Relevant bibliographies by topics / Agricultural productivity

Academic literature on the topic 'Agricultural productivity'

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Published: 4 June 2021
Last updated: 31 July 2024

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Journal articles on the topic "Agricultural productivity"

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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.

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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.
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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.

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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.
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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.

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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.

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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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Dissertations / Theses on the topic "Agricultural productivity"

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BALDONI, EDOARDO. "Agricultural Productivity in Space." Doctoral thesis, Università Politecnica delle Marche, 2017. http://hdl.handle.net/11566/245559.

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Questa tesi ha l’obiettivo di misurare la produttività totale dei fattori in agricoltura in Italia nel periodo 2008-2014 e di capirne i caratteri salienti. Attraverso l’utilizzo di micro dati delle aziende agricole commerciali campionate dalla RICA (Rete di Informazione Contabile Agricola) e dei numeri indice, si derivano indici di produttività a livello nazionale, regionale, provinciale, e a livello di specializzazione produttiva e di dimensione economica. Gli indici di produttività sono generati attraverso la procedura del minimum spanning tree (Hill, 1999; Hill 2004) e sono quindi comparabili tra le varie unità nel tempo. I risultati mostrano una performance decrescente nell’arco dei sette anni considerati. Sia a livello nazionale che a livello di specializzazione produttiva e di dimensione economica, gli indici mostrano un andamento decrescente. A livello di dimensione si registra una relazione positiva tra produttività e dimensione economica delle aziende con ampi differenziali tra le classi dimensionali esaminate. Per quanto riguarda le specializzazioni produttive, livelli maggiori di produttività si registrano per quelle specializzazioni che possono essere considerate di carattere maggiormente professionale. In particolare i bovini da latte, l’ortofloricoltura, la frutticoltura e la viticoltura sono le specializzazioni produttive a più alta performance. I granivori, gli erbivori, la cerealicoltura, i seminativi e le aziende miste mostrano invece una performance inferiore rispetto alle prime. A livello regionale si evidenziano due cluster di regioni ad alta produttività. Uno è composto da Emilia-Romagna, Lombardia, Trentino, Alto Adige, Veneto e Friuli Venezia Giulia e l’altro al Sud è composto da Calabria e Basilicata. La produttività sembra essere legata alla composizione delle singole agricolture regionali in termini di tipologia di produzione e di dimensione economica. Ulteriori analisi saranno comunque necessarie al fine di stabilire una relazione tra la composizione agricola regionale e la performance economic. Nella seconda parte della ricerca gli indici a livello provinciale vengono utilizzati per capire i differenziali di produttività tenendo in considerazione il più possibile la variabilità territoriale italiana. La TFP a livello provinciale mostra una lieve tendenza alla clusterizzazione spaziale. Il grado di dipendenza spaziale viene quantificato in un modello lineare che assume dipendenza spaziale, dipendenza temporale e la presenza di una serie di variabili esogene. Il modello viene stimato con lo stimatore BCLSDV (Bias Corrected Least Squares Dummy Variable). Le stime mostrano un basso grado di dipendenza temporale e un alto grado di dipendenza spaziale assumendo però una struttura di correlazione spaziale limitata a 50-70 chilometri. I risultati delle stime vengono utilizzati per quantificare gli effetti di diffusione a seguito di uno shock esogeno di produttività nelle varie province. Ciò che emerge è che, data la struttura di correlazione spaziale assunta e data la bassa dipendenza temporale, gli effetti di uno shock di produttività sono limitati nel tempo e nello spazio. Questi si estendono anche a province lontane dall’epicentro dello shock ma con caratteri diversi rispetto a quelle vicine. In particolare, l’effetto spillover di lungo periodo è maggiore nelle regioni limitrofe allo shock e viene raggiunto in un tempo decisamente inferiore rispetto alle province più lontane. Questi risultati vengono interpretati come evidenza dello stretto legame tra territorio e produzione agricola. Questo legame è in grado di influenzare i caratteri e lo sviluppo delle agricolture locali.
The research aims at measuring agricultural total factor productivity in Italy over the period 2008-2014 and at understanding its main features. It leverages farm-level information from the FADN (Farm Accountancy Data Network) database e the index number methodology to derive indexes at either geographical level and at the level of farm types. At geographical level, indexes are derived at national level, at the level of FADN regions and at NUTS3 level. Then, indexes are derived at the level of farm typology and of economic size. Indexes are derived using the minimum spanning tree method and are comparable across spatial units over time. Results point to a decline in aggregate productivity over time. Indexes at national level, at the level of economic size and at the level of farm typologies all exhibit general downward trends. A positive relationship between TFP and economic size is found with large productivity differentials across size classes. Large differentials are also found across types of farms. The types associated to a more entrepreneurial nature, such as dairy, horticulture, fruit production and grapes and wine production, are all associated with higher productive performance with respect to the others. At the level of FADN regions, there seem to be two clusters of highly productive regions. One is in the North and is composed of Emilia-Romagna, Lombardia, Veneto, Friuli Venezia Giulia, Trentino and Alto Adige. The other is in the South and is composed of Calabria and Basilicata. TFP seems to be linked to the structure of the regional agricultures in terms of types of farming and size of farms. However, further analyses would be required to establish a relationship between productivity and agricultural composition of geographical regions. In the second part of the research, measurements at NUTS3 level are used to inspect productivity differentials considering the spatial variability of the Italian territory. A limited degree of productivity clustering is found at NUTS3 level. Spatial dependence is quantified in a linear model that assumes also temporal dependence of TFP and controls for covariates. The model is estimated with the BCLSDV (Bias Corrected Least Squares Dummy Variable) estimator. Assuming a narrow spatial correlation structure, estimates show a limited degree of temporal dependence and a high degree of spatial dependence. Coefficient estimates are then used to model the diffusion process of a productivity shock hitting specific NUTS3. Evidences from the exercise show that, due to the narrow spatial correlation structure assumed and the limited temporal dependence, the effects of a shock are limited in space and over time. Effects of a shock differ depending on the distance of NUTS3 from the epicenter of the shock. Neighboring NUTS3 receive, in a shorter time frame, a larger long-run spillover effect with respect to NUTS3 that are further away. This results is an evidence of the site-specificity of agricultural production. The close link that exists between locations and agriculture influence production practices and their development.
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Gurung, Ananda Bahadur. "Impact of Agricultural Productivity Changes on Agricultural Exports." Thesis, North Dakota State University, 2008. https://hdl.handle.net/10365/29760.

