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Exotic sward grassland of good pastoral quality and vigour reflecting relatively high soil fertility and intensive grazing management. Clover species, ryegrass and cocksfoot dominate with lucerne and plantain locally important, but also including lower-producing grasses exhibiting vigour in areas of good soil moisture and fertility.

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Land regularly cultivated for the production of cereal, root, and seed crops, hops, vegetables, strawberries and field nurseries, often including intervening grassland, fallow land, and other covers not delineated separately.

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8 things calves need in order to be fit for transport:

Be at least four days old

Be healthy, with no visible disease (e.g. scours), deformity, injury, blindness or disability

Have bright eyes and upright ears

Have the correct ear tag

Be strong, able to rise from a lying position unassisted, move freely around the pen and bear weight on all four limbs

Have firm hooves on which the soles show wear (indicating that they have been mobile)

Have a dry and withered navel

Have a full tummy – no antibiotic milk

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Courtesy of Google Earth

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This indicator measures the numbers of farmed dairy cattle, beef cattle, deer, and sheep over time nationally, by region, and territorial authority, and approximates the density of these livestock across New Zealand.

Between 1990 and 2019:

dairy cattle numbers increased by 82 percent nationally from 3.4 million to 6.3 million

Canterbury dairy cattle increased tenfold (973 percent) from 113,000 to 1.2 million

Southland dairy cattle increased sixteen-fold (1,584 percent) from 38,000 to 636,000

beef cattle decreased by 15.3 percent nationally from 4.6 million to 3.9 million

sheep decreased by 53.6 percent nationally from 57.9 million to 26.8 million.

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Limitations to data analysis:

We only report on nitrate-nitrogen leaching from four types of livestock, not from fertiliser application, which was included previously. Fertiliser application is captured in Nitrogen and phosphorous in fertilisers. For this reason, and the difference in methods detailed above, values presented here are not directly comparable with previously reported values. Future research is needed to determine with greater confidence the overall magnitude of nitrate-nitrogen leaching.

The map should not be used to infer nitrate-nitrogen leaching rates for individual farms, as average farming practices were assumed and actual stock numbers per farm were not available. The spatial extent of nitrate-nitrogen leaching should be considered as an update from Environment Aotearoa 2015 and Our freshwater 2017, and should not be used for comparing time differences between information presented here and previous versions.

I clipped the TIF file down to a NZ admin boundary (downloaded from https://www.diva-gis.org/datadown), set all 0 values to transparent, and used a continuous color ramp to show low to high leaching values.

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Courtesy of Google Earth

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Courtesy of Google Earth

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Courtesy of Google Earth

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Courtesy of me

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Courtesy of me

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Courtesy of me

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Courtesy of me

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Courtesy of AI in Padlet, added for similarity check to my photos (spooky)

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Abstract:

The shapefile provides counts and densities of livestock numbers within a hexagonal grid from data derived from the Agricultural Production Survey census final results for 2017.

I put the data into QGIS and symbolized based solely on the density of dairy cattle. The green to red color ramp goes from no dairy cattle to highest density of dairy cattle by 8 equal intervals.

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Note the mean (mu) and first standard deviation (sigma). I used equal intervals to denote density groups despite the drastic difference in data per category.

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** since downloading, this data has disappeared from the public site

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Cropland and Pastures 8.61% of NZ Landcover dataset 2018

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Cropland and Pastures 8.61% of NZ Landcover dataset 2018

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*Cropland = current cropland

I recognize that code 41 Low Producing Grassland is also considered to be impacted by high agricultural grazing, but I interpret this to mean it is not currently used as grazing pastureland and is not relevant to the current topic.

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Livestock farming is a widespread land use in New Zealand and a significant contributor to our economy. However, livestock adversely affects the environment, with the extent of impact dependent on the type, size, and animal density. Dairy cattle have a higher environmental impact than beef cattle, and both have a higher impact than sheep.

Livestock, particularly cattle, generate methane emissions, which contribute to climate change. Livestock can adversely affect indigenous biodiversity and soil health (for example, through erosion, habitat loss, compaction, and nutrient concentration).

Water quality can also be adversely affected, as nutrients, sediment, and bacteria from urine and faeces can leach or run off the land into rivers, lakes, and groundwaters. This can affect the health of aquatic ecosystems, as well as recreation and cultural values associated with rivers and lakes. The diminishment of water quality is of great concern to Māori and impacts mātauranga (knowledge systems), tikanga (cultural practice), whakapapa (geneology), and mahinga kai (traditional food resources) connected to water. Reduced and degraded habitats have a profound effect on Māori wellbeing and the reflection of identity in natural landscapes.

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We report on the use of land for primary production purposes, specifically agricultural land uses, such as beef and sheep farming and dairying, horticultural land uses, such as fruit and berry, and vegetable growing, and other land uses, such as forestry. We do this for the period 2002 to 2019.

Between 2002 and 2019:

The area of land used across New Zealand for:

agriculture and horticulture decreased steadily with an overall decrease of 1.9 million hectares or 14 percent

dairy farming increased 991,000 hectares (81 percent)

sheep farming decreased 1.7 million hectares (29 percent) and beef farming decreased 1.3 million hectares (32 percent).

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Understanding agricultural and horticultural land use can allow us to monitor changes in land use and activity in primary industries. Almost half of New Zealand’s total land area is used for agricultural and horticultural purposes. Each land use places various pressures on the land and on receiving environments such as waterways. These pressures can be both positive (for example, increased productivity) and negative (for example, impact on soil and water quality, biodiversity loss, and reduced functioning of ecosystems).

Māori agricultural and horticultural land use is expanding through the aggregation of smaller land parcels to form larger farms for primary production. To balance kaitiaki responsibilities, Māori are determined to link cultural values with commercial objectives to maintain and extend Māori community and environmental wellbeing.

