Key Performance Indicators as Predictors of Enterprise Gross Margin in English and Welsh Suckler Beef and Sheep Farms

Abstract

A large proportion of the lowest annual farm profits in the United Kingdom in recent years comes from lowland and Less Favoured Area (LFA) beef and sheep farms. Benchmarking the performance of a business through routine data collection can provide the information needed to make changes to enterprise management and performance. Key performance indicators (KPIs) are globally recognised measures that can provide farmers with this capability. However, it is largely unknown if there are specific KPIs relating to livestock production that have a significant effect on financial performance. The aim of this study was to determine whether KPIs could be used as predictors of financial performance (gross margin, GM), on suckler beef and sheep farms in England and Wales. This was completed using data from the Farm Business Survey (FBS), which is the largest stratified financial survey of its kind in the UK. Following data extraction, multiple linear regression models were developed for four enterprise types: LFA suckler beef, lowland suckler beef, LFA ewe and lowland ewe. Several KPIs were significantly associated with gross margin per head in all enterprise types. KPIs that were positively associated with GM were measures of livestock productivity, which were lambs per breeding stock and calves per cow. The increased expenditure on concentrate feed had a significantly negative association within all enterprise types, except for LFA suckler beef enterprises, where cow mortality had the greatest significantly negative association. This is the first study to demonstrate the influence livestock production KPIs have on the financial performance of suckler beef and sheep enterprises in both England and Wales, highlighting the importance of routine data collection and benchmarking.

1. Introduction

Agriculture occupies 69% of the total land area in the United Kingdom (UK) [1]. As a temperate country with moderate to high rainfall, the UK is well suited to grass production, with 42% and 85% of the farmed land area in England and Wales, respectively, being used for grazing [2,3]. Grassland can be classified into lowland, which is typically flat and situated slightly above sea level, whereas upland land areas are classified as Less Favoured Areas (LFAs). The LFA classification was developed to financially support and recognise hill farming areas, as LFAs are typically difficult to farm due to their geology, altitude, and climate, unlike lowland grazing land. LFA land is largely seen in the north and southwest of England and accounts for three-quarters of the grazing land in Wales [4].

Grazing livestock play a crucial role in lowland and LFA agricultural systems [5], and as ruminants, they offer the ability to convert grass into a nutrient-dense product for human consumption in the form of red meat [6]. Livestock farms also support rural culture and heritage and provide many socio-economic benefits [7,8]; however, they are often the farms with the lowest farm business incomes in the United Kingdom. In England, 58% of LFA and 77% of lowland livestock farms generated profits of less than GBP 25,000 during 2021 [9], with lowland cattle and sheep farming enterprises being the least profitable farm types in Wales, at GBP 22,900 and GBP 18,400 a year in 2021, respectively [10]. The industry has faced a multitude of challenges, which includes change in agricultural payment schemes due to Brexit and the pressures on land use and net zero targets [11]. This is also reflected in the fluctuating numbers of beef and sheep stock during recent years, particularly suckler beef cows and breeding ewes [12]. Nevertheless, in an analysis of English farms, Betts [13] found that 70% of the variation in the financial performance of grazing livestock farms was attributable to the characteristics of the farm business itself rather than external factors such as agricultural policy. Changes in business management, particularly livestock management, could provide the opportunity for lowland and LFA farmers to improve their efficiency and profitability.

Regular benchmarking of enterprise key performance indicators (KPIs) is a globally recognised and used tool [14]. Performance indicators can be defined as a set of measures focusing on an aspect of business performance that is most critical for the current and future success of the business [15]. The application of KPIs in measuring and benchmarking the performance of livestock is a well-used and documented tool [16,17]. Calculated through routine data collection and when used regularly for benchmarking, KPIs offer the ability for producers to identify areas for improvement within production systems [18] and improve farm business resilience and performance [19]. A study by Ramsbottom et al. [20] found that dairy farms that annually benchmarked over a period of 9 years and made routine use of the data had increases in productivity compared to farms that did not carry out any benchmarking. Farms who annually benchmarked produced significantly (p < 0.001) higher milk yields and milk solids for every kilogram of meal fed, resulting in 168 kg less feed meal consumed per cow, improving feed efficiency and net margin.

