『Abstract
Agro-ecological indicators are simple conceptual models to carry
out agro-environmental assessments. Their use requires data that
need to be obtained at low cost, frequently avoiding direct measurements.
The quality of input data thus may limit the usefulness of the
indicators. One source of uncertainty is the spatial interpolation
of inputs, used to provide indicator values throughout an area.
This paper explores the uncertainty of the inputs, and its effect
on the output of one indicator, the phosphorus indicator (IP).
The indicator evaluates the appropriateness of phosphorus (P)
use by farmers, assigning bad scores to over- and under-fertilization.
We evaluated its use for P management in the Sud Milano Agricultural
Park (northern Italy). We used data contained in a large database
of soil and farm properties as well as crop management information
at the cadastral parcel level to calculate IP values. The uncertainty
of a single input variable (extractable soil P) was tested to
quantify the corresponding uncertainty of the indicator. The results
show that the variability of IP is high and within 80％ of the
analyzed area excessive applications of P fertilizers are made,
in particular in animal farms. In most cases, uncertainty is not
relevant, as it is either very low, or (if high) it is related
to extremely low indicator values: in these cases, the assessment
of P management is unaffected by the uncertainty of the indicator.
The results show that in this area P fertilizers should be applied
at lower doses, or not applied at all. An extension service might
help farmers with fertilizer management, reducing resource use,
environmental pollution and costs. This study shows that uncertainty
analysis is a crucial component of environmental assessments,
and that the importance of uncertain input data needs to be evaluated
on a case-by-case basis.
Keywords: Soil fertility; Fertilizers; Animal manure; Crop management;
Spatial interpolation; Database; Kriging』
1. Introduction
2. Materials and methods
2.1. The phosphorus indicator
2.2. Study area and database
2.3. Spatial interpolation procedures
2.4. Deterministic and stochastic calculation of the indicator
3. Results
3.1. Deterministic versus stochastic evaluation
3.2. Variability of IP among parcels, crop types, rotation types
and farm types
3.3. Uncertainty of IP
4. Discussion
5. Conclusions
Acknowledgments
References