|
|
Minet, J., Laloy, E., Tychon, B., & François, L. (2015). Bayesian inversions of a dynamic vegetation model at four European grassland sites. Biogeosciences, 12(9), 2809–2829.
Abstract: Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters, using the DREAM((ZS)) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo (MCMC) sampler. We focus on comparing model inversions, considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root mean square errors (RMSEs) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m(-2) day(-1) and 0.50 to 1.28 mm day(-1), respectively. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modelled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides the residual error treatment, differences between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics.
|
|
|
|
Özkan Gülzari, Ş., Åby, B. A., Persson, T., Höglind, M., & Mittenzwei, K. (2017). Combining models to estimate the impacts of future climate scenarios on feed supply, greenhouse gas emissions and economic performance on dairy farms in Norway. Agric. Syst., 157, 157–169.
Abstract: • This study combines crop, livestock and economic models.
• Models interaction is through use of relevant input and output variables.
• Future climate change will result in increased grass and wheat dry matter yields.
• Changes in grass, wheat and milk yields in future reduce farm emissions intensity.
• Changes in future dry matter yields and emissions lead to increased profitability.
There is a scientific consensus that the future climate change will affect grass and crop dry matter (DM) yields. Such yield changes may entail alterations to farm management practices to fulfill the feed requirements and reduce the farm greenhouse gas (GHG) emissions from dairy farms. While a large number of studies have focused on the impacts of projected climate change on a single farm output (e.g. GHG emissions or economic performance), several attempts have been made to combine bio-economic systems models with GHG accounting frameworks. In this study, we aimed to determine the physical impacts of future climate scenarios on grass and wheat DM yields, and demonstrate the effects such changes in future feed supply may have on farm GHG emissions and decision-making processes. For this purpose, we combined four models: BASGRA and CSM-CERES-Wheat models for simulating forage grass DM and wheat DM grain yields respectively; HolosNor for estimating the farm GHG emissions; and JORDMOD for calculating the impacts of changes in the climate and management on land use and farm economics. Four locations, with varying climate and soil conditions were included in the study: south-east Norway, south-west Norway, central Norway and northern Norway. Simulations were carried out for baseline (1961–1990) and future (2046–2065) climate conditions (projections based on two global climate models and the Special Report on Emissions Scenarios (SRES) A1B GHG emission scenario), and for production conditions with and without a milk quota. The GHG emissions intensities (kilogram carbon dioxide equivalent: kgCO2e emissions per kg fat and protein corrected milk: FPCM) varied between 0.8 kg and 1.23 kg CO2e (kg FPCM)− 1, with the lowest and highest emissions found in central Norway and south-east Norway, respectively. Emission intensities were generally lower under future compared to baseline conditions due mainly to higher future milk yields and to some extent to higher crop yields. The median seasonal above-ground timothy grass yield varied between 11,000 kg and 16,000 kg DM ha− 1 and was higher in all projected future climate conditions than in the baseline. The spring wheat grain DM yields simulated for the same weather conditions within each climate projection varied between 2200 kg and 6800 kg DM ha− 1. Similarly, the farm profitability as expressed by total national land rents varied between 1900 million Norwegian krone (NOK) for median yields under baseline climate conditions up to 3900 million NOK for median yield under future projected climate conditions.
|
|
|
|
Ventrella, D., Stellacci, A. M., Castrignanò, A., Charfeddine, M., & Castellini, M. (2016). Effects of crop residue management on winter durum wheat productivity in a long term experiment in Southern Italy. European Journal of Agronomy, 77, 188–198.
