The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Yang Gao - One of the best experts on this subject based on the ideXlab platform.
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Research on the use of digital finance and the adoption of green control techniques by Family Farms in China
Technology in Society, 2020Co-Authors: Duanyang Zhao, Zihao Xue, Yang GaoAbstract:Abstract Green control techniques are conducive to ensuring the quality and safety of agricultural products, the ecological environment and agricultural production in China, while the credit constraints of traditional financial services make it difficult for them to be successfully promoted. However, the differences between digital financial services and traditional financial services have not been considered in the existing research, and the impact of digital financial use on farmers ‘adoption of green control techniques is rarely discussed from a micro perspective. Taking 441 Family Farms in Shandong and Henna provinces as an example, this paper adopts the mediating effect model to investigate the influence and mechanism of digital finance on the adoption of green control techniques in Family Farms and addresses possible endogeneity problems with the help of the instrumental variable method. It is found that the use of digital finance has a positive impact not only on the adoption of green control techniques in Family Farms but also on the adoption of green control techniques in Family Farms through three transmission mechanisms: improving credit availability, promoting information acquisition and enhancing social trust. This approach not only helps enrich the research on digital finance and clarify the differences between digital financial services and traditional financial services but also provides theoretical support for making full use of the development opportunity of digital finance to promote farmers to adopt green control techniques and ultimately achieve sustainable agricultural development.
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social capital land tenure and the adoption of green control techniques by Family Farms evidence from shandong and henan provinces of china
Land Use Policy, 2019Co-Authors: Yang Gao, Haoran Yang, Bei Liu, Shijiu YinAbstract:In China, land tenure security refers to the stability of land management rights in the context of the Three Rights Separation Policy, according to which rural land ownership rights, land contract rights, and land management rights can be separated and land management rights can be freely transferred. We apply endogenous switching probit models to a dataset of 443 Family Farms in Shandong and Henan Provinces to investigate the influence of social capital and land tenure security on Family Farms’ adoption of green control techniques (GCTs). We develop simplified equations to verify whether social capital strengthens the positive effect of land tenure security on Family Farms’ GCT adoption. Specifically, we focus on the different effects of embedded and disembedded social capital. Our findings show that both social capital and land tenure security have significant positive effects on Family Farms’ adoption of GCTs. Furthermore, social capital strengthens the positive effect of land tenure security on GCT adoption, but the effects of embedded and disembedded social capital are different. Government policies should strengthen the stability of land management rights and cultivate the social capital of Family Farms, especially disembedded social capital.
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Impact of green control techniques on Family Farms' welfare
Ecological Economics, 2019Co-Authors: Yang Gao, Ziheng Niu, Haoran YangAbstract:Abstract Using survey data of 375 Family Farms in five provinces of the Huang-Huai-Hai Plain, this paper conducts a comprehensive measurement of Family Farms' welfare within the framework of the capability approach theory. Furthermore, using an endogenous switching regression model and a multinomial treatment effects model, this paper evaluates the impact of the adoption or non-adoption of green control techniques on Family Farms' welfare and estimate the welfare effects of the degree and timing of adoption. This research finds that the average treatment effect on Family farm welfare with and without adopting green control techniques is significant, at 0.084 and 0.046, respectively. Therefore, green control techniques help to improve the welfare level of Family Farms. Compared with Family Farms that do not adopt green control techniques, the welfare level of Family Farms adopting a high or low degree of green control techniques increases by 22.63% and 16.42%, respectively, and the welfare level of Family Farms given the early or late adoption of green control techniques increases by 5.87% and 7.57%, respectively. Therefore, the welfare effect of a high degree of adoption on Family Farms is greater, and the welfare level of Family Farms with late adoption is higher.
