scholarly journals Evaluation and Influencing Factors of Agricultural Green Efficiency in Jianghuai Ecological Economic Zone

2021 ◽  
Vol 14 (1) ◽  
pp. 30
Author(s):  
Genzhong Li ◽  
Decai Tang ◽  
Valentina Boamah ◽  
Zhiwei Pan
Keyword(s):  
Influencing Factors ◽  
Agricultural Production ◽  
Technological Progress ◽  
Production Efficiency ◽  
Environmental Efficiency ◽  
Policy Implications ◽  
Environmental Damage ◽  
Desirable Output ◽  
Full Analysis ◽  
Positive Effect

Traditionally, in the pursuit of economic development, ecological health was sacrificed, but this is no longer tenable considering the vast environmental damage that is difficult to control and expensive to repair. This is especially true for China’s Jianghuai River Basin (JRB). As a result, this paper uses the slacks-based measure (SBM) directional distance function and the tobit model on panel data from various cities and counties in the Jiangsu section of the HRB to empirically analyze regional green agricultural production efficiency and influencing factors from 2005 to 2019. The results illustrate that agricultural and environmental efficiency fluctuate upward in provincial areas. Still, a downward trend is observed in both redundancy and insufficiency rates of undesirable and desirable output. While this indicates improving regional agricultural and environmental efficiency, there is no readily detectable positive effect of technological progress and technical efficiency on green agricultural production efficiency improvements. Following a full analysis, policy implications are presented and discussed.

Calculation of regional agricultural production efficiency and empirical analysis of its influencing factors-based on DEA-CCR model and Tobit model

2021 ◽  
pp. 1-14
Author(s):  
Pengfei Zhou ◽  
Siyuan Yang ◽  
Xiaohang Wu ◽  
Yang Shen
Keyword(s):  
Empirical Analysis ◽  
Influencing Factors ◽  
Agricultural Production ◽  
Production Efficiency ◽  
Malmquist Index ◽  
Tobit Model ◽  
Annual Growth Rate ◽  
Productivity Level ◽  
Agricultural Modernization ◽  
Ccr Model

The improvement of agricultural production efficiency and the transformation of production mode are the core of promoting agricultural modernization. Taking Chongqing as a sample case, this paper uses DEA-CCR Model, Malmquist Index and Tobit Model to calculate and analyze its agricultural production efficiency and its influencing factors, and accurately identifies the problems existing in its agricultural transformation and development. It has an important policy reference value for improving agricultural innovation and competitiveness, promoting the steady development of rural revitalization, and realizing agricultural modernization, which also provides some reference and enlightenment for countries or regions with similar characteristics of mountain agriculture development in the world to enhance regional agricultural production efficiency. Through empirical analysis and investigation, it is found that the overall agricultural production efficiency of Chongqing remains at the productivity level of 0.8 from 2009 to 2018, with an average annual growth rate of 12.8%, but there is a large gap in the level of regional development. Through Malmquist Index decomposition, it is found that agricultural technology progress has the greatest contribution to the improvement of production efficiency. Financial support for agriculture, urbanization level, regional economic development level and highway mileage have a significant positive impact on production efficiency, while the level of farmers’ disposable income has a negative impact on the increase of production efficiency, and the income gap between urban and rural residents fails to pass the significance test.

scholarly journals Analysis of the Spatial Variations of Determinants of Gully Agricultural Production Transformation in the Chinese Loess Plateau and Its Policy Implications

Land ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 901
Author(s):  
Lulu Qu ◽  
Yurui Li ◽  
Yunxin Huang ◽  
Xuanchang Zhang ◽  
Jilai Liu
Keyword(s):  
Land Use ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Agricultural Production ◽  
Policy Implications ◽  
Chinese Loess Plateau ◽  
Distribution Area ◽  
Chinese Loess ◽  
General Law ◽  
The Chinese Loess Plateau

