Journal articles on the topic 'Agricultural productivity'
To see the other types of publications on this topic, follow the link: Agricultural productivity.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Agricultural productivity.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Tripathi, Amarnath, and A. R. Prasad. "Agricultural Productivity Growth in India." Journal of Global Economy 4, no. 4 (December 31, 2008): 322–28. http://dx.doi.org/10.1956/jge.v4i4.113.
Full textAbstract:
The case of Indian agricultural performance was impressive. The food production and increases in productivity are essential for meeting the growing demands for food in the future. There is widespread opinion that this growing demand can be met by increased use of inputs or increases in agricultural productivity. Productivity growth of agriculture in India over the past four decades was the result of a combination of factors such as new incentives to farmers offered by the government who considered them as autonomous economic agents, and physical factors such as land, labour, capital (in the form of machines, working animals, irrigation system, and so on), and intermediate inputs such as fertilizer. Indian agricultural growth has been less dependent on the conventional inputs of capital. Capital was computed as the sum of the value of agricultural machinery, farm equipment and tools, transport equipment in farm business, land improvements, investments in private and public irrigation, and farm houses in Indian agriculture. As the growth of agriculture increases the importance of conventional inputs of capital becomes lesser in comparison to modern inputs of capital. Since mid 1960s, a package of modern inputs of capital such as high yield variety seeds, chemical fertilizers, tractor etc. has been continuously used with increasing trend in Indian agriculture. This was main cause of the remarkable growth in output of agriculture during 1970s and 1980s decades. This paper is aimed at analyzing the impact of some production variables (input) on agricultural productivity growth (output) in Indian agriculture from 1969-70 to 2005-06. The question here is whether or not these different variables have an impact on agricultural production.
2
Gao, Dandan, and Xiaogang Lyu. "Agricultural total factor productivity, digital economy and agricultural high-quality development." PLOS ONE 18, no. 10 (October 4, 2023): e0292001. http://dx.doi.org/10.1371/journal.pone.0292001.
Full textAbstract:
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.
3
Ball, V. Eldon, Jean‐Christophe Bureau, Richard Nehring, and Agapi Somwaru. "Agricultural Productivity Revisited." American Journal of Agricultural Economics 79, no. 4 (November 1997): 1045–63. http://dx.doi.org/10.2307/1244263.
Full text
4
Khobarkar, Dr Vanita Khushalrao, and Dr S. W. Jahagirdar Dr. S. W. Jahagirdar. "Impact Of Agricultural Mechanization On Productivity." Indian Journal of Applied Research 1, no. 3 (October 1, 2011): 3–4. http://dx.doi.org/10.15373/2249555x/dec2011/2.
Full text
5
Sapolaite, Vaida, and Tomas Balezentis. "Growth in Agricultural Productivity." Journal of Global Information Management 31, no. 4 (April 7, 2023): 1–16. http://dx.doi.org/10.4018/jgim.320815.
Full textAbstract:
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.
6
Said, Fatimah, Saad Mohd Said, and Azimah Haji Othman. "Malaysian Agricultural Development and Productivity." Indonesian Management and Accounting Research 5, no. 1 (November 10, 2016): 21–40. http://dx.doi.org/10.25105/imar.v5i1.1271.
Full textAbstract:
This paper analyzes the long run changes of aggregate input-output relationships and assesses the impact of agricultural development on the growth rate of Malaysian agricultural productivity over the period 1966-2000. We find that despite the intensified effort to modernize and revitalize the agricultural sector, the average annual growth rate of agricultural production decreased from 8.2 percent in the initial phase of agricultural development (1966-1970) to 5.1 percent in the intermediate phase (1971-1990) and subsequently to 0.3 percent in the modernization phase (1991-2000). During 1966-2000, labor productivity recorded the highest annual rate of growth of 4.6 percent as compared to 2.1 percent of land productivity and 2.9 percent of total productivity. All productivity measures recorded an increasingly slower rate of growth throughout the period of study. This reflects the deterioration in production efficiency in Malaysian agriculture presumably due to technological adjustment and inputs subsidies.
7
Ahmad, Mumtaj, Pasarul Islam, and Shamsul Haque Siddiqui. "Role of Agricultural Technology on Socio-Economic Development in Hathras District, Uttar Pradesh." National Geographical Journal of India 66, no. 3 (September 30, 2020): 222–35. http://dx.doi.org/10.48008/ngji.1743.
Full textAbstract:
Agriculture in India has experienced a significant transformation in the past fifty years, with agriculture being more and more oriented to a productivist form of socio-economic production. Introduction of new agricultural technologies, high yielding varieties of seeds, improve access to irrigation, education, efficient use of fertilizers and extension services are capable of enhanced productivity per unit of land. Increased production further reflects on socio-economic transformation in rural communities. The study uses secondary data from various sources published by the Government of India and the Government of Uttar Pradesh. The study covers the period between 2000-01 and 2014-15 to analyze the role of agricultural technologies on socio-economic transformation in Hathras district. The methodology adopted for the present study are Data Interpolation or Extrapolation, Yang’s Crop Yield Index, Dayal’s Labour Productivity, Data Standardisation technique Z- score, and Composite Z score. The study concludes that the district has experienced tremendous technological changes in agricultural practices, agriculture induced better productivity and productivity further leads to overall socio-economic transformation.
8
Sampson, Devon. "Productivism, Agroecology, and the Challenge of Feeding the World." Gastronomica 18, no. 4 (2018): 41–53. http://dx.doi.org/10.1525/gfc.2018.18.4.41.
Full textAbstract:
Among all of the possible approaches to reducing hunger in the world, efforts to increase agricultural productivity dominate in development institutions and large philanthropies. In this productivist paradigm, the function of agriculture is narrow, and further investments in industrial agriculture are the greatest need. This view clashes with the intricate diversity and multiple functions of farms and gardens in Yucatan, Mexico. Agroecosystems there are spectacularly diverse. Besides providing many products to eat and sell, those farms are uniquely well suited to feed households in the increasingly erratic weather of Yucatan, where droughts and storms often wipe out certain crops. In a diverse garden, there is nearly always something to eat. There is little evidence that increasing agricultural production alone promotes food security, and there are many instances in which the drive for productivity has exacerbated hunger. In this article, I investigate why productivism has dominated development policy and discourse for so long.
