The impact of climate change and production technology heterogeneity on China's agricultural total factor productivity and production efficiency

We use multiple measures of agricultural total factor productivity (TFP) change to examine the relationship between agricultural productivity and poverty in developing countries. We employ a stochastic frontier analysis to estimate agricultural TFP changes for 113 countries using output distance function in a multi input multi output framework. We then make alternative groupings of countries to allow for the possibility of different production frontiers for countries with different income level, and we examine the effect of these various measurements of agricultural TFP on poverty reduction. Results from the TFP analysis show that TFP change estimates by income groups differ from those estimated using all countries in a pooled model. This indicates that agricultural technology and production frontiers may differ across countries based on income levels. Preliminary results show that TFP change from the pooled model has significant impact on poverty reduction. However, TFP estimates from different income groups didn’t indicate significant impact on poverty. The relationship between TFP change and poverty is therefore sensitive to the method used to estimate agricultural productivity.

PurposeThe purpose of this paper is to analyze agricultural total factor productivity (TFP) and input redundancies in different regions of China, and to bring out the policy implications for improving efficiency in agricultural production as well as environment protection.Design/methodology/approachBased on the provincial panel data during 1995-2014, the agricultural productivity of China and its regional disparity are analyzed. First, the agricultural TFP and its decomposition are dynamically evaluated by means of data envelopment analysis-Malmquist productivity index. Second, the agricultural radial production efficiency in year 2014 and the input redundancy changes from 1995 to 2014 are measured based on the BCC-slacks-based measure model.FindingsThe results showed that the overall agricultural TFP of China grew 4.3 percent annually during 1995-2014, mainly as a result of technical progress. However, the declines of technical efficiency and scale efficiency slowed down the agricultural TFP growth. The TFP growth in the Western region and Central region far exceeded the Eastern region in last few years. In 2014, most effective decision-making units were in the Western region. The input redundancies in the agricultural production increased substantially after 2006, especially for the pesticide use amount, reservoir capacity and agricultural machinery power.Originality/valueCombining the dynamic and static analyses, the paper fulfilled the study of China’s agricultural productivity and the input redundancies in recent years, and also presented the regional disparities.

The scientific and reasonable measurement of agricultural green total factor productivity is helpful to grasp the direction of rural-factor-market reform. This study constructs a Malmquist productivity index based on a non-radial and non-angular SBM directional distance function. This study calculates the agricultural green total factor productivity of 28 provinces (cities and autonomous regions) in China from 1997 to 2020 by considering unexpected outputs such as carbon emissions and agricultural non-point-source pollution. Finally, this study uses the spatial Dobbin model to explore the spatial impact of agricultural green total factor productivity under the distortion of the factor market. The results show that the agricultural green total factor productivity, considering the unexpected outputs, is more in line with the level of high-quality green development in China’s agriculture. Regardless of whether the unexpected output is included, the increase in China’s agricultural total factor productivity is primarily due to progress in agricultural technology, and the double boost is little in agricultural technology progress and technical efficiency. Agricultural green total factor productivity shows an increasing trend, but the growth rate is slow, and differences in different regions are significant. Factor market distortion negatively impacts agricultural green total factor productivity, and other factors improve the agricultural total green factor productivity. However, factor market distortion has a particular spatial spillover effect, which hinders the synchronous growth of agricultural green total factor productivity in different regions. Therefore, the government should promote the reform of the agricultural mode of production and agricultural green production, eliminate the blocking effect of factor market distortion on the improvement in agricultural green total factor productivity, narrow the regional gap of agricultural total factor productivity, and establish a policy system for high-quality green development of modern agriculture.

Productivity growth, technological progress, and efficiency change in chinese agriculture after rural economic reforms: A DEA approach

