Dynamic Mechanistic Model Research Articles

E‐photosynthesis: a comprehensive dynamic mechanistic model of C3 photosynthesis: from light capture to sucrose synthesis

Photosynthesis is arguably the most researched of all plant processes. A dynamic model of leaf photosynthesis that includes each discrete process from light capture to carbohydrate synthesis, e-photosynthesis, is described. It was developed by linking and extending our previous models of photosystem II (PSII) energy transfer and photosynthetic C3 carbon metabolism to include electron transfer processes around photosystem I (PSI), ion transfer between the lumen and stroma, ATP synthesis and NADP reduction to provide a complete representation. Different regulatory processes linking the light and dark reactions are also included: Rubisco activation via Rubisco activase, pH and xanthophyll cycle-dependent non-photochemical quenching mechanisms, as well as the regulation of enzyme activities via the ferredoxin-theoredoxin system. Although many further feedback and feedforward controls undoubtedly exist, it is shown that e-photosynthesis effectively mimics the typical kinetics of leaf CO₂ uptake, O₂ evolution, chlorophyll fluorescence emission, lumen and stromal pH, and membrane potential following perturbations in light, [CO₂] and [O₂] observed in intact C3 leaves. The model provides a framework for guiding engineering of improved photosynthetic efficiency, for evaluating multiple non-invasive measures used in emerging phenomics facilities, and for quantitative assessment of strengths and weaknesses within the understanding of photosynthesis as an integrated process.

Prediction of Crizotinib-Midazolam Interaction Using the Simcyp Population-Based Simulator: Comparison of CYP3A Time-Dependent Inhibition between Human Liver Microsomes versus Hepatocytes

Crizotinib (Xalkori) is an orally available potent inhibitor of multiple tyrosine kinases, including anaplastic lymphoma kinase and mesenchymal-epithelial transition factor. Objectives of the present study were as follows: 1) to characterize crizotinib time-dependent inhibition (TDI) potency for CYP3A in human liver microsomes (HLM) and cryopreserved human hepatocytes suspended in human plasma (HSP); 2) to characterize crizotinib enzyme induction potency on CYP3A4 in cryopreserved human hepatocytes; 3) to predict crizotinib steady-state plasma concentrations in patients (e.g., autoinhibition and autoinduction) using the mechanistic dynamic model, Simcyp population-based simulator; and 4) to predict a clinical crizotinib-midazolam interaction using the dynamic model as well as the static mathematical model. Crizotinib inactivation constant (K(I)) and maximum inactivation rate constant (k(inact)) for TDI were estimated as, respectively, 0.37 µM and 6.9 h(-1) in HLM and 0.89 µM and 0.78 h(-1) in HSP. Thus, crizotinib inactivation efficiency (k(inact)/K(I)) was ∼20-fold lower in HSP relative to HLM. Crizotinib E(max) and EC(50) for CYP3A4 induction (measured as mRNA expression) were estimated as 6.4- to 29-fold and 0.47 to 3.1 µM, respectively. Based on these in vitro parameters, the predicted crizotinib steady-state area under plasma concentration-time curve (AUC) with HLM-TDI was 2.1-fold higher than the observed AUC, whereas that with HSP-TDI was consistent with the observed result (≤1.1-fold). The increase in midazolam AUC with coadministration of crizotinib (21-fold) was significantly overpredicted using HLM-TDI, whereas the prediction using HSP-TDI (3.6-fold) was consistent with the observed result (3.7-fold). Collectively, the present study demonstrated the value of HSP to predict in vivo CYP3A-mediated drug-drug interaction.

Artemia protein is processed very fast in Solea senegalensis larvae: A dynamic simulation model

