Abstract: To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.

Abstract: This paper summarizes currently available data on greenhouse gas (GHG) emissions from African natural ecosystems and agricultural lands. The available data are used to synthesize current understanding of the drivers of change in GHG emissions, outline the knowledge gaps, and suggest future directions and strategies for GHG emission research. GHG emission data were collected from 75 studies conducted in 22 countries (n = 244) in sub-Saharan Africa (SSA). Carbon dioxide (CO2) emissions were by far the largest contributor to GHG emissions and global warming potential (GWP) in SSA natural terrestrial systems. CO2 emissions ranged from 3.3 to 57.0 Mg CO2 ha(-1) yr(-1), methane (CH4) emissions ranged from -4.8 to 3.5 kg ha(-1) yr(-1) (-0.16 to 0.12 Mg CO2 equivalent (eq.) ha(-1) yr(-1)), and nitrous oxide (N2O) emissions ranged from -0.1 to 13.7 kg ha(-1) yr(-1) (-0.03 to 4.1 Mg CO2 eq. ha(-1) yr(-1)). Soil physical and chemical properties, rewetting, vegetation type, forest management, and land-use changes were all found to be important factors affecting soil GHG emissions from natural terrestrial systems. In aquatic systems, CO2 was the largest contributor to total GHG emissions, ranging from 5.7 to 232.0 Mg CO2 ha(-1) yr(-1), followed by -26.3 to 2741.9 kgCH(4) ha(-1) yr(-1) (-0.89 to 93.2 Mg CO2 eq. ha(-1) yr(-1)) and 0.2 to 3.5 kg N2O ha(-1) yr(-1) (0.06 to 1.0 Mg CO2 eq. ha(-1) yr(-1)). Rates of all GHG emissions from aquatic systems were affected by type, location, hydrological characteristics, and water quality. In croplands, soil GHG emissions were also dominated by CO2, ranging from 1.7 to 141.2 Mg CO2 ha(-1) yr(-1), with -1.3 to 66.7 kgCH(4) ha(-1) yr(-1) (-0.04 to 2.3 Mg CO2 eq. ha(-1) yr(-1)) and 0.05 to 112.0 kg N2O ha(-1) yr(-1) (0.015 to 33.4 Mg CO2 eq. ha(-1) yr(-1)). N2O emission factors (EFs) ranged from 0.01 to 4.1 %. Incorporation of crop residues or manure with inorganic fertilizers invariably resulted in significant changes in GHG emissions, but results were inconsistent as the magnitude and direction of changes were differed by gas. Soil GHG emissions from vegetable gardens ranged from 73.3 to 132.0 Mg CO2 ha(-1) yr(-1) and 53.4 to 177.6 kg N2O ha(-1) yr(-1) (15.9 to 52.9 Mg CO2 eq. ha(-1) yr(-1)) and N2O EFs ranged from 3 to 4 %. Soil CO2 and N2O emissions from agroforestry were 38.6 Mg CO2 ha(-1) yr(-1) and 0.2 to 26.7 kg N2O ha(-1) yr(-1) (0.06 to 8.0 Mg CO2 eq. ha(-1) yr(-1)), respectively. Improving fallow with nitrogen (N)-fixing trees led to increased CO2 and N2O emissions compared to conventional croplands. The type and quality of plant residue in the fallow is an important control on how CO2 and N2O emissions are affected. Throughout agricultural lands, N2O emissions slowly increased with N inputs below 150 kg N ha(-1) yr(-1) and increased exponentially with N application rates up to 300 kg N ha(-1) yr(-1). The lowest yield-scaled N2O emissions were reported with N application rates ranging between 100 and 150 kg N ha(-1). Overall, total CO2 eq. emissions from SSA natural ecosystems and agricultural lands were 56.9 +/- 12.7 x 10(9) Mg CO2 eq. yr(-1) with natural ecosystems and agricultural lands contributing 76.3 and 23.7 %, respectively. Additional GHG emission measurements are urgently required to reduce uncertainty on annual GHG emissions from the different land uses and identify major control factors and mitigation options for low-emission development. A common strategy for addressing this data gap may include identifying priorities for data acquisition, utilizing appropriate technologies, and involving international networks and collaboration.

Abstract: The Mediterranean region has always shown a marked inter-annual variability in seasonal weather, creating uncertainty in decisional processes of cultivation and livestock breeding that should not be neglected when modeling farmers’ adaptive responses. This is especially relevant when assessing the impact of climate change (CC), which modifies the atmospheric variability and generates new uncertainty conditions, and the possibility of adaptation of agriculture. Our analysis examines this aspect reconstructing the effects of inter-annual climate variability in a diversified farming district that well represents a wide range of rainfed and irrigated agricultural systems in the Mediterranean area. We used a Regional Atmospheric Modelling System and a weather generator to generate 150 stochastic years of the present and near future climate. Then, we implemented calibrated crop and livestock models to estimate the corresponding productive responses in the form of probability distribution functions (PDFs) under the two climatic conditions. We assumed these PDFs able to represent the expectations of farmers in a discrete stochastic programming (DSP) model that reproduced their economic behaviour under uncertainty conditions. The comparison of the results in the two scenarios provided an assessment of the impact of CC, also taking into account the possibility of adjustment allowed by present technologies and price regimes. The DSP model is built in blocks that represent the farm typologies operating in the study area, each one with its own resource endowment, decisional constraints and economic response. Under this latter aspect, major differences emerged among farm typologies and sub-zones of the study area. A crucial element of differentiation was water availability, since only irrigated C3 crops took full advantage from the fertilization effect of increasing atmospheric CO2 concentration. Rainfed crop production was depressed by the expected reduction of spring rainfall associated to the higher temperatures. So, a dualism emerges between the smaller impact on crop production in the irrigated plain sub-zone, equipped with collective water networks and abundant irrigation resources, and the major negative impact in the hilly area, where these facilities and resources are absent. However intensive dairy farming was also negatively affected in terms of milk production and quality, and cattle mortality because of the increasing summer temperatures. This provides explicit guidance for addressing strategic adaptation policies and for framing farmers’ perception of CC, in order to help them to develop an awareness of the phenomena that are already in progress, which is a prerequisite for effective adaptation responses.