Pub Date : 2022-02-15DOI: 10.1108/caer-07-2021-0130
Kolawole Ogundari
PurposeThis study aims to address two research questions. First, do the agricultural extension services have an impact on the potential outcomes considered in the primary studies, and to what extent? Second, how sensitive is the reported impact to the study-specific characteristics in the primary studies?Design/methodology/approachThe paper synthesizes 45 studies that assessed the causal impact of agricultural extension services published in 2004–2021, using meta-regression analysis. It considers three measures of effect sizes – Cohen’s, Hedges and principal correlation coefficient (PCC) – to standardize the reported impact of agricultural extension services in the primary studies.FindingsThe empirical results show that, on average, agricultural extension services have statistically significant and positive impacts on the potential outcomes identified in the primary studies. However, the magnitude of the impact is considered medium-sized. Other results show that the effect size estimates of agricultural extension services' impact significantly vary with the data type (cross-sectional data vs. panel data), research design (non-experimental vs. experimental design) and econometric methods employed in the primary studies.Practical implicationsOne can argue that the medium-sized impact we estimated indicates evidence of a moderate, weak relationship between agricultural extension services and the potential outcomes considered in the primary studies. This means that agricultural extension services need to be restructured in the current form to stimulate change in the agricultural sector globally. In addition, the sensitivity of effect sizes to study attributes (i.e. data types, research design and econometric methods) shows that researchers and academicians need to pay attention to these attributes to provide more reliable estimates for policy purposes.Originality/valueThis is the first study that attempts to shed light on the overall performance of agricultural extension services using a meta-regression analysis approach.
{"title":"A meta-analysis of the impact of agricultural extension services","authors":"Kolawole Ogundari","doi":"10.1108/caer-07-2021-0130","DOIUrl":"https://doi.org/10.1108/caer-07-2021-0130","url":null,"abstract":"PurposeThis study aims to address two research questions. First, do the agricultural extension services have an impact on the potential outcomes considered in the primary studies, and to what extent? Second, how sensitive is the reported impact to the study-specific characteristics in the primary studies?Design/methodology/approachThe paper synthesizes 45 studies that assessed the causal impact of agricultural extension services published in 2004–2021, using meta-regression analysis. It considers three measures of effect sizes – Cohen’s, Hedges and principal correlation coefficient (PCC) – to standardize the reported impact of agricultural extension services in the primary studies.FindingsThe empirical results show that, on average, agricultural extension services have statistically significant and positive impacts on the potential outcomes identified in the primary studies. However, the magnitude of the impact is considered medium-sized. Other results show that the effect size estimates of agricultural extension services' impact significantly vary with the data type (cross-sectional data vs. panel data), research design (non-experimental vs. experimental design) and econometric methods employed in the primary studies.Practical implicationsOne can argue that the medium-sized impact we estimated indicates evidence of a moderate, weak relationship between agricultural extension services and the potential outcomes considered in the primary studies. This means that agricultural extension services need to be restructured in the current form to stimulate change in the agricultural sector globally. In addition, the sensitivity of effect sizes to study attributes (i.e. data types, research design and econometric methods) shows that researchers and academicians need to pay attention to these attributes to provide more reliable estimates for policy purposes.Originality/valueThis is the first study that attempts to shed light on the overall performance of agricultural extension services using a meta-regression analysis approach.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42418009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-15DOI: 10.1108/caer-09-2019-0174
T. Meng, Qijun Jiang, W. Florkowski
PurposeThis paper examines pre- and post-production water treatment practices among food processors and investigates factors, especially managerial perceptions of environmental pressure that encourage or preclude either process.Design/methodology/approachTo consider potential spillover effects across two water-treatment practices, the bivariate probit model based on random utility theory is used to investigate how practices are influenced by managerial perceptions of environmental pressure and measured by manager perceptions on water costs, water availability, water safety and quality.FindingsResults indicate that firms with a managerial perception that water costs are low are less likely to conduct both pre- and post-production water treatment practices, while the perception of high water quality has a negative effect on water treatment prior to use. This study also confirms the positive correlation of the pre- and post-water treatment practices among food processors. Practices also change with firm features including production scope, scale, target market and expected future sales growth.Practical implicationsThis study provides unique insights about water treatment practices and generates knowledge to enhance food safety and environmental sanitation in the food industry. Results are helpful to design and provide additional training and educational programs that target the enhancement of environmental and water quality awareness among food company managers and modify food safety policy instruments and environmental regulations pertaining to surface water resources.Originality/valueResearch exploring water-treatment practices in the food industry has been limited. Using a representative sample of food processors in the city of Shanghai, this study contributes to the literature on the examination of internal drivers of voluntary environmental management (VEM) with a focus on managerial perceptions of environmental pressure, establishes the correlation between pre- and post-production water treatment practices and identifies and quantifies the effects of relevant factors.
