Description and validation of the Teagasc Lamb Production Model

Agricultural Systems 148 (2016) 124–134

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Description and validation of the Teagasc Lamb Production Model A. Bohan a,b,⁎, L. Shalloo a, B. Malcolm d,e, C.K.M. Ho d, P. Creighton c, T.M. Boland b, N. McHugh a a

Animal & Grassland Research and Innovation Centre, Teagasc, Moorepark, Fermoy, Co. Cork, Ireland School of Agriculture & Food Science, University College Dublin, Ireland c Animal & Grassland Research and Innovation Centre, Teagasc, Athenry, Co. Galway, Ireland d Department of Economic Development, Jobs, Transport and Resources, Carlton, Vic. 3053, Australia e University of Melbourne, Vic. 3010, Australia b

a r t i c l e

i n f o

Article history: Received 27 October 2015 Received in revised form 1 July 2016 Accepted 22 July 2016 Available online 30 July 2016 Keywords: Whole-farm stochastic budgeting Monte Carlo simulation Bio-economic model Sheep production systems

a b s t r a c t A stochastic budgetary simulation model of a sheep farm was developed to investigate the effects of changes in lamb production systems on farm profitability. Model inputs included: land, labour, capital, animal numbers, as well as, input and output prices. Model outputs were simulated on a monthly basis and included: flock sales and purchases, net energy demand, grass supply and demand, lamb growth and slaughtering pattern, as well as, land and labour utilization. Grass growth, ewe and lamb mortality, fertiliser and concentrate price along with lamb and mutton price were all included in the model as stochastic variables. Farm earnings before interest and tax and net profit, both including and excluding owner/operator labour, were calculated from total receipts from lamb, culled animals and wool less variable and fixed costs. Validation of the model was undertaken by comparing the model outputs to real farm data recorded on 20 Irish commercial sheep farms as well as comparing the model outputs to those of three individual Irish commercial sheep farms. The model outputs were similar to the real farm data indicating that the model provides a realistic representation of actual farm performance, output and profit. To demonstrate potential application of the model, two lambing date scenarios were investigated; a mid-season lambing flock, with a mean lambing date of March 1st and an early lambing flock, with a mean lambing date of January 1st. Both lambing date scenarios had the same farm area, overall herbage utilization, pregnancy scanning rate and number of lambs weaned per ewe joined to the ram, but the early lambing flock had a higher stocking rate (13.23 ewes/ha versus 9.46 ewes/ha for March 1st lambing) which increased due to the higher proportion of concentrate in the total flock diet compared with March 1st lambing. The annual return on investment (ROI) of the mid-season lambing flock was 0.95% with a net profit of €11,045 excluding owner/operator labour and management. The early lambing system produced a ROI of −0.70% and a net profit of −€4862. When owner/operator labour and management cost were included the ROI was −0.38% with net profits of −€5462 for March 1st lambing and a ROI of −2.46% and net profits of −€26,735 for January 1st lambing. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction The national Irish sheep flock currently consists of 2.5 million breeding ewes, with the lowland sector accounting for 75% of the ewe population (Teagasc, 2011a). Average sheep farm returns from the 2013 Teagasc sheep e-Profit Monitor (EPM), an online financial analysis tool for assessing farm profitability, showed that the average net profit was −€22/ha, with the top third of sheep farms, generating a net profit of €229/ha excluding owner/operator labour costs. Over the next decade (2013 to 2023) sheep farmers will experience rising costs for key inputs, while prices received for meat and wool are unlikely to increase at a corresponding rate (Tocker et al., 2013). This will further erode farm profit ⁎ Corresponding author at: Animal & Grassland Research and Innovation Centre, Teagasc Moorepark, Fermoy, Co. Cork, Ireland. E-mail address: [email protected] (A. Bohan).

http://dx.doi.org/10.1016/j.agsy.2016.07.008 0308-521X/© 2016 Elsevier Ltd. All rights reserved.

unless off-setting productivity gains are achieved. Therefore increasing efficiency is imperative at farm level to ensure the continued viability of sheep farms in Ireland and internationally. Many studies have used simulation modelling to increase understanding of farm systems (White et al., 1983; Cacho et al., 1995; Shalloo et al., 2004a) with the aim of explaining, understanding or improving farm performance (Qureshi et al., 1999). Simulation modelling allows alternative scenarios to be defined and investigates the effect of the underlying biological processes and their interactions (Cros et al., 2004). One type of simulation model is the bio-economic model, which has the ability to describe both bio-physical and economic components of a farm system (Knowler, 2002). A bio-economic model allows for the linkage of the bio-physical and economic components of the model and enables information to feed from one component to the other, thereby enabling the bio-physical component to alter the economic component and vice versa (Kragt, 2012) ultimately facilitating

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the testing of various scenarios in a simulation setting. The bio-economic model enables changes to policy, technological innovations or other variables within a farming system to be assessed and the net profit to be quantified (Janssen and van Ittersum, 2007). Many farm system models have been developed previously for sheep farm systems (Edelsten and Newton, 1977; Finlayson et al., 1995; Jackson et al., 2014), as well as, for Irish dairy (Shalloo et al., 2004a) and beef (Crosson et al., 2006), however, to date no whole farm systems model has been developed for the seasonal grass based Irish sheep industry. The Irish lowland sheep sector operates a grass based system that aims to wean a high number of lambs per ewe at high stocking rate levels and consists of two main sheep production systems; mid-season and early lambing flocks, accounting for 80% and 10% of the sheep flocks in Ireland, respectively (Teagasc, 2014). The mid-season lambing system is a low input grass-based system that aims to slaughter lambs from grass only diets. The early lambing system is a higher input system, with a larger proportion of the total diet comprising of concentrate feed. The early lambing system aims to produce lambs for the Easter market where premium carcass price are attainable (Connolly, 2000a). To date no study has investigated the relative profitability of the mid-season and early production systems. The objectives in this study are: to provide a detailed description of the development of a bio-economic sheep farm model; to validate the model against real Irish farm data and to demonstrate an application of the model by comparing the profitability of two lambing date scenarios. 2. Materials and methods 2.1. Bio-economic model development A whole farm bio-economic simulation model describing an Irish lowland sheep system was developed in an Excel spread-sheet. The Teagasc Lamb Production Model (TLPM) was developed using a similar approach to the Moorepark Dairy Systems Model (Shalloo et al., 2004a). Performance of the flock was simulated in each month of a production year, with lambing date defined based on the scenario under investigation. The financial and economic outputs included: variable costs, fixed costs, capital investment, earnings before interest and tax, net profit and return on total capital and return on equity. Net profit was defined as farm income less variable costs, fixed costs (including depreciation) and interest. Return on total capital was calculated as gross income less variable and fixed costs (excluding interest on term loan or tax) and was expressed as a percentage of the total capital investment, namely, land, infrastructure and livestock. Capital associated with land was valued at €20,000 per hectare, corresponding to the national average land price (DAFM, 2011). Return on equity was gross income less variable and fixed costs (excluding interest) expressed as a percentage of equity. The simulation was a 12 month production cycle that commenced at mating start date. The flock comprised of various animal categories including: lambs (0 to 7 months of age), ewe lambs (N 7 to 19 months of age), hoggets (N19 to 31 months of age), mature ewes (N 31 months of age) and rams. Net energy (NE) requirements were calculated for each group on a monthly basis and a monthly feed budget was derived; this varied by stage of production and time of year. Animal numbers and valuations were calculated at the start and end of each month. All animal movements in and out of the flock, including mortality and number of lambs drafted for slaughter, were included in the TLPM. The base farm scenario modelled was a mid-season lambing system, with a base area of 42 ha equating to a stocking rate of 9.46 ewes/ha, with 1.42 lambs weaned per ewe joined to the ram. Land could be rented in or out and therefore stocking rate is an output of the model. Stocking rate was defined as the number of ewes joined to the ram divided by the total land area for the year including any land rented in or out for temporary grazing. With the exception of ewe lambs retained for breeding, all lambs were sold for slaughter once target slaughter weights were attained. A

