Agricultural Total Factor Productivity Research Articles

Agriculture–Tourism Integration’s Impact on Agricultural Green Productivity in China

Currently, the integrated development of agriculture and tourism is one of the most critical strategic measures in China. The rapid growth of agricultural tourism integration presents the typical characteristics of expanding regional differences. Exploring the impact of agricultural tourism integration on the growth of green total factor productivity in agriculture has important theoretical and practical significance. This study constructs a comprehensive index system for agricultural tourism integration, measuring the development level of agricultural tourism integration in 30 sample provinces from 2008 to 2018. Using the generalized system method of moments approach and Tobit model, the impact of agricultural tourism integration on agriculture was empirically tested, and the regulatory role of rural human capital was discussed. It was found that agricultural tourism integration contributes significantly to the improvement in green total factor productivity in agriculture, with rural mobility human capital, education human capital, and health human capital all playing a significant positive moderating role in this process. Finally, it is recommended that priority be given to agricultural tourism integration in the policy framework, promoting industrial chain upgrading, raising investment in rural infrastructure, and upgrading rural human capital levels to contribute the rural economic development.

Agricultural total factor productivity, digital economy and agricultural high-quality development.

The long-term and stable development of agriculture is the key to China's economic development and social stability. Agricultural total factor productivity and the digital economy have become new kinetic energy and new engines driving agricultural high-quality development. It is of great significance to verify whether there are significant spatial and threshold effects in the process of high-quality development of agriculture and to explore the intrinsic relationship between high-quality development of agriculture and agricultural total factor productivity and digital economy. This paper takes 31 provinces in China from 2011 to 2020 as the research object. The coefficient of variation method is used to estimate the comprehensive evaluation index of agricultural high-quality development and digital economy. And Dea-Malmquist index method is used to estimate agricultural total factor productivity. On this basis, the spatial Durbin model and threshold regression model are constructed to explore the spatial and threshold effects of agricultural total factor productivity, digital economy and other factors and high-quality agricultural development. The conclusion is as follows: the high-quality development of agriculture has significant spatial autocorrelation. Agricultural total factor productivity and digital economy have significant direct effect and indirect spillover effect on the high-quality development of agriculture. Agricultural total factor productivity has stage differences in each range of digital economy level, but its influence on agricultural high-quality development shows a positive state. Based on this, the paper puts forward some countermeasures to promote the high-quality development of agriculture.

The econometric analysis of voluntary environmental regulations and total factor productivity in agribusiness under digitization.

Under the new development pattern, both "digital" and "low-carbon" development have entered the fast track, and digital transformation has become an important path to promote green development and enhance total factor productivity in agriculture. Based on the data of agricultural companies, this paper empirically verifies the impact of voluntary environmental regulations on total factor productivity. The empirical results show that voluntary environmental regulation has a significant positive impact on total factor productivity of agribusiness. In the mechanistic analysis, it is found that voluntary environmental regulations accelerate the digital transformation process of firms, which in turn increases their total factor productivity. In addition, the level of government environmental concern contributes to the increase of voluntary environmental regulations on firms' total factor productivity. The findings have practical implications for the sustainable development of agribusiness, providing empirical evidence for policy formulation and adjustment, and helping the agricultural economy to achieve high-quality development.

Total-Factor Energy Efficiency and Its Driving Factors in China’s Agricultural Sector: An Empirical Analysis of the Regional Differences

Improving agricultural energy efficiency is essential in reducing energy consumption and achieving agricultural sustainable development. This paper aims to measure the agricultural total-factor energy efficiency in China rather than the partial-factor energy efficiency while taking full account of regional heterogeneity and to investigate the driving factors of agricultural total-factor energy efficiency. The empirical results showed that the average value of agricultural total-factor energy efficiency is 0.814 in China, and the technological gap ratio is 0.853. The regional difference in agricultural total-factor energy efficiency was quite obvious. Higher agricultural energy inputs are associated with higher agricultural total-factor productivity. The total value of potential agricultural energy savings in 30 provinces of China reached 1704.41 billion tons of standard coal. In terms of the absolute amount of agricultural energy saving, the amount was largest in the low-energy-input area, which was 113.87 million tons of standard coal, accounting for 66.81% of the total potential saving amount. Furthermore, we used the Tobit model to analyze the influencing factors of agricultural total-factor energy efficiency. We found that the proportion of agriculture to GDP has a positive impact on agricultural total-factor energy efficiency, while the per capita income of farmers, fiscal support for agriculture, the illiteracy rate of farmers, agricultural labor input, and agricultural capital stock have a negative impact on agricultural total-factor energy efficiency. Finally, we proposed policy implications in terms of agricultural technological progress, agricultural infrastructure, technical training, etc.