{"title":"Pre- and post-production water treatment in the food processing industry: managerial perceptions of environmental pressure increase adoption of voluntary environmental management","authors":"T. Meng, Qijun Jiang, W. Florkowski","doi":"10.1108/caer-09-2019-0174","DOIUrl":"https://doi.org/10.1108/caer-09-2019-0174","url":null,"abstract":"PurposeThis paper examines pre- and post-production water treatment practices among food processors and investigates factors, especially managerial perceptions of environmental pressure that encourage or preclude either process.Design/methodology/approachTo consider potential spillover effects across two water-treatment practices, the bivariate probit model based on random utility theory is used to investigate how practices are influenced by managerial perceptions of environmental pressure and measured by manager perceptions on water costs, water availability, water safety and quality.FindingsResults indicate that firms with a managerial perception that water costs are low are less likely to conduct both pre- and post-production water treatment practices, while the perception of high water quality has a negative effect on water treatment prior to use. This study also confirms the positive correlation of the pre- and post-water treatment practices among food processors. Practices also change with firm features including production scope, scale, target market and expected future sales growth.Practical implicationsThis study provides unique insights about water treatment practices and generates knowledge to enhance food safety and environmental sanitation in the food industry. Results are helpful to design and provide additional training and educational programs that target the enhancement of environmental and water quality awareness among food company managers and modify food safety policy instruments and environmental regulations pertaining to surface water resources.Originality/valueResearch exploring water-treatment practices in the food industry has been limited. Using a representative sample of food processors in the city of Shanghai, this study contributes to the literature on the examination of internal drivers of voluntary environmental management (VEM) with a focus on managerial perceptions of environmental pressure, establishes the correlation between pre- and post-production water treatment practices and identifies and quantifies the effects of relevant factors.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49440478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-10DOI: 10.1108/caer-02-2021-0043
Fang Li, S. Feng, Hualiang Lu, F. Qu, M. D’haese
PurposeThis paper investigates the relationship between plot size and fertilizer use efficiency (FE) in Chinese large-scale farming and searches for the underlying mechanisms that explain this relationship.Design/methodology/approachBased on a household- and plot-level data set of large-scale production units (LSPUs) from Jiangsu and Jiangxi Provinces, the technical and fertilizer use efficiency of large-scale rice production is estimated by applying a translog stochastic frontier production function. The authors impose a monotonicity condition on the translog frontier using a three-step procedure to get theoretically consistent efficiency estimates. A beta regression model is then used to explore the association between plot size and LSPUs' efficiency in fertilizer application.FindingsThe average FE for the sampled plots is around 30%, which shows a large potential for LSPUs to reduce fertilizer use. A U-shaped relationship is observed between plot size and FE. The authors relate this non-linear pattern to the substitution of labour with capital-intensive technology and the efficiency differences in terms of farming performance between family and hired workers.Originality/valueFirst, according to the authors’ knowledge, this paper is a first attempt to study the size–efficiency relationship focussing on fertilization practices of large-scale farming. The second contribution lies in the large-scale ranges of the plot-level data set. Third, efforts are made to reveal the mechanisms determining the plot size–FE relationship. Fourth, the authors provide guiding evidence for policymaking, as they show that the size of individual plots deserves equal attention in land consolidation decisions. Methodologically, this paper improves existing estimates of single-factor technical efficiency issued from a restricted production frontier model.