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lamb drafting sub model was included in the TLPM which used real lamb live weight data to obtain mean and standard deviation growth rates for lambs across the production year. Using these data the numbers of lambs above the desired drafting weight were chosen for drafting by the drafting sub model. The soil on the farm was a free draining brown earth of sandy loam texture. The Irish temperate maritime climate (Kiely, 1999) allows for a long grass growing season with the lowest growth rates recorded in the winter months (O'Donovan et al., 2011). Grass growth in the summer (May to August) is relatively high, with high summer rainfall negating the occurrence of serious drought. A schematic diagram of the TLPM is provided in Fig. 1. Further details of the development of the model are described subsequently. 2.1.1. Lambing pattern and feed system Within the TLPM female replacements could be retained from the existing flock or purchased and additionally could be mated as ewe lambs (one year old at first lambing) or as hoggets (two years old at first lambing). Breeding ewes were mated five months prior to the target lambing date and the success of the mating was determined based on pregnancy scanning rates. The performance of the grass-based system is reliant on high utilization of grass to supply the flock NE requirements with minimal concentrate supplementation. The flock was housed during the winter months (mid-December to early-March) and fed grass silage; breeding ewes were supplemented with concentrates for a period of eight weeks pre-lambing. Scanning rate was an input to the model and weaning rate was calculated from the scanning rate after accounting for lamb mortality. Scanning rate also impacted on the energy requirement of the ewe in late pregnancy, as well as, lamb growth rate as an increase in litter size resulted in lower milk availability per lamb and in turn lower lamb performance (McDonald et al., 2011). The diet of lambs varied based on the lambing date scenario under investigation. For early lambing flocks (lambs born in the months of November to January) lambs were supplemented with concentrates along with grass from birth to slaughter. Mid-season lambs (lambs born in the months of February to May) received only grazed grass until October when concentrate supplementation was introduced to compensate for reductions in the quantity and quality of available grass. 2.1.2. Livestock movements and values The number of lambs, replacement ewes (ewe lambs and/or hoggets), mature ewes and rams at the start and end of the month, as well as, animals culled, slaughtered, purchased or died were calculated within the model. The number of females retained or purchased as replacements were calculated based on the culling and mortality rate of the mature flock, such that flock numbers were maintained regardless of overall mortality and culling levels. The proportion of breeding ewes culled for voluntary and in-voluntary reasons at pregnancy scanning (25%) and pre-mating (75%) were taken from Irish experimental data (Creighton, Pers. Comm., 2014a). No voluntary culling of the barren primiparous ewes (ewe lambs or hoggets) occurred at first pregnancy scan. The modelled culling rate for ewe lambs (when mated), hoggets and mature ewes was 6%, 8% and 16%, respectively. Mortality rates in the flock varied by animal category. Annual mortality rates for ewe lambs (when mated), hoggets, mature ewes and rams were 6%, 5%, 4% and 2%, respectively. This resulted in an annual ewe replacement rate of 20%, which is in line with Irish sheep data (McHugh, 2011). Rams have a replacement rate of 25% (Teagasc, 2011a). The monthly mortality of the breeding flock (hoggets and mature ewes) varied across the months of the year, with 20% of ewe mortality occurring one month pre-lambing, 40% at lambing and 20% in the month post-lambing. A total of 2.22% of ewe mortality occurred per month over the remaining months of the production year (Creighton, Pers. Comm., 2014a).

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Farm Resources

Flock structure

Lambing Pattern

Feed System

Grass Growth

Livestock Movements and Valuation

Land

Feed Demand

Lamb Drafting

Labour

Costs

Income

Output Indicators

Physical Indicators

Capital Introduced

Financial & Economic Indicators

Feed Budget

Profit and Loss

Nutrient Balance Sheet

Balance Sheet

Physical Ratios

Cash Flow

Fig. 1. A schematic diagram of the Teagasc Lamb Production Model.

Lamb mortality varied by age, with 30% of losses occurring from pregnancy scanning to lambing, 49% occurring in the first 48 h of life, 11% occurring from 48 h to 14 days post lambing and the remaining 10% mortality between day 15 and sale (EBLEX, 2013). Total lamb mortality from pregnancy scan to sale varied by pregnancy scanning rate (Benoit, 2014). In the base farm scenario (ML), total lamb mortality from pregnancy scanning to sale was 10.8%. Stock valuation was set for each animal category, with lamb valuations based on live weight. A total stock value was calculated for the start and end of each month. 2.1.3. Lamb slaughter pattern and carcass pricing Lamb slaughter pattern was determined based on lamb live weight using the drafting sub model. Lamb daily growth rates for the first two

months of life were determined by ewe milk yield; growth rates thereafter were predicted using data from Irish literature (Flanaghan, 1999; Keady and Hanrahan, 2013). The carcass weight and kill out percentage varied by month and age. The optimal carcass weight increased from 19 kg to 21 kg and kill out percentage decreasing from 50% to 43% between month three and ten of the lambs' life (Earle et al., 2016). Drafting weight for slaughter was based on a target carcass weight and kill out percentage which were defined within the TLPM; the number of lambs slaughtered monthly was determined by the drafting sub model. Real farm data over three years from a Teagasc research flock (Creighton, 2014b) along with historical carcass prices from Bord Bia (Bord Bia, 2014) were used to calculate monthly carcass prices, which included bonuses, or penalties for all conformation and fat classes.

