Machine learning for food security: Principles for transparency and usability-Reference-Cited by-同舟云学术

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Machine learning for food security: Principles for transparency and usability

Published:2021-11-30 Issue: Volume: Page:
ISSN:2040-5790
Container-title:Applied Economic Perspectives and Policy
language:en
Short-container-title:Applied Economic Perspectives and Policy

Author:

Zhou Yujun¹,Lentz Erin²,Michelson Hope¹,Kim Chungmann¹,Baylis Kathy³

Affiliation:

1. Department of Agricultural and Consumer Economics University of Illinois, Urbana‐Champaign Champaign Illinois USA

2. Lyndon B Johnson School of Public Affairs University of Texas, Austin Austin Texas USA

3. Department of Geography University of California, Santa Barbara Santa Barbara California USA

Publisher

Wiley

Subject

Economics and Econometrics,Development

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aepp.13214

Reference36 articles.

1. Policy Research Working Paper No. 9412;Andree Bo Pieter Johannes,2020

2. The NASA Hydrological Forecast System for Food and Water Security Applications;Arsenault Kristi R.;Bulletin of the American Meteorological Society,2020

3. Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data

4. Measuring Food Insecurity;Barrett Christopher;Science,2010

5. “The promise and pitfalls of conflict prediction: evidence from Colombia and Indonesia”.;Bazzi Samuel;The Review of Economics and Statistics,2019

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Drought-sensitive targeting and child growth faltering in Southern Africa;World Development;2024-10

2. Evaluation of fiscal policy with text mining under "dual carbon" target in China;Heliyon;2024-07

3. Predictors of persistent moderate and severe food insecurity in a longitudinal survey in Mexico during the COVID-19 pandemic;Frontiers in Public Health;2024-06-26

4. A Data-Driven Approach for Early Detection of Food Insecurity in Yemen's Humanitarian Crisis;Policy Research Working Papers;2024-05-09

5. Forecasting the prevalence of child acute malnutrition using environmental and conflict conditions as leading indicators;World Development;2024-04

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