Interpreting spatial heterogeneity of crop yield with a process model and remote sensing

A process-based crop growth model (Vegetation Interface Processes (VIP) model) is used to estimate crop yield with remote sensing over the North China Plain. Spatial pattern of the key parameter—maximum catalytic capacity of Rubisco (V_cmax) for assimilation is retrieved from Normalized Difference of Vegetation Index (NDVI) from Terra-MODIS and statistical yield records. The regional simulation shows that the agreements between the simulated winter wheat yields and census data at county-level are quite well with R² being 0.41-0.50 during 2001-2005. Spatial variability of photosynthetic capacity and yield in irrigated regions depend greatly on nitrogen input. Due to the heavy soil salinity, the photosynthetic capacity and yield in coastal region is less than 50 μmol C m⁻² s⁻¹ and 3000 kg ha⁻¹, respectively, which are much lower than that in non-salinized region, 84.5 μmol C m⁻² s⁻¹ and 5700 kg ha⁻¹. The predicted yield for irrigated wheat ranges from 4000 to 7800 kg ha⁻¹, which is significantly larger than that of rainfed, 1500-3000 kg ha⁻¹. According to the path coefficient analysis, nitrogen significantly affects yield, by which water exerts noticeably indirect influences on yield. The effect of water on yield is regulated, to a certain extent, by crop photosynthetic capacity and nitrogen application. It is believed that photosynthetic parameters retrieved from remote sensing are reliable for regional production prediction with a process-based model.     

Dissolved organic carbon concentrations and fluxes in forest catchments and streams: DOC-3 model

Dissolved organic carbon (DOC) concentrations in south-western Nova Scotia streams, sampled at weekly to biweekly intervals, varied across streams from about 3 to 40 mg L⁻¹, being highest mid-summer to fall, and lowest during winter to spring. A 3-parameter model (DOC-3) was proposed to project daily stream DOC concentrations and fluxes from modelled estimates for daily soil temperature and moisture, year-round, and in relation to basin size and wetness. The parameters of this model refer to (i) a basin-specific DOC release parameter “k_DOC^”, related to the wet- and open-water area percentages per basin, (ii) the lag time “τ” between DOC production and subsequent stream DOC emergence, related to the catchment area above the stream sampling location; and (iii) the activation energy “Ea”, to deal with the temperature effect on DOC production. This model was calibrated with the 1988-2006 DOC concentration data from three streams (Pine Marten, Moosepit Brook, and the Mersey River sampled at or near Kejimkujik National Park, or KNP), and was used to interpret the biweekly 1999-2003 DOC concentrations data (stream, ground and lake water, soil lysimeters) of the Pockwock-Bowater Watershed Project near Halifax, Nova Scotia. The data and the model revealed that the DOC concentrations within the streams were not correlated to the DOC concentrations within the soil- and groundwater, but were predictable based on (i) the hydrologically inferred weather-induced changes in soil moisture and temperature next to each stream, and (ii) the topographically inferred basin area and wet- and open-water area percentages associated with each stream (R² = 0.53; RMSE = 3.5 mg L⁻¹). Model-predicted fluxes accounted 74% of the hydrometrically determined DOC exports at KNP.     

Relationships between simulated water stress and mortality and growth rates in underplanted Toona ciliata Roem. in subtropical Argentinean plantations

