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Syncronizing plant available water with soil nitrogen (N) remains a critical aspect of agronomic management to enhance crop yield, grain quality, farmers' profit, and environmental sustainability. Their interaction is essential expecially in landscapes characterized by a highly spatial and temporal range of pedoclimatic conditions. To support farmers in making more informed decisions, validate dynamic process based crop simulation models have been successfully used to optimize N fertlization. In this study, we aimed to develop a tactical N fertilizer management strategy to increase profitability, improve grain quality and reduce N losses. The SALUS model was tested against measured durum wheat grain yield and grain quality data collected across independent farmers' fields in Tuscany (Italy). The model was then used to optimize N fertilization under different potential plant extractable soil water (PESW) conditions at the second topdressing N fertilization timing. The model was tested against measurements of grain yield and protein concentration at harvest, as well as phenological stage, biomass, and plant N content during the growing season. Simulations were carried out for 30 years of available weather, using different N rates. The simulations allowed the identification of optimal N rates for each PESW condition and soil type concerning economic and environmental sustainability. Results showed higher yield and higher leaching for the silty clay soil (Quercia; QUE) than for the loamy soil (Arbia; ARB). No major differences were predicted for protein content across soils. Profitability and emissions increased as N rate increased. The N fertilization strategy locally adopted by farmers was also analyzed across different PESW conditions at 2nd topdressing fertilization in comparison to other adopted N management strategies (timing of application and fertilization rates). The model showed that the conventional fertilization strategy does not maximize socio-ecological benefits, but was also the lowest among all the strategies tested. The maximum economic benefit for farmers was reached by applying 90 kg N at 1st and 60 kg N at 2nd topdressing fertilization, both in dry (due to sufficient soil water storage in the soil) and wet years. These results provide valuable insights for developing strategies that balance sustainability, resilience, quality, and profitability in wheat crop production in central Italy. • Use of SALUS model to optimize nitrogen (N) fertilization. • Simulation of different potential plant extractable soil water (PESW) conditions. • Simulations were carried out for 30 years, using different N rates. • Identification of N fertilization strategy that maximizes economic returns. [ABSTRACT FROM AUTHOR]

Crop simulation models have the potential to assess the risk associated with the selection of a specific N fertilizer rate, by integrating the effects of soil-crop interactions on crop growth under different pedo-climatic and management conditions. The objective of this study was to simulate the environmental and economic impact (nitrate leaching and N2O emissions) of a spatially variable N fertilizer application in an irrigated maize field in Italy. The validated SALUS model was run with 5 nitrogen rates scenarios, 50, 100, 150, 200, and 250kgNha−1, with the latter being the N fertilization adopted by the farmer. The long-term (25 years) simulations were performed on two previously identified spatially and temporally stable zones, a high yielding and low yielding zone. The simulation results showed that N fertilizer rate can be reduced without affecting yield and net return. The marginal net return was on average higher for the high yield zone, with values ranging from 1550 to 2650€ha−1 for the 200 N and 1485 to 2875€ha−1 for the 250 N. N leaching varied between 16.4 and 19.3kgNha−1 for the 200 N and the 250 N in the high yield zone. In the low yield zone, the 250 N had a significantly higher N leaching. N2O emissions varied between 0.28kgN2Oha−1 for the 50kgNha−1 rate to a maximum of 1.41kgN2Oha−1 for the 250kgNha−1 rate. [ABSTRACT FROM AUTHOR]

An objective for applying a Crop Simulation Model (CSM) in precision agriculture is to explain the spatial variability of crop performance and to help guide decisions related to the site-specific management of crop inputs. CSMs require inputs related to soil, climate, management, and crop genetic information to simulate crop yield. In practice, however, measuring these inputs at the desired high spatial resolution is prohibitively expensive. We propose a Bayesian modeling framework that melds a CSM with sparse data from a yield monitoring system to deliver location specific posterior predicted distributions of yield and associated unobserved spatially varying CSM parameter inputs. These products facilitate exploration of process-based explanations for yield variability. The proposed Bayesian melding model consists of a systemic component representing output from the physical model and a residual spatial process that compensates for the bias in the physical model. The spatially varying inputs to the systemic component arise from a multivariate Gaussian process, while the residual component is modeled using a univariate Gaussian process. Due to the large number of observed locations in the motivating dataset, we seek dimension reduction using low-rank predictive processes to ease the computational burden. The proposed model is illustrated using the Crop Environment Resources Synthesis (CERES)-Wheat CSM and wheat yield data collected in Foggia, Italy. [ABSTRACT FROM AUTHOR]

Abstract: The integration of site-specific management principles and conservation tillage practices is a rather unexploited field of research despite their economical and environmental benefits. The objectives of this research were: (1) to investigate the farm economic net return of three conservation tillage practices (NIT – Non-Inversion Tillage, MT – Minimum Tillage and NT – No-Tillage) performed at variable intensity within predefined management zones; the HS – area with a consistently higher yield and LS – area with a consistently low yield, of a maize (Zea mays, L.) field in NE-Italy (2) to identify the most economically sound tillage practice for each management zone using long-term simulation results; (3) to assess the environmental impact of the three tillage systems with regards to soil organic carbon changes, CO2 losses and nitrate leaching using the SALUS model. Field trials were carried out on an 8-ha flat field, situated near Rovigo, NE Italy, on maize (Z. mays, L.). The farm gross margin was higher for NT in the year of study as well as the long-term simulated scenarios that resulted in higher yields over time. The NT tillage practices resulted in higher economic return in the both the HS and LS areas. Total soil carbon was higher in NT due to the crop residues retained on the surface. Nitrate leaching was higher in for the MT and for the LS area. [Copyright &y& Elsevier]