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Background: Climate change leads to more frequent and severe extreme weather events including floods, heatwaves, heavy rainfalls, and droughts. In contrast to the majority of research on weather extremes in sub-Saharan Africa, which focus primarily on how a lack of rainfall causes droughts, this paper aims to elucidate the effect of flooding on harvest failure in rural Burkina Faso., Methods: We conducted a case study in north-western Nouna, Burkina Faso, between August and December 2021 covering a study population of n = 180 participants. The study comprised four components: (i) interviews with farmers ( n = 180) on whether any of their fields had been inundated and if so, on harvest loss on these fields; (ii) determining the feasibility of using Sentinel-2 satellite images to validate study participants reports of floods; (iii) characterizing short-term weather including frequency and duration, of extreme rainfall events within the study area, as well as comparing cumulative rainfall (long-term) over the past 50 years; and (v), estimating both the food energy and economic loss of harvest failure due to flooding., Results: 49% of interviewed farmers ( n = 88) reported that floods had damaged at least one of their fields. Some fields ( n = 13, 7%) had no harvest due to flooding, while some farmers ( n = 14, 8%) had lost part of their harvest. Images from the Sentinel-2-Satellite indicated that reported and remotely observed flooding were consistent. According to time series of data from the local weather station, there has been an increase irregular rainfall distribution and at the same time of cumulative annual rainfall in Nouna. Furthermore, a first illustrative calculation allowed us to estimate the amount of energy lost when one hectare of a common crop is flooded., Conclusion: This case study demonstrated that flood-related harvest failures leading to crop losses in sub-Saharan Africa, exemplified by Burkina Faso, are likely to be substantial. This study serves as a proof-of-principle for flooding effects on food security. This could provide more detail for agricultural adaptation and mitigation strategies. Inundation-vulnerable fields need alternative and novel management practices, which may only be effectively implemented if agricultural institutions and national policy-making bodies receive evidence of flooding e.g., from remote sensing., Competing Interests: MS was employed by the Remote Sensing Solutions GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Müller, Ouédraogo, Schwarz, Barteit and Sauerborn.)

Adaptive management of crop growing periods by adjusting sowing dates and cultivars is one of the central aspects of crop production systems, tightly connected to local climate. However, it is so far underrepresented in crop-model based assessments of yields under climate change. In this study, we integrate models of farmers' decision making with biophysical crop modeling at the global scale to simulate crop calendars adaptation and its effect on crop yields of maize, rice, sorghum, soybean and wheat. We simulate crop growing periods and yields (1986-2099) under counterfactual management scenarios assuming no adaptation, timely adaptation or delayed adaptation of sowing dates and cultivars. We then compare the counterfactual growing periods and corresponding yields at the end of the century (2080-2099). We find that (i) with adaptation, temperature-driven sowing dates (typical at latitudes >30°N-S) will have larger shifts than precipitation-driven sowing dates (at latitudes <30°N-S); (ii) later-maturing cultivars will be needed, particularly at higher latitudes; (iii) timely adaptation of growing periods would increase actual crop yields by ~12%, reducing climate change negative impacts and enhancing the positive CO 2 fertilization effect. Despite remaining uncertainties, crop growing periods adaptation require consideration in climate change impact assessments., (© 2022. The Author(s).)

Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, especially below national scales, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys of 3,008 households and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries (Indonesia, Madagascar, Papua New Guinea, Philippines, and Tanzania). Our study reveals three key findings: First, overall potential losses to fisheries are higher than potential losses to agriculture. Second, while most locations (> 2/3) will experience potential losses to both fisheries and agriculture simultaneously, climate change mitigation could reduce the proportion of places facing that double burden. Third, potential impacts are more likely in communities with lower socioeconomic status., (© 2022. The Author(s).)

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate-related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process-based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no-adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing-zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies., (© 2021 John Wiley & Sons Ltd.)

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32-multi-model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO 2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low-rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO 2 . Introducing genotypes adapted to warmer temperatures (and also considering changes in CO 2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by -1.1 percentage points, representing a relative change of -8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production., (© 2018 John Wiley & Sons Ltd.)

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations., (© 2018 John Wiley & Sons Ltd.)