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2.1.4. Energy demand Net energy (NE) requirement was the central component of the model, with grass growth the largest supplier of energy to the flock; however in times of energy deficit additional grazing was rented in or concentrates were introduced to meet flock energy requirements when required. Net energy requirements were calculated for each month, and each animal category, for maintenance, growth, change in body condition, and where applicable pregnancy and lactation (O'Mara, 1996). Unité fourragère du lait (UFL) was the unit of energy used in the NE system, with one UFL equating to one kg of air dried barley (Jarrige, 1989). Total flock NE requirement was portioned into grazed grass, grass silage, or concentrate depending on time of the year and stage of production (described below). The NE requirement of the flock for maintenance, growth, body condition change, pregnancy and lactation were calculated based on equations developed by O'Mara (1996). Energy required for maintenance: h
i UFL=d ¼ 0:033  LW0:75 x AA where UFL/d = UFL required per day, LW = live weight and AA = activity allowance (10% increased energy demand when at pasture). The NE requirement for lamb growth varied by age; as the age of the lamb increases the efficiency of the lamb in converting energy to weight gain decreases due to the greater deposition of fat (Table 1; Rattray et al., 1973). Rattray et al. (1973) expressed net energy requirements using kilocalories (kcals), for the purpose of this study these figures were converted to UFL with one UFL equating to 1700 kcal (Jarrige, 1989). The ewe's body condition is assumed to change across the production year depending on stage of production: pregnancy, lactation and dry period requirements. In month three of pregnancy the ewe loses 0.2 of a body condition score (BCS) unit; she then loses 0.15 of a BCS unit in months four and five of pregnancy. The ewe is lactating for four months and loses 0.4, 0.3, 0.2 and 0.1 of a BCS unit in each subsequent month. During the dry period, after weaning, the ewe regains her BCS again over four months at a rate of 0.4 BCS in the first month, 0.5 BCS in the second and third months and 0.1 in the fourth month (Teagasc, 2003). Energy required for BCS gain: UFL=d ¼ ððLW  0:13Þ  5:6Þ  CBCS where LW = Live weight and CBCS = Change in BCS. Energy received from BCS Loss: UFL=d ¼ ððLW  0:13Þ  4:36Þ  CBCS where LW = Live weight and CBCS = Change in BCS. Energy required for milk production (McDonald et al., 2011): UFL=d ¼ MY  ðð0:0071  PCÞ þ ð0:0043  FCÞ þ 0:2224Þ where MY = Milk Yield, PC = Protein content and FC = Fat content. Energy required for pregnancy: Table 1 The energy requirement for growth (UFL/kg) for various life stages (Rattray et al., 1973). Animal life stage

Energy requirement

Lamb (one month old) Lamb (two months of age) Lamb (three months of age) Lamb (four months of age to slaughter) Ewe lambs Hoggets

1.40 2.12 2.71 3.42 2.60 2.60

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Net energy requirement for pregnancy varied by stage of pregnancy and number of foetuses and was only quantifiable in the final two months of pregnancy (O'Mara, 1996). The equation used to calculate requirement for the fourth month of pregnancy:

 UFL=d ¼ 0:0107 þ −0:0062  PS2 þ ð0:0613  PSÞ þ 0:0107 The equation used to calculate the energy requirement for the fifth month of pregnancy was:

 UFL=d ¼ 0:0139 þ −0:0588  PS2 þ ð0:4163  PSÞ þ 0:0139 where PS = flock average pregnancy scanning rate. 2.1.5. Feed demand and grass supply Feed demand varied by: time of year, stage of production and lambing date with the demand being met by grazed grass, grass silage and/or concentrates. The average monthly grass growth figures were derived from 10 years of grass growth data from Teagasc Moorepark (Teagasc, 2015); these values were used to derive minimum, average and maximum monthly grass growth values, included in the model as a stochastic variable. The ewe flock was housed for the winter and at lambing and was allocated a diet of grass silage. The high energy demand for foetal development and colostrum production resulted in all breeding ewes being supplemented with concentrates pre-lambing. Post-lambing, it was assumed that adequate grass supply was available and the ewe's diet consisted of grazed grass only; lambs did not require concentrate supplementation. As forage quality changes the intake and intake capacity of the animal will also change. An intake capacity component was built into the TLPM to ensure the amount of forage consumed was biologically possible; this check used the fill values of different types of feed such as grass (of varying qualities), silage and concentrate to ensure that the biological maximum intake capacity of each feedstuff was not exceeded. If the energy requirement of the animal could not be met because maximum intake capacity was reached, concentrates were introduced into the diet as an energy dense feed source. Animal numbers and performance were maintained throughout the year irrespective of grass growth. If grass growth did not meet flock demand, additional land area was rented for short term grazing to fill the deficit. Similarly if grass growth was greater than flock demand, excess land was rented out for grazing. The area closed for first and second cut silage (ratio 3:2 respectively) was based on flock silage demand. Silage yields, preservation losses and DMD values were based on Irish data (O'Kiely, 2014). The fertiliser application and costs for silage production (including contractor, additives, and polythene covers) were based on the calculated silage area. Fertiliser, lime and reseeding levels, as well as, grass production were dependent on flock grass demand. Nitrogen (N), Phosphorus (P) and Potassium (K) requirements were calculated using the Teagasc fertiliser calculator (Teagasc, 2011a) which accounts for grass growth and total land area assigned for silage production. At maximum grass production levels (14.8 t DM/ha; Teagasc, 2015), the calculated N, P and K application rates were 215, 13 and 41 kg/ha, respectively. The NE values of concentrate and grass silage were 0.94 UFL/kg DM (Kavanagh, 2011), and 0.75 UFL/kg DM (McCarty et al., 2011), respectively. The UFL value of grass varied throughout the year, ranging from 0.92 UFL/kg DM in the winter months to 1.00 UFL/kg DM in the spring and early summer (O'Neill et al., 2013). 2.1.6. Land and capital Land improvements and farm buildings were depreciated at 4% per annum using the straight line method and farm machinery was depreciated using the reducing balance method at 8% per annum (Malcolm

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et al., 2005). Farm buildings and static machinery input values were calculated based on the assumption that the buildings and machinery were in the 12th year of their working life. A 15 year bank loan at a nominal interest rate of 5% was used to fund the cost of the buildings and land improvements. The bank term loan was assumed to be in its 7th year and the interest was considered a finance expense. 2.1.7. Labour Labour requirements for Irish sheep farms have been described previously (Connolly, 2000b) and were proportioned into seven main categories: lambing (19%), veterinary (9%), feeding and forage (17%), herding (17%), overhead activities (19%), marketing (5%) and miscellaneous (14%). The number of labour hours required per ewe varied depending on flock size, with larger flocks requiring less labour input per ewe (Connolly, 2000b). One labour unit is equivalent to 1800 h worked on the farm (Hanrahan et al., 2013). In the TLPM it was assumed that an owner/operator worked up to 300 h a month during busy periods (lambing), any additional labour required was included as hired labour. The cost of owner/operator and hired labour was set at €10 per hour. 2.1.8. Animal health and welfare Health and veterinary costs varied by animal category, all animals received routine vaccinations for clostridia diseases and pneumonia, as well as, routine worm, fluke, vitamins and minerals drenches. All animals also incurred a cost for foot bathing, fly-strike preventative pour on, dipping and shearing. Animals also received group specific treatments such as tagging lambs, vaccination of hoggets against chlamydia (enzootic) and toxoplasma abortion, pregnancy scanning of ewes and synchronising early lambing ewes. The costs of health and veterinary inputs were calculated based on a survey of 70 Irish commercial farms (Varley, 2014). 2.1.9. Farm sales There were two main sources of income in the TLPM livestock and wool sales. Livestock sales included slaughtered lambs and cull animals. The price received per kilogram of lamb carcass was calculated based on industry data for the years 2004 to 2014 (Bord Bia, 2014). The average monthly price for culled ewe lambs, hoggets, mature ewes and rams was based on historical prices from January 2010 to December 2013 (Carty, pers. comm., 2014). A constant kill out percentage of 40% was assumed for hoggets, mature ewes and rams. Carcass weight was calculated by multiplying the animals draft weight by the kill out percentage. Wool price was based on average prices from 2011 to 2014 based on an assumed fleece weight of 2.75 kg per fleece (Teagasc, 2011b). 2.1.10. Farm costs Variable costs fluctuated by stock numbers and were calculated to a monthly basis. All variable costs (concentrates, fertiliser, reseeding, machinery hire, silage making, veterinary medicine and animal housing costs) were based on Irish industry prices in 2014. Fixed costs (farm vehicle, electricity, telephone, and depreciation) were also based on 2014 Irish industry costs. Two thirds of the total cost associated with vehicle maintenance and operation were included in the farm fixed costs with the other third a personal expense to the farmer (O'Sullivan and O'Neill, 2002). 2.1.11. Outputs Outputs from the model include: annual cash flow budget, profit and loss, balance sheet. Physical outputs such as feed supply and demand, livestock trading schedule and physical ratios were also provided as outputs from the model. Cash flows from the model were summarised monthly and indicated cash surpluses or deficits. The estimated annual farm profit was presented on a total farm basis, as well as, per hectare, per ewe joined, per lamb slaughtered and per kilogram of carcass sold. Farm earnings before interest and tax was calculated as total farm

