Projections of climate change impacts on crop yields are inherently uncertain1. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate2. ...However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models1, 3 are difficult4. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development and policymaking.
Model projections suggest that although increased temperature and decreased soil moisture will act to reduce global crop yields by 2050, the direct fertilization effect of rising carbon dioxide ...concentration (CO₂) will offset these losses. The CO₂ fertilization factors used in models to project future yields were derived from enclosure studies conducted approximately 20 years ago. Free-air concentration enrichment (FACE) technology has now facilitated large-scale trials of the major grain crops at elevated CO₂ under fully open-air field conditions. In those trials, elevated CO₂ enhanced yield by ~50% less than in enclosure studies. This casts serious doubt on projections that rising CO₂ will fully offset losses due to climate change.
Climate change and global wine quality JONES, Gregory V; WHITE, Michael A; COOPER, Owen R ...
Climatic change,
12/2005, Letnik:
73, Številka:
3
Journal Article
Recenzirano
From 1950 to 1999 the majority of the world's highest quality wine-producing regions experienced growing season warming trends. Vintage quality ratings during this same time period increased ...significantly while year-to-year variation declined. While improved winemaking knowledge and husbandry practices contributed to the better vintages it was shown that climate had, and will likely always have, a significant role in quality variations. This study revealed that the impacts of climate change are not likely to be uniform across all varieties and regions. Currently, many European regions appear to be at or near their optimum growing season temperatures, while the relationships are less defined in the New World viticulture regions. For future climates, model output for global wine producing regions predicts an average warming of 2 super()C in the next 50 yr. For regions producing high-quality grapes at the margins of their climatic limits, these results suggest that future climate change will exceed a climatic threshold such that the ripening of balanced fruit required for existing varieties and wine styles will become progressively more difficult. In other regions, historical and predicted climate changes could push some regions into more optimal climatic regimes for the production of current varietals. In addition, the warmer conditions could lead to more poleward locations potentially becoming more conducive to grape growing and wine production.
A simulation analysis was carried out using the InfoCrop-rice model to quantify impacts and adaptation gains, as well as to identify vulnerable regions for irrigated and rain fed rice cultivation in ...future climates in India. Climates in A1b, A2, B1 and B2 emission scenarios as per a global climate model (MIROC3.2.HI) and a regional climate model (PRECIS) were considered for the study. On an aggregated scale, the mean of all emission scenarios indicate that climate change is likely to reduce irrigated rice yields by ~4 % in 2020 (2010–2039), ~7 % in 2050 (2040–2069), and by ~10 % in 2080 (2070–2099) climate scenarios. On the other hand, rainfed rice yields in India are likely to be reduced by ~6 % in the 2020 scenario, but in the 2050 and 2080 scenarios they are projected to decrease only marginally (<2.5 %). However, spatial variations exist for the magnitude of the impact, with some regions likely to be affected more than others. Adaptation strategies comprising agronomical management can offset negative impacts in the near future—particularly in rainfed conditions—but in the longer run, developing suitable varieties coupled with improved and efficient crop husbandry will become essential. For irrigated rice crop, genotypic and agronomic improvements will become crucial; while for rainfed conditions, improved management and additional fertilizers will be needed. Basically climate change is likely to exhibit three types of impacts on rice crop: i) regions that are adversely affected by climate change can gain in net productivity with adaptation; ii) regions that are adversely affected will still remain vulnerable despite adaptation gains; and iii) rainfed regions (with currently low rainfall) that are likely to gain due to increase in rainfall can further benefit by adaptation. Regions falling in the vulnerable category even after suggested adaptation to climate change will require more intensive, specific and innovative adaptation options. The present analysis indicates the possibility of substantial improvement in yields with efficient utilization of inputs and adoption of improved varieties.
► The sensitivity of vegetation phenology to climate change varies among biomes. ► Key weaknesses in our current understanding of phenology drivers are identified. ► Phenology controls many feedbacks ...of vegetation to the climate system. ► The size and seasonality of these feedbacks will shift as phenology shifts. ► Models that couple the land surface to the climate system need better phenology.
Vegetation phenology is highly sensitive to climate change. Phenology also controls many feedbacks of vegetation to the climate system by influencing the seasonality of albedo, surface roughness length, canopy conductance, and fluxes of water, energy, CO2 and biogenic volatile organic compounds. In this review, we first discuss the environmental drivers of phenology, and the impacts of climate change on phenology, in different biomes. We then examine the vegetation-climate feedbacks that are mediated by phenology, and assess the potential impact on these feedbacks of shifts in phenology driven by climate change. We finish with an overview of phenological modeling and we suggest ways in which models might be improved using existing data sets. Several key weaknesses in our current understanding emerge from this analysis. First, we need a better understanding of the drivers of phenology, particularly in under-studied biomes (e.g. tropical forests). We do not have a mechanistic understanding of the role of photoperiod, even in well-studied biomes. In all biomes, the factors controlling senescence and dormancy are not well-documented. Second, for the most part (i.e. with the exception of phenology impacts on CO2 exchange) we have only a qualitative understanding of the feedbacks between vegetation and climate that are mediated by phenology. We need to quantify the magnitude of these feedbacks, and ensure that they are accurately reproduced by models. Third, we need to work towards a new understanding of phenological processes that enables progress beyond the modeling paradigms currently in use. Accurate representation of phenological processes in models that couple the land surface to the climate system is particularly important, especially when such models are being used to predict future climate.