Toona ciliata Roem. (Australian red cedar) requires a nurse-tree overstory to prevent damage from drought and irradiation in some regions of north-eastern Argentina. T. ciliata was planted in the understory of Pinus taeda L. (625 stems/ha), Pinus elliottii Engelm. × Pinus caribaea Morelet (625 stems/ha), and Grevillea robusta A. Cunn. (833 stems/ha) nurse trees, which were thinned to 0, 25, 50, 75 and 100% of the initial densities. We measured initial T. ciliata mortality and growth as well as Leaf Area Index (LAI) based on light transmission. T. ciliata soil water availability and its effect on early growth and mortality were examined by modelling drought stress using the two-dimensional forest hydrology model ForWaDy. Simulated patterns in T. ciliata water stress for the different overstory treatments were consistent with observed patterns of mortality. Early mortality was lowest with a G. robusta overstory, with corresponding lowest drought stress values and high modelled soil water contents in the top soil layer in intermediate and high overstory densities. Mortality was highest with a P. elliottii × P. caribaea overstory in treatments with the highest modelled drought stress values in the most open treatments. The model supported our field observations by indicating that water stress was an important limitation to T. ciliata survival and growth on our study sites. The linkage between T. ciliata establishment success, early growth and soil water availability as indicated by ForWaDy, leads us to conclude that the model is a suitable stand management tool for guiding establishment of T. ciliata plantations.     

The MONICA model: Testing predictability for crop growth, soil moisture and nitrogen dynamics

A fundamentally revised version of the HERMES agro-ecosystem model, released under the name of MONICA, was calibrated and tested to predict crop growth, soil moisture and nitrogen dynamics for various experimental crop rotations across Germany, including major cereals, sugar beet and maize. The calibration procedure also included crops grown experimentally under elevated atmospheric CO₂ concentration. The calibrated MONICA simulations yielded a median normalised mean absolute error (nMAE) of 0.20 across all observed target variables (n = 42) and a median Willmott's Index of Agreement (d) of 0.91 (median modelling efficiency (ME): 0.75). Although the crop biomass, habitus and soil moisture variables were all within an acceptable range, the model often underperformed for variables related to nitrogen. Uncalibrated MONICA simulations yielded a median nMAE of 0.27 across all observed target variables (n = 85) and a median d of 0.76 (median ME: 0.30), also showing predominantly acceptable results for the crop biomass, habitus and soil moisture variables. Based on the convincing performance of the model under uncalibrated conditions, MONICA can be regarded as a suitable simulation model for use in regional applications. Furthermore, its ability to reproduce the observed crop growth results in free-air carbon enrichment experiments makes it suited to predict agro-ecosystem behaviour under expected future climate conditions.     

Modelling and mapping topographic variations in forest soils at high resolution: A case study

This article examines the utility of a digitally derived cartographic depth-to-water (DTW) index to model and map variations in drainage, vegetation and soil type and select soil properties within a forested area (40 ha) of the Swan Hills, Alberta, Canada. This index was derived from a LiDAR (Light Detection and Ranging) derived digital elevation model (DEM), with at least 1 ground return per m². The resulting DTW pattern was set to be zero along all DEM-derived flow channels, each with a 4 ha flow-initiation threshold. Soil type (luvisol, gleysol, mesisol), drainage type (very poor to excessive), vegetation type (hydric to xeric) and forest floor depth were determined along hillslope transects. These determinations conformed more closely to the DEM-derived log₁₀(DTW) variations (R² > 60%) than to the corresponding variations of the widely used topographic wetness index (TWI) (R² < 25%). Setting log₁₀(DTW) thresholds to represent the wet to moist to dry transitions between vegetation, drainage and soil type enabled a high-resolution mapping of these types across the study area. Also determined were soil moisture content, coarse fragment and soil particle composition (sand, silt, clay), pH, total C, N, S, P, Ca, Mg, K, Fe, Al, Mn, Zn, and available Ca, Mg, K, P and NH₄, by soil layer type and depth. Most of these variables were also more correlated with log₁₀(DTW) than with TWI, with and without soil layer depth as an additional regression variable. These variables are, therefore, subject to topographic controls to at least some extent, and can be modelled and mapped accordingly, as illustrated for soil moisture, forest floor depth and pH across the study area, from ridge tops to depressions. Further examinations revealed that the DEM-produced DTW and TWI patterns complemented one another, with DTW delineating soils in relation to local water-table influences, and with TWI delineating where the water would flow and accumulate.     

