scholarly journals MODERNIZATION AS A BASIS FOR INCREASING THE COMPETITIVENESS OF AGRICULTURAL INDUSTRY OF THE REGION

2021 ◽  
Vol 2 (№4 2021) ◽  
pp. 183-187
Author(s):  
R.N. Galikeev
Keyword(s):  
Agricultural Industry

Tracking the Transmission Channels of Fiscal Deficit and Food Inflation Linkages: A Structural VAR Approach

2020 ◽  
pp. 165-172
Keyword(s):  
Positive Impact ◽  
Structural Var ◽  
Fiscal Deficit ◽  
Agricultural Industry ◽  
Vector Autoregressive ◽  
Transmission Channels ◽  
Government Deficit ◽  
Import And Export ◽  
The Impact ◽  
Food Inflation

This empirical analysis aspired to unearth the transmission channels of fiscal deficit and food inflation linkages in the Indian perspective by reasonably exerting the data for 1991 to 2017. The precise results of structural vector autoregressive (SVAR) analysis proffered that there were three different mechanisms of transmission such as consumption, general inflation, and import channels that led to food inflation in response to the high fiscal deficit. The first channel revealed that government deficit spending had a positive impact on income which further led to food inflation through surging the household consumption expenditure. It was concluded that fiscal deficit passed through general inflation finally leading to a food price surge in the economy and seemed to work as cost-push inflation for the food and agricultural industry. The outcome also revealed that the impact of fiscal deficit passed to food inflation through external linkages such as import and export.

scholarly journals Evaluation of China’s Circular Agriculture Performance and Analysis of the Driving Factors

2021 ◽  
Vol 13 (4) ◽  
pp. 1643
Author(s):  
Biao Li ◽  
Yunting Feng ◽  
Xiqiang Xia ◽  
Mengjie Feng
Keyword(s):  
Rapid Development ◽  
Arable Land ◽  
Indicator System ◽  
Resource Scarcity ◽  
Entropy Method ◽  
Agricultural Industry ◽  
Evaluation Indicator ◽  
Evaluation Indicator System ◽  
Per Capita ◽  
The Government

Along with industry upgrading and urbanization, the agricultural industry in China has been experiencing a stage of rapid development, on the bright side. On the other side, ecological environment deterioration and resource scarcity have become prevalent. Called by the current situation, circular agriculture arises as a direction for the industry to achieve sustainable development. This study develops an evaluation indicator system for circular agriculture using an entropy method, and evaluates factors that could drive the Chinese agricultural industry to achieve better performance. We employ the method using provincial data collected from the province of Henan, in which around 10% of the total grain in China is produced. It was found that agricultural technology and water resources per capita are positively related to circular performance in agriculture. In contrast, urbanization and arable land per capita are negatively related to circular performance. This article provides support to the government in policy-making related to the improvement of circular agricultural performance.

scholarly journals Performance Evaluation of Soft Computing for Modeling the Strength Properties of Waste Substitute Green Concrete

2021 ◽  
Vol 13 (5) ◽  
pp. 2867
Author(s):  
Muhammad Izhar Shah ◽  
Muhammad Nasir Amin ◽  
Kaffayatullah Khan ◽  
Muhammad Sohaib Khan Niazi ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Predictive Models ◽  
Parametric Study ◽  
Cross Validation ◽  
Strength Properties ◽  
Fine Aggregate ◽  
Agricultural Industry ◽  
Green Concrete ◽  
Concrete Properties ◽  
Water To Cement Ratio ◽  
Sugarcane Bagasse Ash

The waste disposal crisis and development of various types of concrete simulated by the construction industry has encouraged further research to safely utilize the wastes and develop accurate predictive models for estimation of concrete properties. In the present study, sugarcane bagasse ash (SCBA), a by-product from the agricultural industry, was processed and used in the production of green concrete. An advanced variant of machine learning, i.e., multi expression programming (MEP), was then used to develop predictive models for modeling the mechanical properties of SCBA substitute concrete. The most significant parameters, i.e., water-to-cement ratio, SCBA replacement percentage, amount of cement, and quantity of coarse and fine aggregate, were used as modeling inputs. The MEP models were developed and trained by the data acquired from the literature; furthermore, the modeling outcome was validated through laboratory obtained results. The accuracy of the models was then assessed by statistical criteria. The results revealed a good approximation capacity of the trained MEP models with correlation coefficient above 0.9 and root means squared error (RMSE) value below 3.5 MPa. The results of cross-validation confirmed a generalized outcome and the resolved modeling overfitting. The parametric study has reflected the effect of inputs in the modeling process. Hence, the MEP-based modeling followed by validation with laboratory results, cross-validation, and parametric study could be an effective approach for accurate modeling of the concrete properties.

scholarly journals Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 646
Author(s):  
Bini Darwin ◽  
Pamela Dharmaraj ◽  
Shajin Prince ◽  
Daniela Elena Popescu ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Deep Learning ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Soil Parameters ◽  
Learning Models ◽  
Weed Detection ◽  
Agricultural Industry ◽  
Agricultural Robots ◽  
Detection Techniques ◽  
Learning Techniques

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

scholarly journals Robotic applications on agricultural industry. A review

2020 ◽  
Vol 997 ◽  
pp. 012081
Author(s):  
C A Morar ◽  
I A Doroftei ◽  
I Doroftei ◽  
M G Hagan
Keyword(s):  
Agricultural Industry ◽  
Robotic Applications
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