Analysis of adoption trends of in-parlor technologies over a 10-year period for labor saving and data capture on pasture-based dairy farms

Assessing the eco-efficiency of milk production systems using water-energy-labor-food nexus

Globally, massive resource inputs and undesired outputs hindered the further development of the dairy industry. This study proposed a method applying data envelopment analysis to the water-energy-labor-food nexus to assess the eco-efficiency of the milk production system (MEE) from a systemic perspective. Using national statistics on scale farms for the period 2014-2021, we illustrated the effects of scale and intensification on MEE in China. In the study period, the production cost increased by 23 % and milk production rose by 30 % at the same time. Despite the increases in both water and energy inputs, the rise in milk production weakened the resource burdens and thus lifted MEE by 24 %. The resource investment pattern shifted from water- and labor-oriented to energy-oriented. Under current conditions, production technology and system management were at higher superiority to advance than farm scale, while mechanization and on-farm clean energy production are the keys to further lifting MEE.

Identifying strategies to enhance the milking and operator efficiency of herringbone and rotary parlor systems in Ireland

International trends of increasing dairy herd sizes coupled with scarcity of labor has necessitated the enhancement of labor efficiency for dairy production systems. This study quantified the effects of infrastructure, automation, and management practices on the milking and operator efficiency of herringbone and rotary parlors used on pasture-based farms in Ireland. Data from 592 milkings across 26 farms (16 herringbones and 10 rotaries) was used. The metrics of cows milked per hour (cows/h), cows milked per operator per hour (cows/h per operator) and liters of milk harvested per hour (L/h) described milking efficiency. The metrics of total process time per cow (TPT, s/cow), milk process time per cow (MPT, s/cow), work routine time (WRT, s/cow), cluster time (CT, s/cluster), and attachment time per cow (ATC, s/cow) described operator efficiency. Automations investigated were backing gates, cluster flush, plant wash, cluster removers (ACRs), feeders, entry gates, rapid-exit, and teat spray. Additional operator presence at milking was also investigated. Herringbone and rotary parlors were assigned to quartiles from their cows/h per operator values to examine infrastructure, automations, and management practices variations. Fourth quartile herringbones based on cows/h per operator values (Q4) averaged 93 cows/h per operator using average system sizes of 24 clusters with 5 parlor automations. Q4 rotaries averaged 164 cows/h per operator using average system sizes of 47 clusters and an average CT of 13 s/cluster. Cows/h per operator values for Q4 herringbone and rotary parlors were 82% and 54% higher, respectively, than values observed on Q1 parlors, indicating the considerable potential to improve efficiency. To determine if infrastructure, automations, or additional operators at milking significantly affected operator efficiencies, general linear mixed models were developed. For parlor infrastructure, additional clusters had greater significance on operator efficiencies (MPT) for herringbones (-1.3 s/cow) as opposed to rotaries (-0.2 s/cow). Hence, increases in system size was likely to result in improved efficiencies for herringbones but less so for rotaries. For automations, ACRs significantly reduced herringbone TPT, CT, and WRT values by 13.3 s/cow, 18.9 s/cluster, and 32.6 s/cow, respectively, whereas rapid-exit significantly lowered CT by 18.6 s/cluster. We found no significant effect on rotary TPT, MPT, CT, or WRT values from the use of automatic teat sprayers. An additional operator at milking was found to significantly reduce herringbone TPT but not MPT or CT. For rotaries, a second operator had no significant effect on TPT, MPT, CT, or WRT values. We documented strong negative correlations between operator efficiencies (TPT, MPT) and milking efficiency (cows/h) for both herringbone (-0.91, -0.84) and rotaries (-0.98, -0.89). Strong negative correlations between the herringbone automation count and TPT (-0.80), MPT (-0.72), and CT (-0.75) suggested highly automated parlors were likely to achieve greater operator efficiencies than less automated parlors. The strong negative correlation (-0.81) between rotary milking efficiency (cows/h) and CT suggested lower CT values (i.e., rotation speed) resulted in increased milking efficiency. In conclusion, our study quantified the effects of parlor infrastructure, automation, and management practices on the milking and operator efficiency of herringbone and rotary parlors.

Survey of farm, parlour and milking management, parlour technologies, SCC control strategies and farmer demographics on Irish dairy farms

BACKGROUND

This cross-sectional study describes a survey designed to fill knowledge gaps regarding farm management practices, parlour management practices and implemented technologies, milking management practices, somatic cell count (SCC) control strategies, farmer demographics and attitudes around SCC management on a sample of Irish dairy farms.

