Spatial Dependence Evaluation of Agricultural Technical Efficiency—Based on the Stochastic Frontier and Spatial Econometric Model

Yin, Ziqi; Wu, Jianzhai

doi:10.3390/su13052708

Cited by 7 publications

(8 citation statements)

References 59 publications

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“…Given the inherent crop diversity in agricultural production, it is assumed that all deviations from the frontier are associated with inefficiency (as assumed in the data envelopment analysis approach), which is challenging to accept in this sector. Based on the following previous literature in the agriculture field [12,31,32], this study narrows down the focus to cotton output and digs into the possibility of realizing similar production frontier and efficiency relationships with the SFA method. Following Battese and Coelli, this paper applies the one stage modeling approach to more comprehensive data representing various factor included in the production frontier and efficiency analysis.…”

Section: Theoretical Conceptmentioning

confidence: 99%

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Evolution of Overall Cotton Production and Its Determinants: Implications for Developing Countries Using Pakistan Case

Arshad¹,

Yuan-feng

Hanif

et al. 2022

Sustainability

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Managing the declining yield of non-food crops has opened new strategic challenges amidst global uncertainties. The COVID-19 scenario has increased awareness of natural lifestyle and eco-friendly products, largely dependent on non-food crop material. This strategic shift requires moving beyond traditional farm practices to improve agricultural production efficiency, and developing countries in particular have shown a consistent loss in their self-sufficiency of industrial crops despite being major exporters of non-food crop materials. However, existing studies analyze production efficiencies of non-food crops from general or theoretical aspects often by virtual estimates from breaking down the multiple factors of crop productivity. This study examined multiple factors of crop production to identify “which crop inputs have been inefficiently used overtime” by tracking efficiency changes and various input issues in overall cotton production from practical aspects, i.e., scaling non-constant returns of those multiple factors would allow for the violation of various situations. Accordingly, a stochastic frontier approach was employed to measure the production frontier and efficiency relationship using time-series data of Pakistan’s cotton production from 1971–2018—a specific non-food crop perspective from a top-ranked cotton-producing country that has recently been shifted towards being a non-exporter of cotton due to low yield. The coefficient of area, seed, and labor indicates the positive relationship with cotton production, while fertilizer, irrigation, electricity, and machinery are statistically negative. This implies that policymakers need priority-based strategies for the judicial use of synthetic fertilizers, irrigation, a subsidy policy, and technology adoption, which could significantly improve the efficiencies of cotton productivity from the same land resources. Being adaptable to other developing economies, the analysis would strategically facilitate designing and developing affordable technology-driven solutions and their customized extensions towards sustainable non-food crop production practices and Agri-Resources efficiencies.

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Section: Theoretical Conceptmentioning

confidence: 99%

“…is a non-negative technical inefficiency effect representing management factors, and it is assumed to be independently distributed with mean U it and variance σv 2 [32].…”

Section: Measurment Of Technical Inefficienymentioning

confidence: 99%

Evolution of Overall Cotton Production and Its Determinants: Implications for Developing Countries Using Pakistan Case

Arshad¹,

Yuan-feng

Hanif

et al. 2022

Sustainability

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“…In terms of the spatiotemporal evolution characteristics of APE, Hou and Yao [22] constructed traditional and spatial Markov transition probability matrices to explore the spatiotemporal evolution characteristics of agricultural eco-efficiency in China and predict the trend of its long-term evolution. Most previous research focused on the spatiotemporal dynamic evolution and differentiation characteristics of APE by Kernel density estimation [23], global or local Moran's I of exploratory spatial data analysis (ESDA) series methods, or hot spot analysis (Getis-Ord Gi*) [24][25][26] based on APE measured by DEA. However, little attention has been paid to the imbalance of the spatiotemporal transfer of the center of gravity (COG) and standard deviation ellipse (SDE) of APE, and therefore the spatial transfer dynamics of APE have not been deeply understood.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The plantation is primarily an agricultural production sector that cultivates plant crops such as food crops, cash crops, and fodder crops. According to the availability of data and the consistency of statistical caliber, the input indicators of APE include traditional agricultural elements such as land, labor, mechanical power, irrigation, fertilizers, and pesticides [19,21,24], and climate indicators such as precipitation, temperature, sunshine hours are incorporated into the input factors. The output indicators include total agricultural output value and total grain production as desirable output, and agricultural non-point source pollution emissions and agricultural carbon emissions as undesirable output.…”

Section: Core Variables Of Ape Under Dual Constraintsmentioning

confidence: 99%

Re-Estimation of Agricultural Production Efficiency in China under the Dual Constraints of Climate Change and Resource Environment: Spatial Imbalance and Convergence

