Weighted Goal Programming and Penalty Functions: Whole-farm Planning Approach Under Risk

The paper presents multiple criteria approach to deal with risk in farmer’s decisions. Decision making process is organised in a framework of spreadsheet tool. It is supported by deterministic and stochastic mathematical programming techniques applying optimisation concept. Decision making process is conceptually divided into seven autonomous modules that are mutually linked up. Beside the common maximisation of expected income through linear programming it enables also reconstruction of current production practice. Income risk modelling is based on portfolio theory resting on expected value, variance (E,V) paradigm. Modules dealing with risk are therefore supported with quadratic and constrained quadratic programming. Non-parametric approach is utilised to estimate decision maker’s risk attitude. It is measured with coefficient of risk aversion, needed to maximise certainty equivalent for analysed farms. Multiple criteria paradigm is based on goal programming approach. In contribution focus is put on benefits and possible drawbacks of supporting weighted goal programming with penalty functions. Application of the tool is illustrated with three dairy farm cases. Obtained results confirm advantage of utilizing penalty function system. Beside greater positiveness it proves as useful approach for fine tuning of the model enabling imitation of farmer’s behaviour, which is due to his/her conservative nature not perfect or rational. Results confirm hypothesis that single criteria decision making, based on maximisation of expected income, might be biased and does not necessary lead to the best - achievable option for analysed farm.goal programming, risk modelling, risk aversion, production planning, Risk and Uncertainty,

Change in Farm Production Structure Within Different CAP Schemes – an LP Modelling Approach

After accession to European Union in 2004 direct payments became very important income source also for farmers in Slovenia. But agricultural policy in place at accession changed significantly in year 2007 as result of CAP reform implementation. The objective of this study was to evaluate decision making impacts of direct payments scheme implemented with the reform: regional or more likely hybrid scheme. The change in farm production structure was simulated with model, applying gross margin maximisation, based on static linear programming approach. The model has been developed in a spreadsheet framework in MS Excel platform. A hypothetical farm has been chosen to analyse different scenarios and specializations. Focus of the analysis was on cattle sector, since it is expected that decoupling is going to have significant influence on its optimal production structure. The reason is high level of direct payments that could in pre-reform scheme rise up to 70 % of total gross margin. Model results confirm that the reform should have unfavourable impacts on cattle farms with intensive production practice. The results show that hybrid scheme has minor negative impacts in all cattle specializations, while regional scheme would be better option for sheep specialized farm. Analysis has also shown growing importance of CAP pillar II payments, among them particularly agri-environmental measures. In all three schemes budgetary payments enable farmers to improve financial results and in both reform schemes they alleviate economic impacts of the CAP reform.CAP reform, optimal farm structure, linear programming

Analysis of indemnification of income risk at sector level: the case of Slovenia

Using Monte Carlo simulations, the impact of diff erent levels of risks on indemnification through an income stabilisation tool is investigated at the sector level. The presented approach, using the IACS database, allows analyses of diff erences across farms with respect to farm type and farm size, applying average-based approaches. Such preliminary information is useful for policy makers responsible for the design and introduction of measures to tackle income risk issues and to identify potential beneficiary groups among farmers. The analysis shows that on average 25 per cent of farms would be indemnified annually, the majority in fruit production, the dairy sector and hop production. Mixed farm types, with a share of 34 per cent, receive only 15 per cent of the total sum of indemnity. However, if EUR 12,000 of average income is set as the threshold for participation in such a tool, only 6 per cent of farms participate and only 13.3 per cent of them would be indemnified. Indemnity at farm level would range between EUR 82 and 40,870. Taking into account all farms in the sector, the average indemnity is EUR 918 per farm and almost EUR 13,500 for the second case

SIMULATION MODEL BASED ON IACS DATA; ALTERNATIVE APPROACH TO ANALYSE SECTORAL INCOME RISK IN AGRICULTURE

We develop a static simulation model to analyse income losses and income risks at aggregated agriculture sector level. Our empirical case study is based on farm level records for direct payments claims (IACS data) and covers the period 2010–2011. Using Monte Carlo simulations, we investigate the impact of different levels of risk on income trends. Results show that 80% of farms are extremely dependent on direct payments. Farm production types highly supported by direct payments consequentially fall into the low-risk group. Results show that a significant share of income loss at sector level is carried by small farms (by economic class). Average probability of larger losses at the sector level ranges between 2% and 64%. Our results also indicate that larger farms often have better risk-return ratios and thus face lower relative income risks