{"title":"Exploring the relationship between plot size and fertilizer use efficiency: evidence from large-scale farming in China","authors":"Fang Li, S. Feng, Hualiang Lu, F. Qu, M. D’haese","doi":"10.1108/caer-02-2021-0043","DOIUrl":"https://doi.org/10.1108/caer-02-2021-0043","url":null,"abstract":"PurposeThis paper investigates the relationship between plot size and fertilizer use efficiency (FE) in Chinese large-scale farming and searches for the underlying mechanisms that explain this relationship.Design/methodology/approachBased on a household- and plot-level data set of large-scale production units (LSPUs) from Jiangsu and Jiangxi Provinces, the technical and fertilizer use efficiency of large-scale rice production is estimated by applying a translog stochastic frontier production function. The authors impose a monotonicity condition on the translog frontier using a three-step procedure to get theoretically consistent efficiency estimates. A beta regression model is then used to explore the association between plot size and LSPUs' efficiency in fertilizer application.FindingsThe average FE for the sampled plots is around 30%, which shows a large potential for LSPUs to reduce fertilizer use. A U-shaped relationship is observed between plot size and FE. The authors relate this non-linear pattern to the substitution of labour with capital-intensive technology and the efficiency differences in terms of farming performance between family and hired workers.Originality/valueFirst, according to the authors’ knowledge, this paper is a first attempt to study the size–efficiency relationship focussing on fertilization practices of large-scale farming. The second contribution lies in the large-scale ranges of the plot-level data set. Third, efforts are made to reveal the mechanisms determining the plot size–FE relationship. Fourth, the authors provide guiding evidence for policymaking, as they show that the size of individual plots deserves equal attention in land consolidation decisions. Methodologically, this paper improves existing estimates of single-factor technical efficiency issued from a restricted production frontier model.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48479621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-02DOI: 10.1108/caer-07-2021-0127
Chang Xu, Baodong Cheng, Mengzhen Zhang
PurposeThis article's purpose is to examine the effect of a Classification-Based Forest Management (CFM) program on farmers' income and determine whether its effect varies with the degree of farmers' concurrent occupations.Design/methodology/approachThe authors use representative panel survey data from Longquan to explore the welfare effects of CFM on farmers. The analysis uses differences-in-differences with propensity score matching (PSM-DID) estimation techniques to deal with endogeneity problems when farmers make the decision to participate in CFM.FindingsThe results show that CFM has a positive effect on part-time forestry households (where forestry income accounts for between 5 and 50% of total income). In contrast, it has a negative impact on full-time forestry households (forestry income accounts for more than 50%), and no clear effect on nonforestry households whose forestry income is less than 5%. This research also shows that the positive effect of CFM on farmers' total income is mainly due to increase of off-farm income driven by CFM, while the negative effects consist of CFM's reduction of forestry income.Originality/valueThe extent of CFM's economic benefits to farmers is uncertain and largely unexplored. This paper analyzes the impact of CFM on income structure to explore the mechanisms explaining its effects on farmers' income. There are still challenges in ensuring the reliability and accuracy of CFM assessment. This paper collected natural experimental data and used the estimation technology of PSM-DID to solve the possible endogeneity problems.
