Roberta Farina

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Soil organic carbon sequestration and tillage systems in the Mediterranean Basin: a data mining approach

This study has reviewed 66 long-term experimental comparisons on Soil Organic Carbon (SOC) and tillage systems in Mediterranean arable crops (from 15 sites located in Greece, Italy, Morocco and Spain), with the aim to identify the biophysical and agronomic variables most associated with C sequestration rate. Data were organized in a dataset containing basic environmental descriptors (elevation, temperature, rainfall), information on soil tillage system (conventional, minimum, no-tillage), soil attributes (pH, particle size distribution and texture), crop rotation, fertilization, time length of the experiment, initial and final SOC stocks. The collected information were analyzed using a data mining approach including Spearman non-parametric correlations, Principal Component Analysis (PCA), hierarchical clustering and step-wise multiple regression. Tillage, crop rotation, and fertilization were the most significant factors affecting C sequestration rate. Non-parametric correlations reported negative coefficients for initial SOC stock, length of the experiment, mineral fertilization, tillage and production system. C sequestration rate increased significantly under no-tillage. Hierarchical clustering indicates that geographical proximity reflects similarity in biophysical conditions and agronomic practices. PCA outlined a positive correlation of SOC with soil depth, elevation and sites located in Spain and a negative correlation with mean air temperature, mineral fertilization, irrigation, experiment’s length and sites located in Greece. C sequestration rate was positively associated with mean air temperature. Finally, a step-wise multiple regression indicated that C sequestration rate increased in sites exposed to colder climate conditions and under no-tillage.

Nitrogen Release from Slow-Release Fertilizers in Soils with Different Microbial Activities

Abstract Soil microbial activity is recognized as an important factor affecting nitrogen (N) release from slow-release fertilizers. However, studies on the effect of size and activity of soil microflora on fertilizer degradation have provided contrasting results. To date, no clear relationships exist between soil microbial activity and the release of N from slow-release fertilizers. Hence, the aim of this study was to better understand such relationships by determining the release of N from three slow-release fertilizers in soils with different microbial activities. Soils were amended with urea-formaldehyde (UF), isobutylidene diurea (IBDU), and crotonylidene diurea (CDU). Urea, a soluble fertilizer, was used as the control. Fertilized soil samples were placed in a leaching system, and the release of N was determined by measuring ammonium-N and nitrate-N concentrations in leachates during 90 d of incubation. Non-linear regression was used to fit N leaching rate to a first-order model. In all the treated soils, N was released in the order: urea (89%–100%) > IBDU (59%–94%) > UF (46%–73%) > CDU (44%–56%). At the end of incubation, N released from CDU did not differ (P > 0.05) among soils. On the contrary, UF and IBDU released significantly lower (P