The impact of swine diseases on total factor productivity of pig farms of different scales in China.

This paper reports a comprehensive analysis on farm production and resources sustainability in Northwest China during 1978 to 1999. Besides the five provincial regions (Shaanxi, Gansu, Ningxia, Qinghai, and Xinjiang), the upper and middle parts of the Yellow River Basin were also included as a nested area for detailed evaluation. The gross value of farm production was chosen to be the dependent variable in data analysis. Independent variables include farm inputs of irrigation ratio, agricultural labor, fertilizer application, and farm machinery use. In the study period, farm production increased four- to five-fold in the regions, strongly associated with the increased application of technological inputs of fertilizer and machinery. Multivariate regressions were performed based on Cobb-Douglas and Frontier production functions between farm production and inputs. Based on the function parameters, the growth of farm production was decomposed into the contributions by farm inputs and total factor productivity. 10-20% of the growth in farm production was attributed to the increased irrigation ratio in Shaanxi, Gansu, and Ningxia, where the irrigation ratio was relatively lower than other regions and increased significantly during the study period. Productivity in irrigated farmland was found to be over 10 times higher than that of rainfed areas in the regions. For the provincial regions, technological input of fertilizer and machinery contributed to more than half of the growth in farm production in the period of 1978-1998. For the upper and middle parts of the Yellow River Basin, total factor productivity accounted for two-thirds of the farm output growth after 1985. In late 1990s, the regress of total factor productivity caused a significant reduction in farm production due to the drop in grain price and basin-wide drought. Total factor productivity in the basin was decomposed as two components of technological progress and relative technological efficiency. The relative technological efficiency was below 70% with a decreasing trend during 1980 to 1999. To integrate hydrological and economic processes in this study, a spatial model was developed to predict water supply and demand. Some preliminary results of this model were also presented.

Farm Production Growth in the Upper and Middle Parts of the Yellow River Basin, China, During 1980–1999

Rice is still a staple food for the people of Indonesia. If Indonesia relies on imported rice, it will be very politically vulnerable if there is a shortage of rice supply in the international market. Therefore, the productivity of rice farming should be kept rising in line with the rate of population increase. This paper analyzes the growth of total factor productivity of paddy farming efforts. Total productivity is decomposed into four parts: the advancement of technology, technical efficiency, allocative efficiency and the effect of business scale. If each component of productivity growth is known, it will be determined strategies to increase rice production. Data analysis using secondary data published by the Indonsian Statistics Agency (BPS). Analyses were performed using an econometric approach. The results show that growth in total factor productivity declined with the slowdown. Positive contributor to the growth of total factor productivity is the change in the technical and business scale effects; whereas negative contributor is the technical and allocative efficiency. Growth in rice production is because of growth in the use of inputs and other factors such as the expansion and increase in cropping index. The growth in total factor productivity can be increased by improving technical and allocative efficiencies.

The scale of China’s textile industry has grown to be the largest in the world with massive factor input. There is a strong demand China’s textile industry, and as a traditional industry, should improve total factor productivity (TFP) to realize technology-driven and sustainable development. TFP is a commonly used indicator to measure the level of technological progress. But regional textile industry development in China is seriously unbalanced and regional TFP is quite different from each other. It is worthwhile to estimate the textile industry TFP of China and different regions, analyse the changing trend and test for their convergences. This paper firstly uses the nonparametric DEA-Malmquist index method to measure and analyse the TFP and its evolution of China’s textile industry during 2007–2018 at the nation, region and province levels. Then it uses the coefficient of variation to test for σ-convergence of China’s and regional textile industry TFP. It also constructs an absolute β-convergence regression equation and panel data model, respectively to test for absolute β-convergence and conditional β-convergence and determine whether the TFP of each region will also converge to its own steady-state or not. The research results help explore the future development model of China’s textile industry and provide corresponding policy suggestions for the upgrading and sustainable development of the industry.