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This study uses linear programming and econometric tools to determine the impact of agricultural productivity (technology) on agricultural exports. The study determines total factor productivity (TFP) using the Malmquist index method for a panel of 64 countries. Productivity impact on exports is determined by a two-stage estimation procedure. The results show agricultural productivity affects agricultural exports. This has important implications for developing countries. A 1 unit change in cumulative TFP increases agricultural output by .79% and a 1% increase in estimated agricultural output increases exports by .37%. Therefore, the total effect of technology on exports of primary and processed commodities is .29%. Developed countries generally have higher TFP rates, leading to higher export earnings; meanwhile, developing countries are not getting the benefits from agricultural exports because they have a relatively lower level of agricultural productivity. Investing in research and development for agriculture can improve technology, which, in turn, can Increase agricultural exports.
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Carvalho, Rosemeiry Melo. "Three Essays About Agricultural Productivity." Universidade Federal do CearÃ, 2003. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=1291.

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FundaÃÃo de Amparo à Pesquisa do Estado do CearÃ
This thesis studies agricultural productivity both from an empirical and theoretical perspective. On the empirical side, a stochastic frontier is estimated for Brazilian states and the Data Envelopment Analysis methodology is employed for South American countries. Regarding the theoretical perspective, the impact of an increase on agricultural productivity upon economic growth and welfare is analyzed by means of an endogenous growth model. We conclude that agricultural productivity plays a major role in determining the growth rate of economies.
Nesta Tese faz-se um estudo sobre produtividade agrÃcola tanto do ponto de vista empÃrico quanto teÃrico. Quanto ao primeiro aspecto, realiza-se estimativas usando o mÃtodo da fronteira estocÃstica para os estados brasileiros e o Data Envelopment Analysis - DEA para os paÃses da AmÃrica do Sul. Do ponto de vista teÃrico analisa-se atravÃs de um modelo de crescimento endÃgeno o impacto do aumento da produtividade agrÃcola no crescimento econÃmico e no nÃvel de bem-estar. A conclusÃo do trabalho à que a produtividade no setor agrÃcola tem importantes implicaÃÃes na taxa de crescimento da economia
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Alia, Didier Y. "AGRICULTURAL INPUT INTENSIFICATION, PRODUCTIVITY GROWTH, AND THE TRANSFORMATION OF AFRICAN AGRICULTURE." UKnowledge, 2017. http://uknowledge.uky.edu/agecon_etds/59.

