Performance analysis at enterprise/ production branch level

Transcription

1 Federal Department of Economic Affairs, Education and Research EAER Agroscope Performance analysis at enterprise/ production branch level Markus Lips 19 th Meeting of the OECD Network for Farm-Level Analysis Paris, May 22, I good food, healthy environment

2 Starting point (1/2) Most Swiss farms have several enterprises (production branches or activities, e.g. milk production or wheat) Average: 5.7 enterprises per farm Typical combinations: Milk production and arable corps Milk production and pig fattening The gross margin (earnings minus direct costs) is normally applied to assess profitability at the enterprise level. There is only a limited academic debate about economic analyses at enterprise level. 2

3 Starting point (2/2) As public institution Agroscope analyses research questions submitted by stakeholder such as farm managers, farmers unions, farm consultants, fiduciaries and civil servants at national and province level. Stakeholders are strongly interested in the performance at enterprise level: Profitability is hard to assess based on gross margins: Risk of disorientation of farm managers. Demand for total production costs, full costing Goal of Agroscope s working agenda: To show the performance of all relevant enterprises of Swiss agriculture. 3

4 Performance indicator Family farms are predominant in Swiss agriculture. The farm manager s family earns the agricultural income to cover the remuneration of the family own labour forces and capital. Given the current interest rate the remuneration of labour is 15 times higher than the remuneration of capital (Lips & Gazzarin, 2016). Remuneration of labour is expressed as wage rate per hour, assuming 10 working hours per Normal Working Day (NWD). Remuneration per working hour serves as performance indicator. 4

5 FADN as data base Swiss Farm Accountancy Data Network (FADN) is hosted by Agroscope Unbalanced panel with up to 3000 farm observations per year 10 farm types (e.g. mixed dairy and arable crop farms) Sales and direct costs are available at enterprise level Indirect cost items are available at farm level Labour is reported in Normal Working Days (NWD) Use of opportunity costs for family own factors labour (CHF 27.- per hour), capital and land 5

6 Data at enterprise level Revenue positions/ cost items + Earnings - Full costs = Profit/Loss + Labour costs Sold products Direct payments Direct costs Seed Purchased feed Data availability Other direct costs Land Land Indirect or joint costs = Total Remuneration of labour / Labour input (in hours) = Remuneration of labour per hour Labour Machinery Buildings Other joint costs 6

7 Allocation (1/2) Allocation of direct payments (especially cross compliance) to enterprises by means of allocation factors such as number of hectares and livestock units Allocation of indirect or joint costs (labour, machinery, buildings) to enterprises by means allocation factors (indirect costing) Standard costs serve as allocation factors (e.g. machinery costs per hectare of wheat). To achieve standard costs is challenging but possible. Standard costs represent costs in a comprehensive manner. Calculation of alpha, the relation of actual costs of the farm and the sum of standard costs of all enterprises. In a proportional allocation all allocation factors are multiplied by alpha. 7

8 Allocation (2/2) Allocation of indirect costs represents a knowledge gap For the allocation a maximum entropy model is applied, providing a disproportionate allocation. Large allocation factors face a stronger adjustment than small ones. On the field, enterprises with large allocation factors have more possibilities for adjusting standard costs (e.g. plant protection for wheat and potatoes). Standard costs are differentiated by size of the enterprise (e.g. 20 milking cows versus 40 milking cows). For each farm and each indirect cost item: The allocation factors are chosen from the standard cost data base. A specific maximum entropy model is formulated and solved. 8

9 Resulting costs per hectare (β i ) 45 Proportional adjustment (αlpha<1) P prop P ME W ME Maximum entropy adjustment W prop Wheat Lips, 2014 & 2016 Potatoes Standard costs (μ i ) 9

10 Descriptive statistics Two enterprises: Milk production (including also roughage and breeding) Wheat Mixed dairy and arable crop farm type in the plain region Years Subdivision of the two samples according to the remuneration of labour: Best performing group (best quarter) Weak performing group (least quarter) Mann-Whitney-U Test to compare the two groups 10

