Features of Peasant Households
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The U.S. population has more than doubled in the last six decades, as has agricultural output. U.S. agriculture now uses about 25 percent less farmland and 78 percent less labor than in 1948, so agricultural productivity is largely responsible for the increased production. Agricultural productivity can be defined in terms of total output per unit of a single input— partial factor productivity (PFP) measures such as land productivity (yield) and labor PRINT PDF EMAIL
Multifactor productivity
Agricultural land
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The study analyses the correlations among different economies of selected EU-12 member states based on comparison of agricultural economics variances, namely the output value of the agricultural industry, productivity of input, agricultural gross value added, subsidies on production, agricultural labour input and agricultural income per annual working unit in the period of 2010-2016, based on the Special Program for Social Sciences, as statistical methods. The EU-12 achieved a higher increase in productivity of input, output value of agricultural industry, agricultural gross valued added, as well as agricultural income per agricultural annual working unit compared to the average results of EU-28 for 2010-2016. The output value of agricultural industry and agricultural gross value added per intermediate consumption decreased by 1.35% and by 3.3%, but the factor income - net value added at factor cost - per annual working unit increased by 21%, because of the subsidies on production increased by 3.4% for 2010-2016. In EU-28, the factor income per annual working unit increased, but most of this income was for developing agricultural production technology.
Gross output
Gross value added
Value (mathematics)
Value added
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The aim of this study was to measure total factor productivity, technical change, and efficiency change of agricultural production in the EU, the new member countries and Turkey. Data Envelopment Analysis (DEA) and Malmquist Productivity Index were used as an approach in this research. Total agricultural production value was considered as output; agricultural land, agricultural labor, tractors, nitrogenous, potash and phosphate fertilizers and live animal stocks were considered as inputs in this paper. First of all, total factor productivity, technical change, and efficiency change of agricultural production were analyzed in EU (15) for 1963-2001 period. Secondly, total factor productivity, technical change, and efficiency change of agricultural production were analyzed related to two different groups concerning 15 EU and 12 new member countries and Turkey just as candidate country for 1993-2001 period. Subsequently, in order to compare EU (15), new member (12) countries and Turkey, total factor productivity, technical change, and efficiency change of agricultural production were analyzed for all countries (28) for 1993-2001 period. As a result total factor productivity increased 2.1% in 1963-2001 period for EU (15). This increase occurred as a technical change. Total factor productivity increased 1.4% in 1993-2001 period for EU (15). This increase occurred as a technical change, too. Total factor productivity decreased 5.2% in 1993-2001 period for new member countries and Turkey. This decrease occurred mostly due to lack of technical change. Considering the all 28 countries total factor productivity decreased 1.9% in 1993-2001 period for the EU, the new nember countries and Turkey. This decrease again occurred mostly due to lack of technical change. Consequently, it is noticeable that compared to other countries, new member countries had a better performance in achieving high levels of productivity and efficiency in agricultural production.
Malmquist index
Technical change
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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.
Agricultural machinery
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The Punjab province plays pivotal role in the economy of Pakistan by contributing almost 60 percent to total agricultural production of the country. This contribution can further be enhanced by identifying the factors that affect agricultural production in the province. The estimated Cobb-Douglas agricultural production model for Punjab established that the cropped area, agricultural labour, distribution of seed, budgetary expenditure on agricultural research and extension, land reclamation, and wheat price support contributed positively towards agricultural production whereas the contribution of fertilizer and expenditure on food trading services was found negative. The provincial government should accord high priority to these factors to boost agricultural production in the province.
Agricultural land
Agricultural policy
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Based on spatial heterogeneity of agricultural productivity,productivity growth effect of agricultural research expenditure was empirically analyzed using geographical weighted regression model to provide reference for optimizing agricultural research expenditure allocations.The results showed that the effects of agricultural research esxpenditure on agriculture productivity growth in some agricultural provinces were relatively better than that in Western provinces whose expenditure level was lower.Considering the actuality that the focuses of agricultural production in China are North and West,the proposal was put forward that agricultural research expenditure in North-Easter agricultural provinces and mid-western area should be enhanced.
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Abstract Agricultural finance has a leading role in financing agricultural projects in order to enhance productivity growth in the agricultural sector. This study aimed to show the impact of productive agricultural loans and cultivated area on the gross agricultural product in Iraq during the period (1990-2020). The validity of the model was tested using statistical tests and analysis of variance (ANOV) software. The results of analysis of the loans of the Agricultural Cooperative Bank, on the one hand, and each of the gross agricultural product, agricultural production, productivity and cultivated area of the same period, on the other hand, showed a weak positive relationship between loans and agricultural production. The results proved the statistical significance of this relationship at the level of (5%), as the significance value was (0.049), while the value of the correlation coefficient was (0.375). As for other variables, there was no significant correlation at the levels (5%), (1%) and (1%), as the significance value between the loans of the Agricultural Cooperative Bank in Iraq during the period (1990-2020), on the one hand, and the gross agricultural product, productivity and cultivated area, on the other hand, reached (0.874), (0.071) and (0.53), respectively. The study recommended providing cash and in-kind agricultural loans by the Agricultural Cooperative Bank for farmers to perform agricultural operations and implement their agricultural plans in Iraq.
Gross output
Cash crop
Econometric model
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This paper presents a quantitative analysis of long-term economic growth and living standards in ancient Japan during the roughly 480 years comprising the Nara (710-794) and Heian (794-1195) periods. The analysis is conducted by estimating production in agriculture, which was the pillar of the economy of ancient Japan. Specifically, arable land, land productivity, and agricultural output are estimated for three benchmark years – 725, 900, and 1150 – using quantitative data obtained from ancient records. The results indicate that (1) economic growth in ancient Japan was relatively steady and moderate, and (2) agricultural output per capita was very low and ordinary farmers must have struggled to survive on their income from agricultural sources alone. The findings thus suggest that ancient Japan was reliant on agriculture, which however, was poorly developed and showed little technological development, so that there was little economic growth.
Arable land
Agricultural land
Pillar
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Based on provincial panel data from 1997 to 2010,making use of the grass agricultural production of pre capita agricultural employment(PEO),grass agricultural production of unit investment(PIO),agricultural production of unit area(PAO) as performance indicator of agricultural transformation,per capita arable land area of operation(PCA) as performance indicator of the agricultural land institutional change,by the static panel estimation and dynamic panel estimation,an empirical study about the effects of agricultural land institutional change on agricultural transformation can be worked out.The research results show that per capita arable land area of operation(PCA) plays a positive promoting role in the grass agricultural production of pre capita agricultural employment(PEO) and agricultural production of unit area(PAO),and no significant effects on the grass agricultural production of unit investment(PIO).
Arable land
Investment
Agricultural land
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