Changes in global agriculture are the result of millions of farmers' decision making on what type of product and in which way they produce it. Hence, understanding farmers' decision making means understanding global agriculture.
Farmers not only have to choose between different crops – they can also decide between crop and livestock production. Furthermore, they can use more or less of purchased inputs such as seeds, fertilizers or feedstock.
Finally, they have to take into account rather complex interactions between various production parameters:
Against this background it becomes obvious that an improved and detailed understanding of the status quo and the changes in production and production systems as well as determinants of farmers’ decision making is key for understanding the future of global agricultural production. Forecasting based on previous trends and farmers reactions to market signals will not yield meaningful results.
To address this issue, agricultural economists could (a) run large scale interviews of growers in order to capture farm level reactions to changing framework conditions on a regular basis or (b) collect and monitor extended farm accounting data.
In reality both options are extremely and very often prohibitively expensive and option (b) – as far as many emerging and developing countries are concerned – is frequently not available at all. Therefore the agri benchmark concept of typical farms (more) has been developed as a feasible compromise.
Understanding agricultural production systems and farmers decision making requires an accurate picture of the real farm situation. However, the real situation in an individual case study always contains some particularities. Therefore we use data from a group of farmers who run farms similar to the envisaged typical farm and we include the know-how from an advisor who knows his clients in the region selected. This group of experts we call a “focus group”.
Together with the focus group we identify all relevant details of the regional production systems such as the duration of fattening periods and the feed rations in beef or the types, number and the dates of plant protection applications in crops. Expenditures for variable inputs are added to the information. In crop we also allocate time and diesel consumption to individual operations as well as quantities and prices for inputs used such as fertilizers or seeds (see example for the scheme here).
In order to gain representativeness of the figures we establish typical farms in regions which are of the highest importance for the national output of interest: US corn in Iowa, Brazilian beef in Mato Grosso do Sul, German wheat in the Magdeburger Börde. The decision about the location of the typical farm as well as the decision about the size of the farm and the degree of specialization is derived from an analysis of official statistics – as far as available.
For forward-looking projects we invite members of the focus group for in-depth analysis of strategic issues. We have been using this approach to identify beef producers' options to adopt to a reduction of EU tariff rates and the subsequent reduction in beef prices (see Brüggemann). Another study investigated the options of technology transfer from overseas arable production systems to improve the profitability of arable production on marginal production sites in Germany (see Krug).