Molecular cloning and sequence comparison of a cDNA encoding α-glucan, water dikinase (GWD) from potato (Solanum tuberosum L.), and analysis of gene expression

Starch phosphorylation is an important biochemical aspect of plant starch metabolism as it influences the overall structure of the starch granule, and a prerequisite for its degradation. There is a growing interest on the isolation and characterization of α-glucan/glucan-like, water dikinases (GWDs) from plants, particularly agriculturally important crops, because GWD is known to catalyze starch phosphorylation both in leaves and different plant storage organs. In the present study, a 4,789-bp full-length cDNA encoding a GWD isoform was isolated from a commercially important Indian potato cultivar, Kufri Chipsona-1 by RT-PCR approach using tuber RNA. The predicted protein consisted of 1,463 amino acids having N-terminal 77-amino acid transit peptide, and 1,386-amino acid mature protein shorter by one amino acid as compared to the other mature GWDs from potato and tomato. The mature GWD showed 98 % sequence identity with the GWD isolated earlier from the potato cv. Desiree. Variations were found at 25 locations representing mostly non-conservative substitutions. The GWD represents a distinct isoform from potato, as revealed by sequence and phylogenetic analyses. Amino acid composition, segment-wise hydrophobic characters, predicted secondary structures were also analyzed and documented in this report. Broadly, the level of GWD expression as analyzed by semi-quantitative RT-PCR approach was found to be nearly uniform both in the mature tubers and leaves from most of the potato cultivars. By immunodetection technique, a band corresponding to ~155 kDa protein was detected only in the tuber protein extracts. The tuber starch-bound phosphorus content data showed minor variations between the potato cultivars.     

The plastidial glucan, water dikinase (GWD) catalyses multiple phosphotransfer reactions

The plant genome encodes at least two distinct and evolutionary conserved plastidial starch-related dikinases that phosphorylate a low percentage of glucosyl residues at the starch granule surface. Esterification of starch favours the transition of highly ordered α-glucans to a less ordered state and thereby facilitates the cleavage of interglucose bonds by hydrolases. Metabolically most important is the phosphorylation at position C6, which is catalysed by the glucan, water dikinase (GWD). The reactions mediated by recombinant wild-type GWD from Arabidopsis thaliana (AtGWD) and from Solanum tuberosum (StGWD) were studied. Two mutated proteins lacking the conserved histidine residue that is indispensible for glucan phosphorylation were also included. The wild-type GWDs consume approximately 20% more ATP than is required for glucan phosphorylation. Similarly, although incapable of phosphorylating α-glucans, the two mutated dikinase proteins are capable of degrading ATP. Thus, consumption of ATP and phosphorylation of α-glucans are not strictly coupled processes but, to some extent, occur as independent phosphotransfer reactions. As revealed by incubation of the GWDs with [γ-(33) P]ATP, the consumption of ATP includes the transfer of the γ-phosphate group to the GWD protein but this autophosphorylation does not require the conserved histidine residue. Thus, the GWD proteins possess two vicinal phosphorylation sites, both of which are transiently phosphorylated. Following autophosphorylation at both sites, native dikinases flexibly use various terminal phosphate acceptors, such as water, α-glucans, AMP and ADP. A model is presented describing the complex phosphotransfer reactions of GWDs as affected by the availability of the various acceptors.     

