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Presently, climate change has increased the frequency of extreme meteorological events such as tropical cyclones. In the western Pacific basin, these cyclones are called typhoons, and in this area, around Taiwan Island, their frequency has almost doubled since 2000. When approaching landmasses, typhoons have devastating effects on coastal vegetation. The increased frequency of these events has challenged the survival of coastal plant species and their posttyphoon recovery. In this study, a population of coastal gynodioecious Ficus pedunculosa var. mearnsii (Mearns fig) was surveyed for two years to investigate its recovery after Typhoon Morakot, which occurred in August 2009. Similar to all the Ficus species, the Mearns fig has an obligate mutualistic association with pollinating fig wasp species, which requires syconia (the closed Ficus inflorescence) to complete its life cycle. Moreover, male gynodioecious fig species produces both pollen and pollen vectors, whereas the female counterpart produces only seeds. The recovery of the Mearns fig was observed to be rapid, with the production of both leaves and syconia. The syconium:leaf ratio was greater for male trees than for female trees, indicating the importance of syconium production for the wasp survival. Pollinating wasps live for approximately 1 day; therefore, receptive syconia are crucial. Every typhoon season, few typhoons pass by the coasts where the Mearns fig grows, destroying all the leaves and syconia. In this paper, we highlight the potential diminution of the fig population that can lead to the extinction of the mutualistic pair of species. The effects of climate change on coastal species warrant wider surveys. [ABSTRACT FROM AUTHOR]

Adaptation, as a strategy to respond to climate change, has limits: there are conditions under which adaptation strategies fail to alleviate impacts from climate change. Research has primarily focused on identifying absolute bio-physical limits. This paper contributes empirical insight to an emerging literature on the social limits to adaptation. Such limits arise from the ways in which societies perceive, experience and respond to climate change. Using qualitative data from multi-stakeholder workshops and key-informant interviews with representatives of the fisheries and tourism sectors of the Great Barrier Reef region, we identify psycho-social and structural limits associated with key adaptation strategies, and examine how these are perceived as more or less absolute across levels of organisation. We find that actors experience social limits to adaptation when: i) the effort of pursuing a strategy exceeds the benefits of desired adaptation outcomes; ii) the particular strategy does not address the actual source of vulnerability, and; iii) the benefits derived from adaptation are undermined by external factors. We also find that social limits are not necessarily more absolute at higher levels of organisation: respondents perceived considerable opportunities to address some psycho-social limits at the national-international interface, while they considered some social limits at the local and regional levels to be effectively absolute. [ABSTRACT FROM AUTHOR]

Various models and datasets related to aflatoxins in the maize and dairy production chain have been developed and used but they have not yet been linked with each other. This study aimed to investigate the impacts of climate change on aflatoxin B1 production in maize and its consequences on aflatoxin M1 contamination in dairy cow’s milk, using a full chain modelling approach. To this end, available models and input data were chained together in a modelling framework. As a case study, we focused on maize grown in Eastern Europe and imported to the Netherlands to be fed–as part of dairy cows’ compound feed–to dairy cows in the Netherlands. Three different climate models, one aflatoxin B1 prediction model and five different carryover models were used. For this particular case study of East European maize, most of the calculations suggest an increase (up to 50%) of maximum mean aflatoxin M1 in milk by 2030, except for one climate (DMI) model suggesting a decrease. Results from all combinations of carryover and climate models suggest a similar or slight increase (up to 0.6%) of the chance of finding aflatoxin M1 in milk above the EC limit of 0.05 μg/kg by 2030. Results varied mainly with the climate model data and carryover model considered. The model framework infrastructure is flexible so that forecasting models for other mycotoxins or other food safety hazards as well as other production chains, together with necessary input databases, can easily be included as well. This modelling framework for the first time links datasets and models related to aflatoxin B1 in maize and related aflatoxin M1 the dairy production chain to obtain a unique predictive methodology based on Monte Carlo simulation. Such an integrated approach with scenario analysis provides possibilities for policy makers and risk managers to study the effects of changes in the beginning of the chain on the end product. [ABSTRACT FROM AUTHOR]

