Does Agricultural Mechanization Improve the Green Total Factor Productivity of China’s Planting Industry?

Development of a new environmentally friendly and efficient centrifugal variable diameter metering device

Introduction

The design of the maize metering device involves centrifugal variable diameter pneumatic and cleaning mechanisms, aiming to enhance the performance and power efficiency of pneumatic maize metering devices. Leveraging the impact of changes in centrifugal diameter and the guidance and positioning of airflow, we optimize the hole insert, seeding plate, seed limit board, and integrated front shell. This optimization facilitates the adjustment of both the quantity and posture of seed filling. As a result, seeds can form a uniform flow within the annular cavity, reducing the wind pressure necessary for regular operation and decreasing power consumption.

Methods

A quadratic regression orthogonal rotation combination experiment is conducted using a self-made experiment bench, considering ground speed, wind pressure, and seeding rate as the experiment factors. Furthermore, a comparative experiment involving a novel centrifugal variable-diameter type metering device.

Results

The results indicate optimal seeding performance when the ground speed is 13.2 km/h, the wind pressure is 1.2 kPa, and the feeding rate is 25 seeds/s. Under these conditions, the quality of feed index reaches 95.20%, the multi-index is 3.87%, and the miss index is 0.93%. Findings reveal that the developed seed metering device achieved a quality of feed index exceeding 93.00% across varying speeds of 12~18 km/h, aligning with the production requirements. Moreover, the actual power consumption of Type B and C is about 85.00% and 98.00% lower than Type A, standing at only 32.90 W at 18 km/h. The COP of Type C is about 86 times and 12 times that of Type A and B, respectively, meeting the demands for efficient production of maize seed metering devices.

Discussion

In comparison to traditional design and structural parameter optimization methods for maize seed metering device, this study is helpful to the sustainable development of maize industry and reduce environmental pollution.

Digitalization of rice value chain in Nigeria with circular economy inclusion for improved productivity - A review

Agriculture is a primary sector of production that is important for providing basic raw materials for many sectors. There seemed to be more people practicing it traditionally, and this situation is limiting the buoyancy of agricultural productivity. Traditional agriculture cannot fully thrive in the 21st century. This is because the global human population is currently growing at an alarming rate that is much higher than the agricultural productivity requirements to strike a balance, especially between food demand and supply. Hence, there is a need to take advantage of technology and incorporate it into agriculture with a view to bridging the gaps created by traditional agriculture. In this review paper, agricultural productivity information as it relates to the human population is presented. Also presented was background information on digital technology and its connections to agriculture through the use of some existing digital technology devices for improved productivity. Furthermore, matters relating to the rice value chain, with specific attention to Nigeria, were given extensive consideration. The circular economy (CE) approach was presented as a means of converting the three (3) major rice value chain by-products or wastes (straw, husk, and bran) to other value-added products. The CE will protect the environment and make it more friendly. Also, it will improve productivity, increase income, and create better living conditions for those in the rice value chain in Nigeria.

The impact of digital financial inclusion and urbanization on agricultural mechanization: Evidence from counties of China

This paper expounds the theoretical logic among digital inclusive finance, urbanization, and agricultural mechanization level, puts forward the research hypothesis, and then selects the county unbalanced panel data of 1309 counties in China from 2014 to 2020 based on the two-way fixed model with standard error clustering to county level and mediating effect model for empirical data regression analysis. Through baseline regression analysis, mediation effect analysis, and heterogeneity analysis, the findings of this paper are as follows. First, digital financial inclusion has a significant positive effect on the growth of agricultural mechanization. Second, digital inclusive finance at the county level can also indirectly affect the growth of agricultural mechanization through urbanization. That is, agricultural mechanization has an intermediary effect between the financial agglomeration at the county level and the growth of farmers' income. Third, the impact of county-level digital financial inclusion on the growth of agricultural mechanization level is significantly heterogeneous, and the promoting effect is significant in areas with balanced grain production, national-level poor county or contiguous areas of dire poverty, and areas with a good foundation for digital financial inclusion. By analyzing digital inclusive finance, urbanization, and agricultural mechanization, this paper proposes targeted policy recommendations. First, the government can promote agricultural mechanization by developing digital financial inclusion. Second, the government should guide and accelerate the process of digital financial inclusion, promoting urbanization thereby amplifying the positive impact of digital financial inclusion on agricultural mechanization. Third, given the heterogeneity of the impact of digital financial inclusion on agricultural mechanization, local development should focus on developing different dimensions of digital financial inclusion according to specific conditions.