Abstract. PROBLEM STATEMENT Total factor productivity (TFP) measures aggregate output produced per unit of aggregate input used. Thus TFP accounts for effects in total output not caused by the traditionally measured input such as labor and capital investments. When TFP grows, more output is obtained per unit of input. This brings benefits to both producers and consumers. Falling TFP, on the other hand, promises less reward for effort committed and is a harbinger of worsening economic welfare. As such, TFP is often considered as the real driver of growth within an economy, and can be taken as a measure of an economy’s long-term technological dynamism. TFP change has proven to be useful to policy makers in regulating agricultural productivity for economy growth. On average, agricultural TFP in the U.S. has grown at a rate of somewhere between 1 and 2% per year for the last half century. That growth, attributed primarily to technological advances, has led to an amazing expansion of the productive ability of the U.S. agricultural sector. This average, however, masks a remarkable instability in the growth of U.S. agricultural TFP. The causes for such instability may include technological changes, efficiency improvements introduced by producers, increased knowledge, and random shocks or fluctuations resulted from forces beyond the control of individual producers. It is not currently possible to decompose this variability into contributions from actual improvements in technical know-how and agriculture’s inherent vulnerability to climate. Not surprisingly, numerous studies have examined the effect of climate change on the U.S. agriculture. They have taken two general approaches using detailed data on weather patterns. One focuses on determining the impact of climate on a common partial productivity measure, crop yields (Schlenker and Roberts 2009; Lobell et al. 2011). Another focuses on the effect of climate on economic returns to farmers in the form of either land values or measured profitability (Mendohlson et al., 1994; Quiggin and Horowitz 1999; Deschenes and Greenstone 2007; Fisher et al. 2012). None has investigated how climate affects the overall U.S. agricultural productivity.

The most important aggregate measure of the long run health of the productive component of the agricultural economy is agricultural total factor productivity (TFP). Between 1948 and 2011, average annual input growth in US agriculture averaged approximately 0.07% while annual average output growth averaged roughly 1.5%. That translates into an annual average agricultural TFP growth rate of approximately 1.43%. That growth has led to a remarkable expansion of the productive ability of the US agricultural sector. However, climate change poses unprecedented challenges to U.S. agricultural production because of the sensitivity of agricultural productivity and costs to changing climate conditions. Some studies have examined the effect of climate change on U.S. agriculture. But none has investigated how climate affects the overall U.S. agricultural productivity. This study intends to find out climate change impacts on U.S. agricultural TFP change (TFPC). By correlation analysis with data in 1979-2005, we found that precipitation and temperature had significant positive or negative correlations with U.S. agricultural TFPC. Those correlation coefficients ranged from -0.8 to 0.8. And significant correlations, whether positive or negative, existed in different regions and different seasons. This is important information for policy-makers in decisions to support U.S. agriculture sustainability.

The growth of agricultural total factor productivity (TFP) is seen as a driving force for the sustainable development of agriculture. Meanwhile, the promotion of urbanization in China has exerted a profound impact on agricultural production. This paper calculates the agricultural TFP and analyzes the effect of urbanization. Firstly, the DEA-Malmquist method is used to calculate the dynamic change in agricultural TFP in China from 2004 to 2016. Secondly, the spatial spillover effect of urbanization on agricultural TFP is investigated by the spatial Durbin model. We found that: the average annual growth rate of agricultural TFP in China is 4.8% from 2004 to 2016; and the spillover effect of urbanization on agricultural TFP shows a U-shaped relationship, which means that urbanization has exerted a negative effect first and then a positive effect on agricultural TFP. Finally, the paper puts forward policy suggestions from the perspective of sustainable coordination of urbanization and agricultural production.

The present study aims at improving our understanding of the individual contribution of the components of total factor productivity (TFP) change to U.S. agricultural productivity. A novel sequential primal‐dual estimation routine to calculate TFP change is proposed, using a multi‐output input distance function in the first stage, followed by a cost minimization routine in the second stage. TFP change is estimated as the direct sum of the estimates of technical change, technical efficiency change, allocative efficiency change, input price effects, changes in output markup, and changes in returns to scale in each state. The validity of the proposed methodology is supported by the remarkable overlap and high correlation of our annual estimates of TFP change with the USDA's measures of change in TFP by state. Although technical change tends to be the largest contributor to productivity change, it bears a low and statistically insignificant correlation with TFP change on an annual basis, whereas annual changes in the markup effect and returns to scale are highly and significantly correlated with TFP changes. This is the first study to find a slowdown of technical progress in the U.S. farm sector in the 1990s and 2000s, and technical regress during the farm crisis of the 1980s. While technical efficiency shows a positive overall trend, allocative efficiency shows a negative overall trend, and their combined effect (i.e., the overall cost efficiency) slows down TFP growth. The policy recommendations from previous studies on the drivers of TFP should be revised in light of these findings.