There can also be substantial financial benefits when the correct metric or combination of metrics are used on farm [21]. Increased margins of up to GBP 50/ewe have been achieved by making improvements using performance recording in breeding ewe systems [22], and improvements in suckler beef KPIs have increased net margin per cow bred by GBP 19.96 [23]. A study by Gray Betts et al. [24] used data from the Farm Business Survey (FBS) to determine the association between the financial performance of cereal enterprises and measures of on farm nitrogen usage in England. Previous literature [20,22] has developed and provided the industry with recommended KPIs that can measure and benchmark livestock production; however, there is limited literature looking at the association between the financial and physical performance of livestock enterprises. Consequently, the current paper seeks to contribute to the current literature by firstly calculating the livestock performance selected beef and sheep farms in England and Wales, and secondly by evaluating the impact that livestock performance has on financial performance. Therefore, in the current study, our aim was to investigate the association between beef and sheep KPIs and gross margin on farms in England and Wales using FBS data.

2. Materials and Methods

2.1. Farm Business Survey

Commissioned by the Department of Environmental, Food, Rural Affairs in England and the Welsh Government in Wales [25,26], the FBS is a stratified survey that gathers detailed financial, physical, and environmental data from a representative number of farms across England and Wales. Further information on the FBS can be found within the technical notes and guidance (see https://www.gov.uk/guidance/farm-business-survey-technical-notes-and-guidance; accessed on 7 January 2025). The study used 3 years of data from 2019/20–2021/22, as these were the most recent data sets available from the UK data service at the point of application. MATLAB software (version R2023a, [27]) was used to extract livestock enterprises from all data sets using the livestock enterprise code used to calculate gross margin. This allowed for the identification of suckler beef and breeding ewe enterprises and for the use of their farm identification numbers as a filter for variable selection. The FBS further classifies enterprise types as either lowland or LFA; information on how farm classifications are calculated can be found within the UK Farm Classification Document (https://assets.publishing.service.gov.uk/media/641073c8e90e076cd09acda9/fbs-uk-farmclassification-2014-14mar23.pdf; accessed on 7 January 2025). The study retained these classifications, resulting in four groups for analysis: LFA ewe, LFA suckler beef, lowland ewe, and lowland suckler beef.

2.2. Key Performance Indicators

Using the suckler beef and breeding ewe enterprise codes, farm-level information regarding beef and sheep production was extracted for each financial year, and the data were averaged across the 3 years. This information was used to develop KPIs that represented livestock performance. To ensure KPIs were relevant to the livestock sector, the study utilised the KPIs recommended by the Agriculture and Horticulture Development Board (AHDB) [28] and the KPI toolkit developed by Hewitt et al. [29]. However, the structure and nature of the survey did mean that there were limitations to the number of KPIs that could be calculated. For example, data on livestock weights were not collected, which meant that KPIs such as weight weaned per suckler cow or ewe could not be calculated. All KPIs calculated for the current study can be seen in

Table 1. Lowland and LFA suckler beef KPIs calculated using FBS data.

Suckler Beef KPIs	Units	Calculation
Gross Margin	GBP per cow	${\displaystyle \frac{\mathrm{Enterprise}\mathrm{Gross}\mathrm{Margin}}{\mathrm{No}\mathrm{of}\mathrm{suckler}\mathrm{cows}}}$
Vet and Med	GBP per cow	${\displaystyle \frac{\mathrm{Cos}\mathrm{t}\mathrm{of}\mathrm{enterprise}\mathrm{Vet}\mathrm{and}\mathrm{Med}}{\mathrm{No}\mathrm{of}\mathrm{suckler}\mathrm{cows}}}$
Concentrates	GBP per cow	${\displaystyle \frac{\mathrm{Cos}\mathrm{t}\mathrm{of}\mathrm{enterprise}\mathrm{Concentrates}}{\mathrm{No}\mathrm{of}\mathrm{suckler}\mathrm{cows}}}$
Forage Area	ha per cow	${\displaystyle \frac{\mathrm{Forage}\mathrm{area}\left(\mathrm{ha}\right)\mathrm{of}\mathrm{the}\mathrm{enterprise}}{\mathrm{No}\mathrm{of}\mathrm{suckler}\mathrm{cows}}}$
Calf Mortality	%	$({\displaystyle \frac{\mathrm{No}\mathrm{of}\mathrm{calf}\mathrm{deaths}}{\mathrm{No}\mathrm{of}\mathrm{calves}}})$ × 100
Calves	Number per cow	${\displaystyle \frac{\mathrm{Production}\mathrm{of}\mathrm{calves}}{\mathrm{No}\mathrm{of}\mathrm{cows}}}$
Cow Mortality	%	$\left({\displaystyle \frac{\mathrm{No}\mathrm{of}\mathrm{cow}\mathrm{deaths}}{\mathrm{No}\mathrm{of}\mathrm{cows}}}\right)$ ×100