Further improvement of growth performance in fish larviculture is closely linked to better understanding of the dietary amino acid (AA) requirements, and therefore of the processes involved in AA metabolism. In recent years, major advances in the understanding of fish larvae amino acid metabolism have been accomplished, in particular through the use of tracer studies. Modelling is a holistic approach to integrate knowledge on growth and metabolism and identify gaps in current understanding. A dynamic mechanistic model that simulates AA metabolism of fish larvae was developed. It aims to improve the understanding of larval digestion and absorption of dietary AA, and the postprandial AA metabolism and growth. The model also assists in the interpretation of results obtained from tracer studies. The model is driven by amino acid intake, with the absorbed dietary AA being used for energy production or for biosynthetic processes. The model is implemented for Senegalese sole (Solea senegalensis) larvae fed Artemia, and was parameterized using literature data. The model allows to integrate the results obtained after feeding a single meal with tracer AA, and following these tracer AA in the free AA and protein pools of larval gut and larval body at different time points after the meal. Model simulations suggest that there is a sharp dynamic change in the FAA pool after a meal while the protein pool is little affected. This suggests that the AA composition of the food has a major contribution to the FAA pool composition. This implies that sole larvae is highly sensitive to dietary AA imbalances, having high AA unavoidable losses unless the dietary AA profile is well balanced. The model also suggests that rates of protein synthesis and AA catabolism rapidly increases after the meal, with the peak for this postprandial metabolism occurring only 1h after the meal, and the rates returning to “basal” values 2h after the meal. This suggests a rapid processing of the Artemia protein by the larvae, and supports the need for feeding sole larvae at a high frequency in order to fully use its growth potential. Mechanistic modelling is useful and an important complement in evaluation of metabolism kinetics in nutrient flux studies. Moreover, due to its mechanistic nature, the present model can be used with different AA tracers, and also for other fish species.

Multi-objective optimisation of a model of the decomposition of animal slurry in soil: Tradeoffs between simulated C and N dynamics

To formulate best management practices for animal slurry, it is important to understand and predict its decomposition in the soil. Slurry decomposition dynamics can be studied by measuring CO2 fluxes and soil mineral nitrogen concentration during laboratory incubations and subsequently calibrating a simulation model. Carbon and nitrogen dynamics are linked and both should be properly simulated. In this work we wanted to identify the tradeoffs between errors in the simulation of C respiration and of soil inorganic N concentration.We optimised six parameters of CN-SIM (a mechanistic dynamic simulation model), using data of respired C and soil inorganic N measured during a 180-day laboratory incubation of five dairy slurries on three soils. Optimisation was carried out with a multi-objective genetic algorithm (NSGA-II), by minimising the Relative Root Mean Squared Error (RRMSE) between observations and simulations.The simulation of C respiration was frequently conflicting with the simulation of inorganic N, i.e. low RRMSE–CO2 corresponded with high RRMSE–N and vice versa. When minimising RRMSE–CO2 a set of parameters was obtained that enhanced microbial N immobilisation and reduced the turnover of the organic pools, to match the observed decrease of inorganic N in the 28 days after slurry addition to soil. Remineralisation occurring in the following 150 days caused a marked overestimation of inorganic N. When minimising RRMSE–N, the optimisation provided parameters that strongly reduced remineralisation of immobilised N by markedly diminishing C respiration, with a consequent underestimation of CO2 emission. A modified version of the model, containing a simple implementation of denitrification and of clay fixation/release of ammonium, performed better than the original model for most treatments.We conclude that the mineralisation/immobilisation turnover in the model is not fully adequate to represent C and N dynamics. We also discuss the implementation of changes (time-varying microbial efficiency and C to N ratio; simulation of ammonium clay fixation and emissions of N2/N2O) to improve model performance.

Developing Dynamic Mechanistic Species Distribution Models: Predicting Bird-Mediated Spread of Invasive Plants across Northeastern North America

Species distribution models are a fundamental tool in ecology, conservation biology, and biogeography and typically identify potential species distributions using static phenomenological models. We demonstrate the importance of complementing these popular models with spatially explicit, dynamic mechanistic models that link potential and realized distributions. We develop general grid-based, pattern-oriented spread models incorporating three mechanisms--plant population growth, local dispersal, and long-distance dispersal--to predict broadscale spread patterns in heterogeneous landscapes. We use the model to examine the spread of the invasive Celastrus orbiculatus (Oriental bittersweet) by Sturnus vulgaris (European starling) across northeastern North America. We find excellent quantitative agreement with historical spread records over the last century that are critically linked to the geometry of heterogeneous landscapes and each of the explanatory mechanisms considered. Spread of bittersweet before 1960 was primarily driven by high growth rates in developed and agricultural landscapes, while subsequent spread was mediated by expansion into deciduous and coniferous forests. Large, continuous patches of coniferous forests may substantially impede invasion. The success of C. orbiculatus and its potential mutualism with S. vulgaris suggest troubling predictions for the spread of other invasive, fleshy-fruited plant species across northeastern North America.