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Agricultural and horticultural land use - Stats NZ

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Agricultural and horticultural land use | Stats NZ

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The total area of land being used for agriculture and horticulture decreased by 2 percent (208,000 hectares) between 2017 and 2019.

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From 1. Introduction:

Surplus or unwanted dairy calves, referred to as bobby calves in New Zealand and Australia, are considered a by-product of the dairy industry. Bobby calves are defined as unweaned calves that are transported for slaughter at meat processing premises for human consumption or pet food, usually within the first week of life (Anonymous, 2015). In New Zealand, around 4 million dairy calves are born between July and September each year (Anonymous, 2017b). Approximately one quarter of these are kept as replacement dairy animals (Hickson et al., 2015), with the remainder sold as bobby calves or to be raised as beef cattle, or killed on farm (Wesselink, 1998; Stafford et al., 2001; Mellor, 2011). Thus, each year in New Zealand approximately 2.2 million bobby calves aged between four and approximately seven days are transported to slaughter.

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From the abstract:

Factors associated with an increased risk of calf mortality included time in the farm of origin’s calving season, duration of travel from farm to the processor and processing slaughter schedule (same day or next day). Every additional week into the farm’s calving season increased the odds of mortality by a factor of 1.2 (95%CI 1.06, 1.35). Similarly, each additional hour of travel time increased the odds of mortality by a factor of 1.45 (95% CI 1.18, 1.76). Risk of mortality was significantly greater for calves processed at premises with a next day slaughter schedule than those processed at premises with a same day slaughter schedule (OR 3.82, 95% CI 1.51, 9.67). However, when the data set was limited to those cases that died or were condemned in the yards (i.e. excluding calves that were dead or condemned on arrival) the effect of same day slaughter was not significant. In order to reduce bobby calf mortality and morbidity, transport duration should be kept as short as possible and a same day slaughter schedule applied. While these factors can be regulated, New Zealand’s pastoral dairy system means that calves will inevitably be transported for slaughter across several months each spring. Although farm management factors did not apparently influence the risk of mortality in this study, the effect of time in farm’s calving season suggests there may be farm-management related factors that change over the season. This requires further investigation.

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Risk factors for bobby calf mortality across the New Zealand dairy supply chain - NIH 

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APA Citation:

Boulton, A. C., Kells, N. J., Cogger, N., Johnson, C. B., O'Connor, C., Webster, J., Palmer, A., & Beausoleil, N. J. (2020). Risk factors for bobby calf mortality across the New Zealand dairy supply chain. Preventive veterinary medicine, 174, 104836. https://doi.org/10.1016/j.prevetmed.2019.104836

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Abstract

The objective of this study was to identify risk factors for morbidity and mortality of bobby calves across the whole dairy supply chain in New Zealand. A case-control study was carried out in the 2016 spring calving season. A total of 194 bobby calves, comprising 38 cases (calves that died or were condemned for health or welfare reasons before the point of slaughter) and 156 controls (calves deemed acceptable and presented for slaughter) were included in the study. Case and control calves were selected by veterinarians located at 29 processing premises across New Zealand. Information regarding management of selected calves on-farm, during transport and at the processor was obtained retrospectively via questionnaires administered to supplying farmers, transport operators and processing premises personnel. Associations between management variables and calf mortality (death or condemnation) were examined using multivariable logistic regression models. Factors associated with an increased risk of calf mortality included time in the farm of origin's calving season, duration of travel from farm to the processor and processing slaughter schedule (same day or next day). Every additional week into the farm's calving season increased the odds of mortality by a factor of 1.2 (95%CI 1.06, 1.35). Similarly, each additional hour of travel time increased the odds of mortality by a factor of 1.45 (95% CI 1.18, 1.76). Risk of mortality was significantly greater for calves processed at premises with a next day slaughter schedule than those processed at premises with a same day slaughter schedule (OR 3.82, 95% CI 1.51, 9.67). However, when the data set was limited to those cases that died or were condemned in the yards (i.e. excluding calves that were dead or condemned on arrival) the effect of same day slaughter was not significant. In order to reduce bobby calf mortality and morbidity, transport duration should be kept as short as possible and a same day slaughter schedule applied. While these factors can be regulated, New Zealand's pastoral dairy system means that calves will inevitably be transported for slaughter across several months each spring. Although farm management factors did not apparently influence the risk of mortality in this study, the effect of time in farm's calving season suggests there may be farm-management related factors that change over the season. This requires further investigation.

Keywords: Bobby calf, Mortality, Morbidity, Risk factors

1. Introduction

Surplus or unwanted dairy calves, referred to as bobby calves in New Zealand and Australia, are considered a by-product of the dairy industry. Bobby calves are defined as unweaned calves that are transported for slaughter at meat processing premises for human consumption or pet food, usually within the first week of life ( Anonymous, 2015). In New Zealand, around 4 million dairy calves are born between July and September each year ( Anonymous, 2017b). Approximately one quarter of these are kept as replacement dairy animals ( Hickson et al., 2015), with the remainder sold as bobby calves or to be raised as beef cattle, or killed on farm ( Wesselink, 1998; Stafford et al., 2001; Mellor, 2011). Thus, each year in New Zealand approximately 2.2 million bobby calves aged between four and approximately seven days are transported to slaughter.

