Search

Objetivou-se com a presente pesquisa, propor um zoneamento bioclimatico para classificar o conforto e desconforto termico de trabalhadores rurais no Estado de Minas Gerais, considerando um periodo historico e um cenario futuro. Series historicas de dados mensais do periodo de (1976-2014) de indice de temperatura e umidade (ITU) minimo, medio e maximo, determinados a partir de valores medios mensais de temperatura (minima, media e maxima) e umidade relativa do ar de 48 estacoes meteorologicas localizadas no Estado de Minas Gerais foram usadas para analise de tendencias por meio do teste de Mann-Kendall e regressao linear. O zoneamento bioclimatico do conforto humano, obtido por meio de analise geoestatistica, foi desenvolvido em funcao dos valores de ITU minimo, medio e maximo para o periodo historico (1976-2014) e o cenario futuro (2024). Os resultados indicam a tendencia geral de aumento das condicoes de desconforto termico nas diversas mesorregioes do Estado de Minas Gerais nas estacoes do ano, sendo mais incisivas no verao e primavera.

Sunflower (Helianthus annuus L.) grain and oil quality are defined by grain weight and oil percentage, oil fatty acid composition and the amount of antioxidants. The aim of this work was to establish and validate a simple model, based on published relationships, which can estimate not only yield and its components, but also grain and oil quality aspects which are of relevance for industrial processes or human health. The model we developed provided good estimations of grain yield (similar to those of a more complex model) and oil quality from independent experiments. It explained known differences in potential yield and grain and oil quality between locations, in terms of differences in incident radiation, mean or minimum temperature. Simulations showed that recent climatic changes could have caused a decrease in sunflower yield and changes in oil quality. Our results suggest that at locations at lower latitudes, sunflower oil with high nutritious value and oxidative stability could compensate for relatively low yields, while at higher latitudes, high-linoleic acid oil production should be compatible with high yield potentials. Our model could facilitate the selection of the best location, sowing date or density for the production of sunflower oil with specific quality characteristics., 13 pages, 4 figures, 5 tables

Cedar leaf blight (Didymascella thujina) is considered to be the most important disease of western redcedar in British Columbia. The disease is most prevalent in warm-moist coastal low-elevation environments causing mortality among seedlings and significant loss of incremental growth and branch death among mature trees. In this study we used a principle component regression model to spatially project the disease risk under observed climate (2003–2008) and multiple future climate scenarios for the 2020s, 2050s, and 2080s. We found that while the majority of future climate scenarios predicted coastal environments will continue to favor occurrence of the disease, intensity is predicted to decrease toward the 2080s. Projected reductions of available summer climate moisture (cumulative precipitation – potential evapotranspiration), corresponding to the time of ascospore discharge and germination, contribute significantly to this finding. Disease intensity is however, projected to increase under moderate temperature and precipitation increases for the 2020s. We therefore recommend current reforestation efforts deploy disease resistant western redcedar seedlots in high risk environments common to hypermaritime coastal regions such as Haida Gwaii and northern maritime, to avoid significant mortality and growth reduction.

The study addressed the effect of year-to-year variation in air temperature on the early needle frost hardening of first-year Scots pine ( Pinus sylvestris L.) seedlings in autumn. To this end a novel method, combining biometeorological and plant ecophysiologal research, was introduced. In the biometeorological part, climatic year-to-year variation in air temperature during August and September was examined by analysing a 51-year set of daily air temperature data from central Finland. The cumulative occurrence of low air temperatures in August and September was quantified by calculating a cold sum for that period each year, and the climatic year-to-year variation of the cold sum accumulation was subsequently determined. Similar cold sum calculations were carried out for different air temperature treatments in hypothetical growth chamber experiments. By comparing the results of these two sets of calculations, experimental designs were defined for air temperature treatments covering the climatic year-to-year variation of cold sum accumulation in August and September. In the ecophysiological part of the study, the effects of low air temperatures on the early needle frost hardening of Scots pine seedlings were studied experimentally over two years in central Finland, using the air temperature treatments defined in the first part of the study. The hardening treatments were implemented in growth chambers under conditions simulating natural autumn conditions: gradually increasing night length combined with decreasing and diurnally fluctuating air temperature. Out of the eight air temperature treatments applied, only one had a clear effect of accelerating the hardening. Comparisons of the cold sum accumulations in the experimental treatments with those in natural conditions suggest that low air temperatures do not accelerate the early frost hardening of Scots pine seedlings in most years; further experimental studies are needed in order to determine the frequency of the years when they do. The results of the cold sum comparisons in the present study will help to identify the experimental designs needed in forthcoming studies.

Simultaneous biometric measurements of aboveground net primary production (ANPP) and eddy-covariance measurements of gross primary production (GPP) were made at 18 forest stands with 80 site-years of data across Canada – to assess the fraction of photosynthesis that is used to produce plant tissues and the consistency of carbon allocation patterns across forest ecosystems. The stands included boreal and temperate forests and spanned very young to mature stand ages. Across all sites, ANPP averaged 298 ± 138 g C m−2 yr−1 (mean ± 1 s.d.), with the highest values for temperate white pine plantations (307–630 g C m−2 yr−1) and harvested Douglas-fir stands (219–459 g C m−2 yr−1), and the lowest values for boreal harvested jack pine stands (97–185 g C m−2 yr−1). ANPP more than doubled from newly established (≤12 years) to young (13–25 years) stands, then stabilized in young to mature (≥51 years) stands but with diverging trends among species. Inter-site variations in ANPP and GPP were closely related to site characteristics, in particular, to leaf area index, which explained 66% of the variation in ANPP and 80% of the variation in GPP, and absorbed photosynthetically active radiation, which explained 80% of the variation in ANPP and 82% of the variation in GPP. Both ANPP and GPP were also positively correlated with mean annual air temperature, mean annual precipitation, and total soil nitrogen in the upper 10 cm of the mineral soil. ANPP was strongly, positively correlated with GPP and the ANPP/GPP ratio was relatively constant (0.29 ± 0.06), with no consistent differences among species or age classes. The results support the use of a constant ANPP/GPP ratio as a reasonable assumption in models of forest productivity for boreal and northern temperate forests. A similar conclusion is reached for the NPP/GPP ratio when published values of belowground NPP are considered.

This study evaluated the performance of digital photography for measuring the green leaf area index (LAI) and green plant area index (GAI) of field crops by quantifying gap fraction either at nadir or at a 57.5° view angle. Clumping index was estimated using the logarithmic averaging method of Lang and Xiang (1986) to derive the total GAI. Measurement uncertainty associated with foliage inclination and view angles was assessed analytically, as was image classification. The GAI derived for corn, soybean and wheat crops was strongly linearly correlated with the destructive GAI for both the nadir and the 57.5° photographic methods (R2 > 0.83, root mean square error < 0.63). Because the destructive LAI and GAI exhibited strong linear correlations for soybean and corn, LAI could be predicted from photographic methods. The LAI and GAI of the wheat canopy were poorly correlated because of a significant proportion of non-leaf tissues contributing to photosynthesis after the start of stem elongation. Clumping had a greater effect on the nadir photographic method than on the 57.5° method. The effective GAI from nadir viewing was better correlated to the destructive GAI than the photographic GAI, whereas the effective GAI from the 57.5° photographic method provided a greater underestimation of the destructive GAI than the photographic GAI. The extinction coefficient at nadir viewing was found to be 22% larger than the assumed value (i.e. k = 0.61 vs. the theoretical value of 0.5). When gap reduction reached the asymptotic saturation level with increasing LAI, higher classification accuracy was required to achieve acceptable LAI measurement accuracy. The 57.5° photographic method was more affected by classification accuracy when LAI was higher than 1.5. With crop canopy development, the decrease in gap size and the decline in signal strength from shadows mean that high-quality photos are required to effectively differentiate gaps from plant tissues.