Modelling is a key tool to explore agricultural impacts of and adaptations to climate change. Here we report recent progress made especially referring to the large project initiatives MACSUR and AgMIP; in particular, in modelling potential crop impacts from field to global using multi-model ensembles. We identify two main fields where further progress is necessary: a more mechanistic understanding of climate impacts and management options for adaptation and mitigation; and focusing on cropping systems and integrative multi-scale assessments instead of single season and crops, especially in complex tropical and neglected but important cropping systems. Stronger linking of experimentation with statistical and eco-physiological crop modelling could facilitate the necessary methodological advances., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Most climate mitigation scenarios involve negative emissions, especially those that aim to limit global temperature increase to 2°C or less. However, the carbon uptake potential in land-based climate change mitigation efforts is highly uncertain. Here, we address this uncertainty by using two land-based mitigation scenarios from two land-use models (IMAGE and MAgPIE) as input to four dynamic global vegetation models (DGVMs; LPJ-GUESS, ORCHIDEE, JULES, LPJmL). Each of the four combinations of land-use models and mitigation scenarios aimed for a cumulative carbon uptake of ~130 GtC by the end of the century, achieved either via the cultivation of bioenergy crops combined with carbon capture and storage (BECCS) or avoided deforestation and afforestation (ADAFF). Results suggest large uncertainty in simulated future land demand and carbon uptake rates, depending on the assumptions related to land use and land management in the models. Total cumulative carbon uptake in the DGVMs is highly variable across mitigation scenarios, ranging between 19 and 130 GtC by year 2099. Only one out of the 16 combinations of mitigation scenarios and DGVMs achieves an equivalent or higher carbon uptake than achieved in the land-use models. The large differences in carbon uptake between the DGVMs and their discrepancy against the carbon uptake in IMAGE and MAgPIE are mainly due to different model assumptions regarding bioenergy crop yields and due to the simulation of soil carbon response to land-use change. Differences between land-use models and DGVMs regarding forest biomass and the rate of forest regrowth also have an impact, albeit smaller, on the results. Given the low confidence in simulated carbon uptake for a given land-based mitigation scenario, and that negative emissions simulated by the DGVMs are typically lower than assumed in scenarios consistent with the 2°C target, relying on negative emissions to mitigate climate change is a highly uncertain strategy., (© 2018 John Wiley & Sons Ltd.)

The magnitude of future climate change could be moderated by immediately reducing the amount of CO 2 entering the atmosphere as a result of energy generation and by adopting strategies that actively remove CO 2 from it. Biogeochemical improvement of soils by adding crushed, fast-reacting silicate rocks to croplands is one such CO 2 -removal strategy. This approach has the potential to improve crop production, increase protection from pests and diseases, and restore soil fertility and structure. Managed croplands worldwide are already equipped for frequent rock dust additions to soils, making rapid adoption at scale feasible, and the potential benefits could generate financial incentives for widespread adoption in the agricultural sector. However, there are still obstacles to be surmounted. Audited field-scale assessments of the efficacy of CO 2 capture are urgently required together with detailed environmental monitoring. A cost-effective way to meet the rock requirements for CO 2 removal must be found, possibly involving the recycling of silicate waste materials. Finally, issues of public perception, trust and acceptance must also be addressed.

Fisheries and aquaculture make a crucial contribution to global food security, nutrition and livelihoods. However, the UN Sustainable Development Goals separate marine and terrestrial food production sectors and ecosystems. To sustainably meet increasing global demands for fish, the interlinkages among goals within and across fisheries, aquaculture and agriculture sectors must be recognized and addressed along with their changing nature. Here, we assess and highlight development challenges for fisheries-dependent countries based on analyses of interactions and trade-offs between goals focusing on food, biodiversity and climate change. We demonstrate that some countries are likely to face double jeopardies in both fisheries and agriculture sectors under climate change. The strategies to mitigate these risks will be context-dependent, and will need to directly address the trade-offs among Sustainable Development Goals, such as halting biodiversity loss and reducing poverty. Countries with low adaptive capacity but increasing demand for food require greater support and capacity building to transition towards reconciling trade-offs. Necessary actions are context-dependent and include effective governance, improved management and conservation, maximizing societal and environmental benefits from trade, increased equitability of distribution and innovation in food production, including continued development of low input and low impact aquaculture.

Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO 2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population., Competing Interests: The authors declare no conflict of interest.

The land use sector of agriculture, forestry, and other land use (AFOLU) plays a central role in ambitious climate change mitigation efforts. Yet, mitigation policies in agriculture may be in conflict with food security related targets. Using a global agro-economic model, we analyze the impacts on food prices under mitigation policies targeting either incentives for producers (e.g., through taxes) or consumer preferences (e.g., through education programs). Despite having a similar reduction potential of 43-44% in 2100, the two types of policy instruments result in opposite outcomes for food prices. Incentive-based mitigation, such as protecting carbon-rich forests or adopting low-emission production techniques, increase land scarcity and production costs and thereby food prices. Preference-based mitigation, such as reduced household waste or lower consumption of animal-based products, decreases land scarcity, prevents emissions leakage, and concentrates production on the most productive sites and consequently lowers food prices. Whereas agricultural emissions are further abated in the combination of these mitigation measures, the synergy of strategies fails to substantially lower food prices. Additionally, we demonstrate that the efficiency of agricultural emission abatement is stable across a range of greenhouse-gas (GHG) tax levels, while resulting food prices exhibit a disproportionally larger spread.

Invasive populations often shift phenotypically during introduction. Moreover, they are postulated to show an increased phenotypic plasticity compared with their native counterparts, which could be advantageous. However, less is known about trait selection across populations along the invasion gradient in response to environmental factors, such as increasing drought caused by climate change. In this study, we investigated the impacts of drought on growth, regrowth, and various leaf traits in plants of different origin. Therefore, seeds of 18 populations of the perennial Tanacetum vulgare were collected along the invasion gradient (North America, invasive; West Europe, archaeophyte; East Europe, native) and grown in competition with the grass Poa pratensis under control or dry conditions in a common garden. Above-ground biomass was cut once and the regrowth was measured as an indicator for tolerance over a second growth period. Initially, drought had little effects on growth of T. vulgare, but after cutting, plants grew more vigorously. Against expectations, phenotypic plasticity was not higher in invasive populations, but even reduced in one trait, which may be attributable to ecological constraints imposed by multiple stress conditions. Trait responses reflected the range expansion and invasion gradient and were influenced by the latitudinal origin of populations. Populations of invaded ranges may be subject to faster and more extensive genetic mixing or had less time to undergo and reflect selective processes.

Drained peat soils are a significant source of greenhouse gas (GHG) emissions to the atmosphere. Rewetting these soils is considered an important climate change mitigation tool to reduce emissions and create suitable conditions for carbon sequestration. Long-term monitoring is essential to capture interannual variations in GHG emissions and associated environmental variables and to reduce the uncertainty linked with GHG emission factor calculations. In this study, we present GHG balances: carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O) calculated for a 5-year period at a rewetted industrial cutaway peatland in Ireland (rewetted 7 years prior to the start of the study); and compare the results with an adjacent drained area (2-year data set), and with ten long-term data sets from intact (i.e. undrained) peatlands in temperate and boreal regions. In the rewetted site, CO 2 exchange (or net ecosystem exchange (NEE)) was strongly influenced by ecosystem respiration (R eco ) rather than gross primary production (GPP). CH 4 emissions were related to soil temperature and either water table level or plant biomass. N 2 O emissions were not detected in either drained or rewetted sites. Rewetting reduced CO 2 emissions in unvegetated areas by approximately 50%. When upscaled to the ecosystem level, the emission factors (calculated as 5-year mean of annual balances) for the rewetted site were (±SD) -104 ± 80 g CO 2 -C m -2  yr -1 (i.e. CO 2 sink) and 9 ± 2 g CH 4 -C m -2  yr -1 (i.e. CH 4 source). Nearly a decade after rewetting, the GHG balance (100-year global warming potential) had reduced noticeably (i.e. less warming) in comparison with the drained site but was still higher than comparative intact sites. Our results indicate that rewetted sites may be more sensitive to interannual changes in weather conditions than their more resilient intact counterparts and may switch from an annual CO 2 sink to a source if triggered by slightly drier conditions., (© 2016 John Wiley & Sons Ltd.)