Evaluating weather effects on interannual variation in net ecosystem productivity of a coastal temperate forest landscape: A model intercomparison

Forest productivity is strongly affected by seasonal weather patterns and by natural or anthropogenic disturbances. However weather effects on forest productivity are not currently represented in inventory-based models such as CBM-CFS3 used in national forest C accounting programs. To evaluate different approaches to modelling these effects, a model intercomparison was conducted among CBM-CFS3 and four process models (ecosys, CN-CLASS, Can-IBIS and 3PG) over a 2500 ha landscape in the Oyster River (OR) area of British Columbia, Canada. The process models used local weather data to simulate net primary productivity (NPP), net ecosystem productivity (NEP) and net biome productivity (NBP) from 1920 to 2005. Other inputs used by the process and inventory models were generated from soil, land cover and disturbance records. During a period of intense disturbance from 1928 to 1943, simulated NBP diverged considerably among the models. This divergence was attributed to differences among models in the sizes of detrital and humus C stocks in different soil layers to which a uniform set of soil C transformation coefficients was applied during disturbances. After the disturbance period, divergence in modelled NBP among models was much smaller, and attributed mainly to differences in simulated NPP caused by different approaches to modelling weather effects on productivity. In spite of these differences, age-detrended variation in annual NPP and NEP of closed canopy forest stands was negatively correlated with mean daily maximum air temperature during July-September (T_amax) in all process models (R² = 0.4-0.6), indicating that these correlations were robust. The negative correlation between T_amax and NEP was attributed to different processes in different models, which were tested by comparing CO₂ fluxes from these models with those measured by eddy covariance (EC) under contrasting air temperatures (T_a). The general agreement in sensitivity of annual NPP to T_amax among the process models led to the development of a generalized algorithm for weather effects on NPP of coastal temperate coniferous forests for use in inventory-based models such as CBM-CFS3: NPP′ = NPP − 57.1 (T_amax − 18.6), where NPP and NPP′ are the current and temperature-adjusted annual NPP estimates from the inventory-based model, 18.6 is the long-term mean daily maximum air temperature during July-September, and T_amax is the mean value for the current year. Our analysis indicated that the sensitivity of NPP to T_amax was nonlinear, so that this algorithm should not be extrapolated beyond the conditions of this study. However the process-based methodology to estimate weather effects on NPP and NEP developed in this study is widely applicable to other forest types and may be adopted for other inventory based forest carbon cycle models.     

Modeling amphibian energetics, habitat suitability, and movements of western toads, Anaxyrus (=Bufo) boreas, across present and future landscapes

Effective conservation of amphibian populations requires the prediction of how amphibians use and move through a landscape. Amphibians are closely coupled to their physical environment. Thus an approach that uses the physiological attributes of amphibians, together with knowledge of their natural history, should be helpful. We used Niche Mapper™ to model the known movements and habitat use patterns of a population of Western toads (Anaxyrus (=Bufo) boreas) occupying forested habitats in southeastern Idaho. Niche Mapper uses first principles of environmental biophysics to combine features of topography, climate, land cover, and animal features to model microclimates and animal physiology and behavior across landscapes. Niche Mapper reproduced core body temperatures (T_c) and evaporation rates of live toads with average errors of 1.6 ± 0.4 °C and 0.8 ± 0.2 g/h, respectively. For four different habitat types, it reproduced similar mid-summer daily temperature patterns as those measured in the field and calculated evaporation rates (g/h) with an average error rate of 7.2 ± 5.5%. Sensitivity analyses indicate these errors do not significantly affect estimates of food consumption or activity. Using Niche Mapper we predicted the daily habitats used by free-ranging toads; our accuracy for female toads was greater than for male toads (74.2 ± 6.8% and 53.6 ± 15.8%, respectively), reflecting the stronger patterns of habitat selection among females. Using these changing to construct a cost surface, we also reconstructed movement paths that were consistent with field observations. The effect of climate warming on toads depends on the interaction of temperature and atmospheric moisture. If climate change occurs as predicted, results from Niche Mapper suggests that climate warming will increase the physiological cost of landscapes thereby limiting the activity for toads in different habitats.     