Estimation of enteric methane emissions trends (1990–2008) from Manitoba beef cattle using empirical and mechanistic models

Alemu, A. W., Ominski, K. H. and Kebreab, E. 2011. Estimation of enteric methane emissions trends (1990–2008) from Manitoba beef cattle using empirical and mechanistic models. Can. J. Anim. Sci. 91: 305–321. The objective of this study was to estimate and assess trends in enteric methane (CH4) emissions from the Manitoba beef cattle population from the base year of 1990 to 2008 using mathematical models. Two empirical (statistical) models: Intergovernmental Panel on Climate Change (IPCC) Tier 2 and a nonlinear equation (Ellis), and two dynamic mechanistic models: MOLLY (v3) and COWPOLL were used. Beef cattle in Manitoba were categorized in to 29 distinct subcategories based on management practice, physiological status, gender, age and production environment. Data on animal performance, feeding and management practices and feed composition were collected from the literature as well as from provincial and national sources. Estimates of total enteric CH4 production from the Manitoba beef cattle population varied between 0.9 and 2.4 Mt CO2 eq. from 1990 to 2008. Regardless of the type of models used, average CH4 emissions for 2008 were estimated to be 45.2% higher than 1990 levels. More specifically, CH4 emissions tended to increase between 1990 and 1996. Emissions were relatively stable between 1996 and 2002, increased between 2003 and 2005, but declined by 13.2% between 2005 and 2008, following the same trend as that observed in the beef cattle population. Models varied in their estimates of CH4 conversion rate (Ym, percent gross energy intake), emission factor (kg CH4 head−1 yr−1) and CH4 production. Total CH4 production estimates ranged from 1.2 to 2.0 Mt CO2 eq. for IPCC Tier 2, from 0.9 to 1.5 Mt CO2 eq. for Ellis, from 1.3 to 2.1 Mt CO2 eq. for COWPOLL and from 1.5 to 2.4 Mt CO2 eq. for MOLLY. The results indicate that enteric CH4 estimates and emission trends in Manitoba were influenced by the type of model and beef cattle population. As such, it is necessary to use appropriate models for reliable estimates for enteric CH4 inventory. A more robust approach may be to integrate different models by using mechanistic models to estimate regional Ym values, which may then be used as input for the IPCC Tier 2 model.

The effect of high-sugar grass on predicted nitrogen excretion and milk yield simulated using a dynamic model

High-sugar grass varieties have received considerable attention for their potential to reduce nitrogen (N) excretion and increase milk yield in cattle. However, considerable variation exists in the magnitude of response in published results. The purpose of this study is to explain the variation in response using a dynamic mechanistic model to predict observed N and milk yield results from the literature, and from simulated data. Examined effects were (1) water-soluble carbohydrate [WSC; g/kg of dry matter (DM)] increase; (2) change in crude protein (CP) and neutral detergent fiber (NDF) content of the plant with WSC increase; and (3) the level of N fertilization. The database for evaluation of model N and milk yield predictions consisted of 4 published studies with 28 treatment means for which high-sugar grasses were being evaluated. Water-soluble carbohydrate content of the diets ranged from 95 to 248g/kg of DM, CP content ranged from 115 to 263g/kg of DM, and the NDF content ranged from 400 to 568g/kg of DM. Urine N, milk N, and total N excretion were predicted well by the model and followed the directional pattern of observed values within each study. Simulation results showed that the N utilization ratio increased as the WSC content of the diet increased, but to varying degrees depending on the grass scenario examined. The greatest benefit in terms of N utilization ratio and urine N levels were seen when the WSC content of grass increased at the expense of CP, followed by a 50:50 CP and NDF mix, followed by a trade for NDF. Simulated milk yield decreased slightly when WSC increased at the expense of CP, increased slightly when it increased at the expense of a CP and NDF mix, and increased most when WSC increased at the expense of NDF. Results were amplified slightly under conditions of low-N fertilization and in the absence of grain feeding. Overall, modeling is useful as an explanatory tool. The variation from results in the literature with high-WSC grass feeding may be, at least in part, the result of the level of WSC (g/kg of DM) increase, concurrent changes occurring within the CP and NDF fractions of the plant, and the plane of nutrition of the diet (grain feeding and N fertilization levels).