9
Chen, Yanling, Weiwei Fu, and Jingyun Wang. "Evaluation and Influencing Factors of China’s Agricultural Productivity from the Perspective of Environmental Constraints." Sustainability 14, no. 5 (February 28, 2022): 2807. http://dx.doi.org/10.3390/su14052807.
Full textAbstract:
Based on provincial panel data for the past 15 years in China, the SBM-ML index method was used to measure agricultural productivity under the environmental-constraint perspective with agricultural surface source pollution as the non-desired output. A dynamic panel regression model was used to empirically analyze the factors influencing agricultural productivity to provide a reference for formulating policies to alleviate the conflict between economic development and environmental pollution. The results show that the green total factor productivity of Chinese agriculture exhibits a slow, incremental trend year by year. The growth of green total factor productivity in agriculture mainly comes from the increase in the rate of green technological progress. In terms of geographical disparity, the eastern, central, and western regions show a high-to-low gradient of agricultural green total factor productivity. Agricultural green total factor productivity showed a significant positive spatial correlation in some years. As for the influencing factors, foreign trade in agricultural products is conducive to enhancing green total factor productivity in agriculture, whereas foreign direct investment in agriculture and agricultural technology input inhibit the growth of green total factor productivity in agriculture. This research also found a significant U-shaped relationship between environmental management inputs and green total factor productivity in agriculture. Accordingly, suggestions are provided to optimize the international trade structure of agricultural products, selectively introduce high-quality green foreign investment projects, drive the efficiency of R&D investment through digital technology, and increase investment in special funds for agricultural pollution control.
10
Gollin, Douglas, David Lagakos, and Michael E. Waugh. "Agricultural Productivity Differences across Countries." American Economic Review 104, no. 5 (May 1, 2014): 165–70. http://dx.doi.org/10.1257/aer.104.5.165.
Full textAbstract:
Recent studies argue that cross-country labor productivity differences are much larger in agriculture than in the aggregate. We reexamine the agricultural productivity data underlying this conclusion using new evidence from disaggregate sources. We find that for the world's staple grains-maize, rice, and wheat-cross-country differences in the quantity of grain produced per worker are enormous according to both micro- and macrosources. Our findings validate the idea that understanding agricultural productivity is at the heart of understanding world income inequality.
11
Tan, Khee Giap, Nurina Merdikawati, and Ramkishen S. Rajan. "Agricultural Productivity in Indonesian Provinces." International Journal of Asian Business and Information Management 7, no. 3 (July 2016): 26–39. http://dx.doi.org/10.4018/ijabim.2016070102.
Full textAbstract:
Indonesia has been recognized as a country with significant potential in agriculture, not only to be self-sufficient in terms of food, but also to be the “food basket” for the world. However, given limited and competing use of resources, raising agricultural productivity is of paramount importance. To date, most of the existing work on Indonesia's agricultural sector is at the national level. Considering the extent of Indonesia's regional diversity, a provincial-level analysis of the country's agricultural sector would be more useful from a policy perspective. In this light, this paper examines agricultural productivity growth in Indonesian provinces during 2000-2011 and draws policy implications from such empirical analysis. The paper uses two methodologies, namely growth accounting and Malmquist index data envelopment analysis. Results suggest that technological change has been improving for most provinces, though there is wide variation in technical efficiency change which in turn is driving differences in total factor productivity growth across provinces.
12
Liao, Xisheng, Shaoyi Qin, Yajuan Wang, Hongbo Zhu, and Xuexiang Qi. "Effects of Land Transfer on Agricultural Carbon Productivity and Its Regional Differentiation in China." Land 12, no. 7 (July 7, 2023): 1358. http://dx.doi.org/10.3390/land12071358.
Full textAbstract:
Facing the realistic threat of natural environment deterioration and frequent extreme weather, improving agricultural carbon productivity has become an objective requirement for achieving the goal of double carbon and for promoting the high-quality development of agriculture. As an important path toward improving land-use efficiency and promoting agricultural technological progress, land transfer has a potential positive effect on improving agricultural output and inhibiting agricultural carbon emissions. Based on the current situation of land transfer and the characteristics of agricultural carbon productivity in China, this study used the panel data of 30 provinces, from 2006 to 2019, in China to empirically test the relationship between land transfer and agricultural carbon productivity via the spatial Durbin model. The results show that (1) land transfer has a positive effect on agricultural carbon productivity; (2) agricultural carbon productivity has a spatial correlation, and the impact of land transfer on agricultural carbon productivity has a spillover effect; and (3) there are regional differences in the impact of land transfer on agriculture carbon productivity. Based on the results of the study, this paper puts forward policy recommendations from three aspects through which to optimize land transfer and enhance agricultural carbon productivity.
13
Yang, Yang, Heng Ma, and Guosong Wu. "Agricultural Green Total Factor Productivity under the Distortion of the Factor Market in China." Sustainability 14, no. 15 (July 29, 2022): 9309. http://dx.doi.org/10.3390/su14159309.
Full textAbstract:
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.
14
Duan, Meiyi. "Analysis on the Development Trend and Influencing Factors of Intelligent Agriculture in Anhui Province." Academic Journal of Science and Technology 1, no. 2 (May 16, 2022): 93–97. http://dx.doi.org/10.54097/ajst.v1i2.370.
Full textAbstract:
With the implementation of the "Internet +" strategy, the modern integration of information technology and agriculture has also entered the fast lane of development. A large number of high-tech equipment and technologies have been gradually integrated into agricultural production, greatly improving the efficiency of agriculture. Realizing agricultural intellectualization and modernization has become an important goal of China's agricultural and rural work at this stage. This paper establishes a model to analyze the current development trend and main influencing factors of smart agriculture in Anhui Province, so as to provide empirical reference for the development of smart agriculture. Firstly, the key factors affecting agricultural productivity in Anhui Province are the change of agricultural scale and agricultural productivity by using tobit-u model, and then the research results show that the change of agricultural productivity in Anhui Province is the key factor to improve agricultural productivity The level of industrialization has a significant role in promoting the production efficiency of smart agriculture in Anhui Province, and the level of financial agricultural expenditure and urbanization rate have a significant negative effect.
15
Trpeski, Predrag, and Marijana Cvetanoska. "The Impact of the Main Determinants and Changes in Agricultural Labour Productivity in Macedonia." European Scientific Journal, ESJ 14, no. 10 (April 30, 2018): 119. http://dx.doi.org/10.19044/esj.2018.v14n10p119.