{"title":"Classification-based forest management program and farmers' income: evidence from collective forest area in southern China","authors":"Chang Xu, Baodong Cheng, Mengzhen Zhang","doi":"10.1108/caer-07-2021-0127","DOIUrl":"https://doi.org/10.1108/caer-07-2021-0127","url":null,"abstract":"PurposeThis article's purpose is to examine the effect of a Classification-Based Forest Management (CFM) program on farmers' income and determine whether its effect varies with the degree of farmers' concurrent occupations.Design/methodology/approachThe authors use representative panel survey data from Longquan to explore the welfare effects of CFM on farmers. The analysis uses differences-in-differences with propensity score matching (PSM-DID) estimation techniques to deal with endogeneity problems when farmers make the decision to participate in CFM.FindingsThe results show that CFM has a positive effect on part-time forestry households (where forestry income accounts for between 5 and 50% of total income). In contrast, it has a negative impact on full-time forestry households (forestry income accounts for more than 50%), and no clear effect on nonforestry households whose forestry income is less than 5%. This research also shows that the positive effect of CFM on farmers' total income is mainly due to increase of off-farm income driven by CFM, while the negative effects consist of CFM's reduction of forestry income.Originality/valueThe extent of CFM's economic benefits to farmers is uncertain and largely unexplored. This paper analyzes the impact of CFM on income structure to explore the mechanisms explaining its effects on farmers' income. There are still challenges in ensuring the reliability and accuracy of CFM assessment. This paper collected natural experimental data and used the estimation technology of PSM-DID to solve the possible endogeneity problems.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41669366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-01DOI: 10.1108/caer-05-2021-0096
Diandian Chen, Yong Ma
PurposeSince 1978, China has made tremendous economic achievements through industrial upgrading. However, these achievements are accompanied by an expanding income gap between rural and urban areas. The purpose of this paper is to examine the relationship between industrial structure and urban–rural income inequality in China.Design/methodology/approach Using the fixed-effects model and provincial data for the period 1985–2019, this paper estimates a linear relationship between industrial structure and urban–rural income inequality. By decomposing total income inequality into four components, the paper then analyzes how industrial structure affects each component.FindingsThe results show that industrial structure imbalance and industrial upgrading are positively associated with urban–rural income inequality. The positive effect of industrial imbalance mainly comes from widening the wage gap, while that of industrial upgrading mainly comes from aggravating business income inequality and property income inequality. Moreover, industrial balance and upgrading are conducive to increasing the share of wage income at the cost of property income.Originality/valueBy progressively examining the total inequality and the inequality of income components, this paper provides a better understanding of how industrial structure affects urban and rural income inequality. The findings of this study highlight the “inequality cost” associated with industrial structure adjustment, which provide policy-related insights on the balance development of urban and rural areas.
{"title":"Effect of industrial structure on urban–rural income inequality in China","authors":"Diandian Chen, Yong Ma","doi":"10.1108/caer-05-2021-0096","DOIUrl":"https://doi.org/10.1108/caer-05-2021-0096","url":null,"abstract":"PurposeSince 1978, China has made tremendous economic achievements through industrial upgrading. However, these achievements are accompanied by an expanding income gap between rural and urban areas. The purpose of this paper is to examine the relationship between industrial structure and urban–rural income inequality in China.Design/methodology/approach Using the fixed-effects model and provincial data for the period 1985–2019, this paper estimates a linear relationship between industrial structure and urban–rural income inequality. By decomposing total income inequality into four components, the paper then analyzes how industrial structure affects each component.FindingsThe results show that industrial structure imbalance and industrial upgrading are positively associated with urban–rural income inequality. The positive effect of industrial imbalance mainly comes from widening the wage gap, while that of industrial upgrading mainly comes from aggravating business income inequality and property income inequality. Moreover, industrial balance and upgrading are conducive to increasing the share of wage income at the cost of property income.Originality/valueBy progressively examining the total inequality and the inequality of income components, this paper provides a better understanding of how industrial structure affects urban and rural income inequality. The findings of this study highlight the “inequality cost” associated with industrial structure adjustment, which provide policy-related insights on the balance development of urban and rural areas.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45703665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-02-01DOI: 10.1108/caer-05-2021-0100
Xu He, T. Sakurai
PurposeTotal farmland value exceeds its value in agriculture but is not directly perceptible to villagers in China. Thus, the exceeded part is often neglected when discussing farmer’s land transaction decision. This study aims to revisit the question about how land titling project affects farmer’s land renting-out and investigate how this unobservable land value would distort the intentional effects of land titling.Design/methodology/approachThis paper first modifies a two-period model by incorporating the unobservable part of land value into the farmers’ leasing decision problem. Following the implications from the theoretical analysis, this study then exploits the difference-in-differences and the triple-differences approach to confirm the distorting effects that are resulted from the unobservable land value.FindingsThe modified theoretical model of this study reveals that land titling would encourage farmers to rent out land when the unobservable land value is predicted to be low but discourage farmers’ willingness to rent-out when this value is predicted to be high. The core reason for this significant conclusion lands in the uncertainty of the unobservable land value. Empirical analysis then provided two evidences for this presumption. Furthermore, this study also gave a disproof of the argument that the uncovered discouraging effect is due to a stronger endowment effect.Originality/valueThis paper contributes to the literature by highlighting the unobservable land value in the farmers’ land-related decisions. This part of land value is always neglected in previous discussions about the land tenure system, but it would cause distorting effects especially in regions without private land ownership.