Spatial differences, dynamic evolution, and convergence of carbon productivity in China

China is currently developing a green economy, and improving carbon productivity (CAP) is an important part of this process. The current study applied a minimum distance to strong efficient frontier (MinDS) model to measure China's CAP. The Dagum Gini coefficient and kernel density estimation methods were further used to reveal its spatial differences and dynamic evolution, while the coefficient of variation and spatial convergence models were employed to examine its convergence characteristics. The results showed significant spatial differences in China's CAP, with primarily high and low spatial distribution characteristics in the east and west, respectively. Between-regional differences were the main sources of the overall differences. Moreover, the differences between overall, eastern, central, and western regions of China all exhibited a widening trend. Although none showed σ convergence, all had significant absolute β spatial convergence and conditional β spatial convergence characteristics. Collectively, the findings of this study objectively reflect the real level, distribution characteristics, and spatial convergence characteristics of CAP in China as a whole and in each region, while also providing a reference basis for achieving peak carbon neutrality.

Impact of social networks on fertilizer use efficiency in China: Evidence from kiwifruit production in Shaanxi province

Improving fertilizer use efficiency (FUE) is an effective way to reduce the fertilizer use and non-point source pollution in agriculture production in China. Existing research related to fertilizer use efficiency ignores the impact of famers' social networks, which can significantly affect the fertilizer use and its efficiency. This paper analyzed the impact of social networks on fertilizer use efficiency and the mediator effect of green fertilization technology adoption, based on the calculation of fertilizer use efficiency of 569 kiwifruit farmers in Shaanxi province of China surveyed in 2021 by using stochastic frontier method. The results show that the FUE of kiwifruit production ranged within 0.1027-0.9945, with an average value of 0.333, which showed great variability, and fertilizer input had an average 66.7% reduction potential without reducing output. Social networks and its four dimensions all had a significant positive impact on FUE, in which network trust and network learning had a greater impact on FUE. Farmers' adoption of green fertilization technology played a positive mediator effect in the process of social networks influencing their fertilizer use efficiency, which was mainly promoted by network trust and learning. Farmers' experience in agricultural production, joining the cooperative, the quantity of village agricultural supply points had a significant positive impact on fertilizer use efficiency, while age and gentle of the head of household had a significant negative impact of fertilizer use efficiency. Planting scale had a U-shape impact on FUE. Policies aimed at improving FUE and reducing non-point source pollution should, therefore, focus on improving the social networks of farmers, especially young farmers, strengthening network trust, promoting network learning, network reciprocity and network interaction, strengthening the research and development and extension of green fertilization technology, encouraging farmers to join agricultural cooperative, improving village production conditions, guiding sustainable agricultural production.

Towards sustainable development: research on the green growth effect of digital agriculture in China

With the rapid development of the Internet, modern agricultural digital technology will play a more critical role in the sustainable development of Chinese agriculture. In this paper, we used China's provincial data to examine the impact factors of digital transformation of agriculture and agricultural green total factor productivity indicators using the entropy value method and the SBM-GML index method from 2013 to 2019. We investigated the impact of digital agriculture on green agricultural growth using methods such as the fixed effects model and mediated effects model. Our findings suggest that green growth in agriculture is driven by the digital transformation of agriculture. It significantly enhances green technology innovation, agricultural scale operation, and agricultural cultivation structure optimization, thus contributing to green growth. Notably, the digital agricultural infrastructure and industrialization level catalyzed green agricultural development, while the role of digital agricultural subject quality could have been more significant. Therefore, strengthening the rural digital infrastructure and improving rural human capital promote sustainable agricultural development.