Bobby calves are at particular risk of welfare compromise, morbidity and mortality due to the very young age at which they are removed from their dam, transported, mixed and held off feed prior to slaughter ( Wesselink, 1998; Fisher et al., 2009). In the 2015 New Zealand dairy season, bobby calf mortality before the point of slaughter was 0.25 %, dropping to 0.12 % in 2017 ( Anonymous, 2017a, b), equating to between 4800 and 5500 calves per year. While the current mortality rate is low, it may be important to further reduce this for animal welfare, economic and reputational reasons by understanding and addressing the underlying risk factors in each stage of the supply chain. Factors associated with the rearing, management, handling, transport and yarding of bobby calves at the processor before slaughter have the potential to impact on their health and welfare, which may in turn influence mortality rate.

Several studies have identified risk factors for on-farm morbidity and mortality in calves (e.g. Gulliksen et al., 2009; Khan et al., 2011; Al Mawly et al., 2015; de Passillé et al., 2016; Renaud et al., 2018), with fewer studies focussing on the effects of transport and processing. No studies were found that explored risk factors for mortality in surplus dairy calves across the entire supply chain. The aim of this research was to identify risk factors for calf condemnation and mortality across the New Zealand dairy supply chain.

2. Materials and methods

Because bobby calf mortality is a rare event, a case-control design was selected as the most appropriate way to explore risk factors for mortality associated with the farm, transport and processing stages of the supply chain. Cases were defined as calves that died or were condemned before the point of slaughter and controls were acceptable calves that were slaughtered as per standard procedure. Case and control calves were selected by Ministry for Primary Industries Verification Services (MPI VS) veterinarians at meat processing premises around New Zealand. Their management from the farm to the point of arrival at the processor was traced back using questionnaires completed by interviewing the supplier of the calf (farmer/farm manager) and transporter of the calf as soon after the event as possible and by recording features of management at the processing premises. At the time of the interview, neither the researchers nor the farmers/transporters knew whether the calf was a case or a control.

Calf selection and retrospective data collection occurred over an 18-week period in six regions of New Zealand between June and October 2016. In each region, data were collected over three non-consecutive weeks at the start, peak and end of the season ( Table 1 ).

Table 1

Rotation

Study week

Regions

Number of premises visited

1 (Early)

1

Bay of Plenty/Waikato

1

2 (Peak)

7

Bay of Plenty/Waikato

1

3 (Late)

13

Bay of Plenty/Waikato

2

2.1. Selection of case and control calves

Researchers visited each region for one week in each stage of the calving season. In total, 29 meat processing premises were visited over the season, with each premises visited between one and three times. When more than one premises was to be visited during the researcher's week in that region ( Table 1 ), calves were selected on alternating days. All calves that died or were condemned (cases) at a premises during the week of the visit were included in the study and a case calf details form, including identification, classification, condemnation and post mortem information (Supplementary Table1) was completed by the attendant veterinarian. Case calves were recorded as either dead on arrival (DOA), condemned on arrival (COA), dead in yard (DIY) or condemned in yard (CIY). The decision to condemn a calf was made by the yard operator or supervisor and/or animal welfare officer and/or MPI veterinarian, according to the premises standard operating procedure.

Up to 10 control calves per day were randomly selected from those classified as 'acceptable' by verification services veterinarians at the processing premises, in accordance with the MPI VS Animal Welfare Procedure for Bobby Calves document ( Anonymous, 2015). To be deemed acceptable, calves were required to be at least 4 days old; have an umbilicus that was wrinkled, withered and shrivelled; and be in good health, mobile, active, bright in the eye and have upright ears ( Anonymous, 2016). Only one control per transport consignment was selected on a given day. Thus, the numbers of control calves varied throughout the season depending on the number of consignments arriving at the processing premises. If no case calves were present on the allocated day then only control calves were selected. To ensure unbiased selection, control calves were chosen using random number tables.

In 2016, all calves that died or were condemned were subject to post mortem examination. Therefore, the case calf details form completed by the attendant veterinarian included a section that captured findings of the post-mortem examination. For the purposes of the research, guidelines were provided to the veterinarians to assist with completion of the case calf form and the post-mortem form to ensure consistency in reporting of the reason for condemnation and interpretation of results (Supplementary Table 1).

2.2. Questionnaires

A separate questionnaire was developed for each stage of the supply chain and these were approved by the Massey University Human Ethics Committee (MUHEC SOB # 16/18). The development of the questionnaires was informed by a systematic literature review in conjunction with input from members of a project steering committee who represented various industry stakeholders. Feedback was sought from the steering committee on multiple occasions during questionnaire development and modifications were made accordingly. Brief details of each of the questionnaires are provided below; complete versions are available in Supplementary Table 2. All interviews were written/conducted in English. Interviewing and completion of the questionnaires was undertaken by a single researcher to reduce variability.

2.2.1. Farm questionnaire

The questionnaire was based on a previously validated questionnaire used in a Canadian study conjointly undertaken by the Université Laval, Agriculture and Agri-Food Canada, Valacta and the Université de Montréal ( Vasseur et al., 2010) which identified the most important factors affecting successful dairy heifer rearing. It comprised 99 questions (77 closed and 22 open) relating to on-farm calf management during the first week of life. Questions focussed on the topics of calving, neonatal care, feeding, housing, health and collection for transport as undertaken during the specific week the selected calf was sent to the processing premises (i.e. in the last 7 days). Information was collected on the management of both bobby calves and replacement heifer calves, with the latter excluded from the risk analysis. The researchers were provided with farm of origin details for selected calves by the processing premises veterinarian. Supplying farms were then contacted by telephone and asked to participate in the study. Where consent was received, a meeting was arranged on-farm between the researcher and farmer or farm manager to complete the questionnaire. The questionnaire took 20-30 minutes to complete during the face-to face meeting

2.2.2. Transport questionnaire

This comprised 35 questions (29 closed and 6 open) regarding transport factors specific to the week the selected calf was sent to the processing premises (i.e. in the last 7 days). Questions included variables related to staff and training, vehicle details, calf numbers and transhipping. The researchers were provided with transport company details for selected calves by the processing premises veterinarian. The questionnaire was completed by either the company dispatcher or the operator who transported the selected calf during a telephone interview with the researcher.

