查看更多>>摘要：? 2022 Elsevier B.V.Advancement of grapevine phenological stages due to climate change has been well documented. There is less information regarding which phenological intervals in the grapevine growth cycle are most affected by temperature and thus drive this advancement. This study focused on investigating the relationship between temperature and the phenological interval lengths between budburst, flowering, veraison (onset of grape ripening) and maturity, to identify the interval most influenced by temperature change. Historical data from four climatically different vineyards in Victoria, Australia were used that included 15 cultivars and covered 7 years, 2012–2018, to investigate trends in the intervals between phenological stages. The interval between budburst and flowering shortened significantly more than the subsequent intervals between flowering and veraison and between veraison and maturity, as related to average daily springtime temperature (max). We found the best relationships between temperature and interval length were for the budburst to flowering interval, and for the relationship between the average daily maximum temperature during this interval and interval length. We found this relationship was best described by a curvilinear rather than linear function, which also varied between cultivars. These findings indicated that with increasing spring maximum temperatures, the rate of decrease in length of the interval between budburst and flowering will slow and plateau. The study showed the importance of the budburst to flowering interval to the length of the grapevine growth cycle and indicates that there is cultivar diversity of response to temperature. This knowledge helps our ability to understand and predict phenological timing and assists development of our adaptation strategies to climate change.
查看更多>>摘要：? 2022Seasonal climate forecasts (SCF) are evolving rapidly alongside improvements in climate modelling and downscaling research, and have great potential for weather-sensitive sectors, especially agriculture, by reducing weather-related risks and increasing productivity. Skilful yield forecasts at the beginning of, or before, a cropping season can provide farmers and other stakeholders in agribusiness with the necessary information for early planning and actions. Only a few yield forecast studies have a forecast lead time of four months or longer due to the problem complexity. To enable SCFs from Global Climate Models (GCMs) to be used for early-season yield forecasts, this paper uses a statistical downscaling technique, Extended Copula Post-Processing (ECPP) and the Schaake shuffle, to downscale four climate variables to generate weather-like daily data that are suitable for agricultural applications. Climate forecasts drive a process-based crop model APSIM (Agricultural Production Systems sIMulator) to simulate crop forecasts on 50 stations, well-distributed across the Australian grain zone. To focus on yield forecast skills attributable to SCF, we propose best practice management rules to predict water-limited winter wheat yield. Yield forecasts from ECPP have a significant improvement over quantile mapping downscaling and raw SCF from the Australian recent seasonal forecast model ACCESS-S1 in terms of bias, accuracy, reliability, and overall forecast skill. In addition, even at the beginning of a cropping season with a forecast lead time of four or more months, yield forecasts driven by ECPP illustrate higher skill than climatology, a benchmark for yield forecast. Early-season yield forecasts driven by SCFs provides a promising alternative to regression/machine-learning-based forecasts. Performance sensitivity and issues, and gaps on using skilful SCFs to help growers with their farming decision-making are discussed.
查看更多>>摘要：? 2022 Elsevier B.V.Over the last decades, fruit trees in Europe have tended to flower earlier due to warmer winters. The impact of destructive spring frosts remains considerable as exemplified by damage during pome fruit flowering in Belgium between 2017 and 2021. While several regional studies on the impact and evolution of this phenomenon exist, pear fruit (Pyrus communis) has received little attention. We focused on the commercially important pear cultivar ‘Conference’ in Belgium using data from 1971 to 2018, and climate projections to 2068. A calibrated phenological model was applied to selected members of the CMIP5 EURO[sbnd]CORDEX regional climate model ensemble for emission scenarios RCP 4.5 and RCP 8.5 to determine the timing of the flowering period and the coinciding frost events (<-2 °C). A recursive and multivariate quantile mapping correction on monthly series of minimum and maximum temperature provided the most consistent reduction of negative temperature bias in the climate time series as compared to the observations. Flowering in the current pear production area in Belgium started on average 7.5 (10.8) days earlier under scenario RCP 4.5 (8.5), and the last frost occurred on average 12.8 (17.9) days earlier in 2019–2068 compared to 1971–2018. Sen's slope coefficient indicated an overall advancement of 1.25 (1.55) days per decade for flowering; 2.06 (2.23) days per decade for the last frost and around 25% fewer frosts during a projected future flowering period. Pear orchards in northern lowlands were less exposed to frosts than orchards in regions with elevations above 300 m. While frosts remain a potential threat in a projected future, climate change did not induce more frequent frosts and production relocation is therefore not recommended.
