Cucci G., Lacolla G., Mastro M. A., Caranfa G. (2016): Leaching effect of rainfall on soil under four-year saline water irrigation. Soil & Water Res., In the context of the overall competition for water resources it is important to understand the complex dynamics of crop water management including evapotranspiration, water quality, and leaching requirement, each of them depending on the site-specific conditions. The research started with grain maize and continued with sunflower, grain maize, and wheat, at the experimental field. On both grain maize and sunflower, 10 irrigation treatments were compared that resulted from the factorial combination of two types of water (fresh and brackish water) with five irrigation regimes; the scheduled treatments were applied by furrow irrigation. The amount of salts brought into the soil with the irrigation water during the three irrigation seasons of our trial increased shifting from the lowest to the highest irrigation regime and with the increase of salinity in the irrigation water. From the study of salt distribution in the soil it follows that at the end of the irrigation season the salt concentration increased by passing from the middle of the furrow, a zone more subject to leaching during irrigation, to the intermediate zone between the furrow and the ridge, and in the middle of the ridge between two contiguous furrows, an area of confluence of the wetting and salt accumulation fronts. The leaching water supplied during the irrigation season was poorly efficient in leaching the salts brought in through irrigation, whereas the rainfall water of the autumn-winter period after the irrigation season ensured a good control of soil salinity.

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Hyperspectral (HS) data represents an extremely powerful means for rapidly detecting crop stress and then aiding in the rational management of natural resources in agriculture. However, large volume of data poses a challenge for data processing and extracting crucial information. Multivariate statistical techniques can play a key role in the analysis of HS data, as they may allow to both eliminate redundant information and identify synthetic indices which maximize differences among levels of stress. In this paper we propose an integrated approach, based on the combined use of Principal Component Analysis (PCA) and Canonical Discriminant Analysis (CDA), to investigate HS plant response and discriminate plant status. The approach was preliminary evaluated on a data set collected on durum wheat plants grown under different nitrogen (N) stress levels. Hyperspectral measurements were performed at anthesis through a high resolution field spectroradiometer, ASD FieldSpec HandHeld, covering the 325-1075 nm region. Reflectance data were first restricted to the interval 510-1000 nm and then divided into five bands of the electromagnetic spectrum [green: 510-580 nm; yellow: 581-630 nm; red: 631-690 nm; red-edge: 705-770 nm; near-infrared (NIR): 771-1000 nm]. PCA was applied to each spectral interval. CDA was performed on the extracted components to identify the factors maximizing the differences among plants fertilised with increasing N rates. Within the intervals of green, yellow and red only the first principal component (PC) had an eigenvalue greater than 1 and explained more than 95% of total variance; within the ranges of red-edge and NIR, the first two PCs had an eigenvalue higher than 1. Two canonical variables explained cumulatively more than 81% of total variance and the first was able to discriminate wheat plants differently fertilised, as confirmed also by the significant correlation with aboveground biomass and grain yield parameters. The combined approach proved to be effective, being able to synthesise the redundant radiometric information in a reduced number of indicators of plant nutritional status, which could be utilized to delineate homogeneous within-field areas to be submitted to site-specific fertilization

The knowledge of soil water retention vs. soil water matric potential is applied to study irrigation and drainage scheduling, soil water storage capacity (plant available water), solute movement, plant growth and water stress. To measure field capacity and wilting point is expensive, laborious and is time consuming, so, frequently, matemathic models, called pedo-transfer functions (PTFs) are utilized to estimate field capacity and wilting point through physical-chemical soil characteristics. Six PTFs have been evaluated (Gupta and Larson, 1979;Rawls et al., 1982;De Jong et al., 1983;Rawls and Brakensiek, 1985;Saxton et al., 1986;Vereecken et al., 1989) by comparing measured soil moisture values with estimated ones at soil water matric potential of -33 and -1500 kPa. Soil samples were collected (361)

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