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Ammonia emission from dairy barns can be reduced by measures that improve removal of urine from floors. Information characterizing physical and chemical properties of urine puddles on floors are essential to improve mitigation measures, however information representative for practical barn conditions is scanty. The objective of this paper is to assess the area ( A p ) and depth ( D p ) of fresh urine puddles in commercial dairy barns, and to investigate the effect of floor type, season and manure scraping on these variables. Sixteen farms were measured in a factorial design of four Floor-Management types ( FMTypes ). Each farm was measured in two seasons and underwent an intense-floor-cleaning treatment ( PREclean ) before puddle creation for the D p measurement, which was compared with those created under normal floor conditions with on-farm manure scraping. Overall mean values were 0.83 m 2 for A p and 1.0 mm for D p . For both A p and D p the variation within a farm was large but negligible between farms. FMType significantly affected both variables. The V-shaped asphalt floor resulted in larger A p (1.04 m 2 ) and D p (1.5 mm) than those of slatted and grooved floors (mean values 0.76 m 2 , 0.93 mm). Our study demonstrates that the draining capacity of solid floors is a critical design issue in lowering ammonia emission. The PREclean treatment resulted in D p values that were 3 times lower than values for puddles created under normal floor conditions. We conclude that there is a considerable potential to improve draining of excreted puddles by increasing the cleaning performance of manure scrapers. [ABSTRACT FROM AUTHOR]

Ammonia emission mainly originates from urea in urine puddles on floors in dairy cow houses. This emission process can be modelled. However, required model inputs have not been updated recently. In addition, values for the model variables pH , Urinary Urea Nitrogen concentration ( UUN ), and their relation with farm and feed management are unknown for commercial dairy cow houses. Moreover, their effect on ammonia emission is unknown. Therefore, the objective of this paper was to investigate the pH and UUN in livestock practice. Sixteen commercial farms were measured in a factorial design of four Floor-Management types ( FMTypes ). Each farm was measured in two seasons and a Diet factor was defined, based on the amount of grass in total roughage. Overall mean values were 4.27 kg m −3 for UUN , an initial pH of 8.3, both in fresh puddles, and a pH ( ξ ) of 9.0 for random puddles at a random time. For UUN both the variation within and between farms was large, whereas the variation for pH was small. The Diet was the only factor that resulted in a significant effect, with a 0.1 difference in pH ( ξ ). Compared to the reference values, both the mean UUN and pH showed smaller values. The calculated potential ammonia in kg puddle −1 , however, showed a huge range and was considerably larger than the commonly used reference values in the Netherlands. [ABSTRACT FROM AUTHOR]

The paper presents a novel method for assessing the local structural identifiability question for a general non-linear state-space model. The method is a combination of (i) the application of a singular value decomposition to a parametric output sensitivity matrix that is created by simply integrating the model once and, (ii) a symbolic computation for a reduced model that is guided by the SVD results and allows a confirmation of the conclusions regarding identifiability obtained in the first step. In case there is a lack of identifiability, the symbolic computation quickly results in determination of the exact structure of the nullspace and a suitable re-parametrisation. The method is discussed in detail and applied to three case studies, of which the last two are considerably large, containing 22 and 43 parameters to be identified, respectively. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) emission can cause acidification and eutrophication of the environment, is an indirect source of nitrous oxide, and is a precursor of fine dust. The current mechanistic NH3 emission base model for explaining and predicting NH3 emissions from dairy cow houses with cubicles, a floor and slurry pit is based on measured data from a limited number of studies. It requires input values for numerous variables, but the empirical equations for the model parameters in the literature vary. Furthermore, many of the input variables cannot be assessed accurately, and their actual influence on the prediction is unknown. We aimed to improve NH3 emission modelling, by assessing the contribution to the variation in NH3 emission of each input variable and each model parameter related to a single urine puddle. We did so for 27 candidate models, created by each possible combination of three equations per model parameter: the acid dissociation constant, Henry's law constant, and the mass transfer coefficient. After analysing each candidate model with a Global Sensitivity Analysis we found that at least 71% of the model variation in NH3 emission for each candidate model was explained by five puddle related input variables: pH, depth, area, initial urea concentration and temperature. NH3 emission was not sensitive to the other four variables: air temperature, air velocity, maximum rate of urea conversion and the Michaelis–Menten constant for urea conversion. Based on these results we recommend simplifying the model structurally and reducing the number of input variables. [Copyright &y& Elsevier]

