Appraising farm business financial performance indicators.

Abstract

The measurement of financial performance is a challenge to all factions of the
agricultural industry. Knowing what indicators are available and understanding
their interpretation is becoming increasingly important, so businesses can react
appropriately to changing circumstances.
This thesis considers the. indicators of success and failure in farm business
through percentage change in net worth and percentage equity, and their
associated causes. Predicting problematic or sound trends or levels means that
appropriate steps may be taken by the business.
The study describes the existing measures and how are they classified, and looks at how performance varies over time, farm type and region. The main models of financial performance and their ability to explain or predict future trends in terms of success or failure are evaluated and other models constructed. The methodology used by banks to assess repayment capability is also reviewed and challenged. Two hypotheses are addressed: firstly, that future farm performance is determined by measures in each of the classification categories and secondly, that a farm business' pre-rent and finance cash flow surplus is a superior indicator of financial surplus and efficiency than the more commonly used servicing ratio adopted by many financial institutions.
Data in the form of audited accounts were collected from 212 farms over the
period 1996-2001, giving coverage of most of the main farm types in the
different regions of England and Wales. A detailed review categorises the
financial indicators into measures of liquidity, solvency, profitability, financial
efficiency, repayment capacity and other. Models are also reviewed, with
particular regard to the independent variables used and how they have been
constructed to predict aspects of financial stress, credit management, bankruptcy and success. Results consider the data grouped into the resultant six financial areas, the mean for each of the various ratios is considered by year, farm type and region and analysed and significant differences discussed. Statistical models tested the predictors of farm business performance using multiple regression for modelling where the dependent variable was continuous with stepwise regression used to select from the many alternative variables in the six categories. Logistical regression was used to test the study data where the outcome variable is a dichotomy.
Having considered the measures appropriate to the industry and classified them
in financial categories {I-VI), the findings showed distinct links between farm
type and region, and although reflecting the downward trend of the industry
during the period covered by the study indicators were worse than expected with
liquidity, solvency and profitability being below the desired level, financial
efficiency varying substantially between farm types, and repayment capacity
confirming that the industry was under pressure during the period of the study.
The findings show that models considered in the literature review were
statistically inferior to the models developed in the study. The results from the
performance models lead to an acceptance of the hypotheses that percentage
change in net worth is dependent on factor in the categories: liquidity, solvency,
profitability, financial efficiency and repayment capacity, but the relationship is a
weak one. The lesser work undertaken on pre-rent and finance cash flow surplus
had positive findings and has the potential to change the way the financial sector
reviews repayment capacity and financial efficiency.
The thesis concludes that more businesses are at risk than might have been
expected, many aspects of financial performance influence the measures of
success of the business, and data used by some institutions may be inaccurate and therefore could lead to erroneous conclusions being drawn.

Date of Award	Mar 2005
Original language	English
Awarding Institution	University of Wales, Bangor
Sponsors	Harper Adams University, Newport
Supervisor	Geoff Bright (Supervisor)