Luca Bechini

Modelling, interpolation and stochastic simulation in space and time of global solar radiation.

Global solar radiation data used as daily inputs for most cropping systems and water budget models are frequently available from only a few weather stations and over short periods of time. To overcome this limitation, the Campbell–Donatelli model relates daily maximum and minimum air temperatures to solar radiation. In this study, calibrated values of model site specific parameters and efficiencies of radiation estimates are reported for 29 stations in northern Italy. Their average root mean squared error equals 2.9 MJ m−2 per day. Model inputs and model output show a clear spatial and temporal structure. For large scale model application, atmospheric transmissivity is calculated with ‘calculate first, interpolate later’ (CI) procedures and ‘interpolate the inputs, calculate later’ (IC) procedures. The mean squared error for CI equals 0.0359, whereas that for IC equals 0.0636. Comparison of ‘calculate first, simulate later’ (CS) procedures with ‘simulate the inputs, calculate later’ (SC) procedures shows a higher spatial sensitivity of SC procedures. The study shows how the model can be best applied to estimate global solar radiation, both at visited and unvisited locations, over a large and productive agricultural area in Italy, and hence, to better use water budget/crop productivity models. In addition, CS procedures show the associated error.

Parameterization of a crop growth and development simulation model at sub-model components level. An example for winter wheat (Triticum aestivum L.)

Abstract Dynamic simulation models are frequently used for assessing agronomic and environmental effects of different management practices, under various pedo-climatic conditions. CropSyst is a suitable cropping systems simulation model for such applications. However, available CropSyst crop parameters for winter wheat, one of the most important cereals in the world, are limited. In this work we show that it is possible to parameterize separate sub-model components by using existing experimental data and literature. The experiments, carried out in northern Italy between 1986 and 2001, quantified the dynamics of aboveground biomass (AGB), plant nitrogen (N) concentration (PNC) and N uptake (UPTK) by means of periodical measurements. The relative root mean square error (calculated by dividing the root mean square error by the average of observations) obtained after model calibration and validation on an independent data set was, respectively, in the range 9–30% and 17–32% for AGB, 10% and 6–40% for PNC, 8–28% and 9–24% for UPTK. AGB was frequently underestimated. Despite the limited accuracy of simulations, we argue that calibrated crop parameters are adequate for scenario analysis as most differences between years and fertilization levels were reproduced by the model and final AGB and cumulative UPTK were also correctly simulated.

A component-Based Framework for Simulating Agricultural Production and Externalities

Although existing simulation tools can be used to study the impact of agricultural management on production activities in specific environments, they suffer from several limitations. They are largely specialized for specific production activities: arable crops/cropping systems, grassland, orchards, agro-forestry, livestock etc. Also, they often have a restricted ability to simulate system externalities which may have a negative environmental impact. Furthermore, the structure of such systems neither allows an easy plug-in of modules for other agricultural production activities, nor the use of alternative components for simulating processes. Finally, such systems are proprietary systems of either research groups or projects which inhibits further development by third parties.

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.

Spatial interpolation of soil physical properties for irrigation planning. A simulation study in northern Italy

Abstract To calculate water balances at a regional scale, a frequently adopted approach (choropleth mapping) consists of using soil profile observations to identify ‘homogeneous areas’, to which simulation models are applied. However, spatial variability of soil properties within ‘homogeneous areas’ is a potential source of error, if the relationship between model inputs and model outputs is not linear. The aim of this work is to assess the feasibility of using spatially variable soil information for providing more detailed inputs to simulation models and to evaluate its effects on calculated irrigation water requirements. Point observations of soil properties in the topsoil layer were collected in a plain area near Milano (northern Italy). Particle size distribution was determined on 154 samples. The cropping systems simulation model cropsyst was applied at the study area by using four different sets of soil input data: the first one was derived from the soil map (1 datum per soil mapping unit), the other three were obtained by the use of geostatistical procedures applied to point observations (several data per soil mapping unit). The results of cropsyst s multi-year simulation for grain-maize were used to calculate the amount of grain biomass produced, actual crop evapotranspiration (ET), irrigation water needed and soil water drainage (SWD) for each soil unit (SU), their standard deviation (S.D.) in time and their S.D. in space within each SU. A clear spatial structure could be identified for all georeferenced model inputs and for model outputs related to crop growth (yield, ET). Simulated values for grain yield (GY), actual ET, irrigation water applied (IWA) and SWD were very similar for choropleth mapping and for geostatistics-based procedures. The S.D. in time was low for variables related to crop growth and was increasing for IWA and SWD. For all simulated variables the S.D. in space was always very low. In general, the spatial variability of model results was much lower than the spatial variability of model inputs: this smoothing effect was due to the application of kriging, pedotransfer functions (PTF) and simulation modeling. These results suggest that for evaluating water management scenarios at this scale, when hydrological properties are not measured, georeferenced soil data are available only for topsoil, and variability of soil particle distribution within SUs is not too high, the choropleth mapping method can be successfully used.