Analysis of Spatial Unbalance and Convergence of Agricultural Total Factor Productivity Growth in China—Based on Provincial Spatial Panel Data from 1978 to 2020

Using the provincial panel data from 1978 to 2020 as the research object, this study employs the fixed effect SFA-Malmquist model to measure the agricultural total factor productivity of each province and city, and the spatial correlation of China’s agricultural total factor productivity is determined by Moran’s I. On this basis, three weights (adjacency, economy, geography) are included as spatial factors in three spatial β-convergence models (SAR, SEM and SDM), and the spatial convergence characteristics of China’s agricultural total factor productivity are analyzed in different time periods and different regions. The study found that: First, China’s agricultural total factor productivity shows a growing trend, but as time goes on, its growth rate gradually slows down, and the growth rate in the eastern region is higher than that in the central and western regions. Second, China’s agricultural total factor productivity has significant spatial correlation and spatial convergence characteristics. The differences in agricultural total factor productivity in various regions are shrinking over time, and the spatial spillover effect significantly shortens the convergence process. Due to spatial convergence, while carrying out agricultural production, all regions should thoroughly consider the advantages of agricultural resources in neighboring regions and strengthen cooperation and exchanges between regions.

Empirical Analysis of China’s Agricultural Total Factor Productivity and the Reform of “County Administrated by Province”: Insights from Agricultural Enterprise Data

The study focuses on examining how the total factor productivity (TFP) of China’s agriculture-related enterprises is affected by the reform of “County Administrated by Province” (CAP). Using panel data from 1998 to 2013, with a sample size of 292, 423 agriculture-related enterprises, the study investigates the influence of CAP reform on the TFP. The findings reveal a significant dampening effect of the CAP reform on the TFP of agriculture-related enterprises. These results are further supported with a series of robustness tests including placebo test, multidimensional fixed effects test, and clustering of cities or counties. Heterogeneity analysis reveals that the CAP reform has significantly restrained the TFP of agriculture-related enterprises in high level cities, while showing no significant impact on cities with lower administrative levels. In the eastern region, the impact of the CAP reform on the TFP of agriculture-related enterprises is significantly negative, whereas it is not statistically significant in the central and western regions. the study on mechanisms elucidates that the inhibitory impact of CAP policy on the TFP of agriculture-related enterprises is enhanced by urban property prices, labor resource allocation, and banking competition. The research conclusion is of significance in guiding the practices of agriculture-related enterprises and deepening the reform of “County Administrated by Province”.

Farmland Leasing, misallocation Reduction, and agricultural total factor Productivity: Insights from rice production in China

Reducing factor misallocation is crucial for enhancing agricultural aggregate total factor productivity (TFP) and narrowing the income gap between urban and rural areas in China. This paper presents a theoretical framework illustrating how farmland leasing affects aggregated TFP via the reduction of misallocation. Utilizing a comprehensive dataset of cost and benefit survey for Jiangsu rice farmers, monthly surface climatological data, and soil characteristic information from FAO, this study finds that efficient factor reallocation could theoretically result in a 23.6% increase in aggregate TFP. In practice, a 1% increase in farmland leasing leads to a 1.087% gain in aggregate TFP and a 0.267% reduction in misallocation using IV regression. These effects become larger after the Three Rights Separation (TRS) reform compared to prior conditions. The mediation effects of farmland leasing on TFP through misallocation reduction are only observed in Southern Jiangsu, possibly due to farmland concentration.

Spatial double dividend from China’s main grain-producing areas policy: total factor productivity and the net carbon effect