Exploring the gully agricultural production transformation and its influencing factors is of considerable significance to the evolution of the human–land relationship and multifunctional transformation of gully agriculture in the context of new development. This paper tries to reveal intensive land use under the background of population contraction in the Chinese Loess Plateau and its transformation trend by defining the gully agricultural production transformation (GAPT). Given the representativeness of land-use change in the loess hilly and gully region (LHGR) was taken as a case study, and ArcGIS spatial analysis techniques and geographically and temporally weighted regression model (GTWR) were used to detect the spatio-temporal differentiation pattern and influencing factors. The results show that: (1) GAPT shifts from the high elevation area of 1000–1300 m to the low elevation area of <1000 m, and the transformation process remains within the range of slope 0–20° and topographic relief between 40 m and 180 m. (2) GTWR coupled with time non-stationary and spatial heterogeneity has a better fitting effect, which verifies its applicability in the study of GAPT. Social and economic factors were the main driving forces of GAPT in Yan’an City in the past 20 years, and they were increasing year by year. (3) The spatial-temporal distribution of the driving factors of the agricultural production transformation in Yan’an City is different. The intensity of the population factor and the slope factor is always in the dominant position, and the high value distribution area of the land average GDP factor forms a funnel-shaped pattern of “core edge” in the north and the central and western regions, and its changes tend to “flow” to the core. (4) The gully agricultural production transformation can reflect the general law of rural land use transition in gully areas, and thereby provide policy ideas for gully development. Overall, this study’s content can provide scientific guidance for the sustainable development of gully agriculture and the revitalization of watershed and land consolidation in gully areas.

scholarly journals An Analysis of Agricultural Production Efficiency of Yangtze River Economic Belt Based on a Three-Stage DEA Malmquist Model

2022 ◽  
Vol 19 (2) ◽  
pp. 958
Author(s):  
Zhiwei Pan ◽  
Decai Tang ◽  
Haojia Kong ◽  
Junxia He
Keyword(s):  
Agricultural Production ◽  
Yangtze River ◽  
Technological Progress ◽  
Production Efficiency ◽  
Malmquist Index ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Strategic Development ◽  
Technological Efficiency ◽  
Development Area

The Yangtze River Economic Belt (YREB) is a major national strategic development area in China, and the development of the YREB will greatly promote the development of the entirety China, so research on its agricultural production efficiency is also of great significance. This paper is committed to studying the agricultural production efficiency of 11 provinces in the YREB and adopts a combination of the Data Envelopment Analysis (DEA) model and the Malmquist index to make a dynamic and static analysis on the YREB’s agricultural production efficiency from 2010 to 2019. Then, a three-stage DEA Malmquist model that eliminates the factors of random interference and management inefficiency is compared to a model without elimination. The results show that the adjusted technological efficiency changes, technological progress, and total factor productivity increased by −0.1%, 0.24%, and 0.22%, respectively. When comparing these values to the pre-adjustment values, the results indicate that the effect of environmental variables cannot be ignored when studying the agricultural production efficiency of the YREB. At the same time, the differences in the agricultural production efficiency in the YREB are reasonably explained, and feasible suggestions are put forward.

scholarly journals An Empirical Study on Environmental Efficiency Measurements and Influencing Factors

2020 ◽  
Vol 27 (4) ◽  
pp. 543-553
Author(s):  
Yuxiu Chen ◽  
Long Li ◽  
Jian Yu ◽  
Sang-Bing Tsai ◽  
Jiangtao Wang
Keyword(s):  
Influencing Factors ◽  
Environmental Efficiency ◽  
Practical Significance ◽  
Load Factor ◽  
Significant Positive Effect ◽  
Tobit Regression ◽  
Factors Influencing ◽  
Negative Effect ◽  
Positive Effect ◽  
Tobit Regression Model

Abstract Airlines are an important part of the comprehensive transportation system. Therefore, it is of great practical significance to empirically analyse the measurement of airlines’ environmental efficiency and influencing factors. Adopting the SBM-DEA model, this study measured the environmental efficiency of 20 Chinese airlines between 2010 and 2017 and empirically analysed the factors influencing their environmental efficiency using a Tobit regression model. Spring Airlines, China Southern Airlines, and Hainan Airlines were found to have the highest environmental efficiency. Tianjin Airlines, Hebei Airlines, and China Express Airlines had the lowest environmental efficiency. The Tobit regression results showed that average flight distance, load factor, market share, and proportion of cargo and mail turnover had a significant positive effect on airline environmental efficiency. Fuel consumption per ton-km had a significant negative effect on airline environmental efficiency.