Continuous cotton ( Gossypium hirsutum L.) production was examined using data from Alabama's long‐term Old Rotation experiment (c. 1896). Index values were used to examine trends in productivity and sustainability for 95 yr. Treatments studied were those receiving (i) no N fertilizers and no winter legumes for 95 yr, (ii) only winter legumes as a source of N, and (iii) chemical fertilizer N. Three sets of index numbers were calculated from all inputs and outputs involved in the production systems: (i) total factor productivity (TFP), which accounts for all direct production inputs, but which does not consider production externalities; (ii) productivity relative to a base plot;and (iii) total social factor productivity (TSFP), which accounts for all direct production inputs as well as externalities of soil erosion and pesticide use. Viewed from the 95‐yr perspective of the Old Rotation experiment, all three treatments fulfill at least one criterion required for a system to be considered sustainable. Output per unit of input is higher in 1991 than in 1896, even when externalities are valued. None of the systems showed a linear trend in output or TFP over the life of the experiment;productivity cycles are present in all three systems, despite a positive overall trend. An average annual rate of TSFP growth of 1.8%/yr was attained. Accounting for erosion and pesticide externalities reduced the annual productivity growth rate by 0.2%/yr. The system that has neither an organic nor a chemical source of added N was less productive and less sustainable than the two other systems, with a 0.3%/yr TSFP growth rate. The plots using organic and chemical sources of N had similar productivity impacts. Valuing soil erosion and pesticide externalities had only a modest effect on measured productivity. The most dramatic single event to affect the productivity of cotton farming was the introduction of the mechanical cotton picker. The impact of this technology was powerful enough to offset the effect of many other changes in the system. Research Question Is cotton production in the southeastern USA sustainable? How do we measure sustainability of a crop that has been produced for almost 200 yr in the same region but has a reputation for depleting the soil of nutrients, extensive soil erosion, and high pesticide use? The objective of this study was to use input and output indexes and a calculation of total factor productivity (TFP) to determine if cotton production using different management strategies is sustainable over nearly a century of continuous production. Literature Summary Most researchers agree that a sustainable system should maintain or enhance agricultural production, reduce the level of production risk for the farmer, protect natural resources, be economically viable, and be socially acceptable. Measuring all of these attributes of a production system is very difficult. However, using the extensive data available from historical, long‐term experiments should provide insight as to sustainability of certain production systems. Alabama's Old Rotation (c. 1896) is the oldest continuous cotton experiment in the world. Input and output (yield) records and estimates allow calculation of TFP indexes over the 95‐yr history of continuous cotton production. Different cotton production systems can be compared. Study Description Three continuous cotton systems from the Old Rotation were chosen for comparison: (i) No N and no winter legumes since 1896 (No N), (ii) winter legumes (crimson clover and/or vetch) as the only source of N since 1896 (winter legumes), and (iii) no winter cover crop and 120 lb N/acre as ammonium nitrate since 1956 (N fertilizer). Where input records were not recorded (e.g., labor, costs, machinery, etc.), they were estimated from USDA, Alabama Agricultural Experiment Station, and Alabama Cooperative Extension Service publications. Soil erosion estimates for the three cropping systems on a Pacolet fine sandy loam, were made using Erosion Productivity Index Calculator modeling. Input, output, TFP, and total social factor productivity (TSFP) indexes for 95 yr were calculated. Total social factor productivity includes estimated values for the negative offsite effects of soil erosion and pesticide use. Applied Questions Is continuous cotton production sustainable? Viewed from the 95‐yr perspective of the Old Rotation, the no N, winter legume, and N‐fertilized continuous cotton plots all fulfill at least one criterion required for a system to be sustainable. Output per unit of input is higher in 1991 than in 1896, even when externalities (erosion and pesticides) are valued. The average growth rates on the No N plot are 0.5%/yr for TFP and 0.3%/yr for TSFP. On the winter legume plot, TFP and TSFP grew at a rate of 2.0%/yr and 1.8%/yr, respectively. The plots using organic and chemical sources of N had similar productivity records. None of the systems shows a linear trend in TFP over the history of the experiment. Productivity cycles are present in all three systems, despite the positive overall trend. An important focus of future research will be to explain whether these cycles are related to weather, technology, or changes in the resource base. As one would expect, the system that has neither an organic or a chemical source of added N is less productive than the two other systems. This system compares even more poorly when externality costs are assigned. Organic and chemical sources of N have similar productivity impacts. How have externalities such as soil erosion and the negative impact of pesticide use on the environment affected TFP? Soil erosion and pesticide externalities have had only a modest effect on measured productivity. The no N plot indexes are not changed at all; TFP on the legume and N‐fertilized plots decreased by 4 and 6%, respectively. The main conclusions of the previous question are therefore unaffected. How have technological advancements affected long‐term productivity/sustainability of continuous cotton production? The most dramatic single event to affect productivity was the introduction of the mechanical cotton picker around 1960. The impact of this technology is powerful enough to offset the effect of many other changes in the system. This advancement allowed cotton production to move from a labor‐intensive environment with increasing labor costs per pound of yield to an environment where harvesting costs were not seriously affected by increasing yields. Because technological advancements cannot be predicted into the future, predicting the long‐term sustainability of a system becomes very difficult.