不同气象数据空间内插对区域玉米生长模拟结果的影响

作物模型区域模拟已成为作物模型应用的一个新方向。运用作物模型进行区域研究时,遇到的问题之一就是输入模型的空间数据质量问题,研究不同空间内插法获得的气象数据对作物模型区域模拟结果的影响,可以为区域模拟对输入数据的敏感性研究提供一定的参考。利用区域校准的CERES-Maize模型,将3类内插方法(几何内插、统计内插、动力模型内插)产生的网格化天气数据分别输入到CERES-Maize模型中,模拟了50km×50km网格水平下1961—1990年我国玉米生产状况,并选取1980—1990年模拟的平均产量与同期农调队调查产量进行比较,以了解区域模拟中,不同空间内插方法所得的逐日气象数据对区域模拟结果的影响。结果表明:(1)作物模型区域应用时,所采用的3种内插方法都能满足作物模型区域模拟对网格化天气数据的要求,采用3种天气数据的区域模拟结果都能反映出玉米平均产量的空间变化特征,与网格调查平均产量之间具有极显著的相关关系,但采用不同内插天气数据对模拟结果造成了8%以内的偏差。(2)采用不同内插天气数据,在进行作物区域模拟时,各方法的模拟结果之间呈极显著的相关关系,但这些模拟结果之间,在全国大部分地区是差异显著。     

用露点法和湿度法测定叶片水势及其组分

本文讨论了样品水势差异对叶室平衡时间和热电偶冷却时间的影响;比较了湿度法和露点法的测定结果,活体测定与离体测定的结果以及两种渗透势测定方法的结果;并用离体法测定了大麦在NaCl胁迫下叶片Yw,Ys和Yp 的动态变化;用活体法测定了小麦和蚕豆在大田条件下叶片Yw、Ys和YP 的昼夜变化。     

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level

Understanding large‐scale crop growth and its responses to climate change are critical for yield estimation and prediction, especially under the increased frequency of extreme climate and weather events. County‐level corn phenology varies spatially and interannually across the Corn Belt in the United States, where precipitation and heat stress presents a temporal pattern among growth phases (GPs) and vary interannually. In this study, we developed a long short‐term memory (LSTM) model that integrates heterogeneous crop phenology, meteorology, and remote sensing data to estimate county‐level corn yields. By conflating heterogeneous phenology‐based remote sensing and meteorological indices, the LSTM model accounted for 76% of yield variations across the Corn Belt, improved from 39% of yield variations explained by phenology‐based meteorological indices alone. The LSTM model outperformed least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) approaches for end‐of‐the‐season yield estimation, as a result of its recurrent neural network structure that can incorporate cumulative and nonlinear relationships between corn yield and environmental factors. The results showed that the period from silking to dough was most critical for crop yield estimation. The LSTM model presented a robust yield estimation under extreme weather events in 2012, which reduced the root‐mean‐square error to 1.47 Mg/ha from 1.93 Mg/ha for LASSO and 2.43 Mg/ha for RF. The LSTM model has the capability to learn general patterns from high‐dimensional (spectral, spatial, and temporal) input features to achieve a robust county‐level crop yield estimation. This deep learning approach holds great promise for better understanding the global condition of crop growth based on publicly available remote sensing and meteorological data.     

The analysis of the different functions of starch‐phosphorylating enzymes during the development of Arabidopsis thaliana plants discloses an unexpected role for the cytosolic isoform GWD2

The genome of Arabidopsis thaliana encodes three glucan, water dikinases. Glucan, water dikinase 1 (GWD1; EC 2.7.9.4) and phosphoglucan, water dikinase (PWD; EC 2.7.9.5) are chloroplastic enzymes, while glucan, water dikinase 2 (GWD2) is cytosolic. Both GWDs and PWD catalyze the addition of phosphate groups to amylopectin chains at the surface of starch granules, changing its physicochemical properties. As a result, GWD1 and PWD have a positive effect on transitory starch degradation at night. Because of its cytosolic localization, GWD2 does not have the same effect. Single T‐DNA mutants of either GWD1 or PWD or GWD2 have been analyzed during the entire life cycle of A. thaliana. We report that the three dikinases are all important for proper seed development. Seeds from gwd2 mutants are shrunken, with the epidermal cells of the seed coat irregularly shaped. Moreover, gwd2 seeds contain a lower lipid to protein ratio and are impaired in germination. Similar seed phenotypes were observed in pwd and gwd1 mutants, except for the normal morphology of epidermal cells in gwd1 seed coats. The gwd1, pwd and gwd2 mutants were also very similar in growth and flowering time when grown under continuous light and all three behaved differently from wild‐type plants. Besides pinpointing a novel role of GWD2 and PWD in seed development, this analysis suggests that the phenotypic features of the dikinase mutants in A. thaliana cannot be explained solely in terms of defects in leaf starch degradation at night.     