scholarly journals Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China

2021 ◽  
Vol 9 ◽  
Author(s):  
Hanxiao Xu ◽  
Bei Ma ◽  
Qiang Gao
Keyword(s):  
Environmental Factors ◽  
Influencing Factors ◽  
Production Efficiency ◽  
Technological Development ◽  
Spillover Effects ◽  
Development Stage ◽  
Environmental Efficiency ◽  
Spatial Effects ◽  
Grain Production ◽  
Production Areas

Environmental factors have a profound impact on the evaluation of grain production efficiency. Taking environmental factors into account can more accurately measure grain production efficiency and identify the development stage of grain production. Based on the global super-efficiency SBM model, environmental factors are included in the grain production efficiency measurement system, while the temporal and spatial evolution characteristics of grain production environmental efficiency and the sources of inefficiency are analyzed. In addition, the Spatial Durbin Model is used to investigate the influencing factors and spatial spillover effects of China’s grain production environmental efficiency. The results show that: environmental factors have a significant impact on the efficiency of grain production in China; the environmental efficiency of grain production in China fluctuates with a downward trend; the environmental efficiency of grain production in different provinces show obvious spatial differentiation and geographical agglomeration; the main influencing factors of the environmental efficiency loss of grain production in major grain production areas are carbon emissions, non-point source pollution of grain production and labor input; the level of education, the level of technological development, and labor transfer have a significant positive spillover effect on the environmental efficiency of China’s grain production, yet disaster rate has a significantly negative one. Thus, it is important to pay attention to resource conservation and pollution control, strengthen cooperation and exchanges between provinces, and take the path of coordinated development between the environment and grain production.

Protein-energy ratio for forage production efficiency

2019 ◽  
Author(s):  
Н. Зезин ◽  
М. Намятов
Keyword(s):  
Agricultural Production ◽  
Production Efficiency ◽  
Agricultural Research ◽  
Perennial Grasses ◽  
Forage Production ◽  
Milk Productivity ◽  
Agricultural Research Institute ◽  
Oil Crops ◽  
Protein Energy ◽  
Research Institute

Исследования Уральского НИИСХ и опыт передовых хозяйств Свердловской области показывают, что для повышения эффективности и стабильности кормопроизводства необходимо в структуре кормовых культур увеличивать удельный вес высокобелковых, высокоэнергетических и засухоустойчивых культур, производить подсев многолетних трав ежегодно на площади не менее 60 тыс. га, а в перспективе 7080 тыс. га. За последние 7 лет (20112018 годы) площади люцерны в области увеличились в 2 раза (до 23 тыс. га), кукурузы по зерновой технологии в 2,4 раза (до 20,9 тыс. га) и масличных культур в 3,1 раза (до 30,8 тыс. га). Опыт СПК Килачёвский Ирбитского района показывает, что освоение научно обоснованных биологизированных севооборотов, прошедших изучение в стационарных опытах Уральского НИИСХ, позволяет ежегодно получать высокие и стабильные урожаи всех сельскохозяйственных культур. В этом хозяйстве каждая кормовая культура размещается в своём севообороте. В СПК Килачёвский и ряде других хозяйств Свердловской области большое значение придаётся возделыванию люцерны, кукурузы и масличных культур. Площадь этих культур в расчёте на одну корову мы назвали белковоэнергетическим коэффициентом (БЭК). Анализ показал тесную взаимосвязь между белковоэнергетическим коэффициентом и молочной продуктивностью. Так, в СПК Килачёвский в 2016 году при удое 10196кг БЭК был равен 1,25 в 2017 году удой достиг 10798кг, БЭК 1,43 в 2018 году соответственно 11493 кг и 1,47 при количестве коров 29002977 голов. According to the investigations conducted at the Ural Agricultural Research Institute and experience of the leading farms in the Sverdlovsk region high effectiveness and stability of Forage Production requires wide cultivation of droughtresistant crops rich in protein and energy as well as seeding over 60 thousand ha (in the future 7080 thousand ha) by perennial grasses annually. For the last 7 years (20112018) areas of alfalfa increased by 2 times (up to 23 thousand ha), grain maize by 2.4 times (up to 20.9 thousand ha), oil crops by 3.1 times (up to 30.8 thousand ha). SPK Kilachevskiy (agricultural production cooperative) reported that the use of approved crop rotations tested at the Ural Agricultural Research Institute resulted in annual high and stable yields of all the crops. SPK Kilachevskiy and other farms of the region widely grow alfalfa, maize and oil crops. Land area for the cultivation of these crops per one cow is called proteinenergy coefficient (PEC). Proteinenergy coefficient was shown to have a significant correlation with milk productivity. SPK Kilachevskiy reported that in 2016 PEC and milk yield reached 1.25 and 10196 kg in 2017 1.43 and 10798 kg in 2018 1.47 and 11493 kg, respectively under 29002977 cows.