According to the economics literature, there are two main sources of economic growth. While the first of the resources is the accumulation of production factors, the other is the part of the output that cannot be explained by the amount of input used in production, in other words, the total factor productivity. The level of total factor productivity is measured according to how efficiently the inputs are used in the production process. In this study, the hypothesis that public spending affects real economic growth through total productivity is investigated. In the first stage, whether the changes in public expenditures affect the total factor productivity or not; if it does, to what extent and in what direction it has been tried to be revealed. In the second stage, the effect of total factor productivity on economic growth was examined and the statistical significance, direction and extent of the relationship between variables were investigated. Annual data were used in the study and the year range is 2000-2017. The sampling economies were selected according to data availability, and there are a total of 20 developed and developing economies. Research was conducted using multiple panel regression analysis. According to the findings, the relationship between public expenditures and total factor productivity is statistically significant. An increase in public expenditures reduces the total factor productivity. The relationship between total factor productivity and economic growth is statistically significant, and an increase in total factor productivity also increases economic growth. An increase in public expenditures affects economic growth negatively by reducing the total factor productivity.

Man has always thought of efficient utilization of available potentials and sources. Today this subject drives more serious attention compared to the past. Limited available resources, increasing population and growing human needs and demands of those involved make the economy, politics and management and community organizations increase productivity in its priority programs. Productivity has positive effect on phenomena such as competition in international markets, equitable distribution of income, raising living standards, economic development and even political power of a government. However, the study in this field requires knowledge about its development process. So far there has been no comparison of total factor productivity factor in Iran with other countries in the cement industry with regard to position and valuable role in the economy. This research should be considered a step toward eliminating the deficiencies outlined. In this study, using the relative index of total factor productivity factor, the relative total factor productivity factor in Iran and Turkey, South Korea and the United States has been evaluated and analyzed between the years (2007 to 1990) in the cement industry. Also, using panel data approach, the effect of macro and institutional factors such as the role of government, the degree of openness, inflation, and human capital on total factor productivity factor is evaluated. The findings indicate that there is a wide gap between total factor productivity of Iran's cement industry and that of the United States and the trend is not a proper one. This is an alarm for Iran's policy makers and planning managers to plan and utilize proper policies and take necessary actions to close or reduce this wide gap. It is also adversely shown that interference of the government may negatively affect the total factor productivity but, developed human resources and an open economic environment will have positive effect on the productivity. It is also noted that inflation has an adverse effect on total productivity. Key words: Total productivity, cement industry board data, equal purchasing power.