2.2.3. Processing premises questionnaire

This comprised 62 questions (53 closed and 9 open) pertaining to processing premises management as undertaken during the seven days prior to the interview. Questions related to features of the unloading & holding facilities, staff details and training programmes, and calf management. The questionnaire was administered only once during the 18-week study period. Therefore, only information that was relevant to all calves processed across the season could be used in the case control study. Any questions that related to a practice undertaken during the week the questionnaire was completed were excluded from analysis on the basis that they may not have reflected the practice undertaken during the specific week the case or control calf was processed. Completion of the questionnaires took place either in person at the processor or via telephone interview.

It should be noted that retrospective data collection meant that information on individual calf age or disease status could not be obtained. Therefore, associations between these variables and risk of bobby calf mortality were not explored in the present study.

2.3. Data management and analysis

Data from the written questionnaires were entered into Excel spreadsheets. For the purposes of subsequent analyses, open questions with more than 6 unique responses were collapsed into fewer categories based on the response distribution. For example, the question "How often is bedding added to the pen for replacements" elicited 30 unique responses, which were subsequently collapsed into five categories: weekly, fortnightly, monthly, once or twice per season and never.

2.4. Statistical analyses

Summary statistics were calculated for all variables in the farm, transport, processing premises and case calf details data sets. Categorical variable data were summarised using counts and percentages. The method used to summarise continuous variables varied depending on whether the data were normally distributed. Normally distributed data were summarised by means and standard deviation while non-normally distributed data were summarised by median, minimum, maximum and the 25 th and 75 th percentiles.

The risk factor analysis was conducted on two separate data sets. The first comprised all cases (n = 38) and controls (n = 156). The second included all controls (n = 156) and only those cases that were condemned or died in the yards at the processing premises (n = 18). The separate data sets were created to allow a better understanding of the significance of processor-related factors. Importantly, for calves that died or were condemned on arrival at the processing premises (DOA/COA), processor variables may not have influenced the risk of being a case.

A multivariable model was constructed in a six-step process. Firstly, a binary outcome variable was generated that coded for whether the calf was a case or control and exact logistic regression models were then used to determine the association between mortality and each explanatory variable. The second step involved checking correlations between all variables associated with the outcome at P < 0.2. Correlations between pairs of variables eligible for inclusion were determined. For the pairs of continuous variables correlation was assessed using the Pearson correlation coefficient, whereas the Kendall's correlation was used for the pairs of categorical variables. In both cases one variable in the pair was included in the model if the correlation exceeded an absolute value of 0.7. Thirdly, a multivariable model was constructed that included all variables that were associated with the outcome at P < 0.20. In the fourth step, a preliminary multivariable model was created by stepwise removal of the least significant variable (assessed using the Deviance test statistic) until all remaining variables were statistically significant at P < 0.05. When a variable was dropped from the model, changes in co-efficient and standard error were examined. The fifth step was an assessment of whether the continuous variables modelled had a linear relationship with the logit through the inclusion of a quadratic term. The quadratic term was retained in the model, or the variable converted to a categorical variable, if it significantly improved the fit as determined by the likelihood ratio test statistic. In the final step, the model was applied to the restricted data set (excluding cases that died or were condemned in the yards). For both models, the fit was assessed using the Hosmer & Lemeshow Goodness of Fit test and by examining the Pearson's residuals.

All analyses were conducted in SAS Version 9.4 (SAS Institute Inc., Cary NC, USA).

3. Results

3.1. Response rates

A total of 606 dairy farmers across New Zealand were contacted as suppliers of a selected case or control calf. Of these, 194 (32 %) agreed to participate and completed the questionnaire. Of the 194 participating farms, 38 had supplied case calves and 156 had supplied control calves. Fig. 1 shows the distribution of selected calves by region of origin.

Seventy different transport companies transported selected calves during the study period. Calf transport information was obtained from 38/70 (53 %) transport companies. The 38 respondents were responsible for transporting 99/194 (51 %) selected calves, including 7/38 (18 %) cases and 91/156 (58 %) controls. Where a transporter did not respond or declined to complete the questionnaire, transport duration information (from time of collection on farm to arrival at the processor) for most calves was instead obtained from the transport docket at the processing premises. Thus, transport duration data were available for 185/194 (95%) calves (n = 34 cases and n = 151 controls). No detailed transport data were available for nearly half of all selected calves, including more than 80 % of control calves, therefore the only transport-related variable that could be included in the risk factor analysis was duration of travel.

Processing premises data were obtained from all 29 premises that supplied case and control calves. Although each premises involved in the study processed multiple case and/or control calves during the season, data were only collected on a single occasion meaning that data could not be linked to the specific week in which a selected calf was processed. Therefore, only those processing variables that were stable across the season could be included in the risk analysis.

In total, 73 unique variables were included in the risk analysis. These are presented in Table 2 .

Table 2

Data source

Variable name

Description

Response categories (binary/categorical data)

3.2. Case calf characteristics

Of the 38 case calves for which retrospective information could be obtained, 18 (47.4 %) were condemned on arrival at the processing premises and a further 10 (26.3 %) were condemned at some point in the yards. Thus, nearly three quarters of case calves were condemned, rather than dying before the point of slaughter. Half of those condemned on arrival (9/18) were unable to walk off the truck. Of the 10 calves that died, eight died in the yards while waiting to be slaughtered, whilst two were dead on arrival at the processing premises. The most frequently recorded reasons for calf condemnation were weakness, recumbency, thin body condition and dehydration ( Table 3 ).