Application of a three-dimensional model for assessing effects of small clear-cuttings on radiation and soil temperature

A three-dimensional model Mixfor-3D of soil–vegetation–atmosphere transfer (SVAT) was developed and applied to estimate possible effects of tree clear-cutting on radiation and soil temperature regimes of a forest ecosystem. The Mixfor-3D model consists of several closely coupled 3D sub-models describing: forest stand structure; radiative transfer in a forest canopy; turbulent transfer of sensible heat, H₂O and CO₂ between ground surface and the atmospheric surface layer; evapotranspiration of ground surface vegetation and soil; heat and moisture transfer in soil. The model operates with the horizontal grid resolution, 2 m × 2 m; vertical resolution, 1 m and primary time step, 1 h.     

Water budget and the consequent duration of canopy carbon gain in a teak plantation in a dry tropical region: Analysis using a soil–plant–air continuum multilayer model

A soil–plant–air continuum multilayer model was used to numerically simulate canopy net assimilation (A_n), evapotranspiration (ET), and soil moisture in a deciduous teak plantation in a dry tropical climate of northern Thailand to examine the influence of soil drought on A_n. The timings of leaf flush and the end of the canopy duration period (CDP) were also investigated from the perspective of the temporal positive carbon gain. Two numerical experiments with different seasonal patterns of leaf area index (LAI) were carried out using above-canopy hydrometeorological data as input data. The first experiment involved seasonally varying LAI estimated based on time-series of radiative transmittance through the canopy, and the second experiment applied an annually constant LAI. The first simulation captured the measured seasonal changes in soil surface moisture; the simulated transpiration agreed with seasonal changes in heat pulse velocity, corresponding to the water use of individual trees, and the simulated A_n became slightly negative. However, in the second simulation, A_n became negative in the dry season because the decline in stomatal conductance due to severe soil drought limited the assimilation, and the simultaneous increase in leaf temperature increased dark respiration. Thus, these experiments revealed that the leaflessness in the dry season is reasonable for carbon gain and emphasized the unfavorable soil water status for carbon gain in the dry season. Examining the duration of positive A_n (DPA) in the second simulation showed that the start of the longest DPA (LDPA) in a year approached the timing of leaf flush in the teak plantation after the spring equinox. On the other hand, the end appeared earlier than that of all CDPs. This result is consistent with the sap flow stopping earlier than the complete leaf fall, implying that the carbon assimilation period ends before the completion of defoliation. The model sensitivity analysis in the second simulation suggests that a smaller LAI and slower maximum rate of carboxylation likely extend the LDPA because soil water from the surface to rooting depth is maintained longer at levels adequate for carbon gain by decreased canopy transpiration. The experiments also suggest that lower soil hydraulic conductivity and deeper rooting depth can postpone the end of the LDPA by increasing soil water retention and the soil water capacity, respectively.     

Development of a hydrothermal time seed germination model which uses the Weibull distribution to describe base water potential

Seed germination has been modelled extensively using hydrothermal time (HTT) models, that predict time to germination as a function of the extent to which seedbed temperature, T, and water potential, Ψ, exceed the base temperature, T_b, and base water potential, Ψ_b, of each seed percentile, g. Within a seed population the variation in time to germination arises from variation in Ψ_b(g) modelled by a normal distribution. We tested the assumption of normality in the distribution of Ψ_b(g) by germinating seed of two unrelated species with non-dormant seed (Buddleja davidii (Franch.) and Pinus radiata D. Don) across a range of constant Ψ at sub-optimal T. When incorporated into a HTT model the Weibull distribution more accurately described both the right skewed distribution of Ψ_b(g) and germination time course over sub-optimal T than the HTT based on the normal distribution, for both species. Given the flexibility of the Weibull distribution this model not only provides a useful method for predicting germination but also a means of determining the distribution of Ψ_b(g).     

