R/Estimations.R<br>An R script that imports the prepared data set (file R/dataFoodNutrition.R), conducts all the analyses presented in the paper, creates Tables 2 and 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes" and "OLS and IV regression results of the conditional associations between organic farming and mediating outcomes", LaTeX files paper/tables/estMainReg.tex and paper/tables/estMedReg.tex), creates Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes", 12 PDF files paper/figures/*.pdf), and 45 Tables that are included in the Supplementary Information: 36 tables with detailed regression results (LaTeX files paper/tables/tabels/est*.tex), one table with results of the first-stage probit regression (LaTeX file paper/tables/tabels/estProbit.tex), 6 tables with detailed regression results of estimations for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011) (LaTeX files paper/tables/tabels/estOLS*Falco.tex), and 2 tables with coefficient bounds obtained as suggested by Oster (2019) (LaTeX files paper/tables/tabels/Oster*.tex).</p> <p>* R/GridSearch.R<br>An R script that re-runs our regression analyses with different units of measurement of IHS-transformed variables and calculates various indicators that can can be used to assess the appropriateness of different units of measurement as suggested by Aihounton and Henningsen (2021) and that creates 28 Tables that are included in the Supplementary Information (LaTeX files paper/tables/tabels/grid*.tex).</p> <p>* R/functions/calcOsterBounds.R<br>An R script that defines the R function calcOsterBounds() that calculates coefficient bounds using the method suggested by Oster (2019). This function is used by the R script R/FoodNutritionImpact.R.</p> <p>* R/functions/calcSemiElaOrg.R<br>An R script that defines the R function calcSemiElaOrg() that calculates the semi-elasticity of various log-transformed or IHS-transformed variables with respect to the dummy variable for organic farming. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/createFormula.R<br>An R script that defines the R function createFormula() that creates the regression formulas for the various empirical analyses that are presented in the paper. This function is used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/functionsTables.R<br>An R script that defines various R functions that are used to create tables in LaTeX format. These functions are used by the R scripts R/FoodNutritionImpact.R and R/GridSearchFoodSecurity.R.</p> <p>* R/functions/predR2.R<br>An R script that defines the R function predR2() that calculates the predictive R-squared value. This R script has been obtained from the replication package of the article:<br>Aïhounton, G. B. D. and Henningsen, A. (2021). Units of measurement and the inverse hyperbolic sine transformation. The Econometrics Journal, 24(2):334–351. https://doi.org/10.1093/ectj/utaa032<br>The function consists of a slightly modified version of the code that is available at: https://tomhopper.me/2014/05/16/can-we-do-better-than-r-squared/ This function is used by the R script R/GridSearchFoodSecurity.R.</p> <p>* paper/figures/*.pdf<br>12 LaTeX files that are the (sub)figures in Figures 1 and 2 of the paper ("Estimated conditional associations of organic farming with outcomes" and "Estimated conditional associations of organic farming with mediating outcomes"). These 12 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/DescriptiveStat.tex<br>A LaTeX file that creates Table 1 of the paper ("Descriptive statistics"). This file is created by the R script R/DescriptiveTab.R.</p> <p>* paper/tables/estMainReg.tex<br>A LaTeX file that creates Table 2 of the paper ("OLS and IV regression results of the conditional associations between organic farming and outcomes"). This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/estMedReg.tex<br>A LaTeX file that creates Table 3 of the paper ("OLS and IV regression results of the conditional associations between organic farming and mediating outcomes"). This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/est*.tex<br>36 LaTeX files that create 36 tables that are included in the Supplementary Information and present detailed regression results. These 36 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/estProbit.tex<br>A LaTeX files that creates a table that is included in the Supplementary Information and presents the results of the first-stage probit regression. This file is created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/estOLS*Falco.tex<br>6 LaTeX files that create 6 tables that are included in the Supplementary Information and present detailed regression results for testing the exogeneity of the instrument as suggested by Di Falco et al. (2011). These 6 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/Oster*.tex<br>2 LaTeX files that create 2 tables that are included in the Supplementary Information and present coefficient bounds obtined as suggested by Oster (2019). These 2 files are created by the R script R/FoodNutritionImpact.R.</p> <p>* paper/tables/tabels/grid*.tex<br>28 LaTeX files that create 28 tables that are included in the Supplementary Information and present various indicators for assessing the appropriateness of different units of measurement of IHS-transformed variables as suggested by Aihounton and Henningsen (2021). These 28 files are created by the R script R/GridSearchFoodSecurity.R</p>
Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data