Table 3

Reason

Frequency

Percentage¹

The most frequently recorded post-mortem findings were diarrhoea and/or enteritis (n = 11; 29 %), inflamed umbilicus/urachus (n = 6; 16 %), musculature bruising (n = 5; 13 %) and peritonitis (n = 4; 11 %). Approximately one third of post-mortem examinations of case calves recorded no significant findings (n = 13; 34.2 %). It should be noted that in some cases (n = 3; 8 %), no significant findings were recorded alongside other findings.

3.3. Risk factor analyses

The results of the univariate exact logistic regression analyses for those variables with p-values <0.20 when using the full data set (all cases and controls) are provided in Table 4 . Full results of the univariate screening of all variables are available in Supplementary Table 3 . Of the continuous variables considered for inclusion in the model, a significant association was found between the two variables Time in farm's season (weeks since first calving on the farm of origin) and Time in processor's season (weeks since start of processing at the premises where the calf was processed). This was unsurprising, given that both were measures of time in season. The decision was made to include Time in farm's season in the model, as the association with calf mortality was strongest for this variable.

Table 4

Variable

Case calves (n = 38)

Control calves (n = 156)

Odds ratio (95% CI)

p-value

No

100

91.7

NC ¹

While there is some value to viewing the relationship between a single variable and risk of mortality (the unadjusted odds ratio); we refer to the odds ratio obtained from the multivariable model (the adjusted odds ratio), as this accounts for the effects of other variables in the model.

The results of the multivariable model for both data sets are shown in Table 5 . Both data sets gave similar estimates for the effect of Weeks into farm's season and Travel time on the odds of mortality. After adjusting for travel time and premises slaughter schedule, the odds of mortality increased by a factor of 1.2 for every additional week into the farm's season. Similarly, for every additional hour travelled the odds of mortality increased approximately 1.5 times. When using data from all cases and adjusting for Time in farm's season and Travel time, the odds of mortality was 3.8 times higher when calves were processed at a premises with a next day slaughter schedule. However, when the data set was limited to those cases that died or were condemned in yards the effect was not significant. When the model was applied to the full data set, the Hosmer-Lemeshow goodness-of fit test indicated that the model was a good fit for the data (χ ² (8, 194) = 14.2, P = 0.08). Examination of Pearson's residuals indicated that 2.06 % of values were greater than |3|. When applied to the restricted data set, the Hosmer-Lemeshow goodness-of fit test again indicated that the model was a good fit for the data (χ ² (8, 174) = 3.5, P = 0.9) and examination of Pearson's residuals revealed 2.87 % of values greater than |3|.

Table 5

All cases

DIY/CIY cases only

Variable

OR (95% CI)

P-value

OR (95% CI)

P-value

3.4. Time in farm's season

On average, case calves (all cases) were collected and transported later in the farm's calving season (8.62 ± 3.04 weeks) than were control calves (5.98 ± 4.05 weeks). When cases were restricted to those that died or were condemned in yards, the case calf average was 8.93 ± 4.51 weeks into the season.

3.5. Transport duration

On average, case calves (all cases) travelled for longer (5.38 ± 2.45 h) than did control calves (3.11 ± 1.97 h). When cases were restricted to those that died or were condemned in yards, the average travel duration for case calves was 5.44 ± 2.43 h.

3.6. Slaughter schedule

Of the 38 case calves, 30 (79 %) died or were condemned at processing premises that were operating a next day slaughter schedule that week while 21 % of cases died or were condemned at premises operating a same day slaughter schedule. In comparison, 37 % of control calves were processed at premises operating a next-day slaughter schedule and 63 % at premises operating a same day schedule ( Table 6 ). When cases were restricted to those that died or were condemned in yards, 67 % of case calves died or were condemned at premises operating a next day schedule.

Table 6

Slaughter schedule

Classification

Same day

Next day

Total

Total ¹

106 (54.6%)

88 (45.4%)

194 (100%)

4. Discussion

The New Zealand bobby calf supply chain consists of three main stages: on-farm rearing, transport from farm to processor and holding in yards for slaughter. Each of these has features that may influence the risk of calf morbidity and mortality. Despite existing guidelines governing pre-transport feeding, fitness for transport, transport duration and time off feed prior to slaughter ( Anonymous, 2018b), some bobby calves do not survive the journey to the processor or their time in the yards prior to slaughter or are so seriously compromised that they are humanely killed. The objective of the present study was to investigate the integrated risk factors across farm, transport and processing premises for bobby calf mortality or condemnation prior to slaughter.

Three significant risk factors for calf morbidity/mortality were identified: time into the farm of origin's calving season, duration of travel from the farm to the processor and whether calves were processed at premises operating a same day or next day slaughter schedule.

4.1. Time in farm's calving season

Calves collected later in the calving season of the supplying farm were more likely to die or be condemned on arrival or in the holding yards. The persistence of this variable in the multivariate model suggests that the effect was due to some feature of the farms which changed over the duration of their season. Furthermore, the fact that this effect on mortality remained significant after accounting for travel time and slaughter schedule indicates that it was not due to seasonal changes in travel time.

It may be that seasonal changes in farm management, which altered the likelihood of gastrointestinal infection or of nutritional scouring contributed to the observed seasonal effect on mortality. Such changes might include alterations in staffing or staff behaviour, or in features of bobby calf management. An example of the latter might be management of bedding in the calf pens, such that pathogen load might increase as the farm's calving season progressed, increasing the risk of infection later in the season. The severity of infectious disease in calves has been shown to be influenced by management and hygiene practices ( Castro-Hermida et al., 2002) as well as immune status ( Meganck et al., 2014). Furthermore, disease transmission among infected calves may also be affected by management factors such as housing, group size and hygiene ( Trotz-Williams et al., 2007; Lorenz et al., 2011), which may change over a farm's season. Although no significant associations were found in the multivariable model between farm variables such as frequency of bedding change or housing cleaning/disinfection and calf mortality in the present study, it should be noted that the number of case calves was relatively small (n = 38 for the full data set and n = 18 in the restricted data set). In addition, given that considerable variation was observed among farms in factors such as frequency of cleaning/disinfection of housing and frequency of bedding change, the associations between time in the farm's season and such factors may warrant further investigation.