Improving farm productivity is often touted as essential for the future prospects of Australian agriculture, particularly for the export-oriented broadacre farm sector. This paper draws on farm panel data for the period 2002 to 2011. The annual components of productivity of the same group of 223 farms are measured each year for a decade by using a multiplicatively complete Fare-Primont index number and applying DEA methods. Results often show pronounced variability in the annual productivity of these farms. Farms are classed according to the geometric mean of their total factor productivity and the variance of this productivity. The convexity of this relationship suggests that to achieve high growth in productivity in broadacre farming, farm businesses are exposed to greater volatility in productivity. There are only a small proportion of farms that over the decade were able to achieve high, stable growth in productivity. Most farms either experienced high growth and high variability in productivity or low growth and low variability in productivity. The characteristics of farm businesses in both categories are examined to ascertain links between farm characteristics and change in farm productivity. Key findings are that overall most farms experienced growth in their total factor productivity with the principal cause of this growth being greater technical efficiency rather than technical change. Farms that experienced the highest growth in their total factor productivity typically increased their farm size, became more crop dominant, often operated farms in lower rainfall regions, generated more profit and were less exposed to debt and generated more crop yield and more livestock income per millimetre (mm) of growing season rainfall.

Purpose of the Study: Egyptian agriculture suffers from many problems related to the use of available economic resources, the most important of which is lack of optimal utilization of resources, wasteful use of agricultural production inputs, reduced efficiency of irrigation water use, and the fertility of agricultural lands are deteriorating, in addition to increasing rates of encroachments on agricultural lands and shifting it from agricultural use to other non-agricultural uses, which limits the agricultural sector ability to achieve high growth rates, especially with the increasing global production of biofuels from crops that individuals consume as food, including wheat and corn, which constitutes an explicit threat to Egyptian food and national security.
 Objectives: The research aimed to:
 
 Estimate the changes in the sources and components of the total productivity of the factors for the main cereal crops in Egypt in the presence and absence of carbon dioxide emissions,
 Environmental impact assessment of changes in the productivity of these crops.
 
 Methods: The study applied analytical approaches to measure changes in productivity, as parameter analysis methods are used as methods of the aggregate production function, and non-parameterized methods of estimation, in addition to (Malmquist, 1953) which is one of the most important indicators of measurement changes in productivity and relies on a Data Envelopment Analysis (DEA) to measure efficiency and changes in TFP productivity and identify the sources of changes in productivity through changes in technical competence and technological change, as the two most important components of the change in total productivity.
 Results: Wheat Crop: Wheat crop by estimating the change in the different efficiencies of the wheat crop with co2 emissions, it was clear that a decrease in technological change (TC) during the study period, and thus a decrease in the average change in the total factor productivity (TFP), while without co2 emissions effect, the average change in the total factor productivity of (TFPc) indicates an increase in the actual wheat efficiency which is low due to the environmental impact of the emissions.
 Rice Crop: Rice crop by estimating the change in the different efficiencies of the rice crop with co2 emissions, it became clear that a decrease in the average technological change (TC), thus increasing the average change in the total factor productivity of the (TFP), whereas, without co2 emissions, it was found that the average change in the total factor productivity of the (TFPc) for the study areas was higher.
 Summer Maize Crop: It was clear that the average technological change (TC) for the summer maize crop with co2 emissions, decreased during the study period, and therefore a decrease in the average change in the total factor productivity of the (TFP), but without co2, an increase in the annual average of the change in technical efficiency (TEC), and a decrease in the average technological change (TC), i.e. in the use of technology, and an increase in the average change in the total factor productivity (TFPc).