income less variable and fixed costs (including depreciation). Net profit was calculated as total income less variable costs, fixed costs (including depreciation) and interest. Interest costs were divided into two categories: interest on short term credit and interest on term liabilities. Interest earned on the cash flow in the current account was distinguished from other farm receipts. The assets and the liabilities of the business and net worth were all summarised on the farm balance sheet. The return on investment, current ratio, working capital, debt to asset ratio, equity to asset ratio and debt to equity ratio were also model outputs. 2.2. Model validation The TLPM was validated by comparing simulated model outputs for a mid-season lambing flock to real farm data from the Teagasc e-Profit Monitor (EPM) on 20 commercial Irish sheep farms for the 2013 production year. The 20 EPM validation farms were a subset of 223 midseason EPM farms and were selected by removing farms with incomplete data. Two validation approaches were undertaken; the first validation approach involved simulating the 20 EPM farms using the average input variables such as farm size, ewe numbers, stocking rate and weaning rate from all 20 EPM farms and comparing the simulated physical and economic outputs from the TLPM with the real outputs from the 20 EPM farms. A subsequent validation of the TLPM based on three individual farms from the subset of 20 EPM farms was also undertaken. For this validation approach, the individual input variables from each of the three farms were simulated within the TLPM, the TLPM physical and economic outputs were then compared to the individual farm outputs of each farm. Due to a lack of industry data, the early lambing section of the TLPM was validated by construct, using a steering committee of sheep researchers and industry experts who assessed the inputs and outputs of the early lambing sections, as described as “face validity” by Sargent (2013). 2.3. Model application To demonstrate an application of the TLPM the two main lowland lambing systems in Ireland, early and mid-season lambing (Connolly, 2000a) were compared in the TLPM. The mid-season lambing flock (ML) had a mean lambing date of March 1st and 90% of ewes lambed within a 3 week spread. The early lambing flock (EL) had a mean lambing date of January 1st with all ewes lambing in two weeks. For a valid comparison, the labour requirement per ewe was assumed to be the same in ML and EL, regardless of flock size. The ML and the EL farm scenarios were modelled to represent a land area of 42 ha. A pregnancy scanning rate of 160% with 1.42 lambs weaned per ewe joined were simulated. The replacement rate of 20% was assumed which resulted in a flock composition of 16% ewe lambs, 16% hoggets and 68% mature ewes. Farm size, pregnancy scanning rate, weaning rate and replacement were maintained across the two scenarios. 2.4. Risk analysis Risk was included in the model to account for uncertainty around key variables. The stochastic variables considered as risk variables were: lamb mortality, ewe replacement mortality, ewe mortality, grass growth, fertiliser cost, concentrate costs, lamb price and mutton price. Risk analysis was carried out using “@Risk” (Palisade, 2013) which used Monte Carlo simulation to assign a probability distribution to each risk variable. During Monte Carlo simulation, values are sampled at random from the input probability distributions and the results form an outcome probability distribution (Palisade, 2013). To accurately describe the probability distributions of the outputs using the Monte Carlo simulation approach 10,000 iterations were estimated for each risk variables (Isukapalli et al., 1998). For each variable, a minimum, most likely and maximum figure was generated based on industry data recorded between the years 2004 to 2014; the most likely figure

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was based on the average value for each variable between the years 2012 to 2014. The data for each stochastic variable was sourced from real farm data from the Athenry research farm (lamb mortality, ewe replacement mortality, ewe mortality), Teagasc Moorepark historical data (grass growth), the central statistics office (fertiliser and concentrate costs), Bord Bia (lamb price) and the Irish Farmers Journal (mutton Price). The ranges for each stochastic variable are outlined in Table 2. A Program Evaluation and Review Technique (PERT) distribution was fitted to each stochastic variable (Palisade, 2013).

2.5. Sensitivity analysis In the base farm scenario land was rented in or leased out for short term grazing to deal with surpluses or deficits in grass supply. In order to test the robustness of the model assumptions and to assess the effect of limited land availability on profit, a scenario with limited land was investigated. In this scenario land was limited and no additional grazing was introduced to the system when grass supply was in deficit, instead the energy deficit was met by introducing purchased concentrate, when there was a surplus grass supply, grass was sold in the form of baled silage.

3. Results The outputs of the TLPM for farm performance, livestock inventory, lamb production, feed requirements, land use and labour requirement for each month for the base farm scenario (ML) are presented in Table 3. The average number of breeding ewes, including hoggets, on the farm across the year was 375. A total of 400 ewes (326 ewes and 74 hoggets) were joined to the ram in October, with 382 ewes lambing in March. A total of 22 ewes died and 54 were culled throughout the year and replaced by 76 ewe lambs. A weaning rate of 1.42 lambs weaned per ewe joined was calculated for the base farm scenario, this corresponded to 491 lambs slaughtered, with a further 76 ewe lambs retained as replacements. The average carcass weight was 20.72 kg and lamb price averaged €92.47. Drafting for slaughter of lambs commenced in June and continued until November with 1%, 9%, 45%, 18%, 23% and 4% drafted each month from June to November, respectively (Table 3). The flock required 6800 kg DM/ha of grass, 657 kg DM/ha of grass silage and 359 kg DM/ha concentrates (Table 3), which equated to 87.1%, 8.3% and 4.6% of the total diet, respectively. Total land area required for grass silage was 6.81 ha, and total labour requirement was 1822 h (1.01 labour units, Table 3). The gross margin from the base farm scenario was €23,538, with a net profit of €11,045 with a standard deviation of €2768.