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adoption behavior of green control techniques by Family Farms in china evidence from 676 Family Farms in huang huai hai plain
Crop Protection, 2017Co-Authors: Yang Gao, Xiao Zhang, Shijiu YinAbstract:Abstract Technique adoption behavior by Family Farms comprises three stages with progressive relation, including information collection, adoption willingness, and adoption intensity. To explore the influence factors of the adoption of green control techniques (GCTs) by Family Farms, this study focused on sample selection problems, namely, understanding (or not) and willingness (or not), using bivariate probit and regression linear models based on a field survey data from 676 Family Farms in Huang-huai-hai Plain. Estimation results showed that: 1) the frequency of neighbor communication, the strengths of the extension of agricultural technique sector and media publicity, education, and degree of risk preference of farmers had significant positive influences on information collection individually; however, the gender of farmers had a significant negative influence. 2) The perceived ease of use and usefulness about the technique, the number of laborers, the strength of the extension of agricultural technique sector, education, and degree of risk preference of farmers had significant positive influences on adoption willingness. 3) Finally, the perceived ease of use and usefulness about the technique, fund status, the strengths of media publicity and the extension of agricultural technique sector, and education of farmers had significant positive influences on adoption intensity; whereas the frequency of neighbor communion, gender, and degree of risk preference of farmers had significant negative influences on adoption intensity. Thus, to develop the GCTs successfully, the Chinese government should improve the effects of technique training, ameliorate financing environment, focus on publicity and guidance, establish and improve the system of education and training for Family farmers, and strengthen the team concerning the extended construction of grassroots technique.
Terri Raney - One of the best experts on this subject based on the ideXlab platform.
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the number size and distribution of Farms smallholder Farms and Family Farms worldwide
World Development, 2016Co-Authors: Sarah K Lowder, Jakob Skoet, Terri RaneyAbstract:Summary Numerous sources provide evidence of trends and patterns in average farm size and farmland distribution worldwide, but they often lack documentation, are in some cases out of date, and do not provide comprehensive global and comparative regional estimates. This article uses agricultural census data (provided at the country level in Web Appendix) to show that there are more than 570 million Farms worldwide, most of which are small and Family-operated. It shows that small Farms (less than 2 ha) operate about 12% and Family Farms about 75% of the world’s agricultural land. It shows that average farm size decreased in most low- and lower-middle-income countries for which data are available from 1960 to 2000, whereas average farm sizes increased from 1960 to 2000 in some upper-middle-income countries and in nearly all high-income countries for which we have information. Such estimates help inform agricultural development strategies, although the estimates are limited by the data available. Continued efforts to enhance the collection and dissemination of up-to date, comprehensive, and more standardized agricultural census data, including at the farm and national level, are essential to having a more representative picture of the number of Farms, small Farms, and Family Farms as well as changes in farm size and farmland distribution worldwide.
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the number size and distribution of Farms smallholder Farms and Family Farms worldwide
World Development, 2016Co-Authors: Sarah K Lowder, Jakob Skoet, Terri RaneyAbstract:Numerous sources provide evidence of trends and patterns in average farm size and farmland distribution worldwide, but they often lack documentation, are in some cases out of date, and do not provide comprehensive global and comparative regional estimates. This article uses agricultural census data (provided at the country level in Web Appendix) to show that there are more than 570 million Farms worldwide, most of which are small and Family-operated. It shows that small Farms (less than 2ha) operate about 12% and Family Farms about 75% of the world’s agricultural land. It shows that average farm size decreased in most low- and lower-middle-income countries for which data are available from 1960 to 2000, whereas average farm sizes increased from 1960 to 2000 in some upper-middle-income countries and in nearly all high-income countries for which we have information.
Sarah K Lowder - One of the best experts on this subject based on the ideXlab platform.
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the number size and distribution of Farms smallholder Farms and Family Farms worldwide
World Development, 2016Co-Authors: Sarah K Lowder, Jakob Skoet, Terri RaneyAbstract:Summary Numerous sources provide evidence of trends and patterns in average farm size and farmland distribution worldwide, but they often lack documentation, are in some cases out of date, and do not provide comprehensive global and comparative regional estimates. This article uses agricultural census data (provided at the country level in Web Appendix) to show that there are more than 570 million Farms worldwide, most of which are small and Family-operated. It shows that small Farms (less than 2 ha) operate about 12% and Family Farms about 75% of the world’s agricultural land. It shows that average farm size decreased in most low- and lower-middle-income countries for which data are available from 1960 to 2000, whereas average farm sizes increased from 1960 to 2000 in some upper-middle-income countries and in nearly all high-income countries for which we have information. Such estimates help inform agricultural development strategies, although the estimates are limited by the data available. Continued efforts to enhance the collection and dissemination of up-to date, comprehensive, and more standardized agricultural census data, including at the farm and national level, are essential to having a more representative picture of the number of Farms, small Farms, and Family Farms as well as changes in farm size and farmland distribution worldwide.
