Mapping management zones in a sandy pasture soil using an objective model and multivariate techniques

• Published in: Precision agriculture Vol. 22; no. 3; pp. 800 - 817
• Main Authors: Moral, F. J., Rebollo, F. J., Serrano, J. M., Carvajal, F.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2021; Springer Nature B.V
• Subjects: Agriculture; Arid zones; Atmospheric Sciences; Biomedical and Life Sciences; Cation exchange; Cation exchanging; Chemistry and Earth Sciences; Clay; Clay soils; Clustering; Computer Science; Crop yield; Electrical conductivity; Electrical resistivity; Fertilizer application; Fertilizers; Identification methods; Life Sciences; Normalized difference vegetative index; Organic matter; Organic phosphorus; Organic soils; Pasture; Phosphorus; Physics; Rasch model; Remote Sensing/Photogrammetry; Sampling; Sandy soils; Soil fertility; Soil properties; Soil Science & Conservation; Statistics for Engineering; Vegetation; Vegetation index; Rasch model; Pasture; Soil fertility; Homogeneous zones
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-020-09756-w

Soils occupied by dryland pastures usually have low fertility but can exhibit a high spatial variability. Consequently, logical application of fertilisers should be based on an appropriate knowledge of spatial variability of the main soil properties that can affect pasture yield and quality. Delineation of zones with similar soil fertility is necessary to implement site-specific management, reinforcing the interest of methods to identify these homogeneous zones. Thus, the formulation of the objective Rasch model constitutes a new approach in pasture fields. A case study was performed in a pasture field located in a montado (agrosilvopastoral) ecosystem. Measurements of some soil properties (texture, organic matter, nitrogen, phosphorus, potassium, cation exchange capacity and soil apparent electrical conductivity) at 24 sampling locations were integrated in the Rasch model. A classification of all sampling locations according to pasture soil fertility was established. Moreover, the influence of each soil property on the soil fertility was highlighted, with the clay content the most influential property in this sandy soil. Then, a clustering process was undertaken to delimit the homogeneous zones, considering soil pasture fertility, elevation and slope as the input layers. Three zones were delineated and vegetation indices (normalized difference vegetation index, NDVI, and normalized difference water index, NDWI) and pasture yield data at sampling locations were employed to check their differences. Results showed that vegetation indices were not suitable to detect the spatial variability between zones. However, differences in pasture yield and quality were evident, besides some key soil properties, such as clay content and organic matter.