The Poverty Resolution as Peacelessness and the Future of Food Security : Competing for Marine Genetic Resources and Countermeasures

2017 ◽  
Vol 3 ◽  
pp. 61-83
Author(s):  
Woochoel Joo ◽  
◽  
Kyungmin Ko
Keyword(s):  
Food Security ◽  
Genetic Resources ◽  
The Future ◽  
Future Of Food

Plant genomics in view of plant genetic resources – an introduction

2014 ◽  
Vol 12 (S1) ◽  
pp. S6-S8 ◽  
Author(s):  
Ronald L. Phillips
Keyword(s):  
Genetic Diversity ◽  
Food Security ◽  
Plant Breeding ◽  
Genetic Resources ◽  
Plant Genetic Resources ◽  
Food Prices ◽  
New Era ◽  
Global Food Security ◽  
The Future ◽  
Global Food

Genetic resources form the basis of the new era of global food security. The food crises in many developing countries, reflected by food riots correlated with food prices, have been termed the Silent Tsunami. Plant genetic resources are clearly essential to food security for the future. Fortunately, genetic resources are generally considered a public good and shared internationally. Wild relatives of crop species and their derivatives represent the reservoir of genetic diversity that will help to meet the food demands of nine billion people by 2050. New technologies from genomics bolster conventional plant breeding for enhancing traits to meet these food demands. Genetic diversity is the lifeblood of traditional and modern plant breeding. The dramatic increase in the number of biotech crops reveals the value of new genetic resources. Genetic resources will provide a gateway to a new era of global food security. Although 7.4 million plant accessions are stored in 1750 germplasm banks around the world, only a small portion of the accessions has been used so far to produce commercial varieties. Our challenge is to find better ways to make more efficient use of gene bank materials for meeting food demands in the future.

The future of food security: where to from here?

2019 ◽  
pp. 152-161
Author(s):  
S. Smyth ◽  
W. A. Kerr ◽  
P. W. B. Phillips
Keyword(s):  
Food Security ◽  
The Future ◽  
Future Of Food

scholarly journals Prediction of Food Production Using Machin Learning Algorithms of Multilayer Perceptron and ANFIS

2021 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Sina Ardabili ◽  
Zoltan Lakner ◽  
Csaba Mako ◽  
Amir Mosavi
Keyword(s):  
Food Security ◽  
Multilayer Perceptron ◽  
Agricultural Production ◽  
Food Production ◽  
Livestock Production ◽  
Series Data ◽  
Accurate Estimation ◽  
Inference System ◽  
The Future ◽  
Future Of Food

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

scholarly journals Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

2021 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Sina Ardabili ◽  
Zoltan Lakner ◽  
Csaba Mako ◽  
Amir Mosavi
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Multilayer Perceptron ◽  
Agricultural Production ◽  
Food Production ◽  
Livestock Production ◽  
Series Data ◽  
Accurate Estimation ◽  
The Future ◽  
Future Of Food

Abstract Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

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