Characterizing drought stress and trait influence on maize yield under current and future conditions

Global climate change is predicted to increase temperatures, alter geographical patterns of rainfall and increase the frequency of extreme climatic events. Such changes are likely to alter the timing and magnitude of drought stresses experienced by crops. This study used new developments in the classification of crop water stress to first characterize the typology and frequency of drought‐stress patterns experienced by European maize crops and their associated distributions of grain yield, and second determine the influence of the breeding traits anthesis‐silking synchrony, maturity and kernel number on yield in different drought‐stress scenarios, under current and future climates. Under historical conditions, a low‐stress scenario occurred most frequently (ca. 40%), and three other stress types exposing crops to late‐season stresses each occurred in ca. 20% of cases. A key revelation shown was that the four patterns will also be the most dominant stress patterns under 2050 conditions. Future frequencies of low drought stress were reduced by ca. 15%, and those of severe water deficit during grain filling increased from 18% to 25%. Despite this, effects of elevated CO₂ on crop growth moderated detrimental effects of climate change on yield. Increasing anthesis‐silking synchrony had the greatest effect on yield in low drought‐stress seasonal patterns, whereas earlier maturity had the greatest effect in crops exposed to severe early‐terminal drought stress. Segregating drought‐stress patterns into key groups allowed greater insight into the effects of trait perturbation on crop yield under different weather conditions. We demonstrate that for crops exposed to the same drought‐stress pattern, trait perturbation under current climates will have a similar impact on yield as that expected in future, even though the frequencies of severe drought stress will increase in future. These results have important ramifications for breeding of maize and have implications for studies examining genetic and physiological crop responses to environmental stresses.     

Support vector machine-based open crop model (SBOCM): Case of rice production in China

Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.     

三色(Bougainvillea peruviana‘Thimma’)在转录水平的甜菜色素和类黄酮积累比较

Bougainvillea peruviana‘Thimma’属于三角梅属,该属植物积累甜菜色素而不是像绝大多数高等植物一样积累花青素。该材料特征同株出现3种颜色：白色、洋红色和白/洋红相间。本研究首次使用3种花色特征的花序（红色Yp、混合色的Ym、白色Yw）作为研究材料进行高通量测序。并通过real-time PCR方法对探测到的花色代谢基因进行验证。共获得平均长度为616 bp的73 325条基因。3种材料的差异显示基因（DEGs）中有327个被注释到甜菜色素合成基因,308个被注释到类黄酮合成基因,466个被注释到花青素合成基因。我们选出8个基因：4个甜菜色素合成基因（PPO,CYP76AD1,cDOPA-5-GT,DODA）和4个花青素合成基因（FLS,DFR,LDOX,3-GT）进行验证。其中,4个甜菜色素合成基因在3种花色材料中的表达较好的正相关于甜菜色素含量。花青素合成途径末端的3个基因（DFR,LDOX,3-GT）在B.peruviana中首次被验证。real-time PCR的验证结果很好的吻合转录组测序的结果。同时,B.peruviana也提供了一个很好的三角梅属植物的生理、生化和分子生物学研究的工具,有效的摒除其他生物学干扰。     

Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (gamma, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966-1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of gamma near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.     

Increasing influence of heat stress on French maize yields from the 1960s to the 2030s