A Dynamic View of Trauma/Hemorrhage-Induced Inflammation in Mice: Principal Drivers and Networks

BackgroundComplex biological processes such as acute inflammation induced by trauma/hemorrhagic shock/ (T/HS) are dynamic and multi-dimensional. We utilized multiplexing cytokine analysis coupled with data-driven modeling to gain a systems perspective into T/HS.Methodology/Principal FindingsMice were subjected to surgical cannulation trauma (ST) ± hemorrhagic shock (HS; 25 mmHg), and followed for 1, 2, 3, or 4 h in each case. Serum was assayed for 20 cytokines and NO2 −/NO3 −. These data were analyzed using four data-driven methods (Hierarchical Clustering Analysis [HCA], multivariate analysis [MA], Principal Component Analysis [PCA], and Dynamic Network Analysis [DyNA]). Using HCA, animals subjected to ST vs. ST + HS could be partially segregated based on inflammatory mediator profiles, despite a large overlap. Based on MA, interleukin [IL]-12p40/p70 (IL-12.total), monokine induced by interferon-γ (CXCL-9) [MIG], and IP-10 were the best discriminators between ST and ST/HS. PCA suggested that the inflammatory mediators found in the three main principal components in animals subjected to ST were IL-6, IL-10, and IL-13, while the three principal components in ST + HS included a large number of cytokines including IL-6, IL-10, keratinocyte-derived cytokine (CXCL-1) [KC], and tumor necrosis factor-α [TNF-α]. DyNA suggested that the circulating mediators produced in response to ST were characterized by a high degree of interconnection/complexity at all time points; the response to ST + HS consisted of different central nodes, and exhibited zero network density over the first 2 h with lesser connectivity vs. ST at all time points. DyNA also helped link the conclusions from MA and PCA, in that central nodes consisting of IP-10 and IL-12 were seen in ST, while MIG and IL-6 were central nodes in ST + HS.Conclusions/SignificanceThese studies help elucidate the dynamics of T/HS-induced inflammation, complementing other forms of dynamic mechanistic modeling. These methods should be applicable to the analysis of other complex biological processes.

Dynamic optimization of a FCC converter unit: numerical analysis

Fluidized-bed Catalytic Cracking (FCC) is a process subject to frequent variations in the operating conditions (including feed quality and feed rate). The production objectives usually are the maximization of LPG and gasoline production. This fact makes the FCC converter unit an excellent opportunity for real-time optimization. The present work aims to apply a dynamic optimization in an industrial FCC converter unit, using a mechanistic dynamic model, and to carry out a numerical analysis of the solution procedure. A simultaneous approach was used to discretize the system of differential-algebraic equations and the resulting large-scale NLP problem was solved using the IPOPT solver. This study also does a short comparison between the results obtained by a potential dynamic real-time optimization (DRTO) against a possible steady-state real-time optimization (RTO) application. The results demonstrate that the application of dynamic real-time optimization of a FCC converter unit can bring significant benefits in production.

Models of biological nitrogen fixation of legumes. A review

Leguminous crops have the ability to fix nitrogen (N) biologically from the atmosphere. This can benefit not only the legumes themselves but also any intercropped or subsequent crops, thus reducing or removing the need to apply N fertilizers. Improved quantification of legume biological nitrogen fixation (BNF) will provide better guidance for farmers on managing N to optimise productivity and reduce harmful losses to the environment. There are many techniques available for the direct quantitative measurement of legume BNF in the field and in controlled environments. However, these are time-consuming and therefore expensive, and generate data relevant only to the time and place of measurement. Alternatively, legume BNF can be estimated by either empirical models or dynamic mechanistic simulation models. Comparatively, simulation by a dynamic model is preferable for quantifying legume BNF, because of its capability to simulate the response of N fixation to a wide range of environmental variables and legume growth status. Currently there is no published review of the approaches used to simulate, rather than measure, legume BNF. This review of peer-reviewed literature shows that most simulation models estimate the N fixation rate from a pre-defined potential N fixation rate, adjusted by the response functions of soil temperature, soil/plant water status, soil/plant N concentration, plant carbon (C) supply and crop growth stage. Here, we highlight and compare the methods used to estimate the potential N fixation rate, and the response functions to simulate legume BNF, in nine widely-cited models over the last 30 years.We then assess their relative strengths in simulating legume BNF with varying biotic and abiotic factors, and identify the discrepancies between experimental findings and simulations. After this comparison, we identify the areas where there is the potential to improve legume BNF simulation in the future. These include; (1) consideration of photosynthetic C supply, (2) refining the various effects of soil mineral N concentration, (3) characterization and incorporation of excess soil water stress and other factors into models, and (4) incorporation of the effects of grazing, coexistence and competition with intercrops and weeds into models to improve their practical relevance to sustainable agricultural systems. This review clarifies, for the first time, the current progress in legume BNF quantification in simulation models, and provides guidance for their further development, combining fundamental experimental and modelling work.