Full textAbstract:
The aim of this paper is to analyze changes and impacts on the level of labour productivity in the agricultural sector in Macedonia in the period from 2006 to 2017. Labour productivity is an important determinant for establishing the competitiveness of a particular sector or overall economy and helps in creating the necessary conditions for economic development. Agricultural sector in many countries represents the basis for growth in gross domestic product. Agriculture plays a key role in development of the national economy in Macedonia as a third largest sector after services and industry. Therefore, in order to increase the agricultural labour productivity, it is necessary to increase agricultural production, i.e., the part of gross domestic product created by the agriculture sector. In this direction, the paper also analyzes the relationship between agricultural labour productivity and gross domestic product and employment in agriculture. Synthesis and analysis, induction and deduction, descriptive statistics, comparative analysis, correlation analysis and regression analysis are used for the purpose of the paper. The results show that changes in gross domestic product in agricultural sector in Macedonia have a greater impact on agricultural labour productivity for the analyzed period compared to the impact of changes in the number of employees in the agriculture sector where the relationship is weak to moderate. Research results also showed that there is a positive and strong quantitative relationship between agricultural labour productivity growth rate and GDP growth rate in Macedonian economy. Agricultural GDP is the determinant which has to be influenced through intensification of agricultural production in order to increase the agricultural productivity.
16
Caunedo, Julieta, and Elisa Keller. "Capital Obsolescence and Agricultural Productivity*." Quarterly Journal of Economics 136, no. 1 (December 11, 2020): 505–61. http://dx.doi.org/10.1093/qje/qjaa028.
Full textAbstract:
Abstract This article argues that accounting for capital-embodied technology greatly increases the importance of capital in explaining cross-country differences in agricultural labor productivity. To do so, we draw on a novel data set of agricultural capital prices. We document that new capital is more expensive in richer countries, both in absolute terms and relative to old capital. A model of endogenous adoption of capital of different quality links these price differences to the path of capital-embodied technology. In particular, our model recovers the level of embodied technology from the price of new capital and the growth rate of embodied technology from the price of new capital relative to old capital. We then measure the stocks of quality-adjusted capital in agriculture for a sample of 16 countries at different stages of development. We find that adjusting for differences in quality almost doubles the importance of capital in accounting for cross-country differences in agricultural labor productivity: from 21% to 37%. In addition, improvements in capital quality have been an important source of agricultural labor productivity growth over the past 25 years, accounting for 21% and 35% of the productivity growth in poor and rich countries, respectively.
17
DVORNYK, Inna. "LABOR PRODUCTIVITY IN AGRICULTURE." Ukrainian Journal of Applied Economics 6, no. 2 (June 30, 2021): 245–51. http://dx.doi.org/10.36887/2415-8453-2021-2-30.
Full textAbstract:
The essence of labor productivity is substantiated in the article. It is determined that it is the production of a certain amount of products (or work performed of the established quality) per unit of working time. Labor productivity is one of the main indicators that determines the development of the industry, enterprise. The analysis of agricultural products showed that in general there is a positive trend, in particular crop production. Livestock production is characterized by a decrease. Enterprises produce the most agricultural products - 66% (11.6% of farms), respectively households – 34%. Analysis of the dynamics of labor productivity per employee in agriculture of Ukraine showed positive results. The growth rate in the industry over the past 5 years is 48.81%, including in crop production - 44.61%, in animal husbandry - 61.78%. However, the achieved level does not meet modern requirements. Factors of labor productivity growth are singled out: organizational-economic, technical-economic, socio-economic, natural-climatic. Correlation-regression analysis was used to study the influence of individual factors on labor productivity. It is established that the following have the greatest direct impact: the average engine power of the tractor, the cost of fixed assets, the average monthly salary. The following have the opposite effect: population, number of employees in the agriculture. A positive financial result in agriculture does not increase labor productivity. Investments, wage growth in the agricultural sector, predictability of state regulatory policy, improved land relations, mechanisms of state support for farmers and improving the efficiency of public administration will help increase labor productivity in agriculture. Keywords: labor productivity, agriculture, wages, production volume, investments.
18
Gollin, Douglas, David Lagakos, and Michael E. Waugh. "The Agricultural Productivity Gap *." Quarterly Journal of Economics 129, no. 2 (December 11, 2013): 939–93. http://dx.doi.org/10.1093/qje/qjt056.
Full textAbstract:
Abstract According to national accounts data, value added per worker is much higher in the nonagricultural sector than in agriculture in the typical country, particularly in developing countries. Taken at face value, this “agricultural productivity gap” suggests that labor is greatly misallocated across sectors. In this article, we draw on new micro evidence to ask to what extent the gap is still present when better measures of sector labor inputs and value added are taken into consideration. We find that even after considering sector differences in hours worked and human capital per worker, as well as alternative measures of sector output constructed from household survey data, a puzzlingly large gap remains.
19
Kakar, Mehmood, Adiqa Kiani, and Asia Baig. "Determinants of Agricultural Productivity: Empirical Evidence from Pakistan’s Economy." Global Economics Review I, no. I (December 30, 2016): 1–12. http://dx.doi.org/10.31703/ger.2016(i-i).01.
Full textAbstract:
This article examines the determinants of the total productivity of the agriculture sector which enhances the total agricultural productivity in Pakistan and analyzes the relations among variables used for the analysis from 1990 - 2017. The application of the auto regressive distributed lag technique ARDL was used to approximate various determinants. The area under cultivation, fertilizer consumption, agriculture credit, and rainfall show a positive effect on agriculture productivity, whereas agriculture employment and pesticide consumption show a positive but statistically insignificant effect on agricultural productivity in the long run. While in the short-run all determinants have a positive and significant effect on total agriculture productivity convergence towards equilibrium is shown by error correction term is 0.829.
20
Abiola, Abidemi, and Rasak A. Adefabi. "Rural Structural Transformation and Agricultural Productivity in Nigeria." Athens Journal of Business & Economics 8, no. 2 (January 5, 2022): 119–38. http://dx.doi.org/10.30958/ajbe.8-2-2.