This study illustrates and quantifies how overlooking the impact of weather shocks can affect the measurement and decomposition of agricultural total factor productivity (TFP) change. The underlying technology is represented by a flexible input distance function with quasi‐fixed inputs estimated with Bayesian methods. Using agricultural production and weather data for 16 states in the Pacific Region, Central Region, and Southern Plains of the United States, we estimate TFP change as the direct sum of multiple components, including a net weather effect. To assess the role of weather, we conduct a comparative analysis based on two distinct sets of input and output variables. A traditional set of variables that ignore weather variations, and a new set of “weather‐filtered” variables that represent input and output levels that would have been chosen under average weather conditions. From this comparative analysis, we derive biases in the decomposition of TFP growth from the omission of weather shocks. We find that weather shocks accelerated productivity growth in 12 out of 16 states by the equivalent of 11.4% of their group‐average TFP growth, but slowed down productivity by the equivalent of 6.5% of the group‐average TFP growth in the other four states (located in the Northern‐most part of the country). We also find substantial biases in the estimated contribution of technical change, scale effects, technical efficiency change, and output allocation effects to TFP growth (varying in magnitude and direction across regions) when weather effects are excluded from the model. This is the first study to present estimates of those biases based on a counterfactual analysis. One major implication from our study is that the official USDA's measures of TFP change would appear to overestimate the rate of productivity growth in U.S. agriculture stemming from technical change, market forces, agricultural policies, and other nonweather drivers.

Purpose: This paper aimed at evaluating changes in agricultural productivity in the group of new (NMS) and old (OMS) member states of the European Union. Design/Methodology/Approach: The analysis covered the years 2007-2016. The calculations made use of unit data from farms participating in FADN (Farm Accountancy Data Network). Surveys were carried out based on the Malmquist productivity index and partial indicators of land, workforce and capital productivity. Findings: The outcome pointed to increased total productivity of agriculture in NMS (9.5%), resulting from positive technological changes and improvement in technical efficiency. A small decrease in productivity was noted in a group of EU-15 countries, which was due to a decrease in technical efficiency. Despite the growth in total productivity, the value of partial productivity indicators in NMS remained at a much lower level than in OMS. Practical Implications: Identification of the determinants of growth in agricultural productivity is the precondition to make up differences occurring between member states in this respect. Originality/Value: This study contributes to reference literature concerning productivity of agriculture for a number of reasons. First, the scope of the survey is extensive, as it covers a group of 27 EU member states split into new and old members. The second aspect of the survey is taking into account both changes in total factor productivity and in productivity of respective production factors. Thirdly, the Malmquist index adopted for the needs of the survey made it possible to identify the sources of change in total factor productivity.

This study applies a Data Envelopment Analysis (DEA) approach to analyze total factor productivity, technology, and efficiency changes in Chinese agricultural production from 1984 to 1993. Twenty-nine provinces in China were classified into advanced-technology and low-technology categories. The Malmquist productivity measures were decomposed into two components: technical change index and efficiency change index. The results showed that total factor productivity has risen in most provinces for both technology categories. Technical progress has been the most important factor to Chinese agricultural productivity growth since 1984 and will remain crucial to productivity growth in low-technology provinces. Low efficiency in many important agricultural provinces indicates a great potential for China to increase productivity through improving technical efficiency. Continuously expanding market economy and enhancing rural education may also help farmers to improve technical efficiency and productivity in agricultural production.

In an attempt to support the push for second generation biofuels in the United States, this research investigates the role that soil organic matter plays in explaining changes in technical efficiency and agricultural productivity across counties in Nebraska. We estimate optimum biomass harvest potentials for forty seven counties in Nebraska. These estimates reveal the percentage of biomass that can be harvested that would not negatively affect current levels of agricultural production. We also give an account of the status of inter-county changes in agricultural productivity in Nebraska. We use an output measure of technical efficiency from non-parametric data envelopment analysis to estimate technical efficiency measures. Total factor productivity change was estimated using an output-based Malmquist index approach. Biomass harvest potentials were obtained by shrinking/contracting only soil organic matter in our linear programming constraints. Results show that SOM does contribute to explaining changes in technical efficiency and total factor productivity across counties in Nebraska. Also, an average measure of TFP growth of 3.7% was obtained for the 41 years period, 99% of which was accounted for by technological change while the contribution of efficiency change was very minimal. 55% of counties in Nebraska have zero harvest potentials while only 45% of counties have excess biomass potentials for harvest. The highest average potential of 35% was reported for Lincoln, Cass, Gosper and Colfax counties.