Synergistic study of the green efficiency and economic resilience of agriculture from a sustainable development perspective: evidence from Northeast China

Under the scenario of the complex external environment and internal structural changes in agricultural development, exploring the synergistic relationship between agricultural green efficiency (AGE) and agricultural economic resilience (AER) can offer a new path for fostering sustainable regional agricultural development. This paper fills the research gap in the interaction between efficiency and resilience in agriculture. We explore the synergistic mechanism of the two based on the perspective of sustainable development, providing a reference for constructing a synergistic theoretical system of AGE and AER. Moreover, we apply the Haken model to the field of the agricultural economy and scientifically evaluate the level of AGE and AER synergy in Northeast China from 2010 to 2020. Finally, we analyze the influencing factors of AGE and AER synergy in Northeast China from three dimensions: economic, social, and natural. The results show that AER is dominant in the AGE-AER synergistic system. The synergy level of AGE and AER in Northeast China is mainly in the higher and advanced synergy stages, with obvious spatio-temporal differences and insufficient inter-regional radiation effects. Social factors are the main factors of spatial differentiation of AGE and AER synergy.

Carbon reduction effect of agricultural green production technology: A new evidence from China

Both contemporary policy makers and scholars focus on the role of agricultural sector in better achieving carbon reduction and green transition. This paper empirically analyzes the impact and mechanism of agricultural green production technology (AGPT) on agricultural carbon emission intensity at the national level using the panel data of China's 31 provinces from 2000 to 2019 as well as the panel OLS method and spatial Durbin model. The results show that China's AGPTs adoption level have improved greatly and the agricultural carbon emission intensity has declined drastically in the period of 2000 to 2019. No-till planting and straw returning significantly inhibited agricultural carbon emission intensity, while water-saving irrigation had the opposite effect. Chemical fertilizers are substituted effect through zero-tillage planting and straw-return, thus reducing the agricultural carbon emission intensity. Water-saving irrigation serves as a supplement effect to chemical fertilizers application, inhibiting fertilizer's carbon increase effects. The role of AGPTs reduce carbon emissions is particularly dominant in main grain-producing areas. Straw-return played a significant negative spatial spillover effect on the agricultural carbon emission intensity of neighboring regions through technology dissemination across regions, and the spillover effect of zero-tillage planting and water-saving irrigation was not significant. According to local conditions, no-tillage planting and straw returning should be promoted, water-saving irrigation efficiency should be improved, and the input characteristics of high carbon elements in traditional agriculture should be gradually changed. A regional collaborative emission reduction mechanism should be established to achieve a long-term mechanism for agricultural carbon emission reduction and green development.

The driving influence of multidimensional urbanization on green total factor productivity in China: evidence from spatiotemporal analysis

China is experiencing a swift transformation from a rural to an urban society. This societal transition is the result of a country's ambition toward economic development. Rapid urbanization impacts on human wellbeing, environment, and infrastructure. Unlike many cases of urban extension, China's urbanization has led to increase in environmental issues, rather than to contain, and posed serious challenges to achieve sustainable development. To date, nevertheless, there is a little empirical enquiry on the spatiotemporal effects of multidimensional urbanization on green total factor productivity. Taking this into account, this article constructs a spatial panel data model based on 283 Chinese cities during 2006-2019. The results showed that the three types of urbanization (i.e., population, land, and economic) have positive effect on green total factor productivity (GTFP). Population urbanization has indirect positive effect on GTFP. Local land urbanization inhibits while surrounding land urbanization promotes GTFP. Economic urbanization has positive direct effect on GTFP. Considering regional heterogeneity, population and economic urbanization have positive effect on GTFP in eastern region. In central region, land urbanization significantly promotes GTFP, while economic urbanization has significant negative effect on GTFP. In the western region, the three types of urbanization are not conducive to GTFP. Considering sized heterogeneity, population and economic types of urbanization have significant negative effect on GTFP in small- and medium-sized cities. In large cities, the three types of urbanization have significant positive effects on GTFP. Considering temporal heterogeneity, the three types of urbanization significantly promote GTFP before 2014, but economic urbanization deteriorates GTFP after 2014. Our results recommend loosening Hukou system, improving land use efficiency, and developing technology and knowledge-intensive industries.