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the number size and distribution of Farms smallholder Farms and Family Farms worldwide
World Development, 2016Co-Authors: Sarah K Lowder, Jakob Skoet, Terri RaneyAbstract:Numerous sources provide evidence of trends and patterns in average farm size and farmland distribution worldwide, but they often lack documentation, are in some cases out of date, and do not provide comprehensive global and comparative regional estimates. This article uses agricultural census data (provided at the country level in Web Appendix) to show that there are more than 570 million Farms worldwide, most of which are small and Family-operated. It shows that small Farms (less than 2ha) operate about 12% and Family Farms about 75% of the world’s agricultural land. It shows that average farm size decreased in most low- and lower-middle-income countries for which data are available from 1960 to 2000, whereas average farm sizes increased from 1960 to 2000 in some upper-middle-income countries and in nearly all high-income countries for which we have information.
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what do we really know about the number and distribution of Farms and Family Farms in the world background paper for the state of food and agriculture 2014
2014Co-Authors: Sarah K Lowder, Jakob Skoet, Saumya SinghAbstract:The agricultural economics literature provides various estimates of the number of Farms and small Farms in the world. This paper is an effort to provide a more complete and up to date as well as carefully documented estimate of the total number of Farms in the world, as well as by region and level of income. It uses data from numerous rounds of the World Census of Agriculture, the only dataset available which allows the user to gain a complete picture of the total number of Farms globally and at the country level. The paper provides estimates of the number of Family Farms, the number of Farms by size as well as the distibution of farmland by farm size. These estimates find that: there are at least 570 million Farms worldwide, of which more than 500 million can be considered Family Farms. Most of the world's Farms are very small, with more than 475 million Farms being less than 2 hectares in size. Although the vast majority of the world's Farms are smaller than 2 hectares, they operate only a small share of the world's farmland. Farmland distribution would seem quite unequal at the global level, but it is less so in low- and lower-middle-income countries as well as in some regional groups. These estimates have serious limitations and the collection of more up-to-date agricultural census data, including data on farmland distribution is essential to our having a more representative picture of the number of Farms, the number of Family Farms and farm size as well as farmland distribution worldwide.
Haoran Yang - One of the best experts on this subject based on the ideXlab platform.
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social capital land tenure and the adoption of green control techniques by Family Farms evidence from shandong and henan provinces of china
Land Use Policy, 2019Co-Authors: Yang Gao, Haoran Yang, Bei Liu, Shijiu YinAbstract:In China, land tenure security refers to the stability of land management rights in the context of the Three Rights Separation Policy, according to which rural land ownership rights, land contract rights, and land management rights can be separated and land management rights can be freely transferred. We apply endogenous switching probit models to a dataset of 443 Family Farms in Shandong and Henan Provinces to investigate the influence of social capital and land tenure security on Family Farms’ adoption of green control techniques (GCTs). We develop simplified equations to verify whether social capital strengthens the positive effect of land tenure security on Family Farms’ GCT adoption. Specifically, we focus on the different effects of embedded and disembedded social capital. Our findings show that both social capital and land tenure security have significant positive effects on Family Farms’ adoption of GCTs. Furthermore, social capital strengthens the positive effect of land tenure security on GCT adoption, but the effects of embedded and disembedded social capital are different. Government policies should strengthen the stability of land management rights and cultivate the social capital of Family Farms, especially disembedded social capital.