Hou

Huo

2022

Agriculture

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Climate change and farmland environmental pollution have put greater pressure on the sustainability of agricultural production. Based on the provincial panel data of mainland China from 1978 to 2018, climate variables such as precipitation, temperature, and sunshine hours are included into the input indicators, and agricultural non-point source pollution and carbon emissions are taken as undesirable outputs, the agricultural production efficiency (APE) under the dual constraints of climate change and the resource environment was estimated by the super slacks-based measure (SBM)-undesirable model. On the basis of the trajectory of the imbalanced spatiotemporal evolution of APE shown by Kernel density estimation and the standard deviational ellipse (SDE)–center of gravity (COG) transfer model, the spatial convergence model was used to test the convergence and differentiation characteristics of APE. Under the dual constraints, APE presents a “bimodal” distribution with a stable increase in fluctuation, but it is still at a generally low level and does not show polarization, among which the APE in the northeast region is the highest. The COG of APE tends to transfer towards the northeast, and the coverage of the SDE is shrinking, so the overall spatial pattern is characterized by a tendency of clustering towards the north in the north-south direction and a tendency of imbalance in the east-west direction. APE has significant spatial convergence, and there is a trend of “latecomer catching-up” in low-efficiency regions. The introduction of spatial correlation accelerates the convergence rate and shortens the convergence period. The convergence rate is the highest in the central and western regions, followed by that in the northeastern region, and the convergence rate is the lowest in the eastern region. In addition, the convergence rate in different time periods has a phase change. The process of improving the quality and efficiency of agricultural production requires enhancing the adaptability of climate change, balancing the carrying capacity of the resource environment, and strengthening inter-regional cooperation and linkage in the field of agriculture.

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“…At present, the mainstream research methods of spatial metrology include the spatial lag model (SAR) [59], spatial error model (SEM) [60], and spatial Durbin model (SDM) [61]. Compared with the SAR and SEM models, the SDM model considers the spatial correlation of dependent variables and the spatial correlation of independent variables and has both spatial autocorrelation and spatial interaction effects [62]. At the same time, for endogeneity problems, the SDM model can be used to obtain estimates that are not biased by amplification [63].…”

Section: Spatial Dubin Modelmentioning

confidence: 99%

An Integrated Assessment of Different Types of Environment-Friendly Technological Progress and Their Spatial Spillover Effects in the Chinese Agriculture Sector

et al. 2022

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The progress of environment-friendly technology is an important means and fundamental way to achieve high-quality agricultural development. Based on the panel data of 30 provinces of China from 2000 to 2010, the study used the slack-based models (SBM) to measure the progress of China’s environment-based technology and its different types and discusses its dynamic evolution characteristics over time. First, the study adopted MATLAB software and used a slack-based models (SBM) method to split the environment-friendly technology progress (AGTP) into agricultural emission-reduction environment-friendly technology progress (AEGTP) and the agricultural re-source-saving environment-friendly technology progress (ARGTP). Then, global and local spatial autocorrelation analysis, spatial model testing, and Spatial Durbin Model (SDM) were performed on different types of environment-friendly technology progress using STATA15. Moreover, OpenGeoDa and ArcGIS software was used for visualization. The empirical results showed that: (i) from the perspective of time and space, the AGTP showed a slightly higher level in technological regression trend from 2000 to 2012, and rebounded rapidly from 2012 to 2019. In the spatial dimension, the spatial autocorrelation test results of environment-friendly technology progress at the global Moran I level showed a significant positive correlation; however, the phenomenon of the regional level showed a negative correlation. (ii) From the perspective of the type of heterogeneity, only the spatial distribution has a high degree of chance, and the aggregation area is more concentrated. Various influencing factors have a very significant impact on ACGTP but are less significant on agricultural resource-saving environment-friendly technology progress. However, various influencing factors have a more significant impact on the ACGTP than AEGTP. (iii) From the perspective of the spatial spillover effect, labor level, per capita agricultural gross product, and agricultural internal structure are positively and significantly related to environment-friendly technology progress and its different types. Agricultural price policy, financial support policy, economic environmental regulation, and administrative environmental regulation have significant negative effects on the progress of environment-friendly technology and its different types.

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Spatial Dependence Evaluation of Agricultural Technical Efficiency—Based on the Stochastic Frontier and Spatial Econometric Model

Cited by 7 publications

References 59 publications

Evolution of Overall Cotton Production and Its Determinants: Implications for Developing Countries Using Pakistan Case

Evolution of Overall Cotton Production and Its Determinants: Implications for Developing Countries Using Pakistan Case

Re-Estimation of Agricultural Production Efficiency in China under the Dual Constraints of Climate Change and Resource Environment: Spatial Imbalance and Convergence

An Integrated Assessment of Different Types of Environment-Friendly Technological Progress and Their Spatial Spillover Effects in the Chinese Agriculture Sector

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