Alessia Perego

Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data

The agricultural sector faces the challenge of ensuring food security without an excessive burden on the environment. Simulation models provide excellent instruments for researchers to gain more insight into relevant processes and best agricultural practices and provide tools for planners for decision making support. The extent to which models are capable of reliable extrapolation and prediction is important for exploring new farming systems or assessing the impacts of future land and climate changes. A performance assessment was conducted by testing six detailed state-of-the-art models for simulation of nitrate leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to nitrate leaching. Three consecutive phases were distinguished to gain insight in the predictive power of the models: 1) a blind test for 2005-2008 in which only soil hydraulic characteristics, meteorological data and information about the agricultural management were accessible; 2) a calibration for the same period in which essential information on field observations was additionally available to the modellers; and 3) a validation for 2009-2011 with the corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root mean squared error, index of agreement, model efficiency, root relative squared error, Pearsons linear correlation coefficient) was applied for testing the results and comparing the models. None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high-input farming systems had difficulties in accurately predicting leaching in low-input farming systems that are strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to identify years and crops with high- and low-leaching rates.

A new approach for determining rice critical nitrogen concentration

SUMMARYA reliable evaluation of crop nutritional status is crucial for supporting fertilization aiming atmaximizing qualitative and quantitativeaspects of production and reducing the environmental impactof cropping systems. Most of the available simulation models evaluate crop nutritional statusaccording to the nitrogen (N) dilution law, which derives critical N concentration as a function ofabove-ground biomass. An alternative approach, developed during a project carried out with studentsof the Cropping Systems Masters course at the University of Milan, was tested and compared withexisting models (N dilution law and approaches implemented in EPIC and DAISY models). The newmodel (MAZINGA) reproduces the effect of leaf self-shading in lowering plant N concentration(PNC) through an inverse of the fraction of radiation intercepted by the canopy. The models weretested using data collected in four rice (Oryza sativa L.) experiments carried out in Northern ItalyunderpotentialandN-limited conditions.MAZINGAwasthemostaccurateinidentifyingthecriticalN concentration, and therefore in discriminating PNC of plants growing under N-limited and non-limited conditions, respectively. In addition, the present work proved the effectiveness of crop modelswhen used as tools for supporting education.INTRODUCTIONEvaluating nitrogen (N) nutritional status is a keyissue for analysing, monitoring and managing cro-pping systems (e.g. Naylor & Stephen 1993;Senanayake et al. 1996; Jeuffroy et al. 2002; Jaggardet al. 2009). A reliable estimation of crop requirementallows optimization of both qualitative and quantitat-ive aspects of production (Ghosh et al. 2004), andincreases N use efficiency, therefore reducing theimpacts on the environment. For these reasons,different typologies of tools for supportingN manage-ment have been developed. They differ in their scope(i.e. supporting fertilization, evaluating losses andidentifying scenarios suitable for specific contexts), inthe reference spatial scale (e.g. field and region), in theeconomic and time resources they demand and inthe skills required by the user. Some of these tools(e.g. leaf colour charts (LCC); Alam et al. 2005) arevery simple and cheap, being conceived to supportfarmers directly for N-management at field level.Others (i.e. SPAD-502; Konica Minolta Inc., Tokyo,Japan) are more complex and use plant chlorophyll

Modelling nitrogen leaching from sewage sludge application to arable land in the Lombardy region (northern Italy)

Sewage sludge can be used as fertiliser, offering the possibility of safely recycling this waste product as a resource in agricultural applications. As the environmental concerns related to waste recycling in agricultural applications are well-known, restrictions on the use of sewage sludge have been implemented by the EU and local authorities. This work aimed to evaluate the nitrogen leaching associated with the application of sludge and the effectiveness of the temporal restrictions on its application implemented to safeguard the environment in the Lombardy region of northern Italy (120 days in Nitrate Vulnerable Zones and 90 days elsewhere) using the CropSyst model which was first validated. The effects of fertilisation using four different sludge types on N leaching were simulated at five sites under cultivation with maize and rice crops; six different timing schemes for sludge application were tested, three of which involved dates that were in agreement (AT) with the regulation, while the other three were not in agreement (NAT). We detected a significant effect of the sludge type and application timing, whereas the effect of their interaction was never significant. The mean annual leaching was 22 to 154 kg N ha(-1). The higher the ammonium N content in the sludge was, the greater the potential for N leaching was found to be. For the maize crop, the distribution of sludge in the late fall period resulted in significantly greater N leaching (61 kg N ha(-1)) and led to lower yields (9 t DM ha(-1)) compared to late winter fertilisation (49 kg N ha(-1); 10 t DM ha(-1)), whereas no differences in N leaching or yield were detected between AT and NAT, which was also observed for the rice crop. Therefore, the applied temporal constraints did not always appear to be advantageous for protecting the environment from leaching.