Furthermore, research into the prevalence failure of passive transfer (FPT) of maternal antibodies in New Zealand dairy calves found that FPT was more prevalent in the middle compared to the early calving period ( Cuttance et al., 2017b). If this were also the case for calves in the present study, this may have contributed to the observed seasonal effect on risk of mortality.

4.2. Travel time

In the present study, the duration of calf transport from farm to processing premises ranged from 0.1-10 hours. Calves that travelled for a longer duration were more likely to die or be condemned on arrival or in the yards, regardless of the time in the farm's calving season. For every additional hour of travel time, the risk of death or condemnation was 1.45 times higher overall and calves were 1.53 times more likely to die or be condemned in the yards. The increase in risk with travel time was linear across the 0.1-10 -h range, meaning that there was no threshold below which travel time did not affect risk of mortality; rather, any increase in travel time increased the risk, and shorter travel times posed lower risk.

Although the effect of transport duration on calf mortality has not previously been investigated, others have looked at the effect of a related variable: transport distance. In a retrospective analysis of bobby calf mortality data from Northern Victoria, Australia between 1998 and 2000, Cave et al. (2005) identified an exponential increase in mortality with increasing transport distance (from farm to processor) over the range 100-800 km. Although travel duration was not known, the authors assumed that travel duration and travel distance were correlated and that distance was therefore a proxy for duration ( Cave et al., 2005).

Interestingly, the reported effects of travel distance on mortality applied only to mortality on arrival at the processor (equivalent of DOA or COA in the present study), with no effect of travel distance found on mortality in yards (defined as 'dead overnight') ( Cave et al., 2005). This contrasts with the present study, in which transport duration was significantly associated with mortality in yards as well as overall. This apparent difference may be due to the definitions applied to death/condemnation in yards. In the present study, death or condemnation in yard could occur at any point prior to presentation for slaughter, whereas the previous study defined this as 'dead overnight', implying death or condemnation after overnight holding only. According to the Victorian Code of Accepted Farming Practice for the Welfare of Cattle bobby calves held overnight must be fed as soon as practicable ( Anonymous, 1996). Thus, the provision of feed may have offset any deleterious effects of transport in calves held overnight.

There are a number of possible reasons for the observed effect of travel time on calf mortality in the present study. Transportation of young animals from the farm to the processor imposes stressors that affect their biochemical, hormonal and metabolic status ( Trunkfield and Broom, 1990). Longer journey distances ( Cave et al., 2005; Večerek et al., 2006; Uetake et al., 2011) loading and unloading ( Kent and Ewbank, 1986; Cave et al., 2005), novel human-animal contact ( Lensink et al., 2001), and the inability to lie down ( Uetake et al., 2011) have all been shown to negatively affect calf health and welfare and increase mortality.

In the current study, transport duration was the only transport-related factor that was included in the risk analysis, due to the unavailability of transport data for half of the selected calves. While it is possible that this was the only transport-related factor that influenced calf mortality, the effect of other variables cannot be ruled out without further study. Given that for the foreseeable future bobby calves will have to be transported some distance for processing, further research should focus on identification of transport factors that influence the risk of mortality and animal welfare status, to allow mitigation of these.

4.3. Slaughter schedule

Calves that were processed at premises operating a next-day slaughter schedule were more likely to be cases than those processed at premises operating a same-day schedule. The precise reason for this finding is not clear. Intuitively, it might be assumed that a next day slaughter schedule equates to more time spent in the yards prior to processing. However, it is conceivable that calves arriving at a premises late in the evening and being processed early the next day could spend less time in yards than calves arriving in the morning and being processed in the evening of the same day.

Given that there is no requirement in New Zealand to provide feed to calves in yards, provided they are slaughtered within 24 h of their last feed on-farm ( Anonymous, 2016), longer yarding times may be associated with longer time off feed. Prolonged feed withdrawal may negatively impact on calf energy and hydration status. For example, young dairy calves deprived of feed for up to 30 h demonstrated progressive reductions in plasma glucose and increases in plasma beta hydroxy butyrate, indicating the development of hypoglycaemia and switch to lipid for energy metabolism ( Todd et al., 2000). Additionally, although free water is provided in the yards, it is not known whether calves consume this. Therefore, it is possible that prolonged feed withdrawal may be accompanied by water loss, leading to dehydration.

While information was available on the length of time between arrival at the processing premises and death/condemnation of case calves, no information was available on time in yards prior to processing for control calves. Therefore, the relationship between slaughter schedule and time in yards could not be determined in this study. It is possible that other factors, such as staffing and frequency of monitoring of calves in yards may also vary according to processing schedule. Further research is required to determine what feature or features of a next-day processing schedule contribute to increased mortality risk.

Although processing premises slaughter schedule was significantly associated with calf mortality, the precise impact of a next day slaughter schedule on the odds of mortality was unclear. Analysis of data from all cases indicated a 4-fold greater risk of mortality when the processor had a next day slaughter schedule. When cases were limited to those calves that were condemned or died in the yards, there was no significant effect of slaughter schedule on mortality risk. This was unexpected, given that these latter calves were more likely to be impacted by a policy of next day slaughter.