Increasing nutrient inputs into terrestrial ecosystems affect not only plant communities but also associated soil microbial communities. Studies carried out in predominantly unmanaged ecosystems have ...found that increasing nitrogen (N) inputs generally decrease soil microbial biomass; less is known about long-term impacts in managed systems such as agroecosystems. The objective of this paper was to analyze the responses of soil microorganisms to mineral fertilizer using data from long-term fertilization trials in cropping systems. A meta-analysis based on 107 datasets from 64 long-term trials from around the world revealed that mineral fertilizer application led to a 15.1% increase in the microbial biomass (Cmic) above levels in unfertilized control treatments. Mineral fertilization also increased soil organic carbon (Corg) content and our results suggest that Corg is a major factor contributing to the overall increase in Cmic with mineral fertilization. The magnitude of the effect of fertilization on Cmic was pH dependent. While fertilization tended to reduce Cmic in soils with a pH below 5 in the fertilized treatment, it had a significantly positive effect at higher soil pH values. Duration of the trial also affected the response of Cmic to fertilization, with increases in Cmic most pronounced in studies with a duration of at least 20 years. The input of N per se does not seem to negatively affect Cmic in cropping systems. The application of urea and ammonia fertilizers, however, can temporarily increase pH, osmotic potential and ammonia concentrations to levels inhibitory to microbial communities. Even though impacts of fertilizers are spatially limited, they may strongly affect soil microbial biomass and community composition in the short term. Long-term repeated mineral N applications may alter microbial community composition even when pH changes are small. How specific microbial groups respond to repeated applications of mineral fertilizers, however, varies considerably and seems to depend on environmental and crop management related factors.
•In the long term mineral fertilizer increases microbial biomass in cropping systems.•The long-term effect of mineral fertilizer on microbial biomass is pH dependent.•Responses of specific microbial groups depend on environment and crop management.
Climate change can bring positive and negative effects on Chinese agriculture, but negative impacts tend to dominate. The annual mean surface temperature has risen about 0.5–0.8 °C. The precipitation ...trends have not been identified during the past 100 years in China, although the frequency and intensity of extreme weather/climate events have increased, especially of drought. Water scarcity, more frequent and serious outbreaks of insects and diseases, and soil degradation caused by climate change have impacted agro-environmental conditions. However, temperature rise prolonged the crop growth seasons and cold damages have reduced in Northeast China. The projection of climate change indicates that the surface temperature will continue to increase with about 3.9 to 6.0 °C and precipitation is expected to increase by 9 to 11 % at the end of 21st century in China. Climate warming will provide more heat and as a consequence, the boundary of the triple-cropping system (TCS) will extend northwards by as much as 200 to 300 km, from the Yangtze River Valley to the Yellow River Basin, and the current double-cropping system (DCS) will move to the central part of China, into the current single cropping system (SCS) area which will decrease in SCS surface area of 23.1 % by 2050. Climate warming will also affect the optimum location for the cultivation of China’s main crop varieties. If no measures are taken to adapt to climate changes, compared with the potential yield in 1961–1990, yields of irrigated wheat, corn and rice are projected to decrease by 2.2–6.7 %, 0.4 %–11.9 % and 4.3–12.4 % respectively in the 2050s. Climate warming will enhance potential evaporation and reduce the availability of soil moisture, thus causing a greater need for agricultural irrigation, intensifying the conflict between water supply and demand, especially in arid and semi-arid areas of China. With adequate irrigation, the extent of the reduction in yield of China’s corn and wheat can be improved by 5 % to 15 %, and rice by 5 % or so than the potential yield in 1961–1990. Adaptive measures can reduce the agricultural loss under climate change. If effective measures are taken in a timely way, then climate change in the next 30–50 years will not have a significant influence on China’s food security.
•Overview of currently available measurement methods to separately assess E and T.•Models validated for their ability to partition between E and T are discussed briefly.•52 papers were found to ...attempt ET partitioning.•E/ET ratio was found to exceed 30% in 32 of the studies.•Despite measurement variability ∼30% of the papers show over 90% agreement for ET=E+T.
Partitioning of evapotranspiration (ET) into evaporation from the soil (E) and transpiration through the stomata of plants (T) is challenging but important in order to assess biomass production and the allocation of increasingly scarce water resources. Generally, T is the desired component with the water being used to enhance plant productivity; whereas, E is considered a source of water loss or inefficiency. The magnitude of E is expected to be quite significant in sparsely vegetated systems, particularly in dry areas or in very wet systems such as surface irrigated crops and wetlands. In these cases, ET partitioning is fundamental to accurately monitor system hydrology and to improve water management practices. This paper aims to evaluate and summarize available methods currently used to separately determine E and T components. We presuppose that, to test the accuracy of ET partitioning methods (measurements and/or modeling), all three components, i.e., E, T and ET, must be estimated independently, but recognize that sometimes one of the components is taken as the residual of the other two. Models that were validated against measurements for their ability to partition between E and T are briefly discussed. To compare approaches, 52 ET partitioning studies were considered regarding estimates of the relative amount of E and for success of agreement in closing the ET=E+T equation. The E/ET ratio was found to exceed 30% in 32 of the studies, which confirms the hypothesis that E often constitutes a large fraction of ET and deserves independent consideration. Only 20 studies estimated E and T as well as ET, and had varied results. A number of studies succeeded to estimate E+T to within 10% of measured ET. Future challenges include development of models simulating the components of ET separately and advancement of methods for continuous measurement of E, T and/or the ratio between the two.
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