Total factor productivity change in dairy farming: Empirical evidence from southern Chile

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.

Impact of Environmental Regulation on the Green Total Factor Productivity of Dairy Farming: Evidence from China

Environmental regulation is essential to promote green and sustainable development in dairy farming. Nevertheless, limited studies have focused on the impact of environmental regulation on the green total factor productivity (GTFP) of dairy farming. This study measures the GTFP of dairy farming in 27 provinces in China during 2009–2020 using the Slack Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index. In addition, random effects and threshold regression models are used to measure the impact of environmental regulations on the GTFP of dairy farming. The results demonstrate the fluctuating growth of the GTFP of dairy farming and that technical efficiency is the primary driver of the GTFP growth. The annual growth rate of GTFP is the highest in large-scale dairy farming (3.27%), followed by medium-scale dairy farming (2.73%) and small-scale dairy farming (1.98%). Furthermore, environmental regulation positively affects the GTFP and has a threshold effect on the GTFP, with the urban–rural income gap as the threshold variable in medium-scale dairy farming and small-scale dairy farming. The impact on the GTFP can be significantly negative if the urban–rural income gap crosses the threshold value. Overall, this study provides some policy recommendations for attaining green and sustainable dairy farming development in China.

Dynamic sustainable productivity growth of Dutch dairy farming

The economic, environmental and social sustainability of Dutch dairy farms have attracted increasing societal concern in the past decades. In this paper, we propose a recently developed dynamic Luenberger indicator based on the by-production model to measure dynamic productivity growth in the economic, environmental and social dimensions of sustainability of Dutch dairy farms. Subsequently, we investigate the statistical associations between productivity growth and socio-economic factors using the OLS bootstrap regression model. We find that dairy farms have suffered a decline in dynamic sustainable productivity growth, especially in the environmental dimension where it is more pronounced than in the economic and social dimensions. Furthermore, we find that both technical and scale inefficiency change contribute to the decline of environmental productivity growth. Specialization and government support are associated with a higher economic and environmental sustainability productivity growth, and with, a decreased growth of social sustainable productivity. We found no significant association between the age of the oldest entrepreneur, financial structure, farm size or cost of advisory service and dynamic productivity growth in the three sustainability dimensions. The results provide insights into potential pathways towards improving the three pillars of sustainability.

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

Agricultural mechanization is an important factor to improve the green total factor productivity of the planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of the output-oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sinks, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The main findings of this paper are as follows: (1) Agricultural mechanization can promote the planting green total factor productivity significantly, and this basic conclusion is still robust after using instrumental variables and sub sample regression. (2) The path of agricultural mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover, while the mechanisms of operation scale expansion, factor allocation optimization and technical efficiency change are not significant. (3) With the improvement in the mechanization level, the promotion effect of mechanization on planting GTFP will become clearer. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications.

Productivity growth of Indonesian rice production: sources and efforts to improve performance

Purpose

The purpose of this paper is to investigate the productivity of rice production by decomposing the growth of total factor productivity (TFP) into four components: technological change, scale effects, technical and allocative efficiencies.

Design/methodology/approach

This study employed an econometric approach to decompose TFP growth into four components: technological change, technical efficiency, allocative efficiency and scale effect. Unbalanced panel data used in this study were surveyed in 1994, 2004 and 2014 from 360 rice farming operations. The model used the stochastic frontier transcendental logarithm production technology to estimate the technology parameters.

Findings

The results indicate that the primary sources of TFP growth were technological change and allocative efficiency effects. The contribution of technical efficiency was low because it grew sluggishly.

Research limitations/implications

This study has several shortcomings, such as very low R2 and the insignificant elasticity of labour presented in the findings. Another limitation is the limited time period panel covering long interval, which resulted in unbalanced data.

Practical implications

The government should improve productivity growth by allocating more areas for rice production, which enhances the scale and efficiency effects and adjusting the use of capital and material inputs. Extension services should be strengthened to provide farmers with training on improved agronomic technologies. This action will enhance technical efficiency performance and lead to technological progress.

Social implications

As Indonesian population is still growing at a significant rate and the fact that rice is the primary staple food for Indonesian people, the productivity of rice production should increase continually to ensure social security at a national level.

Originality/value

The productivity growth is decomposed into four components using the transcendental logarithm production technology based on farm-level data. The measure has not been conducted previously in Indonesia, even in rice-producing countries.