A nonparametric analysis of the determinants of family farm efficiency dynamics in Lithuania

Baležentis, Tomas; Kriščiukaitienė, Irena; Baležentis, Аlvydas

doi:10.1111/agec.12108

Cited by 21 publications

(13 citation statements)

References 20 publications

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“…Similar results were obtained by Balezentis et al. () using a DEA bootstrap approach, suggesting that increasing area under rice may raise the technical efficiency. This implies that increasing the scale of farming systems could improve the efficiency of the use of inputs.…”

Section: Discussionsupporting

confidence: 87%

“…Hall () suggests 1,000 replications, Balezentis et al. (), Simar and Wilson (), and Yu () used 2,000 replications. A larger number of replications can be used to achieve more accurate estimates at the cost of waiting time depending on the CPU power of the computer (Simar and Wilson, ; Yu, ). In step 3.1, for each i = 1,…, m , we draw

ε_{i}

(the residual) from the N (0,

{\hat{σ}}_{ε}^{2}

) distribution with left‐truncation at (0−

z_{i} \hat{β}

). In step 3.2, for each i = 1,…, m , we computed

δ_{i}^{*} = z_{i} \hat{β} + ε_{i}

. In step 3.3, we used the maximum likelihood method to estimate the truncated regression of

δ_{i}^{*}

z_{i}

, yielding estimates parameters

({\overset{β}{true}}^{*}, {\hat{σ}}_{ε}^{*})

and marginal effects of explanatory variables. …”

Section: Methodsmentioning

confidence: 99%

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Inefficiency of manual weeding in rainfed rice systems affected by parasitic weeds

N'Cho

Mourits

Rodenburg

2019

Agricultural Economics

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Manual weeding is the predominant weed control practice and the most labor-consuming activity in smallholder, rainfed rice systems in sub-Saharan Africa. This study investigates the technical inefficiency of weeding labor, other labor, and overall inputs, and identifies sources of technical inefficiency of weeding labor in the context of parasitic weed infestation. The analysis applies a two-stage approach. First, a directional input distance function DEA approach was used to compute input-specific technical inefficiencies. Second, sources of technical inefficiency of weeding labor were identified using a truncated bootstrap regression. Data from 406 randomly selected smallholder farmers from Benin (n = 215) and Côte d'Ivoire (n = 191) were used. The technical inefficiency of weeding labor was high in both countries (58% in Côte d'Ivoire and 69% in Benin). This implies that a substantial fraction of weeding labor could be saved without reducing rice productivity or increasing the use of other inputs. A decrease in the technical inefficiency of weeding labor with an increase in production scale was observed. In addition, weeding regime and education level were each associated to significant changes in the technical inefficiency of weeding labor.JEL classifications: C01, C02, C14, C34

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Section: Discussionsupporting

confidence: 87%

ε_{i}

(the residual) from the N (0,

{\hat{σ}}_{ε}^{2}

) distribution with left‐truncation at (0−

z_{i} \hat{β}

). In step 3.2, for each i = 1,…, m , we computed

δ_{i}^{*} = z_{i} \hat{β} + ε_{i}

. In step 3.3, we used the maximum likelihood method to estimate the truncated regression of

δ_{i}^{*}

z_{i}

, yielding estimates parameters

({\overset{β}{true}}^{*}, {\hat{σ}}_{ε}^{*})

and marginal effects of explanatory variables. …”

Section: Methodsmentioning

confidence: 99%

Inefficiency of manual weeding in rainfed rice systems affected by parasitic weeds

N'Cho

Mourits

Rodenburg

2019

Agricultural Economics

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“…This confirms the finding (Alwarritzi et al 2015;Coelli and Fleming 2004) that farm diversification activities seem to increase efficiency because the farmers might have the opportunity to choose other farming activities that complement the given input of each other resource. However, Baležentis et al (2014) argue that specialization increases efficiency.…”

mentioning

confidence: 99%

Technical Efficiency of Smallholder Agriculture in Developing Countries: The Case of Ethiopia

Tenaye

2020

Economies

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The efficient use of inputs is indispensable in many developing countries, such as Ethiopia. This study assesses the level and determinants of technical efficiency of smallholder farmers using the true fixed effects (TFE) model. The TFE model separates inefficiency from unobserved heterogeneity. Empirical data come from four rounds of panel data (1994–2009) from the Ethiopian rural household survey (ERHS). A one-step maximum likelihood estimator was employed to estimate the Cobb-Douglas stochastic frontier production function and factors influencing technical efficiency. The results indicated that the major variables affecting technical efficiency are policy responsive, albeit to varying degrees: education of the household head, family size, farm size, land fragmentation, land quality, credit use, extension service, off-farm employment, and crop share. The analyses also identify variables amenable to policy changes in the production function: labor, traction power, farm size, seeds, and fertilizer. The mean household-level efficiency for the surveyed farmers is 0.59, indicating that farmers could improve technical efficiency. This implies that smallholder farms in Ethiopia can reduce the input requirement of producing the average output by 41% if their operations become technically efficient. This study recommends that the above policy variables be considered to make Ethiopian smallholder farmers more efficient.

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“…The post-accession period features structural changes in terms of farm size and specialisation. Specifically, an increase in the mean farm size due to the exits of small-scale farms has been observed alongside a switching to crop farming in lieu of animal farming (Baležentis, Kriščiukaitienė, & Baležentis, 2014). However, the average farm size is still below that in other countries with similar farming possibilities (e.g.,…”

Section: Introductionmentioning

confidence: 99%

Dynamics of the total factor productivity in Lithuanian family farms with a statistical inference: the bootstrapped Malmquist indices and Multiple Correspondence Analysis

Baležentis

2016

Economic Research-Ekonomska Istraživanja

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The paper combines the bootstrapped Malmquist productivity index and the Multiple Correspondence Analysis to measure the changes in the total factor productivity. The bootstrapped Malmquist productivity index enables us to identify insignificant change in the total factor productivity, whereas the Multiple Correspondence Analysis relates the estimates to the environmental variables. A sample of Lithuanian family farms is utilised to test the proposed framework. Specifically, the research covers 200 family farms and the period of [2004][2005][2006][2007][2008][2009]. The analysis showed that the total factor productivity decreased by some 15-18% during 2004-2009 depending on the farming type. Multiple Correspondence Analysis suggested that all of the farming types exhibited change in the total factor productivity close to the average, although the crop farming was located in the more stochastic area.

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A nonparametric analysis of the determinants of family farm efficiency dynamics in Lithuania

Cited by 21 publications

References 20 publications

Inefficiency of manual weeding in rainfed rice systems affected by parasitic weeds

Inefficiency of manual weeding in rainfed rice systems affected by parasitic weeds

Technical Efficiency of Smallholder Agriculture in Developing Countries: The Case of Ethiopia

Dynamics of the total factor productivity in Lithuanian family farms with a statistical inference: the bootstrapped Malmquist indices and Multiple Correspondence Analysis

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