Climate change threatens agricultural productivity worldwide, resulting in higher food prices. Associated economic gains and losses differ not only by region but also between producers and consumers and are affected by market dynamics. On the basis of an impact modeling chain, starting with 19 different climate projections that drive plant biophysical process simulations and ending with agro-economic decisions, this analysis focuses on distributional effects of high-end climate change impacts across geographic regions and across economic agents. By estimating the changes in surpluses of consumers and producers, we find that climate change can have detrimental impacts on global agricultural welfare, especially after 2050, because losses in consumer surplus generally outweigh gains in producer surplus. Damage in agriculture may reach the annual loss of 0.3% of future total gross domestic product at the end of the century globally, assuming further opening of trade in agricultural products, which typically leads to interregional production shifts to higher latitudes. Those estimated global losses could increase substantially if international trade is more restricted. If beneficial effects of atmospheric carbon dioxide fertilization can be realized in agricultural production, much of the damage could be avoided. Although trade policy reforms toward further liberalization help alleviate climate change impacts, additional compensation mechanisms for associated environmental and development concerns have to be considered.

Climate change has impacts on agricultural yields, which could alter cropland requirements and hence deforestation rates. Thus, land-use responses to climate change might influence terrestrial carbon stocks. Moreover, climate change could alter the carbon storage capacity of the terrestrial biosphere and hence the land-based mitigation potential. We use a global spatially explicit economic land-use optimization model to (a) estimate the mitigation potential of a climate policy that provides economic incentives for carbon stock conservation and enhancement, (b) simulate land-use and carbon cycle responses to moderate climate change (RCP2.6), and (c) investigate the combined effects throughout the 21st century. The climate policy immediately stops deforestation and strongly increases afforestation, resulting in a global mitigation potential of 191 GtC in 2100. Climate change increases terrestrial carbon stocks not only directly through enhanced carbon sequestration (62 GtC by 2100) but also indirectly through less deforestation due to higher crop yields (16 GtC by 2100). However, such beneficial climate impacts increase the potential of the climate policy only marginally, as the potential is already large under static climatic conditions. In the broader picture, this study highlights the importance of land-use dynamics for modeling carbon cycle responses to climate change in integrated assessment modeling.

Development efforts for poverty reduction and food security in sub-Saharan Africa will have to consider future climate change impacts. Large uncertainties in climate change impact assessments do not necessarily complicate, but can inform development strategies. The design of development strategies will need to consider the likelihood, strength, and interaction of climate change impacts across biosphere properties. We here explore the spread of climate change impact projections and develop a composite impact measure to identify hotspots of climate change impacts, addressing likelihood and strength of impacts. Overlapping impacts in different biosphere properties (e.g. flooding, yields) will not only claim additional capacity to respond, but will also narrow the options to respond and develop. Regions with severest projected climate change impacts often coincide with regions of high population density and poverty rates. Science and policy need to propose ways of preparing these areas for development under climate change impacts., (© 2014 John Wiley & Sons Ltd.)

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information., (© 2014 John Wiley & Sons Ltd.)

Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400-1,400 Pcal (8-24% of present-day total) when CO2 fertilization effects are accounted for or 1,400-2,600 Pcal (24-43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20-60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600-2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.

Climate change impact assessments on agriculture are subject to large uncertainties, as demonstrated in the present review of recent studies for Africa. There are multiple reasons for differences in projections, including uncertainties in greenhouse gas emissions and patterns of climate change; assumptions on future management, aggregation, and spatial extent; and methodological differences. Still, all projections agree that climate change poses a significant risk to African agriculture. Most projections also see the possibility of increasing agricultural production under climate change, especially if suitable adaptation measures are assumed. Climate change is not the only projected pressure on African agriculture, which struggles to meet demand today and may need to feed an additional one billion individuals by 2050. Development strategies are urgently needed, but they will need to consider future climate change and its inherent uncertainties. Science needs to show how existing synergies between climate change adaptation and development can be exploited.