The KPIs listed above are the industry standard, as published and recommended by [27,28].

and

Table 2. Lowland and LFA ewe KPIs calculated using FBS data.

Ewe KPIs	Unit	Calculation
Gross Margin	GBP per ewe	${\displaystyle \frac{\mathrm{Enterprise}\mathrm{Gross}\mathrm{Margin}}{\mathrm{No}\mathrm{of}\mathrm{ewes}}}$
Vet and Med	GBP per ewe	${\displaystyle \frac{\mathrm{Cos}\mathrm{t}\mathrm{of}\mathrm{enterprise}\mathrm{Vet}\mathrm{and}\mathrm{Med}}{\mathrm{No}\mathrm{of}\mathrm{ewes}}}$
Concentrates	GBP per ewe	${\displaystyle \frac{\mathrm{Cos}\mathrm{t}\mathrm{of}\mathrm{enterprise}\mathrm{concentrates}}{\mathrm{No}\mathrm{of}\mathrm{ewes}}}$
Forage Area	ha per ewe	${\displaystyle \frac{\mathrm{Forage}\mathrm{area}\left(\mathrm{ha}\right)\mathrm{of}\mathrm{the}\mathrm{enterprise}}{\mathrm{No}\mathrm{of}\mathrm{ewes}}}$
Ewe Mortality	%	${\displaystyle \frac{\mathrm{No}\mathrm{of}\mathrm{ewe}\mathrm{deaths}}{\mathrm{No}\mathrm{of}\mathrm{ewes}}}$ × 100
Lambs	Number per breeding stock	${\displaystyle \frac{\mathrm{No}\mathrm{of}\mathrm{lambs}\mathrm{produced}}{\mathrm{No}\mathrm{of}\mathrm{Ewes}+\mathrm{Ewe}\mathrm{Hoggs}\mathrm{for}\mathrm{breeding}}}$
Ewe:Ram	ratio	${\displaystyle \frac{\mathrm{No}\mathrm{of}\mathrm{ewes}}{\mathrm{No}\mathrm{of}\mathrm{rams}}}$

The KPIs listed above are the industry standard, as published and recommended by [27,28].

, with mostly identical KPIs for LFA and lowland enterprises to ensure consistency.

2.3. Data Visualisation and Statistical Analysis

A total of 910 LFA and 365 lowland farms were extracted between 2019/20 and 2021/22 using their farm identification numbers. Only farms present in all three years were retained for analysis, resulting in of 354 LFA and 148 lowland farms (

Table 3. Number of farms and average number of breeding livestock in each enterprise type that was included in each of the final models.

Enterprise Type	Number of Farms	Average Number of Breeding Livestock	SD
LFA suckler beef	195	46	37
Lowland suckler beef	58	62	34
LFA ewe	159	642	630
Lowland ewe	90	429	392

). Averaging KPIs across the three years meant that seasonal changes could be accounted for, providing stability in the data. Before conducting data visualisation, a total of 25 farms were removed due to incomplete data. All statistical analyses were undertaken using R (version 4.0.3, [30]). Initial data quality checks were performed using QQ plots, histograms, and correlation plots. Multiple linear regression with a stepwise AIC procedure was conducted using the gross margin as the response variable and KPIs as the predictor variables. Furthermore, the direction of the stepwise procedure was set as forward and backward to identify the most statistically significant variables in the model. The Akaikae’s Information Criterion was used within a stepwise modelling framework, eliminating predictors in the model to identify the most parsimonious model that balances goodness of fit against model complexity [31]. Results were considered significant at p < 0.05. The distribution of model residuals was examined to ensure there was no significant deviation from normality using the package ‘DHARMa’ [32]. Outliers were investigated, and it was decided that suckler beef farms with fewer than 10 cows in a financial year were to be removed. This was also the case in a study of dairy farms by Gonzalez-Mejia et al. [33] using FBS data. Consequently, a total of 23 LFA suckler beef farms and 6 lowland suckler beef farms were removed from the data set. Model validation was performed again, and following no significant deviation of the residual data points, the models were accepted

