A dynamic programming model for optimising feeding and slaughter decisions regarding fattening pigs

Costs of purchasing new piglets and of feeding them until slaughter are the main variable expenditures in pig fattening. They both depend on slaughter intensity, the nature of feeding patterns and the technological constraints of pig fattening, such as genotype. Therefore, it is of interest to examine the effect of production technology and changes in input and output prices on feeding and slaughter decisions. This study examines the problem by using a dynamic programming model that links genetic characteristics of a pig to feeding decisions and the timing of slaughter and takes into account how these jointly affect the quality-adjusted value of a carcass. The model simulates the growth mechanism of a pig under optional feeding and slaughter patterns and then solves the optimal feeding and slaughter decisions recursively. The state of nature and the genotype of a pig are known in the analysis. The main contribution of this study is the dynamic approach that explicitly takes into account carcass quality while simultaneously optimising feeding and slaughter decisions. The method maximises the internal rate of return to the capacity unit. Hence, the results can have vital impact on competitiveness of pig production, which is known to be quite capital-intensive. The results suggest that producer can significantly benefit from improvements in the pig's genotype, because they improve efficiency of pig production. The annual benefits from obtaining pigs of improved genotype can be more than €20 per capacity unit. The annual net benefits of animal breeding to pig farms can also be considerable. Animals of improved genotype can reach optimal slaughter maturity quicker and produce leaner meat than animals of poor genotype. In order to fully utilise the benefits of animal breeding, the producer must adjust feeding and slaughter patterns on the basis of genotype. The results suggest that the producer can benefit from flexible feeding technology. The flexible feeding technology segregates pigs into groups according to their weight, carcass leanness, genotype and sex and thereafter optimises feeding and slaughter decisions separately for these groups. Typically, such a technology provides incentives to feed piglets with protein-rich feed such that the genetic potential to produce leaner meat is fully utilised. When the pig approaches slaughter maturity, the share of protein-rich feed in the diet gradually decreases and the amount of energy-rich feed increases. Generally, the optimal slaughter weight is within the weight range that pays the highest price per kilogram of pig meat. The optimal feeding pattern and the optimal timing of slaughter depend on price ratios. Particularly, an increase in the price of pig meat provides incentives to increase the growth rates up to the pig's biological maximum by increasing the amount of energy in the feed. Price changes and changes in slaughter premium can also have large income effects. Key words: barley, carcass composition, dynamic programming, feeding, genotypes, lean, pig fattening, precision agriculture, productivity, slaughter weight, soybeans

The success of the diversified farm : resource-based view

Farms and rural areas have many specific valuable resources that can be used to create non-agricultural products and services. Most of the research regarding on-farm diversification has hitherto concentrated on business start-up or farm survival strategies. Resource allocation and also financial success have not been the primary focus of investigations as yet. In this study these specific topics were investigated i.e. resource allocation and also the financial success of diversified farms from a farm management perspective. The key question addressed in this dissertation, is how tangible and intangible resources of the diversified farm affect the financial success. This study’s theoretical background deals with resource-based theory, and also certain themes of the theory of learning organisation and other decision-making theories. Two datasets were utilised in this study. First, data were collected by postal survey in 2001 (n = 663). Second, data were collected in a follow-up survey in 2006 (n = 439). Data were analysed using multivariate data analyses and path analyses. The study results reveal that, diversified farms performed differently. Success and resources were linked. Professional and management skills affected other resources, and hence directly or indirectly influenced success per se. In the light of empirical analyses of this study, tangible and intangible resources owned by the diversified farm impacted on its financial success. The findings of this study underline the importance of skills and networks for entrepreneur(s). Practically speaking all respondents of this study used either agricultural resources for non-farm businesses or non-farm resources for agricultural enterprises. To share resources in this way was seen as a pragmatic opportunity recognised by farmers. One of the downsides of diversification might be the phenomenon of over-diversification, which can be defined as the situation in which a farm diversifies beyond its optimal limit. The empirical findings of this study reveal that capital and labour resource constrains did have adverse effects on financial success. The evidence indicates that farms that were capital and labour resource constrained in 2001 were still less profitable than their ‘no problems’ counterparts five years later.