Abstract: A long-term experiment comparing different crop residue (CR) managements was established in 1977 in Foggia (Apulia region, southern Italy). The objective of this study was to investigate the long-term effects of different types of crop residue management on main yield response parameters in a continuous cropping system of winter durum wheat. In order to correctly interpret the results, models accounting for spatial error autocorrelation were used and compared with ordinary least square models. Eight crop residue management treatments, based on burning of wheat straw and stubble or their incorporation with or without N fertilization and irrigation, were compared. The experimental design was a complete randomized block with five replicates. Results indicated that the dynamics of yield, grain protein content and hectolitric weight of winter durum wheat did not show any decline as usually expected when a monoculture is carried out for a long time. In addition, the temporal variability of productivity was more affected by meteorological factors, such as air temperature and rainfall, than CR management treatments. Higher wheat grain yields and hectolitric weights quite frequently occurred after burning of wheat straw compared with straw incorporation without nitrogen fertilization and autumn irrigation and this was attributed to temporary mineral N immobilization in the soil. The rate of 50 kg ha(-1) of N seemed to counterbalance this negative effect when good condition of soil moisture occurred in the autumn period, so yielding the same productive level of straw burning treatment. (C) 2016 Elsevier B.V. All rights reserved.
|
|
|
|
Wolf, J., Ouattara, K., & Supit, I. (2015). Sowing rules for estimating rainfed yield potential of sorghum and maize in Burkina Faso. Agricultural and Forest Meteorology, 214-215, 208–218.
Abstract: To reduce the dependence on local expert knowledge, which is important for large-scale crop modelling studies, we analyzed sowing dates and rules for maize (Zea mays L.) and sorghum (Sorghum bicolor (L)) at three locations in Burkina Faso with strongly decreasing rainfall amounts from south to north. We tested in total 22 methods to derive optimal sowing dates that result in highest water-limited yields and lowest yield variation in a reproducible and objective way. The WOFOST crop growth simulation model was used. We found that sowing dates that are based on local expert knowledge, may work quite well for Burkina Faso and for West Africa in general. However, when no a priori information is available, maize should be sown between Julian days 160 and 200, with application of the following criteria: (a) cumulative rainfall in the sowing window is >= 3 cm or available soil moisture content is >2 cm in the moderately dry central part of Burkina Faso, (b) cumulative rainfall in this period is >= 2 cm or available soil moisture content is >1 cm in the more humid regions in the southern part of Burkina Faso. Sorghum should also be sown between Julian days 160 and 200 with application of the following criteria: (a) in the dry northern part of Burkina Faso the long duration sorghum variety should be sown when cumulative rainfall is >2 cm in the sowing window, and the short duration sorghum variety should be sown later when cumulative rainfall is >= 3 cm, (b) in central Burkina Faso sowing should start when cumulative rainfall in this period is >= 2 cm or when available soil moisture content is >1 cm. Sowing date rules are shown to be generally crop and location specific and are not generic for West Africa. However, the required precision of the sowing rules appears to rapidly decrease with increasing duration and intensity of the rainy season. Sowing delay as a result of, for example, labour constraints, has a disastrous effect on rainfed maize and sorghum yields, particularly in the northern part of West Africa with low rainfall. Optimization of sowing dates can also be done by simulating crop yields in a time window of two months around a predefined sowing date. Using these optimized dates appears to result in a good estimate of the maximal mean rainfed yield level. (C) 2015 Elsevier B.V. All rights reserved.
|
|
|
|
Yang, H., Dobbie, S., Ramirez-Villegas, J., Feng, K., Challinor, A. J., Chen, B., et al. (2016). Potential negative consequences of geoengineering on crop production: A study of Indian groundnut. Geophys. Res. Let., 43(22), 11786–11795.
Abstract: Geoengineering has been proposed to stabilize global temperature, but its impacts on crop production and stability are not fully understood. A few case studies suggest that certain crops are likely to benefit from solar dimming geoengineering, yet we show that geoengineering is projected to have detrimental effects for groundnut. Using an ensemble of crop-climate model simulations, we illustrate that groundnut yields in India undergo a statistically significant decrease of up to 20% as a result of solar dimming geoengineering relative to RCP4.5. It is somewhat reassuring, however, to find that after a sustained period of 50 years of geoengineering crop yields return to the nongeoengineered values within a few years once the intervention is ceased.
|
|