Because of the reductions in China’s cultivated land area and the significant impact on the agricultural market, the main grain-producing areas (MGPA) policy has become vitally important for ensuring China’s food security. However, guaranteed food security and sustainability require ecological security, which raises the question of whether food production should come at the expense of the environment. This study used 1998–2020 panel data from 30 Chinese provinces and a spatial difference-in-differences model (SDID) to explore the MGPA policy effects and mechanism paths on agricultural total factor productivity (ATFP) and the net carbon effect (NCS). It was found that economically, the MGPA policy promoted local ATFP improvements and had positive spillover effects on surrounding provinces through factor diffusion, and environmentally, the MGPA policy had a positive effect in the MGPA policy implementation areas but inhibited the NCS in surrounding provinces. Further explorations of the environmental performances revealed that the MGPA policy promoted growth in agricultural carbon sinks and agricultural carbon emissions, with the environmental performances being primarily reflected in an increase in carbon sinks and a decline in the carbon emissions density. The mechanism test showed that the MGPA policy’s local environmental performance was achieved through economic performance. The ATFP was refined into technical innovation (TI), technology adoption (TA), and business scale (OS), with the mechanistic roles of these three refining paths being OS > TI > TA. A further mechanism test revealed that the MGPA policy effects on the ATFP were influenced by the various adjustment of production support, government support, and collaborative support. Based on the above analysis, this study gives policy recommendations to ensure food security and the realization of China’s dual carbon target.

Comparative performance of agricultural productivity in 44 SSA countries for a period of 59 years (1961-2019): A Malmquist productivity index approach.

We investigate the extent and nature of productivity growth in Sub-Saharan African countries using non-parametric frontier techniques. In this work, we examine agricultural total factor productivity (TFP) change in 44 countries in Sub-Saharan Africa (SSA), covering a period of fifty-nine (59) years (1961-2019). We make use of data drawn from the Food and Agriculture Organization of the United Nations. Due to the non-availability of reliable input price data, the study uses data envelopment analysis (DEA) to derive Malmquist productivity indices. The results demonstrate a decline in agricultural TFP growth in the region from 1961 to 2019. This notwithstanding, a general average growth was seen in some of the conventional inputs measured (land, labour, capital, and fertilizer) which are the main drivers of growth in the region. This finding implies that the increase in agricultural output over the past five decades in SSA is mainly due to an increased use of conventional inputs over time, including land, and not due to an increase in the ratio of output over inputs. As a generally acceptable and preferred indicator of technical and efficiency changes, we use TFP to calculate the Malmquist productivity index (MPI).

Improvement of Agricultural Supply Quality in China: Evidence from Jiangsu Province

Promoting the reform of the agricultural supply side and its quality improvement are crucial for realizing agricultural modernization. This paper tests the varied trajectory of agricultural total factor productivity (ATFP) in Jiangsu Province over the past 21 years. The paper used the three-stage DEA empirical analysis method—Stochastic Frontier Approach (SFA), to address the uncertainty in the development of the agricultural industry. The paper introduces environmental variables such as urbanization level, import and export trade, financial support for agriculture, and transportation convenience. The research results show that: (1) the ATFP growth in Jiangsu Province presents a fluctuating trend; (2) The further sub-index research of ATFP in Jiangsu Province shows that the average rate of growth for agricultural technology efficiency (AEC) in Jiangsu is negative, indicating that the input cost of agricultural factors in Jiangsu increases and marginal efficiency decreases; (3) The empirical analysis of ATFP growth by region shows that there are still large differences in agricultural economic development level, the level of modern agricultural technology and ATFP in the southern, the central and the northern parts of Jiangsu. (4) Stage III: DEA empirical results showed that improving urbanization level, net export trade, and transportation convenience is conducive to improving agricultural production efficiency; financial support for agriculture is weakly conducive to improving agricultural production efficiency. On this basis, the paper puts forward countermeasures and suggestions to promote agricultural structural reform.

Impact of Temperature Extremes on Carbon Emissions from Crop Production in Hebei Province, China

The study investigated the impact of temperature extremes on carbon emissions (CE) from crop production. (1) Background: Many scholars have studied climate extremes. However, the research on the relationship between temperature extremes and CE is not extensive, which deserves attention. (2) Methods: The study adopted a fixed-effect model to analyze the impact of temperature extremes on CE from crop production, and the moderating effect was tested using total factor productivity (TFP) in agriculture. (3) Results: Temperature extremes in Hebei Province were mainly reflected in a decline in the cold day index (TX10p) and a rise in the warm spell duration index (WSDI) and the number of summer days (SU25). Additionally, TX10p was positively correlated with CE. For every 1% reduction in TX10p, CE dropped by 0.237%. There was no significant correlation between WSDI and CE. Finally, the agricultural TFP had a significant moderating effect on CE, with each 1% increase resulting in a corresponding 0.081% decrease in CE. (4) Conclusions: The results indicated a warming trend in Hebei Province, which resulted in a decrease in the number of winter days, and reduced CE from crop production. The improvement of input efficiency in agricultural production factors helped moderate the CE.