The Use of HepaRG and Human Hepatocyte Data in Predicting CYP Induction Drug-Drug Interactions via Static Equation and Dynamic Mechanistic Modelling Approaches

The method of predicting CYP induction drug-drug interactions (DDIs) from a relative induction score (RIS) calibration has been developed to provide a novel model facilitating predictions for any CYP-inducer substrate combination by inclusion of parameters such as the fraction of hepatic clearance mediated by a specific CYP and fraction of the dose escaping intestinal extraction. In vitro HepaRG CYP3A4 induction data were used as a basis for the approach and a large number of DDIs were well predicted. Primary human hepatocyte data were also used to make predictions, using the HepaRG calibration as a foundation. Similar predictive accuracy suggests that HepaRG and primary hepatocyte data can be used inter-changeably within the same laboratory. A comparison of this 'indirect' calibration method with a direct in vitro-in vivo scaling approach was made and investigations undertaken to define the most appropriate in vivo inducer concentration to use. Additionally, a reasonably effective prediction model based on F(2) (the concentration of inducer taken to increase the CYP mRNA 2-fold above background) was established. An accurate prediction for the CYP1A2-dependent omeprazole-caffeine interaction was also made, demonstrating that the methods are useful for the evaluation of DDIs from induction involving mechanisms other than PXR activation. Finally, a dynamic mechanistic model accounting for the simultaneous influence of CYP induction and reversible and irreversible CYP inhibition in both the liver and intestine was written to provide a prediction of the overall DDI when several interactions occur concurrently. The rationale for using the various models described, alongside commercially available prediction tools, at various stages of the drug discovery process is described.

Why the human microbiome project should motivate epidemiologists to learn ecology.

If you are not an astronomer (amateur or otherwise), looking at the stars can be an exercise in frustration. Where are the constellations? The planets? A telescope doesn’t necessarily help: you can see things more clearly, but without knowing where to look and what to look for, you can’t tell what you are seeing.

Simulation of low rigidity part machining applied to thin-walled structures

The aim of this study is to evaluate the modelling of machining vibrations of thin-walled aluminium workpieces at high productivity rate. The use of numerical simulation is generally aimed at giving optimal cutting conditions for the precision and the surface finish needed. The proposed modelling includes all the ingredients needed for real productive machining of thin-walled parts. It has been tested with a specially designed machining test with high cutting engagement and taking into account all the phenomena involved in the dynamics of cutting. The system has been modelled using several simulation techniques. On the one hand, the milling process was modelled using a dynamic mechanistic model, with time domain simulation. On the other hand, the dynamic parameters of the system were obtained step by step by finite element analysis; thus the variation due to metal removal and the cutting edge position has been accurately taken into account. The results of the simulations were compared to those of the experiments; the discussion is based on the analysis of the cutting forces, the amplitude and the frequency of the vibrations evaluating the presence of chatter. The specific difficulties to perfect simulation of thin-walled workpiece chatter have been finely analysed.

On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter

On-line estimation of unmeasurable biological variables is important in fermentation processes, directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product. In this study, a novel strategy for state estimation of fed-batch fermentation process is proposed. By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model, a state space model is developed. An improved algorithm, swarm energy conservation particle swarm optimization (SECPSO), is presented for the parameter identification in the mechanistic model, and the support vector machines (SVM) method is adopted to establish the nonlinear measurement model. The unscented Kalman filter (UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process. The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.