Angular distribution of leaves is an important parameter determining the transmission and reflection of radiation by vegetation canopies. When inverting canopy transmittance measurements for estimating the leaf area index or canopy clumping, incorrect assumptions on leaf angles may lead to considerable errors. To address this issue, we measured an extensive data set of leaf angle distributions for 58 deciduous broadleaf tree species commonly found in temperate and boreal ecoclimatic regions. Additionally, a small subset of species was sampled several times during the growing season. We found that reliable estimates of leaf angle distributions at the level of a whole tree can be obtained by measuring the leaf inclination angles of 75 leaves distributed across the vertical tree profile using a simple technique based on digital photography. According to our measurements, the common assumption of a spherical leaf angle distribution often used when no measurement data is available is not a valid assumption for most of the studied tree species. Our multitemporal measurements indicated no seasonality of leaf inclination angles of the selected tree species, except directly after budburst. Finally, using a gap fraction model, we illustrate the influence of different leaf angle distributions on the clumping factor and leaf area index. We advocate the use of species-specific leaf angle distributions from actual leaf inclination measurements. However, a planophile or a plagiophile leaf angle distribution appears to be a more appropriate assumption than a spherical one for modeling radiation transmission through temperate and boreal broadleaf stands when no leaf inclination angle measurements are available.

Differences in drop size distribution (DSD) of leaf drips among tree species have been estimated and physically interpreted to clarify the leaf drip generation process. Leaf drip generation experiments for nine species were conducted in an indoor location without foliage vibration using an automatic mist spray. Broad-leaved species produced a similar DSD among species whose leaves had a matte surface and a second similar DSD among species whose leaves had a coated surface. The matte broad leaves produced a larger and wider range of DSDs than the coated broad leaves. Coated coniferous needles had a wider range of DSDs than the coated broad leaves and different DSDs were observed for different species. The species with shorter dense needles generated a larger DSD. The leaf drip diameter was calculated through the estimation of a state of equilibrium of a hanging drop on the leaves based on physical theory. The calculations indicated that the maximum diameter of leaf drips was determined by the contact angle, and the range of DSDs was determined by the variation in contact length and the contact diameter at the hanging points. The results revealed that leaf drip DSD changed due to variations in leaf hydrophobicity, leaf roughness, leaf geometry and leaf inclination among the different tree species. This study allows the modelization of throughfall DSD. Furthermore, it indicates the possibility of interpreting canopy water processes from canopy water storage to drainage through the contact angle and leaf drip DSD.

Transient type canopy chambers are still the only currently available practical solution for rapid screening of gas-exchange in agricultural fields. The technique has been criticized for its effect on canopy microclimate during measurement which affects the transport regime and regulation of plant gas-exchange. Field studies in which the technique has been compared against independent methods are still fragmentary. The aims of this study were to quantify the changes of the physical environment during chamber placement, to determine optimum flux measuring windows, to compare three flux-calculation procedures, and to test the performance of the method against independent measurements of sap-flow. Two wheat experiments were conducted for these purposes under German temperate climate conditions. Leaf transpiration and sap-flow remained relatively constant during the first 120 s after chamber deployment, but changed considerably afterwards. Canopy H 2 O fluxes could thus be inferred from concentration measurement series during this interval. A saturation function, previously not mentioned in the literature, was compared against the commonly used constant and quadratic regression methods and identified as the most suitable method for calculating vapor fluxes. The study reconfirmed that small proportional changes of CO 2 concentrations during the calculation interval facilitate the application of the frequently applied quadratic regression method for calculating CO 2 fluxes. Sap-flow, leaf and canopy gas-exchange were severely perturbed after chamber removal. Revisiting times of same sample locations need to be planned accordingly. The study confirms that the transient chamber technique can be applied for determining canopy gas-exchange, provided that characteristic time intervals within concentration measuring series are determined and their non-linearity tested to establish appropriate flux calculation procedures.

a b s t r a c t Vegetation structure parameters are key elements in the study of ecosystem functioning and global scale ecosystemic interactions. The detailed retrieval of many of these parameters by direct measurements is impractical due to the quantity of plant material in trees. Terrestrial LiDAR Scanners (TLSs) have been shown to hold great potential as an indirect means of estimating plant structure parameters with a high level of detail, while some studies identified a number of challenges inherent to this approach. In this study we investigate the use of a voxel-based approach to retrieve leaf area distribution of individual trees. The approach is based on the contact frequency method applied to co-registered TLS returns from two or more scanning positions. The contact frequency was computed for voxels being 10, 30, and 50 cm in size and subsequently corrected for the influence of occlusion effects, leaf inclination, the presence of non-photosynthetic material, and the laser beam size. The leaf area of voxels for which occlusion effects were too pronounced was estimated using modeled values based on the availability of light. We compared the TLS derived leaf area estimates against direct measurements, obtained by the harvesting of leaves, in a broad-leaved savanna of central Mali. The measured leaf area values of the sampled trees ranged from 30 to 530 m 2 , and crown LAI values between 0.8 and 7.2. The leaf area estimates lay on average 14% from the reference measurements (general bias). Our method provides vertical as well as radial distributions of leaf area in individual trees, and lends itself to the estimation of savanna vegetation structural parameters with a high level of detail.

Climate change is recognized to increase the frequency and severity of extreme temperature events that lead to declining crop yield, but this impact has not been well evaluated in China. We examined the changes in extreme temperature stress over the past five decades by quantifying the indices of temperature stress (TSI) during different growth stages of irrigated rice across mainland China. Our results suggest that the indices of low- or high-temperature stress can be used to explain the year-to-year changes in rice yield. Analysis using the TSI indicated that low-temperature stress (LTS) in the seedling and heading-flowering stages of single rice in northeast China, the seedling stage of early rice and the heading-flowering stage of late rice in the double rice regions has reduced over the period of 1961–2008. No significant trends in LTS were detected during the booting stage. Moreover, global warming did not enhance high-temperature stress (HTS) in the heading-flowering stage over the same period, except in early rice in the mid-lower Yangtze River Valley where the HTS in the 2000s was higher than in previous decades.

a b s t r a c t Crop gross primary productivity (GPP) is an important characteristic for evaluating crop nitrogen content and yield, as well as the carbon exchange. Based on the close relationship observed between GPP and total chlorophyll content in crops, we applied a model that relies on a product of chlorophyll-related vegetation index and incoming photosynthetically active radiation for remote estimation of GPP in maize. In this study, we tested the performance of this model for maize GPP estimation based on spectral reflectance collected at a close range, 6 m above the top of the canopy, over a period of eight years from 2001 through 2008. Fifteen widely used chlorophyll-related vegetation indices were employed for GPP estimation in irrigated and rainfed maize, and accuracy and uncertainties of the models were compared. We also explored the possibility of using a unified algorithm in estimating maize GPP in fields that are different in irrigation, field history and climatic conditions. The results showed that vegetation indices that closely relate to total canopy chlorophyll content and/or green leaf area index were accurate in GPP estimation. Both green and red edge Chlorophyll Indices, MERIS Terrestrial Chlorophyll Index as well as Simple Ratio were the best approximations of the widely variable GPP in maize under different crop managements and climatic conditions. They were able to predict daily GPP reaching 30 gC/m 2 /d with RMSE below 2.75 gC/m 2 /d.

To evaluate the spatial variability of throughfall amount, raindrops, and erosivity under a single canopy during calm meteorological conditions, indoor experiments were conducted using a 9.8-m-tall transplanted Japanese cypress (Chamaecyparis obtusa) and a large-scale rainfall simulator. Drop size distribution, drop velocity, and kinetic energy of throughfall varied spatially under a single canopy as did throughfall amount and rain rate. Compared with throughfall rain rate, the variability was similar in drop size distribution, lower in drop velocity, and higher in kinetic energy. The results suggest that the spatial distribution of throughfall amount was dominated by the canopy shape and position of branches inside the canopy, and thus the spatial distribution was correlated with the radial distance from the trunk. Throughfall amount and rate were lower at the midway point between the trunk and the canopy edge. Throughfall drop size indices (drop size distribution, drop velocity, and unit kinetic energy) varied spatially while did not differ significantly. On the other hand, time-specific throughfall kinetic energy was correlated with the radial distance from the trunk. The dependence the throughfall kinetic energy on the radial distance from the trunk was dominated by the spatial distribution of throughfall amount. The trend in the spatial distribution of throughfall revealed in this study will aid in modelling canopy water processes and in predicting soil erosion on the bare forest floor.