Studying the dynamic of Neolithic settlement on a local scale and its connection to climate variability is often difficult due to missing on-site climate reconstructions from natural archives. Here we bring together archaeological settlement data and a regional climate reconstruction from precipitation-sensitive trees. Both archives hold information about regional settlement dynamics and hydroclimate variability spanning the time of the first farming communities, the so called Linearbandkeramik (LBK) in Bavaria, Germany. Precipitation-sensitive tree-ring series from subfossil oak are used to develop a spring-summer precipitation reconstruction (5700–4800 B.C.E.) representative for southern Germany. Early Neolithic settlement data from Bavaria, mainly for the duration of the LBK settlement activities, are critically evaluated and compared to this unique regional hydroclimate reconstruction as well as to reconstructions of Greenland temperature, summer sea surface temperature, delta 18O and global solar irradiance to investigate the potential impact of climate on Neolithic settlers and their settlement dynamic during the LBK. Our hydroclimate reconstruction demonstrates an extraordinarily high frequency of severe dry and wet spring-summer seasons during the entire LBK, with particularly high year-to-year variability from 5400 to 5101 B.C.E. and with lower fluctuations until 4801 B.C.E. A significant influence of regional climate on the dynamic of the LBK is possible (e.g. around 4960 B.C.E.), but should be interpreted very carefully due to asynchronous trends in settlement dynamics. Thus, we conclude that even when a climate proxy such as tree rings that has excellent spatio-temporal resolution is available, it remains difficult to establish potential connections between the settlement dynamic of the LBK and climate variability. [ABSTRACT FROM AUTHOR]

Climate change affects agriculture through a range of direct and indirect pathways. These include direct changes to impacts of pests and diseases on crops and indirect effects produced by interactions between organisms. It remains unclear whether the net effects of these biotic influences will be beneficial or detrimental to crop yield because few studies consider multiple interactions within communities and the net effects of these on community structure and yield. In this study, we created two experimental grapevine communities in field cages, and quantified direct and indirect effects of key pest and disease species under simulated climate change conditions (elevated temperature and reduced humidity). We found that the net impact of simulated climate change on total yield differed for the two communities, with increased yield in one community and no effect in the other. These effects, and the interactions between pests and pathogens, may also have been affected by the prevailing abiotic conditions, and we discuss how these may contribute to our findings. These results demonstrate that future research should consider more of the interactions between key organisms affecting crops under varying abiotic conditions to help generate future recommendations for adapting to the effects of climate change. [ABSTRACT FROM AUTHOR]

Knowledge about the relative importance of influencing-factors on rice yield gap is crucial to rice production, especially in southwestern China where topography is extremely complicated. In the current study, the data of rice yield from a total of 76 experiments were collected in 2008 and 2009 in Chongqing, southwest China. For each location, two treatments with fertilizer and without fertilizer were carried out, each treatment was performed with three replications, and yield gap was calculated using fertilized yield minus unfertilized yield. Seventeen influencing-factors including variety, fertilization, climate, terrain, and soil properties were obtained at each location. Regression tree (RT) model were employed to investigate relative important of influencing-factors to rice yield gap variability. The result of Pearson correlation analysis suggested yield gap of rice was positively correlated with sunshine hours, phosphorous and potassium fertilizers, while negatively correlated with soil available nitrogen content. The results of RT showed that the selected influencing-factors explained about 74.1% of rice yield gap variation. Meanwhile, the result also indicated variety followed by others had more influence on rice yield gap variation. Our findings analyzed by regression model at a regional scale suggested that more precise fertilization recommendation should be formulated based on comprehensive factors (e.g., soil, climate, terrain, variety), which reasonably guided farmer and government for rice production. [ABSTRACT FROM AUTHOR]