RESULTS

We categorized 376 complete responses by herd size quartile and calving pattern. The average respondent herd was 131 cows with most (82.2%) operating a seasonal calving system. The median monthly bulk tank somatic cell count for seasonal calving systems was 137,000 cells/ml (range 20,000 - 1,269,000 cells/ml), 170,000 cells/ml for split-calving systems (range 46,000 - 644,000 cells/ml) and 186,000 cells/ml for 'other' herds (range 20,000 - 664,000 cells/ml). The most common parlour types were swing-over herringbones (59.1%) and herringbones with recording jars (22.2%). The average number of units across herringbone parlours was 15, 49 in rotary parlours and two boxes on automatic milking system (AMS) farms. The most common parlour technologies were in-parlour feeding systems (84.5%), automatic washers on the bulk tank (72.8%), automatic cluster removers (57.9%), and entrance or exit gates controlled from the parlour pit (52.2%). Veterinary professionals, farming colleagues and processor milk quality advisors were the most commonly utilised sources of advice for SCC management (by 76.9%, 50.0% and 39.2% of respondents respectively).

CONCLUSIONS

In this study, we successfully utilised a national survey to quantify farm management practices, parlour management practices and technology adoption levels, milking management practices, SCC control strategies and farmer demographics on 376 dairy farms in the Republic of Ireland. Rotary and AMS parlours had the most parlour technologies of any parlour type. Technology add-ons were generally less prevalent on farms with smaller herds. Despite finding areas for improvement with regard to frequency of liner changes, glove-wearing practices and engagement with bacteriology of milk samples, we also found evidence of high levels of documentation of mastitis treatments and high use of post-milking teat disinfection. We discovered that Irish dairy farmers are relatively content in their careers but face pressures regarding changes to the legislation around prudent antimicrobial use in their herds.

Effect of milk flow-rate switch-point settings on milking duration and udder health throughout lactation

The objective of this study was to quantify the effects of different milk flow-rate switch-point settings on milking duration, somatic cell count (SCC), strip milk, teat condition, and milk yield in a grass-based system in a long-term experiment. Much work has already been conducted providing strong support for significant reduction in milking duration without effects on yield through increasing the flow-rate switch-point at which vacuum to the milking cluster ceases and the cluster is removed from the cow by means of a retracting cord. However, in practice many farms have not adopted this labor-saving technology on the basis that it may increase milk SCC. Recent research on commercial Irish dairy herds identified the contagious mastitis-causing pathogen Staphylococcus. aureus as the most prevalent pathogen detected. Staph. aureus could have a cyclical shedding pattern which would inhibit detection at certain time points. Therefore, to reliably assess the effect of milk flow-rate switch-points on SCC, a long-term study was required, consisting of multiple observations on cow-level SCC. The present study filled this gap in knowledge by informing on any effect that ceasing milking at different flow rates may have on milking duration and SCC levels, particularly with regard to spring calving grass-based systems. Four treatments, consisting of milk flow-rate switch-points increasing from 0.2 kg/min to 0.8 kg/min in steps of 0.2 kg/min, were deployed for 31 wk to cows at the Teagasc Research Centre at Moorepark, Ireland. The effect of treatment on daily milking duration was significant. The milking duration for a milk flow-rate switch-point of 0.8 kg/min was 95 s (14%) shorter than for 0.2 kg/min. We did not find a significant effect of increasing the milk flow-rate switch-point from 0.2 to 0.8 kg/min on milk yield or SCC in this long-term study. We did find a significant effect of week of experiment on milk SCC, whereby the SCC of the cows on the experiment increased similarly among treatment groups as lactation progressed. A significant reduction in dead time (time from cluster attachment to reach a milk flow rate of 0.2 kg/min) was also noted as the milk flow-rate switch-point increased. On average, reductions in dead time contributed 12% to the overall reductions in milking duration. Similarly, reductions in low flow time (time from a flow rate of 0.2 kg/min to cluster detachment at the end of milking) contributed 26% to the overall reductions in milking duration. Reductions in dead time and low flow time played a greater role in reducing p.m. milking duration rather than a.m. milking duration due to the milking interval practiced on the research farm.

Factors associated with intensity of technology adoption and with the adoption of 4 clusters of precision livestock farming technologies in Irish pasture-based dairy systems