In this study, we used a pair of optical disdrometers, one under and one outside the maize canopy, to measure the drop-size and the velocity distribution of hydrometeors during 12 storms and identified four distinct regions by examining how the fraction of drops recorded on the throughfall changed with respect to the drop diameter. The first region, containing drops with diameters smaller than 0.5 mm, showed an elevated number on the throughfall. Their numbers were comparable to the numbers of drops that lacked enough energy to adhere to the leaf. The second region, featuring drops with diameters larger than 0.5 mm and smaller than 3 mm, had a number ratio very close to the canopy gap fraction, indicating that their most likely origin was direct throughfall. The third region entailed drops with diameters ranging from 3 mm to 5.5 mm that featured a high throughfall to rainfall count ratio, with one of the diameter classes having higher counts under the canopy than outside of it. Through simplified calculations, we showed that drop weights in this region should exceed surface tension forces and lead to their detachment. In the fourth and final region, the throughfall to rainfall ratio decreased converging to the canopy gap fraction. By comparing the number of drops on each of the 440 diameter/velocity classes under and outside of the canopy, we were able to identify preferential drop sizes on the throughfall, i.e. classes of drop diameters with higher drop-size distributions under the canopy. The drop classes presenting higher counts under the canopy had diameters ranging from 3.25 mm to 5.75 mm with velocities between 1.4 m s −1 and 5 m s −1 . We were able to trace the origin of those drops to heights of between 0.10 m and 1.05 m within the canopy, confirming that these drops constitute the indirect throughfall. The capability to estimate the detachment height of drops allows us to reconstruct the drop-size distribution at different levels of the canopy and offers unique insight into the mechanics of interception, indirect throughfall formation and re-interception of raindrops by the maize canopy.

This study was undertaken to quantify interannual variations of total evaporation (ET) in a tropical rainforest in Sarawak, Malaysia. To this end, we conducted 10-year meteorological measurements and formulated a simplified big-leaf model that reproduces transpiration (Et), rainfall interception (Ei), and ET as the sum of these. The model was validated independently using eddy covariance fluxes, rainfall interception based on throughfall and stemflow measurements, and sap flow measurements conducted for more than 2 years. Using the model, Et, Ei, and ET were estimated for the period 2000–2009. Annual Et, Ei, and ET averaged over 10 years were estimated as 1114, 209, and 1323 mm, respectively, with small seasonal fluctuations. The derived estimates showed conservative year-to-year variations in annual Et, Ei, and ET (CV = 5–7%) in contrast to considerable year-to-year variations in annual rainfall (CV = 11%). Specific rainfall characteristics (e.g. intense short duration storms) at this site can explain the conservative year-to-year Ei variations. Small interannual variations in meteorological conditions and absence of severe drought during the study period can explain the small year-to-year Et variations. To characterize ET at our site, we also compared Ei and ET at our site with those of other tropical forests. Based on the derived ET characteristics, we discuss possible ET changes in response to changes in rainfall regime at this site.

Deciduous fruit trees require exposure to cold winter temperatures to fulfil chilling requirements allowing production of normal harvests. Trends in chill accumulation over the last 100 years were investigated at a number of important horticultural locations in Australia. Historical analysis is a necessary first step when considering potential impacts of climate change. Chill was examined using four chill models; the 0–7.2 °C, Modified Utah, Positive Utah and Dynamic models. Differences between locations were found with notable recent declines at Orange, Lenswood, Tatura, Yarra Valley and Bacchus Marsh. Other locations have remained stable with no location exhibiting a consensus increase in chill across all models. Trends in chill were shown to differ between models with results indicating that the 0–7.2 °C model frequently behaved differently to the other three chill models. These results highlight the need for multi-model analysis for chill trends, especially in a climate change context, to avoid maladaptation.

Evapotranspiraton ( ET ) estimation based on scaling up from leaf stomatal conductance ( g s ) to canopy conductance ( g c ) is important in improving effective use and evaluation of agricultural water resources. Taking a summer maize field in north China as an example, after the response of g s to main environmental factors was analyzed based on the measured value, the Jarvis model for g s was established and calibrated. Then the weighted integration model (WI model) was established on the basis of weighted model (W model) after considering the difference of intercept diffuse radiation by shaded leaves in different canopy heights and nonlinear relationship between g s and the photosynthetically active radiation ( PAR ) to improve g c estimation for shaded leaves using integration equation. Meanwhile the estimation accuracy of W and WI models for g c was compared, and then field ET was estimated using the Penman–Monteith equation. Results indicate that the variation of g s was similar to that of PAR and the Jarvis model could better express the response of g s to PAR , vapour pressure deficit and air temperature. Compared to the W model, WI model could effectively improve the estimation accuracy of g c , with the relative error of 4.4%. Penman–Monteith equation overestimated λET by 9.4% using the estimated g c by the W model, but underestimated λET by 2.3% using the estimated g c by the WI model. Therefore, Penman–Monteith equation can estimate maize field ET using the estimated g c by WI model in the region.

The quality and quantity of solar radiation are crucial for growth and competition within forest ecosystems. The spectral waveband between 400 and 700 nm is mainly responsible for photosynthesis and thus for plant growth. Spectral ratios such as red/far red (R/FR) and blue/red (B/R) give important information about the light quality within stands. Changes in R/FR and B/R trigger or inhibit effects such as seed germination, stem growth, dormancy, leaf expansion and flowering. Blue light strongly influences development and growth of plants e.g. an increase of B/R may lead to higher photosynthetic rates per unit leaf area. In addition to spectral properties, a higher fraction of diffuse radiation on overcast days can be used more effectively by plant organs. In this study, the spectral composition and variability of solar radiation were analyzed for different sky conditions and solar angles in six different vertical layers of a mixed European beech and Norway spruce stand in Southern Germany. The results showed lower R/FR ratios in beech than in spruce. Spruce showed markedly higher B/R values in comparison to beech under clear sky conditions. A steep increase of the B/R ratio at the height of the sun crown in both species was observed. Cloud cover had a major effect, elevating R/FR and reducing B/R values compared to clear sky days, due to a higher fraction of diffuse, unattenuated radiation within the canopy. The penetration of blue and red light into the canopy strongly depends on both the diffuse index and solar elevation angle.

The methane exchange in an oligotrophic mire complex was measured on the ecosystem and microform scale with the eddy covariance (EC) and the closed chamber technique, respectively. Information about the distribution of three distinct microform types in the area of interest and in each 30min EC flux source area was derived from a high-resolution (1m) landcover map in combination with an analytical source weight model (Kormann and Meixner, 2001). The mean weighted coverage of flark, lawn and hummock microforms in the EC source area (0.3%:57%:43%) closely mirrors the overall distribution in the area of interest (0.5%:50.1%:49.4%), despite great differences in microform coverage between individual 30min EC source areas. The measured ecosystem flux was fitted to the sum of three microform flux models based on environmental variables and weighted by their fractional coverage in the EC source area. This method resulted in a better representation of the ecosystem flux compared to an approach based on only one flux model for the whole ecosystem (R=0.87, RMSE=0.44 vs. R=0.74, RMSE=0.61, n=5181) and thus constitutes a successful down-scaling of measured ecosystem scale flux to the microform scale. A comparison of down-scaled and measured microform fluxes reveals a good agreement for lawn microforms and systematic differences for flark and hummock microforms. Reasons for the differences are thought to be the limited resolution of the landcover classification and the systematic underestimation of hummock fluxes by the closed chamber technique. As a result, hummock fluxes derived by down-scaling of EC fluxes are considered to be more dependable than closed chamber fluxes. The seasonal ecosystem methane budget from gap-filled EC measurements was 9.4±0.2gCHm; the budget derived from up-scaled microform measurements was 8.0±0.8gCHm. The lower value of the latter budget is attributed to the underestimation of flark and hummock fluxes by closed chamber measurements and to the microform gap-filling procedure. Generally, estimates from up-scaled microform measurements are found to be less certain than estimates from EC measurements.