Full textAbstract:
Rural structural transformation is best defined as structural changes in the rural areas occasioned by government policies and programmes with the intention of altering the contributions of major sector of the economy for the enhancement of agricultural sector. The study aimed at investigating the impact of rural structural transformation on agricultural productivity in Nigeria. The methodology adopted for the study was Structural Autoregression (SVAR). Six variables of expenditure on education (EXPE), expenditure on health (EXPH), expenditure on electricity (EXPEL), expenditure on telecommunication (EXPTC), expenditure on roads and construction (EXPRC) and expenditure on agriculture (EXPA). Of the six explanatory variables only expenditure on agriculture was found to be negatively related to agricultural productivity, while the others were positively related to it. Several reasons of which of official corruption by the handlers of agricultural funds could possibly be one of the reasons for the negative relationship between expenditure on agriculture and agricultural productivity. Among many other recommendations was the need to provide clinics and health centres to the rural areas, provision of good and accessible roads, provision of electricity and internet facilities. This will act as motivating factors in curbing rural-urban migration, and by extension improve the lots of agricultural productivity in Nigeria. Keywords: rural, structural transformation, agricultural productivity, agricultural policies and structural VAR
21
Stifel, David, and Bart Minten. "Isolation and agricultural productivity." Agricultural Economics 39, no. 1 (July 2008): 1–15. http://dx.doi.org/10.1111/j.1574-0862.2008.00310.x.
Full text
22
Grabowski, Richard. "Agricultural Productivity and Industrialization." Forum for Development Studies 40, no. 2 (June 2013): 309–25. http://dx.doi.org/10.1080/08039410.2013.775965.
Full text
23
Xu, Yingfeng. "Agricultural productivity in China." China Economic Review 10, no. 2 (September 1999): 108–21. http://dx.doi.org/10.1016/s1043-951x(99)00008-5.
Full text
24
Accorti, Marco. "Bees and agricultural productivity." Ethology Ecology & Evolution 3, sup1 (January 1991): 162. http://dx.doi.org/10.1080/03949370.1991.10721933.
Full text
25
Fuglie, Keith O. "Is agricultural productivity slowing?" Global Food Security 17 (June 2018): 73–83. http://dx.doi.org/10.1016/j.gfs.2018.05.001.
Full text
26
Adeyeye, Babatunde, Lanre Amodu, Oscar Odiboh, Kehinde Oyesomi, Evaristus Adesina, and Darlynton Yartey. "Agricultural Radio Programmes in Indigenous Languages and Agricultural Productivity in North-Central Nigeria." Sustainability 13, no. 7 (April 1, 2021): 3929. http://dx.doi.org/10.3390/su13073929.
Full textAbstract:
This study investigated the influence of agricultural radio programmes in indigenous languages on farmers’ productivity and their implications for agricultural productivity in North-Central Nigeria. It specifically explored how farmers’ knowledge of agricultural radio programmes produced in indigenous languages influences their productivity; how farmers’ acceptance of agricultural radio programmes produced in indigenous languages influences productivity; and whether behavioural changes result from agricultural radio programmes aired in indigenous languages affect farmers’ productivity. Data were gathered through a survey of 663 farmers selected through the three states’ multi-stage sampling technique (Benue, Nasarawa, and Plateau). The hypotheses were tested using regression analysis and structural equation modelling. They revealed that the R value was 0.677, suggesting a highly significant relationship between farmers’ knowledge of agricultural radio programmes in indigenous languages and farmers’ productivity. Results also revealed that farmers’ behavioural changes resulting from agricultural radio programmes in indigenous languages greatly influence farmers’ productivity (F value was 558.358 at the 0.000 significant level). The study concludes that farmers’ knowledge, acceptance and behavioural change towards agricultural radio programmes in indigenous languages significantly influence farmers’ productivity in agriculture. Thus, farmers should be encouraged to continue participating in agricultural radio programmes in indigenous languages to be kept abreast of happenings in the field.
27
Samatar, Elmi Hassan. "Assessing the determinants of agricultural productivity in Somalia: An application of an ARDL model." Asian Journal of Agriculture and Rural Development 13, no. 3 (June 19, 2023): 154–62. http://dx.doi.org/10.55493/5005.v13i3.4819.
Full textAbstract:
This study delves into the factors that boost agricultural productivity while taking five macroeconomic variables into account. The investigated variables are agricultural productivity, which is used as the dependent variable, while employment in agriculture, gross capital formation, arable land, and rainfall are the independent variables. Employing an autoregressive distributed lags (ARDL) model, this paper examines the determinants of agricultural productivity in Somalia from 1991 to 2020. The cointegration between the model’s variables was verified using a bounds-testing approach to cointegration. Employment in agriculture was found to have both a short-run and long-run positive impact on agricultural productivity. Similarly, it was discovered that both gross capital formation and the availability of arable land had a favorable influence on agricultural productivity in the short and long run. Additionally, the study indicated a positive short-run and long-run correlation between rainfall and agricultural productivity, although this correlation is statistically insignificant at a five percent level. In the long run, the amount of available arable land has a positive impact on agricultural productivity. However, in the short run, this determinant has the opposite effect. Based on the results, the study advises the government, policymakers, and other concerned authorities to prioritize technological innovation and climate-smart agricultural systems to boost sector productivity.
28
Mohan, Geetha, Hirotaka Matsuda, Samuel A. Donkoh, Victor Lolig, and Gideon Danso Abbeam. "Effects of Research and Development Expenditure and Climate Variability on Agricultural Productivity Growth in Ghana." Journal of Disaster Research 9, no. 4 (August 1, 2014): 443–51. http://dx.doi.org/10.20965/jdr.2014.p0443.
Full textAbstract:
This paper examines the effects of agricultural research expenditure and climate change on agricultural productivity growth by region in Ghana. A panel dataset is constructed for 2000-2009 from the Food and Agriculture Organization of the United Nations; the Ministry of Food and Agriculture, Ghana; and the Agriculture Science and Technology Indicators (ASTI) database of the International Food Policy Research Institute. A Malmquist index was used to compute agricultural productivity growth, including decomposition components efficiency change and technical change. The determinants of productivity growth are examined using a fixed effects regression model. The results specify that significant causal factors impact positively on Ghana’s agricultural productivity growth, include climate variability, infrastructure, and agricultural research and development expenditure. The study confirms there is a need to strengthen and develop new technological progress for sustainable agricultural production in Ghana.
29
Hallam, D. "AGRICULTURAL RESEARCH EXPENDITURES AND AGRICULTURAL PRODUCTIVITY CHANGE." Journal of Agricultural Economics 41, no. 3 (September 1990): 434–39. http://dx.doi.org/10.1111/j.1477-9552.1990.tb00659.x.