The Intergovernmental Panel on Climate Change (IPCC) assessment of major risks for African agriculture and food security caused by climate change during coming decades is confirmed by a review of more recent climate change impact assessments (14 quantitative, six qualitative). Projected impacts relative to current production levels range from -100% to +168% in econometric, from -84% to +62% in process-based, and from -57% to +30% in statistical assessments. Despite large uncertainty, there are several robust conclusions from published literature for policy makers and research agendas: agriculture everywhere in Africa runs some risk to be negatively affected by climate change; existing cropping systems and infrastructure will have to change to meet future demand. With respect to growing population and the threat of negative climate change impacts, science will now have to show if and how agricultural production in Africa can be significantly improved.

• Multi-modal energy system planning approach to support political decision makers. • Energy sector coupling enables efficient transition pathway to achieve climate goals. • Main drivers: electrification of heat and mobility sectors and use of synthetic fuels. • High impact of the electricity exchange on the operation and costs of the system. In order to reach the goals of the United Nations Framework Convention on Climate Change, a stepwise reduction of energy related greenhouse gas emissions as well as an increase in the share of renewable energies is necessary. For a successful realization of these changes in energy supply, an integrated view of multiple energy sectors is necessary. The coupling of different energy sectors is seen as an option to achieve the climate goals in a cost-effective way. In this paper, a methodical approach for multi-modal energy system planning and technology impact evaluation is presented. A key feature of the model is a coupled consideration of the sectors electricity, heat, fuel and mobility. The modeling framework enables system planners to optimally plan future investments in a detailed transition pathway of the energy system of a country, considering politically defined climate goals. Based on these calculations, in-depth analyses of energy markets as well as electrical transmission and distribution grids can be performed using the presented optimization models. Energy demands, conversion and storage technologies in households, the Commerce, Trade and Services (CTS) area and the industry are modeled employing a bottom-up modeling approach. The results for the optimal planning of the German energy system until 2050 show that the combination of an increased share of renewable energies and the direct electrification of heat and mobility sectors together with the use of synthetic fuels are the main drivers to achieve the climate goals in a cost-efficient way. [ABSTRACT FROM AUTHOR]

In the course of climate change, crop plants are exposed to both altered precipitation volumes and frequencies in many cultivation areas. Reduced water availability and longer periods without precipitation can lead to changes in plant physiology and productivity.In this greenhouse study, we investigated physiological responses, including maximum quantum yield of photosystem II (Fv/Fm) and water use efficiency (WUE) as well as diverse productivity‐related parameters, including grain yield per plant, to continuous (irrigated three times a week) versus pulsed (once a week) irrigation in spring wheat (Triticum aestivum L.). In both drought regimes, plants were exposed to four different irrigation volumes.Fv/Fm was not influenced by irrigation treatment but was significantly higher after 6 weeks of drought than after 2 weeks. WUE increased with decreasing irrigation volume and based on generative biomass; it was higher in continuously‐watered compared to pulsed‐watered plants that received a similar amount of water over the experiment. Infrequent irrigation led to a reduction in grain yield of up to 51%. This lower productivity was primarily caused by fewer tillers in pulsed‐watered plants. Additionally, at low water volume the thousand grain weight (TGW) was lower in infrequently‐compared to frequently‐watered plants. In contrast, the number of grains per ear was higher in pulsed‐watered plants.In conclusion, reduced irrigation frequency, next to a decrease in irrigation volume, negatively affects WUE and grain yield of spring wheat. These results might have important implications for irrigation scheduling and breeding programmes under a changing climate. [ABSTRACT FROM AUTHOR]

Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity to climate variability and climate change is comparable for impact models designed for either scale. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climate change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a better reproduction of reference conditions. However, the sensitivity of the two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases, but have distinct differences in other cases, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability. Whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models calibrated and validated against observed discharge should be used. [ABSTRACT FROM AUTHOR]