Table 2 Minimum and maximum ranges for the stochastic variables included in the Teagasc Lamb Production Model (TLPM). Stochastic variable

Minimum

Maximum

Lamb mortality (singles) Lamb mortality (twins) Lamb mortality (triplets) Ewe mortality (annual) Hogget mortality (annual) Ewe lamb mortality (annual) Lamb price (€/kg) Mutton price (€/kg) Grass growth (Kg DM/ha/day) Concentrate price (ewes) Concentrate price (lambs) Fertiliser cost (C.A.N) Fertiliser cost (UREA) Fertiliser cost (18-6-12) Fertiliser cost (0-7-30)

5% 6% 7% 3% 3% 0.8% 2.49 (August) 1.48 (October) 2 (February) €197/T €224/T €190/T €252/T €235/T €206/T

13% 16% 22% 7% 8% 2.4% 5.86 (May) 3.30 (March) 122 (May) €341/T €397/T €394/T €454/T €507/T €526/T

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3.1. Model validation The physical inputs and outputs simulated in the model such as: stocking rate, scanning rate, lambing rate, weaning rate and average price received per lamb were similar to those from the average EPM farms (Table 4). The number of lambs slaughtered in the TLPM (490.3) and the 20 EPM farms (489.8) were almost identical. The total concentrates fed and cost of concentrate feeding per ewe were 49 kg and €14.35 lower, respectively, in the TLPM compared to the EPM farm data. The TLPM physical outputs were similar to the average performance of the 20 EPM farms; and were within the range of the 20 EPM farms. The economic performance of the 20 EPM farms and the TLPM are shown in Table 5. Farm receipts, total farm costs and net profit of both the model outputs and 20 EPM farms were similar. There were some differences for other economic parameters such as variable costs and gross margin, but the TLPM outputs were similar to the average of the 20 EPM farms considering the large variation between individual farms. Net profit per hectare was €29 greater for TLPM compared to the average of the 20 EPM farms. This is attributed to the greater variable costs associated with the EPM farms, most notably concentrate and machinery operation costs. Despite these differences, net profit generated by the TLPM was within the range reported for the 20 EPM farms (−€117/ha to €407/ha; Table 5). Similarly, a greater net profit per ewe was reported for the TLPM (+€3.05) compared with the 20 EPM farms. The TLPM also simulated three individual farms from the sub-set of 20 EPM farms and the individual results of the three flocks are outlined in Table 6. The three individual flocks varied in farm size (21 to 52 ha), ewe numbers (170 to 509), stocking rate (8.05 to 10.5 ewes/ha) and weaning rates (1.39 to 1.55). The difference in net profit per hectare between the TLPM and farm 1, 2 and 3 was €7, €35 and €15, respectively (Table 6). As expected, there were some deviations between the TLPM results and the real farm data, with the TLPM assigning higher total receipts to each farm. The individual components of the variable and fixed costs varied across each simulated farm, for example in farm 2 the concentrate costs were €339 higher per hectare than the simulated concentrate costs, however the simulated total variable, total fixed and total farm costs were similar to the corresponding real farm data (Table 6). 3.2. Model application The physical outputs of ML and EL, as well as, the difference between the two systems are shown in Table 7. For comparison purposes farm size, pregnancy scanning rate, lambing percentage, weaning rate and replacement were held constant in both scenarios. The stocking rate for ML was 9.46 ewes/ha, which equated to pasture production of 8.56 t DM/ha/yr. (58% of the maximum grass potential in the TLPM). Applications of N, P and K were 72, 12 and 32 kg/ha, respectively. The EL farm scenario had the same farm area (42 ha) and the same level of pasture production (8.56 t DM/ha/yr.) but had a higher stocking rate of 13.23 ewes/ha. Applications of N, P and K were 72, 13 and 41 kg/ha, respectively. The EL flock had a lower proportion of grazed grass (−25%) in the diet and higher proportion of concentrate (+ 24%) in the flock's diet compared with ML. The lower grazed grass requirement per ewe in EL resulted in the increased stocking rate of 13.23 ewes/ha compared to 9.46 ewes/ha in ML which corresponded to more ewes mated and in turn more lambs slaughtered. Total farm receipts were €51,460 (€1220/ha) and €79,817 (€1894/ha) for ML and EL, respectively. The resulting net profit for ML and EL were €11,045 (€263/ha) and -€4862 (−€114/ha), respectively (Table 8). The return on investment (ROI) was 0.95% for ML and −0.70% for EL. When owner/operator labour was included, the ROI was reduced to −0.38% for ML and − 2.46% for EL. Lamb sales accounted for 88% and 89% of the farm receipts for ML and EL, respectively, with the remaining farm receipts coming from cull (8% and 7%) and wool sales (4% and 4%).

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Table 3 Physical inputs and outputs of the Teagasc Lamb Production Model (TLPM) for the base farm scenario. Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Total

9.3

9.1

8.9

8.8

8.7

8.7

8.7

8.20

7.7

9.5

9.4

9.4

–

392 0 318 74 75 0

384 0 310 73 75 0

377 382 305 72 75 605

371 0 300 71 75 570

368 0 298 70 75 569

368 0 298 70 75 566

367 0 297 70 75 541

346 0 279 67 74 408

324 0 261 64 74 216

400 0 325 74 75 77

399 0 325 74 75 10

399 0 325 74 75 0

– – – – – –

Mortality & culling Mature ewes died Mature ewes culled Hoggets Died Hoggets culled Lambs died

0.4 12 0.1 0 0

3 0 0.9 0 0

7 0 1.8 0 69

3 0 0.9 0 1

0.4 0 0.1 0 1

0.4 0 0.1 0 1

0.4 0 0.1 0 1

0.4 36 0.1 6 1

0.4 0 0.1 0 0

0.4 0 0.1 0 0

0.4 0 0.1 0 0

0.4 0 0.1 0 0

17 48 5 6 74

Sales and purchases Mature ewes sold Hoggets sold Lambs sold

12 0 0

0 0 0

0 0 0

0 0 0

0 0 0

0 0 4

0 0 44

36 6 220

0 0 88

0 0 114

0 0 20

0 0 0

48 6 491

Lamb production Lamb numbers (MTH start) Lamb slaughter pattern Lamb numbers (MTH end) Lamb carcass value (c/kg) Lamb carcass value (€)

0 0% 0 454 94

0 0% 0 470 97

640 0% 570 503 104

570 0% 570 545 113

570 0% 569 542 112

569 1% 564 511 106

564 9% 519 478 99

519 45% 298 444 92

298 18% 134 452 94

134 23% 20 432 89

20 4% 0 446 92

0 0% 0 457 95

– – – – –

Feed requirements Grass demand, T DM/Flock Silage demand, T DM/flock Conc. demand, T DM/flock Total demand, T DM/flock

0 11 3 14

0 10 8 17

27 0 3 30

33 0 0 33

35 0 0 35

36 0 0 36

45 0 0 45

40 0 0 41

31 0 0 31

20 0 1 21

15 0 0 16

5 7 0 12

289 28 15 332

Land use (ha) Area for grazing Area for silage Grass utilized kg DM/ha

42 0 49

42 0 72

42 0 294

38 4 1042

38 4 1248

38 4 948

42 0 1161

39 3 1019

39 3 745

42 0 410

42 0 151

42 0 43

– 7 7184

Labour requirement (Hrs) Flock requirement Hired labour

129 0

126 0

471 171

121 0

121 0

120 0

120 0

113 0

106 0

132 0

132 0

131 0

1822 171

Stocking rate (ewes/ha) Flock inventory Total breeding ewes Total sheep lambing Mature ewes Hoggets Ewe lambs Lambs

The proportion of total costs assigned to variable, fixed and depreciation costs for the ML system were 72%, 22% and 6%, respectively. The corresponding proportions for EL were 86%, 11% and 3%, respectively. Owner/operator labour and management cost was €16,507 (€41.29/ ewe) for ML and €21,873 (€39.15/ewe) for EL. When owner/operator labour was included in the farm costs, net profit was -€5462 for ML and -€26,735 for EL. The total labour requirement for ML was 1822 including 171 h of hired labour. Total labour requirement for EL was 2539 h including 352 h of hired labour.

