Estimation of technical efficiency of dairy farms in central zone of Tigray National Regional State

Girma, Haile

doi:10.1016/j.heliyon.2019.e01322

Cited by 9 publications

(10 citation statements)

References 9 publications

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“…This was approved by Jondrow et al [26]. In addition, [22] also found similar result for the two. Therefore, for the analysis of the data in this study, we rely on the truncated-normal model.…”

Section: Empirical Analysis Maximum-likelihood Estimatorssupporting

confidence: 61%

Analysis of technical efficiency in maize production in Guji Zone: stochastic frontier model

Belete

2020

Agric & Food Secur

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Background The Guji Zone in particular and Oromia region in general have a huge potential for crop production that can even contribute for the country as a whole in the struggle for achieving food security. Maize is Ethiopia’s largest cereal crop in terms of total production, acreage, and the number of farm holdings. It is one of the staple crops produced in the study area. However, the farm productivity per hectare is low compared to the national average which indicates the presence of farming inefficiency in the study area. Thus, this study was undertaken with the objective of estimating the level of farmers’ technical efficiency in maize production and identifying the factors which determine the variation in the level of technical efficiency among the farmers. Results This study was relied on the primary data collected from a sample of maize producing farm household of Adola Rede and Anna Sorra districts of the Guji Zone using a survey questionnaire. A simple random sampling technique was found appropriate for selecting the respondents, since the populations of these study area are homogeneous. The analysis was relied on a cross-sectional data collected from 234 sample farm households. A one-step stochastic frontier normal/truncated-normal model was used to estimate the farmers’ technical efficiency as well as to identify the determinants of yield per hectare of maize which was estimated by maximum-likelihood method. The empirical result indicates that there are large numbers of inefficient farmers in both districts. The estimated technical efficiency for maize producing farmers is equal to 69.03%. Besides, the current study revealed that 69.23% of maize producing farmers have been operating within a technical efficiency score interval equal to (0.5–0.75) in both districts. In addition, the maize yield per hectare in the study area is very low compared to the national average, i.e., the mean yield is 19.2 quintal per hectare. Moreover, the maximum-likelihood results show that gender of the head of the household, age of the head of the household, farm income, row planting, access to credit, number of active labor force, land size owned, access to improved seed and seed type used, and number of livestock were significantly determine the farmers technical inefficiency. Conclusion The results of the study revealed that there is an opportunity which is being squandered in maize production in the study area. For instance, with a given technology, it is possible to raise the maize yield per hectare by 30.07% in the study area. Thus, the government and any concerned stakeholders should facilitate access to improved seed and fertilizer, credit, farm technology, and short-term training. Besides, all stakeholders should work in a cooperative and organized manner to implement the full agricultural package on farm level.

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Section: Empirical Analysis Maximum-likelihood Estimatorssupporting

confidence: 61%

Analysis of technical efficiency in maize production in Guji Zone: stochastic frontier model

Belete

2020

Agric & Food Secur

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“…It can also be seen that between these two types of farms there is no significant difference in the size of the herd of cows, while the level of milk yield was slightly different. This may result from the difference in the proper use of inputs (the level of technical efficiency of dairy farms) and differences in other socio-economic factors characterizing these units [62]. It also indicates that milk production on a farm can be increased without the need to incur additional production costs.…”

Section: Discussionmentioning

confidence: 99%

Measurement and Evaluation of the Efficiency and Total Productivity of Dairy Farms in Poland

2021

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The aim of the article is to measure and evaluate the economic and technical efficiency as well as the total productivity of dairy farms in Poland. The research used data from the Polish FADN. The analysis covered the years 2008–2017. The study group consisted of 730 farms. An iteration-optimization method, k-means clustering, non-parametric DEA method, and aggregated Färe–Primont indexes were used to achieve the goal. The conducted research allowed for the distinction of four types of dairy farms with different levels of economic efficiency. We prove that the costs of labor, energy, maintenance of machines and buildings, fodder, depreciation, and overheads, as well as agricultural land, affect the technical efficiency and total productivity of dairy farms. We also prove that over the analyzed years, technical efficiency decreased in all groups of dairy farms, while the Färe–Primont index of total productivity changes slightly increased. The validity of these studies and their practical significance result from the need to improve the competitiveness of dairy farms and increase their profitability. Our research fills a gap in research on measuring and evaluating the efficiency and productivity of dairy farms, and at the same time represents an original approach to the studied phenomenon. Moreover, it allows for the explanation of differences in the effectiveness of different types of dairy farms, providing useful information for the design of effective targeted policy interventions to improve the competitiveness of the Polish dairy sector. There are opportunities to improve both the technical efficiency and productivity of dairy farms in Poland. This increase will be possible thanks to the improvement of agricultural advisory services and ensuring that farmers participate in training in proper nutrition, reproduction, milk marketing and other management skills.

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“…Figure 8 illustrates the medium to low dry matter productivity and high THI conditions in the clusters Inda Silase–Axum and Mekelle–Adigrat, which shows that expansion of dairy farming in these areas would be challenging. Girma ( 2019 ) identified several inefficiencies in dairy production around these clusters and recommended that these could be overcome by improving farmers’ access to education and extension services as well as improving the enabling environment.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Integrating knowledge on biophysical and socioeconomic potential to map clusters for future milk production in Ethiopia

Ndambi

Ceccarelli

Zijlstra

et al. 2021

Trop Anim Health Prod

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Despite growing milk demand and imports, market-oriented milk production and formal processing in Ethiopia is limited to areas around Addis Ababa, notwithstanding its competing land use demand. This study assessed biophysical and market potential for developing the dairy sector, characterizing Ethiopian dairy clusters. Biophysical data from geographic information system (GIS) sources and information from key informants were combined in mapping and ranking these clusters on milk production potential. Twenty-four indicators in six major categories were applied for this assessment: feed availability, environmental conditions for dairy cattle, current production status, access to inputs and services, output market access, and production expansion potential. Feed availability (fodder, crop residues, and agro-industrial by-products as well as land availability and affordability) were the main drivers for dairy development, followed by the current production status, mainly driven by number of (improved) dairy cattle and (formal) milk volumes. Dairy clusters close to Addis Ababa had the highest overall scores for development potential, mainly determined by local demand and access to inputs. For dairy sustainable dairy development in Ethiopia, companies seeking long-term opportunities may avoid the Addis Ababa area and develop dairy production and processing in other clusters especially in Amhara and Tigray regions, with good milk production potential but less developed market infrastructure. The combination of biophysical data and key informant knowledge offered key strengths in delivering valuable results within a short time span. It however requires a careful selection of knowledgeable key informants whose expertise cover a broad scope of the dairy value chain.

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Estimation of technical efficiency of dairy farms in central zone of Tigray National Regional State

Cited by 9 publications

References 9 publications

Analysis of technical efficiency in maize production in Guji Zone: stochastic frontier model

Analysis of technical efficiency in maize production in Guji Zone: stochastic frontier model

Measurement and Evaluation of the Efficiency and Total Productivity of Dairy Farms in Poland

Integrating knowledge on biophysical and socioeconomic potential to map clusters for future milk production in Ethiopia

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