Exploring the water–energy–food nexus from a perspective of agricultural production efficiency using a three-stage data envelopment analysis modelling evaluation method: a case study of the middle and lower reaches of the Yangtze River, China

Water Policy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 49-72 ◽  
Author(s):  
Jiazhong Zheng ◽  
Weiguang Wang ◽  
Dan Chen ◽  
Xinchun Cao ◽  
Wanqiu Xing ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Agricultural Production ◽  
Yangtze River ◽  
Production Efficiency ◽  
Evaluation Method ◽  
Data Envelopment ◽  
The Yangtze River ◽  
Agricultural Resources ◽  
Important Challenge

Abstract A coordinated nexus of agricultural resources is vital to achieve food security and sustainable development in China. Comprehensively considering the water–energy–food nexus as well as the external environment, this study adopts a three-stage data envelopment analysis (DEA) modelling evaluation method to assess the agricultural production efficiency (APE) of seven provinces in the middle and lower reaches of the Yangtze River (MLYR) during 1996–2015. The results show that the three-stage DEA modelling evaluation method reveals real APE and is considered to be a better quantitative method than conventional approaches. A gradually widening range of APE is an important challenge for this region. Significantly, this region generates huge demands for agricultural resources. Moreover, regional emissions of greenhouse gases (GHG) decreased from 34.20 million tons standard coal in 1996 to 32.11 million tons standard coal in 2015, though APE has continued to decrease by 2.56% in the past two decades. In general, the management and technology levels should be improved simultaneously, even though specific opportunities for APE improvement vary across provinces in MLYR. However, understanding the temporal and spatial variation of APE along with the WEF nexus from a production-based insight is a vital step toward appropriately targeted policy making for nationwide resources savings and emissions reduction.

scholarly journals Structural Change and Its Impact on the Energy Intensity of Agricultural Sector in China

2018 ◽  
Vol 10 (12) ◽  
pp. 4591 ◽  
Author(s):  
Hongyun Han ◽  
Shu Wu
Keyword(s):  
Agricultural Production ◽  
Energy Intensity ◽  
Agricultural Sector ◽  
Scale Economy ◽  
Policy Implications ◽  
Vegetable Production ◽  
Regional Heterogeneity ◽  
Fuel Price ◽  
Agricultural Structure ◽  
The Impact

China’s agricultural structure has undergone significant changes for the past four decades, mainly presenting as the fall of sown proportion of grain crops and the rise of vegetables, as has its energy consumption. Employing the panel data on 30 provinces during 1991–2016, this paper empirically explores the impact of agricultural structure changes (ASC) on the energy intensity of agricultural production (EIAP), direct energy intensity of agricultural production (DEIAP) and indirect energy intensity of agricultural production (IEIAP) in China. Besides, the regional heterogeneity of such impact is examined. The results show that: (1) ASC increases EIAP and IEIAP significantly, while ASC decreases DEIAP, which is explained by the structural effect and different planting modes of different crops; (2) the impact in the three administrative regions is similar to national situation, except the impact of ASC on DEIAP in the West Region, which is explained by regional differences of vegetable mechanization; (3) the result of the six vegetable production regions reveals greater regional heterogeneity, and this is attributed to the scale economy effect and the incremental effect of vegetable mechanization; and (4) fuel price, income, agricultural labor, old dependency ratio, and fiscal expenditure have different but significant impacts on EIAP, DEIAP, and IEIAP. Finally, some policy implications are given.

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