Does farmland inflow improve the green total factor productivity of farmers in China? An empirical analysis based on a propensity score matching method

Promoting the "double security" of agricultural economy and ecology is the key to the agricultural modernization strategy, and the large-scale development of agriculture is an essential way for modern agriculture. Based on the micro-survey of 697 corn growers from August to September 2020 in China, the super-efficiency SBM model was used to calculate farmers' green total factor productivity. We further used the propensity score matching method to identify the impact of farmland inflow on farmers' green total factor productivity and dissect the internal mechanism. The study found that: firstly, compared with the non-inflowed households, the green total factor productivity of the inflowed households increased by 14.66%; secondly, farmland inflow can significantly improve farmers' green total factor productivity through the marginal output leveling effect, transaction benefit effect, and technology adoption effect; thirdly, the influence of farmland inflow on the green total factor productivity of farmers has heterogeneity in age, identity, and geographical location. Therefore, governments should establish a differentiated farmland inflow mechanism according to local conditions, enhance factor mobility and soil fertility monitoring capabilities, and drive a "win-win" between economic development and ecological protection.

How Does Agricultural Mechanization Service Affect Agricultural Green Transformation in China?

Agricultural mechanization service (AMS) is a critical path to achieving agricultural green transformation with smallholders as the mainstay of agricultural production. Based on the panel data of 30 Chinese provinces from 2011 to 2020, this paper measures the AGTFP using the Super-SBM model and examines the effects of different AMS supply agents on AGTFP and spatial spillover effects through the spatial Durbin model. The main conclusions are as follows: First, China's AGTFP showed a stable growth trend, with the mean value increasing from 0.1990 in 2011 to 0.5590 in 2020. Second, the specialization (SPO) and large-scale (LSO) of AMS supply organizations have significantly positive effect on the AGTFP of the local province. However, SPO has a significantly positive effect on the AGTFP of the neighboring provinces, while LSO has the opposite effect. Third, the specialization of AMS supply individuals (SPI) has significantly negative effect on the AGTFP of the local province. In contrast, the large-scale AMS supply individuals (LSI) has the opposite effect. Furthermore, the spatial spillover effects of both are insignificant. Fourth, the spatial spillover effect of AGTFP shows asymmetry among different regions and indicates that AMS resources flow from non-main grain production and economically developed regions to main grain production and less developed regions. These findings provide helpful policy references for constructing and improving the agricultural mechanization service system and realizing the agricultural green transformation in economies as the mainstay of agricultural production.

Internet technology and green productivity in agriculture

The high-quality development of agriculture requires not only sustainable growth of agricultural productivity but also green agricultural production. Internet technology has played an essential role in agricultural production and marketing in China over the past decades. This paper estimates provincial agricultural green growth in China from 1997 to 2019 and decomposes it into technological progress (TP) and efficiency changes (EC) based on the Luenberger productivity indicator method. Then an econometric model is employed to analyze the impact of the Internet on the growth of agricultural green productivity and each sub-component, and moderating role of farmer education in such effect. The empirical results indicated that annual average growth rate of agricultural green productivity in China is 1.33% from 1997 to 2019, and technological progress dominates its growth. The development of Internet technology has a significant positive impact on agricultural green productivity and its decomposition. Farmer education has strengthened the effect of Internet technology on agricultural green productivity and its decomposition TP and EC.

Does the National Credit Demonstration Policy Affect Urban Green Economy Efficiency? Evidence from the Yangtze River Delta Region of China

