Influence of demographic factors on the use of farm management tools by New Zealand farmers

Corner-Thomas, René A.; Kenyon, P. R.; Morris, S. T.; Ridler, Anne; Hickson, R. E.; Greer, Andrew W.; Logan, Christopher M.; Ht, Blair

doi:10.1080/00288233.2015.1063513

Cited by 27 publications

(18 citation statements)

References 15 publications

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“…This could be attributed to the fact that as farmers spend more years on formal education they turn to adopt technologies that in turn reduce inefficiency. This result is somewhat consistent with Corner- Thomas et al (2015) that education of the farmer positively influences the use farm management tools that results in increase productivity.…”

Section: Effect Of Mechanisation Intensity On the Productivity Of Ricesupporting

confidence: 87%

Effect of mechanisation use intensity on the productivity of rice farms in southern Ghana

Apiors

Kuwornu

Kwadzo

2016

AAS

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<p>This study analysed the effect of farm mechanisation on productivity of rice farms in southern Ghana. The empirical results of the stochastic frontier model of primary data solicited from 360 rice farmers in southern Ghana revealed that land size cultivated, agrochemical expenditure, tillage intensity, threshing intensity, education and transportation intensity were significant factors that positively influenced partial factor productivity with respect to mechanisation. On the other hand, reaping intensity, over use of fertilizers, and age of farmers negatively influenced partial factor productivity with respect to mechanisation. These results have implications for capacity building and government support to increase productivity on rice farms.</p>

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Section: Effect Of Mechanisation Intensity On the Productivity Of Ricesupporting

confidence: 87%

Effect of mechanisation use intensity on the productivity of rice farms in southern Ghana

Apiors

Kuwornu

Kwadzo

2016

AAS

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“…A farm’s productivity has been found to decrease progressively with farmers over 45 years of age [ 27 ]. This can be explained when considering the findings of a New Zealand study, in which it was found that farmers older than 50 years were using fewer health management tools and using them less frequently than younger farmers, and this was the case even for procedures as basic as anti-clostridial vaccinations in sheep flocks [ 28 ]. This finding could be of great importance in diseases, such as mastitis, that require complex health management.…”

Section: Discussionmentioning

confidence: 99%

Extensive Countrywide Field Investigation of Somatic Cell Counts and Total Bacterial Counts in Bulk-Tank Raw Milk in Sheep Flocks in Greece

Lianou

Michael

Vasileiou

et al. 2021

Foods

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Objectives were to investigate somatic cell counts (SCC) and total bacterial counts (TBC) in the raw bulk-tank milk of sheep flocks in Greece, to study factors potentially influencing increased SCC and TBC in the bulk-tank milk of sheep and to evaluate possible associations of SCC and TBC with milk content. Throughout Greece, 325 dairy sheep flocks were visited for collection of milk sampling for somatic cell counting, microbiological examination and composition measurement. Geometric mean SCC were 0.488 × 106 cells mL−1; geometric mean TBC were 398 × 103 cfu mL−1; 228 staphylococcal isolates were recovered form 206 flocks (63.4%). Multivariable analyses revealed annual incidence risk of clinical mastitis, age of the farmer and month into lactation period (among 53 variables) to be significant for SCC > 1.0 × 106 cells mL−1 and month into lactation period at sampling and availability of mechanical ventilators (among 58 variables) to be significant for TBC > 1500 × 103 cfu mL−1. Negative correlation of SCC with fat, total protein and lactose and positive correlation of SCC with added water were found. With SCC > 1.0 × 106 cells mL−1, significant reduction of protein content (2%) was observed, whilst in flocks with SCC > 1.5 × 106 cells mL−1, significantly lower annual milk production per ewe (42.9%) was recorded.

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“…Our results show that other known sociodemographic factors seen as influencing technology uptake, such as age or enterprise size, did not significantly influence adoption of EID technology. Effect of age on adoption of technology has been variable with some studies suggesting this as a significant factor and poor adoption of technology with increasing age [ 54 , 55 ] whilst other suggesting age as not a barrier for adoption [ 42 , 53 ].…”

Section: Discussionmentioning

confidence: 99%

Drivers for precision livestock technology adoption: A study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales

et al. 2018

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The UK is the largest lamb meat producer in Europe. However, the low profitability of sheep farming sector suggests production efficiency could be improved. Although the use of technologies such as Electronic Identification (EID) tools could allow a better use of flock resources, anecdotal evidence suggests they are not widely used. The aim of this study was to assess uptake of EID technology, and explore drivers and barriers of adoption of related tools among English and Welsh farmers. Farm beliefs and management practices associated with adoption of this technology were investigated. A total of 2000 questionnaires were sent, with a response rate of 22%. Among the respondents, 87 had adopted EID tools for recording flock information, 97 intended to adopt it in the future, and 222 neither had adopted it, neither intended to adopt it. Exploratory factor analysis (EFA) and multivariable logistic regression modelling were used to identify farmer beliefs and management practices significantly associated with adoption of EID technology. EFA identified three factors expressing farmer’s beliefs–external pressure and negative feelings, usefulness and practicality. Our results suggest farmer’s beliefs play a significant role in technology uptake. Non-adopters were more likely than adopters to believe that ‘government pressurise farmers to adopt technology’. In contrast, adopters were significantly more likely than non-adopters to see EID as practical and useful (p≤0.05). Farmers with higher information technologies literacy and intending to intensify production in the future were significantly more likely to adopt EID technology (p≤0.05). Importantly, flocks managed with EID tools had significantly lower farmer- reported flock lameness levels (p≤0.05). These findings bring insights on the dynamics of adoption of EID tools. Communicating evidence of the positive effects EID tools on flock performance and strengthening farmer’s capability in use of technology are likely to enhance the uptake of this technology in sheep farms.

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Influence of demographic factors on the use of farm management tools by New Zealand farmers

Cited by 27 publications

References 15 publications

Effect of mechanisation use intensity on the productivity of rice farms in southern Ghana

Effect of mechanisation use intensity on the productivity of rice farms in southern Ghana

Extensive Countrywide Field Investigation of Somatic Cell Counts and Total Bacterial Counts in Bulk-Tank Raw Milk in Sheep Flocks in Greece

Drivers for precision livestock technology adoption: A study of factors associated with adoption of electronic identification technology by commercial sheep farmers in England and Wales

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