Optimization of ecosystem model parameters using spatio-temporal soil moisture information

Parameters in process-based terrestrial ecosystem models are often nonlinearly related to the water flux to the atmosphere, and they also change temporally and spatially. Therefore, for estimating soil moisture, process-based terrestrial ecosystem models inevitably need to specify spatially and temporally variant model parameters. This study presents a two-stage data assimilation scheme (TSDA) to spatially and temporally optimize some key parameters of an ecosystem model which are closely related to soil moisture. At the first stage, a simplified ecosystem model, namely the Boreal Ecosystem Productivity Simulator (BEPS), is used to obtain the prior estimation of daily soil moisture. After the spatial distribution of 0–10 cm surface soil moisture is derived from remote sensing, an Ensemble Kalman Filter is used to minimize the difference between the remote sensing model results, through optimizing some model parameters spatially. At the second stage, BEPS is reinitialized using the optimized parameters to provide the updated model predictions of daily soil moisture. TSDA has been applied to an arid and semi-arid area of northwest China, and the performance of the model for estimating daily 0–10 cm soil moisture after parameter optimization was validated using field measurements. Results indicate that the TSDA developed in this study is robust and efficient in both temporal and spatial model parameter optimization. After performing the optimization, the correlation (r²) between model-predicted 0–10 cm soil moisture and field measurement increased from 0.66 to 0.75. It is demonstrated that spatial and temporal optimization of ecosystem model parameters can not only improve the model prediction of daily soil moisture but also help to understand the spatial and temporal variation of some key parameters in an ecosystem model and the corresponding ecological mechanisms controlling the variation.     

Integrating soil carbon cycling with that of nitrogen and phosphorus in the watershed model SWAT: Theory and model testing

In this paper we describe and test a sub-model that integrates the cycling of carbon (C), nitrogen (N) and phosphorus (P) in the Soil Water Assessment Tool (SWAT) watershed model. The core of the sub-model is a multi-layer, one-pool soil organic carbon (S_C) algorithm, in which the decomposition rate of S_C and input rate to S_C (through decomposition and humification of residues) depend on the current size of S_C. The organic N and P fluxes are coupled to that of C and depend on the available mineral N and P, and the C:N and N:P ratios of the decomposing pools. Tillage explicitly affects the soil organic matter turnover rate through tool-specific coefficients. Unlike most models, the turnover of soil organic matter does not follow first order kinetics. Each soil layer has a specific maximum capacity to accumulate C or C saturation (S_x) that depends on texture and controls the turnover rate. It is shown in an analytical solution that S_x is a parameter with major influence in the model C dynamics. Testing with a 65-yr data set from the dryland wheat growing region in Oregon shows that the model adequately simulates the S_C dynamics in the topsoil (top 0.3 m) for three different treatments. Three key model parameters, the optimal decomposition and humification rates and a factor controlling the effect of soil moisture and temperature on the decomposition rate, showed low uncertainty as determined by generalized likelihood uncertainty estimation. Nonetheless, the parameter set that provided accurate simulations in the topsoil tended to overestimate S_C in the subsoil, suggesting that a mechanism that expresses at depth might not be represented in the current sub-model structure. The explicit integration of C, N, and P fluxes allows for a more cohesive simulation of nutrient cycling in the SWAT model. The sub-model has to be tested in forestland and rangeland in addition to agricultural land, and in diverse soils with extreme properties such high or low pH, an organic horizon, or volcanic soils.     