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This dissertation studies agricultural input intensification, defined as the increased use of modern inputs such as hybrid seeds, mineral fertilizer, herbicide, and pesticide in African agriculture. It also analyses the potential of this intensification to accelerate productivity growth and tests the effectiveness of two policies, input subsidies and land reforms, in promoting it and consequently in increasing crop yield. In the first essay, we argue that to create the conditions for the emergence of a green revolution in Africa, modern agricultural technologies have to be adopted as a package, not in a piecemeal fashion. This argument is consistent with a conceptual framework that we develop to illustrate the importance of harnessing strategic complementarities among agricultural technologies by adopting them simultaneously rather than sequentially. Based on this framework, we propose a methodology to estimate an index to measure agricultural input intensification in its many dimensions. The index provides a simple and intuitive measure to quantify joint adoption of several inputs and compare it across plots, crops, farmers, and regions. Applying this methodology to maize producers in Burkina Faso and Tanzania, we show that our estimated index is a valid measure of joint input adoption and effectively captures the relative importance of each input as well as the number of different inputs adopted. Using the estimated index, we find that simultaneous adoption of modern inputs in Burkina Faso and Tanzania is limited but not rare. Most importantly, we find that the impact of the adoption of individual modern input on yield is increasing with the level of intensification for others. In the subsequent two essays, we assess the effectiveness of government’s direct intervention through input subsidies and indirect intervention through land reforms in promoting agricultural input intensification and increasing productivity. Our empirical analyses focus on Burkina Faso, a country that has recently implemented a fertilizer subsidy program and is undertaking profound land reforms to improve land tenure security and land transferability among households. The second essay tests the hypothesis that subsidizing only one input might promote or discourage the use of other inputs. We find that fertilizer subsidy for maize farmers in Burkina Faso crowds in the use of hybrid seeds and crop protection chemicals, but discourages the use of manure. The last essay assesses whether the development of rural land rental markets can facilitate land transferability among farmers and increase input intensification and productivity. The findings suggest that land rental transfers land from less talented or committed farmers to the more able but have minimal impact on input intensification. However, our results show that land renters are more productive and better farm managers. These results suggest that the short-term gains from policies that foster the development of land rental markets in Burkina Faso, and more generally Africa, will likely be in term of efficiency rather than widespread adoption of modern agricultural technologies.
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Liu, Yucan. "Risk, induced innovation, and productivity convergence in U.S. agriculture." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Dissertations/Spring2007/y_Liu_043007.pdf.

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Kulkarni, Kedar <1991&gt. ""Indian Agriculture – Productivity, Climate Change and Institutions An essay in Agricultural Economics"." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8815.

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"Agricultural sector in India has recorded tremendous growth since Independence. This has been largely possible due to the new agricultural reforms and the arrival of the green and white revolutions. The impact of the new agricultural reforms can be felt in the massive increase in the productivity of coarse cereals and pulses which has enabled India to attain self-sufficiency in food grains. A by-product of this has been the gradual rise of energy inputs. In particular, fertilizer consumption, diesel use and electricity consumption, have seen a dramatic rise post 1960. There also has been a large scale substitution of capital for labour. This is a direct consequence of the increasing population size and food grain demand as India strives to maintain self-sufficiency. However, more importantly, the extravagant use of energy inputs and substitution of capital for labour coupled with new agricultural technology has had an adverse effect on the climate. This thesis makes an attempt to analyse the growth in Indian Agriculture and derive its implications in relation to energy use and CO2 emissions. The specific objective is to estimate the relationship between carbon emissions and agricultural productivity. Although agricultural production in India has witnessed a tremendous growth, it is unclear whether the high intake of energy has an adverse impact on climate. Over the past years, the northern states of India have blossomed partly due to favourable climatic conditions, while the western and southern states have experienced drastic climatic conditions that have adversely impacted agricultural productivity, repercussions of which are felt in farmer suicides and rural to urban migration. This thesis also investigates this issue by throwing light on the role of institutions in the development of agriculture and its implications on climate change. The findings of the study show the presence of a positive relationship between agricultural productivity and the level of carbon emissions. Further, the study also finds that states with good institution are able to perform better than their competitors endowed with bad institutions."
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Benu, Fredrik Lukas. "Farm productivity and farmers' welfare in West Timor, Indonesia." Thesis, Curtin University, 2003. http://hdl.handle.net/20.500.11937/333.