Cell-cycle times and the tumour control probability

Mechanistic dynamic cell population models for the tumour control probability (TCP) to date have used a simplistic representation of the cell cycle: either an exponential cell-cycle time distribution (Zaider & Minerbo, 2000, Tumour control probability: a formulation applicable to any temporal protocol of dose delivery. Phys. Med. Biol., 45, 279-293) or a two-compartment model (Dawson & Hillen, 2006, Derivation of the tumour control probability (TCP) from a cell cycle model. Comput. Math. Methods Med., 7, 121-142; Hillen, de Vries, Gong & Yurtseven, 2009, From cell population models to tumour control probability: including cell cycle effects. Acta Oncol. (submitted)). Neither of these simplifications captures realistic cell-cycle time distributions, which are rather narrowly peaked around the mean. We investigate how including such distributions affects predictions of the TCP. At first, we revisit the so-called 'active-quiescent' model that splits the cell cycle into two compartments and explore how an assumption of compartmental independence influences the predicted TCP. Then, we formulate a deterministic age-structured model and a corresponding branching process. We find that under realistic cell-cycle time distributions, lower treatment intensities are sufficient to obtain the same TCP as in the aforementioned models with simplified cell cycles, as long as the treatment is constant in time. For fractionated treatment, the situation reverses such that under realistic cell-cycle time distributions, the model requires more intense treatment to obtain the same TCP.

When is optimal experimental design advantageous for the analysis of Michaelis–Menten kinetics?

A systematic analysis is performed for the estimation of parameters for the Michaelis–Menten kinetic using the dynamic mechanistic model. As quality measure the Cramer–Rao lower bond (CRLB) is used. Depending on three different optimization criteria, two different measurement errors, the process operation mode (batch and fed-batch) as well as the error of the rough estimates of the parameters used for optimal experimental design the CRLB is calculated. These values are used to decide, if optimal experimental design is favourable compared to equidistant measurement points. It will be demonstrated, that optimization criteria A and E are just slightly more favourable than criteria D. The considered measurement errors give different results, but will not change the design and the precision of the parameters significant. The fed-batch process operation gives always a significant higher precision of the parameters compared to the batch mode. If the rough estimated parameter values, with which the optimal experimental design is carried out, are just known with low accuracy equidistant measurement points are in favour to model-based optimal experimental design. Using a batch process the error of the rough value of k 2 (= v max/ E tot) must be smaller than 17% and of K m smaller than 40% so an optimal experimental design is worth for the parameter estimation. For fed-batch the errors of the rough estimates can be even bigger (up to 40% for k 2 and 68% for K m).

Online model-based control of an emulsion terpolymerisation process

A multivariable constrained model predictive control was formulated and implemented to control online an emulsion terpolymerisation process. A generic multilayer control architecture for real-time implementation of optimal control policies for particulate processes was also developed. The methodology was implemented within a multi-tiered software environment for real-time implementation of model-based control strategies. A full mechanistic dynamic model for emulsion terpolymerisation, developed in our previous work, is used as a real-time soft-sensor and incorporated within the MPC strategy. The simultaneous control of key polymer properties such as the particle size distribution (PSD), average molecular weight (Mn) and terpolymer composition were investigated using case studies involving manipulation of flow rates of monomers (styrene, MMA, MA), surfactant and initiator, and the reactor temperature. Although some offsets were observed with a degree of model mismatch, the experimental results agreed well with predictions.

Modelling the dynamic chemical interactions of atmospheric ammonia with leaf surface wetness in a managed grassland canopy

Abstract. Ammonia exchange fluxes between grassland and the atmosphere were modelled on the basis of stomatal compensation points and leaf surface chemistry, and compared with measured fluxes during the GRAMINAE intensive measurement campaign in spring 2000 near Braunschweig, Germany. Leaf wetness and dew chemistry in grassland were measured together with ammonia fluxes and apoplastic NH4+ and H+ concentration, and the data were used to apply, validate and further develop an existing model of leaf surface chemistry and ammonia exchange. Foliar leaf wetness which is known to affect ammonia fluxes may be persistent after the end of rainfall, or sustained by recondensation of water vapour originating from the ground or leaf transpiration, so measured leaf wetness values were included in the model. pH and ammonium concentrations of dew samples collected from grass were compared to modelled values. The measurement period was divided into three phases: a relatively wet phase followed by a dry phase in the first week before the grass was cut, and a second drier week after the cut. While the first two phases were mainly characterised by ammonia deposition and occasional short emission events, regular events of strong ammonia emissions were observed during the post-cut period. A single-layer resistance model including dynamic cuticular and stomatal exchange could describe the fluxes well before the cut, but after the cut the stomatal compensation points needed to numerically match measured fluxes were much higher than the ones measured by bioassays, suggesting another source of ammonia fluxes. Considerably better agreement both in the direction and the size range of fluxes were obtained when a second layer was introduced into the model, to account for the large additional ammonia source inherent in the leaf litter at the bottom of the grass canopy. Therefore, this was found to be a useful extension of the mechanistic dynamic chemistry model by keeping the advantage of requiring relatively little site-specific information.