Field experiments that expose terrestrial ecosystems to climate change factors by manipulations are expensive to maintain, and typically only last a few years. Plant biomass is commonly used to assess responses to climate treatments and to predict climate change impacts. However, response to the treatments might be considerably different between the early years and a decade later. The aim of this data analysis was to develop and apply a method for evaluating changes in plant biomass responses through time, in order to provide a firm basis for discussing how the 'short-term' response might differ from the 'long-term' response. Across 22 sites situated in the northern hemisphere, which covered three continents, and multiple ecosystems (grasslands, shrublands, moorlands, forests, and deserts), we evaluated biomass datasets from long-term experiments with exposure to elevated CO2 (eCO2), warming, or drought. We developed methods for assessing biomass response patterns to the manipulations using polynomial and linear (piecewise) model analysis and linked the responses to site-specific variables such as temperature and rainfall. Polynomial patterns across sites indicated changes in response direction over time under eCO2, warming, and drought. In addition, five distinct pattern types were confirmed within sites: 'no response', 'delayed response', 'directional response', 'dampening response', and 'altered response' patterns. We found that biomass response direction was as likely to change over time as it was to be consistent, and therefore suggest that climate manipulation experiments should be carried out over timescales covering both short- and long-term responses, in order to realistically assess future impacts of climate change. [ABSTRACT FROM AUTHOR]

In the ongoing political debate on climate change, global mean temperature change (▵Tglob) has become the yardstick by which mitigation costs, impacts from unavoided climate change, and adaptation requirements are discussed. For a scientifically informed discourse along these lines, systematic assessments of climate change impacts as a function of ▵Tglob) are required. The current availability of climate change scenarios constrains this type of assessment to a narrow range of temperature change and/or a reduced ensemble of climate models. Here, a newly composed dataset of climate change scenarios is presented that addresses the specific requirements for global assessments of climate change impacts as a function of ▵Tglob). A pattern-scaling approach is applied to extract generalised patterns of spatially explicit change in temperature, precipitation and cloudiness from 19 Atmosphere-Ocean General Circulation Models (AOGCMs). The patterns are combined with scenarios of global mean temperature increase obtained from the reducedcomplexity climate model MAGICC6 to create climate scenarios covering warming levels from 1.5 to 5 degrees above pre-industrial levels around the year 2100. The patterns are shown to sufficiently maintain the original AOGCMs' climate change properties, even though they, necessarily, utilise a simplified relationships between ▵ Tglob) and changes in local climate properties. The dataset (made available online upon final publication of this paper) facilitates systematic analyses of climate change impacts as it covers a wider and finer-spaced range of climate change scenarios than the original AOGCM simulations. [ABSTRACT FROM AUTHOR]

Abstract: Maize (Zea mays L.) is one of the most important food crops and very common in all parts of sub-Saharan Africa. In 2010 53million tons of maize were produced in sub-Saharan Africa on about one third of the total harvested cropland area (~33million ha). Our aim is to identify the limiting agroclimatic variable for maize growth and development in sub-Saharan Africa by analyzing the separated and combined effects of temperature and precipitation. Under changing climate, both climate variables are projected to change severely, and their impacts on crop yields are frequently assessed using process-based crop models. However it is often unclear which agroclimatic variable will have the strongest influence on crop growth and development under climate change and previous studies disagree over this question. We create synthetic climate data in order to study the effect of large changes in the length of the wet season and the amount of precipitation during the wet season both separately and in combination with changes in temperature. The dynamic global vegetation model for managed land LPJmL is used to simulate maize yields under current and future climatic conditions for the two 10-year periods 2056–2065 and 2081–2090 for three climate scenarios for the A1b emission scenario but without considering the beneficial CO2 fertilization effect. The importance of temperature and precipitation effects on maize yields varies spatially and we identify four groups of crop yield changes: regions with strong negative effects resulting from climate change (<−33% yield change), regions with moderate (−33% to −10% yield change) or slight negative effects (−10% to +6% yield change), and regions with positive effects arising from climate change mainly in currently temperature-limited high altitudes (>+6% yield change). In the first three groups temperature increases lead to maize yield reductions of 3 to 20%, with the exception of mountainous and thus cooler regions in South and East Africa. A reduction of the wet season precipitation causes decreases in maize yield of at least 30% and prevails over the effect of increased temperatures in southern parts of Mozambique and Zambia, the Sahel and parts of eastern Africa in the two projection periods. This knowledge about the limiting abiotic stress factor in each region will help to prioritize future research needs in modeling of agricultural systems as well as in drought and heat stress breeding programs and to identify adaption options in agricultural development projects. On the other hand the study enhances the understanding of temperature and water stress effects on crop yields in a global vegetation model in order to identify future research and model development needs. [Copyright &y& Elsevier]