Assessing the impact of digital financial inclusion on agricultural total factor productivity in China

Based on panel data from the National rural fixed point survey from 2011 to 2018 and the Peking University digital financial inclusion (DFI) index data, this article uses the dynamic panel fixed effect model to analyze the effect of access to digital inclusive financing platforms on agricultural total factor productivity (TFP) and its contributing factors at the household level. The results show that DFI have a significant hysteresis positive impact on agricultural TFP and its two components, agricultural technical progress and agricultural technical efficiency change. And the usage depth of financial services has the greatest effect in three dimensions of DFI index.

National water-saving city and its impact on agricultural total factor productivity: A case study of nine provinces along the Yellow River, China

As an important water-saving regulation, the construction of national water-saving city (NWSC) plays an important role in agricultural production, however, there is little research on its impact on agriculture. This paper uses super efficient Slacks-Based Measure (SBM) to measure agricultural total factor productivity (ATFP) in the regions along the Yellow River, and uses difference-in-difference (DID) model to explore the impact of NWSC on ATFP based on the theory of production factor flow. The results show that: First, the ATFP in the regions along the Yellow River demonstrated an initial increase followed by a decrease from 2000 to 2019, the ATFP in the regions along the Yellow River is low in the north and high in the south, and the ATFP of the main grain producing areas is generally higher. Second, the establishment of the NWSC can effectively improve the ATFP, the ATFP has increased by 6.57% compared with the control group, and the dynamic effect of the policy has shown an “N" pattern since its implementation. Particularly, in areas with relatively poor water resources, high agricultural factor intensification and major grain production areas, NWSC has a stronger impact on the ATFP. Third, NWSC has promoted the flow of labor production factors and improved ATFP through income effect, industrial synergy effect and technological innovation effect. The transfer of agricultural labor has also injected strong impetus into industrial development, and improved the return on industrial capital, which can promote agricultural capital more significantly, and further improved ATFP. Our findings provide empirical evidence for achieving high-quality agricultural development through the construction of NWSC.

Impact of Climate Change on Total Factor Productivity of Agriculture in District Multan

Agriculture is particularly vulnerable to climate change because it faces open weather conditions. In uncontrolled open weather circumstances, it's difficult to attain better total factor productivity (TFP), which characterizes agriculture growth in an economy. The study estimated the TFP growth of Pakistan’s agriculture in first step by employing the Tornqvist- Theil index number approach for the period 1990-2019. The time series data for thirteen crops and four livestock categories from 1990 to 2019 were collected from different sources to estimate the TFP. The average annual TFP growth of agriculture was estimated to be 2.14 percent for the study period, and it contributed about 56 percent to total agricultural output growth. The results also indicated that TFP growth in agriculture sector was highest (0.05 percent) during the 1990-2000, while lowest (-2.14 percent) during the last decade. After, TFP index that was calculated in first step was used as dependent variable in second step of analysis. Then we estimated the impact of climate change on TFP in agriculture sector in Multan district. For this first we used ARDL bound test approach for estimation of long run impact of climatic variables. The results showed that there are positive effects of both minimum and maximum temperature on TFP in the study area. The drought and floods have negative impact on TFP but in first lag their effects became positive on TFP. One of the important variables of climate is rain that had positive impact on the TFP growth, with 1% increase in rainfall there was 0.22% increase in TFP at first lag. Improving the adaptation and mitigation practices related to floods and droughts would be required for sustainable growth in TFP in agriculture sector in Multan District.

Research on Spatial-Temporal Characteristics and Affecting Factors of Agricultural Green Total Factor Productivity in Jiangxi Province

Increasing green total factor productivity is the key to achieving green development in agriculture. This study measured the green total factor productivity of Jiangxi’s agriculture, and its regional and temporal evolution characteristics were examined. The fixed-effects model was then used to investigate the model’s fundamental components empirically. The study’s findings reveal the following: During the period under review, technical change was the primary element driving the rise in the green total factor productivity of agriculture. A rising “U”-shaped trend with notable regional variances characterizes the spatial and temporal evolution. The primary factors that affect changes in green total factor productivity in agriculture include the ease of transportation, the per capita disposable income of rural residents, the level of agricultural mechanization, the degree of urbanization, the level of financial support for agriculture, and the percentage of workers in secondary industries, with the impact effect values of 0.581, 0.647, −0.126, −0.729, −0.326, and −0.559, respectively. As a result, it is suggested that in order to substantially increase agricultural green total factor productivity, agricultural green technological support should be strengthened, more fully developed, and promoted in a multi-pronged approach.