In the current era of promoting rural revitalization, it is very important to improve the efficiency of agricultural production and then improve the level of agricultural modernization, which will help achieve poverty alleviation and build a beautiful countryside. Focusing on the theme of the improvement of agricultural total factor productivity in the Central Plains region, this paper uses the agricultural production efficiency theory and the nonparametric index method of DEAP-Malmquist to construct a measurement model of agricultural production efficiency in the Central Plains region. Data, the agricultural total factor production efficiency (TFP) and its decomposition values, such as: pure technical efficiency index and scale efficiency change index, carry out horizontal and vertical comparative analysis, to explore the differences in agricultural productivity in various regions and their sources of growth. Then use the panel data regression model to analyze many influencing factors of agricultural TFP, such as: economic development level, labor quality and so on. Finally, according to the empirical results, some policy references are put forward to ensure the safety of grain production in the Central Plains and improve the level of agricultural modernization.

Agricultural production can be observed to reach 3-4 percent annual growth rates with the exception of drought like shocks in Turkey. In recent years, significant level of investments, qualified input use and more irrigated land led to productivity improvements. However, there are not enough studies on the change in total factor productivity (TFP) or on regional dynamics of the change in TFP in the agricultural sector. Using Turkish statistical classification of territorial units (NUTS-2 classification of the EU), detailed data on economic and social indicators were collected so as to examine the change in regional TFP figures to point out the results and policy priorities. In this context, agricultural output change is determined not only by the labor, capital and other agricultural production inputs but also by the factors that are effective on the change in total factor productivity, that are dealt with in this study. At this point, macroeconomic and institutional factors play an important role in the change in total factor productivity; inflation, exchange rate, economic volatility, share of high-tech exports, rural development supports, human capital. Since data were not available for the structure of the soil at regional level it could not be considered as a factor in estimations. As a result in this study, especially technological progress, human capital and rural development support have significant impacts on the change in total factor productivity whereas changes in the exchange rate have negative effects. An important result here is that the impact of agricultural supports other than rural development supports could not be determined statistically as a cause changing factor productivity.

Purpose – China and India have made significant strides in transforming their agricultural sectors to cut hunger and poverty for the masses through improved agricultural productivity. Given limited land and shift of labor to non-agricultural sector, increasing productivity will continue to be central in agricultural growth in the twenty-first century. The purpose of this paper is to provide comparative analysis of the agricultural total factor productivity (TFP) growth in the two countries. It complements existing literature by examining the evolution and drivers of TFP at disaggregated sub-national level. Richer data allows a deeper understanding of the nature and drivers of TFP growth in the two countries. Design/methodology/approach – This paper applies different analytical framework to address different research questions using data since 1980. China study estimates a parametric output-based distance function using a translog stochastic frontier function. Productivity growth index and its multiple components are calculated using parameters derived from the parametric approach to identify the characteristics of technology such as structural bias. India study first applies data envelopment analysis to estimate the aggregate productivity growth index, technical change (TC), and efficiency change. Next productivity indexes by for traditional crops are estimated using growth accounting framework at state level. Finally, a panel regression links TFP on its determinants. Findings – Several common themes emerge from this comparative study. Faced with similar challenges of limited resources and growing demand, improving productivity is the only way to meet long-term food security. Agriculture sector has performed impressively with annual TFP growth beyond 2 percent in China and between 1 and 2 percent in India since the 1980s. The TFP growth is mainly propelled by technological advance but efficiency had been stagnant or even deteriorated. This study provides a granular picture of within country heterogeneity: fast growth in the North and Northeast part of China, South and East of India. Research limitations/implications – The study suggests some possible policy interventions to improve agricultural productivity, including investment in agricultural R & D to create advanced production technology, effective extension programs and supportive policies to increase efficiency, and diversification from staple crops for sector-wide growth. The India study suggests certain policies may not be contributing much to productivity growth in the long run due to a negative impact on environment. Further studies are needed to expand the productivity analysis to take into consideration of the negative externalities to the society. Data enhancement to account for quality-adjusted inputs could improve the estimation of productivity growth. Originality/value – Each country study reveals certain prospects of the agricultural sector and production technology. China analysis statistically confirms the existence of technical inefficiency and technology progress, suggests the translog form is appropriate to capture the production technology and satisfies conditions stipulated in theoretical models. The results indicate TC does not influence the contribution of output or input to the production process. India study pinpoints the lagging productivity growth of traditional crops, which still derives growth from input expansion. Although different states benefited from different crops, sector-wide productivity gain is primarily the result of diversification to high-value crops and livestock products.