Benefits of using production factors in assessing farm risk: a simulation on the role of irrigation data

Risk assessment at the farm level is pivotal to developing appropriate managerial choices. This is specifically needed to identify and use the appropriate mix of available risk management tools. Among these is the appropriate choice of the level of use of inputs such as irrigation water and chemicals. Several pieces of information and methods are used for this scope. However, the type and amount of data available often represent a constraint to improving the analyses. In economic research, the input use is generally not measured in terms of quantity but in terms of costs farmers pay for these categories of inputs due to the heterogeneous nature of the inputs. The inputs generally considered are irrigation water, fertilizers, chemicals, labour and other inputs. The purpose of the analysis is to assess whether shifting from using the costs of water only to also using the physical amount of water changes the assessment of farm risk and, finally, whether this can improve the quality of the assessment. This paper resorts to an econometric approach that allows assessing the role of variable inputs, as well as other farm characteristics, on the risk profile of farms. The latter is described according to the moments of the deviation of observed income from predicted incomes. However, because data on the amount of irrigation water is provided, the model is estimated two times: one using the cost of water only and another using data in quantity (m3). The preliminary results of the analysis suggest that including data on the physical amount of water used for irrigation does indeed change the outcome of the risk assessment generating some improvements, at least in the case study. These results, even if referred to only irrigation water, suggest that the availability of quantitative data could improve risk assessment. Therefore, it is possible to argue that the application of new technologies that allow measuring input use at the individual farm level, such as those related to the Internet of Things (IoT) and, more in general, to Agriculture 4.0, could indeed ameliorate the results of similar analyses.