Table 4 Physical performance of the Teagasc Lamb Production Model (TLPM) and the average (standard deviation in parentheses), minimum and maximum of 20 Teagasc e-Profit Monitor (EPM) farms. Performance variable

TLPM

Hectares (ha) Stocking rate (ewe/ha) Ewes Joined to the ram Ewes lambed per ewe joined Lambs weaned per ewe joined Total lambs slaughtered Average lamb price (€/lamb) Concentrate usage (kg/ewe) Concentrate costs (€/ewe)

42 9.46 400 95% 1.42 490 92.47 38 11.35

EPM Average (±SD)

Minimum

Maximum

42 (28.5) 9.46 (2) 400 (308) 95% (3%) 1.42 (0.19) 489 (390) 96.55 (5.43) 87 (62) 25.70 (18.51)

15 5 130 90% 1.03 153 83.84 13 3.88

131 12.4 1471 98% 1.67 1897 103 249 73.99

3.3. Risk analysis Results for the risk analysis showed that mean net profit excluding owner/operator labour for ML was €11,045 (standard deviation €2768). In comparison, the mean net profit for EL was −€4861 (standard deviation €5844, Fig. 2). The 90% confidence interval (5% to 95%) of the mean net profit ranged from €6061 to €15,102 for ML and −€14,828 to €4711 for EL (Fig. 2), indicating there is a greater spread in profit in EL, and thus greater risk. Results from regression analysis showed that the three variables that explained the greatest variation in net profit for the ML scenario were lamb price in August (r2 = 0.55), lamb price in September (r2 = 0.21), and grass growth in July (r2 = 0.20). The three variables that explained the greatest variation in net profit for the EL scenario were lamb price in May (r2 = 0.45), lamb concentrate price (r2 = −0.45), and lamb price in June (r2 = 0.34).

3.4. Sensitivity analysis In the sensitivity analysis completed in this paper, the energy demand of the flock and the grass production from the base farm area were very similar, indicating that the land limiting scenario had a minimal effect on the financial outcome of the farm as the amount of concentrate purchased or baled silage sold was very low compared to the other feed sources.

A. Bohan et al. / Agricultural Systems 148 (2016) 124–134 Table 5 Economic performance of the Teagasc Lamb Production Model (TLPM) and the mean (standard deviation in parentheses), minimum and maximum of 20 Teagasc e-Profit Monitor (EPM) farms. TLPM € per hectare

EPM Average (±SD)

Minimum Maximum

Receipts Lambs Culls Wool Total receipts

1075 98 47 1220

1114 (296) 158 (181) 30 (13) 1303 (598)

500 – – 452

1608 769 56 2548

Variable costs Concentrates Straw Fertiliser Lime Reseeding Short term grazing Livestock purchases Dead animal disposal Machinery hire Silage making Vet & medicine Carcass processing levies Machinery (repairs & maint.) Other variable costs Total variable costs Gross margin

107 42 159 20 29 -0.25 21 11 25 50 140 12 45 – 661 559

243 (174) 30 (25) 135 (73) 12 (22) −(–) −(–) 67 (87) −(–) 60 (42) 14 (27) 87 (48) 7 (10) 125 (134) 31 (70) 811 (266) 491 (198)

36 – 40 – – – – – – – 12 – – – 219 167

735 103 280 73 – – 297 – 218 89 229 24 509 257 1226 835

85 30 41 33 23 43 – 255 916 304 0.95% -0.38% -0.54 42 263

40 (25) 22 (29) 27 (61) 32 (32) 27 (66) 33 (22) 62 (87) 245 (244) 1057 (467) 246 (83) −(–) −(–) 3 (5) 9 (18) 234 (170)

– – – – – – – 39 259 -63 – – – – −117

171 72 135 207 189 85 338 880 2030 595 – – 17 58 407

−128

−(–)

–

–

Fixed costs Car use Electricity & phone Hire labour Farm insurance Buildings depreciation Machinery depreciation Other fixed costs Total fixed costs Total farm costs Earnings before interest & tax Return on investment ROI (incl. owner labour) Net interest on O/D Interest on term loan Net profit (pre-tax excl. owner labour) Net profit (pre-tax incl. owner labour)

4. Discussion 4.1. Model assumptions The base farm scenario input assumptions in the TLPM are representative of the predominant production system of sheep farming in

Table 6 Physical and economic performance of the Teagasc Lamb Production Model (TLPM) and three of the Teagasc e-Profit Monitor (EPM) farms. € per hectare

TLPM

Farm 1

TLPM

Farm 2

TLPM

Farm 3

Farm size (ha) Ewes joined to the ram Stocking rate (ewes/ha) Lambs weaned/ewe joined Total receipts Total variable costs Gross margin Total fixed costs Total farm costs Net profit (pre-tax excl. owner labour)

21 170 8.1 1.55 1292 734 558 327 1061 232

21 170 8.1 1.55 1059 500 559 334 834 225

52 509 9.8 1.53 1557 829 727 242 1072 438

52 509 9.8 1.53 1516 866 650 247 1113 403

30.4 320 10.5 1.39 1547 872 675 328 1200 295

30.4 320 10.5 1.39 1329 720 609 329 1049 280

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Table 7 Comparison of physical details for the mid-season lambing (ML) and early lambing (EL) systems including farm size, animal numbers, animal performance and feed requirements. Physical details

ML

EL

Difference

Farm size (ha) Stocking rate (ewes/ha) No. of ewes (annual average) No. of ewes joined to the ram No. of ewes lambing Percentage of ewes lambing (of ewes joined) Pregnancy scanning rate (lambs per ewe joined) Weaning rate (lambs per ewe joined) No. of lambs slaughtered Replacements retained Average lamb price (€) Grass DM (% of diet) Silage DM (% of diet) Conc. DM (% of diet) Total labour (hours)

42 9.46 375 400 382 95% 1.60 1.42 491 76 92.47 87% 8% 4% 1822

42 13.23 523 559 533 95% 1.60 1.42 686 106 104.19 62% 9% 28% 2546

0 +3.77 +148 +159 +151 0 0 0 +195 +30 +11.72 −25% +1% +24% +724

Ireland (Connolly, 2000a), with a mean lambing date in March and lambs drafted from grass only diets from July onwards. Of the 279 sheep farmers that completed the 2013 EPM, 223 (80%) were mid-season lambing (Teagasc, 2014). This production system ensures that the resumption of spring grass growth coincidences with lambing date and peak feed demand coincides with high grass growth rates. This results in a large proportion of the flock diet being composed of grazed grass which is a high energy, low cost feed (Finnernan et al., 2010).

