Environmental time series are often affected by the "presence" of missing data, but when dealing statistically with data, the need to fill in the gaps estimating the missing values must be considered. At present, a large number of statistical techniques are available to achieve this objective; they range from very simple methods, such as using the sample mean, to very sophisticated ones, such as multiple imputation. A brand new methodology for missing data estimation is proposed, which tries to merge the obvious advantages of the simplest techniques (e.g. their vocation to be easily implemented) with the strength of the newest techniques. The proposed method consists in the application of two consecutive stages: once it has been ascertained that a specific monitoring station is affected by missing data, the "most similar" monitoring stations are identified among neighbouring stations on the basis of a suitable similarity coefficient; in the second stage, a regressive method is applied in order to estimate the missing data. In this paper, four different regressive methods are applied and compared, in order to determine which is the most reliable for filling in the gaps, using rainfall data series measured in the Candelaro River Basin located in South Italy.
Soil & Water Management & ConservationLong-term field experiments and multivariate analysis techniques represent research tools that may improve our knowledge on soil physical quality (spq) assessment. These techniques allow us to measure relatively stable soil conditions and to improve soil quality judgment, thereby reducing uncertainties. A monitoring of spq under long-term experiments, aimed at comparing crop residue management strategies (burning vs. incorporation of straw, Fe1) and soil management (minimum tillage vs. no tillage, Fe2), was established during the crop growing season of durum wheat. The relationships between five spq indicators (bulk density [BD], macroporosity [p MAC ], air capacity [AC], plant available water capacity [pAWC], and relative field capacity [rFC]) were evaluated, and two techniques of multivariate analysis (principal component analysis and stepwise discriminant analysis) were applied to select key indicators for spq assessment. According to the used indicators, an spq from optimal to intermediate (i.e., not definitely poor) was detected in 65% of the observations in Fe1 and in 54% in Fe2. The main results showed a significant negative relationship between rFC and AC, and multivariate analysis identified rFC as a key spq indicator, mainly in Fe2. plant available water capacity and BD showed the highest discriminating capability in the Fe1 dataset. The highest scores of rFC assessment were highlighted for burning and minimum tillage treatments (+1 and +2). An optimal AC range, derived from optimal rFC limits, was obtained and was suggested to better assess the AC of agricultural soils (0.10 £ AC £ 0.26 cm 3 cm -3 ).
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