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This study examines agricultural productivity and farmers' welfare in West Timor, Indonesia. The driving force behind this study is to understand why the welfare of farmers has lagged behind others despite significant growth in the agricultural sector. The main research problem in this study is that while agricultural production has increased significantly in West Timor, the welfare of farmers has not increased as fast as that of non-farmers. To reduce the gap in income between farmers and nonfarmers, the growth of income of West Timor's farmers, as the indicator of their welfare, has to accelerate at least as fast as the growth of non-farmers' income. This target might be achieved if there is an appropriate policy of agricultural development implemented by the government. For this reason, evaluation of the structure of agricultural production, as well as the welfare of the farmers of West Timor, is an important issue that needs to be addressed. This study analyses the structure of agricultural production and the welfare of farmers in West Timor. An econometric method (Three Stage Least Squares) was used in modelling the agricultural system to evaluate the structure of the agricultural production as well as farmers' welfare in West Timor. A simultaneous equations model which consists of eight structural and four identity equations was constructed for the analysis of the structure, the estimation of elasticities from the regression coefficients and the subsequent policy analysis. The data used for the analysis are secondary data published by the Indonesian government.All data used in the model were time series data from 1979 to 1998 and gathered in the period between January and July 1999. The results of this research found that technical factors such as water availability. pasture capacity and irrigation channels influence the production of agriculture more than economic factors such as the price of products and cost of inputs. Too, population growth and the availability of socio-economic institutions such as cooperatives at the village level. have a significant influence on the agricultural production. Although technical factors influence the production of agriculture more than economic factors, subsequent policy analysis shows that an increase in agricultural credit as well as a reduction in the cost of production will still have a positive impact on the production of agriculture. A policy to increase the price of agricultural commodities at the farm gate, especially the price of live cattle and rice, will increase the profit of farmers, further motivating them to increase their overall production. There are six scenarios of the policy alternatives that are simulated in this study. These are: (1) the scenario of a 10 per cent increase in the size of irrigated areas, (2) the scenario of a 10 per cent increase in the amount of credit, (3) the scenario of a 35 per cent decrease in total cost per hectare of maize cultivation, (4) the scenario of a 10 units increase in the number of cooperatives, (5) the scenario of a 10 per cent increase in the price of live cattle at the farm gate, and (6) the scenario of a 10 per cent increase in the price of rice at the farm gate.The results of the policy analysis found that the largest positive impact on the agricultural sector output as well as farmers per capita income is derived from the scenario of a 10 per cent increase in the size of irrigated area. The scenarios of increasing amount of agricultural credit and the number of co-operatives have also generated a large positive impact on the agricultural sector output, but with a high increase in farmer population growth. Two other scenarios that have a large impact on the agricultural sector output as well as farmers' per capita income are the scenario of a 10 per cent increase in the price of live cattle and the price of rice. Based on the results of the policy analysis, two main policies that might be undertaken by the government to promote the growth of the agricultural sector and farmers' per capita income are expansion of irrigated areas and improving farmers' access to agricultural credit.
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Akudugu, Mamudu Abunga. "Farm credit and agricultural productivity in Ghana." Thesis, University of Reading, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.715414.

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Nimo, Michael Kwabi. "Agricultural productivity and supply responses in Ghana." Thesis, University of Nottingham, 2012. http://eprints.nottingham.ac.uk/12583/.