Table 8 Trading profit and loss accounts for mid-season lambing (ML) and early lambing (EL) on a net profit per hectare (€/ha) and net profit per ewe joined to the ram (€/ewe) basis. ML (€/ha)

EL (€/ha)

ML (€/ewe)

EL (€/ewe)

Receipts Wool Lamb Culls Total farm receipts

47 1075 97 1220

66 1697 132 1894

5 113 10 129

5 128 10 143

Variable costs Concentrates Straw Fertiliser Lime Reseeding Short term grazing rental Livestock purchases Dead animal disposals Machinery hire Silage making Vet & medicine Carcass processing levies Machinery (R&M) Total variable costs Gross margin

107 42 159 20 29 0 21 11 25 50 140 12 45 661 559

986 36 186 20 29 0 29 15 22 77 179 16 45 1639 255

11 4 17 2 3 0 2 1 3 5 15 1 5 70 59

74 3 14 2 2 0 2 1 2 6 14 1 3 124 19

85 30 41 33 23 43 255 916 304 42 263

85 42 85 40 23 43 318 1957 −63 51 −114

9 3 4 5 3 2 27 97 32 4 28

6 3 6 3 3 2 24 148 −5 4 −9

−129

−634

−14

−48

Fixed costs Car use Electricity & phone Hired labour Farm insurance Buildings depreciation Machinery depreciation Total fixed costs Total farm costs Earnings before interest and tax Interest term loan Net profit (pre-tax excl. owner labour) Net profit (Pre-tax incl. owner labour)

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Fig. 2. Box and whisker plot of the mean net profit, 5th and 95th percentile, as well as, the interquartile ranges of ML compared to EL.

The farm size, stocking rate and number of lambs weaned per ewe joined were simulated based on the average of the EPM farms used in the model validation. This is equivalent of producing 13.3 lambs/ha which is low compared to the UK (18.3 lambs/ha), but is similar to that in New Zealand (12.7 lambs/ha; Connolly, 1999).

4.2. Comparison of models A number of alternative modelling approaches could have been undertaken in the designing of the TLPM. The simulation modelling approach was chosen because it allows flexibility in the system and this is crucial for the TLPM as it will be used as an experimental incubator to answer many research questions in the future. Simulation modelling allows system changes to be tested without actually implementing them on farm and also allows the simulation of systems that do not exist in practise, as well as, allowing the study of long term effects of system changes as demonstrated by Tocker et al. (2013). The use of a bioeconomic modelling approach allowed for the cost effective analysis of system changes as well as policy change or technical innovations. Another use of bio-economic modelling is pre-experimental analysis to predict outcomes and likely benefits from experimental trials Jackson et al., 2014) and this will be a future use of the TLPM. Other systems analysis approaches such as liner programming (Crosson et al., 2006, Butler, 2006; McCall and Clark, 1999) have been used to identify the optimal economic performance of a farm system under certain constraints such as animal nutritional requirements. The linear programming option was not chosen to develop the TLPM as the TLPM needed more flexibility in its system analysis, including stochastic budgeting which can be easily incorporated into simulation models. Although the TLPM is not an optimisation model at present if future uses required the optimisation approach the model could be adjusted. A bio-economic approach has been used previously to derive economic values for breeding traits such as ewe mature weight, lamb mortality and carcass quality for sheep systems in the UK and Ireland (Conington et al., 2004; Byrne et al., 2010). These models differed from the current model as the previous systems were modelled on a trait by trait basis rather than using the whole farm approach. The TLPM allows for the interactions between different components of the model when assessing net profit and is therefore more accurate in assessing a wide range of system changes on the overall farm profitability in a whole farm system compared to using a partial budgeting approach.

The development of the bio-physical component of the TLPM allowed for relevant Irish data to be used to derive the main assumptions in the model. A similar bio-physical approach was undertaken in Australia by Jackson et al. (2014), although energy demand was represented in that study on a daily basis. Many Australian models (Young et al., 2010; Tocker et al., 2013) used the GrassGro® tool to simulate the bio-physical aspects of an Australian sheep production system. GrassGro® has been validated and used extensively for Australian conditions; however this approach was not suitable for Ireland as the climatic conditions and grass growth patterns differ drastically between both countries. Model inputs such as ewe live weight, lamb growth rates, lamb mortality along with the general management of the flock differ dramatically in Irish and Australian flocks indicating that the best approach was to develop the biophysical aspect of the TLPM specifically for an Irish sheep flock using accurate Irish farm data rather than adapting the GrassGro® programme for an Irish sheep farm. An Irish dairy (Shalloo et al., 2004a) and beef (Crosson et al., 2006) whole farm system model were developed using the bio-physical approach, and similar to the TLPM used the NE system for the calculation of energy demand. Previous sheep models have used the metabolisable energy (ME) system (White et al., 1983; Conington et al., 2004; Tocker et al., 2013) rather than the NE system to calculate energy demand. The NE system as described by Jarrige (1989) is used extensively in Ireland to calculate energy demands as it is considered the more appropriate measurement of feed value and energy requirement for the Irish production systems. A previous study has shown that the ME system can over-estimate the energy value of poor quality feed relative to high quality feed (Kavanagh and Murphy, 2000). The current model, as well as, the previous Irish dairy and beef bioeconomic models (Shalloo et al., 2004a; Crosson et al., 2006) have used a single year steady-state approach; this is in contrast to the approach of Australian models (Tocker et al., 2013; Jackson et al., 2014) where the effect of system changes to the farm system were assessed over a period of time. Such studies have examined alternative systems of production for the farm, such as a change in the initial farm enterprise, in favour of another enterprise and would therefore require several years to reach a steady state. Depending on the analysis undertaken the Irish dairy model has been used to assess investment appraisal over time with different time horizons evaluated depending on the investment (Shalloo et al., 2004b; Hutchinson et al., 2012, 2013; McDonald et al., 2013). Similar to the current study, the Irish dairy and beef models (Shalloo et al., 2004a; Crosson et al., 2006) have mainly focused on comparing changes to the farm system once the change has been implemented fully. The TLPM was designed to investigate farm system changes, such as stocking rate, rather than the study of alternative systems of production, such as change of enterprise, and for this reason it was decided to use the steady state approach however the TLPM could be adjusted to assess the effect of system changes over multiple years if required. 4.3. Model validation Model validation is the major component of describing the utility of any model (Jansen, 1997). For a model to be validated and fit for purpose, the outcomes of the model should closely reflect reality (Gutierrez-Aleman et al., 1986). Previous studies have taken a similar approach to the validation of the ML scenario in the TLPM and used real farm data to validate their models (Shalloo et al., 2004a; Tocker et al., 2013). Crosson et al. (2006) used the ‘validation by construct’ provisions of McCarl and Apland (1986). Due to a lack of industry data the EL scenario was validated using the “validation by construct” or ‘face validity’ method as described by Sargent (2013). Validation of the TLPM showed that the model provides a realistic representation of the average of the 20 EPM farm data. In addition the validation of three individual farms of contrasting size and production system also showed that the TLPM can provide a realistic representation of individual flocks.