Improved crop yield forecasts could enable more effective adaptation to climate variability and change. Here, we explore how to combine historical observations of crop yields and weather with climate model simulations to produce crop yield projections for decision relevant timescales. Firstly, the effects on historical crop yields of improved technology, precipitation and daily maximum temperatures are modelled empirically, accounting for a nonlinear technology trend and interactions between temperature and precipitation, and applied specifically for a case study of maize in France. The relative importance of precipitation variability for maize yields in France has decreased significantly since the 1960s, likely due to increased irrigation. In addition, heat stress is found to be as important for yield as precipitation since around 2000. A significant reduction in maize yield is found for each day with a maximum temperature above 32 °C, in broad agreement with previous estimates. The recent increase in such hot days has likely contributed to the observed yield stagnation. Furthermore, a general method for producing near‐term crop yield projections, based on climate model simulations, is developed and utilized. We use projections of future daily maximum temperatures to assess the likely change in yields due to variations in climate. Importantly, we calibrate the climate model projections using observed data to ensure both reliable temperature mean and daily variability characteristics, and demonstrate that these methods work using retrospective predictions. We conclude that, to offset the projected increased daily maximum temperatures over France, improved technology will need to increase base level yields by 12% to be confident about maintaining current levels of yield for the period 2016–2035; the current rate of yield technology increase is not sufficient to meet this target.     

Comparing predicted yield and yield stability of willow and Miscanthus across Denmark

To achieve the goals of energy security and climate change mitigation in Denmark and the EU, an expansion of national production of bioenergy crops is needed. Temporal and spatial variation of yields of willow and Miscanthus is not known for Denmark because of a limited number of field trial data. The semi‐mechanistic crop model BioCro was used to simulate the production of both short‐rotation coppice (SRC) willow and Miscanthus across Denmark. Predictions were made from high spatial resolution soil data and weather records across this area for 1990–2010. The potential average, rain‐fed mean yield was 12.1 Mg DM ha^?1 yr^?1 for willow and 10.2 Mg DM ha^?1 yr^?1 for Miscanthus. Coefficient of variation as a measure for yield stability was poorest on the sandy soils of northern and western Jutland, and the year‐to‐year variation in yield was greatest on these soils. Willow was predicted to outyield Miscanthus on poor, sandy soils, whereas Miscanthus was higher yielding on clay‐rich soils. The major driver of yield in both crops was variation in soil moisture, with radiation and precipitation exerting less influence. This is the first time these two major feedstocks for northern Europe have been compared within a single modeling framework and providing an important new tool for decision‐making in selection of feedstocks for emerging bioenergy systems.     

Down-regulation of Glucan, Water-Dikinase activity in wheat endosperm increases vegetative biomass and yield

A novel mechanism for increasing vegetative biomass and grain yield has been identified in wheat (Triticum aestivum). RNAi-mediated down-regulation of Glucan, Water-Dikinase (GWD), the primary enzyme required for starch phosphorylation, under the control of an endosperm-specific promoter, resulted in a decrease in starch phosphate content and an increase in grain size. Unexpectedly, consistent increases in vegetative biomass and grain yield were observed in subsequent generations. In lines where GWD expression was decreased, germination rate was slightly reduced. However, significant increases in vegetative growth from the two leaf stage were observed. In glasshouse pot trials, down-regulation of GWD led to a 29% increase in grain yield while in glasshouse tub trials simulating field row spacing and canopy development, GWD down-regulation resulted in a grain yield increase of 26%. The enhanced yield resulted from a combination of increases in seed weight, tiller number, spikelets per head and seed number per spike. In field trials, all vegetative phenotypes were reproduced with the exception of increased tiller number. The expression of the transgene and suppression of endogenous GWD RNA levels were demonstrated to be grain specific. In addition to the direct effects of GWD down-regulation, an increased level of α-amylase activity was present in the aleurone layer during grain maturation. These findings provide a potentially important novel mechanism to increase biomass and grain yield in crop improvement programmes.     

Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi‐year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO₂ concentration [CO₂]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model‐based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10% of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well‐controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO₂] and temperature.     