{"title":"Unobservable land value in rural China and its microeconomic implication on land lease behavior","authors":"Xu He, T. Sakurai","doi":"10.1108/caer-05-2021-0100","DOIUrl":"https://doi.org/10.1108/caer-05-2021-0100","url":null,"abstract":"PurposeTotal farmland value exceeds its value in agriculture but is not directly perceptible to villagers in China. Thus, the exceeded part is often neglected when discussing farmer’s land transaction decision. This study aims to revisit the question about how land titling project affects farmer’s land renting-out and investigate how this unobservable land value would distort the intentional effects of land titling.Design/methodology/approachThis paper first modifies a two-period model by incorporating the unobservable part of land value into the farmers’ leasing decision problem. Following the implications from the theoretical analysis, this study then exploits the difference-in-differences and the triple-differences approach to confirm the distorting effects that are resulted from the unobservable land value.FindingsThe modified theoretical model of this study reveals that land titling would encourage farmers to rent out land when the unobservable land value is predicted to be low but discourage farmers’ willingness to rent-out when this value is predicted to be high. The core reason for this significant conclusion lands in the uncertainty of the unobservable land value. Empirical analysis then provided two evidences for this presumption. Furthermore, this study also gave a disproof of the argument that the uncovered discouraging effect is due to a stronger endowment effect.Originality/valueThis paper contributes to the literature by highlighting the unobservable land value in the farmers’ land-related decisions. This part of land value is always neglected in previous discussions about the land tenure system, but it would cause distorting effects especially in regions without private land ownership.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49666971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-27DOI: 10.1108/caer-11-2020-0275
R. Kalli, P. Jena
PurposeClimate change is the most concerned issue in the global economy; increase in climate variability and uncertain climate events have caused distress in agriculture sector. The study estimates economic effect of climate change on agriculture income for the Indian state of Karnataka. The study reports the difference of result from past studies, where estimates from present study indicate higher negative impact of rise in temperature.Design/methodology/approachFixed effect panel regression method was used to examine change in agriculture revenue to climate response. Climate variables were classified based on the crop calendar to capture the damage caused by climate change. The authors use fine scale climate data set constructed at regional context for 20 districts and time period of 21 years (1992–2012).FindingsThe result showed that with 1-degree rise in average maximum temperature, the revenue declined by 17–21%. The prediction behavior of the different models was evaluated using out-of-sample forecast approach by training and testing historical data set.Originality/valueThe study adopts recent data sets on agriculture and the updated climate variables to estimate the climate change impact on agriculture. The study yields the better results when compared to previous traditional models applied in literature in Indian context. The study further evaluates the prediction behavior and robustness of the estimated models using out-of-sample forecast method.