3. Results

3.1. Lowland and LFA Ewe Performance

Descriptive statistics for LFA and lowland ewe enterprises (

Table 4. Descriptive statistics for lowland and LFA ewe enterprise KPIs.

	Lowland Ewes			Less Favoured Area Ewes
Key Performance Indicator	Mean	SD	95% CI	Mean	SD	95% CI
Concentrates (GBP per ewe)	20.37	13.24	1.40	19.42	10.62	0.84
Ewe:Ram (ratio)	37.07	12.57	1.33	42.84	19.42	1.54
Lambs, per breeding stock (no.)	1.32	0.36	0.04	1.24	0.28	0.02
Ewe Mortality (%)	8.28	3.57	0.38	7.60	3.70	2.37
Vet and Med (GBP per ewe)	7.30	3.61	0.38	5.68	3.03	0.24
Forage area (ha per ewe)	7.30	3.48	0.37	7.60	3.72	0.29
Gross Margin (GBP per ewe)	71.97	37.48	3.95	70.05	29.82	0.01

Data were averaged across years 2019/20–2021/22 of the FBS.

) show that concentrates (GBP per ewe) had a greater cost than vet and med per ewe in both enterprise types. Lowland ewes had consistently higher KPIs compared to LFA ewes, with the exception of the ewe:ram ratio.

Interactions between appropriate variables were examined but did not significantly improve model fit across all models. Statistical analysis of the lowland ewe data set found that lambs per breeding stock had a highly significant and positive association with gross margin per ewe (73.540, p < 0.001). Concentrates costs per ewe was also highly significant in the model, but had a negative association with gross margin per ewe (−0.085, p < 0.001) (

Table 5. Statistical results of lowland ewe enterprises.

Key Performance Indicator	Coefficient	95% CI	p Value
Intercept	−0.03	−30.13–14.06	ns
Concentrates (GBP per ewe)	−0.09	−1.30–−0.40	<0.001
Lambs per breeding stock (no.)	73.54	57.05–90.03	<0.001

Lowland ewe enterprise (n = 90) results from a stepwise linear regression where KPIs were the dependant variable and gross margin was the independent variable. R² = 0.4814. ns = Not significant (p > 0.05).

).

The results were similar for the LFA data, with concentrates per ewe (−0.833, p < 0.001) and lambs per breeding stock (56.247, p < 0.001) having a negative and positive association, respectively, with the gross margin per ewe. However, in addition to these KPIs, LFA ewe enterprises had two additional KPIs with a negative and positive association with gross margin per ewe. These were the ewe:ram ratio (−0.238, p < 0.05) and the vet and med cost per ewe (1.184, p < 0.05;

Table 6. Statistical results of LFA ewe enterprises.

Key Performance Indicator	Coefficient	95% CI	p Value
Intercept	17.16	−4.85–39.17	ns
Concentrates (GBP per ewe)	−0.83	−1.21–−0.46	<0.001
Ewe:Ram (ratio)	−0.24	−0.44–−0.04	<0.05
Lambs, per breeding stock (no.)	56.25	41.98–70.51	<0.001
Vet and med (GBP per ewe)	1.18	0.13–2.24	<0.05

LFA ewe enterprises (n = 159) stepwise linear regression, where KPIs were the dependant variable and gross margin was the independent variable. R² = 0.3642. ns = Not significant (p > 0.05).

).

3.2. Lowland and LFA Suckler Beef Performance

On average, the greatest cost to both enterprises was the concentrate cost per cow, valued at GBP 70.55 and GBP 52.88/cow for lowland and LFA systems, respectively. The KPI incurring the lowest cost for both suckler beef enterprise types was vet and med per cow, at GBP 30.99 and GBP 40.91/cow for lowland and LFA systems, respectively (

Table 7. Descriptive statistics for lowland and LFA suckler beef enterprise KPIs.

