Estimating technical efficiency of Chinese table grape wholesalers

Jiao, Weihua; Fu, Zetian; Mu, Weisong; Zhang, Xiaoshuan; Lu, Jianjun; Xu, Mark

doi:10.1108/bfj-03-2014-0134

Cited by 7 publications

(7 citation statements)

References 42 publications

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“…Wholesalers are the hub of table grape supply chain (Han and Yang, 2010), which is different from most developed countries (Rieple and Singh, 2010). Given the fact that table grape production system is dominated by small-scale and dispersed vinegrowers (Jiao et al, 2015), a high search costs are faced by vinegrowers and wholesalers. As a result, most vinegrowers sell table grapes to their preferred wholesalers, and change the buyer seems to be very difficult.…”

Section: The Wholesale Of Table Grape Value Chainmentioning

confidence: 99%

“…Consequently, retailers and wholesalers both have many alternatives. However, given the fact that wholesalers still dominate table grape trade (Jiao et al, 2015), retailers have more dependence on wholesalers for revenue. Meanwhile, retailers' switching costs are much higher than wholesalers due to the quality and trust issues.…”

Section: Retailers Often Rely On Many Different Chains For Sourcing Table Grapes As Well As Vegetablesmentioning

confidence: 99%

“…Moreover, the table grape production system is dominated by small-scale vinegrowers in China (Jiao et al, 2015), in other words, vinegrowers are unorganized, which implies the low bargaining power in price negotiations with wholesalers (Asem-Bansah et al, 2012). Given the characteristics of small-scale and dispersed production of table grape farms, wholesalers may have many alternatives and low switching cost, but have to face high search costs (Humphrey and Schmitz, 2001;Prowse and Moyer-Lee, 2014).…”

Section: Table VIImentioning

confidence: 99%

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Assessing the table grape supply chain performance in China - a value chain analysis perspective

Deng

Wang

Tian

et al. 2016

British Food Journal

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Purpose The paper aims to understand the cost-benefits, financial performance and relationships of the key actors in the table grape supply chain by using value chain analysis method. Design/methodology/approach This study presents the result of analysis on financial performance of key actors in three table grape supply chains and the relationship among them using value chain analysis. The table grape supply chain was analyzed using data collected from a field survey in 5 regions of China. The key actors of these chains were interviewed. The costs, benefits, operational and financial performance of supply chain actors were analyzed. Findings The results show that, despite receiving the highest proportion of total net profit and making the highest value creation, vinegrowers are facing fluctuant and uncertain commercial returns due to the production and market risks, fluctuation of farm-gate price and the buyer dominant relationship with wholesalers. Moreover, although it is profitable to all key actors, table grape supply chain still faces several challenges including unorganized and dispersed production systems, power asymmetry and lack of information sharing, all of which are the barriers to the improvement of the whole chain performance and the long term sustainability of this important industry. Originality/value This paper not only helps to understand the relationship among the key actors and the critical factors impacting on the whole chain performance, but also offer useful guidance for the policy makers and key actors to achieve further improvement of the table grape supply chain performance and provide valuable implications for the future studies on agriculture products.

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Section: The Wholesale Of Table Grape Value Chainmentioning

confidence: 99%

Section: Retailers Often Rely On Many Different Chains For Sourcing Table Grapes As Well As Vegetablesmentioning

confidence: 99%

Section: Table VIImentioning

confidence: 99%

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Assessing the table grape supply chain performance in China - a value chain analysis perspective

Deng

Wang

Tian

et al. 2016

British Food Journal

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“…However, postharvest table grapes are prone to phenomena such as decay, rachis browning and dry branches during storage, which influence the consumers’ desire to buy (Deng et al , 2016; Farzaneh and Carvalho, 2015; Feng et al , 2014). Shelf life (SL) is one of the important prerequisites for consumers to understand quality and ensure safety of fresh foods, it is of great significance in predicting the SL of table grape for protecting consumers’ rights and reducing economic losses (Jiao et al , 2012, 2015; Kasper et al , 2018; Raab et al , 2008).…”