The aim of the paper is to employ Empirical Orthogonal Function (EOF) analysis to drought indices including Palmer Drought Severity Index (PDSI), Palmer Hydrological Drought Index (PHDI), Palmer Moisture Anomaly (Z) Index (Z-Index), Weighted Palmer Drought Severity Index (WPDSI), and Water Deficit (P–PET) Index and to compare their resultant spatial patterns across Turkey. In this respect, the PDSI, PHDI, Z, WPDSI, Aridity Index (AI) and P–PET Index values are calculated based on observed monthly temperature and precipitation values of the 96 Turkish meteorological stations for the 1929–2009 period, and gridded available water holding capacities (AWHC) in 1-m soil depth are extracted from the soil data sets of the Oak Ridge National Laboratory Distributed Active Archive Centre (ORNL DAAC). By considering the strong match among the PDSI, PHDI, and WPDSI based on the results of the patterns of the first EOFs (EOF1) with the highest eigenvalue, we suggest that using and applying one of these drought indices could be adequate for a drought analysis in Turkey. On the other hand, it is evident that the significant results from the EOF2, EOF3 and EOF4 loadings of the PHDI and WPDSI indices have a similar pattern across Turkey. Therefore, with respect to the geographical autocorrelation and magnitudes of the loading values, it is explained that the PDSI and WPDSI in particular could have originated from the same physical process, and one may have a preference for the effective usage of a combination of PDSI, PHDI and Z-Index. Consequently, in a multi-purpose medium- and long-term drought management plan for Turkey or in any country having similar climatic and physical geographical conditions, using one of the three drought indices along with the Z-Index could be acceptable for successful applications.

Leaf area index (LAI) is an important index in ecological and meteorological studies. The litter trap method is commonly used to measure LAI in deciduous forests. To reduce the time consumed in sorting leaf litterfall by species in the litter trap method, we developed four models to predict LAI using litter traps and tree census data. The local dominance model, which estimates the leaf litterfall amount of each species by their local dominance, predicted mean and spatial variability of LAI most accurately compared to the 2 models that did not take into account spatial heterogeneity of species distribution within a forest or the model that estimated litterfall amount from leaf dispersal function. Therefore, this model can be employed instead of sorting leaf litter by species. Furthermore, we found that leaf mass per area (LMA) of at least 10 dominant species are essential for accurate estimation of LAI. Present results suggest that spatial variability of LAI is mainly due to spatial variance of leaf litterfall followed by spatial heterogeneity of species distribution within a forest, and difference in LMA among species.

Climate model projections indicate that wintertime temperatures will warm and change snowfall patterns in the northeastern U.S. Snow provides insulation for soils from air temperature fluctuations. Therefore, these snowfall changes will have consequences, particularly for soil surface temperatures. Changes in minimum soil surface temperatures were investigated using a heat flow soil temperature model, driven using daily average air temperature and snow depth data. Two scenarios of three coupled atmosphere-ocean circulation models were used to run the soil temperature model. Modeled projections of minimum soil surface temperatures in the northeastern U.S. indicate that warming will occur over the majority of the region over the 2000–2099 period. In contrast, soil surface temperature projections in the northernmost and snowiest regions of the Northeast indicate minimum soil surface temperatures will be colder. In these northern regions, the coldest soil surface temperatures are also projected to occur later in winter, but show little change in other regions. Most trends throughout the Northeast are enhanced using the higher emission scenario A1fi. Changes to snow depth drive the changes in the minimum soil surface temperatures where snow persists during winter, whereas average air temperatures drive changes in the rest of the northeastern U.S.

We analyse air temperature data from 14 sites in Switzerland, each with two weather stations in close proximity, one under a forest canopy and the other in the open. We use the statistics of extremes to investigate how extremely high maximum and extremely low minimum temperatures depend on the effect of forest cover. Our analysis shows that temperature maxima at two nearby stations are less dependent than are temperature minima. Maxima under the canopy are influenced by altitude: for higher sites, the maxima are less variable and depend less on the open-field data. Southerly orientations increase the dependence of minimum temperatures and so reduce the sheltering effect during cold periods. Extreme maximum and minimum temperatures occur less within conifer forests, indicating that the insulation provided by conifers all over the year is more efficient than that provided by deciduous species. Steepness of slopes has a complex impact on distributions of extremes and on their dependence. (C) 2011 Elsevier B.V. All rights reserved.

Crop yields are affected by many factors, related to breeding, management and climate. Understanding these factors, and their relative contributions to historical yield increases, is important to help ensure that these yield increases can continue in the future. Two important factors that can affect yields are planting dates and the crop's growing degree day (GDD) requirements. We analyzed 25 years of data collected by the USDA in order to document trends in planting dates, lengths of the vegetative and reproductive growth periods, and the length of time between maturity and harvest for corn and soybeans across the United States. We then drove the Agro-IBIS agroecosystem model with these observations to investigate the effects of changing planting dates and crop GDD requirements on crop yields and fluxes of water and energy. Averaged across the U.S., corn planting dates advanced about 10 days from 1981 to 2005, and soybean planting dates about 12 days. For both crops, but especially for corn, this was accompanied by a lengthening of the growth period. The period from corn planting to maturity was about 12 days longer around 2005 than it was around 1981. A large driver of this change was a 14% increase in the number of GDD needed for corn to progress through the reproductive period, probably reflecting an adoption of longer season cultivars. If these changes in cultivars had not occurred, yields around 2005 would have been 12.6 bu ac−1 lower across the U.S. Corn Belt, erasing 26% of the yield increase from 1981 to 2005. These changes in crop phenology, together with a shortening of the time from maturity to harvest, have also modified the surface water and energy balance. Earlier planting has led to an increase in the latent heat flux and a decrease in the sensible heat flux in June, while a shorter time from maturity to harvest has meant an increase in net radiation in October.

The role of different sky conditions on diffuse PAR fraction (ϕ), air temperature (Ta), vapor pressure deficit (vpd) and GPP in a deciduous forest is investigated using eddy covariance observations of CO2 fluxes and radiometer and ceilometer observations of sky and PAR conditions on hourly and growing season timescales. Maximum GPP response occurred under moderate to high PAR and ϕ and low vpd. Light response models using a rectangular hyperbola showed a positive linear relation between ϕ and effective quantum efficiency (α = 0.023ϕ + 0.012, r2 = 0.994). Since PAR and ϕ are negatively correlated, there is a tradeoff between the greater use efficiency of diffuse light and lower vpd and the associated decrease in total PAR available for photosynthesis. To a lesser extent, light response was also modified by vpd and Ta. The net effect of these and their relation with sky conditions helped enhance light response under sky conditions that produced higher ϕ. Six sky conditions were classified from cloud frequency and ϕ data: optically thick clouds, optically thin clouds, mixed sky (partial clouds within hour), high, medium and low optical aerosol. The frequency and light responses of each sky condition for the growing season were used to predict the role of changing sky conditions on annual GPP. The net effect of increasing frequency of thick clouds is to decrease GPP, changing low aerosol conditions has negligible effect. Increases in the other sky conditions all lead to gains in GPP. Sky conditions that enhance intermediate levels of ϕ, such as thin or scattered clouds or higher aerosol concentrations from volcanic eruptions or anthropogenic emissions, will have a positive outcome on annual GPP, while an increase in cloud cover will have a negative impact. Due to the ϕ/PAR tradeoff and since GPP response to changes in individual sky conditions differ in sign and magnitude, the net response of ecosystem GPP to future sky conditions is non-linear and tends toward moderation of change.