{"title":"How large is the farm income loss due to climate change? Evidence from India","authors":"R. Kalli, P. Jena","doi":"10.1108/caer-11-2020-0275","DOIUrl":"https://doi.org/10.1108/caer-11-2020-0275","url":null,"abstract":"PurposeClimate change is the most concerned issue in the global economy; increase in climate variability and uncertain climate events have caused distress in agriculture sector. The study estimates economic effect of climate change on agriculture income for the Indian state of Karnataka. The study reports the difference of result from past studies, where estimates from present study indicate higher negative impact of rise in temperature.Design/methodology/approachFixed effect panel regression method was used to examine change in agriculture revenue to climate response. Climate variables were classified based on the crop calendar to capture the damage caused by climate change. The authors use fine scale climate data set constructed at regional context for 20 districts and time period of 21 years (1992–2012).FindingsThe result showed that with 1-degree rise in average maximum temperature, the revenue declined by 17–21%. The prediction behavior of the different models was evaluated using out-of-sample forecast approach by training and testing historical data set.Originality/valueThe study adopts recent data sets on agriculture and the updated climate variables to estimate the climate change impact on agriculture. The study yields the better results when compared to previous traditional models applied in literature in Indian context. The study further evaluates the prediction behavior and robustness of the estimated models using out-of-sample forecast method.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42412486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-24DOI: 10.1108/caer-10-2020-0250
Zhigang Xu, Kerong Zhang, Li Zhou, Ruiyao Ying
PurposeWhile the peer effects of technology adoption are well established, few studies have considered the variation in peer effects resulting from the mutual proximity between leaders and followers and the heterogeneity of farmers' learning technology. This study addresses the gap in the literature by analyzing the peer effects of technology adoption among Chinese farmers.Design/methodology/approachDrawing on a government-led soil testing and formulated fertilization program, this study uses survey data of farmers from three Chinese provinces to examine the peer effects of technology adoption. This study uses a probit model to examine how mutual proximity influences peer effects and their heterogeneity. Accordingly, farmers were divided into two groups, namely small- and large-scale farmers, and then into leaders or followers depending on whether they were selected by the government as model farmers.FindingsBoth small- and large-scale farmers are more likely to use formula fertilizer if their peers do so. However, a large-scale farmer is more likely to adopt formula fertilizer if the average adoption behavior of other large-scale model (leader) farmers is higher, while a small-scale farmer is more likely to adopt formula fertilizer if other small-scale non-model (follower) farmers have higher average adoption behavior. Moreover, the peer effect was weakened by geographic distance among small-scale farmers and by economic distance among large-scale farmers.Originality/valueThis study elucidates the means of optimizing social learning and technology adoption among farmers.
{"title":"Mutual proximity and heterogeneity in peer effects of farmers' technology adoption: evidence from China's soil testing and formulated fertilization program","authors":"Zhigang Xu, Kerong Zhang, Li Zhou, Ruiyao Ying","doi":"10.1108/caer-10-2020-0250","DOIUrl":"https://doi.org/10.1108/caer-10-2020-0250","url":null,"abstract":"PurposeWhile the peer effects of technology adoption are well established, few studies have considered the variation in peer effects resulting from the mutual proximity between leaders and followers and the heterogeneity of farmers' learning technology. This study addresses the gap in the literature by analyzing the peer effects of technology adoption among Chinese farmers.Design/methodology/approachDrawing on a government-led soil testing and formulated fertilization program, this study uses survey data of farmers from three Chinese provinces to examine the peer effects of technology adoption. This study uses a probit model to examine how mutual proximity influences peer effects and their heterogeneity. Accordingly, farmers were divided into two groups, namely small- and large-scale farmers, and then into leaders or followers depending on whether they were selected by the government as model farmers.FindingsBoth small- and large-scale farmers are more likely to use formula fertilizer if their peers do so. However, a large-scale farmer is more likely to adopt formula fertilizer if the average adoption behavior of other large-scale model (leader) farmers is higher, while a small-scale farmer is more likely to adopt formula fertilizer if other small-scale non-model (follower) farmers have higher average adoption behavior. Moreover, the peer effect was weakened by geographic distance among small-scale farmers and by economic distance among large-scale farmers.Originality/valueThis study elucidates the means of optimizing social learning and technology adoption among farmers.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48468501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-19DOI: 10.1108/caer-09-2021-0167
Liang Lu, Guang Tian, Patrick L. Hatzenbuehler
PurposeThe purpose of this paper is to describe the main ways in which large amounts of information have been integrated to provide new measures of food consumption and agricultural production, and new methods for gathering and analyzing internet-based data.Design/methodology/approachThis study reviews some of the recent developments and applications of big data, which is becoming increasingly popular in agricultural economics research. In particular, this study focuses on applications of new types of data such as text and graphics in consumers' online reviews emerging from e-commerce transactions and Normalized Difference Vegetation Index (NDVI) data as well as other producer data that are gaining popularity in precision agriculture. This study then reviews data gathering techniques such as web scraping and data analytics tools such as textual analysis and machine learning.FindingsThis study provides a comprehensive review of applications of big data in agricultural economics and discusses some potential future uses of big data.Originality/valueThis study documents some new types of data that are being utilized in agricultural economics, sources and methods to gather and store such data, existing applications of these new types of data and techniques to analyze these new data.