Full text
30
R. N. ZHANGIROVA. "LABOR PRODUCTIVITY– ONE OF THE MAJOR EFFICIENCY CRITERIA OF THE AGRICULTURAL SECTOR OF THE REPUBLIC OF KAZAKHSTAN." Bulletin of the National Engineering Academy of the Republic of Kazakhstan 3, no. 77 (October 15, 2020): 148–52. http://dx.doi.org/10.47533/2020.1606-146x.25.
Full textAbstract:
The article considers the relationship between the category of labor productivity and efficiency. The factors affecting the level of labor productivity in the agricultural industry are investigated. The problems of increasing labor productivity and the efficiency of agricultural production are shown. The analysis of labor productivity in agriculture of the Republic of Kazakhstan. The main directions of increasing labor productivity in the agricultural sector of Kazakhstan are presented.
31
Bocean, Claudiu George. "A Cross-Sectional Analysis of the Relationship between Digital Technology Use and Agricultural Productivity in EU Countries." Agriculture 14, no. 4 (March 25, 2024): 519. http://dx.doi.org/10.3390/agriculture14040519.
Full textAbstract:
Amidst the rapid evolution of digital technologies and their prospective implications for agricultural productivity, farmers are increasingly turning to Agriculture 4.0. As digitization permeates every facet of agriculture, the potential for boosting productivity while ensuring sustainability and resilience becomes increasingly tangible. The objective of this study is to understand how the adoption of digital technologies influences agricultural productivity within the diverse socioeconomic and agricultural landscapes of EU nations. The research of this study aims to address questions concerning the impact of digital technology use on agricultural productivity across EU countries. This study employs a robust analytical framework combining equation modeling (SEM), artificial neural networks, and cluster analysis. SEM analysis reveals significant associations and influences between digital technology use and productivity related to the total labor force across EU countries. Moreover, cluster analysis outlines distinct clusters of EU member states distinguished by varying degrees of digital technology incorporation and corresponding agricultural productivity, emphasizing the diverse socioeconomic contexts that influence these associations. These findings underscore the significance of embracing digital technology as a catalyst for enhancing agricultural productivity across EU nations. Future research could focus on devising strategies to promote the widespread adoption of digital technologies in agriculture across EU member states, and longitudinal analyses could offer insights into the dynamic relationship between digital technology use and agricultural output, informing policy interventions.
32
Awuor, Fredrick Mzee, and Daisy Mbucu Ireri. "E-Agriculture Framework to Improve Agricultural Productivity: Literature Review." Modern Economy 13, no. 08 (2022): 1126–38. http://dx.doi.org/10.4236/me.2022.138059.
Full text
33
Fuglie, Keith O., and Sun Ling Wang. "New Evidence Points to Robust but Uneven Productivity Growth in Global Agriculture." Global Journal of Emerging Market Economies 5, no. 1 (January 2013): 23–30. http://dx.doi.org/10.1177/0974910112469266.
Full textAbstract:
This article is drawn from Productivity Growth in Agriculture: An International Perspective, edited by Fuglie, Wang, and Ball. It is a review of agricultural productivity around the world, with an analysis of prices, population, and productivity over the past 50 years. In developing and transition countries, agricultural productivity growth has been found to be strong over the past 10 years. Developed countries have also experienced robust agricultural total factor productivity growth, though it is now slowing in many countries.
34
Zhou, Zhiqiang, Wenyan Liu, Huilin Wang, and Jingyu Yang. "The Impact of Environmental Regulation on Agricultural Productivity: From the Perspective of Digital Transformation." International Journal of Environmental Research and Public Health 19, no. 17 (August 30, 2022): 10794. http://dx.doi.org/10.3390/ijerph191710794.
Full textAbstract:
China’s goal of becoming a strong agricultural country cannot be achieved without the modernization and digital transformation of the agricultural sector. Presently, China’s agriculture has ushered in the era of digital economy transformation. The digital transformation of agriculture has played a huge role in improving agricultural productivity, promoting sustainable development of China’s agricultural economy, and achieving sustainable development goals. The deep integration of digital economy and agricultural economy has become an important issue of The Times. This study uses a two-way fixed-effects model and an instrumental variable method to examine the impact of environmental regulation on agricultural total factor productivity. Using the method of mechanism analysis, the conduction path of improving agricultural productivity under the means of environmental regulation is discussed. Therefore, the visualization analysis results based on the panel data of Chinese agricultural enterprises from 2011 to 2019 show that the distribution of digital transformation and productivity level of enterprises is uneven and tends to be stable in space. The empirical analysis results show that there is a direct and significant positive relationship between voluntary environmental regulation and agricultural total factor productivity. The results of mechanism analysis show that, under the means of environmental regulation, digital transformation plays an indirect role in improving agricultural productivity. On the basis of enriching and deepening the theoretical extension of the “Porter Hypothesis”, this study subtly incorporates environmental regulation, digital transformation, and agricultural productivity into a unified framework, expanding existing research.
35
Herasymenko, Alina. "THE INFLUENCE OF SOCIO-ECONOMIC FACTORS OF MOTIVATION ON LABOR PRODUCTIVITY IN THE EFFECTIVE AGRIBUSINESS SYSTEM." Financial and credit activity problems of theory and practice 1, no. 48 (February 28, 2023): 378–87. http://dx.doi.org/10.55643/fcaptp.1.48.2023.3980.
Full textAbstract:
The article examines the impact of labor productivity on the development of agriculture. It was established that labor productivity is the main lever for creating additional value and a source of economic growth of agricultural production. An assessment of the achieved level of labor productivity in the agriculture of Ukraine was carried out on the basis of economic and statistical analysis. The monitoring of wage dynamics as the main socioeconomic factor of increasing the productivity of agricultural labor was carried out. A comparative analysis of the rate of increase in labor productivity and the rate of growth of its payment was done. The obtained results made it possible to reveal the presence of disproportions in the agrarian sphere of Ukraine between these indicators. A comparative assessment of the level of labor productivity in domestic agriculture and EU countries proved that the rate of growth of agricultural labor productivity in Ukraine is significantly behind the average European indicators. A set of socio-economic motivators for increasing labor productivity in agricultural production is substantiated on the basis of the research results. It was established that remuneration systems for hired labor remain the main socioeconomic factor stimulating the growth of labor productivity in agrarian business. At the same time, a set of socio-economic motivators, which, in addition to wages, can be effective in stimulating the growth of agricultural labor productivity has been determined. Among the key motivators, the following ones are proposed for use in the modern HR policy of agrarian formations: opportunities for professional development of personnel, opportunities for employees to influence making important management decisions, increasing the prestige of agricultural work, appreciation and self-realization of workers. It was determined that the principles of sustainable development and the elimination of inclusive gaps in the industry should be an important prerequisite for increasing labor productivity in the agricultural economy of Ukraine.