A more scientific green economy efficiency indicator is constructed based on OH (2010), and a multiperiod spatial DID model is used to examine the impact of national credit demonstration policies on urban green economy efficiency in a sample of cities above the prefecture level in the Yangtze River Delta. The study confirms the following: (1) The national credit demonstration policy makes a significant contribution to the green economic efficiency of cities, and it is conducive to strengthening awareness of the rule of law in the market to regulate market order. (2) The demand for credit regulation in coastal areas has increased under the new development pattern, and the national credit demonstration policy has effectively enhanced green economy efficiency through institutional supply. (3) Under the national credit demonstration policy, the subprovincial level and above can mobilise more resources for policy refinement and support, reducing transaction costs and improving the efficiency of the green economy. (4) The impact of the national credit demonstration policy on the efficiency of Zhejiang's green economy is more obvious; but, under the overall framework of the Yangtze River Delta, the policy has a more prominent role in promoting green economy efficiency in other provinces. Policy insights are as follows: (1) Different cities have different degrees of impact on the efficiency of the green economy from the national credit demonstration policy, and they should implement differentiated measures based on regional heterogeneity; (2) regulating the use of administrative resources and avoiding undue administrative intervention are important prerequisites for promoting regional integration to enhance the efficiency of the green economy; and (3) strengthening interprovincial credit policy synergies can help to alleviate administrative distortions of policy implementation and enhance the efficiency of the regional green economy.

Can Agricultural Productive Services Promote Agricultural Environmental Efficiency in China?

Agricultural productive services are important paths to realize the development of green agriculture, while the effect of agricultural productive services on the agricultural environment and its influencing mechanism are not yet clear. With the panel data of agricultural production in China from 2004 to 2019, by using multi-output stochastic frontier analysis with an output-oriented distance function, this study investigates agricultural environmental efficiency based on net carbon sinks. Then, this study explores the effect of agricultural productive services on agricultural environmental efficiency and its mechanisms by adopting ordinary least squares regression with fixed-effect panel model, causal steps approach, and spatial econometric method. The main findings are as follows: Firstly, agricultural productive services enhance agricultural productivity and agricultural environment by optimizing inputs and increasing outputs, and thus improve agricultural environmental efficiency. This result holds steadily after using instrumental variables to deal with endogeneity, changing the measurement of the dependent and independent variables, and subdividing the sample. Secondly, the pathways of agricultural productive services affecting agricultural environmental efficiency are mainly reflected in technology progress, planting structure adjustment, factor allocation optimization, and spatial spillover. Thirdly, due to the law of diminishing marginal returns, the impact of agricultural productive services on agricultural environmental efficiency is more significant when the level of agricultural productive services is relatively low. To improve agricultural environmental efficiency, we suggest implementing different productive service strategies in different regions, strengthening information integration, and improving infrastructure.

Agricultural Services: Another Way of Farmland Utilization and Its Effect on Agricultural Green Total Factor Productivity in China

Improving agricultural green total factor productivity (AGTFP) is an important aspect of sustainable agricultural development. Agricultural services, a new way of farmland utilization in agricultural production, solved the problem of ‘who and how to farm’ in the context of labor off-farm migration. The literature has analyzed different factors that affect AGTFP, but there is a relative dearth of research into agricultural services and AGTFP. Therefore, based on the panel data of 31 provinces from 2011 to 2020, this study firstly measured carbon emissions in agricultural production and then took it as an unexpected output to measure the AGTFP by using the global Malmquist–Luenberger (GML) productivity index. Finally, the effect of agricultural services on AGTFP and its decomposition were empirically verified. The main findings are as follows: (1) Between 2011 and 2020, agricultural carbon emissions increased from 85.63 million tons to 90.99 million tons in the first five years and decreased gradually to 78.64 million tons in 2020; the government policy significantly affects carbon emissions reduction. (2) AGTFP has been increasing for the past decade, and the average growth rate of AGTFP reached 1.016, and agricultural services promoted AGTFP growth significantly, in which technological progress was the crucial driving factor. (3) Taking the Heihe–Tengchong line as the demarcation, the improving effect of agricultural services on AGTFP in the eastern region is better than the western region.

Evaluation of Agricultural Machinery Using Multi-Criteria Analysis Methods

To achieve the highest possible agricultural production, it is necessary to procure the appropriate agricultural machinery. A tractor is the most useful machine in agriculture that performs various functions. Therefore, the selection of a tractor is one of the key decisions in the agriculture-production process. This study aims to evaluate heavy tractors for agricultural production in Bosnia and Herzegovina. Since this is a selection between different tractors, which are evaluated using several criteria, the methods of multi-criteria analysis (MCDA) were used in this study. Five different methods were used to determine the weight of the criteria, of which a modified standard-deviation method is a new method used in practice, while the tractor ranking was performed using the CRADIS (compromise ranking of alternatives from distance to ideal solution) method. The results showed that the best-ranked tractor is A4, while the most deviations from the ranking occur when the entropy method is used. The contribution of this study is in the systematization of the methods for the objective determination of the criteria weights and the development of new methods to facilitate decision-making in agriculture and other industries.