There are two possible reasons for these findings. Firstly, calves that were classified as dead or condemned on arrival included animals that died/were condemned within two hours of arrival. It is plausible that assessors at premises reporting a next day schedule would be more likely to condemn a 'borderline' animal to minimise suffering and/or to have the death associated with transport rather than with the processor. Secondly, we may not have detected a true association in the restricted data set because of a lack of statistical power as there were only 18 cases in the CIY/DIY subset. A post hoc power analysis estimated that the power to detect an association in the limited data set was relatively low (0.43). It is not possible to postulate as to which reason is more likely. Given that day of slaughter is something that can be controlled, undertaking further research to determine the impact of slaughter schedule should be a priority.

It should be noted that since the time of data collection, new legally enforceable regulations have come into effect. These limit the travel duration for young calves to a maximum of 12 h and require that calves be slaughtered as soon as possible after arrival at the processing premises and within 24 h of their last feed ( Anonymous, 2016). These new regulations are consistent with the findings of this study, namely that limiting travel duration and time to slaughter is associated with reduced mortality. Accordingly, the bobby calf mortality rate in New Zealand has declined since this research was undertaken, from 0.25 % in 2015 to 0.12 % in 2016, and 0.06 % in 2017 ( Anonymous, 2017a, 2018a). This likely reflects both the introduction of new regulations, as well as recent education and extension efforts by various industry stakeholders.

It was somewhat surprising that variables reflecting on-farm management and implying calf health status were not associated with mortality in the current study. However, retrospective data collection meant that information on individual calf age, health and immune status or age at separation from the dam was not available. Therefore, the influence of these factors on calf mortality could not be assessed. Previous studies of dairy calf mortality on New Zealand farms have identified disease status and failure of passive transfer of immunity (FPT) as risk factors for mortality. For example, a prospective study of calf and replacement heifer mortality on 32 New Zealand dairy farms in 2015 found that risk of mortality on-farm in the first week of life was higher in herds with disease problems, such as scours, and among calves that were removed from the dam within 12 h of birth and hand-fed colostrum ( Cuttance et al., 2017a). A related study investigating passive transfer of immunity and colostrum quality found that calves that remained with the dam for ∼24 h had lower prevalence of FPT ( Cuttance et al., 2017b). The authors postulated that feeding poor quality, pooled, stored colostrum (with low antibody and high bacterial concentrations) to calves less that 24 h old was responsible for the observed effects ( Denholm et al., 2017). The present study collected information on colostrum feeding practices (e.g. true or mixed) and quality management (whether or not colostrum quality was checked) on-farm. Neither of these factors were found to significantly influence mortality risk in the final multivariable model. Given the relatively small sample size in the present study, along with the small number of farms that reported checking colostrum quality, further investigation of the effects of colostrum management on bobby calf mortality may be informative.

4.4. Limitations

A major limitation in this study was the relatively small sample size and the subsequent impact on study power. The requirement for data collection to take place over a single season, combined with the low response rate from supplying farmers, meant that we were unable to achieve the required sample size of 150. As a result, it may well be that variables other than those in the final model had a significant impact oncalf mortality risk.

As this was an exploratory analysis, there was limited a priori decision making regarding the inclusion/exclusion of variables. However, this study represents an important first step in developing a focus for further research and potential intervention strategies to improve calf welfare.

The study design ensured that recall bias was limited, as both the researchers delivering the questionnaire and those completing it were blinded as to whether a given calf was a case or control. However, the information obtained may have been influenced by participant memory, such that respondents may have answered according to what was the norm for them, as opposed to being able to recall specific details for the day in question. This may have biased the results toward the null.

5. Conclusions

The present study identified three significant risk factors for bobby calf mortality in New Zealand: time in farm of origin's calving season, duration of travel and processing premises slaughter schedule. Whilst the current mortality rate is low, it may be possible to further reduce this by addressing these risk factors, as evidenced by the decline in mortality since the introduction of new regulations consistent with these findings. Although travel time and slaughter schedule can be, and are now, subject to regulation, time in season is more difficult to regulate due to the wide calving spread in New Zealand's pastoral-based dairy system. However, the identification of this as a risk factor provides some insight into factors of potential importance on farm. It is possible that further education and extension within the industry could be effective in reducing animal welfare compromise and mortality associated with seasonal effects. As the present study design did not permit the collection of information on individual calf age or disease status, the influence of these factors on risk of mortality in bobby calves should also be investigated.

Declaration of Competing Interest

None.

Acknowledgements

The authors would like to thank the members of the Bobby Calf Steering Committee for their input and guidance in the development of this study, and Richard Laven for technical advice. We would also like to acknowledge the contributing MPI VS veterinarians at the slaughter premises and those farmers and transport operators who provided information on selected calves. This research was supported by the New Zealand Ministry for Primary Industries (Research contract # 17556).

Appendix A. Supplementary data

The following are Supplementary data to this article:

References

Anonymous . In: Code of Accepted Farming Practice for the Welfare of Cattle. Minister for Agriculture and Resources, editor. Agriculture Victoria; Victoria, Australia: 1996. [Google Scholar]

Anonymous . In: Animal Welfare (Calves) Regulations 2016. NZ Government, editor. Parliamentary Counsel Office; Wellington, New Zealand: 2016. [Google Scholar]

Mellor D.J. production animals: ethical and welfare issues raised by production-focused management of newborn livestock. In: Wathes C.M., Corr S.A., May S., McCulloch S., Whiting M., editors. Veterinary & Animal Ethics. 2011. pp. 174-187. London, UK. [Google Scholar]

Trotz-Williams L.A., Wayne Martin S., Leslie K.E., Duffield T., Nydam D.V., Peregrine A.S. Calf-level risk factors for neonatal diarrhea and shedding of [[Cryptosporidium parvum in Ontario dairy calves. Prev. Vet. Med. 2007;82:12-28. PMC free article] [PubMed] Google Scholar]

Vasseur E., Rushen J., de Passillé A.M., Lefebvre D., D, P A Canadian intervention strategy to encourage changes in calves and heifers management to improve welfare in dairy farms. J. Dairy Sci. 2010:4414-4426. [[PubMed] Google Scholar]

Articles from Preventive Veterinary Medicine are provided here courtesy of Elsevier

# data[].attributes.attachment_link.title
Risk factors for bobby calf mortality across the New Zealand dairy supply chain

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The objective of this study was to identify risk factors for morbidity and mortality of bobby calves across the whole dairy supply chain in New Zealand. A case-control study was carried out in the 2016 spring calving season. A total of 194 bobby calves, ...

