Abstract: The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i) seven crops in rotations, ii) across various pedo-climatic and agro-management conditions in Europe, under both continuous simulation and single year simulation, and for iv) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat (Triticum aestivum L.), winter barley (Hordewn vulgare L.) and spring barley (Hordeum vulgare L.) compared to spring oat (Avena saliva L.), winter rye (Secale cereale L.), pea (Piswn sativum L.) and winter oilseed rape (Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism.

Abstract: A field experiment was conducted in Southern Italy to evaluate the effects of different water quality and fertilizers on yield performance of tomato crop. In mineral nitrogen (N) fertilizer and irrigation with fresh water (Electrical Conductivity, EC, = 0.9 dS m⁻¹) (FWF); mineral N fertilizer and irrigation with saline water (EC = 6.0 dS m⁻¹) (SWF); municipal solid waste (MSW) compost and irrigation with fresh water (EC = 0.9 dS m⁻¹) (FWC); MSW compost and irrigation with saline water (EC = 6.0 dS m⁻¹) (SWC). At harvest, weight and number of fruits and refractometric index (°Brix) were measured, total and marketable yield and dry matter of fruit were calculated. The results indicated that MSW compost, applied as amendment, could substitute the mineral fertilizer. In fact, in the treatments based on compost application, the tomato average marketable yield increased by 9% compared with treatments with mineral fertilizer. The marketable yield in the SWF and SWC treatments (with an average soil EC in two years to about 3.5 dS m⁻¹) decreased respectively of 20 and 10%, in respect to fresh water treatments. At the end of the experiment, application of compost significantly decreased the sodium absorption rate (SAR) of SWC treatment in respect of SWF (−29.9%). Significant differences were observed among the four treatments both on soil solution cations either exchangeable cations. In particular compost application increased the calcium (Ca) and potassium (K) contents in saturated soil paste respect to the SWF ones (31.4% and 59.5%, respectively). At the same time saturated soil paste sodium (Na) in SWC treatment recorded a decrease of 17.4% compared to SWF.

Abstract: Salinization is one of the most serious problems confronting sustainable agriculture in semi-arid and arid regions. Accurate mapping of soil salinization and the associated risk represent a fundamental step in planning agricultural and remediation activities. Geostatistical analysis is very useful for soil quality assessment because it makes it possible to determine the spatial relationships between selected variables and to produce synthetic maps of spatial variation. The main objective of this paper was to map the soil salinization risk in the Delia-Nivolelli alluvial basin (south-western Sicily, southern Italy), using multivariate geostatistical techniques and a set of topographical, physical and soil hydraulic properties. Elevation data were collected from existing topographic maps and analysed preliminarily to improve the estimate precision of sparsely sampled primary variables. For interpolation multi-collocated cokriging was applied to the dataset, including textural and hydraulic properties and electrical conductivity measurements carried out on 128 collected soil samples, using elevation data as auxiliary variable. Spatial dependence among elevation and physical soil properties was explored with factorial kriging analysis (FKA) that could isolate and display the sources of variation acting at different spatial scales. FKA isolated significant regionalised factors which give a concise description of the complex soil physical variability at the different selected spatial scales. These factors mapped, allowed the delineation of zones at different salinisation risk to be managed separately to control and prevent salinization risk. The proposed methodology could be a valid support for land use and soil remediation planning at regional scale.