In this study, estimates for yield of short rotation plantations are provided based on climatic variables using the k nearest neighbour method. The calculations were based on climatic data and yield records from 1790 willow plantations in central and southern Sweden, divided into three categories based on local performance. The chosen neighbours were weighted proportionally to the inverse squared distance measured in the feature space defined by the climatic variables. The climatic variables included monthly averages of maximum, minimum and mean temperatures and precipitation. These were weighted using empirical constants after an optimisation process. The best accuracy was obtained with k = 4 for the group of high performance plantations, and k = 5 for the other groups. The relative RMSE values were 37.9%, 24.4% and 38.9% for the high, medium and low local performance, respectively, and the corresponding relative biases were 2.10%, −0.95% and −1.30%. The method was applied to interpolate the yield values in order to perform maps of potential productivity for the whole area. The results of this approach indicate that it can provide faster and more accurate predictions than previous modelling approaches, and can offer interesting approaches in the field of yield modelling.

A 27-year soil temperature record at five depths and soil heat flux record at one depth were analyzed for a grassland area in The Netherlands. The annual mean soil temperature of the last 23 years of soil measurements (no data gaps) showed a statistically significant increase of about 1.0 °C, consistent with the observed air temperature increase of about 1.3 °C for the same period. This positive trend correlates well with global brightening of 5.3 W m-2 per decade. The 10-day mean soil temperature varied smoothly throughout the year with relatively small inter-annual variability. The deeper the measurement depth, the smoother the annual cycle and the smaller the variability. In February and at the end of the year the variability appears to be somewhat larger. A Fast Fourier Transform was applied to the measurements and revealed an annual and daily damping depth of 1.80 m and 0.10 m, respectively. An example of the usefulness of this data is provided for agriculture, where an aggressive root-knot nematode is affected by the mean soil temperature increase. It appears that the 600 degree day life-cycle threshold is reached 16 days earlier, which may lead to a potential serious increase in agriculture crop damage.

This research uses a quantitative methodology for directly comparing the responses of observed crop yields in the SE USA to ENSO phenomena classified using dissimilar ENSO indices. ENSO condition has been related to agricultural yields in many parts of the world. It has been generally recognized that the strongest effects on spring and summer crops occur during the boreal winter, and therefore most directly affect spring–summer field crops in the southern hemisphere. However, some ENSO effects on northern hemisphere spring and summer crops have been found, particularly when researchers have used sub-annual indicators of ENSO conditions that, unlike annual ENSO indices, distinguish between continuity and change prior to or during the crop season. To evaluate the utility of such sub-annual ENSO indicators for agriculture in the SE USA, a tercile-based scoring system was devised to compare four distinct ENSO indices: three monthly ENSO indices and the JMA annual ENSO index. Each index was scored in its ability to predict crop yields differing from the historically normal tercile for corn (Zea mays L.), cotton (Gossypiumhirsutum L.), and peanut (Arachis hypogaea L.). Annual crop yield data were used from selected counties in five Southeastern USA states. No geographic differentiation among the data was included in the analysis. This aggregation of county data increased the sample size for each crop, to address the limitation of a short time-series (47 years) distributed among up to 9 ENSO categories. Statistical significance was compared using contingency tables and the Fisher exact test. Peanut and corn yield response matched best to the Multivariate ENSO Index (MEI) and cotton, to the Oceanic Nino Index (ONI). The MEI and ONI are quantitative indices, while the lower-scoring JMA and Modified-JMA indices are categorical. Therefore, future efforts may reduce the focus on categorical (Nino, Nina, Neutral) classification, and explore the response of crop yields to quantitative ENSO data.

Intensive studies reveal that there is much uncertainty regarding how ecosystem and soil respiration will respond to warming and grazing, especially in the alpine meadow ecosystem. We conducted a first of its kind field-manipulative warming and grazing experiment in an alpine meadow on the Tibetan plateau to determine the effects of warming and grazing on ecosystem and soil respiration for 3-years, from 2006 to 2008. Generally, warming and grazing did not affect seasonal average ecosystem respiration (Re), and there was no interaction between grazing and warming. However, they significantly affected the Re early in the growing season and by the end of the growing season. Warming significantly increased seasonal average soil respiration (Rs) by 9.2%, whereas the difference mainly resulted from data gathered early in the growing season, before June 2007. Positive correlations between soil temperature and Re and Rs were observed, and soil temperature explained 63-83% of seasonal Re variations during the 3-year study and 19-34% of Rs variations in 2007. Seasonal Re in 2008 and Rs in 2007 were slightly negatively correlated to soil moisture, but interannual average Re decreased with a decrease in precipitation for all treatments. Warming and grazing reduced the Q(10) value of Re in 2007 and 2008 but did not affect the Q(10) value of Rs. The Q(10) values of Rs were much lower than the Q(10) values of Re in 2007. These results suggest that grazing may reduce the temperature sensitivity of Re and that Re was mainly controlled by soil temperature rather than moisture which varied with timescale in the alpine meadow. (C) 2011 Elsevier B.V. All rights reserved.

As a weather element, clouds can affect CO(2) exchange between terrestrial ecosystems and the atmosphere by altering environmental conditions, such as solar radiation received on the ground surface, temperature, and moisture. Based on the flux data measured at five typical ecosystems of China during mid-growing season (June-August) from 2003 to 2006, we analyzed the responses of net ecosystem exchange of carbon dioxide (NEE), light use efficiency (LUE, defined as Gross ecosystem photosynthesis (GEP)/Photosynthetically active radiation (PAR)), and water use efficiency (WUE, defined as GEP/Evapotranspiration (ET)) to the changes in cloudiness. The five ecological sites included Changbaishan temperate mixed forest (CBS), Dinghushan subtropical evergreen broad-leaved forest (DHS), Xishuangbanna tropical rainforest (XSBN), Inner Mongolia semi-arid Leymus chinensis steppe (NMG), and Haibei alpine frigid Potentilla fruticosa shrub (HB). Our analyses show that cloudy sky conditions with cloud index (k(t)) values between 0.4 and 0.6 increased NEE, LUE, and WUE of the ecosystems at CBS, DHS, NMG and HB from June to August. The LUE of tropical rainforest at XSBN was higher under cloudy than under clear sky conditions, but NEE and WUE did not decrease significantly under clear sky conditions from June to August. The increase in GEP with increasing diffuse radiation received by ecosystems under cloudy skies was the main reason that caused the increases in LUE and net carbon uptake in forest ecosystem at CBS, DHS, and alpine shrub ecosystem at HB, compared with clear skies. Moreover, for the ecosystem at CBS, DHS, and HB, when sky condition became from clear to cloudy, GEP increased and ET decreased with decreasing VPD, leading to the increase in WUE and NEE under cloudy sky conditions. The decrease in Re with decreasing temperature and increase in GEP with decreasing VPD under cloudy skies led to the increase in LUE. WUE, and net carbon uptake of semi-arid steppe at NMG, compared to clear skies. These different responses among the five ecosystems are attributable to the differences in canopy characteristics and water conditions. From June to August, the peaks of the k(t) frequency distribution in temperate ecosystems (e.g., CBS, NMG, and HB) were larger than 0.5, but they were smaller than 0.4 in subtropical/tropical forest ecosystems (e.g., DHS and XSBN). These results suggest that the pattern of cloudiness during the years from 2003 to 2006 in the five ecosystems was not the best condition for their net carbon uptake. This study highlights the importance of cloudiness factor in the prediction of net carbon absorption in the Asia monsoon region under climate change. (C) 2011 Elsevier B.V. All rights reserved.