Abstract: Multiple cropping systems provide more harvest security for farmers, allow for crop intensification and furthermore influence ground cover, soil erosion, albedo, soil chemical properties, pest infestation and the carbon sequestration potential. We identify the traditional sequential cropping systems in ten sub-Saharan African countries from a survey dataset of more than 8600 households. We find that at least one sequential cropping system is traditionally used in 35% of all administrative units in the dataset, mainly including maize or groundnuts. We compare six different management scenarios and test their susceptibility as adaptation measure to climate change using the dynamic global vegetation model for managed land LPJmL. Aggregated mean crop yields in sub-Saharan Africa decrease by 6–24% due to climate change depending on the climate scenario and the management strategy. As an exception, some traditional sequential cropping systems in Kenya and South Africa gain by at least 25%. The crop yield decrease is typically weakest in sequential cropping systems and if farmers adapt the sowing date to changing climatic conditions. Crop calorific yields in single cropping systems only reach 40–55% of crop calorific yields obtained in sequential cropping systems at the end of the 21st century. The farmers’ choice of adequate crops, cropping systems and sowing dates can be an important adaptation strategy to climate change and these management options should be considered in climate change impact studies on agriculture. [Copyright &y& Elsevier]

The article presents a newly composed dataset of climate change scenario to pacify requirements for global assessments of climate change impacts. It is evident that global mean temperature change has become the yardstick to gauze impact of unavoidable climate change and requirement of adaptation. It is informed that the dataset helps in analyzing impact of climate change and implement climate model.

Abstract: Weather data are essential inputs for crop growth models, which are primarily developed for field level applications using site-specific daily weather data. Daily weather data are often not available, especially when models are applied to large regions and/or for future projections. It is possible to generate daily weather data from aggregated weather data, such as average monthly weather data, e.g. through a linear interpolation method. But, due to the nonlinearity of many weather–crop relationships, results of simulations using linearly interpolated data will deviate from those with actual (daily) data. The objective of this study was to analyse the sensitivity of different modelling approaches to the temporal resolution of weather input data. We used spring wheat as an example and considered three combinations of summarized and detailed approaches to model leaf area index development and associated radiation interception and biomass productivity, reflecting the typical range of detail in the structure of most models. Models were run with actual weather data and with aggregated weather data from which day-to-day variation had been removed by linear interpolation between monthly averages. Results from different climatic regions in Europe show that simulated biomass differs between model simulations using actual or aggregated temperature and/or radiation data. In addition, we find a relationship between the sensitivity of an approach to interpolation of input data and the degree of detail in that modelling approach: increasing detail results in higher sensitivity. Moreover, the magnitude of the day-to-day variability in weather conditions affects the results: increasing variability results in stronger differences between model results. Our results have implications for the choice of a specific approach to model a certain process depending on the available temporal resolution of input data. [Copyright &y& Elsevier]

In the coming decades, world agricultural systems will face serious transitions. Population growth, income and lifestyle changes will lead to considerable increases in food demand. Moreover, a rising demand for renewable energy and biodiversity protection may restrict the area available for food production. On the other hand, global climate change will affect production conditions, for better or worse depending on regional conditions. In order to simulate these combined effects consistently and in a spatially explicit way, we have linked the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ) with a "Management model of Agricultural Production and its Impact on the Environment" (MAgPIE). LPJ represents the global biosphere with a spatial resolution of 0.5 degree. MAgPIE covers the most important agricultural crop and livestock production types. A prototype has been developed for one sample region. In the next stage this will be expanded to several economically relevant regions on a global scale, including international trade. The two models are coupled through a layer of productivity zones. In the paper we present the modelling approach, develop first joint scenarios and discuss selected results from the coupled modelling system. [ABSTRACT FROM AUTHOR]