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The importance of Agricultural Supply Response (ASR) modelling cannot be over emphasised. Knowledge of its size provides a roadmap for designing a tailored agricultural policy based on suppliers’ responses to price and non-price incentives. In spite of its policy importance, limited amount of studies exist for Ghana. This study seeks to fill the gap and also sheds some light on how future agricultural policies in Ghana should be formulated. This study is conducted on a regional (ecological) group basis and at a crop-level. Apart from price and non-price factors, we have also accounted for technical inefficiencies, a problem that impedes the growth of agricultural production in Ghana. We employed the duality modelling technique (based on the profit function). This technique provides a more intuitive way of modelling and interpreting ASRs. We used the fourth wave of the Ghana Living Standard Survey (GLSS4), a cross-sectional dataset collected between 1998 and 1999. The analysis is based on six crops, grouped into industrial (cocoa and groundnut), food (maize, rice and cowpea) and staple (sorghum and millet combined and termed migso in the study). A sensitivity analysis is carried out to check the robustness of results. We found high national and ecological technical inefficiency scores. Nationally, technical inefficiency is in the neighbourhood of 53%. At the ecological levels, groundnut (industrial crop) farmers in the Coastal zone recording the highest inefficiency (83%) with the least inefficiency score coming from cowpea (food) farmers in the Savannah zone (30%). In a related outcome we found that technical inefficiency estimates and patterns are sensitive to the structure and composition of the dataset. Our supply elasticities support claims that farmers in Ghana will respond to both market (price) and non-price incentives. In terms of price incentives we found that, with or without technical inefficiency, farmers of food crops in the Coastal zone will respond the most to changes to outputs prices. Farmers in the Savannah zone for all crops but staples will be the least to respond to output price change. We found, however, that with production inefficiency accounted for, supply responses were relatively lower, reinforcing the arguments that earlier supply response estimates from other studies could have been inaccurately estimated especially where analysis failed to account for non-price factors. Moreover, the study estimates revealed that farmers in Ghana are would record a larger output supply responses to changes in inputs prices than output prices. Besides price, the study also found that all four non-price incentives - plot size, animal capital, family labour and education of household head - are important to the development of an effective agricultural policy regardless of whether technical inefficiency is accounted for or not. In some cases, output supply responses from non-prices factors outweighed price elasticities, again supporting the argument that ASR estimates are likely to be biased if non-price factors are omitted. These findings provide two policy signposts for the design of Ghana’s future agricultural policies. Firstly, the policy - aimed at increasing output and/or improving the sector’s competitiveness - must identify and address technical inefficiencies among smallholder agricultural farmers. Failure to address such inefficiencies would lead to suboptimal performance - operating on a lower production frontier. Secondly, the differences in crop-level ecological supply elasticities support regional-based agricultural policies rather than a one-size-fits all centralised agricultural policy.
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Tran, Huu-Cuong. "Market access and agricultural productivity in Vietnam." Beuren Stuttgart Grauer, 2005. http://d-nb.info/989887405/04.

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Books on the topic "Agricultural productivity"

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Ball, V. Elton, and George W. Norton, eds. Agricultural Productivity. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9.

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W, Robertson James. Improving Canadian agriculture. Ottawa: Commission of Conservation, 1997.

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B, Tweddle A., and British Columbia. Dept. of Agriculture. Statistics Branch., eds. Agricultural statistics, 1916. Victoria, B.C: W.H. Cullin, 1997.

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1959, Stewart G. H., and British Columbia. Dept. of Agriculture. Statistics Branch., eds. Agricultural statistics, 1919. Victoria, B.C: W.H. Cullin, 1997.

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M, Capalbo Susan, Antle John M, Resources for the Future. Renewable Resources Division., and National Center for Food and Agricultural Policy (U.S.), eds. Agricultural productivity: Measurement and explanation. Washington, D.C: Resources for the Future, 1988.

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Craig, Barbara J. International agricultural productivity patterns. St. Paul, Minn: Center for International Food and Agricultural Policy, University of Minnesota, 1994.

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Rehman, Hifzur. Energy use in agricultural productivity. New Delhi: Concept Pub. Co., 2003.

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M, Alston Julian, Pardey Philip G, Smith Vincent H, and International Food Policy Research Institute., eds. Paying for agricultural productivity. Baltimore: Johns Hopkins University Press, 1999.

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Taufique, Mohammad. Human dimensions and agricultural productivity. Delhi: Vista International Pub. House, 2005.

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Azam, Qazi Tauqir. Agricultural research productivity in Pakistan. Islamabad: Pakistan Agricultural Research Council, 1991.

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Book chapters on the topic "Agricultural productivity"

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Ball, V. Eldon, and George W. Norton. "Introduction and Overview." In Agricultural Productivity, 1–8. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_1.

