Technical efficiency in milk production in underdeveloped production environment of India*

Bardhan, D.; Sharma, M.L.

doi:10.1186/2193-1801-2-65

Cited by 16 publications

(10 citation statements)

References 5 publications

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“…Holding a contrary view, [29] noted that owners of small scale farms tend to work on their farms and appropriate the full benefits of their efforts unlike large scale farmers who use hired labor which is rarely found to be least concerned with farm progress. Results of Herd size effect on efficiency for [30] was positive and statistically significant while [31] observed a significant and negative relationship between herd size and technical efficiency in milk production in underdeveloped production environment of India. Herd size was expected to affect technical efficiency in either direction.…”

Section: Factors That Influence Technical Efficiencymentioning

confidence: 83%

Technical Efficiency of Milk Producers in Cattle Corridor of Uganda: Kiboga District Case

Nakanwagi¹,

Hyuha²

2015

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Overall objective of the study was to analyze technical efficiency and its determinants among milk producers in cattle corridor parts of Kiboga district. Stochastic frontier approach was employed to analyze technical efficiency and Tobit model regression to establish determinants of technical efficiency. The study used cross sectional edited data of 190 randomly sampled respondents from the three sub counties of Dwaniro, Kapeke and Lwamata. Milk producers achieved average technical efficiency level of 68%. Average technical efficiency indicated that milk producers were operating below the production frontier and were not technically efficient but had potential to improve. Determinants of technical efficiency were herd size, improved cows, hired labour, land ownership, water source and extension services. Policies that will lead to increased investment in valley tanks, increased investment in cross/exotic cows and increased investment in provision of extension are recommended by the study.

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Section: Factors That Influence Technical Efficiencymentioning

confidence: 83%

Technical Efficiency of Milk Producers in Cattle Corridor of Uganda: Kiboga District Case

Nakanwagi¹,

Hyuha²

2015

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“…The cow is considered holy in Indian mythology, and the anti‐viral and anti‐bacterial properties of cow urine and dung has long been established (Samhita, ; Basak et al., ; Jarald et al., ). India is the leading milk producer in the world (More, ), but this achievement is number driven (Birthal et al., ), and native breeds of Indian livestock are known for their low per‐animal productivity (Bardhan and Sharma, ).…”

Section: Discussionmentioning

confidence: 99%

‘Bio-Load’ and Bio-Type Profiles ofMycobacterium aviumsubspeciesparatuberculosisInfection in the Domestic Livestock Population Endemic for Johne's Disease: A Survey of 28 years (1985-2013) in India

Singh

et al. 2014

Transbound Emerg Dis

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Bio-load and bio-profile of Mycobacterium avium subspecies paratuberculosis was studied in the domestic livestock population of the country. Of the 23,429 farm and farmer's animals screened, average bio-load was 23.3% (Period of study; 28 years for goats; 13 years for sheep, cattle and buffaloes). Species-wise, bio-load was 20.1, 32.7, 39.3 and 28.3% in goats, sheep, cattle and buffaloes, respectively. Bio-load was significantly lower in time period A (P < 0.001) and B (P < 0.03), compared with period C. Geographical zone-wise, bio-load of MAP was significantly higher (P < 0.05) in Central zone compared with South, West, East and North zones. Bio-load in 11 states ranged from 16.2 to 87.8%. Of 8450, 5643, 8185 and 1151 samples screened by microscopy, culture, indigenous ELISA and IS900 blood PCR, 20.0, 10.6, 35.1 and 26.6% samples were positive, respectively. Bio-load was 32.8 and 31.6% in farm and farmer's goats and sheep, respectively, and 62.1% in farmer's cattle. MAP bio-load was also monitored in four farm units (three goats and one sheep) for breed improvement and three farm goats units for experimental purposes at Central Institute for Research on Goats in Mathura district. Of the 8025 goats and 1525 sheep that died from 1988 to 2013, 10.9 and 3.0% deaths were due to JD, respectively. On the basis of JD and suspected JD, 10.0 and 28.4% goats and 2.2 and 40.9% sheep, respectively were culled from the farm units in 25 years. Microscopic examination of 214 tissues (mesenteric lymph nodes and intestines) of 107 animals, it was observed that bio-load of MAP was high (25.0-60.0%) in farm animals. 'Indian Bison Type' was the dominant biotype, irrespective of domestic livestock species and the geographical zone.

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“…This section presents the determinants of TE and the formulation of hypotheses derived from the literature. Several authors have discussed the determinants of TE, since understanding these factors can improve the efficiency and performance of dairy farms (Tauer and Belbase, 1987;Bravo-Ureta and Rieger, 1991;Hallam and Machado, 1996;Gelan and Muriithi, 2012;Ma et al, 2012;Nascimento et al, 2012;Bardhan and Sharma, 2013;Luik et al, 2014;Van der Voort et al, 2014;Gül et al, 2016;Travassos et al, 2016;Latruffe et al, 2017;Mehmood et al, 2018). Mareth et al (2017) identified 42 studies that analyse the determinants of TE in dairy farms.…”

Section: Methodsmentioning

confidence: 99%

Analysing the determinants of technical efficiency of dairy farms in Brazil

Mareth

Scavarda

Thomé

et al. 2019

IJPPM

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Purpose The purpose of this paper is to analyse the determinants of technical efficiency (TE) in dairy farms located in the South of Brazil, aiming for a better understanding of the topic for academics, dairy farmers and policymakers to improve the productivity and competitiveness of dairy farms. Design/methodology/approach This study was developed using a two-stage approach. Data envelopment analysis was used to estimate the TE level and regression models to understand the factors affecting TE in dairy farms. The sample size is 253 dairy farms in the South of Brazil. Findings The variation in the mean TE indexes reported in the literature can be explained by the attributes of the analysed studies, including the education of the farm operator, farm size (number of cows and milk), feed and labour costs, and use of services. Additionally, the results suggest that dairy farmers in the sample could increase milk output by 50.1 per cent (level of inefficiency) on average if they improve their TE. Originality/value This study makes three important contributions: first, it formulates hypotheses from the previous literature’s propositions on the estimation of TE in dairy farms; second, it tests the hypotheses in an empirical study to understand the main factors affecting the TE in dairy farms of the selected municipalities in the South of Brazil; and third, it compares previous findings on the determinants of TE in dairy farms serving different stakeholders, such as researchers, farmers and government representatives, to improve the productivity and competitiveness of dairy farms.

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Technical efficiency in milk production in underdeveloped production environment of India*

Cited by 16 publications

References 5 publications

Technical Efficiency of Milk Producers in Cattle Corridor of Uganda: Kiboga District Case

Technical Efficiency of Milk Producers in Cattle Corridor of Uganda: Kiboga District Case

‘Bio-Load’ and Bio-Type Profiles ofMycobacterium aviumsubspeciesparatuberculosisInfection in the Domestic Livestock Population Endemic for Johne's Disease: A Survey of 28 years (1985-2013) in India

Analysing the determinants of technical efficiency of dairy farms in Brazil

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