	Lowland Sucklers			Less Favoured Area Sucklers
Key Performance Indicator	Mean	SD	95% CI	Mean	SD	95% CI
Vet and Med (GBP per cow)	30.99	20.57	5.29	40.91	25.28	3.56
Concentrates (GBP per cow)	70.55	66.00	16.99	52.88	61.19	8.66
Forage Area (ha per cow)	1.27	0.48	0.12	1.11	0.61	0.09
Calves (no. per cow)	0.94	0.11	0.03	0.96	0.17	0.02
Cow Mortality (%)	5.02	6.34	1.63	4.53	3.25	0.38
Calf Mortality, (%)	6.86	5.23	1.35	6.79	4.54	0.64
Gross Margin (GBP per cow)	241.34	201.64	44.54	243.06	173.05	20.26

Data were averaged across years 2019/20–2021/22 from the FBS.

).

Similar to the sheep enterprises, interactions between variables were assessed and did not significantly improve the fit of the AIC model; therefore, the final main effects model with no interactions was accepted. Results from the stepwise linear regression show that calves per cow and concentrates per cow had highly significant associations with gross margin per cow (

Table 8. Statistical results of lowland suckler beef enterprises.

Key Performance Indicator	Coefficient	95% CI	p Value
Intercept	−124.30	−351.25–102.65	ns
Concentrates, GBP per cow	−0.73	−1.14–−0.32	<0.001
Calves (no. per cow)	461.42	231.15–691.68	<0.001

Results of a stepwise linear regression for lowland suckler beef farms (n = 58) where KPIs were the dependant variables and gross margin was the independent variable. Farms with ≤10 cows were not included in the analysis. R² = 0.430. ns = Not significant (p > 0.05).

). Concentrates per cow had a negative association (−0.734, p < 0.001), whereas calves per cow had a positive association (461.416, p < 0.001).

Similarly to the LFA ewe enterprise, there were additional KPIs significantly associated with gross margin per cow in the LFA enterprise. Both cow and calf mortality had a significant (p < 0.001 and p < 0.05) and negative association (−9.883 and −4.568) with gross margin per cow, as did concentrates per cow (−0.528, p < 0.01). However, in contrast to lowland suckler beef enterprises, the vet and med cost per cow had a significantly positive association (1.160, p < 0.01) with the gross margin in LFA suckler beef enterprises (

Table 9. Statistical results of LFA suckler beef enterprises.

Key Performance Indicator	Coefficient	95% CI	p Value
Intercept	123.42	−1.66–248.50	ns
Cow Mortality (%)	−9.89	−16.90–−2.86	<0.001
Calf Mortality (%)	−4.57	−8.76–−0.38	<0.05
Calves (no. per cow)	196.01	86.53–305.48	<0.001
Vet and Med (GBP per cow)	1.16	0.40–1.92	<0.01
Concentrates (GBP per cow)	−0.53	−0.81–−0.19	<0.01

LFA suckler beef farms (n = 195) stepwise linear regression where KPIs are the independent variable and gross margin the independent variable. Farms with 10 or fewer cows have not been included. R² = 0.4075. ns = Not significant (p > 0.05).

).

4. Discussion

This study has shown that a number of KPIs derived from FBS data are significantly associated with the gross margin in beef and sheep enterprises. Most notably, measures of productivity (lambs per breeding stock and calves per cow), of all variables in the stepwise regressions, proved consistent in their significant association with gross margin per head. Calves per cow on lowland and LFA suckler beef farms (Table 7) had a comparable average to Irish suckler beef farms that were classified as high-efficiency systems and had an average of 0.95 calves per cow [34]. As expected, increasing lambs per breeding stock resulted in a positive association with gross margin (Table 5 and Table 6). Similarly, Bohan et al. [35] found that increasing number of lambs weaned per ewe from 1.5 to 1.8 increased the net profit. However, it is important to highlight the differences in KPI calculations. In the current study, breeding stock represents all livestock in the enterprise which are eligible for breeding; this includes breeding ewes and ewe hoggs (6 months and less than 1 year to be used for breeding). Calculating lambs per ewe or reared was not possible using this data set. While this could be seen as a limitation of the study, it is worth noting the relevance of the KPI in potentially measuring the carbon footprint of breeding animals kept on farm by comparing their output and input. This was used in a study by Jones et al. [36], where the percentage of the ewe and replacement ewe lamb flock not mated and the lambs reared per ewe were used as predictor variables in a carbon footprinting model of hill and lowland farms.