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Ball, V. Eldon, Rolf Färe, Shawna Grosskopf, F. Hernandez-Sancho, and Richard F. Nehring. "The Environmental Performance of the U.S. Agricultural Sector." In Agricultural Productivity, 257–75. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_10.

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Chaston, Kelly A., and Frank M. Gollop. "The Effect of Ground Water Regulation on Productivity Growth in the Farm Sector." In Agricultural Productivity, 277–91. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_11.

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Ball, V. Eldon, Ronald G. Felthoven, Richard F. Nehring, and Catherine J. Morrison Paul. "Costs of Production and Environmental Risk: Resource-Factor Substitution in U.S. Agriculture." In Agricultural Productivity, 293–309. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_12.

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Evenson, Robert E., Bruce L. Gardner, Dale W. Jorgenson, and C. Richard Shumway. "Conclusion: The Usefulness of Productivity Measurement." In Agricultural Productivity, 311–19. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_13.

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Ball, V. Eldon, Jean-Pierre Butault, and Richard F. Nehring. "U.S. Agriculture, 1960–96: A Multilateral Comparison of Total Factor Productivity." In Agricultural Productivity, 11–35. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_2.

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Acquaye, Albert K. A., Julian M. Alston, and Philip G. Pardey. "A Disaggregated Perspective on Post-War Productivity Growth in U.S. Agriculture: Isn’t That Spatial?" In Agricultural Productivity, 37–84. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_3.

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Rao, D. S. Prasada, Christopher J. O’Donnell, and V. Eldon Ball. "Transitive Multilateral Comparisons of Agricultural Output, Input, and Productivity: A Nonparametric Approach." In Agricultural Productivity, 85–116. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_4.

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Moss, Charles B., Gregory Livanis, Vince Breneman, and Richard F. Nehring. "Productivity Versus Urban Sprawl: Spatial Variations in Land Values." In Agricultural Productivity, 117–33. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_5.

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O’Donnell, C. J. "Parametric Estimation of Technical and Allocative Efficiency in U.S. Agriculture." In Agricultural Productivity, 137–66. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0851-9_6.

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Conference papers on the topic "Agricultural productivity"

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Sulaimanova, Burulcha, and Daniyar Jasoolov. "The Gender Gap in Agricultural Productivity in Kyrgyzstan." In International Conference on Eurasian Economies. Eurasian Economists Association, 2018. http://dx.doi.org/10.36880/c10.02039.

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More than two third of total population of Kyrgyzstan are living in rural areas, and the agricultural sector of Kyrgyzstan employs nearly the half of labor force and have export oriented output production with over than 384 thousand peasant farms. The share of employed women in agriculture compromised the 44 % of total agricultural labor force. However the low economic efficiency and competitiveness of farmers in regional market, market imperfections in agriculture impedes the economic growth of this sector. This research aims to investigate gender gap in agricultural productivity among farm entrepreneurs in Kyrgyzstan. The agricultural labor productivity gap decomposed by various types of market imperfections, and empirically estimated by “Life in Kyrgyzstan” survey data for 2013 year.
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SUÁREZ BARÓN, Marco Javier, Angie Lizeth GOMEZ AGUDELO, and Juana Valentina GARCIA ARIZA. "PRECISION AGRICULTURE (PA) SUPPORT OF INCREASING AGRICULTURAL PRODUCTIVITY." In 10th International Conference on Management. Mendelova univerzita v Brně, 2021. http://dx.doi.org/10.11118/978-80-7509-820-7-0356.

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Turner, Reed J. "HarvesTrainer-A Combine Productivity Tool." In Agricultural Machinery Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1985. http://dx.doi.org/10.4271/851100.

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"Enhancing agricultural productivity through nanotechnology." In Role of Healthy Soil-Plant Interactions towards Achieving Resilient Agriculture in the Asian and Pacific Region. Food and Fertilizer Technology Center for the Asian and Pacific Region, 2022. http://dx.doi.org/10.56669/xlsh6749.

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Süt, Ali Talih. "The Effect of Urbanization on Agricultural Productivity in Developing Countries." In International Conference on Eurasian Economies. Eurasian Economists Association, 2021. http://dx.doi.org/10.36880/c13.02504.