Facing rising atmospheric CO 2 concentrations, subsoils may play an important role in the global carbon (C) cycle due to the presence of unsaturated mineral surfaces. Further, macroaggregation is considered a crucial process influencing C sequestration. However, analyses on subsoil aggregation and C retention processes under long-term elevated CO 2 (eCO 2 ) are lacking. In this study we investigated the long-term effect of +20% above ambient CO 2 concentration (corresponds to conditions reached 2035–2045) in a temperate grassland ecosystem at the Giessen Free Air CO 2 Enrichment (Gi-FACE), Germany. A depth-dependent response of macroaggregation to eCO 2 was observed: While in subsoil (15–45 cm depth) macroaggregation increased under eCO 2 , no CO 2 induced change in macroaggregation was detected in topsoil (0–15 cm). Increased macroaggregation in subsoil coincided with higher SOC content of large macroaggregates (LM). Mean residence time (MRT) of SOC in aggregate-size classes were not different among each other under eCO 2 . However, macroaggregates and bulk soil differed in their MRT between soil depths. Despite increased macroaggregation and an estimated high SOC sequestration potential in subsoil we could not observe an increase in SOC content of bulk soil. [ABSTRACT FROM AUTHOR]

Palaeoclimate reconstruction on the northern Tibetan Plateau resulted in a large spectrum of different and partly divergent interpretations for the climate evolution during the late glacial and the Holocene. In some cases this is caused by incomplete understanding of the geomorphological processes influencing the different proxies used. To overcome these limitations and to enhance the understanding of the complex process interactions in a sensitive and highly dynamical environment a detailed analysis of different members of the sedimentary system at Lake Heihai on the northern Tibetan Plateau was conducted. Lake level variations during the late Pleistocene were influenced by sediment supply to an alluvial fan. This sediment surplus resulted in the temporary blocking of the outflow of Lake Heihai. High sediment supply presumably occurred during or shortly after large glaciations in the Kunlun Shan. The spatial distribution of aeolian sediments revealed a strong relationship to possible source areas. This resulted in a spatially heterogeneous distribution of the aeolian sediments. Furthermore, topographic effects have an important influence on the preservation of the sediments. Aeolian sediments deposited in sheltered positions might not be comparable with other archives with a similar grain size. Nevertheless, deposition of loess during the mid-Holocene indicates a shift to wetter climate conditions on the northern Tibetan Plateau. This might be caused by the intrusion of the East Asian Summer monsoon into the area. During the late Holocene, the Asian summer monsoon retreated and aeolian sediments were reactivated. [ABSTRACT FROM AUTHOR]

Abstract: Substitution of the Intergovernmental Panel on Climate Change (IPCC) default methodology by country-specific activity data is recommended for improved estimation of baseline soil organic carbon (SOC) stocks and their changes. In the Republic of Ireland (ROI), previous studies focused either predominantly on grassland or on all land cover types but were depth-limited. To improve the accuracy, Tier 2 approaches are proposed by the IPCC. This requires an analysis of high spatial resolution databases (such as the Irish NSDB – National Soil Database) and maps, collated for major land cover, soil types and land use areas in Ireland. In this study, data were overlaid using ArcGIS to derive information for disaggregated soil types and agricultural land use areas. Empirical models were developed using separate measurement data to estimate the NSDB-derived SOC concentrations for deeper layers, using a depth distribution function and the bulk density (ρ d ) using pedotransfer functions. The soil type specific models (R2 =0.87–0.99) had an improved estimate of SOC densities when mineral and organic soils (peat) were treated separately. The estimated SOC densities for grasslands on mineral plus organo-mineral soils at the 0–10, 0–30, 0–50 and 0–100cm depths were 52.2, 127.1, 170.9 and 213.8t C ha−1, respectively. For arable lands, the corresponding SOC densities were 29.9, 81.3, 117.6 and 167.5t C ha−1. Nationally, for all soil types, the corresponding stocks (the products of SOC density and land cover area) were estimated to be 246.9, 608.1, 829.5 and 1079.3Tg for grassland, and 13.5, 36.7, 50.2 and 67.0Tg for arable lands in the three soil layers. The total national SOC stocks were estimated to be 888 at 0–30cm and 1832Tg at 0–100cm reference depths. For the complete soil profile, including peats >100cm depth, the national estimate was 2824Tg. The combined empirical models and Geographical Information System technique provide robust estimates of SOC stocks for disaggregated land covers and soil types, enabling Ireland to consider moving from Tier 1 to Tier 2 accounting methodology. This improved national inventory of the ROI is important for estimates of the C stock related to the Land Use, Land Use Change and Forestry (LULUCF) categories. [Copyright &y& Elsevier]