11 Milk production in CHF per livestock unit Costshare (%) Weak perform. group Well perform. group All observations [652] Wellweak P-value Total revenues *** Sales of milk *** Direct payments Total costs *** Total direct costs *** Concentrates Veterinary & insemination *** Land Total indirect cost *** Labour costs *** Machinery costs *** Buildings *** Other indirect costs *** Calculated profit/loss *** Remuneration of labour [CHF/h] Milk yield [kg/cow/year] *** Producer price [CHF/kg] *** CHF = 1 US$; Hoop et al. forthcoming 11

12 Wheat in CHF per hectare Costshare (%) Weak perform. group Well perform. group All observations [638] Wellweak P-value Total revenues *** Sales *** Direct payments ** Total costs *** Total direct costs * Pesticides *** Land Total indirect cost *** Labour costs *** Machinery costs *** Buildings etc *** Calculated profit/loss *** Remuneration of labour [CHF/h] Yield [t/ha] *** Producer price [CHF/t] *** CHF = 1 US$; Hoop et al. forthcoming 12

13 Overview of enterprises performance Enterprise Share of labour input in % Remuneration of labour CHF/hour Wheat 3 48 Rape seed 1 78 Sugar beets Potatoes 4 38 Milk production 91 9 Whole farm Results of enterprise analyses (working time per livestock unit/ hectare and remuneration of labour) Average size of enterprises of mixed dairy and arable crop farms of Swiss FADN are used to show the situation at the farm level. 13

14 Analysis of heterogeneity Performance (labour remuneration) differs heavily among farms Explaining remuneration of labour at enterprise level by means of random-effects models for each enterprise separately Crop enterprises: wheat, rape seed and potatoes Mixed dairy and arable crop farm type in the plain region Years Unbalanced panels 14

15 Random-effects models for the remuneration of labour in CHF/hectare Variables Wheat Rape seed Potatoes Arable land, ha 1.72 *** 1.84 * 2.49** Arable land squared, ha² ** Share of direct payments in total revenue, % *** Dummy for other livestock farming activities ** Dummy if farm manager works off-farm 3.84 ** ** Share of agricultural income in total income, % 0.14 *** * Leverage, % Share of hired labour, % * -0.30*** -0.28*** Leased land share, % ** Dummy if tenant farmer ** Share of contracting in total machinery cost, % 0.23 *** ** Dummy for young farmer (< lower tercile) * Dummy for older farmer (> upper tercile) * Extenso dummy 7.60 *** 5.98 n.a. Number of crop enterprises *** Number of farm-related business activities Dummy for higher farm education Dummy for seed production 7.80 ** n.a ** Constant *** *** ** Number of observations R² overall CHF = 1 US$; Zorn et al

16 Conclusions Analyses of the performance at enterprise level by means of descriptive statistics and random-effects models Indirect costs dominate the cost structure (e.g. 75% for milk production and 61% for wheat). Accordingly, the gross margin analysis is of limited relevance. Within a mixed dairy and arable crop farm labour remuneration of enterprises differs substantially. The comparison of well and weak performing groups gives a quantitative indication of the management influence (e.g. milk yield). Determinants of performance differ among enterprises. Performance analyses at enterprise level provide important information for farm managers and also agricultural policy makers. 16

17 References Hoop, D., Spörri, M., Zorn, A., Gazzarin, C. & Lips, M., forthcoming. Wirtschaftlichkeitsrechnungen auf Betriebszweigebene, Agroscope Science, Ettenhausen. Lips, M., Calculating full costs for Swiss dairy farms in the mountain region using a maximum entropy approach for joint-cost allocation, International Journal of Agricultural Management, 3(3): Lips, M., Disproportionate Allocation of Indirect Costs at Individual-Farm Level Using Maximum Entropy, working paper, Agroscope, Ettenhausen. Lips, M., & Gazzarin, C., Die finanziellen Auswirkungen von Investitionen im Vorfeld abschätzen, Agrarforschung Schweiz, 7(3): Zorn, A., Hoop, D., Gazzarin, C. & Lips, M Erfolgsfaktoren im Ackerbau, Jahrestagung der Schweizerischen Gesellschaft für Agrarökonomie und Agrarsoziologie, März, Chur. 17

18 Markus Lips, Dr. Head research group farm management Agroscope Tänikon 8356 Ettenhausen Switzerland Tel