An optimisation algorithm, as an essential part of a model-based method to design greenhouses for a broad range of climatic and economic conditions, was described. This algorithm – a modified controlled random search using parallel computing – maximised the annual Net Financial Return (NFR) for a tomato grower by selecting the best alternative to fulfil eight design elements: type of greenhouse structure, material of the cover, outdoor shade screen, whitewash properties, thermal screen, heating system, cooling system and CO2 enrichment system. As an example, the algorithm was applied to two locations with different climatic and economic conditions, Almeria and The Netherlands. Due to the warm climate with high radiation levels in Almeria, a greenhouse with a relatively large specific ventilation area (20% compared to 14% for Dutch conditions), seasonal whitewash and a low-capacity direct air heater (50 W m−2 compared to 200 W m−2 for Dutch conditions) was selected. In contrast, for the relatively cold climate with low radiation levels of the Netherlands, a 100% aluminium thermal screen and no whitewash would give the best result. The design method produced realistic greenhouses and related annual NFR, indicating that the method performs well. An analysis of the close-to-best greenhouses showed that, for both locations, a structure with high light transmissivity considerably enhanced the greenhouse performance whereas an outdoor shade screen, geothermal heating and mechanical cooling would be not economical. These results demonstrate the feasibility of a model-based design approach that produces suitable greenhouse designs for given climatic and economic conditions. [ABSTRACT FROM AUTHOR]

Background: Current research in network reverse engineering for genetic or metabolic networks very often does not include a proper experimental and/or input design. In this paper we address this issue in more detail and suggest a method that includes an iterative design of experiments based, on the most recent data that become available. The presented approach allows a reliable reconstruction of the network and addresses an important issue, i.e., the analysis and the propagation of uncertainties as they exist in both the data and in our own knowledge. These two types of uncertainties have their immediate ramifications for the uncertainties in the parameter estimates and, hence, are taken into account from the very beginning of our experimental design. Findings: The method is demonstrated for two small networks that include a genetic network for mRNA synthesis and degradation and an oscillatory network describing a molecular network underlying adenosine 3′-5′ cyclic monophosphate (cAMP) as observed in populations of Dyctyostelium cells. In both cases a substantial reduction in parameter uncertainty was observed. Extension to larger scale networks is possible but needs a more rigorous parameter estimation algorithm that includes sparsity as a constraint in the optimization procedure. Conclusion: We conclude that a careful experiment design very often (but not always) pays off in terms of reliability in the inferred network topology. For large scale networks a better parameter estimation algorithm is required that includes sparsity as an additional constraint. These algorithms are available in the literature and can also be used in an adaptive optimal design setting as demonstrated in this paper.demonstrated in this paper. [ABSTRACT FROM AUTHOR]

A computer simulation model was developed to compare six alternative management strategies with respect to their economic performance and impact on population size and structure of Buffon’s kob (Kobus kob kob). The model allows for both controlled and illegal harvesting depending on the level of protection enforcement. Additional features include age–sex structure, density dependence, and environmental stochasticity. The compared strategies combine two enforcement levels and three harvest rates in both sexes. Under low enforcement regime, the population size either remained stable or decreased with legal harvesting. By contrast, an increased protection resulted in a population growth that was either logistic or dampened by legal off-take. Within the tested range of parameters, it appeared that a sustainable harvest could be achieved by taking up to 7% of the kob population. The sex ratio was slightly biased towards females under illegal but random shooting or when male harvesting was high. Both recruitment and yearling-to-cow ratios tend to be low in undisturbed, lightly hunted populations and relatively higher in depressed populations. A substantial reduction in the percentage of illegal harvest was achieved by raising the minimum anti-poaching effort from 0.15 to 0.3 (in arbitrary monetary units). Allowing a simultaneous controlled harvesting increased the amount of captured benefits (meat, trophies and their total value) and hence the household shares in the neighbouring community. At lower discount rates (10% or less), ecological and economic goals are better balanced by combining an increased protection with a high harvest rate. A sensitivity analysis revealed that small increments of the harvest quota return larger increase in revenues, especially for quota levels below 5%. Further, the rate of illegal harvest showed low sensitivity (i.e., hardly changed) when the model assumed that illegal off-take was a quadratic rather than a linear function of anti-poaching effort. In practical terms, the reduction in illegal off-take can be expected to decline linearly with the enforcement budget. Based on the obtained results, a number of suggestions are made with respect to the interpretation of survey results and the harvesting of kob populations. [Copyright &y& Elsevier]

