Development of Strategic Consultancy to Farm Managers: Experience from an Action Research Approach

A Danish action research approach to the development of strategic consultancy to farm managers is presented. The development principles adopted include separate investigations of the content and process of strategic consultancy resulting in the formulation of a development matrix and a procedure for knowledge transformations. The project activities were carried out by a self-organised team group with participants from both consultancy and research organisations. The produced knowledge and strategic tools have been tested in a number of farm cases by local consultants and the implemented evaluation programme indicates that the needs of farmers have been fulfilled and the local consultants have increased their strategic competences. Key words: Action research, strategic consultancy, process and content, development matrix, self-organisation, knowledge transformations, complex learning and consultancy processes.Farm Management,

Reversing the road to super farms

The organization of primary agriculture is dependent upon whether the institutions of a country allow for reverse franchising by farmers. If the transaction costs of managing a farm can be minimized by farmers conducting a form of collective action, such as cooperatives, then the size of farms will be smaller. If farms have to make the products in the firm, which are subject to very large economies of scale than super farms will be the result. The key is the existence of institutions, such as collective action and property rights, that allow for the minimization of costs. For this reason the organization of primary agriculture is, among other things, a public policy issue. In this paper we develop this argument, we sketch a theoretical framework based on a model of adaptive relational contracts, and we present two illustrative examples: the Danish cooperative system, and the Canadian Wheat Board.Agribusiness, Farm Management,

Agricultural Risk Management - Experiences from an Action Research Approach

A new model for risk management in agriculture is described in the paper. The risk model is constructed as a context dependent process, which includes four main phases. The model is aimed at agricultural advisors, who wish to facilitate and disseminate risk management to farmers. It is developed and tested by an action research approach in an attempt to make risk management more applicable on family farms. Our obtained experiences indicate that farmers don't apply probabilistic thinking and other concepts according to formal decision theory.Risk management, consulting, action research, farm families, Risk and Uncertainty,

Blocking Gibbs sampling in the mixed inheritance model using graph theory

International audienc

Genomic prediction when some animals are not genotyped

<p>Abstract</p> <p>Background</p> <p>The use of genomic selection in breeding programs may increase the rate of genetic improvement, reduce the generation time, and provide higher accuracy of estimated breeding values (EBVs). A number of different methods have been developed for genomic prediction of breeding values, but many of them assume that all animals have been genotyped. In practice, not all animals are genotyped, and the methods have to be adapted to this situation.</p> <p>Results</p> <p>In this paper we provide an extension of a linear mixed model method for genomic prediction to the situation with non-genotyped animals. The model specifies that a breeding value is the sum of a genomic and a polygenic genetic random effect, where genomic genetic random effects are correlated with a genomic relationship matrix constructed from markers and the polygenic genetic random effects are correlated with the usual relationship matrix. The extension of the model to non-genotyped animals is made by using the pedigree to derive an extension of the genomic relationship matrix to non-genotyped animals. As a result, in the extended model the estimated breeding values are obtained by blending the information used to compute traditional EBVs and the information used to compute purely genomic EBVs. Parameters in the model are estimated using average information REML and estimated breeding values are best linear unbiased predictions (BLUPs). The method is illustrated using a simulated data set.</p> <p>Conclusions</p> <p>The extension of the method to non-genotyped animals presented in this paper makes it possible to integrate all the genomic, pedigree and phenotype information into a one-step procedure for genomic prediction. Such a one-step procedure results in more accurate estimated breeding values and has the potential to become the standard tool for genomic prediction of breeding values in future practical evaluations in pig and cattle breeding.</p