Multi-metric evaluation of the models WARM,CropSyst, and WOFOST for rice

WARM (Water Accounting Rice Model) simulates paddy rice (Oryza sativa L.), based on temperature-driven development and radiation-driven crop growth. It also simulates: biomass partitioning, floodwater effect on temperature, spikelet sterility, floodwater and chemicals management, and soil hydrology. Biomass estimates from WARM were evaluated and compared with the ones from two generic crop models (CropSyst, WOFOST). The test-area was the Po Valley (Italy). Data collected at six sites from 1989 to 2004 from rice crops grown under flooded and non-limiting conditions were split into a calibration (to estimate some model parameters) and a validation set. For model evaluation, a fuzzy-logic based multiple-metrics indicator (MQI) was used: 0 (best) ≤ MQI ≤ 1 (worst). WARM estimates compared well with the actual data (mean MQI = 0.037 against 0.167 and 0.173 with CropSyst and WOFOST, respectively). On an average, the three models performed similarly for individual validation metrics such as modelling efficiency (EF > 0.90) and correlation coefficient (R > 0.98). WARM performed best in a weighed measure of the Akaike Information Criterion: (worst) 0<wk<1$0<w_k<1$ (best), considering estimation accuracy and number of parameters required to achieve it (mean wk=0.983$w_k=0.983$ against 0.007 and ∼0.000 with CropSyst and WOFOST, respectively). WARM results were sensitive to 30% of the model parameters (ratio being lower with both CropSyst, <10%, and WOFOST, <20%), but appeared the easiest model to use because of the lowest number of crop parameters required (10 against 15 and 34 with CropSyst and WOFOST, respectively). This study provides a concrete example of the possibilities offered using a range of assessment metrics to evaluate model estimates, predictive capabilities, and complexity.     

Estimation of annual potential evapotranspiration at regional scale based on the effect of moisture on soil respiration

Potential evapotranspiration (PET) is an important component of water cycle. For traditional models derived from the principle of aerodynamics and the surface energy balance, its calculation always includes many parameters, such as net radiation, water vapor pressure, air temperature and wind speed. We found that it can be acquired in an easier way in specific regions. In this study, a new PET model (PETP model) derived from two empirical models of soil respiration was evaluated using the Penman-Monteith equation as a standard method. The results indicate that the PETP model estimation concur with the Penman-Monteith equation in sites where annual precipitation ranges from 717.71 mm to 1727.37 mm (R² = 0.68, p = 0.0002), but show large discrepancies in all sites (R² = 0.07, p = 0.1280). Then we applied our PETP model at the global scale to the regions with precipitation higher than 700 mm using 2.5° CMAP data to obtain the annual PET for 2006. As expected, the spatial pattern is satisfactory overall, with the highest PET values distributed in the lower latitudes or coastal regions, and with an average of 1292.60 ± 540.15 mm year⁻¹. This PETP model provides a convenient approach to estimate PET at regional scales.     

Effects of climatic data low-pass filtering on the ICBM temperature- and moisture-based soil biological activity factors in a cool and humid climate

The air temperature (T_air), total precipitation (TP) and potential evapotranspiration (PET) are standard input data for soil carbon dynamic models, i.e., for calculating temperature and moisture effects on soil biological activity. The resolution needed depends on objectives, the complexity of models and inbuilt pedotransfer functions. The Introductory Carbon Balance Model (ICBM) soil climate front end model calculates a multiplicative soil-temperature (r_{e_temp}) and -moisture (r_{e_wat}) factor with a daily time-step to estimate soil biological activity, i.e., r_{e_crop} = r_{e_temp} × r_{e_wat}. Our objective was to determine how much r_{e_temp}, r_{e_wat} and r_{e_crop} are affected by low-pass filtering of the climatic input data for a cool, humid temperate region. To achieve this we conducted spectral analysis on T_air, TP, PET and r_{e_crop} in the frequency domain. Thereafter we applied Fourier low-pass filters of 5, 15, 30, 60 and 180 days on T_air, TP, PET and tracked their effects through the soil climate model's state variables and outputs. This was done using a sandy and a heavy clay soil and an 89-year daily time-series from a meteorological station in Quebec (Canada). The Fourier spectra showed that the variance for T_air, PET and r_{e_crop} was dominated by an annual cycle, as could be expected. There was no yearly cycle for TP. The variation in r_{e_temp} explained most of the variance in r_{e_crop}. The soil climate module outputs were not sensitive to low-pass filtering of PET. A daily time-step was needed to avoid overestimating r_{e_crop} for the sandy soil. Using a weekly time-step for TP and T_air allowed us to explain about 80% of the variance in r_{e_crop} for the heavy clay soil. This study also indicates that the standard leaf (and green) area index functions for calculating transpiration should receive more attention, since they have significant effects on the state and output variables.     