查看更多>>摘要：? 2022 Elsevier B.V.Adjusting crop calendars may present an effective adaptation measure to avoid crop yield loss and reduce water use in a changing climate. In order to better understand potentials and limitations of adjusting crop calendars for climate change adaptation of tropical multi-cropping systems with short fallow periods, we used a regionally calibrated Environmental Policy Integrated Climate (EPIC) agronomic model to estimate annual caloric yield and blue water requirement (BWR) of irrigated double-rice and rice-wheat cropping systems in India and Bangladesh. We adjusted crop calendars by (a) single-objective optimization to maximize annual caloric yield and (b) multi-objective optimization to minimize BWR under current and future climate scenarios, focusing on climatic drivers of optimal growing seasons. While the short time intervals between harvest of kharif crops and (trans-)planting of rabi crops limit the space for planting date shift in the study area, our results indicate that crop calendar adjustment has great potential to reverse yield loss induced by temperature rise and decrease BWR by utilizing monsoon precipitation. The study indicates a trend towards earlier planting of rabi wheat to mitigate heat stress during the reproductive stage. Moreover, earlier planting of kharif rice can help to utilize monsoon precipitation, avoid cold stress of kharif rice during anthesis, and allow for early wheat sowing during the historic period. By the 2080s, the increase of heat stress in summer and the decrease of cold stress in winter seems to allow more flexibility for late rice in kharif season, but a conflict between later planting for yield improvement and earlier planting for blue water saving is expected in kharif rice on the Indo-Gangetic plain of India and Bangladesh. Therefore, the trade-off between yield improvement and irrigation water use needs to be carefully considered to promote adaptive adjustment of crop calendars under climate change.
查看更多>>摘要：? 2022 Elsevier B.V.The marked increase in drought extreme frequency in drylands raises concerns about the stability and sustainability of forest productivity. However, the responses of forests to climate extremes in drylands such as Inner Asia have received limited attention thus far despite their importance for forest sustainability and ecosystem services. After examining the changes on tree growth observed during and after droughts as well as the relations of growth increments associated with climate wetness at 32 forest sites across Inner Asia, we found that the growth compensation by climate wetness can reach 278% in Inner Asia (tree growth increasing 0.39 mm during climate wetness vs decreasing 0.14 mm during droughts), 2.5 times higher than the global average of 93%, reflecting the adaptation of forests in water-deficient areas to high-frequency climate extremes. However, we further observed fading extra-compensation of climate wetness on tree growth since the 1980s, namely decreasing growth enhancements occurring during wet periods, while significantly increasing growth declines occurring during dry periods. Fading extra-compensation on tree growth was leading to persistent and pervasive declines on tree growth rate in the study area. Our results indicate that this fading extra-compensation of climate wetness will weaken the resistance of forests to droughts and potentially reduce the carbon sink strength of forests in Inner Asia.
查看更多>>摘要：? 2022 The Author(s)Long-term studies of insect populations in the North American boreal forest have shown the vital importance of long-distance dispersal to the maintenance and expansion of insect outbreaks. In this work, we extend several concepts established previously in an empirically-based dispersal flight model with recent work on the physiology and behavior of the adult eastern spruce budworm (SBW) moth, Choristoneura fumiferana (Clemens). An outbreak of defoliating SBW in Quebec, ongoing since the mid-2000s, already covers millions of hectares of forests in eastern Canada and threatens to spread into neighboring areas through annual summertime episodes of long-distance dispersal. Such flight events in favorable conditions frequently include billions of SBW moths dispersing in the warm atmospheric boundary layer, typically starting around sunset and often lasting through several hours of wind-driven transport over hundreds of kilometers. Successful SBW dispersal to possibly distant host forest areas depends acutely on the weather. Here we describe the components and results of SBW–pyATM, an open-source individual-based modeling framework developed in Python for the simulation of these weather-driven SBW dispersal events. Using seasonal SBW phenology results from BioSIM at known outbreak locations and high-resolution Weather Research and Forecasting (WRF) model output, we focus on modeling dispersal flights over two successive nights in July 2013 in southern Quebec. Our flight model closely reproduces the SBW spatial patterns and motions observed by weather surveillance radar over the St. Lawrence estuary. With SBW–pyATM we can estimate landing locations for both male and female SBW and the resulting spatial patterns of egg distribution, allowing us eventually to forecast future larval defoliation activity in new locations where immigration could help overcome local limitations on SBW populations. This information could then support forest management decisions where SBW outbreaks threaten valuable resources.
查看更多>>摘要：? 2022 Elsevier B.V.The pending extensive rice expansion in northeastern Asia, especially in northeast China, affects regional climate by altering both biogeochemical and biophysical processes. While the biogeochemical effects (e.g., CO2, CH4) of rice expansion have attracted plenty of attention, its biophysical effects have not been well documented, especially its influences on diurnal and seasonal land surface temperature (LST). In this study, we used a pair-wise comparison approach to examine biophysical effects of paddy rice expansion at different temporal scales (diurnal and seasonal) in northeast China, based on satellite-derived biophysical proxies and a high-resolution crop map in 2017. We found that the daily mean LST of rice paddies was 0.5 °C lower than that of corn and soybean fields during the growing season (from May to September), as a result of daytime cooling (-1.8 and -2.0 °C) and nighttime warming (0.8 and 1.1 °C), which subsequently led to a narrower diurnal LST range (-2.6 and -3.0 °C) than in upland crops (i.e. corn and soybean). The cooling effects were stronger in the early period of the growing season (May and June) than in the late season (July to September). Using a temperature response model, we found that the nonradiative processes (i.e., evapotranspiration and sensible heat) dominated the LST response in paddy rice, while the radiative process (i.e., albedo) played a secondary role. The daytime cooling and nighttime warming implies that we need to consider the unsymmetrical diurnal LST dynamics when evaluating the short-term effects of paddy rice expansion. Stronger cooling effects in the early growing season has to be accounted for when modeling its biophysical impacts at seasonal scale. This study explained the local climate effects of rice expansion through the biophysical mechanism with both radiative and nonradiative controls on the surface energy balance, which can contribute to improved modeling of biophysical effects of land use change.