The purchase of concentrates was a considerable cost for all enterprise types, and its negative influence on gross margin has been described previously [37]. It is also important to recognise the rise in input costs with the average price of cattle concentrates increasing by GBP 148 per tonne between 2019 to 2022 [38]. Improving grassland management to increase grass utilisation can reduce concentrate costs for a business, with profitable suckler beef systems being reported to have low purchased feed costs [39]. Decreasing forage area per cow or increasing stocking rate (number of animals divided by land area rather than forage area only) is a strategy farmers can use to increase output per ha, with a previous report showing an increase in stocking rate from 1.75 cows/ha to 2 cows/ha would improve the gross margin by GBP 150/ha [5]. Taylor et al. [40] also found that beef farms with higher stocking rates had higher output (kg/LU and kg/ha); however, farms achieving this were participating in a farm improvement programme and were being compared to the national average, which had a lower output. Rotational grazing is also a technique farmers can use to increase their stocking rate while increasing herbage mass and quality [41]. However, our study did not identify a significant relationship between forage area per head and gross margin per head.

The other notable cost affecting the gross margin of suckler beef and sheep enterprises was vet and med. When comparing with industry averages, AHDB’s Farmbench users in the middle 50% of performers recorded vet and med costs of GBP 40.17 per cow put to the bull in 2022/23, which is similar to FBS lowland suckler farms at GBP 40.91 [41]. For FBS sheep enterprises, the average vet and med per LFA ewe were GBP 5.68 and GBP 7.30 for lowland ewes; in comparison, AHDB’s Farmbench users in the middle 50% of performers recorded GBP 8.72 per ewe, with the top 25% achieving GBP 7.29 per ewe during 2022/23 [42]. Although the vet and med costs were higher in LFA suckler beef compared to lowland suckler beef, the average mortality percentage for both calves and cows was lower. Factors outside the scope of this study, such as management, breed, and health, may explain some of this variation.

Although calf mortality and calves per cow were comparable to previous research [43] additional measures of fertility, such as calving interval, number of months calving, empty rate, and calving difficulty, could have provided the information needed to determine the reproductive performance of cows. Measuring cow age could also explain variations in suckler cow mortality percentage, vet and med per cow, and the mortality percentage of calves as well, all of which would give further indications of the production system’s efficiency [44]. As previously stated, due to the nature of the FBS questionnaire, there were limitations in the number and types of KPIs that could be calculated. However, this study has highlighted the importance of neonatal survival and keeping purchase costs to a minimum, as these variables had the greatest effects on the gross margin per head in all enterprise types. This is significant, as recent agricultural policies [45,46] in devolved administrations across the UK and elsewhere are now moving towards incentivising and rewarding livestock farmers to record and improve livestock performance.

5. Conclusions

Being the largest stratified survey of its kind in the United Kingdom, the FBS allowed this study to use representative farm-level data that reflected the performance of beef and sheep farms in England and Wales. The study has demonstrated significant associations between gross margin and specific KPIs at a system level. The greatest number of variables with a significant association with gross margin per head was seen in LFA suckler beef enterprises, followed by LFA ewe enterprises, and then lowland suckler beef and lowland ewe enterprises, with the fewest variables of significant association. The KPIs calculated within this study are largely comparable with industry averages; however, there is likely variation between farms due to differences in production system types defined by their choice in breed, rearing system, market, and facilities. The ability to identify such factors was beyond the scope of this study; therefore, farm type classifications derived from the FBS were retained throughout the study (LFA and lowland). Further research is needed to identify the contributions and effects that differing production system types can have on livestock KPIs and their association with financial performance. This is the first study using data from England and Wales to demonstrate the influence livestock KPIs have over the financial performance of businesses and the benefits that routine data collection and benchmarking can have on enterprise performance.