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Agriculture is a very important activity for developing countries, and generally takes place in rural areas. Therefore, the urbanization directly affects agricultural activities. In this study, the relationship between agricultural productivity and urbanization is examined. In this context, carbon emission, representing environmental quality, was added as a control variable in the model. Because agricultural activities get their source from natural resources and the environment. The data set covers from 1992 to 2018 for 17 developing countries. According to the analysis, it was understood that the series were not co-integrated. According to the Dumitrescu Hurlin causality test, urbanization and carbon emissions are the cause of agricultural productivity. In addition, according to the same test results, it is not the cause of agricultural productivity, urbanization and carbon emissions.
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Zikra, Naswatun, and Fakhruddin Fakhruddin. "Does Migration Matter for Agricultural Productivity?" In Proceedings of the 1st Aceh Global Conference (AGC 2018). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/agc-18.2019.104.

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Zwiggelaar, Reyer, and Christine R. Bull. "Fourier transforms and fractals in the food and agricultural industry." In Optics for Productivity in Manufacturing, edited by Rolf-Juergen Ahlers, Donald W. Braggins, and Gary W. Kamerman. SPIE, 1994. http://dx.doi.org/10.1117/12.196080.

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Zhao, Zhengnan, Shaofeng Ru, and Guizhi Zhou. "Impact of Agricultural FDI on China’s Agricultural Green Total Factor Productivity." In Hradec Economic Days 2022, edited by Jan Maci, Petra Maresova, Krzysztof Firlej, and Ivan Soukal. University of Hradec Kralove, 2022. http://dx.doi.org/10.36689/uhk/hed/2022-01-090.

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BAUMGARDNER, M., and C. DAUGHTRY. "Remote sensing research in global agricultural productivity." In 23rd Aerospace Sciences Meeting. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1985. http://dx.doi.org/10.2514/6.1985-196.

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Saha, Sourav, Sudip Halder, Siddhartha Paul, and Kanishka Majumder. "Smart agricultural system: Better accuracy and productivity." In 2017 Devices for Integrated Circuit (DevIC). IEEE, 2017. http://dx.doi.org/10.1109/devic.2017.8073960.

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Reports on the topic "Agricultural productivity"

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Hazell, Peter. Topic Guide: Agricultural productivity. Evidence on Demand, May 2014. http://dx.doi.org/10.12774/eod_tg.may2014.hazell.

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Gollin, Douglas, David Lagakos, and Michael Waugh. The Agricultural Productivity Gap. Cambridge, MA: National Bureau of Economic Research, November 2013. http://dx.doi.org/10.3386/w19628.

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Carroll, Christine, Colin Carter, Rachael Goodhue, and C. Y. Cynthia Lin Lawell. Crop Disease and Agricultural Productivity. Cambridge, MA: National Bureau of Economic Research, June 2017. http://dx.doi.org/10.3386/w23513.

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Research Institute (IFPRI), International Food Policy. Intertemporal trends in agricultural productivity. Washington, DC: International Food Policy Research Institute, 2016. http://dx.doi.org/10.2499/9780896298811_02.

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Research Institute (IFPRI), International Food Policy. Spatial patterns of agricultural productivity. Washington, DC: International Food Policy Research Institute, 2016. http://dx.doi.org/10.2499/9780896298811_03.

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Research Institute (IFPRI), International Food Policy. Typology of agricultural productivity zones. Washington, DC: International Food Policy Research Institute, 2016. http://dx.doi.org/10.2499/9780896298811_04.

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Kaumbutho, Pascal, and Hiroyuki Takeshima. Agricultural productivity in Kenya: 2000-2020. Washington, DC: International Food Policy Research Institute, 2023. http://dx.doi.org/10.2499/9780896294651_09.

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Nin-Pratt, Alejandro. Agricultural productivity in Kenya: 2000-2020. Washington, DC: International Food Policy Research Institute, 2023. http://dx.doi.org/10.2499/9780896294651_06.

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Kaumbutho, Pascal, and Hiroyuki Takeshima. Agricultural productivity in Kenya: 2000-2020. Washington, DC: International Food Policy Research Institute, 2023. http://dx.doi.org/10.2499/9780896294561_09.

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Nin-Pratt, Alejandro. Agricultural productivity in Kenya: 2000-2020. Washington, DC: International Food Policy Research Institute, 2023. http://dx.doi.org/10.2499/9780896294561_06.

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