Integrating a spatial micrometeorological model into the risk assessment for arable crops in hilly terrain

Arable crops in hilly terrain may experience additional abiotic stress over crops growing in the plain, which affects crop establishment and productivity. We aim to predict risks and sustainability of crops in hilly terrain for current and future climates. In the STAMINA project we include a proper micrometeorological model (MM) to simulate the effects of terrain on atmospheric variables and their impact on crop growth. A generic crop growth model (CGM) is connected to the MM model, simulating growth and development of an arable crop. The model system estimates the distributed components of the soil water and energy balance with reference to standard weather variables, crop and topographic characteristics. Finally, specific risk indicators, e.g. crop water stress index (CWSI) and thermal stress index (TSI), are calculated to characterize yield reduction and to test mitigation options. The model was successfully calibrated for cereals and sugar beet. Here, we give an overview of this new model and present some results for a 5-year simulation scenario for northern Europe, in which we quantified the effects of terrain (slope and aspect) on meteorological variables and crop yields. The effect of topography on productivity was considerable: south-facing aspects were beneficial for winter wheat in wet years (+2.3 t DM ha−1), similar effects were seen for sugar beet.

Agri-environmental assessment of extractable soil phosphorus at the regional scale

Agri-environmental assessments at the regional scale are restricted by the amount and quality of input data. Such data may be available in public databases. In this study we estimated the extractable soil-P (ESP) in the Sud Milano Agricultural Park in northern Italy. Information about agricultural activities was integrated with ESP values across the area, using a large database of measured soil properties and crop management data collected from individual farms. Four interpolation methods were tested: two forms of ordinary kriging, kriging with external drift and a hybrid form (section-specific mosaic kriging). After splitting the dataset into three sub-sets, ESP was assessed at unsampled farms. High ESP levels were found in maize fields and on animal farms due to large amounts of P fertilizers applied in maize, particularly on animal farms. Using a reference threshold of 20 mg P per kg soil, most of the soils in the area were classified as being rich in ESP. As a result, it was concluded that P fertilization could be suspended in many cases for several years without crop yield decrease. Mosaic kriging showed a lower standard deviation of the prediction error and less smoothing and thus provided a better representation of the actual spatial variation.

Near infrared monitoring of mineralisation of liquid dairy manure in agricultural soils

Liquid dairy manure is a major organic input to cultivated soils. Therefore, a method for monitoring the mineralisation of slurries should be a useful tool for managing soil fertilisation. In order to examine whether the biodegradation of cattle sludge can be monitored by near infrared (NIR) spectroscopy, soil samples from a laboratory incubation experiment were analysed using this rapid and inexpensive method. Five different cattle slurries were added to three soils with increasing clay content in such an amount as to give 130 ppm of total nitrogen. The resulting 18 experimental treatments (three control soils and 15 soil-slurry combinations) were incubated for 180 days under optimal temperature and soil water content. Each treatment was sampled at 0, 2, 8, 12, 16, 21, 29, 41, 72, 121 and 180 days: the respired CO2 was captured in alkali traps and mineral N was extracted using 1 M KCl. Three replicates of each sampling were analysed individually. The resulting 648 samples, air dried and ground at 0.5 mm, were analysed by NIR spectroscopy using an Antaris (Thermo Nicolet) Fourier transform-NIR spectrometer. Although the slurries and soil mineralised carbon represent only a very small part of the total soil organic carbon, the mineralisation of carbon can be clearly monitored by NIR spectroscopy in both amended and unamended soils. Whereas NO3–N evolution was difficult to predict using NIR data, the results for NH4–N were more encouraging. Using measurements of CO2–C respired, a two-pool mineralisation model was developed and the simulated concentration of carbon pools in the soils were used for the development of NIR equations. The results obtained in this work have demonstrated that NIR is a useful tool for monitoring the carbon mineralisation process when cattle sludge is incorporated into agricultural soils.

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.

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