|
|
Sándor, R., Barcza, Z., Acutis, M., Doro, L., Hidy, D., Köchy, M., et al. (2016). Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance. European Journal of Agronomy, .
Abstract: • We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems.
|
|
|
|
Makowski, D. (2017). A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations. Europ. J. Agron., 88, 76–83.
Abstract: Multi-model forecasting has drawn some attention in crop science for evaluating effect of climate change on crop yields. The principle is to run several individual process-based crop models under several climate scenarios in order to generate ensembles of output values. This paper describes a simple Bayesian method – called Bayes linear method- for updating ensemble of crop model outputs using yield observations. The principle is to summarize the ensemble of crop model outputs by its mean and variance, and then to adjust these two quantities to yield observations in order to reduce uncertainty. The adjusted mean and variance combine two sources of information, i.e., the ensemble of crop model outputs and the observations. Interestingly, with this method, observations collected under a given climate scenario can be used to adjust mean and variance of the model ensemble under a different scenario. Another advantage of the proposed method is that it does not rely on a separate calibration of each individual crop model. The uncertainty reduction resulting from the adjustment of an ensemble of crop models to observations was assessed in a numerical application. The implementation of the Bayes linear method systematically reduced uncertainty, but the results showed the effectiveness of this method varied in function of several factors, especially the accuracy of the yield observation, and the covariance between the crop model output and the observation. (C) 2015 Elsevier B.V. All rights reserved.
|
|
|
|
Van Oijen, M., & Höglind, M. (2016). Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design. Euphytica, 207(3), 627–643.
Abstract: Process-based grassland models (PBMs) simulate growth and development of vegetation over time. The models tend to have a large number of parameters that represent properties of the plants. To simulate different cultivars of the same species, different parameter values are required. Parameter differences may be interpreted as genetic variation for plant traits. Despite this natural connection between PBMs and plant genetics, there are only few examples of successful use of PBMs in plant breeding. Here we present a new procedure by which PBMs can help design ideotypes, i.e. virtual cultivars that optimally combine properties of existing cultivars. Ideotypes constitute selection targets for breeding. The procedure consists of four steps: (1) Bayesian calibration of model parameters using data from cultivar trials, (2) Estimating genetic variation for parameters from the combination of cultivar-specific calibrated parameter distributions, (3) Identifying parameter combinations that meet breeding objectives, (4) Translating model results to practice, i.e. interpreting parameters in terms of practical selection criteria. We show an application of the procedure to timothy (Phleum pratense L.) as grown in different regions of Norway.
|
|
|
|
Lehtonen, H. S., & Irz, X. (2013). Impacts of reducing red meat consumption on agricultural production in Finland. Agriculture and Food Science, 22(3), 356–370.
Abstract: This paper summarises the simulated effects on Finnish agrcultural production and trade of a 20% decrease in Finnish demand for red meat (beef, pork, lamb). According to our results, reduced red meat consumption would be offset by increased consumption of poultry meat, eggs, dairy products and fish, as well as small increases in consumption of fruits and vegetables, peas, nuts, cereal products and sweets. By including the derived demand changes in an agricultural sector model, we show that livestock production in Finland, incentivised by national production-linked payments for milk and bovine animals, would decrease by much less than 20% due to the complex nature of agricultural production and trade. Overall, assuming unchanged consumer preferences and agricultural policy, a 20% reduction in red meat consumption is not likely to lead to a substantial decrease in livestock production or changed land use, or greenhouse gas emissions, from Finnish agriculture.
|
|
|
|
Lehtonen, H. (2015). Evaluating adaptation and the production development of Finnish agriculture in climate and global change. Agricultural and Food Science, 24(3), 219–234.
Abstract: Agricultural product prices and policies influence the development of crop yields under climate change through farm level management decisions. On this basis, five main scenarios were specified for agricultural commodity prices and crop yields. An economic agricultural sector model was used in order to assess the impacts of the scenarios on production, land use and farm income in Finland. The results suggest that falling crop yields, if realized due to low prices and restrictive policies, will result in decreasing crop and livestock production and increasing nutrient surplus. Slowly increasing crop yields could stabilise production and increase farm income. Significantly higher crop prices and yields are required, however, for any marked increase in production in Finland. Cereals production would increase relatively more than livestock production, if there were high prices for agricultural products. This is explained by abundant land resources, a high opportunity cost of labour and policies maintaining current dairy and beef production.
|
|