36
Hong, Mingyong, Mengjie Tian, and Ji Wang. "Digital Inclusive Finance, Agricultural Industrial Structure Optimization and Agricultural Green Total Factor Productivity." Sustainability 14, no. 18 (September 13, 2022): 11450. http://dx.doi.org/10.3390/su141811450.
Full textAbstract:
Based on the Peking University Digital Financial Inclusion Index and 2011–2018 provincial panel data, this paper discusses the mechanism of digital financial inclusion on agricultural green total factor productivity from both theoretical and empirical perspectives. The result shows that digital financial inclusion can significantly increase China’s agricultural green total factor productivity, and the optimization of the agricultural industry structure can bring a significant “structural growth effect”. A total of 8.42% of the positive effects of digital financial inclusion on agricultural green total factor productivity are realized through the intermediary effect of agricultural industrial structure optimization. Through further research, it is found that digital financial inclusion has regional heterogeneity in the improvement of agricultural green total factor productivity. At the same time, digital financial inclusion of different dimensions will also have a differential impact on the improvement of agricultural green total factor productivity. In order to promote the green development of agriculture, it is necessary to further improve the financial development environment, optimize the structure of the agricultural industry, and formulate development policies for digital inclusive finance in accordance with local conditions.
37
Hanafiah, M. Ali, Witman Rasyid, and Agus Purwoko. "HUBUNGAN KARAKTERISTIK, MOTIVASI DAN KOMPETENSI TERHADAP PRODUKTIVITAS KERJA PENYULUH PERTANIAN DI KOTA BENGKULU." Jurnal AGRISEP 12, no. 1 (April 7, 2013): 69–84. http://dx.doi.org/10.31186/jagrisep.12.1.69-84.
Full textAbstract:
The objective of this research is to analyze the correlation of the agricultural agents’ characteristics, motivation and competence to their work productivity. This research was conducted in February through April 2013 and used census methods. The study population was defined as many as 33 people of 45 people from the extension of existing civil servants. Data analysis was done by using Product Moment Pearson procedure. The results showed that: 1) some of the agents’ characteristics were correlated significantly with their job productivity, there were period of employment, training, agricultural area in the region of agriculture extension, 2) Some factors of the agents’ motivation were correlated significantly with their job productivity. There are recognition, salary and reward. 3) Some factors of the agents’ competencies were correlated significantly with their job productivity. There are planning, implementating, evaluation of agriculture extension, and communication skills real contact with the work productivity of agricultural extension. 4) Work productivity level of agricultural extension agents still not yet good (“low” category) because four of five factors work productivity as method extension, extension materials and increased knowledge and skills of farmers were low.Keywords: Agricultural Extension Agent, Characteristics, Motivation, Competence, Productivity
38
Ssozi, John, Simplice Asongu, and Voxi Heinrich Amavilah. "The effectiveness of development aid for agriculture in Sub-Saharan Africa." Journal of Economic Studies 46, no. 2 (March 4, 2019): 284–305. http://dx.doi.org/10.1108/jes-11-2017-0324.
Full textAbstract:
Purpose Agriculture is the major source of livelihood for the majority of population in Sub-Saharan Africa but its productivity is not only low it has started showing signs of decline since 2012. The purpose of this paper is to find out whether official development assistance for agriculture is effective. Design/methodology/approach The data for development assistance for agriculture are broken down into the major agricultural sectors in receiving countries. The empirical evidence is based on the two-step system, i.e. generalized method of moments, to assess the degree of responsiveness of agricultural productivity to development assistance. Findings There is a positive relationship between development assistance and agricultural productivity in general. However, when broken down into the major agricultural recipient sectors, there is a substitution effect between food crop production and industrial crop production. Better institutions and economic freedom are found to enable agricultural productivity growth, and to increase the effectiveness of development assistance. The structural economic transformation associated with agricultural development assistance is also found to be weak. Practical implications Allocation of development assistance for agriculture is primarily determined by need, although expected effectiveness also increases the assistance receipts. Agricultural assistance policies could focus more on building productive capacity to reduce the need while boosting effectiveness. Originality/value Breaking down data into agricultural recipient sectors and controlling for the potential spurious correlation under the assumption that more development assistance could be allocated, where agricultural productivity is already increasing due to some other factors.
39
Scheierling, Susanne, David O. Treguer, and James F. Booker. "Water Productivity in Agriculture: Looking for Water in the Agricultural Productivity and Efficiency Literature." Water Economics and Policy 02, no. 03 (September 2016): 1650007. http://dx.doi.org/10.1142/s2382624x16500077.
Full textAbstract:
Expectations are that the agricultural sector will have to expand the use of water for irrigation to meet rising food demand, while at the same time the competition for water resources is growing in many regions. Increasing water productivity in agriculture is widely seen as a critical response to help address this challenge. Yet much of the public debate is vague on the meaning of agricultural water productivity — often emphasizing “more crop per drop” as if water were the only input that mattered —, and approaches for assessing and increasing water productivity are seldom addressed systematically. This paper discusses conceptual issues that should be kept in mind when assessing agricultural water productivity, and presents findings from what may be the first survey of the agricultural productivity and efficiency literature with regard to the explicit inclusion of water aspects in productivity and efficiency measurements. The survey comprises studies applying single-factor productivity (SFP) measures, total factor productivity (TFP) indices and frontier models. Studies using deductive methods are also included. A key finding is that the studies tend to either incorporate field- and basin-level aspects but focus only on a single input (water), or they apply multi-factor approaches but do not tackle the basin-level aspects. It seems that no study has yet presented an approach that accounts for multiple inputs and basin-level issues. Deductive methods may provide the flexibility to overcome some of the limitations of the other methods.
40
Zhang, Jianying, and Xuebin Feng. "Optimal Matching Metaheuristic Algorithm for Potential Areas of Agricultural Economic Resources Development Based on Spatial Relationship." Journal of Food Quality 2022 (March 23, 2022): 1–10. http://dx.doi.org/10.1155/2022/9301098.