不同空间尺度下的ALMANAC模型验证

ALMANAC模型最早作为EPIC模型的一部分,用于模拟土壤侵蚀导致的土地生产力的下降．它将试验数据的统计过程和作物生长的机理过程结合起来,是一种典型的基于过程模拟的应用型作物生长模型．如能在不同的空间尺度上验证模型的适用性,无疑会大大扩展模型的应用范围．从这一目的出发,利用美国得克萨斯州19个试验田和9个县的玉米和高粱产量资料及其相关的作物、土壤、田问管理等数据,模拟了1998年田间尺度,1989～1998年县级尺度的平均作物产量．模拟结果表明,ALMANAC模型能够很好地模拟两种不同空间尺度的作物产量,其相对误差在田问尺度上分别为8．9％(高粱)和9．4％(玉米),在县级尺度上分别达到2．6％(玉米)和—0．6％(高粱)．该模型在进行产量预测、掌握作物生长动态,指导农业生产管理和土地利用等方面具有很好的应用前景．     

Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties?

Extreme climate, especially temperature, can severely reduce wheat yield. As global warming has already begun to increase mean temperature and the occurrence of extreme temperatures, it has become urgent to accelerate the 5–20 year process of breeding for new wheat varieties, to adapt to future climate. We analyzed the patterns of frost and heat events across the Australian wheatbelt based on 50 years of historical records (1960–2009) for 2864 weather stations. Flowering dates of three contrasting‐maturity wheat varieties were simulated for a wide range of sowing dates in 22 locations for ‘current’ climate (1960–2009) and eight future scenarios (high and low CO₂ emission, dry and wet precipitation scenarios, in 2030 and 2050). The results highlighted the substantial spatial variability of frost and heat events across the Australian wheatbelt in current and future climates. As both ‘last frost’ and ‘first heat’ events would occur earlier in the season, the ‘target’ sowing and flowering windows (defined as risk less than 10% for frost (<0 °C) and less than 30% for heat (>35 °C) around flowering) would be shifted earlier by up to 2 and 1 month(s), respectively, in 2050. A short‐season variety would require a shift in target sowing window 2‐fold greater than long‐ and medium‐season varieties by 2050 (8 vs. 4 days on average across locations and scenarios, respectively), but would suffer a lesser decrease in the length of the vegetative period (4 vs. 7 days). Overall, warmer winters would shorten the wheat season by up to 6 weeks, especially during preflowering. This faster crop cycle is associated with a reduced time for resource acquisition, and potential yield loss. As far as favourable rain and modern equipment would allow, early sowing and longer season varieties (i.e. in current climate) would be the best strategies to adapt to future climates.     

From GCM grid cell to agricultural plot: scale issues affecting modelling of climate impact

General circulation models (GCM) are increasingly capable of making relevant predictions of seasonal and long-term climate variability, thus improving prospects of predicting impact on crop yields. This is particularly important for semi-arid West Africa where climate variability and drought threaten food security. Translating GCM outputs into attainable crop yields is difficult because GCM grid boxes are of larger scale than the processes governing yield, involving partitioning of rain among runoff, evaporation, transpiration, drainage and storage at plot scale. This study analyses the bias introduced to crop simulation when climatic data is aggregated spatially or in time, resulting in loss of relevant variation. A detailed case study was conducted using historical weather data for Senegal, applied to the crop model SARRA-H (version for millet). The study was then extended to a 10 degrees N-17 degrees N climatic gradient and a 31 year climate sequence to evaluate yield sensitivity to the variability of solar radiation and rainfall. Finally, a down-scaling model called LGO (Lebel-Guillot-Onibon), generating local rain patterns from grid cell means, was used to restore the variability lost by aggregation. Results indicate that forcing the crop model with spatially aggregated rainfall causes yield overestimations of 10-50% in dry latitudes, but nearly none in humid zones, due to a biased fraction of rainfall available for crop transpiration. Aggregation of solar radiation data caused significant bias in wetter zones where radiation was limiting yield. Where climatic gradients are steep, these two situations can occur within the same GCM grid cell. Disaggregation of grid cell means into a pattern of virtual synoptic stations having high-resolution rainfall distribution removed much of the bias caused by aggregation and gave realistic simulations of yield. It is concluded that coupling of GCM outputs with plot level crop models can cause large systematic errors due to scale incompatibility. These errors can be avoided by transforming GCM outputs, especially rainfall, to simulate the variability found at plot level.     