{"title":"How agricultural economists are using big data: a review","authors":"Liang Lu, Guang Tian, Patrick L. Hatzenbuehler","doi":"10.1108/caer-09-2021-0167","DOIUrl":"https://doi.org/10.1108/caer-09-2021-0167","url":null,"abstract":"PurposeThe purpose of this paper is to describe the main ways in which large amounts of information have been integrated to provide new measures of food consumption and agricultural production, and new methods for gathering and analyzing internet-based data.Design/methodology/approachThis study reviews some of the recent developments and applications of big data, which is becoming increasingly popular in agricultural economics research. In particular, this study focuses on applications of new types of data such as text and graphics in consumers' online reviews emerging from e-commerce transactions and Normalized Difference Vegetation Index (NDVI) data as well as other producer data that are gaining popularity in precision agriculture. This study then reviews data gathering techniques such as web scraping and data analytics tools such as textual analysis and machine learning.FindingsThis study provides a comprehensive review of applications of big data in agricultural economics and discusses some potential future uses of big data.Originality/valueThis study documents some new types of data that are being utilized in agricultural economics, sources and methods to gather and store such data, existing applications of these new types of data and techniques to analyze these new data.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49621448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-14DOI: 10.1108/caer-05-2021-0099
Yuanyuan Xu, Jian Li, Linjie Wang, Chongguang Li
PurposeThis paper aims to present the first empirical liquidity measurement of China’s agricultural futures markets and study time-varying liquidity dependence across markets.Design/methodology/approachBased on both high- and low-frequency trading data of soybean and corn, this paper evaluates short-term liquidity adjustment in Chinese agricultural futures market measured by liquidity benchmark and long-term liquidity development measured by liquidity proxies.FindingsBy constructing comparisons, the authors identify the seminal paper of Fong, Holden and Trzcinka (2017) as the best low-frequency liquidity proxy in China’s agricultural futures market and capture similar historical patterns of the liquidity in soybean and corn markets. The authors further employ Copula-generalized autoregressive conditional heteroskedasticity models to investigate liquidity dependence between soybean and corn futures markets. Results show that cross-market liquidity dependence tends to be dynamic and asymmetric (in upper versus lower tails). The liquidity dependence becomes stronger when these markets experience negative shocks than positive shocks, indicating a concern on the contagion effect of liquidity risk under negative financial situations.Originality/valueThe findings of this study provide useful information on the dynamic evolution of liquidity pattern and cross-market dependence of fastest-growing agricultural futures in the largest emerging economy.
{"title":"Liquidity of China’s agricultural futures market: measurement and cross-market dependence","authors":"Yuanyuan Xu, Jian Li, Linjie Wang, Chongguang Li","doi":"10.1108/caer-05-2021-0099","DOIUrl":"https://doi.org/10.1108/caer-05-2021-0099","url":null,"abstract":"PurposeThis paper aims to present the first empirical liquidity measurement of China’s agricultural futures markets and study time-varying liquidity dependence across markets.Design/methodology/approachBased on both high- and low-frequency trading data of soybean and corn, this paper evaluates short-term liquidity adjustment in Chinese agricultural futures market measured by liquidity benchmark and long-term liquidity development measured by liquidity proxies.FindingsBy constructing comparisons, the authors identify the seminal paper of Fong, Holden and Trzcinka (2017) as the best low-frequency liquidity proxy in China’s agricultural futures market and capture similar historical patterns of the liquidity in soybean and corn markets. The authors further employ Copula-generalized autoregressive conditional heteroskedasticity models to investigate liquidity dependence between soybean and corn futures markets. Results show that cross-market liquidity dependence tends to be dynamic and asymmetric (in upper versus lower tails). The liquidity dependence becomes stronger when these markets experience negative shocks than positive shocks, indicating a concern on the contagion effect of liquidity risk under negative financial situations.Originality/valueThe findings of this study provide useful information on the dynamic evolution of liquidity pattern and cross-market dependence of fastest-growing agricultural futures in the largest emerging economy.","PeriodicalId":10095,"journal":{"name":"China Agricultural Economic Review","volume":" ","pages":""},"PeriodicalIF":5.1,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48083227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：