scholarly journals 60 seasons : a conversation around the future of food

2021 ◽  
Author(s):  
Jeannette Breward ◽  
Pierre Tremblay ◽  
Don Snyder ◽  
Sara Knelman
Keyword(s):  
Human Health ◽  
Small Groups ◽  
Food Production ◽  
Food Choices ◽  
Healthy Food ◽  
Western World ◽  
Current State ◽  
The Future ◽  
Environmentally Sound ◽  
Future Of Food

The current state of food production in the Western world is leading to the devastation of our land, soil, and air. industrial farms are contributing not just to poor human health, but to the ever increasing depletion of our natural esources, a reduction in the biodiversity of plants and animals and in the sustainability of the planet. 60 Seasons – A Conversation around the Future of Food aims to stimulate the dialogue around healthy and sustainable means of food production by depicting the efforts of two small groups within Northumberland County, Ontario. Their aim is to bring sustainable farming methods to their community, while expanding the discourse around environmentally sound food production and providing healthy food choices to those in need.

scholarly journals 60 seasons : a conversation around the future of food

2021 ◽  
Author(s):  
Jeannette Breward ◽  
Pierre Tremblay ◽  
Don Snyder ◽  
Sara Knelman
Keyword(s):  
Human Health ◽  
Small Groups ◽  
Food Production ◽  
Food Choices ◽  
Healthy Food ◽  
Western World ◽  
Current State ◽  
The Future ◽  
Environmentally Sound ◽  
Future Of Food

The current state of food production in the Western world is leading to the devastation of our land, soil, and air. industrial farms are contributing not just to poor human health, but to the ever increasing depletion of our natural esources, a reduction in the biodiversity of plants and animals and in the sustainability of the planet. 60 Seasons – A Conversation around the Future of Food aims to stimulate the dialogue around healthy and sustainable means of food production by depicting the efforts of two small groups within Northumberland County, Ontario. Their aim is to bring sustainable farming methods to their community, while expanding the discourse around environmentally sound food production and providing healthy food choices to those in need.

The future of food production – a text-mining approach

2019 ◽  
Vol 32 (5) ◽  
pp. 516-528
Author(s):  
Pavel Bakhtin ◽  
Elena Khabirova ◽  
Ilya Kuzminov ◽  
Thomas Thurner
Keyword(s):  
Text Mining ◽  
Food Production ◽  
The Future ◽  
Future Of Food

8. Psychotherapy now and in the future

2015 ◽  
pp. 103-115
Author(s):  
Tom Burns ◽  
Eva Burns‐Lundgren
Keyword(s):  
Mentally Ill ◽  
Social Evolution ◽  
Well Being ◽  
Western World ◽  
Current State ◽  
The Future

Psychotherapy has reached into almost every aspect of our lives—how we treat the mentally ill, how we understand our relationships, our appreciation of art and artists, and even how we manage our schools, prisons, and workplaces. ‘Psychotherapy now and in the future’ reviews the current state of psychotherapy in the Western world as well as in non-Western societies, and considers what the future holds for psychotherapy. How we judge psychotherapy’s future will probably reflect what we think of it now: as a profound breakthrough in understanding ourselves and a step forward in social evolution, or as simply one among many technical procedures to reduce distress and improve human well-being.

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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