Evaluation of weather-based rice yield models in India

The objective of this study was to compare two different rice simulation models—standalone (Decision Support System for Agrotechnology Transfer [DSSAT]) and web based (SImulation Model for RIce-Weather relations [SIMRIW])—with agrometeorological data and agronomic parameters for estimation of rice crop production in southern semi-arid tropics of India. Studies were carried out on the BPT5204 rice variety to evaluate two crop simulation models. Long-term experiments were conducted in a research farm of Acharya N G Ranga Agricultural University (ANGRAU), Hyderabad, India. Initially, the results were obtained using 4 years (1994–1997) of data with weather parameters from a local weather station to evaluate DSSAT simulated results with observed values. Linear regression models used for the purpose showed a close relationship between DSSAT and observed yield. Subsequently, yield comparisons were also carried out with SIMRIW and DSSAT, and validated with actual observed values. Realizing the correlation coefficient values of SIMRIW simulation values in acceptable limits, further rice experiments in monsoon (Kharif) and post-monsoon (Rabi) agricultural seasons (2009, 2010 and 2011) were carried out with a location-specific distributed sensor network system. These proximal systems help to simulate dry weight, leaf area index and potential yield by the Java based SIMRIW on a daily/weekly/monthly/seasonal basis. These dynamic parameters are useful to the farming community for necessary decision making in a ubiquitous manner. However, SIMRIW requires fine tuning for better results/decision making.     

Critical temperatures for xylogenesis in conifers of cold climates

Aim To identify temperatures at which cell division and differentiation are active in order to verify the existence of a common critical temperature determining growth in conifers of cold climates. Location Ten European and Canadian sites at different latitudes and altitudes. Methods The periods of cambial activity and cell differentiation were assessed on a weekly time‐scale on histological sections of cambium and wood tissue collected over 2 to 5 years per site from 1998 to 2005 from the stems of seven conifer species. All data were compared with daily air temperatures recorded from weather stations located close to the sites. Logistic regressions were used to calculate the probability of xylogenesis and of cambium being active at a given temperature. Results Xylogenesis lasted from May to October, with a growing period varying from 3 to 5 months depending on location and elevation. Despite the wide geographical range of the monitored sites, temperatures for onset and ending of xylogenesis converged towards narrow ranges with average values around 4–5, 8–9 and 13–14 °C for daily minimum, mean and maximum temperature, respectively. On the contrary, cell division in the cambium stopped in July?August, when temperatures were still high. Main conclusions Wood formation in conifers occurred when specific critical temperatures were reached. Although the timing and duration of xylogenesis varied among species, sites and years, the estimated temperatures were stable for all trees studied. These results provide biologically based evidence that temperature is a critical factor limiting production and differentiation of xylem cells in cold climates. Although daily temperatures below 4?5 °C are still favourable for photosynthesis, thermal conditions below these values could inhibit the allocation of assimilated carbon to structural investment, i.e. xylem growth.     

Stochastic simulation of daily air temperature and precipitation from monthly normals in North America north of Mexico

A simple, stochastic daily temperature and precipitation generator (TEMPGEN) was developed to generate inputs for the study of the effects of climate change on models driven by daily weather information when climate data are available as monthly summaries. The model uses as input only 11 sets of monthly normal statistics from individual weather stations. It needs no calibration, and was parameterized and validated for use in Canada and the continental United States. Monthly normals needed are: mean and standard deviation of daily minimum and maximum temperature, first and second order autoregressive terms for daily deviations of minimum and maximum temperatures from their daily means, correlation of deviations of daily minimum and maximum temperatures, total precipitation, and the interannual variance of total precipitation. The statistical properties and distributions of daily temperature and precipitation data produced by this generator compared quite favorably with observations from 708 stations throughout North America (north of Mexico). The algorithm generates realistic seasonal patterns, variability and extremes of temperature, precipitation, frost-free periods and hot spells. However, it predicts less accurately the daily probability of precipitation, extreme precipitation events and the duration of extreme droughts.