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Impact of green control techniques on Family Farms' welfare
Ecological Economics, 2019Co-Authors: Yang Gao, Ziheng Niu, Haoran YangAbstract:Abstract Using survey data of 375 Family Farms in five provinces of the Huang-Huai-Hai Plain, this paper conducts a comprehensive measurement of Family Farms' welfare within the framework of the capability approach theory. Furthermore, using an endogenous switching regression model and a multinomial treatment effects model, this paper evaluates the impact of the adoption or non-adoption of green control techniques on Family Farms' welfare and estimate the welfare effects of the degree and timing of adoption. This research finds that the average treatment effect on Family farm welfare with and without adopting green control techniques is significant, at 0.084 and 0.046, respectively. Therefore, green control techniques help to improve the welfare level of Family Farms. Compared with Family Farms that do not adopt green control techniques, the welfare level of Family Farms adopting a high or low degree of green control techniques increases by 22.63% and 16.42%, respectively, and the welfare level of Family Farms given the early or late adoption of green control techniques increases by 5.87% and 7.57%, respectively. Therefore, the welfare effect of a high degree of adoption on Family Farms is greater, and the welfare level of Family Farms with late adoption is higher.
Tomas Baležentis - One of the best experts on this subject based on the ideXlab platform.
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Multi-directional program efficiency: the case of Lithuanian Family Farms
Journal of Productivity Analysis, 2016Co-Authors: Mette Asmild, Tomas Baležentis, Jens Leth HougaardAbstract:The present paper analyses both managerial and program efficiencies of Lithuanian Family Farms, in the tradition of Charnes et al. (Manag Sci 27(6):668–697, 1981 ) but with the important difference that multi-directional efficiency analysis rather than the traditional data envelopment analysis approach is used to estimate efficiency. This enables a consideration of input-specific efficiencies. The study shows clear differences between the efficiency scores on the different inputs as well as between the farm types of crop, livestock and mixed Farms respectively. We furthermore find that crop Farms have the highest program efficiency, but the lowest managerial efficiency and that the mixed Farms have the lowest program efficiency (yet not the highest managerial efficiency).
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benefit of the doubt model for financial risk analysis of lithuanian Family Farms
Economics & Sociology, 2016Co-Authors: Dalia Streimikiene, Tomas Baležentis, Irena KrisciukaitienėAbstract:(ProQuest: ... denotes formulae omitted.)IntroductionAgricultural activities differ from other businesses in that the former are subject to much wider range of risks. Specifically, besides commonly known input and output price risk, credit risk, institutional risk etc., farmers are exposed to risks emerging from changes in biophysical environment. Therefore, it is important to foresee the sources of risk, farmers' strategies, and government policies (OECD, 2009). An appropriate interaction among these components of risk management strategy might mitigate loss due to different types of risks.Risk management is of especial importance in the new European Union (EU) Member States, where agricultural sector has been experiencing serious economic, institutional, and social transformations since the 1990s. Indeed, the collapse of planned economy including large-scale collective farming systems induced certain volatility in factor markets and resulted in sub-optimal farming structure in some cases (Bilan & Chmielewska, 2013a,b). Furthermore, accession to the EU allowed receive the funding under the Common Agricultural Policy (Ministry of Agriculture of the Republic of Lithuania, 2015). The latter has been distributed in the form of both direct payments and investment subsidies. Demographic transition implies a decreasing labour supply in rural areas and thus calls for further mechanisation.According to to R. B. M. Huirne et al. (2000) and J. B. Hardaker et al. (2004) there are two broad categories of risk for agricultural activities, viz. business risk and financial risk. Business risk comprises production, market, institutional, and personal risks (Bernat et al. 2014). Financial risk stems from fluctuations at financial markets and farmers' money-related decisions. Specifically, increasing interest rates might render difficulties in repaying loans or create credit constraints. Farmers' decisions regarding capital structure might also impact the financial viability of Farms.As regards the agricultural sector, much of literature has been focused on business risk and, particularly, risk aversion (e.g., Moschini, Hennessy, 2001). The estimation of risk aversion can follow either the attitudinal approach, or empirical approach. The attitudinal approach relies on questionnaire surveys or experiments aimed at identifying farmers' choices under different circumstances. The empirical approach relies on the analysis of factual data and can be carried out either parametrically (Bardsley, Harris, 1987; Bar-Shira et al., 1997) or nonparametrically (Gomez-Limon