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Ventrella, D., & Giglio, L. (2014). Regional analysis of climate change impact and adaptation strategies for winter durum wheat and tomato yield cultivated in Southern Italy. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The most important factors limiting the agriculture in Puglia region in Southern Italy are typically linked to high temperatures and low water availability. In expected future scenarios, increased challenges about such factors could further limit the crop productivity. We adopted an approach based on the simulation analysis carried out through the DSSAT implemented into AEGIS/WIN. This tool has proved to be an useful tool to manage the analysis results about the potential future impact of two regionalized climatic scenarios within the SRES scenario A2. Anomaly2 and Anomaly5, based on a target increase of global temperature of 2° and 5°C. The winter durum wheat and tomato were simulated on the basis of the interaction climate-soil on a regional scale framework interesting the whole area of Puglia (about 20000 km2) subdivided in about 200 units of simulation. The wheat yield has proved to be mainly affected by the variability of precipitation. Conversely, the largest increment of temperature of spring-summer period caused a tomato yield reduction. As second step, in order to individuate the optimal adaptation strategies for both crops, a spatial analysis focused on sowing/transplanting times, nitrogen fertilization and tomato-irrigation has been carried out. The results have clearly indicated the different sensitivity of crops to climate change as influenced by the specific interaction soil-climate and an high degree of uncertainty, especially for the sowing date, depending even on small differences related to the climatic differences characterizing the areas of the Puglia territory.
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Sanna, M., Acutis, M., & Bellocchi, G. (2014). Interrelationship between evaluation metrics to assess agro-ecological models. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: When evaluating the performances of simulation models, the perception of the quality of the outputs may depend on the statistics used to compare simulated and observed data. In order to have a comprehensive understanding of model performance, the use of a variety of metrics is generally advocated. However, since they may be correlated, the use of two or more metrics may convey the same information, leading to redundancy. This study intends to investigate the interrelationship between evaluation metrics, with the aim of identifying the most useful set of indicators, for assessing simulation performance. Our focus is on agro-ecological modelling. Twenty-three performance indicators were selected to compare simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Indicators were calculated on large data sets, collected to effectively apply correlation analysis techniques. For each variable, the interrelationship between each pair of indicators was evaluated, by computing the Spearman’s rank correlation coefficient. A definition of “stable correlation” was proposed, based on the test of heterogeneity, allowing to assess whether two or more correlation coefficients are equal. An optimal subset of indicators was identified, striking a balance between number of indicators, amount of provided information and information redundancy. They are: Index of Agreement, Squared Bias, Root Mean Squared Relative Error, Pattern Index, Persistence Model Efficiency and Spearman’s Correlation Coefficient. The present study was carried out in the context of CropM-LiveM cross-cutting activities of MACSUR knowledge hub.
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Destain, M. - F. (2014). Filtering methods for predicting and modelling wheat yield in the context of climate change. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: In this paper, an Improved Particle Filtering (IPF) based on minimizing Kullback-Leibler divergence will be proposed for biomass prediction of a wheat crop model in the context of climate change including heat and drought stresses.In a first stage, the performances of the proposed technique will be compared with those of the extended Kalman filter (EKF), unscented Kalman filter (UKF), Particle filter (PF). In a second stage, the state estimation techniques EKF, UKF, PF and IPF will be used for updating prediction of the model in order to predict winter wheat biomass, in specific field conditions, during several contrasted weather conditions. In a third stage, the effects of practical challenges on the performances of the state estimation algorithms will be assessed. Such practical challenges include the effect of measurement noise on the estimation performances and the measurement frequency of state variables.The first results show that the UKF provides a higher accuracy than the EKF due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the IPF provides a significant improvement over PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. For all techniques, the practical challenges affect the estimation accuracy as well as the convergence of the estimated states and parameters. However, the IPF can still provide both convergence as well as accuracy over other estimation methods. These advantages are precious in presence of high climate stresses.
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Bellocchi, G., Rivington, M., & Acutis, M. (2014). Deliberative processes for comprehensive evaluation of agro-ecological models. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Biophysical models are acknowledged for examining interactions of agro-ecological systems and fostering communication between scientists, managers and the public. As the role of models grows in importance, there is an increase in the need to assess their quality and performance (Bellocchi et al., 2010). However, the heterogeneity of factors influencing model outputs makes it difficult a full assessment of model features. Where models are used with or for stakeholders then model credibility depends not only on the outcomes of well-structured statistical evaluation but also less tangible factors may need to be addressed using complementary deliberative processes. To expand our horizons in the evaluation of crop and grassland models, approaches have been reviewed with emphasis on using combined metrics. Comprehensive evaluation of simulation models was developed to integrate expectations of stakeholders via a weighting system where lower and upper fuzzy bounds are applied to a set of evaluation metrics. A questionnaire-based survey helped understanding the multi-faceted knowledge and experience required and the substantial challenges posed by the deliberative process. MACSUR knowledge hub holds potential to advance in good modelling practice in relation with model evaluation (including access to appropriate software tools), an activity which is frequently neglected in the context of time-limited projects.
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Hoveid, Ø. (2014). Prototype stochastic general equilibrium model of a global food system. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: A model of a global food system need at least two points in time per year and two locations with different growing seasons so that planting and harvesting have different timing across locations. Moreover, planting decisions reflect soil states affected by stochastic weather since previous point in time, while harvest reflect the planting decisions and the stochastic weather through the growing season up to next point. Decisions on trade, storage and consumption are taken at every point in time. Despite stochastic influence, deterministic stationary general equilibrium is applicable. The world then runs in circles through a likely sequence of N given weather scenarios, while the decision-makers do not know the next scenario. The model will provide a setting in which the consequences of climate change can be assessed both with respect to expectations and variances. It will by construction be an integrated assessment model (IAM) in the sense that outcomes follow from agent choices in a world of biophysical processes. In this case the biophysical world is stochastic. At the prototype stage neither existing behavioral nor bio-physical models will be applied.
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