<p><strong>Assessing ambitious nature conservation strategies in a below 2-degree and food-secure world – supplementary spatial data</strong></p><p><strong>Authors: </strong>Marcel Kok, Johan Meijer, Willem-Jan van Zeist, Jelle Hilbers, Marco Immovilli, Jan Janse, Elke Stehfest, Michel Bakkenes, Andrzej Tabeau, Aafke Schipper, Rob Alkemade</p><p><strong>Point of contact:</strong> <a href="mailto:Marcel.Kok@pbl.nl">Marcel.Kok@pbl.nl</a></p><p><strong>Research paper summary:</strong> Global biodiversity is projected to further decline under a wide range of future socio-economic development pathways, even in sustainability-oriented scenarios. This raises the question how biodiversity can be put on a path to recovery, the core challenge for the implementation of the CBD Kunming-Montreal Global Biodiversity Framework. We designed two ambitious global conservation strategies, 'Half Earth' (HE) and 'Sharing the Planet' (SP), and evaluated their ability to restore terrestrial and freshwater biodiversity and to provide nature's contributions to people (NCP), while also limiting global warming below 2 degrees and ensuring food security. We applied the integrated assessment framework IMAGE with the GLOBIO biodiversity model, using the 'Middle of the Road' Shared Socio-economic Pathway (SSP2) with its projected human population growth as baseline. We found that the HE strategy performs generally better for terrestrial biodiversity (biodiversity intactness (MSA), Area of Habitat, Living Planet Index, Red List Index) in currently still natural regions. The SP strategy yields more improvements for biodiversity in human-used areas, for freshwater biodiversity and for regulating NCP (pest control, pollination, erosion control, water quality). However, both strategies were insufficient to restore biodiversity and corresponded with considerable increases in food security risks and global temperature. Only when we combined the conservation strategies with a portfolio of 'integrated sustainability measures', including climate change mitigation and reductions of food waste and animal product consumption, our scenarios resulted in a restoration of biodiversity and NCP while keeping global warming below two degrees and food security risks below the baseline projection.</p><p><strong>Contents:</strong> This repository contains the supplementary spatial data describing the specific prioritization of conservation areas under the Half Earth (HE) and Sharing the Planet (SP) scenarios, and the resulting scenario land use and MSA data sets for the year 2050, including also a baseline (BL) scenario. All spatial data is in geotiff format at a 10 arcsecond resolution in WGS84 coordinate system. Detailed description of the methodology is provided in the paper listed under "related identifiers".</p><p><strong>Keywords:</strong> Nature conservation, Half Earth, Sharing the Planet, Climate Change, Food Security, Solution-oriented scenarios, Biodiversity, Nature's Contribution to People, NCP</p>
Supplementary Materials for "Food security in Roman Palmyra (Syria) in light of paleoclimatological evidence and its historical implications"
<p>Contained here are the SI files for the article "Food security in Roman Palmyra (Syria) in light of paleoclimatological evidence and its historical implications". With all the materials contained here, as well as the openly accessible datasets cited in S1_File, every step of the study can be reproduced. Detailed instructions are contained within. Includes code for Data Analysis.</p> <p>Article DOI: [forthcoming]</p>
Replication Data for the paper "Predicting Food-Security Crises in the Horn of Africa Using Machine Learning"
<p>This folder contains all input data necessary to run the machine learning model as described in the paper "Predicting Food Security Crises in the Horn of Africa Using Machine Learning". </p><p>This model is developed at the Institute for Environmental Studies, Vrije Universiteit Amsterdam. </p><p>Questions or remarks can be send to tim.busker@vu.nl</p>
Global and regional health and food security under strict conservation scenarios.
<p>Data supporting Nature Sustainability paper 'Global and regional health and food security under strict conservation scenarios'. Zipped folder contains LandSyMM output used to generate results. The tif files are the biodiversity prioritisation areas used in the 50% and 30% strict protection scenarios generated as per the methods in Jung, M., Arnell, A., de Lamo, X. <em>et al.</em> Areas of global importance for conserving terrestrial biodiversity, carbon and water. <em>Nat Ecol Evol</em> <strong>5, </strong>1499–1509 (2021). https://doi.org/10.1038/s41559-021-01528-7. </p>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</p>
Realistic soil carbon sequestration considering food security and climate change
<p>This dataset contains soil organic carbon stocks as described in Keel et al. Global Change Biology (submitted)</p> <p>Annual soil organic carbon (SOC) stocks (t C ha-1, 0-30 cm depth) of Swiss agricultural soils simulated with the model RothC for the years 2020-2100. Simulations were performed for 240 strata (regions with similar agricultural production types, climatic conditions and clay content). The SOC stocks are weighted averages across strata for the national scale. <br> Each column contains SOC stocks for a specific combination of a climate model chains (nine in total) and an emission scenario (three in total: RCP 26, RCP 45, RCP 85) (specified in column header). </p> <p>The results include simulated SOC stocks for a baseline scenario and five soil carbon sequestration (SCS) scenarios (cover crops, biochar amendment at two rates, biochar amendment based on biomass from two agroforestry scenarios). <br> The SCS scenarios were only performed on cropland, therefore there is only a single file for grassland (the baseline scenario). <br> All simulations (i.e. baseline as well as the five scenarios) account for changes in crop shares and organic matter additions associated with growing food demand as well as climate change. </p> <p>The scenarios are described in Keel et al. Global Change Biology (submitted)</p> <p>CL_baseline: Baseline scenario for cropland (CL) <br> GL_baseline: Baseline scenario for permanent grassland (GL)<br> CL_cover_crops: Cover crop scenario for cropland <br> CL_biochar_I: Biochar I scenario for cropland <br> CL_biochar_II: Biochar II scenario for cropland <br> CL_agroforestry_I: Agroforestry I scenario for cropland <br> CL_agroforestry_II: Agroforestry II scenario for cropland </p>
Accompanying material for Article: No cash, no food. Gendered reorganization of livelihoods and food security in Cambodia
<p>Results of the two questionnaire-base surveys carried on in 2016 and 2019 on rural livelihoods in Cambodia</p>
Mehrabi et al. 2022. Research priorities for global food security under extreme events. Supplementary data and code.