A. Bohan et al. / Agricultural Systems 148 (2016) 124–134

Some outputs, such as concentrate costs per hectare differed when simulated in the TLPM and the average of the 20 EPM data as well as on the three individual flocks used for validation. Concentrate cost per hectare was the output with the greatest variation across the 20 EPM farms and between the three individual flocks used in validation, varying from €75/ha in farm 1 to €473/ha in farm 2. The variation in concentrate usage is reflective of the differing management practices and concentrate feeding levels within the industry, with an over reliance on concentrate feed and an underutilization of grazed grass across some flocks. The greater concentrate usage in the EPM farms may be due to the assumption, within the TLPM, that adequate grass supply is available post-lambing and that lambs do not require concentrate supplementation; in practice, this will vary depending on weather conditions, grass quality and pasture management. The modelled concentrate use in the TLPM is based on data from the Athenry research farm and this level of concentrate supplementation has been verified by a steering committee of industry experts. The difference in the reported net profit for the TLPM and the average of the 20 EPM data is reflective of the higher variable costs (€150/ha) associated with the EPM data relative to the TLPM. The average price received per lamb in the TLPM was lower (€92.47) than the EPM farms (€96.55) and may be a result of the underlying assumptions of lamb price. The TLPM lamb prices in the TLPM were based on the average price recorded between 2004 and 2014 whereas the lamb price in the EPM were prices received in 2013 and were relatively high compared to previous years. In addition the average carcass in the TLPM was 20.72 kg and the EPM farms may have achieved slightly heavier carcass weights. The TLPM provided an accurate simulation of the three individual farms, with simular net profit figures for each of the three farms. The TLPM predicted notably higher total receipts figures for both farm 1 (€1292/ha v €1059/ha) and farm 3 (€1547/ha v €1329/ha), this may be due to the assumption that all lambs are sold for slaughter in the TLPM commanding a high price per lamb compared to some farms selling light lambs as stores at a reduced price. Although the simulated fixed costs were similar to the fixed costs on the three individual farms, there was some variation within the variable costs. This may be due to differing management practices on each farm with the TLPM predicting higher veterinary costs for each farm as well as variation of concentrate and fertiliser costs between the TLPM and each farm. If more intricate management details were available for each farm a more optimal parameterisation of the TLPM could have been undertaken, however, such data was not collected as part of the EPM programme. 4.4. Model application The greater net profit achieved by the mid-season lambing flock (ML) compared to early lambing flock (EL) was not surprising given the greater feed costs per ewe associated with the EL (+ €60) compared to the ML system. While EL generated additional lambs and a greater average price per lamb (+€11.72), which equated to an extra €32,859 in lamb receipts, this additional income did not offset the greater feed costs associated with EL. The lower profit for EL was as a result of the greater proportion of grass silage (+1%) and concentrate (+24%) fed to the flock compared to the ML flocks and in turn resulted in greater concentrate, fertiliser and silage costs. This suggests that the greater the proportion of grazed grass in the diet (87% in ML compared to 62% in EL), the lower the production cost, which is reflected in greater net profits for sheep production systems in Ireland. This result corroborates findings from a recent Irish study which estimated the cost of producing grazed grass as €76/t DM compared to €152/t DM for silage and €325/t DM for concentrates (Finnernan et al., 2010). In the analysis of whole farm profitability, the return on investment (ROI) of all scenarios must also be considered as it is the indicator of the economic efficiency of the farm. Greater capital investment such as land, buildings, machinery and stock were associated with EL, which resulted

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in the total fixed and livestock assets of €1,033,097 compared with €999,042 for ML. This resulted in an ROI of 0.95% for ML and − 0.70% for EL. When owner/operator labour costs were included, ROI was − 0.38% for ML and − 2.46% for EL. The negative ROI indicates that when owner/operator labour is included as a cost, the farm does not make a profit under either scenario and that the operator should be looking at alternative opportunities to make a return for their resources. For EL systems to generated the same level of profit as ML an additional €23.18/lamb for early lamb price. 4.5. Risk analysis The risk analysis in this study was undertaken using Monte Carlo simulations and is heavily reliant on the quality of the simulation inputs (Petersen, 2000). In the current study six risk variables were chosen (lamb price, mutton price, lamb, replacement and ewe mortality, grass growth, fertiliser cost and concentrate cost) due to their variability over time and their direct impact on annual profit. As the minimum and maximum value for each variable was available from historic data, a Program Evaluation and Review Technique (PERT) distribution was chosen as the best fit for each variable. This distribution is similar to a triangular distribution as it has three parameters but is superior to a triangular distribution for data with a skewed distribution, as the smooth shape of the curve places less emphasis in the direction of skewness (Palisade, 2013). By defining distributions for key parameters instead of single values, distributions were generated for the outputs of interest (e.g. net profit). The net profit results from the risk analysis showed that at the 90% confidence intervals, there was greater probability of making profit in ML in comparison to EL (Fig. 2). The greater risk associated with EL may be partially attributed to the consistently higher costs associated with the system. The risk associated with concentrate price has a greater impact on the EL compared to the ML because of the greater proportion of concentrate in the diet (28% vs. 4%). The cost of production of grazed grass, which accounted for majority of the mid-season flock's diet, had a small variation in the risk analysis. The risk analysis showed that early lambing is a high risk, low reward system and that the gain in income from increased carcass value does not justify the high production costs. The results of the regression analysis showed that lamb price in August, lamb price in September and grass growth in July were the three stochastic variables with the greatest effect on profit in the ML scenario; this is reflective of the grass based system with the majority of lambs being drafted for slaughter in late summer/early autumn. In the EL the three stochastic variables with the greatest effect on profit were lamb price in May, lamb concentrate price and lamb price in June; this is not unexpected for the EL scenario with many lambs drafted for slaughter in May and June. The EL scenario had a large proportion of concentrate in the diet of the lamb and is not surprising that lamb concentrate price had such an effect on net profit. 5. Conclusions The TLPM, a stochastic bio-economic model of an Irish sheep farm, can be used to simulate the effect of changes, such as lambing date, stocking rate or prolificacy, to a production system and assess the implications for farm profit. Validation of the TLPM showed that the model provided a realistic representation of the performance of a typical Irish sheep farm and could be used with confidence to assess the effects of different scenarios on the outputs and overall profitability of the farm. In this study, an application of the TLPM showed that date of lambing had a significant effect on farm profit. Early lambing flocks sold more lambs for greater prices but were less profitable compared to mid-season lambing flocks because of the increased variable costs, most notably feed costs. Future uses of the TLPM include the calculating economic breeding values to aid in the genetic evaluations of the Irish sheep flock, as well as, providing a means of adding an economic component to other systems research.

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