Assimilation of COSMO-SkyMed-derived LAI maps into the AQUATER crop growth simulation model. Capitanata (Southern Italy) case study

Abstract AQUATER is a Decision Support System (DSS) developed to drive crop management decisions at district level in a Mediterranean area; it integrates information from soil and climatic databases with a crop growth simulation model and provides estimates of crop yield at regional scale. AQUATER can assimilate LAI maps derived from Earth observation data in order to mitigate the risk of erroneous model predictions over large areas. In this study, time-series of LAI maps derived from COSMO-SkyMed SAR images, acquired over the Capitanata plain (Puglia region) in 2010 and 2011, have been assimilated by a forcing procedure in AQUATER and the improvements of its predictions have been assessed. Results indicate that the LAI assimilation leads to significant improvements in the yield forecast of sugar beet and tomato crops, whereas in the case of wheat the improvements are marginal.

Designing a high-yielding maize ideotype for a changing climate in Lombardy plain (northern Italy)

The expected climate change will affect the maize yields in view of air temperature increase and scarce water availability. The application of biophysical models offers the chance to design a drought-resistant ideotype and to assist plant breeders and agronomists in the assessment of its suitability in future scenarios. The aim of the present work was to perform a model-based estimation of the yields of two hybrids, current vs ideotype, under future climate scenarios (2030-2060 and 2070-2100) in Lombardy (northern Italy), testing two options of irrigation (small amount at fixed dates vs optimal water supply), nitrogen (N) fertilization (300 vs 400 kg N ha(-1)), and crop cycle durations (current vs extended). For the designing of the ideotype we set several parameters of the ARMOSA process-based crop model: the root elongation rate and maximum depth, stomatal resistance, four stage-specific crop coefficients for the actual transpiration estimation, and drought tolerance factor. The work findings indicated that the current hybrid ensures good production only with high irrigation amount (245-565 mm y(-1)). With respect to the current hybrid, the ideotype will require less irrigation water (-13%, p<0.01) and it resulted in significantly higher yield under water stress condition (+15%, p<0.01) and optimal water supply (+2%, p<0.05). The elongated cycle has a positive effect on yield under any combination of options. Moreover, higher yields projected for the ideotype implicate more crop residues to be incorporated into the soil, which are positively correlated with the SOC sequestration and negatively with N leaching. The crop N uptake is expected to be adequate in view of higher rate of soil mineralization; the N fertilization rate of 400 kg N ha(-1) will involve significant increasing of grain yield, and it is expected to involve a higher rate of SOC sequestration.

Integration of a Crop Simulation Model and Remote Sensing Information

The monitoring of irrigation requirements at district or regional scale can be based on the use of ecological process-based models and remote sensing data. The former simulates the time evolution (usually at daily scale) of the main biophysical variables which determine crop photosynthesis and water consumption rates; the latter allows to provide the spatial distribution of these variables over a region of interest at a time interval ranging from few days to one month. The evaluation of water balance components and, in particular, the estimate of actual evapotranspiration and the partitioning between soil evaporation and plant transpiration, are crucial issues in semi-arid regions where the scarcity of water resources is becoming an important limiting factor crop growth and yield. The research focused an integrated approach to combine field data, simulation crop model and remote sensing information.

Conservation Agriculture as a Driving Force to Accumulate Carbon in Soils: An Analysis of RDP in Lombardy

Carbon Dioxide (CO2) emission credits and C-sequestration are measures that are largely applied to limit the rising concentration of CO2 in the Earth’s atmosphere. In this context an increasing role is played by conservation agriculture (CA). This chapter aims to present the policies pursued in Lombardy and to calculate, with the soil CN-cycle model ARMOSA, the potential of C-storage in soils with the adoption of CA measures for 20 years. The analysis is performed on 600 farms (24,550 ha), and it is implemented here taking into account the economic incentive provided by the 2007–2013 Rural Development Program (RDP) of Lombardy. The results show that C-accumulation in soils by CA can contribute to achieve Kyoto targets, but it needs a significant economic effort. Suggestions for policy-makers are here briefly outlined in relation to similar policies applied at the international level.