Does agricultural mechanization improve agricultural environment efficiency? Evidence from China's planting industry

Environmental problems caused by energy consumption in the rapid popularization of China's agricultural mechanization (AM) have caused increasing concern. Using the panel data of China's 30 provinces from 2001 to 2019, this article adopts a stochastic frontier analysis method with output-oriented distance function to measure agricultural environment efficiency (AEE) based on net carbon sinks and empirically analyzes the impact of AM on AEE. The main findings are as follows: Firstly, the AEE of the nation and all provinces shows an upward trend over time and has significant spatial positive autocorrelation characteristics. Secondly, there is an inverted U-shaped relationship between AM and AEE. Meanwhile, AM has spatial spillover effect and time cumulative effect on AEE. These basic conclusions are still robust after using instrumental variables, spatial autoregressive model, sub-sample regression, changing spatial weight matrix, and independent variable. Thirdly, the effect of AM on AEE depends on the input effect and output effect caused by AM. The mechanism is mainly reflected in agricultural technology progress, expansion of the scale of farming operation, optimization of resource allocation, and spatial spillover. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications.

A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods

As regional interaction increases in an open economy, a region’s green total factor productivity in agriculture must be considered alongside relationships with other regions. In this study, the slack-based model (SBM) global Malmquist–Luenberger (GML) index is used to measure the green total factor productivity of agriculture in each province of China, and the social network analysis (SNA) and vector autoregressive model (VAR) impulse response function (IRF) are used to examine the spatial network structure and regional interactivity. The research confirms that the absolute value and concentration of agricultural green total factor productivity are generally higher in the south than in the north of China, but the peak is lower in the south than in the north. The network density of agricultural green total factor productivity in China from 2008 to 2019 shows an increase, with the cut-off values of mean, 10, 50, and 100 treated as 4.97%, 2.57%, 3.30%, and 2.43%, respectively. From 2008 to 2019, the central potentials of network entry and network exit of green total factor productivity in China’s agriculture show a “V”-shaped and inverted “V”-shaped evolution path, respectively, with the density of cohesive subgroups growing, which demonstrates that the spatial structure of green total factor productivity in Chinese agriculture has experienced an evolutionary path from polycentric to monocentric to polycentric conditions. The spatial interaction of different cohesive subgroups is intensifying and has a certain degree of self-stability. In terms of regional interaction, the siphon effect of the east on the green development of agriculture in the central and western regions is significant, but the trickle-down effect is not obvious, and the interaction between the central and western regions has a catalytic effect on the efficiency of the green economy of agriculture in both regions. It is recommended that targeted policies be introduced to support the flow of agricultural factors and industrial division of labour between the central and western regions and the south and north, taking into account the actual situation. The novelty of this paper is that it focuses on the green total factor productivity of Chinese agriculture and combines the innovative use of the social network analysis paradigm to analyse the green development of agriculture in a country from a spatial dynamic evolutionary perspective. A limitation of the research methodology in this paper is its poor applicability to closed economy analysis.

Does Digital Inclusive Finance Effectively Promote Agricultural Green Development?-A Case Study of China

Agricultural green development is increasingly being discussed in sustainable development. This paper constructs agricultural green development from four dimensions: resource savings, environmental protection, ecological conservation, and quality industrialization. We apply the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to measure agricultural green development and employ a panel dataset of provinces in China from 2011-2019. Then, the dynamic spatial Durbin model is adopted to estimate the spatial effect of digital inclusive finance on agricultural green development. The main findings are as follows: (1) digital inclusive finance effectively promotes agricultural green development, and the promotional effect shows temporary and spatial spillover; (2) regional heterogeneity exists in the spatial effect in the short and long term; and (3) education, digital infrastructure, and traditional finance are important factors influencing this spatial effect of digital inclusive finance on agricultural green development.