Foliage profiles of individual trees determine competition,self-thinning,biomass and NPP of a Cryptomeria japonica forest stand: A simulation study based on a stand-scale process-based forest model

A simulation study was carried out to investigate simultaneously the effects of eco-physiological parameters on competitive asymmetry, self-thinning, stand biomass and NPP in a temperate forest using an atmosphere–vegetation dynamics interactive model (MINoSGI). In this study, we selected three eco-physiological relevant parameters as foliage profiles (i.e. vertical distribution of leaf area density) of individual trees (distribution pattern is described by the parameter η), biomass allocation pattern in individual tree growth (χ) and the maximum carboxylation velocity (V_max). The position of the maximal leaf area density shifts upward in the canopy with increasing η. For scenarios with η < 4 (foliage concentrated in the lowest canopy layer) or η > 12 (foliage concentrated in the uppermost canopy layer), a low degree of competitive asymmetry was produced. These scenarios resulted in the survival of subordinate trees due to a brighter lower canopy environment when η < 4 or the generation of spatially separated foliage profiles between dominant and subordinate trees when η > 12. In contrast, competition between trees was most asymmetric when 4 ≤ η ≤ 12 (vertically widespread foliage profile in the canopy), especially when η = 8. In such cases, vertically widespread foliage of dominant trees lowered the opportunity of light acquisition for subordinate trees and reduced their carbon gain. The resulting reduction in carbon gain of subordinate trees yielded a higher degree of competitive asymmetry and ultimately higher mortality of subordinate trees. It was also shown that 4 ≤ η ≤ 12 generated higher self-thinning speed, smaller accumulated NPP, litter-fall and potential stand biomass as compared with the scenarios with η < 4 or η > 12. In contrast, our simulation revealed small effects of χ or V_max on the above-mentioned variables as compared with those of η. In particular, it is notable that greater V_max would not produce greater potential stand biomass and accumulated NPP although it has been thought that physiological parameters relevant to photosynthesis such as V_max influence dynamic changes in forest stand biomass and NPP (e.g. the greater the V_max, the greater the NPP). Overall, it is suggested that foliage profiles rather than biomass allocation or maximum carboxylation velocity greatly govern forest dynamics, stand biomass, NPP and litter-fall.     

A model for measuring leaf growth rate of vegetative shoot in the seagrass, Zostera marina

A new model for determining leaf growth in vegetative shoots of the seagrass Zostera marina (eelgrass) is described. This model requires the weights of individual mature and immature whole leaves and leaf plastochrone interval (P_L) as parameters, differing from the conventional leaf marking technique (CLM) that requires cutting and separation between new and old tissue of leaves. The techniques required for the model are the same as for the plastochrone method, but the parameters differ between both methods in use of the weight of individual immature leaves. In a mesocosm study, eelgrass growth was examined, and parameters for the new model and plastochrone method (the weights of individual mature and immature leaves and P_L) were measured. Leaf growth rate was measured using the CLM and determined by the new method and the plastochrone method. The results were then compared between the CLM, the new model, and the plastochrone method. The results obtained with the new model were similar to those obtained with the CLM. However, the results of the plastochrone method differed from those of the CLM, while the weight of immature leaves varied seasonally. The new model was also used to determine leaf growth in a natural eelgrass bed in Mikawa Bay, Japan, and revealed the growth rates in all shoots and those of different ages. This method would be advantageous as an accurate means of direct measurement in fieldwork, and should therefore be a useful tool for monitoring seagrass growth.     

