Simone Bregaglio

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2 ]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2 ] and temperature.

Modelling soil borne fungal pathogens of arable crops under climate change

Soil-borne fungal plant pathogens, agents of crown and root rot, are seldom considered in studies on climate change and agriculture due both to the complexity of the soil system and to the incomplete knowledge of their response to environmental drivers. A controlled chamber set of experiments was carried out to quantify the response of six soil-borne fungi to temperature, and a species-generic model to simulate their response was developed. The model was linked to a soil temperature model inclusive of components able to simulate soil water content also as resulting from crop water uptake. Pathogen relative growth was simulated over Europe using the IPCC A1B emission scenario derived from the Hadley-CM3 global climate model. Climate scenarios of soil temperature in 2020 and 2030 were compared to the baseline centred in the year 2000. The general trend of the response of soil-borne pathogens shows increasing growth in the coldest areas of Europe; however, a larger rate of increase is shown from 2020 to 2030 compared to that of 2000 to 2020. Projections of pathogens of winter cereals indicate a marked increase of growth rate in the soils of northern European and Baltic states. Fungal pathogens of spring sowing crops show unchanged conditions for their growth in soils of the Mediterranean countries, whereas an increase of suitable conditions was estimated for the areals of central Europe which represent the coldest limit areas where the host crops are currently grown. Differences across fungal species are shown, indicating that crop-specific analyses should be ran.

A set of software components for the simulation of plant airborne diseases

Models to evaluate the impact of plant diseases on crop production under current and future climatic conditions are increasingly requested by different stakeholders. This paper presents four software components - InoculumPressure, DiseaseProgress, ImpactsOnPlants, AgromanagementDisease - which implement models to simulate the dynamics of generic polycyclic fungal epidemics and interactions with crop physiological processes. The software architecture adopted allows extending the components with alternate approaches to reproduce specific pathosystems or compare predictive capabilities. As proofs of concept, (i) the components are coupled with two crop simulators to reproduce wheat brown rust and rice blast epidemics and their impacts on leaf area and yield formation; (ii) spatially distributed sensitivity analyses are performed for rice in China and wheat in Europe to investigate model behaviour; (iii) a preliminary evaluation against observations of rice blast severity is performed in Northern Italy. The components are explicitly targeted to the modelling of crop-pathogen interactions to perform scenario analysis. We present four software components to simulate plant-pathogens interactions.Each component is devoted to the modelling of different aspects of plant diseases.The components are framework independent and can be extended by third parties.We couple the components to crop simulators and perform model sensitivity analysis.The main target of the components is their application in scenario analysis.

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

A taxonomy-based approach to shed light on the babel of mathematical models for rice simulation

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance. A taxonomy-based approach was used to classify AgMIP rice simulation models.Different model structures often resulted in similar outputs.Similar structures often led to large differences in outputs.User subjectivity likely hides relationships between model structure and behaviour.Shared protocols are still needed to limit the risks during calibration.

New multi-model approach gives good estimations of wheat yield under semi-arid climate in Morocco

Wheat production in Morocco is crucial for economy and food security. However, wheat production is difficult because the semi-arid climate causes very variable wheat yields. To solve this issue, we need better prediction of the impact of drought on wheat yields to adapt cropping management to the semi-arid climate. Here, we adapted the models WOFOST and CropSyst to agro-climatic conditions in Morocco. Six soft and durum wheat varieties were grown during the 2011–2012 and 2012–2013 growing seasons in the experimental sites of Sidi El Aydi, Khemis Zemamra and Marchouch. Drip irrigation and rainfed treatments were arranged in a randomised-block design with three replicates. We determined the phenological stages of emergence, tillering, stem elongation, flowering and maturity. We measured aboveground biomass six times along the season. These data were used to adapt WOFOST and CropSyst to local conditions. Our results show that both models achieved good estimations, with R2 always higher than 0.91, and positive values for Nash and Sutcliffe modelling efficiencies. Results of spatially distributed simulations were then analysed for the whole country in terms of different response to drought.

AirTemperature: Extensible Software Library to Generate Air Temperature Data

The development of a set of reusable libraries to support custom applications has become a goal in biophysical modeling projects. This is true for weather modeling as well. AirTemperature is a software component providing a collection of deterministic and stochastic approaches to generate atmospheric temperature data on daily and hourly time steps. Data generated on a daily time step consist of maximum and minimum air temperature and dew point temperature. Hourly estimations include air and dew point temperatures. The software design allows for extension of the models implemented without recompiling the component. The component, inclusive of hypertext help documentation files, is released as compiled .NET2 version, allowing application development in either programming environment. A sample client and a sample extension project using AirTemperature are provided as source code. A sample Web service and a Web application are also developed as examples of possible use of the component.

Assimilating seasonality information derived from satellite data time series in crop modelling for rice yield estimation

The agricultural sector is facing important global challenges due to the pressure of food demand, increased price-competition produced by market globalization and food price volatility (G20 Agriculture Action Plan), and the necessity of more environmentally and economically sustainable farming. Earth Observation (EO) systems can significantly contribute to these topics by providing reliable real time information on crop distribution, status and seasonal dynamics. ERMES FP7 project aims to create added-value information for the rice agro-sector by integrating EO-products in crop models. Time series of moderate resolution satellite data are analyzed exploiting the PhenoRice algorithm to retrieve seasonal occurrence of agro-practices and phenological stages. Eleven years (2003-2013) of rice seasonal metrics were derived and used in WARM crop model to set up a crop forecasting systems, with the aim to provide crop yield estimates for regional authorities. Preliminary test conducted in Italy on indica rice ecotype demonstrated that the system can provide rice yield estimates explaining up to 90% of interannual variability.

ISIde : A rice modelling platform for in silico ideotyping

Abstract Ecophysiological models can be successfully used to analyze genotype by environment interactions, thus supporting breeders in identifying key traits for specific growing conditions. This is especially true for traits involved with resistance/tolerance to biotic and abiotic stressors, which occurrence can vary greatly both in time and space. However, no modelling tools are available to be used directly by breeders, and this is one of the reasons that prevents an effective integration of modelling activities within breeding programs. ISIde is a software platform specifically designed for district-specific rice ideotyping targeting (i) resistance/tolerance traits and (ii) breeders as final users. Platform usability is guaranteed by a highly intuitive user interface and by exposing to users only settings involved with genetic improvement. Other information needed to run simulations (i.e., data on soil, climate, management) is automatically provided by the platform once the study area, the variety to improve and the climate scenario are selected. Ideotypes indeed can be defined and tested under current and climate change scenario, thus supporting the definition of strategies for breeding in the medium-long term. Comparing the performance of current and improved genotype, the platform provides an evaluation of the yield benefits exclusively due to the genetic improvement introduced. An example of the application of the ISIde platform in terms of functionalities and results that can be achieved is reported by means of a case study concerning the improvement of tolerance to heat stress around flowering in the Oristanese rice district (Italy). The platform is currently available for the six Italian rice districts. However, the software architecture allows its extension to other growing areas – or to additional genotypes – via dedicated tools available at the application page.