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Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer’s field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term. [ABSTRACT FROM AUTHOR]

Remotely sensed vegetation indices have been extensively used to quantify plant and soil characteristics. The objectives of this study were to: (i) compare vegetation indices developed at different scales for measuring canopy N content (g·N·m-2) and concentration (%); and (ii) evaluate the effects of soil background reflectance, cultivar, illumination and atmospheric conditions on the ability of vegetation indices to estimate canopy N content. Data were collected from two rainfed field sites cropped to wheat in Southern Italy (Foggia) and in Southeastern Australia (Horsham). From spectral readings, 25 vegetation indices were calculated. The Perpendicular Vegetation Index showed the best prediction of plant N concentration (%) (r2 = 0.81; standard error (SE) = 0.41%; p < 0.001). The Canopy Chlorophyll Content Index showed the best predictive capability for canopy N content (g·N·m-2) (r2 = 0.73; SE = 0.603; p < 0.001). Canopy N content was best related to indices developed at the canopy scale and containing a red-edge wavelength. Canopy-scale indices were related to canopy N%, but such relationships needed to be normalized with biomass. Geographical location influenced mainly simple ratio or normalized indices, while indices that contained red-edge wavelengths were more robust and able to estimate canopy parameters more accurately. [ABSTRACT FROM AUTHOR]

Highlights: [•] Spatial and temporal patterns of wheat grain yield were quantified. [•] Optimal amount of N fertilizer is not fixed but varies with the rainfall. [•] N fertilizer needs to be tactically applied and varies over space and years. [ABSTRACT FROM AUTHOR]

Grain protein has recently received attention in several countries because of a premium associated to increased protein concentration (PC) in grains. Nitrogen (N) fertilizer management with its timing of application and rates plays a crucial role in increasing PC. The objectives of the present study were to: (1) quantify the effects of different N fertilizer rates and timing of application on grain yield and grain protein of six durum wheat cultivars, and (2) identify the best cultivar in terms of net return, yield, and protein content in typical pedo-climatic condition of southern Italy. The field experiments were conducted in Foggia during three growing seasons (2002–2003, 2003–2004, and 2004–2005). Results from this study showed that N application at sowing did not influence the cultivars performance. N applied at stem elongation increased yield and protein content provided there is adequate soil water for N uptake. In absolute terms, the best combination was the cultivar Svevo managed using 104 kg N ha−1split between tillering and stem-elongation stage. The excessive N splitting reduces yield and does not provide any improvement in terms of protein content. [ABSTRACT FROM PUBLISHER]

Abstract: Wheat yield production in Mediterranean environment is highly affected by rainfall and amount of soil water stored into the soil before and during the growing season. Agricultural fields have been always considered as uniform entities and managed accordingly. However, uniform agronomic management in fields where spatial variability is present, is economically and environmentally inefficient. The objectives of this study were to: (i) identify spatially and temporally stable areas throughout the field, (ii) understand the influence of fallow and growing season rainfall on spatial and temporal variability of wheat yield. The study was carried out on a 12ha field located in Foggia, Southern Italy during five years wheat monoculture. One hundred geo-referenced points were sampled for deriving spatial maps of soil texture and organic carbon. Spatial maps of grain yield, normalized difference vegetation index (NDVI), soil electrical resistivity tomography (ERT) were collected non-destructively. Total growing season rainfall was correlated with grain yield after dividing it into long fallow (June–November), short fallow (September–November), growing season (December–May), vegetative (December–February), reproductive (March–May). The spatial maps were used to define spatial and temporal yield variability and to identify three stable zones within the field, “low yield stable” (LS), “average yield stable” (AS), “high yield stable” (HS). Long and short fallow rainfall was highly correlated with grain yield of HS zone with correlation coefficients ranging between 0.5 and 1. Growing season rainfall was mostly correlated with the AS zone. The crop response to rainfall was a result of dynamic interaction of spatial static properties such as soil texture, position in the landscape and dynamic properties (soil water content, infiltration and crop water use). [Copyright &y& Elsevier]