Does rural industrial integration improve agricultural productivity? Implications for sustainable food production

Although the literature demonstrates that rural industrial integration can enhance farmers' income, foster rural development, its impact on agricultural total factor productivity (ATFP), a critical aspect of sustainable food systems remains unclear. Using provincial-level data from 2008 to 2018, this paper constructs a composite index of rural industrial integration and examines its effect, heterogeneity, and spatial spillover on ATFP growth in China. The findings indicate that the levels of rural industry integration and ATFP experienced a gradual increase from 2008 to 2018. Rural industry integration promotes ATFP growth through technical progress and improved technical efficiency. An analysis of regional heterogeneity reveals that rural industry integration has the most significant impact on ATFP promotion in the western region, followed by the central region, with the least impact in the eastern region of China. Unconditional quantile regression suggests that rural industrial integration has a more considerable impact on ATFP in regions with higher ATFP. Furthermore, the spatial Durbin model results demonstrate that rural industry integration directly supports rural industry integration development in a region while simultaneously inhibiting ATFP growth in surrounding areas. Finally, the findings also reveal that enhancing rural industrial integration can have positive impacts on sustainable agricultural production in China. These findings offer valuable insights for other developing countries aiming to promote sustainable consumption and production.

POTENTIAL IMPACT OF ARTIFICIAL INTELLIGENCE APPLICATIONS ON AGRICULTURAL PRODUCTIVITY

Development of Artificial Intelligence (AI) methods and their applications are becoming important drivers of innovations which significantly affect all areas of economic activities including agriculture. The aim of the paper is to examine how AI solutions applied in agriculture can influence not only production practices, but the sector Total Factor Productivity (TFP). First, types of AI systems and areas of their use in agriculture and related activities are presented. Second, an attempt is made to indicate effects of such technological changes for agricultural TFP worldwide

The Impact of Green Finance on China’s Agricultural Trade

Enhancing the effectiveness of green development of the agricultural trade economy with green finance is a practical need to promote the healthy development of agricultural trade. This manuscript empirically analyzes the impact of green finance on China’s agricultural products import and export trade by using provincial-level panel data from 30 Chinese provinces from 2001–2019. The findings show that: (1) Green finance positively impacts China’s agricultural import and export trade at the 1% significant level, expanding the scale of agricultural imports and exports. (2) The positive impact of green finance on China’s agricultural import and export trade is heterogeneous across regions. Accordingly, this paper puts forward policy suggestions such as strengthening the support of green finance to agriculture, focusing on the improvement of green total factor productivity in agriculture, and promoting synergistic regional development through the implementation of differentiation of green finance.

Digital inclusive finance and total factor productivity in agriculture—— Evidence from China

Under the strategy of rural revitalization, the development of digital inclusive finance is an effective way to alleviate the long-standing problem of "difficult and expensive financing" in the "three rural areas", and is an inherent requirement for achieving high-quality development of Chinese agriculture. Based on the panel data of Chinese provinces from 2011 to 2021, this paper adopts a three-stage SBM-DEA model to measure the total factor productivity of agriculture and analyzes the impact of digital financial inclusion on total factor productivity of agriculture. The study shows that, firstly, the development of digital inclusive finance plays a more significant role in enhancing total factor productivity in agriculture, and the depth of use plays the strongest contributing role among the sub-indicators. Second, there is heterogeneity in the effects of digital inclusive finance on agricultural total factor productivity in terms of time and geographical location. Third, the mechanism analysis shows that deepening human capital and regional innovation capacity can effectively drive the growth of agricultural total factor productivity. The research in this paper contributes to a deeper understanding of how agricultural total factor productivity is measured, and the theoretical mechanisms by which digital inclusive finance drives agricultural total factor productivity.

Growth in Agricultural Productivity

This study examines agricultural total factor productivity (TFP) from theoretical and empirical perspectives. Specifically, the measures, relevant data, and major sources of the TFP growth are discussed. Using the sector-level growth and productivity data from the EU KLEMS, EUROSTAT, FAOSTAT, and USDA databases, the TFP growth in the EU countries over 1996–2019 is considered. The sources of the TFP growth are analyzed. The results suggest that agricultural TFP increased in almost all EU countries over the period covered. TFP growth appears as an important component of labour productivity and value-added growth in the EU agriculture. The differences among the databases considered are noted in the sense of input and output levels and TFP growth rates.