Full textAbstract:
The agriculture sector is the backbone of the economies of many Asian countries such as India, China, and Bangladesh. The agriculture sector can contribute a major share to the GDP of such countries where the main occupation of the citizens is agriculture or the dependency of the citizens is mainly on the agricultural productivity. It is important to study the potential areas of agricultural economic resource development. The existing methods are not efficient enough to map the potential areas of agricultural productivity with economic resource development, and hence, it has motivated us to study the aspects which impact the economic resource development based on agricultural productivity. There are numerous factors such as low productivity, high irrigation amount, high labor charges, low proportion of planning optimization, and low crop yield that should be considered to study the correlation between economic development and agricultural productivity. Firstly, the spatial relationship of potential areas of agricultural economic resources development is analyzed in this paper. Secondly, the multiobjective linear programming model is proposed. Based on this multiobjective model, the optimal matching model for potential areas of agricultural economic resource development is constructed, and the improved genetic algorithm is used to solve the model to realize the optimal matching of potential areas of agricultural productivity and economic resource development. The experimental results show that the proposed method has high economic benefit, low irrigation amount, and high proportion of planning optimization with high crop yield.
41
Chen, Chaoran. "Untitled Land, Occupational Choice, and Agricultural Productivity." American Economic Journal: Macroeconomics 9, no. 4 (October 1, 2017): 91–121. http://dx.doi.org/10.1257/mac.20140171.
Full textAbstract:
The prevalence of untitled land in poor countries helps explain the international agricultural productivity differences. Since untitled land cannot be traded across farmers, it creates land misallocation and distorts individuals' occupational choice between farming and working outside agriculture. I build a two-sector general equilibrium model to quantify the impact of untitled land. I find that economies with higher percentages of untitled land would have lower agricultural productivity; land titling can increase agricultural productivity by up to 82.5 percent. About 42 percent of this gain is due to eliminating land misallocation, and the remaining is due to eliminating distortions in individuals' occupational choice. (JEL J24, J43, O13, P14, Q12, Q15, Q24)
42
Cheng, XiaoYu. "Digital inclusive finance and total factor productivity in agriculture—— Evidence from China." BCP Business & Management 44 (April 27, 2023): 975–86. http://dx.doi.org/10.54691/bcpbm.v44i.4986.
Full textAbstract:
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.
43
Moon, Wanki. "Are there dynamic productivity gains from agricultural trade?" China Agricultural Economic Review 14, no. 1 (December 16, 2021): 32–46. http://dx.doi.org/10.1108/caer-02-2021-0030.
Full textAbstract:
PurposeThe primary purpose of this paper is to take an in-depth look at the question of whether liberalizing trade in agriculture can generate dynamic productivity gains comparable to those in the manufacturing sector.Design/methodology/approachIn contrast to the manufacturing sector that has generated firm/plant-level trade data, there is a lack of farm-level trade data that are needed for empirical measurement of dynamic productivity gains. Therefore, the authors use thought experiments to analyze the sequence of events that would occur when trade is liberalized for agriculture; delineate the expected behaviors of the actors involved in the trade and draw inferences about whether there would be dynamic productivity gains from agricultural trade.FindingsThe central finding is that there would be little dynamic gain from agricultural trade at the farm level due to the limited role of producers in shaping their international competitiveness. Yet, agricultural trade may generate dynamic gains if states or input supply corporations respond to the freer trade environment by making more investments for research and development (R&D). Further, when intraindustry prevails, there can be productivity gains at the industry level due to the transfer of resources from less to more efficient farm producers.Originality/valueThe findings of the paper are expected to present insights into value for researchers working in the area of agricultural trade; for agricultural trade policymakers in developing countries and for trade negotiators engaged in reforming or designing World Trade Organization (WTO)’s trade rules for agriculture.
44
Priya, M. Likhitha, Jaswanthi Pooja, Lalitha ., and Selva Priya. "Agricultural Crop Recommendation Based on Productivity and Season." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1668–74. http://dx.doi.org/10.22214/ijraset.2023.50383.
Full textAbstract:
Abstract: As a coastal nation, Tamil Nadu is going through agricultural uncertainty, that's lowering its manufacturing. With greaterhumans and area, greater merchandisemight be produced, howeverit cannot be. Inpast a long time, farmers had phrase of mouth, but now they can't be used due to climatic elements. Agricultural statistics and parameters provide insight into agricultural records. The growth of statistics technology brings a few crucial trends in agricultural sciences to help farmers with precise agricultural information. In this strolling state of affairs, information approximately the utility of present day technological strategies inside the subject of agriculture is suited. Machine gaining knowledge of strategies really provide an explanation for the sample with the information and assist us make predictions. Agricultural problems consisting of crop availability, croprotation, water requirements, fertilizer requirements and protection can be addressed. Due to the variousreasons of theclimatic surroundings, it is essential to havean efficient system to facilitate the cultivation of plants and to assist farmers in production and control. This will help future farmers to enhance agriculture. Aadvice device may be furnished to the farmer to assist him get his plants via the mines. To enforce this approach, vegetationare advocated in phrases in their climatic elements and amount. Data analytics pavesthe manner for growing beneficial extracts from agricultural databases. The crop dataset become analyzed and crop pointershave been made based on yield and season
45
Sunge, Regret, and Nicholas Ngepah. "Agricultural Trade Liberalisation and Agricultural Total Factor Productivity Growth Convergence in Africa." Research in Agriculture Livestock and Fisheries 9, no. 2 (September 5, 2022): 71–88. http://dx.doi.org/10.3329/ralf.v9i2.61612.
Full textAbstract:
Reducing income inequality in Africa rests on agricultural total factor productivity (TFP) growth and convergence. Liberalizing agricultural trade has emerged as a force of agricultural TFP growth convergence. Notwithstanding increasing agricultural trade, TFP in Africa is falling while the differences in TFP growth rates remain wide. We provide evidence on the impact of agricultural trade liberalization on agricultural TFP growth convergence. We examine trade by origin, disaggregated into intra-Africa, and rest-of-the-world trade. Also, we recognize the uniqueness of agricultural trade liberalization and analyze the effect of the removal of trade-distorting agriculture support. Using maize and rice data for the period 2005-2016, we apply a Feasible-Generalized- Least-Squares estimation of panel data models derived from Barro and Sala-i-Martin (1990). We find evidence for both absolute and conditional convergence, which is stronger for maize. Moreover, agricultural trade openness speeds up TFP growth convergence for both crops. Convergence speed is higher for intra-Africa trade. Estimations on domestic agriculture support suggest that reduction of support beyond distortion-free levels enhances TFP growth convergence. Our findings call for more agricultural trade liberalization. We appeal that the recently launched Africa Continental Free Trade Area prioritizes intra-Africa agricultural trade liberalization and further elimination of trade-distorting domestic agriculture support.