Wheat yield dynamic in Canada, USA, Russia and Kazakhstan from 1981 till 2015 was related to air temperature and precipitation during wheat season to evaluate the effects of climate change. The study used yield data from the provinces, states and regions and average yield from 19 spring wheat breeding/research sites. Both at production and research sites grain yield in Eurasia was two times lower compared to North America. The yearly variations in grain yield in North America and Eurasia did not correlate suggesting that higher yield in one region was normally associated with lower yield in another region. Minimum and maximum air temperature during the wheat growing season (April-August) had tendency to increase. While precipitation in April-August increased in North American sites from 289 mm in 1981–1990 to 338 mm in 2006–2015 it remained constant and low at Eurasian sites (230 and 238 mm, respectively). High temperature in June and July negatively affected grain yield in most of the sites at both continents. Climatic changes resulted in substantial changes in the dates of planting and harvesting normally leading to extension of growing season. Longer planting-harvesting period was positively associated with the grain yield for most of the locations. The climatic changes since 1981 and spring wheat responses suggest several implications for breeding. Gradual warming extends the wheat growing season and new varieties need to match this to utilize their potential. Higher rainfall during the wheat season, especially in North America, will require varieties with higher yield potential responding to moisture availability. June is a critical month for spring wheat in both regions due to the significant negative correlation of grain yield with maximum temperature and positive correlation with precipitation. Breeding for adaptation to higher temperatures during this period is an important strategy to increase yield. [ABSTRACT FROM AUTHOR]

Understanding the regional relationships between climate change and crop production will benefit strategic decisions for future agricultural adaptation in China. In this study, the combined effects of climatic factors on spring wheat phenophase and grain yield over the past three decades in Inner Mongolia, China, were explored based on the daily climate variables from 1981–2014 and detailed observed data of spring wheat from 1981–2014. Inner Mongolia was divided into three different climate type regions, the eastern, central and western regions. The data were gathered from 10 representative agricultural meteorological experimental stations in Inner Mongolia and analysed with the Agricultural Production Systems Simulator (APSIM) model. First, the performance of the APSIM model in the spring wheat planting areas of Inner Mongolia was tested. Then, the key climatic factors limiting the phenophases and yield of spring wheat were identified. Finally, the responses of spring wheat phenophases and yield to climate change were further explored regionally. Our results revealed a general yield reduction of spring wheat in response to the pronounced climate warming from 1981 to 2014, with an average of 3564 kg·ha-1. The regional differences in yields were significant. The maximum potential yield of spring wheat was found in the western region. However, the minimum potential yield was found in the middle region. The air temperature and soil surface temperature were the optimum climatic factors that affected the key phenophases of spring wheat in Inner Mongolia. The influence of the average maximum temperature on the key phenophases of spring wheat was greater than the average minimum temperature, followed by the relative humidity and solar radiation. The most insensitive climatic factors were precipitation, wind speed and reference crop evapotranspiration. As for the yield of spring wheat, temperature, solar radiation and air relative humidity were major meteorological factors that affected in the eastern and western Inner Mongolia. Furthermore, the effect of the average minimum temperature on yield was greater than that of the average maximum temperature. The increase of temperature in the western and middle regions would reduce the spring wheat yield, while in the eastern region due to the rising temperature, the spring wheat yield increased. The increase of solar radiation in the eastern and central regions would increase the yield of spring wheat. The increased air relative humidity would make the western spring wheat yield increased and the eastern spring wheat yield decreased. Finally, the models describing combined effects of these dominant climatic factors on the maturity and yield in different regions of Inner Mongolia were used to establish geographical differences. Our findings have important implications for improving climate change impact studies and for local agricultural production to cope with ongoing climate change. [ABSTRACT FROM AUTHOR]

Ecoregional differences contribute to genetic environmental interactions and impact animal performance. These differences may become more important under climate change scenarios. Utilizing genetic diversity within a species to address such problems has not been fully explored. In this study Hereford cattle were genotyped with 50K Bead Chip or 770K Bovine Bead Chip to test the existence of genetic structure in five U.S. ecoregions characterized by precipitation, temperature and humidity and designated: cool arid (CA), cool humid (CH), transition zone (TZ), warm arid (WA), and warm humid (WH). SNP data were analyzed in three sequential analyses. Broad genetic structure was evaluated with STRUCTURE, and ADMIXTURE software using 14,312 SNPs after passing quality control variables. The second analysis was performed using principal coordinate analysis with 66 Tag SNPs associated in the literature with various aspects of environmental stressors (e.g., heat tolerance) or production (e.g., milk production). In the third analysis TreeSelect was used with the 66 SNPs to evaluate if ecoregional allelic frequencies deviated from a central frequency and by so doing are indicative of directional selection. The three analyses suggested subpopulation structures associated with ecoregions from where animals were derived. ADMIXTURE and PCA results illustrated the importance of temperature and humidity and confirm subpopulation assignments. Comparisons of allele frequencies with TreeSelect showed ecoregion differences, in particular the divergence between arid and humid regions. Patterns of genetic variability obtained by medium and high density SNP chips can be used to acclimatize a temperately derived breed to various ecoregions. As climate change becomes an important factor in cattle production, this study should be used as a proof of concept to review future breeding and conservation schemes aimed at adaptation to climatic events. [ABSTRACT FROM AUTHOR]