Estimating dyad association probability under imperfect and heterogeneous detection

In animal behaviour studies, association indices estimate the proportion of time two individuals (i.e. a dyad) spend in association. In terms of dyads, all association indices can be interpreted as estimators of the probability that a dyad is associated. However, traditional indices rely on the assumptions that the probability to detect a particular individual (p) is either approximately one and/or homogeneous between associated and not associated individuals. Based on marked individuals we develop a likelihood based model to estimate the probability a dyad is associated (ψ) accounting for p < 1 and possibly varying between associated and not associated individuals. The proposed likelihood based model allows for both individual and dyadic missing observations. In addition, the model can easily be extended to incorporate covariate information for modeling p and ψ. A simulation study showed that the likelihood based model approach yield reasonably unbiased estimates, even for low and heterogeneous individual detection probabilities, while, in contrast, traditional indices showed moderate to strong biases. The application of the proposed approach is illustrated using a real data set collected from a population of Commerson's dolphin (Cephalorhynchus commersonii) in Patagonia Argentina. Finally, we discuss possible extensions of the proposed model and its applicability in animal behaviour and ecological studies.     

Identification of important factors for water vapor flux and CO2 exchange in a cropland

Water vapor flux and carbon dioxide (CO₂) exchange in croplands are crucial to water and carbon cycle research as well as to global warming evaluation. In this study, a standard three-layer feed-forward back propagation neural network technique associated with the Bayesian technique of automatic relevance determination (ARD) was employed to investigate water vapor and CO₂ exchange between the canopy of summer maize and atmosphere in responses to variations of environmental and physiological factors. These factors, namely the photosynthetically active radiation (PAR), air temperature (T), vapor pressure deficient (VPD), leaf-area index (LAI), soil water content in root zone (W), and friction velocity (U*), were used as inputs in neural network analysis. Results showed that PAR, VPD, T and LAI were the primary factors regulating both water vapor and CO₂ fluxes with VPD and W more critical to water vapor flux and PAR and T more crucial to CO₂ exchange. Furthermore, two time variables “day of the year (DOY)” and “time of the day (TOD)” could also improve the simulation results of neural network analysis. The important factors identified by the neural network technique used in this study were in the order of PAR > T > VPD > LAI > U* > TOD for water vapor flux and in the order of VPD > W > LAI > T > PAR > DOY for CO₂ exchange. This study suggests that neural network technique associated with ARD could be a useful tool for identifying important factors regulating water vapor and CO₂ fluxes in terrestrial ecosystem.     

The effect of estimated PAR uncertainties on the physiological processes of biosphere models

Photosynthetically active radiation (PAR) energy reaching on the vegetated surface is a key determinant of plant physiological processes. Most of biosphere or crop models use the ratio of PAR to incoming solar radiation (R_s), PAR/R_s, to convert R_s into PAR in order to reduce weather data-input requirements. Several existing models simply specify a constant ratio, PAR/R_s = 0.5. However, some field experiments have reported that the ratio PAR/R_s may not be constant. Previous empirical equations of PAR/R_s were derived based on the data of monthly or daily timescales collected from only a few measurement sites, hence they may not be appropriate to be used in current global biosphere models usually with hourly simulation time steps. Here, we represent the exponential correlation between PAR/R_s and sky clearness index (0-1) using hourly data from 54 Ameriflux measurement sites. It is found that PAR/R_s increases up to 0.6 in cloudy conditions when the clearness index (CI) is below ∼0.2, whereas it is nearly constant at ∼0.42 when CI is above 0.2. When the identified empirical equation is used in the model simulation, it results in −4 to 2% difference in the stomatal conductance compared to that using the constant ratio PAR/R_s = 0.5.