<p>Data and script for reproducing the final results shown in Mehrabi et al., Research priorities for global food security under extreme events, One Earth (2022), https://doi.org/10.1016/j.oneear.2022.06.008.</p> <p>Simply download and read the Mehrabi2022_EEGFS.pdf or the Mehrabi2022_EEGFS.Rmd file from which it was created.</p>
Data for "The neglected role of abandoned cropland in supporting both food security and climate change mitigation"
<p><strong>Data for "The neglected role of abandoned cropland in supporting both food security and climate change mitigation"</strong></p> <p><strong>All files will be made publicly accessible before publication.</strong></p> <p>Version: 2.0 (Round 2 revision)</p> <p>Content (spatial resolution, data info)</p> <ol> <li>Abandoned cropland map (10arcsec, 1 raster file)</li> <li>Suitabability of abanonded cropland for reforestation and recultivation (5arcmin, 3 raster files)</li> <li>Food production potential of global abandoned cropland (5arcmin,1 raster file)</li> <li>Climate change mitigation potential of global abandoned cropland (5arcmin, 1 raster file)</li> <li>outcomes of key scenarios <ul> <li>4 key Scenarios: Maximizing food production, Maximizing climate change mitigation, Equal Allocation, Maximizing combined potential</li> <li>Data included in the output of each scenario: <ul> <li>Land allocated for reforestation and recultivation (5arcmin/1arcdeg, 5 raster files)</li> <li>Climate change mitigation potential and food production potential (5arcmin/1arcdeg, 5 raster files)</li> <li>Emission for land clearing (5arcmin, 1 raster)</li> <li>Foregone climate change mitigation potential (5arcmin, 1 raster)</li> <li>Summary table</li> </ul> </li> <li>Summary table for 4 key scenarios</li> </ul> </li> <li>Codes for generating 4 key scenarios</li> </ol>
Impact of Online Ordering on Low-Income Adults' Food Security in Online Food Pantry Settings
ClinicalTrials.gov study NCT05752721. IPD Sharing: YES. Countries: 1. Publications: 1.
Applying innovative cloud computing technology for the effective management of Groundwater resources to promote SUStainable food security within the Sokoto Basin, Nigeria (AGSUS)
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Climate change incidence, risk perception, and food security nexus
<p>This dataset supports the manuscript "Climate change incidence, risk perception, and food security among smallholders in Tigray, Ethiopia". The dataset contains three folders and a file from three data sources: (1) the Ethiopia Rural Socioeconomic Survey (ERSS)/Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA), a three-round panel data for Ethiopia, filtered for Tigray region; (2) an ERSS follow-up survey on the beliefs and opinions of respondents on climate change conducted in August 2019 in Tigray; and (3) 4km x 4km monthly grided Climate data (Rainfall, Max & min temperature). The files include socioeconomic data and household features, beliefs and opinions on climate change, and climatological data (monthly rainfall, maximum and minimum temperatures). The dataset covers 34 Enumeration Areas (EA) of the ERSS/LSMS-ISA and represents the region. It can be useful for studies on climate change risk perception and adaptation, environmental protection, and drivers of food insecurity in Tigray, Ethiopia. The data were processed using user-written codes in STATA v.17.</p>
Future Food Security in Africa under Climate Change
<p>This excel file contains data tables (S2-S3) also found in the supplementary materials of the publication titled "Future Food Security in Africa under Climate Change". The tables included here include a regional breakdown of African countries (Table S2), available calories for direct or indirect human consumption under diverse food loss and waste pathways (Table S3), and data on national caloric deficits under different scenarios (Table S4). </p>
Figure 2 in Plant growth promoting bacteria drive food security
Figure 2. Screening steps to obtain efficient bioinoculants.
Figure 1 in Plant growth promoting bacteria drive food security
Figure 1. Mode of actions of PGPB.
Earth Food Security: Use Case Service
<p>This use case service consists in a water availability index computed based on soil status from Earth Observation. For the computation of water availability, long-time series of Soil Moisture is used to establish climatology over West Africa.</p>
Agricultural Intervention for Food Security and HIV Health Outcomes in Kenya
ClinicalTrials.gov study NCT02815579. IPD Sharing: NO. Countries: 1. Publications: 20.
Shamba Maisha: Pilot Agricultural Intervention for Food Security and HIV Health Outcomes in Kenya
ClinicalTrials.gov study NCT01548599. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Effect of Drought Stress on the Genetic Architecture of Photosynthate Allocation and Remobilization in Pods of Common Bean (Phaseolus vulgaris L.), a Key Species for Food Security
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.