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Montesino-San Martín, M., Olesen, J. E., & Porter, J. R. (2014). A genotype, environment and management (GxExM) analysis of adaptation in winter wheat to climate change in Denmark. Agricultural and Forest Meteorology, 187, 1–13.
Abstract: Wheat yields in Europe have shown stagnating trends during the last two decades, partly attributed to climate change. Such developments challenge the needs for increased production, in particular at higher latitudes, to meet increasing global demands and expected productivity reductions at lower latitudes. Climate change projections from three General Circulation Models or GCMs (UKMO-HadGEM1, INM-GM3.0 and CSIRO-Mk3.1) for the A1FI SIZES emission scenario for 2000 to 2100 were downscaled at a northern latitude location (Foulum, Denmark) using LARS-WG5.3. The scenarios accounted for changes in temperature, precipitation and atmospheric CO2 concentration. In addition, three temperature-variability scenarios were included assuming different levels of decreased temperature variability in winter and increased in summer. Crop yield was simulated for the different climate change scenarios by a calibrated version of AFRCWHEAT2 to model several combinations of genotypes (varying in crop growth, development and tolerance to water and nitrogen scarcity) and management (sowing dates and nitrogen fertilization rate). The simulations showed a slight improvement of grain yields (0.3-1.2 Mg ha(-1)) in the medium-term (2030-2050), but not enough to cope with expected increases in demand for food and feed. Optimum management added up to 1.8 Mg ha(-1). Genetic modifications regarding winter wheat crop development exhibit the greatest sensitivity to climate and larger potential for improvement (+3.8 Mg ha(-1)). The results consistently points towards need for cultivars with a longer reproductive phases (2.9-7.5% per 1 degrees C) and lower photoperiod sensitivities. Due to the positive synergies between several genotypic characteristics, multiple-target breeding programmes would be necessary, possibly assisted by model-based assessments of optimal phenotypic characteristics.
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Sanna, M., Acutis, M., & Bellocchi, G. (2014). Interrelationship between evaluation metrics to assess agro-ecological models (Vol. 3).
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. No Label
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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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Ghaley, B. B., & Porter, J. R. (2014). Ecosystem function and service quantification and valuation in a conventional winter wheat production system with the DAISY model in Denmark. Ecosystem Services, 10, 79–83.
Abstract: With inevitable link between ecosystem function (EF), ecosystem services (ES) and agricultural productivity, there is a need for quantification and valuation of EF and ES in agro-ecosystems. Management practices have significant effects on soil organic matter (SOM), affecting productivity, EF and ES provision. The objective was to quantify two EF: soil water storage and nitrogen mineralization and three ES: food and fodder production and carbon sequestration, in a conventional winter wheat production system at 2.6% SOM compared to 50% lower (1.3%) and 50% higher (3.9%) SOM in Denmark by DAISY model. At 2.6% SOM, the food and fodder production was 649 and 6.86 t ha(-1) year(-1) respectively whereas carbon sequestration and soil water storage was 9.73 t ha(-1) year and 684 mm ha(-1) year(-1) respectively and nitrogen mineralisation was 83.58 kg ha(-1) year(-1), AL 2.6% SOM, the two EF and three ES values were US$ 177 and US$ 2542 ha(-1) year respectively equivalent to US$ 96 and US$1370 million year(-1) respectively in Denmark. The EF and ES quantities and values were positively correlated with SOM content. Hence, the quantification and valuation of EF and ES provides an empirical tool for optimising the Er. and ES provision for agricultural productivity. (C) 2014 Elsevier B.V. All rights reserved
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Hutchings, N. (2014). Farm-scale modelling. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: —
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