The Advanced Canopy–Atmosphere–Soil Algorithm (ACASA) model is used to predict energy, water and carbon fluxes over a Mediterranean maquis site located in North-Western Sardinia (Italy) and the model performance is evaluated. Flux simulations are compared with Eddy Covariance field measurements collected from 2004 to 2007. The site experiences a drought season during the summer months in which the vegetation becomes water stressed. Results from the months of January, April, and July are analyzed to demonstrate the model behavior in different environmental conditions. In general, simulated and observed fluxes matched when both the thermal and moisture regime are optimal. During the July water stress period the model underestimated latent heat and carbon fluxes due to a strong stress response linked to soil properties and plant physiological characteristics. The selection of values for key parameters, e.g. maximum ideal photosynthetic capacity (RUBISCO), wilting point, soil water content, and root and leaf area ratio, is crucial to obtain close agreement between simulated and observed fluxes. The model was designed so that the most sensitive parameters are measurable quantities. Using the ACASA model to predict energy and mass fluxes between the vegetation and atmosphere appears promising in this context, and it could significantly improve our ability to estimate fluxes for use in future studies.

An experimental system combining an eddy covariance system, a micrometeorological station and soil chambers placed in planted areas and in root exclusion zones was installed during three successive years in a production crop managed in a traditional way at the Lonzee experimental site (Belgium). Measurements were made successively on seed potato, winter wheat and sugar beet. The general objectives of the study were, first to evaluate the relative contributions to total ecosystem respiration (TER) of heterotrophic, above ground autotrophic and below ground autotrophic respiration over a succession of three agricultural crops (seed potato, winter wheat and sugar beet) cultivated on successive years at the same location and, secondly, to identify the driving variables of these contributions. Results showed that, during the observation periods, TER was dominated by autotrophic respiration (AR) (60–90%) and that AR was dominated by its above ground component (60–80%). HR was found to increase with temperature and to be independent of Gross Primary Production (GPP), whereas AR was driven by GPP and was mostly independent of temperature. The AR response to GPP was specific to the crop: not only AR intensity but also AR distribution between its above- (ARa) and below- (ARb) ground components were found to differ from one crop to another and, in the winter wheat, from one development stage to another. Generally, ARb contribution to AR was found larger when carbon allocation towards roots was more important. An uncertainty analysis was made and showed that the main sources of uncertainties on the estimates were the spatial variability for soil chamber measurements and uncertainties linked to the data gap filling method for eddy covariance measurements.

The partitioning of light absorption is difficult to assess in natural or cultivated mixed vegetation canopies. Previous research has shown that it is possible to estimate light absorption in monospecific canopies from the Leaf Mass per Area ( M A ), which is closely related to the prevailing light conditions experienced by the leaf during its development. The effectiveness of this approach in assessing light partitioning at individual plant level was tested on monospecific and mixed crop/weed plots. Canopy absorbed PAR ( APAR ) was estimated in monocultures or 1:1 mixed stands of Abutilon theophrasti Medicus and Glycine max L. (soybean) using a relationship between intercepted PAR ( IPAR ) and M A , calibrated at individual leaf level against incident light measurements. The accuracy of estimates was evaluated through comparison with APAR calculated from incident light measurements taken independently at various heights at whole canopy level. The use of the experimentally assessed relationship of IPAR vs. M A provided acceptable absorption estimates. The average departure of estimates from measurements, expressed as Root Mean Squared Error ( RMSE ), was 10.2%. By comparison, a numerically optimized version of the model that excluded the effects of experimental errors in single-leaf PAR measurements, yielded estimates with an average RMSE of 4.8%. The lower accuracy of the estimates based on the experimental IPAR – M A relationship was due to experimental error but also to a high sensitivity of the M A -based estimates to the model coefficients. M A variability in monoculture or mixed canopies was confirmed to be highly dependent on the PAR distribution, so M A is potentially suitable for use as a predictor of light absorption. However, due to the sensitivity of the model, a high experimental accuracy in IPAR – M A assessment as well as in M A and LAI distribution has to be guaranteed in order to perform reliable estimates by this method.

The ecosystem fluxes of mass and energy were quantified for a riparian cottonwood (Populus fremontii S. Watson) stand, and the daily and seasonal courses of evapotranspiration, CO2 flux, and canopy conductance were described, using eddy covariance. The ecosystem-level evapotranspiration results are consistent with those of other riparian studies; high vapor pressure deficit and increased groundwater depth resulted in reduced canopy conductance, and the annual cumulative evapotranspiration of 1095 mm was more than double the magnitude of precipitation. In addition, the cottonwood forest was a strong sink of CO2, absorbing 310 g C m−2 from the atmosphere in the first 365 days of the study. On weekly to annual time scales, hydrology was strongly linked with the net atmosphere–ecosystem exchange of CO2, with ecosystem productivity greatest when groundwater depth was ∼2 m below the ground surface. Increases in groundwater depth beyond the depth of 2 m corresponded with decreased CO2 uptake and evapotranspiration. Saturated soils caused by flooding and shallow groundwater depths also resulted in reduced ecosystem fluxes of CO2 and water.

A new Model of Ozone Deposition and Detoxification (MODD) is presented. This model describes stomatal ozone uptake and deposition on external plant surfaces and soil; it accounts for diurnal variability of detoxification processes and reactive ozone uptake on cuticular waxes and soil surface. The mechanistic modelling of plant defense reactions is based on the Plochl et al. (2000) detoxification model in which the dynamics of apoplast chemistry are considered. To estimate ozone deposition fluxes on cuticular waxes and soil surface, we use a revised version of the Morrison and Nazaroff (2002) model developed to account for ozone uptake on material surfaces. This model which has been fully integrated with a soil–plant–atmosphere continuum model ensures a complete coupling between stomatal conductance and O3 exchanges between leaves and the atmosphere. The observed diurnal variations in stomatal conductance which largely control the influx of O3 into the leaf are well reproduced. Model simulations point out that the pool of ascorbate located in the mesophyll cell wall plays a significant role in the detoxification of O3. Besides stomatal conductance, it is the key process involved in the control of ozone flux to the cell wall. A decrease in the pool of ascorbate lengthens the chemical lifetime of O3 in the cell wall then the virtual apoplastic resistance is found to increase with decreasing ascorbate. Although the atmospheric ozone concentration increases as the weather becomes hot and dry, the virtual apoplastic resistance follows the same trend, indicating a decrease of the ascorbate pool in the mesophyll cell wall. Results also indicate that for the pre-senescence period 57% of the ozone is deposited onto the cuticular surfaces, 4% on soil and only 37% is absorbed by stomata. The comparison of modelled and measured data reported in this study indicates that the model is capable of predicting the major features of the patterns of total ozone flux.

We determined key environmental and meteorological drivers that influence the magnitude of the fluxes of carbon dioxide, latent heat, and sensible heat using data from different vegetation types at 56 sites of the AmeriFlux network in combination with a self-organizing feature map neural network. The technique was combined with a subsequent k -means clustering procedure to classify the turbulent fluxes. This method is a direct approach to assess the importance of several environmental parameters for the turbulent exchange rates above vegetated areas, based on eddy covariance measurements. The synthesis of the available dataset was achieved by merging 225 pre-clusters of fluxes that were found by the SOFM into 9 final clusters exhibiting mean CO 2 fluxes from −5.8 μmol m −2 s −1 to 3.8 μmol m −2 s −1 . The spatial and temporal comprehensive dataset covering 305 site years, combined with the independence from mechanistic ecological assumptions of the SOFM network approach provides an opportunity to assess the strength of the influence of various environmental parameters on the turbulent fluxes over vegetated areas. After ranking the environmental and meteorological variables according to their cluster separation strength, the pattern of turbulent fluxes turned out to be three times more sensitive to photosynthetic photon flux density and vegetation type than air temperature. Furthermore, the results indicate a strong influence of the water vapor deficit on the magnitude of the turbulent exchange.