Landowners and game reserve managers are often faced with the decision whether to undertake consumptive (such as hunting) and/or non-consumptive (such as tourism) use of wildlife resources on their properties. Here a theoretical model was used to examine cases where the game reserve management allocated the amount of land devoted to hunting (trophy hunting) and tourism, based on three scenarios: (1) hunting is separated from tourism but wildlife is shared; (2) hunting and tourism co-exist; and (3) hunting and tourism are separated by a fence. The consumptive and non-consumptive uses are not mutually exclusive; careful planning is needed to ensure that multiple management objectives can be met. Further, the analysis indicates that the two uses may be undertaken in the same area. Whether they are spatially, or temporally separated depends on the magnitude of the consumptive use. When consumptive use is not dominant, the two are compatible in the same shared area, provided the wildlife population is sufficiently large. [ABSTRACT FROM PUBLISHER]

A first-order perturbation algorithm for the computation of mean values and variances of transient temperature and moisture fields during coupled heat and mass transfer problems with random field parameters has been developed and implemented. The algorithm is based on the Galerkin finite-element discretization of Luikov's heat and mass transfer equations for capillary porous bodies and is computationally less demanding than the Monte Carlo method. The algorithm has been programmed in MATLAB and applied to a published test case of a drying process for soybean kernels. The simulations indicate that the stochastic fluctuations of the thermophysical properties and the process conditions may cause a considerable level of uncertainty in the predicted temperatures and moisture contents inside the product. [ABSTRACT FROM AUTHOR]

Identifiability is a fundamental concept in parameter estimation, and therefore key to the large majority of environmental modeling applications. Parameter identifiability analysis assesses whether it is theoretically possible to estimate unique parameter values from data, given the quantities measured, conditions present in the forcing data, model structure (and objective function), and properties of errors in the model and observations. In other words, it tackles the problem of whether the right type of data is available to estimate the desired parameter values. Identifiability analysis is therefore an essential technique that should be adopted more routinely in practice, alongside complementary methods such as uncertainty analysis and evaluation of model performance. This article provides an introductory overview to the topic. We recommend that any modeling study should document whether a model is non-identifiable, the source of potential non-identifiability, and how this affects intended project outcomes. Image 1 • Identifiability means the type of data allow parameter values to be distinguished. • Diagnosis involves examining equations or derivatives, visualizing response surface. • Important to know whether parameters are identifiable, and effect on analysis. [ABSTRACT FROM AUTHOR]

Surface water is one of the constraining resources for herbivore populations in semi-arid regions. Artificial waterpoints are constructed by wildlife managers to supplement natural water supplies, to support herbivore populations. The aim of this paper is to analyse how a landowner may realize his ecological and economic goals by manipulating waterpoints for the management of an elephant population, a water-dependent species in the presence of water-independent species. We develop a theoretical bio-economic framework to analyse the optimization of wildlife management objectives (in this case revenue generation from both consumptive and non-consumptive use and biodiversity conservation), using waterpoint construction as a control variable. The model provides a bio-economic framework for analysing optimization problems where a control has direct effects on one herbivore species but indirect effects on the other. A landowner may be interested only in maximization of profits either from elephant offtake and/or tourism revenue, ignoring the negative effects that could be brought about by elephants to biodiversity. If the landowner does not take the indirect effects of waterpoints into consideration, then the game reserve management, as the authority entrusted with the sustainable management of the game reserve, might use economic instruments such as subsidies or taxes to the landowners to enforce sound waterpoint management. [Copyright &y& Elsevier]