查看更多>>摘要：? 2022 Elsevier B.V.Within-season crop yield prediction with a dynamic crop model can provide valuable references for field management practices and regional food security. However, weather ensembles containing the unknown future weather conditions occurring after prediction dates are essential for such predictions using crop models. Two strategies were established for selecting analogue weather years as the target growing season based on a five-year maize experiment conducted at eight sites in the Loess Plateau of China. The first strategy tried weather data from different lengths of years ahead the planting year. The second strategy used the k-nearest neighbor (k-NN) algorithm to select analogue weather according to different combinations of weather variables with daily or accumulative values. The results showed that satisfactory predictions could be obtained after maize tasseling (about 50 d prior to maturity). The mean absolute relative error (ARE) and coefficient of variation (CV) of the daily yield predictions after tasseling were 6.6% and 5.7%, respectively, in 2010 at the Yulin site. In the leading-year strategy, the most reliable predictions were obtained by the weather data from the 10 years ahead of planting, with an overall average ARE of 11.7%. In the k-NN strategy, the most reliable predictions were obtained by using the analogue weather selected with only accumulative precipitation, with an overall average ARE of 11.5%. Additionally, both of the two optimal strategies improved the original predictions in most cases. However, the k-NN strategy was more likely to generate worse predictions in the early part of the growing season. Generally, it was more convenient to use the weather data of 10 leading years before the planting year to represent the unknown weather data after the prediction dates. This strategy provided reliable prediction accuracy without complex programming and requirement for long-term weather records.
查看更多>>摘要：? 2021 Elsevier B.V.The partitioning of evapotranspiration (ET) into surface evaporation (E) and stomatal-based transpiration (T) is essential for analyzing the water cycle and earth surface energy budget. Similarly, the partitioning of net ecosystem exchange (NEE) of carbon dioxide into respiration (R) and photosynthesis (P) is needed to quantify the controls on its sources and sinks. Promising approaches to obtain these components from field measurements include partitioning models based on analysis of conventional high frequency eddy-covariance data. Here, two such existing approaches, based on similarity between non-stomatal (R and E) and stomatal (P and T) components, are considered: the Modified Relaxed Eddy Accumulation (MREA) and Flux-Variance Similarity (FVS) models. Moreover, a simpler technique is proposed based on a Conditional Eddy-Covariance (CEC) scheme. All approaches were evaluated against independent estimates of transpiration and respiration. The CEC method agreed better with measurements of transpiration over a grass field, with a smaller root mean square error (5.9 W m?2) and higher correlation (0.96). At a forest site, better agreement with soil respiration was found for FVS above the canopy, while CEC and MREA performed better below the canopy. Further application of these methods over a vineyard and a pine forest across different seasons provided insight into the main strengths and weaknesses of each approach. FVS and MREA converge less often when ground flux components dominate, while CEC might result in noisy P and R for small NEE. Finally, in the CEC and MREA framework, the ratio T/ET is shown to be related to the correlation coefficient for carbon dioxide and water vapor concentrations, which can thus be used as a qualitative measure of the importance of stomatal and non-stomatal components. Overall, these results advance the understanding of the skill and agreement of all three methods, and inform future studies where the various approaches can be applied simultaneously and intercompared.
查看更多>>摘要：? 2021High elevation mountain forests in the European Alps are strongly affected by climate change. In this study we aimed to investigate the long- and short-term effects of climate on radial stem growth and tree hydraulics of the two dominant tree species at the forest line of the Eastern central Alps, European larch (Larix decidua Mill. and Swiss stone pine (Pinus cembra L). To this end, we analyzed tree ring widths from stem cores from five sites between 1990 and 2320 m above sea level (a.s.l.) and measured sap flow for three years and radial stem variation for four years at three sites between 1990 and 2100 m a.s.l. in an inner alpine dry valley in Northern Italy. We found that tree ring width of larch responded stronger to temperature and increased more with warming in the last decades than in Swiss stone pine. In the short term, low soil moisture reduced sap flow during the summer in Swiss stone pine but not in larch. Additionally, air vapor pressure deficit clearly reduced the number of days with radial stem growth in Swiss stone pine, leading to lower annual growth than in larch. Consequently, larch at high elevation might actually benefit from climate change at the expense of Swiss stone pine.
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