Vol. 9, No. 2, August 2022: 71-88
46
Wang, Yafei, Zihan Zhao, Ming Xu, Zhixiong Tan, Jingwei Han, Lichen Zhang, and Siying Chen. "Agriculture–Tourism Integration’s Impact on Agricultural Green Productivity in China." Agriculture 13, no. 10 (October 5, 2023): 1941. http://dx.doi.org/10.3390/agriculture13101941.
Full textAbstract:
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.
47
Imam, Fakhar, and Allah Bakhsh. "The Impact of Psychological Factors on Productivity of Agricultural Financing: An Evidence from Punjab, Pakistan." Journal of Economic Impact 2, no. 2 (June 15, 2020): 72–79. http://dx.doi.org/10.52223/jei0202205.
Full textAbstract:
The purpose of this study was to estimate the impact of psychological factors of farmers on the productivity of agricultural finance used for agriculture purposes. The independent variables include the socio-economic and psychological factors of the farmers. The psychological factors used in the study are trust, impulsiveness, perceived control, agreeableness, extraversion, organizational commitment, and risk aversion characteristics of the farmers. For this study, the primary data were collected through the questionnaire from 400 farmers from district Multan, Punjab. A binary logit model was used for the analysis. According to the estimated results, the impact of the age of a farmer is negative and significant on the productivity of agriculture financing. There is a positive and significant impact of agricultural land on the productivity of agricultural finance. There is a positive and significant impact of the income level of the farmer on the productivity of the agricultural loan. The farmer’s personality characteristic of having trust in others for the very first time has a positive and significant impact on the productivity of agricultural finance. Lack of perceived control in a farmer’s personality has a negative and significant impact on agricultural productivity while using agricultural finance for the productive purpose at the farm. The impact of agreeableness behavior has negative but significant. Extraversion characteristic has a positive and significant impact on the productivity of the farmers.
48
Osinowo, Olatokunbo Hammed, Esther Toluwatope Tolorunju, and Iyabosola Mary Osinowo. "Drivers of agricultural productivity: Evidence from transforming economies." Agricultura Tropica et Subtropica 54, no. 1 (January 1, 2021): 14–23. http://dx.doi.org/10.2478/ats-2021-0002.
Full textAbstract:
Abstract This study empirically investigates the drivers of agricultural productivity in transforming economy. The study used a 35-year period (1980–2014) panel data sourced from World Development Indicators, Penn World Table, United States Department of Agriculture and Statistics on Public Expenditure for Economic Development. Data used for the study include Agricultural Productivity (AP), Real Gross Domestic Product (GDP), Government Agricultural Expenditure (EXP), Agricultural Trade Barrier (ATB), Consumer Price Index (CPI), Farm Machinery (MACH), Fertiliser (FERT), Human Capital (HCAP) and Irrigation (IRRG). Data were analysed using Impulse Response Function, Levin-Lin-Chu unit root test, Johansen-Fisher Panel Cointegration test and Panel Least Squares regression technique. Impulse Response Function revealed that ln (GDP)reacted negatively to a shock from ln (Agricultural Productivity). Levin-Lin-Chu unit root test revealed that the variables were stationary either at level or at first difference. The result of the Johansen-Fisher panel cointegration test showed that for every case at 5 percent level of significance, we reject null hypothesis of no cointegration. Panel Least Squares revealed that Agricultural Trade Barrier (α = 0.0531, p < 0.05), Human Capital (α = 1.2409, p < 0.01) and Irrigation (α = 0.0771, p < 0.01) increased Agricultural Productivity. However, Fertilizer (α = −0.0730, p < 0.01) decreased Agricultural Productivity. This study therefore concluded that Agricultural Productivity will grow in transforming economy with trade restriction on imported agricultural tradable commodities, increased investment in human capital and expansion in irrigation application. The study therefore recommends measures that will protect domestic agriculture, capacity building of the farmers and improved irrigation infrastructure that will enhance small scale farmers for all-season cropping.
49
SEVEN, UNAL, and SEMIH TUMEN. "AGRICULTURAL CREDITS AND AGRICULTURAL PRODUCTIVITY: CROSS-COUNTRY EVIDENCE." Singapore Economic Review 65, supp01 (March 10, 2020): 161–83. http://dx.doi.org/10.1142/s0217590820440014.
Full textAbstract:
We present cross-country evidence suggesting that agricultural credits have a positive impact on agricultural productivity. In particular, we find that doubling agricultural credits generates around 4–5% increase in agricultural productivity. We use two different agricultural production measures: (i) the agricultural component of GDP and (ii) agricultural labor productivity. Employing a combination of panel-data and instrumental-variable methods, we show that agricultural credits operate mostly on the agricultural component of GDP in developing countries and agricultural labor productivity in developed countries. This suggests that the nature of the relationship between agricultural finance and agricultural output changes along the development path. We conjecture that the development of the agricultural finance system generates entry into the agricultural labor market, which pushes up the agricultural component of GDP and keeps down agricultural labor productivity in developing countries; while, in developed countries, it leads to labor-augmenting increase in agricultural production. We argue that replacement of the informal credit channel with formal and advanced agricultural credit markets along the development path is the main force driving the labor market response.
50
Wang, Lei, Xueshan Wang, Yaling Hu, and Luyuan Yang. "Research on the Path of Efficient Agricultural Development Enabled by New Quality Productivity." Academic Journal of Science and Technology 11, no. 2 (June 12, 2024): 227–32. http://dx.doi.org/10.54097/w3dhgb83.
Full textAbstract:
The 20th National Congress of the Communist Party of China pointed out that the agricultural modernization plan will be realized in 2035. Digital new quality productivity is a combination of digital economy and new quality productivity. This project aims to explore how digital economy can be an important part of new quality productivity to help agricultural development. Some towns in Anhui Province will be taken as an example, with the improvement of rural digital economic infrastructure, the proportion of new talents, the development degree of digital agricultural trade, combined with the agricultural bureau and related data for empirical analysis. The verification of digital new quality productivity can improve the production efficiency of rural agriculture, reduce production costs and improve the quality of agricultural products, effectively safeguard the fundamental interests of the people, and promote the modernization of agriculture and the country.