The most widely used empirical drought indices in forestry and fire risk management were tested under Mediterranean conditions. These were: the Keetch–Byram drought index KBDI, the Nesterov index NI, the Modified Nesterov index MNI, the Zhdanko index ZI which belong to the cumulative type, and the Sweden Angstrom Index which belongs to the daily type. The above indices were selected for fire risk assessment in the European Mediterranean conditions, following the method of correlating indices with real fuel moisture data. Meteorological data were collected for the testing area of the sub-urban forest of Thessaloniki, northern Greece. Time series of the drought indices were constructed for the summers of 2006 and 2007. At the same time, field data were collected in order to estimate the real fuel moisture content including surface soil, litter to represent the dead fuel moisture content, and grass to represent the live fuel moisture content. Statistical analysis of the collected field data and their correlation with the tested drought indices, which calculated from the local meteorological data, showed that KBDI is the most suitable empirical drought index for the area. Consequently, KBDI was modified for a better adaptation to Mediterranean conditions, following the development procedure as in the original paper. Based on the above method a Modified KBDI was calculated for the study area, and the differences from the original KBDI were estimated. The comparison of the two indices showed that: (a) there is a faster response of the Modified KBDI index to meteorological data, (b) the index takes on higher values during the summer months of both studied years, and thus, previously reported problems of underestimation of actual water loss is mitigated, and, (c) the estimated fire risk is higher for the study area, which is in accordance with the very low moisture content values observed.

The main objective of our study was to use Bayesian methods to quantify the uncertainties related to phenological development of maize (Zea mays L.) under various climate conditions. For this purpose, five different phenological methods were implemented in the dynamic crop growth model, which was subsequently optimized, using the data acquired at three different locations in Slovenia. The sensitivity analysis of the crop model was performed in order to find the set of most influential physiological parameters. Subsequent Bayesian model comparison was used in order to quantify the impact of phenological method selection on the final maize yield. The results revealed the importance of using an appropriate phenological method in order to correctly estimate the duration of the growing season and yield, when used within dynamic crop model. The limitations of the phenological methods used in this study are discussed. The selection of phenological method itself did not have a significant influence on the yield estimation, except in years with high temperatures and limiting water conditions. This raises the concern that inaccurate simulation of phenological development may increase the uncertainties of impact assessment on crop yield where crop models are fed with future climate projections.

We describe an approach for evaluating the representativeness of eddy covariance flux measurements and assessing sensor location bias (SLB) based on footprint modelling and remote sensing This approach was applied to the 12 main sites of the Fluxnet-Canada Research Network (FCRN)/Canadian Carbon Program (CCP) located along an east-west continental-scale transect covering grassland forest and wetland biomes For each site monthly and annual footprint climatologies e monthly or annual cumulative footprints) were calculated using the Simple Analytical Footprint model on Eulerian coordinates (SAFE) The resulting footprint climatologies were then overlaid on to Images of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from LANDSAT Thematic Mapper (TM) imagery which were used as surrogates of land surface fluxes to estimate SLB Results indicate that (i) the sizes of annual footprint climatology Increased exponentially with increasing cumulative footprint percentages and for a given percentage of footprint climatology the footprint areas were significantly different among the sites Typically the 90% annual footprint climatology areas varied from 1 1 km(2) to 5 0 km(2) (ii) using either NDVI or EVI as the flux surrogate the SLB was less than 5% for most sites with respect to the reference area of interest (A(r)) at 90% annual footprint climatology (scenario A) and a circular area with radius of 1 km centred at the individual tower (scenario B) with several exceptions (iii) the SLB decreased with increasing size of footprint climatology for all sites for both scenarios A and B (iv) out of 12 eight flux towers represented most of the ecosystem surrounding the towers for an area of 0 3 km(2) up to 10 km(2) with a satisfactorily low bias of

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.

The exchange of CO 2 between the atmosphere and a beech forest near Soro, Denmark, was measured continuously over 14 years (1996–2009). The simultaneous measurement of many parameters that influence CO 2 uptake makes it possible to relate the CO 2 exchange to recent changes in e.g. temperature and atmospheric CO 2 concentration. The net CO 2 exchange (NEE) was measured by the eddy covariance method. Ecosystem respiration (RE) was estimated from nighttime values and gross ecosystem exchange (GEE) was calculated as the sum of RE and NEE. Over the years the beech forest acted as a sink of on average of 157 g C m −2 yr −1 . In one of the years only, the forest acted as a small source. During 1996–2009 a significant increase in annual NEE was observed. A significant increase in GEE and a smaller and not significant increase in RE was also found. Thus the increased NEE was mainly attributed to an increase in GEE. The overall trend in NEE was significant with an average increase in uptake of 23 g C m −2 yr −2 . The carbon uptake period (i.e. the period with daily net CO 2 gain) increased by 1.9 days per year, whereas there was a non significant tendency of increase of the leafed period. This means that the leaves stayed active longer. The analysis of CO 2 uptake by the forest by use of light response curves, revealed that the maximum rate of photosynthetic assimilation increased by 15% during the 14-year period. We conclude that the increase in the overall CO 2 uptake of the forest is due to a combination of increased growing season length and increased uptake capacity. We also conclude that long time series of flux measurements are necessary to reveal trends in the data because of the substantial inter-annual variation in the flux.

Eddy-covariance measurements of net ecosystem exchange of CO2 (NEE) and estimates of gross ecosystem productivity (GEP) and ecosystem respiration (RE) were obtained in a 2–4 year old Eucalyptus plantation during two years with very different winter rainfall. In the first (drier) year the annual NEE, GEP and RE were lower than the sums in the second (normal) year, and conversely the total respiratory costs of assimilated carbon were higher in the dry year than in the normal year. Although the net primary production (NPP) in the first year was 23% lower than that of the second year, the decrease in the carbon use efficiency (CUE = NPP/GEP) was 11% and autotrophic respiration utilized more resources in the first, dry year than in the second, normal year. The time variations in NEE were followed by NPP, because in these young Eucalyptus plantations NEE is very largely dominated by NPP, and heterotrophic respiration plays only a relatively minor role. During the dry season a pronounced hysteresis was observed in the relationship between NEE and photosynthetically active radiation, and NEE fluxes were inversely proportional to humidity saturation deficit values greater than 0.8 kPa. Nighttime fluxes of CO2 during calm conditions when the friction velocity (u*) was below the threshold (0.25 m s−1) were estimated based on a Q10 temperature-dependence relationship adjusted separately for different classes of soil moisture content, which regulated the temperature sensitivity of ecosystem respiration.

Alternaria is a common airborne phytopathogenic fungus that may affect crops in the field or can cause decay of plant products. It can also cause diseases in animals and humans. The study of airborne Alternaria conidia is a necessary step for the control and prevention of the agricultural damage they can provoke. The aim of this paper is to contribute to model the presence and levels of Alternaria conidia in the air using a logistic regression model. Our study is conducted in 12 monitoring stations in Spain corresponding to three geographic regions with different bio-climatic characteristics, which show three different patterns of Alternaria conidia dynamics: a unique main sporulation season from mid spring to autumn in NE Spain, two defined periods (spring and autumn) in SE Spain and a uniform and constant presence in the Canary Islands. Regarding the abundance, NE Spain shows the highest values and the Canary Islands the lowest. Daily Alternaria conidia concentration is positively correlated to daily minimum temperature and daily temperature variation and negatively correlated to daily precipitation. Also, the occurrence of rain in the 3 previous days has a positive effect on Alternaria levels. These effects are modelled in this paper by means of logit regression equations. The three equations used apply to the presence of Alternaria conidia, and to the exceedance of thresholds of 10 and 30 conidia/m 3 . The model is calibrated in the 12 stations using data from years 1995 to 2008 and validated with data from 2009 in 7 stations, showing a reasonable percentage of right prediction (average 78.6%, ranging from 61.3% to 92.5%).