et al., 2003). The analysis of financial risk has been confined to estimation the impacts of certain financial ratios on probability to become unviable (Argiles, 2001). Such a framework rests on the ideas of Altman (1968, 2004). However, such a setting requires a priori specification of the dependent variable, which involves a certain degree of subjectivity. D. Jackson-Smith et al. (2004) and S. Davidova and L. Latruffe (2007) investigated the determinants of financial performance treating different indicators as dependent variables in regression models. However, no aggregate measures were introduced.In Lithuania, as well as in other Central and East European countries, financial risk constitutes an important dimension of farm viability due to investments in response to the aforementioned transformations there. First, excessive investments might be fuelled by investment support measures thus arriving at unreasonable leverage level. Second, credit constraints might be related to increase in interest rates. Therefore, it is important to offer appropriate methodologies for financial risk appraisal in Lithuanian Family Farms. The following scientific problem, therefore, emerges: even though a variety of techniques for analysis of financial risk are available, these usually require longitudinal data for estimation of variance; however, such data are not readily available for Lithuanian Family Farms where extensive time series are not available for multiple holdings. …
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one and multi directional conditional efficiency measurement efficiency in lithuanian Family Farms
European Journal of Operational Research, 2015Co-Authors: Tomas Baležentis, Kristof De WitteAbstract:Abstract This paper analyses farming efficiency by the means of the partial frontiers and Multi-Directional Efficiency Analysis (MEA). In particular, we apply the idea of the conditional efficiency framework to the MEA approach to ensure that observations are compared to their homogeneous counterparts. Moreover, this paper shows that combining the traditional one-directional and multi-directional efficiency framework yields valuable insights. It allows one to identify what factors matter in terms of output production and input consumption. The application deals with Lithuanian Family Farms, for which we have a rich dataset. The results indicate that the output efficiency positively correlates to a time trend and negatively to the subsidy share in the total output. The MEA-based analysis further suggests that the time trend has been positively affecting the productive efficiency due to increase in the labour use efficiency. Meanwhile, the increasing subsidy rate has a negative influence upon MEA efficiencies associated with all the inputs.
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the sources of the total factor productivity growth in lithuanian Family Farms a fare primont index approach
Prague Economic Papers, 2015Co-Authors: Tomas BaležentisAbstract:The Lithuanian agricultural sector still features the processes of land reform, farm structure development, and modernisation. Accordingly, there is a need to utilise the benchmarking techniques in order to fathom the underlying trends and sources of efficiency and productivity. This paper therefore aims at analysing the productive efficiency and the total factor productivity in the Lithuanian Family Farms. The research is based on the Farm Accountancy Network Data covering the period of 2004-2009. The Fare-Primont Indices were employed to estimate and decompose the total factor productivity changes. Furthermore, the stochastic kernels were applied to analyse the distributions of the efficiency scores along with the econometric analysis which aimed at revealing the relationships of the environmental variables and the efficiency scores. The results do indicate that the technical efficiency was a decisive factor causing decrease in TFP efficiency for crop and mixed Farms. Meanwhile, the scale efficiency constituted a serious problem for mixed Farms. Indeed, these Farms were the smallest ones if compared to the remaining farming types. Finally, the mix efficiency was low for all farming types indicating the need for implementation of certain farming practices allowing for optimisation of the input-mix.
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technical efficiency and expansion of lithuanian Family Farms 2004 2009 graph data envelopment analysis and rank sum test
Management Theory and Studies for Rural Business and Infrastructure Development, 2012Co-Authors: Tomas BaležentisAbstract:The aim of this paper is to estimate the impact of the technical efficiency on farm expansion. The research relies on the sample of the Lithuanian Family Farms operated throughout 2004–2009. The graph DEA model was employed to estimate the efficiency scores, whereas the rank-sum test was employed to test the relationships between efficiency and expansion variables. Farm expansion was analyzed by considering multiple criteria. The rank-sum test indicated that the Farms expanded in terms of ESU and UAA were specific with lower efficiency during the preceding periods. Meanwhile, labour input and assets were not related to different populations of efficiency scores. Therefore, one can expect for decrease in efficiency given no managerial decisions are undertaken.