Mechanistic Model Evaluation of Tree Species and Cactae Commonly Consumed by Grazing Goats

Vargas-Villamil, L., Juárez-Reyes, A.S., Ku-Vera, J., Ávila-Vales, E., Vargas-Villamil, F., Medina-Peralta, S. and Cerrillo-Soto, M.A. 2008. Mechanistic model evaluation of tree species and cactae commonly consumed by grazing goats. J. Appl. Anim. Res., 33: 193–198. The mechanistic dynamic model, Kóokay, was used to estimate the initial high degradable phase (QS sc (0)), the medium degradation fractional rate (k ND,NDSm), the fermentation fractional rate (kSm,SmF) and the relation between the produced gas as a function of the fermented mass (f ΔG,ΔF). To obtain these parameters a bounded regression problem was solved using in situ and in vitro data. It was found that: the cactae had much higher value for Qsc(0), k ND,NDSm and k Sm,SmF than tree leaves. The fΔG,ΔF > 40 mLG(mgF)−1 for Q. grisea and Opuntia spp and fΔG,ΔF < 40 mLG(mgF)−1 for the others. At the incubation time of 8 h, the fermentation of the tree leaves, the O. leptocaulis and the Opuntia spp was 20, ∼35 and 60 mgF(100 mgDM)−1, respectively. It was concluded that kSm,SmF is an important parameter to determine the nutritive value of new feeds since it is useful to predict the F into the rumen. Also, the F for Q. grisea, Q. eduardii and A. schaffheri are similar, but lower than Opuntia spp and O. leptocaulis.

Evaluation of CERES-Wheat and CropSyst models for water–nitrogen interactions in wheat crop

Crop simulation models can provide an alternative, less time-consuming and inexpensive means of determining the optimum crop N and irrigation requirements under varied soil and climatic conditions. In this context, two dynamic mechanistic models (CERES (Crop Environment REsource Synthesis)-Wheat and CropSyst (Cropping Systems Simulation Model)) were validated for predicting growth and yield of wheat ( Triticum aestivum L) under different nitrogen and water management conditions. Their potential as N and water management tool was evaluated for New Delhi representing semi-arid irrigated ecosystems in the Indo-Gangetic Plains. The field experiment was carried out on a silty clay loam soil at the Research Farm of the Indian Agricultural Research Institute, New Delhi, India during 2000–2001 to collect the input data for the calibration and validation of both the models on wheat crop (variety HD 2687). The models were evaluated for three water regimes [I4 (4 irrigations within the growing season), I3 (3 irrigations within the growing season) and I2 (2 irrigations within the growing season)] and five N treatments (N 0, N 60, N 90, N 120 and N 150). Both the models were calibrated using data obtained from the treatments receiving maximum nitrogen and irrigations, i.e., N 150 and I4 treatments. The models were then validated against other water and nitrogen treatments. For performance evaluation, in addition to coefficient of determination ( R 2), root mean square error (RMSE), mean absolute error (MAE) and Wilmot's index of agreement (IoA) were estimated. Both CERES-Wheat and CropSyst provided very satisfactory estimates for the emergence, flowering and physiological maturity dates. For CERES-Wheat overall prediction (pooled result of the three water regimes) of grain yield was satisfactory with significant R 2 values (0.88). The model, however, under estimated the biomass under all water regimes and N levels except for N 0 level, under which biomass was overpredicted. CropSyst predicted yield and biomass of wheat more closely than CERES-Wheat. The combined RMSE for the three water regimes between predicted and observed grain yield was 0.36 Mg ha −1 for CropSyst as compared to 0.63 Mg ha −1 for CERES-Wheat. Similarly, RMSE between observed and predicted biomass by CropSyst was 1.27 Mg ha −1 as compared to 1.94 Mg ha −1 between observed and predicted biomass by CERES-Wheat. Wilmot's index of agreement (IoA) also indicated that CropSyst model is more appropriate than CERES-Wheat in predicting growth and yield of wheat under different N and irrigation application situations in this study.