Daniele Cavalli

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.

Use of Spectral and Thermal Imaging Sensors to Monitor Crop Water and Nitrogen Status

High spatial and temporal resolution monitoring methods are the key to improve the efficiency in water and fertilizer input management. In this context, this work presents the set-up and the first results of a greenhouse experiment conducted on two crops with a different canopy geometry (rice and spinach) subjected to four nitrogen treatments. The experiment involves the acquisition of thermal, multispectral and hyperspectral images at three phenological stages for each crop. At each stage, spectral acquisitions are conducted on one-third of the pots, at good water conditions and, later on, at different times after interruption of irrigation. The total number of pots in the experiment is 72 (corresponding to 4 nitrogen levels x 2 crops x 3 phenological stages x 3 replicates). Just after the spectra acquisitions, non-destructive and destructive measurements of variables correlated with the crops nitrogen and water status are conducted. Multivariate regression analysis between the spectra features and measured variables will be used to identify predicting models for the estimation of crop water and nitrogen status. The most significant wavelengths for the detection of water and nitrogen stress could be the subject of a future experimentation in open field conditions using multispectral systems.

Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables

The development of small unmanned aerial vehicles and advances in sensor technology have made consumer digital cameras suitable for the remote sensing of vegetation. In this context, monitoring the in-field variability of maize (Zea mays L.), characterized by high nitrogen fertilization rates, with a low-cost color-infrared airborne system could be the basis for a site-specific nitrogen (N) fertilization support system. An experimental field with different N treatments applied to silage maize was monitored during the years 2014 and 2015. Images of the field and reference destructive measurements of above ground biomass, its N concentration and N uptake were taken at V6 and V9 development stages. Classical normalized difference vegetation indices (NDVI) and the indices adjusted by crop ground cover were calculated and regressed against the measured variables. Finally, image colorgrams were used to explore the potential of band-related information in variable estimation. A colorgram is a linear signal that summarizes the color content of each digital image. It is composed of a sequence of the frequency distribution curves of the camera bands, of their related parameters and of results of the principal components analysis applied to each image. The best predictors were found to be the ground cover and the adjusted green-based NDVI: regression equation at V9 resulted in R2 of 0.7 and RRMSE < 25% in external validation. Colorgrams did not improve prediction performance due to the spectral limitations of the camera. Therefore, the feasibility of the method should be tested in future research. In spite of limitations of sensor setup, the modified camera was able to estimate maize biomass due to the very high spatial resolution. Since the above ground biomass is a robust proxy of N status, the modified camera could be a promising tool for a low-cost N fertilization support system.

Nitrogen availability after repeated additions of raw and anaerobically digested 15N-labelled pig slurry: Nitrogen availability after additions of pig slurry

D . C a v a l l i a , L . B e c h i n i a , A . D i M a t t e o b, M . C o r t i a, P . C e c c o n c & P . M a r i n o G a l l i n a a aDepartment of Agricultural and Environmental Sciences – Production, Landscape, Agroenergy, Università degli Studi di Milano, Milan, Italy, bDepartment of Chemistry, Life Sciences and Environmental Sustainability, Università degli Studi di Parma, Parma, Italy, and cDepartment of Agrifood, Environmental, and Animal Sciences, Università degli Studi di Udine, Udine, Italy