Environmental Efficiency of Apple Production in China: A Translog Stochastic Frontier Analysis

Bai, Xiuguang; Salim, Ruhul; Bloch, Harry

doi:10.1017/age.2018.25

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…Econometrically, the translog production function is specified as follows:

O_{i} = θ_{0} + {false\sum}_{m = 1}^{6} θ_{1} \ln (I_{italicim}) + 0.5 \sum_{m = 1}^{6} θ_{2} \ln {(I_{italicim})}^{2} + {false\sum}_{m = 1}^{6} \sum_{k = 1}^{6} θ_{3} \ln false(I_{im} false) \ln false(I_{ik} false) + ε_{i}

where ln( O i )is the logarithm of the banana yield for farmer i ; ln( I im ) is the logarithm of m input for farmer i ; and ln( I im ) 2 is its squared term; ln( I im ) ln( I ik ) captures the interaction between inputs m and k ,

m \neq k

. The inputs include land, seedlings, fertilisers, pesticides, labour and other inputs (e.g., irrigation and machinery), following the relevant literature (Bai et al, 2019; Ma, Renwick, et al, 2018; Villano et al, 2015; Zheng et al, 2021). Because some farmers in our sample did not purchase seedlings and other inputs in the 2019 survey year, the two input variables have zero‐value observations.…”

Section: Estimation Strategiesmentioning

confidence: 99%

Impacts of cooperative membership on banana yield and risk exposure: Insights from China

Zheng

Yuan

2021

J Agricultural Economics

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The yield‐increasing effects of agricultural cooperative membership have been widely examined in the literature. However, so far, little is known about whether cooperative membership has the potential to reduce farmers’ exposure to production risk. We address this gap by estimating the impacts of cooperative membership on expected yield, yield variance (variability), and yield skewness (exposure to downside risk), using data collected from a survey of 626 banana farmers in China. We employ an endogenous switching regression model to address the selectivity bias issue. The empirical results show that cooperative membership increases banana yield by 3% and reduces the variance and downside risk exposure by 60% and 114%, respectively. The results are supported by robustness check estimates, using propensity score matching and inverse probability weighted regression adjustment models. Additional analysis reveals that the treatment effects of cooperative membership vary among members with different household and farm characteristics. Our findings suggest that agricultural cooperatives can be an effective institutional arrangement for reducing production risk and crop failure and point to the need for policies and programmes in developing cooperatives and increasing membership involvements of smallholder farmers.

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“…Econometrically, the translog production function is specified as follows:

O_{i} = θ_{0} + {false\sum}_{m = 1}^{6} θ_{1} \ln (I_{italicim}) + 0.5 \sum_{m = 1}^{6} θ_{2} \ln {(I_{italicim})}^{2} + {false\sum}_{m = 1}^{6} \sum_{k = 1}^{6} θ_{3} \ln false(I_{im} false) \ln false(I_{ik} false) + ε_{i}

m \neq k

Section: Estimation Strategiesmentioning

confidence: 99%

Impacts of cooperative membership on banana yield and risk exposure: Insights from China

Zheng

Yuan

2021

J Agricultural Economics

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“…1) Previous research has important theoretical value for guiding the development of China's grain production, but most scholars have not considered the impact of environmental factors on the efficiency of grain production. In recent years, although a few scholars have taken environmental factors into consideration when studying the efficiency of grain production, they only considered undesired output (such as chemical oxygen demand, total nitrogen and total phosphorus) without attaching importance to carbon sink output resulted from the positive externalities of grain production (Bai et al, 2019). However, ignoring the environmental factors of grain production (especially grain carbon sinks) will be detrimental to the accuracy of grain production efficiency assessment.…”

Section: Space Effects Of Grain Production Efficiencymentioning

confidence: 99%

Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China

Gao

2021

Front. Environ. Sci.

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Environmental factors have a profound impact on the evaluation of grain production efficiency. Taking environmental factors into account can more accurately measure grain production efficiency and identify the development stage of grain production. Based on the global super-efficiency SBM model, environmental factors are included in the grain production efficiency measurement system, while the temporal and spatial evolution characteristics of grain production environmental efficiency and the sources of inefficiency are analyzed. In addition, the Spatial Durbin Model is used to investigate the influencing factors and spatial spillover effects of China’s grain production environmental efficiency. The results show that: environmental factors have a significant impact on the efficiency of grain production in China; the environmental efficiency of grain production in China fluctuates with a downward trend; the environmental efficiency of grain production in different provinces show obvious spatial differentiation and geographical agglomeration; the main influencing factors of the environmental efficiency loss of grain production in major grain production areas are carbon emissions, non-point source pollution of grain production and labor input; the level of education, the level of technological development, and labor transfer have a significant positive spillover effect on the environmental efficiency of China’s grain production, yet disaster rate has a significantly negative one. Thus, it is important to pay attention to resource conservation and pollution control, strengthen cooperation and exchanges between provinces, and take the path of coordinated development between the environment and grain production.

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“…The translog production frontier model, following Tipi, Dari, and Vural (2021) and Bai, Salim, and Bloch (2019) is expressed as follows:…”

Section: Technical Efficiencymentioning

confidence: 99%

Efficiency of agricultural cooperative members in Indonesia

Sunarwibowo,

Ikhsan,

Mahi

et al. 2024

5002

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The food supply's stability is dependent on agricultural productivity, which, in turn, depends on the efficient allocation of production factors. This research analyzes the efficiency levels of rice farmers and compares them between cooperative members and non-members. This study quantifies efficiency using the stochastic frontier analysis (SFA) method and compares the results using the propensity score matching (PSM) method, which addresses potential selectivity bias resulting from the decision to join a cooperative. The study utilizes cross-sectional data from the Badan Pusat Statistik (BPS) of the Republic of Indonesia, encompassing a sample size of 68,204 farmers. The analysis reveals that the majority of farmers operate within an efficiency range of 70-90 percent. Cooperative members exhibit higher efficiency levels than non-members, with a 2.1 percent increase during the wet season and a 3.1 percent increase during the dry season. Researchers employ an alternative method, coarsened exact matching (CEM), to ensure robustness, yielding results similar to the PSM model. This research provides evidence that cooperatives have the potential to significantly enhance agricultural efficiency. The findings underscore the importance of developing and supporting cooperatives to enhance the overall welfare of farmers.

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Environmental Efficiency of Apple Production in China: A Translog Stochastic Frontier Analysis

Cited by 8 publications

References 25 publications

Impacts of cooperative membership on banana yield and risk exposure: Insights from China

Impacts of cooperative membership on banana yield and risk exposure: Insights from China

Assessing the Environmental Efficiency of Grain Production and Their Spatial Effects: Case Study of Major Grain Production Areas in China

Efficiency of agricultural cooperative members in Indonesia

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