Delineation of management zones in an agrosylvopastoral ecosystem based on the Rasch model

Abstract

Pasture soils can exhibit a high spatial variability which should be characterised to properly manage the yield potential of different within-field areas. Thus, with the aim of proposing an objective methodology to estimate the pasture soil fertility and, later, analyse its spatial pattern, the formulation of the probabilistic Rasch model constitutes a new approach in pasture fields. To illustrate the proposed approach, a case study was performed in a Mediterranean evergreen oak woodland, called montado in Portugal and dehesa in Spain, subject to agrosylvopastoral exploitation. Ten soil properties (moisture content, nitrogen, phosphorus, potassium, pH, organic matter, soil apparent electrical conductivity, and sand, silt, and clay content) were measured at 76 locations in the experimental field and, after their integration in the model, a classification of all sampling places according to the pasture soil fertility was determined. Moreover, the influence on the soil fertility of each soil property was highlighted, being soil moisture, clay and sand content, and nitrogen the most influential properties. Later, geostatistical algorithms were utilised to estimate pasture soil fertility across the field and homogeneous zones were delimited from the kriged map. Information for hazard assessment of pasture soil fertility in the field was also provided by probability maps. NDVI data at each sampling location were used to verify the differences between the management zones. Some statistics shown how data reasonably fit the model, so its formulation to estimate a measure of pasture soil fertility was successful. The use of the Rasch model and geostatistical algorithms constitutes a powerful tool to developing an objective strategy to define management zones not only in agricultural fields but also in pasture systems. The analysis of zonal differences in pasture systems can lead to an optimal application of inputs and a more cost-effective management, with the associated environmental, economic, and energetic benefits.