Improving farm productivity is touted as essential for the future prospects of Australian agriculture, particularly for the export-oriented broadacre farm sector. Accordingly, this paper examines productivity gain in a large sample of Australian farms over the period 2002 to 2011. The annual components of productivity of the same group of 223 farms are measured each year for a decade by using a multiplicatively complete Färe-Primont index number and applying DEA methods. Results show the variability and trends in these farms’ annual productivity. Farms are classed according to the geometric mean of their total factor productivity and the variance of this productivity. This relationship displays convexity, and high growth in productivity is associated with greater volatility in productivity. Only a small proportion of farms over the decade achieved high, stable growth in productivity. Most farms either experienced high growth and high variability in productivity or low growth and low variability in productivity. The characteristics of farm businesses in both categories are examined to ascertain links between farm characteristics and their trend in farm productivity. Key findings are that, overall, most farms experienced growth in their total factor productivity with the principal cause of this growth being greater technical efficiency rather than technical change. Farms that experienced the highest growth in their total factor productivity typically increased their farm size, became more crop dominant, often operated farms in lower-rainfall regions, generated more profit and were less exposed to debt and generated more crop yield and more livestock income per millimetre (mm) of growing season rainfall.

The study estimated the total factor productivity (TFP) of dairy farms and its determinants in the eastern region of India. The results suggested that the overall total factor productivity of dairy farms in the region was 0.1697, and degree of variability existed which could be reduced if the farmers adopt better management practices. The frequency distribution of dairy households by TFP value was positively skewed such that 97 per cent of the farms had TFP less than 0.30. Among all the factors included in the model, herd size, composition, and C-P ratio significantly impacted TFP. Thus, TFP influenced by economies of scale, was technology, and management practices.

The total factor productivity became an interesting concept in the measurement of productivity growth. Productivity is a ratio of output to input. The most common measurement of productivity is single factor productivity or partial productivity such as of land, labor, or capital.A total (factor) productivity is a productivity of all factors of production where the factors are aggregated. In cross-sectional studies this total productivity is a ratio of actual to potential output where the potential output is estimated from ther frontier production function. One of the methods to estimate this frontier function is by using linear programming technique.The total productivity does not always coincide with a single factor productivity of land (yield), that in the study area the larger farms tend to have higher total productivity than yield

The total factor productivity became an interesting concept in the measurement of productivity growth. Productivity is a ratio of output to input. The most common measurement of productivity is single factor productivity or partial productivity such as of land, labor, or capital.A total (factor) productivity is a productivity of all factors of production where the factors are aggregated. In cross-sectional studies this total productivity is a ratio of actual to potential output where the potential output is estimated from ther frontier production function. One of the methods to estimate this frontier function is by using linear programming technique.The total productivity does not always coincide with a single factor productivity of land (yield), that in the study area the larger farms tend to have higher total productivity than yield

The paper combines the bootstrapped Malmquist productivity index and the Multiple Correspondence Analysis to measure the changes in the total factor productivity. The bootstrapped Malmquist productivity index enables us to identify insignificant change in the total factor productivity, whereas the Multiple Correspondence Analysis relates the estimates to the environmental variables. A sample of Lithuanian family farms is utilised to test the proposed framework. Specifically, the research covers 200 family farms and the period of 2004–2009. The analysis showed that the total factor productivity decreased by some 15–18% during 2004–2009 depending on the farming type. Multiple Correspondence Analysis suggested that all of the farming types exhibited change in the total factor productivity close to the average, although the crop farming was located in the more stochastic area.

In order to evaluate the impact of investment and technology stock on total production, a panel date model is used in this paper. By introducing investment in fixed assets as investment's proxy, estimation of the number of in-force issued patents from 30 provincial capitals and 5 cities specially designated in the state plan as technology stock's proxy, the paper model can solve the problem of parameter quantification. Empirical results show that the investment and technology stock have positive impact on the total production, that the elasticity coefficient of investment is more than that of technology stock, that the total factor productivity(TFP) of the western cities is less than that of eastern coastal cities. Not only investment in fixed assets and innovation should be increased, but also the total factor productivity should be be increased in the process of Western Development.