Section: Introductionmentioning

confidence: 99%

Shelf life prediction model of postharvest table grape using optimized radial basis function (RBF) neural network

Chu

et al. 2019

BFJ

Self Cite

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Purpose The purpose of this paper is to develop a common remaining shelf life prediction model that is generally applicable for postharvest table grape using an optimized radial basis function (RBF) neural network to achieve more accurate prediction than the current shelf life (SL) prediction methods. Design/methodology/approach First, the final indicators (storage temperature, relative humidity, sensory average score, peel hardness, soluble solids content, weight loss rate, rotting rate, fragmentation rate and color difference) affecting SL were determined by the correlation and significance analysis. Then using the analytic hierarchy process (AHP) to calculate the weight of each indicator and determine the end of SL under different storage conditions. Subsequently, the structure of the RBF network redesigned was 9-11-1. Ultimately, the membership degree of Fuzzy clustering (fuzzy c-means) was adopted to optimize the center and width of the RBF network by using the training data. Findings The results show that this method has the highest prediction accuracy compared to the current the kinetic–Arrhenius model, back propagation (BP) network and RBF network. The maximum absolute error is 1.877, the maximum relative error (RE) is 0.184, and the adjusted R2 is 0.911. The prediction accuracy of the kinetic–Arrhenius model is the worst. The RBF network has a better prediction accuracy than the BP network. For robustness, the adjusted R2 are 0.853 and 0.886 of Italian grape and Red Globe grape, respectively, and the fitting degree are the highest among all methods, which proves that the optimized method is applicable for accurate SL prediction of different table grape varieties. Originality/value This study not only provides a new way for the prediction of SL of different grape varieties, but also provides a reference for the quality and safety management of table grape during storage. Maybe it has a further research significance for the application of RBF neural network in the SL prediction of other fresh foods.

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“…The choice to use both models was due to our interest in determining the returns to scale of the farms under study and estimating their scale efficiency. Furthermore, the choice of inputoriented approach finds its meaning in the limited inputs characterizing the agricultural sector (Toma et al, 2015), as well as in the major ability of producers to control inputs rather than output levels (Banaeian et al, 2011;Jiao et al, 2015). Therefore, it may be plausible to state that an inputoriented model is more appropriate to quantify the excess use of inputs during the production processes and to identify inputs optimization strategies also to support agriculturally sustainable pathways.…”

Section: Dea Model Implementationmentioning

confidence: 99%

Technical Efficiency Assessment of Intensive and Traditional Olive Farms in Southern Italy

Stillitano

Falcone

Nicolò

et al. 2019

AOL

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Olive-growing plays an important role in Southern Italy's agricultural sector. However, the profitability of many olive growing farms depends, still today, on public subsidies. The current changes in the European Common Agricultural Policy (CAP) 2014-2020, oriented towards the direct payments decreasing, will inevitably have important effects on farmer incomes. This is why the olive farms will have to increase their level of direct profitability to ensure their resilience on the market. Therefore, the measurement of technical efficiency plays a crucial role in identifying more efficient management practices, and for this aim, Data Envelopment Analysis (DEA) represents the most widely used technique in productivity analysis.In this paper, constant returns to scale and variable returns to scale input-oriented models were used to investigate the technical and scale efficiency of intensive and traditional olive farms in Southern Italy, in order to highlight the performance of each farm.Results showed technical inefficiencies in both olive systems and, suggesting that improvements in the input allocation among all farms are needed. Findings could be useful to suggest the adoption of management strategies to optimize the use of inputs, aiming to achieve suitable levels of productive performance.

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Estimating technical efficiency of Chinese table grape wholesalers

Cited by 7 publications

References 42 publications

Assessing the table grape supply chain performance in China - a value chain analysis perspective

Assessing the table grape supply chain performance in China - a value chain analysis perspective

Shelf life prediction model of postharvest table grape using optimized radial basis function (RBF) neural network

Technical Efficiency Assessment of Intensive and Traditional Olive Farms in Southern Italy

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