Seasonal and inter-annual climate variability have become important issues for farmers, and climate change has been shown to increase them. Simultaneously farmers and agricultural organizations are increasingly collecting observational data about in situ crop performance. Agriculture thus needs new tools to cope with changing environmental conditions and to take advantage of these data. Data mining techniques make it possible to extract embedded knowledge associated with farmer experiences from these large observational datasets in order to identify best practices for adapting to climate variability. We introduce new approaches through a case study on irrigated and rainfed rice in Colombia. Preexisting observational datasets of commercial harvest records were combined with in situ daily weather series. Using Conditional Inference Forest and clustering techniques, we assessed the relationships between climatic factors and crop yield variability at the local scale for specific cultivars and growth stages. The analysis showed clear relationships in the various location-cultivar combinations, with climatic factors explaining 6 to 46% of spatiotemporal variability in yield, and with crop responses to weather being non-linear and cultivar-specific. Climatic factors affected cultivars differently during each stage of development. For instance, one cultivar was affected by high nighttime temperatures in the reproductive stage but responded positively to accumulated solar radiation during the ripening stage. Another was affected by high nighttime temperatures during both the vegetative and reproductive stages. Clustering of the weather patterns corresponding to individual cropping events revealed different groups of weather patterns for irrigated and rainfed systems with contrasting yield levels. Best-suited cultivars were identified for some weather patterns, making weather-site-specific recommendations possible. This study illustrates the potential of data mining for adding value to existing observational data in agriculture by allowing embedded knowledge to be quickly leveraged. It generates site-specific information on cultivar response to climatic factors and supports on-farm management decisions for adaptation to climate variability. [ABSTRACT FROM AUTHOR]

Climate is changing across the world, including the major maize-growing state of Iowa in the USA. To maintain crop yields, farmers will need a suite of adaptation strategies, and choice of strategy will depend on how the local to regional climate is expected to change. Here we predict how maize yield might change through the 21st century as compared with late 20th century yields across Iowa, USA, a region representing ideal climate and soils for maize production that contributes substantially to the global maize economy. To account for climate model uncertainty, we drive a dynamic ecosystem model with output from six climate models and two future climate forcing scenarios. Despite a wide range in the predicted amount of warming and change to summer precipitation, all simulations predict a decrease in maize yields from late 20th century to middle and late 21st century ranging from 15% to 50%. Linear regression of all models predicts a 6% state-averaged yield decrease for every 1°C increase in warm season average air temperature. When the influence of moisture stress on crop growth is removed from the model, yield decreases either remain the same or are reduced, depending on predicted changes in warm season precipitation. Our results suggest that even if maize were to receive all the water it needed, under the strongest climate forcing scenario yields will decline by 10–20% by the end of the 21st century. [ABSTRACT FROM AUTHOR]

The space-time pattern and environmental drivers (land cover, climate) of bovine anaplasmosis in the Midwestern state of Kansas was retrospectively evaluated using Bayesian hierarchical spatio-temporal models and publicly available, remotely-sensed environmental covariate information. Cases of bovine anaplasmosis positively diagnosed at Kansas State Veterinary Diagnostic Laboratory (n = 478) between years 2005–2013 were used to construct the models, which included random effects for space, time and space-time interaction effects with defined priors, and fixed-effect covariates selected a priori using an univariate screening procedure. The Bayesian posterior median and 95% credible intervals for the space-time interaction term in the best-fitting covariate model indicated a steady progression of bovine anaplasmosis over time and geographic area in the state. Posterior median estimates and 95% credible intervals derived for covariates in the final covariate model indicated land surface temperature (minimum), relative humidity and diurnal temperature range to be important risk factors for bovine anaplasmosis in the study. The model performance measured using the Area Under the Curve (AUC) value indicated a good performance for the covariate model (> 0.7). The relevance of climatological factors for bovine anaplasmosis is discussed. [ABSTRACT FROM AUTHOR]