Precision livestock farming (PLF) technologies have been widely promoted as important tools to improve the sustainability of dairy systems due to perceived economic, social, and environmental benefits. However, there is still limited information about the level of adoption of PLF technologies (percentage of farms with a PLF technology) and the factors (farm and farmer characteristics) associated with PLF technology adoption in pasture-based dairy systems. The current research aimed to address this knowledge gap by using a representative survey of Irish pasture-based dairy farms from 2018. First, we established the levels of adoption of 9 PLF technologies (individual cow activity sensors, rising plate meters, automatic washers, automatic cluster removers, automatic calf feeders, automatic parlor feeders, automatic drafting gates, milk meters, and a grassland management decision-support tool) and grouped them into 4 PLF technology clusters according to the level of association with each other and the area of dairy farm management in which they are used. The PLF technology clusters were reproductive management technologies, grass management technologies, milking management technologies, and calf management technologies. Additionally, we classified farms into 3 categories of intensity of technology adoption based on the number of PLF technologies they have adopted (nonadoption, low intensity of adoption, and high intensity of adoption). Second, we determined the factors associated with the intensity of technology adoption and with the adoption of the PLF technology clusters. A multinomial logistic regression model and 4 logistic regressions were used to determine the factors associated with intensity of adoption (low and high intensity of adoption compared with nonadoption) and with the adoption of the 4 PLF technology clusters, respectively. Adoption levels varied depending on PLF technology, with the most adopted PLF technologies being those related to the milking process (e.g., automatic parlor feeders and milk meters). The results of the multinomial logistic regression suggest that herd size, proportion of hired labor, agricultural education, and discussion group membership were positively associated with a high intensity of adoption, whereas age of farmer and number of household members were negatively associated with high intensity of adoption. However, when analyzing PLF technology clusters, the magnitude and direction of the influence of the factors in technology adoption varied depending on the PLF technology cluster being investigated. By identifying the PLF technologies in which pasture-based dairy farmers are investing more and by detecting potential drivers and barriers for the adoption of PLF technologies, the current study could allow PLF technology companies, practitioners, and researchers to develop and target strategies that improve future adoption of PLF technologies in pasture-based dairy settings.

Can the use of digital technology improve the cow milk productivity in large dairy herds? Evidence from China's Shandong Province

Introduction

Improving milk productivity is essential for ensuring sustainable food production. However, the increasing difficulty of supervision and management, which is associated with farm size, is one of the major factors causing the inverse relationship between size and productivity. Digital technology, which has grown in popularity in recent years, can effectively substitute for manual labor and significantly improve farmers' monitoring and management capacities, potentially addressing the inverse relationship.

Methods

Based on data from a survey of farms in Shandong Province in 2020, this paper employs a two-stage least squares regression model to estimate the impact of herd size on dairy cow productivity and investigate how the adoption of digital technology has altered the impact of herd size on dairy cow productivity.

Results

According to the findings, there is a significant and negative impact of herd size on milk productivity for China's dairy farms. By accurately monitoring and identifying the time of estrus, coupled with timely insemination, digital technology can mitigate the negative impact of herd size on milk productivity per cow.

Discussion

To increase dairy cow productivity in China, the government should promote both small-scale dairy farming and focus on enhancing management capacities of farm operators, as well as large-scale dairy farms and increase the adoption of digital technologies.

Impact of COVID-19 food supply chain: Comparing the use of IoT in three South African supermarkets

This study aims to understand the impact of the COVID-19 pandemic by comparing the performance of three major supermarkets in South Africa and addressing the following questions. 1) What is the impact of a supply chain disruption on the food system? 2) What interventions (short and long-term) are taken by the food supply chain to mitigate disruption? 3) What does the post-pandemic picture look like for the food retail sector? This study adopts a comparative research approach and investigates direct strategies adopted by various food supply chain actors to mitigate the impact of covid-19. This study compares how retailers Checkers, Woolworths, and Pick n Pay have adapted their business models to remain resilient during COVID-19 lockdown. The results show that the food supply chain remained resilient even with demand management challenges at the lockdown. Food supply chain issues came under a spotlight as borders and production plants were shut down or restricted to contain the spread of the virus. This study establishes that the food shortage is primarily caused by panic buying at the beginning of lockdown, causing shock in the supply chain cadence. The other aspect of food security issue is attributed to food availability and socioeconomic problems resulting from loss of income. On sustainability, there are fears that control measures such as packaging (increased use of plastic), cleaning chemicals, waste and sanitisation of space to maintain hygiene as required for covid-19 can undermine the gains towards preserving the environment.

Integrating Digital Technologies to Aid Grassland Productivity and Sustainability

Digital technologies provide an opportunity to further increase the sustainability and productivity of grasslands and rangelands. Three resources are key to that change. These are the soil on which forage grows, the forages that grow on those soils and the animals that use the forage resource as food. This paper describes elements of technologies to measure and monitor these resources and provides some insights on combining that knowledge and controlling the animal's utilization through virtual fencing. There are many potential challenges to the application of digital technologies to pastoral farming. These often require the calibration of digital signals to define biophysical characteristics. The significant repository of historic data of pasture growth over many geo-climatic regions, for example, provides New Zealand with an opportunity to accelerate that development. Future advances in rangeland use, nutrient deposition, greenhouse gas emissions and the provision and utilization of high quality and quantity will be enabled by the application of digital technologies at scale, under the control of virtual fencing. Digital technologies may provide the means to maintain or enhance ruminant production from grassland in a sustainable operating space into the future.