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Pens designed according to breed and calf comfort

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Shelter required

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Dairy cattle numbers increased by 82 percent nationally from 3.4 million to 6.3 million between 1990 and 2019.

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Information and best practice guidelines regarding the welfare of bobby calves when transporting them safely

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Re_ MfE Data Service - 99906-livestock-numbers-grid-aps-2017

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Description:

Raster layer with 100m * 100m pixels, Each pixel represents the estimated nitrate-N leached in kg/ha/yr. This layer contains all nitrate leaching estimated from dairy cows.

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Nitrate-nitrogen leaching from dairy livestock 2017 | MfE Data Service

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Raster layer with 100m * 100m pixels, Each pixel represents the estimated nitrate-N leached in kg/ha/yr. This layer contains all nitrate leaching estimated...

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Nitrate leaching by animal type, 1990-2017

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Nitrate leaching by animal type, 1990-2017

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The total amount of nitrate-nitrogen leached from livestock increased from 189,000 tonnes nationwide in 1990 to 199,000 tonnes in 2017. The contributions of different regions and livestock types to national nitrate-nitrogen leaching have changed over this time.

The amount of nitrate-nitrogen leached by dairy cattle was more than three times higher than beef cattle in 2017 (130,000 tonnes by dairy cattle in contrast to 37,000 tonnes by beef cattle).

In 1990, nitrate-nitrogen leaching from sheep contributed 34% of national nitrate-nitrogen leaching, which decreased to 15% in 2017. In contrast, dairy cattle contributed 39% in 1990, which rose to 65% in 2017. The contribution of beef to national nitrate-nitrogen leaching has also decreased over this time period, from 26% in 1990 to 19% in 2017.

Livestock nitrate-nitrogen leaching in Canterbury has increased 117% since 1990 (from 15,000 tonnes to 33,000 tonnes in 2017).

25% of all nitrate-nitrogen leached by livestock in New Zealand in 2017 was in Waikato (49,000 tonnes). Leaching in Waikato has increased 12% since 1990.

In 2017, there were clusters of areas with higher leaching rates of nitrate-nitrogen per hectare in Waikato, Bay of Plenty, Taranaki, and Canterbury.

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Nitrogen is an essential nutrient for plant growth. It occurs naturally, but in agricultural systems more nitrogen is commonly added to soils as fertiliser or as urine or dung from livestock. Not all the additional nitrogen can be used by plants and microorganisms, so some nitrate-nitrogen may leach (drain) from the soil. Livestock urine is the dominant source of nitrate-nitrogen leached from soil. Leached nitrate-nitrogen can enter groundwater and waterways, potentially causing ecological harm. The amount of nitrate-nitrogen leaching from the soil varies around the country as a result of different land uses, climates, and soils.

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Nitrate leaching from livestock | Stats NZ

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Of nitrate leached from livestock, 65% was from dairy, and 15% from sheep in 2017.

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The New Zealand Land Cover Database (LCDB) is a multi-temporal, thematic classification of New Zealand's land cover. It identifies 33 mainland land cover classes (35 classes once the offshore Chatham Islands are included). The classification was revised between versions 1, 2, and 3 but has been consistent thereafter, and always with backward compatibility maintained. Land cover features are described by a polygon boundary, a land cover code, and a land cover name at each nominal time step; summer 1996/97, summer 2001/02, summer 2008/09, summer 2012/13, and summer 2018/19. The data set is designed to complement in theme, scale and accuracy, New Zealand’s 1:50,000 topographic database https://www.linz.govt.nz/land/maps/topographic-maps/topo50-maps). LCDB is suitable for use in national and regional environment monitoring, forest and shrubland inventory, biodiversity assessment, trend analysis and infrastructure planning.

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LCDB v5.0 - Land Cover Database version 5.0, Mainland, New Zealand | LRIS Portal

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The New Zealand Land Cover Database (LCDB) is a multi-temporal, thematic classification of New Zealand's land cover. It identifies 33 mainland land cover...

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While the groups have been separated out since this was published, the visuals are still applicable. I've compiled the new groups that are applicable to agriculture.

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Land managed for the production of grapes, pip, citrus and stone fruit, nuts, olives, berries, kiwifruit, and other perennial crops. Cultivation for crop renewal is infrequent and irregular but is sometimes practiced for weed control.

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Exotic sward grassland and indigenous short tussock grassland of poor pastoral quality reflecting lower soil fertility and extensive grazing management or non-agricultural use. Browntop, sweet vernal, danthonia, fescue and Yorkshire fog dominate, with indigenous short tussocks (hard tussock, blue tussock and silver tussock) common in the eastern South Island and locally elsewhere.

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US NLCD legend & descriptions - for comparison only

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US NLCD legend & descriptions - for comparison only

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National Land Cover Database Class Legend and Description | Multi-Resolution Land Characteristics (MRLC) Consortium

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*To view and print the PDF you must obtain and install the Acrobat® Reader, available at no charge from Adobe Systems.