Timothy (Phleum pratense L.) is the most important forage grass in Scandinavia and it is therefore highly interesting to study how it will perform in a changing climate. In order to model winter survival, the dynamics of hardening and dehardening must be simulated with satisfactory precision. We investigated an early timothy frost tolerance model (LT50 model), and an LT50 model for winter wheat. Based on the assumption that timothy has no vernalization requirement, unlike winter wheat, but does have the ability to adapt to cold temperatures in a process linked to stage of development, two alternative versions of the winter wheat model were also constructed. In total, these four candidate models were calibrated by a Bayesian approach for the timothy cultivar Engmo. The candidate models were validated using independent observations on LT50 in timothy at different locations reflecting differences in climate. A sensitivity analysis, using the Morris method, to identify important model parameters suggested that there is a connection between frost tolerance and stage of plant development, even if there is no vernalization requirement. The simplified winter wheat model was selected as the best candidate model for LT50 in timothy based on model selection criteria and its ability to capture the hardening and dehardening processes. The results from the Bayesian calibration suggest that there are no major regional differences in Norway calling for regional calibration. However, cultivar-specific calibration is probably required, since there are hardy and less hardy cultivars within the same species. A functional LT50 model would allow risk assessments to be made of future winter survival using specifically tailored and downscaled climate scenarios.

The accurate prediction of the flowering period and the amount of released pollen of wind-pollinated trees is important for predicting the aerobiological pollen amounts and for estimating the intensity of background pollination of orchard seed production. Current phenological models, such as the commonly used temperature sum (thermal time) model, are efficient for predicting the timing of point events, e.g. the onset of flowering or leaf unfolding. However, the flowering period of boreal trees, including Betula pendula and B. pubescens, may last up to 5 weeks with widely varying flowering intensity. Therefore, the ability to predict the duration of the whole flowering period and the intensity of pollination is very important. In this paper, we present a new phenological model that predicts the whole flowering period and daily (normalized) variation of flowering intensity. The model is based on similar principles as the temperature sum model. The model was calibrated against pollen data recorded with pollen traps placed in several locations in Southern and Central Finland. Our new model predicted accurately the variation in the intensity throughout the flowering period. Moreover, it was able to predict the start and end of the pollination season with accuracy comparable to that achieved with an ordinary temperature sum model for point events. Due to limited amount of test data, the data-dependency of the model had to be tested with a bootstrapping approach. With this method, the model fit and parameter values showed to be independent of the parameter fitting data. The model can be used to predict the whole flowering period of wind-pollinated boreal trees for many different purposes, such as aerobiological forecasts of allergenic pollen, or generation of input data for models of long-range pollen transport.

The goal of this study was to test the utility of terrestrial laser scanning (lidar) as a means to estimate the dry weight and spatial distributions of herbage removals from small pasture plots. High resolution measurements of sward surface heights (SSH) were generated from lidar data acquired at close range over alfalfa ( Medicado sativa subsp. sativa L.) and tall fescue ( Festuca arundinacea Schreb. or Lolium arundinaceum (Schreb.) S.J. Darbyshire) pasture quadrats, before and after the collection of hand-plucked samples designed to mimic grazing by livestock. Removed herbage dry weights ranged from 3 to 65 g for n = 32 samples collected on 1 m 2 quadrats, half in each pasture type. Volume change was calculated based on differences in SSH before and after plucking. Strong relationships were observed between volume change and dry weights of removals for both species (alfalfa r 2 = 0.970, tall fescue r 2 = 0.957). Contour maps gave detailed information on SSH before and after plucking, and the spatial distributions of herbage removed from field plots. Subject to further testing, lidar-based estimates could prove both reliable and non-invasive to livestock in grazing or herbage studies.

Fluxes of CO2, water vapor, sensible heat and several heat storage terms were used to characterize the dependence of the energy balance closure and carbon balance on gaps introduced by screening for high quality or turbulent conditions. The current work is the first study where a sensitivity analysis was applied to the carbon balance and energy balance closure for an Amazonian forest site. The measurements were part of the RACCI/DRY-TO-WET Experiment, within the framework of the Large Scale Biosphere–Atmosphere Experiment in Amazonia (LBA). The energy balance closure varied from 88% to 98%, with an average intercept of −5 W m−2. The level of closure was dependent on the amount of data filtered out according to the quality control flag. A compromise between moderate data quality and good sampling of daytime periods was determined to be the best choice for this site. Different values of friction velocity threshold and data quality used in the screening of data resulted in a carbon balance ranging from −0.10 ± 0.15 to 0.31 ± 0.25 t C ha−1, where positive values indicate a net source of carbon in the period. Applying the screening based on friction velocity and quality control changed this ecosystem in the period analyzed from a sink into a source of carbon to the atmosphere. Year-round continuous measurements would be required to verify if the net release of carbon during this transition period is part of the natural cycle for this forest or if that was caused by disturbances in the ecosystem or local climatology. Our results show that the carbon sink strength associated with this forest site could have been overestimated in previous works due to underestimation of nocturnal respiration, caused by lack of filtering of low turbulence conditions.

Recent changes in climate have had a measurable impact on crop yield in China. The objective of this study is to investigate how climate variability affects wheat yield in China at different spatial scales. First the response of wheat yield to the climate at the provincial level from 1978 to 1995 for China was analysed. Wheat yield variability was only correlated with climate variability in some regions of China. At the provincial level, the variability of precipitation had a negative impact on wheat yield in parts of southeast China, but the seasonal mean temperature had a negative impact on wheat yield in only a few provinces, where significant variability in precipitation explained about 23–60% of yield variability, and temperature variability accounted for 37–41% of yield variability from 1978 to 1995. The correlation between wheat yield and climate for the whole of China from 1985 to 2000 was investigated at five spatial scales using climate data. The Climate Research Unit (CRU) and National Centers for Environmental Prediction (NCEP) proportions of the grid cells with a significant yield–precipitation correlation declined progressively from 14.6% at 0.5° to 0% at 5° scale. In contrast, the proportion of grid cells significant for the yield–temperature correlation increased progressively from 1.9% at 0.5° scale to 16% at 5° scale. This indicates that the variability of precipitation has a higher association with wheat yield at small scales (0.5°, 2°/2.5°) than at larger scales (4°/5.0°); but wheat yield has a good association with temperature at all levels of aggregation. The precipitation variable at the smaller scales (0.5°, 2°/2.5°) is a dominant factor in determining inter-annual wheat yield variability more so than at the larger scales (4°/5°). We conclude that in the current climate the relationship between wheat yield and each of precipitation and temperature becomes weaker and stronger, respectively, with an increase in spatial scale.

Climate models predict rising temperatures and more frequent and prolonged droughts, particularly in the northern hemisphere and in the Canadian Prairies. However, few studies have examined the interannual variation in evapotranspiration (E) of northern terrestrial ecosystems in relation to drought. This study analyses multi-year (1998-2006), eddy-covariance measurements to quantify the seasonal and interannual variability in E from grassland and mature aspen, black spruce, and jack pine forest ecosystems in the dry interior plains of western Canada. It also investigates the response of E to the historic 2001-2003 drought in this region. Leaf area index (LAI) was a primary factor controlling the difference in E among different ecosystems in the same ecozone. Annual E was higher and more variable for the aspen forest (405 ± 84 mm, mean ± s.d.) and grassland (395 ± 90 mm) than the black spruce (374 ± 34 mm) and jack pine (300 ± 20 mm) forests. Interannual variation of E was controlled by early spring soil temperature at all four sites, with warm springs enhancing annual E. Only the aspen forest and grassland showed a significant suppression of E by summer drought, related to reduced surface conductance at the aspen site, and to reduced surface conductance and early leaf senescence at the grassland. We conclude that the potential for drought impacts on annual E of northern ecosystems is greatest for grasslands, moderate for deciduous broadleaf aspen forests, and smallest for coniferous black spruce and jack pine forests. The forests of the Boreal Plains, adjacent to the prairie region, may ameliorate the onset of drought through the recycling of moisture to the atmosphere, whereas the prairie grasslands have only limited capacity to counteract drought through moisture recycling.