scholarly journals SUSTAINABLE INTENSIFICATION OF MODERN AGRICULTURE THROUGH PRODUCTION TECHNOLOGIES ON DIFFERENT READINESS LEVELS

2017 ◽  
Author(s):  
Mariusz MACIEJCZAK ◽  
Janis FALTMANN
Keyword(s):  
Sustainable Intensification ◽  
Modern Agriculture ◽  
Production Technologies

Economic and energy efficiency of agriculture

2020 ◽  
Vol 66 (No. 8) ◽  
pp. 355-364
Author(s):  
Marcin Wysokiński ◽  
Joanna Domagała ◽  
Arkadiusz Gromada ◽  
Magdalena Golonko ◽  
Paulina Trębska
Keyword(s):  
Energy Efficiency ◽  
Economic Development ◽  
Data Envelopment ◽  
Eu Countries ◽  
Modern Agriculture ◽  
Socio Economic Development ◽  
Industrial Energy ◽  
Production Technologies ◽  
High Level ◽  
The Eu

Article concerns economic and energy efficiency of agriculture in European Union countries. The study period concerned 2016. For analysis and presentation of materials, descriptive, tabular and graphic methods and the Data Envelopment Analysis (DEA) method – CCR (Charnes, Cooper and Rhodes) model focused on input-oriented minimisation were used. An assessment of the socio-economic development of the EU countries was made using the following measures: Human Development Index (HDI) and Gross Domestic Product (GDP) per capita (USD per inhabitant). Modern agriculture depends on industrial energy sources and as the socio-economic development changes into more and more energy-intensive production technologies. After presenting the introduction and review of the literature, the economic and energy efficiency of agriculture in the EU countries in 2016 was examined, which was at a high level – the DEA reached 0.67. Then, the correlation between the socio-economic development of countries and their economic and energy efficiency was analysed. It was also found that along with socio-economic development in the EU countries, the economic and energy efficiency of agriculture is increasing.

scholarly journals Assessing Readiness Levels of Production Technologies for Sustainable Intensification of Agriculture

2018 ◽  
Vol 12 (1-2) ◽  
pp. 47-52 ◽  
Author(s):  
Mariusz Maciejczak ◽  
Janis Faltmann
Keyword(s):  
Research Methods ◽  
Agricultural Production ◽  
New Technologies ◽  
Jel Classification ◽  
Sustainable Intensification ◽  
Environmental Threats ◽  
Knowledge Based ◽  
Production Technologies

The modern agricultural production is facing the problem of a growing society connected with the growing asking for food as well as different environmental threats. To solve this issue, agricultural production should be more sustainable and efficient which can be reached by using new technologies. In the paper the most important technologies, which were evaluated by different research methods to find how and when they could be used for a sustainable intensification of agriculture were highlighted by applying technology and market readiness models. By asking professionals from different fields of agriculture in practice as well as academia it was found that technologies that collect or utilize advanced data (sensors, drones) used for knowledge based management are more applicable for use, contrary to nanotechnologies where the costs of development and applications limits the readiness. JEL Classification: Q16

Innovation and investment framework for the development of competitive fisheries and aquaculture

2019 ◽  
pp. 31-38
Author(s):  
Nataliia Vdovenko ◽  
Maryna Pavlenko ◽  
Elena Hryniuk
Keyword(s):  
Economic Conditions ◽  
Technological Parameters ◽  
Critical Situation ◽  
Competitive Activity ◽  
Scientific Approach ◽  
Production Technologies ◽  
Modern Economic ◽  
Innovative Economy ◽  
Combination Of Methods ◽  
Production Plans

The purpose of the article it consists in the substantiation of innovation and investment principles of competitive development of fisheries and aquaculture in order to effectively conduct business in modern economic conditions and to comply with technological parameters of production, introduce innovations in production, and economical attitude to fish and water resources. Methodology research consists in using a combination of methods: economic-statistical, monographic, systemic, economic-mathematical, abstract-logical and others. The scientific novelty the re- sults obtained include the introduction of innovative, economically viable and competitive production technologies based on a scientific approach to organizing an aquaculture business, namely the ability to quickly and efficiently respond to external challenges, find a way out of a critical situation and correctly adjust production plans in accordance with changes in market conditions. Conclusions. Substantially modern principles of the effective functioning of fisheries in order to ensure competitive activity. Regulatory tools are proposed to ensure the profitability of the aquaculture business, which include attracting in- vestments for the acquisition of capital goods, training specialists and constantly improving their skills, and ensuring efficient fisheries production in an innovative economy.

Water Use Features of Typical Crops and Technical Modes of Modern Agriculture Irrigation in China

2018 ◽  
Vol 20 (5) ◽  
pp. 77 ◽  
Author(s):  
Baozhong Zhang ◽  
Zhigong Peng ◽  
Bo Lei ◽  
Lijuan Du ◽  
Lei Wang ◽  
...  
Keyword(s):  
Water Use ◽  
Modern Agriculture ◽  
Agriculture Irrigation

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

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