Simulation model for income risk analyses at the sector level, case of Slovenia

This paper presents possible approach how different sources of data at farm level, national statistics and analytical models could be merged in simulation process to analyse income risk at the sector level. Baseline is production structure resumed out of annual subsidy applications as key information per each agricultural holding within the sector. Presented approach utilises potential of random number generator and random distributions of Monte Carlo to roughly reconstruct different sources of risks in different states of nature that may occur with diverse probabilities at the particular farm. In such a manner income situation at sector level is analysed. The developed approach is tested on the 21 farm types further divided into 13 economic classes. Obtained preliminary results suggest that this could be useful approach for rough estimation of income risk and points on some limitations and drawbacks that should be further improved

Fertilization Planning Based on Economic Efficiency

Formulation of fertilization plan is important task in agricultural production, but frequently performed by advisors without necessary information about individual plots and fertilisation practices applied by individual farmers. With increasing concerns for environment and sustainable production it is therefore important that fertilization planning is done by farmers. High fertilizers' prices also urge to their rational application, achievable only on the basis of plans for several years. In the article an electronic tool for fertilization plan formulation is presented. It is developed as a multi-criteria decision making model based on mathematical programming techniques. Hypothetical case illustrates its application as well as strengths and weaknesses of methodology applied

Farm management support based on mathematical programming; an example of fertilization planning

Th is paper presents electronic tool to support farmers in fertilization planning. It is developed as a spreadsheet model, utilizing optimization potential of mathematical programming techniques. Described problem of fertilization planning is rather simple from technological-expert viewpoint, but methodological application of multi-criteria paradigm makes it more complicated. In the paper focus is put on economic effi ciency of fertilization. Hypothetical case illustrates its application as well as strengths and weaknesses of methodology applied

Modelling the economic impacts of bovine viral diarrhoea virus at dairy herd level; the case of Slovenia

In the last decade Bovine viral diarrhoea (BVD) was listed by the World Organisation for Animal Health (OIE) as a notifiable disease, due to the fact that it causes significant production losses in cattle industry worldwide. The production losses include reduced milk production, reduced conception rate, abortions, growth retardation, early culling, increased mortality and an increased occurrence of other diseases. This paper presents a possible approach to how an economic analysis of BVD virus, could be conducted at the herd level. For this purpose a spreadsheet model in MS Excel has been developed utilizing Monte Carlo Simulations (MCS). Simulation results show that economic losses at the heard level could exceed 18,000 €. Obtained results suggest that this could be promising approach for analysis BVD effect at herd or animal level

Weighted Goal Programming and Penalty Functions: Whole-farm Planning Approach Under Risk

The paper presents multiple criteria approach to deal with risk in farmer’s decisions. Decision making process is organised in a framework of spreadsheet tool. It is supported by deterministic and stochastic mathematical programming techniques applying optimisation concept. Decision making process is conceptually divided into seven autonomous modules that are mutually linked up. Beside the common maximisation of expected income through linear programming it enables also reconstruction of current production practice. Income risk modelling is based on portfolio theory resting on expected value, variance (E,V) paradigm. Modules dealing with risk are therefore supported with quadratic and constrained quadratic programming. Non-parametric approach is utilised to estimate decision maker’s risk attitude. It is measured with coefficient of risk aversion, needed to maximise certainty equivalent for analysed farms. Multiple criteria paradigm is based on goal programming approach. In contribution focus is put on benefits and possible drawbacks of supporting weighted goal programming with penalty functions. Application of the tool is illustrated with three dairy farm cases. Obtained results confirm advantage of utilizing penalty function system. Beside greater positiveness it proves as useful approach for fine tuning of the model enabling imitation of farmer’s behaviour, which is due to his/her conservative nature not perfect or rational. Results confirm hypothesis that single criteria decision making, based on maximisation of expected income, might be biased and does not necessary lead to the best - achievable option for analysed farm