Stocking models of Chinese mitten crab (Eriocheir japonica sinensis) in Yangtze lakes

The fanning of Chinese mitten crab, a quality aquatic product in China and neighbouring Asian countries, has been developing rapidly in China since last decade. It reached a total yield of 3.4 X 10(5) tonnes in 2002. Due to the successive over-stocking year after year, many lakes in the mid-lower Yangtze Basin, the main farming area, are under deterioration, leading to a reduction of crab yield and quality, and, subsequently, a loss of fanning profits. Aiming at a normal development of crab culture and the sustainable use of lakes, an annual investigation dealing with lake environmental factors in relation to stocked crab populations was carried out at 20 farms in 4 lakes. The results show that the submersed macrophyte biomass (B-Mac) is the key factor affecting annual crab yield (CY). Using the ratio of Secchi depth to mean depth (Z(SD)/Z(M)), an easily measured parameter closely correlated to BMac, as driving variable, 10 regression models of maximal crab yields were generated (r(2) ranging 0.49-0.81). Based on the theory of MSY (Maximum Sustainable Yield), in combination with body-weight (BW) and recapture rate (RR) of adult crabs, a general optimal stocking model was eventually formulated. All models are simple and easy to operate. Comments on their applications and prospects are given in brief. (c) 2006 Elsevier B.V. All rights reserved.

Modeling meat yield based on measurements of body traits in genetically improved giant freshwater prawn (GFP) Macrobrachium rosenbergii

A single-generation dataset consisting of 1,730 records from a selection program for high growth rate in giant freshwater prawn (GFP, Macrobrachium rosenbergii) was used to derive prediction equations for meat weight and meat yield. Models were based on body traits [body weight, total length and abdominal width (AW)] and carcass measurements (tail weight and exoskeleton-off weight). Lengths and width were adjusted for the systematic effects of selection line, male morphotypes and female reproductive status, and for the covariables of age at slaughter within sex and body weight. Body and meat weights adjusted for the same effects (except body weight) were used to calculate meat yield (expressed as percentage of tail weight/body weight and exoskeleton-off weight/body weight). The edible meat weight and yield in this GFP population ranged from 12 to 15 g and 37 to 45 %, respectively. The simple (Pearson) correlation coefficients between body traits (body weight, total length and AW) and meat weight were moderate to very high and positive (0.75–0.94), but the correlations between body traits and meat yield were negative (−0.47 to −0.74). There were strong linear positive relationships between measurements of body traits and meat weight, whereas relationships of body traits with meat yield were moderate and negative. Step-wise multiple regression analysis showed that the best model to predict meat weight included all body traits, with a coefficient of determination (R 2) of 0.99 and a correlation between observed and predicted values of meat weight of 0.99. The corresponding figures for meat yield were 0.91 and 0.95, respectively. Body weight or length was the best predictor of meat weight, explaining 91–94 % of observed variance when it was fitted alone in the model. By contrast, tail width explained a lower proportion (69–82 %) of total variance in the single trait models. It is concluded that in practical breeding programs, improvement of meat weight can be easily made through indirect selection for body trait combinations. The improvement of meat yield, albeit being more difficult, is possible by genetic means, with 91 % of the variation in the trait explained by the body and carcass traits examined in this study.

Effects of agricultural management practices on earthworm populations and crop yield: validation and application of a mechanistic modelling approach

There is little consensus on how agriculture will meet future food demands sustainably. Soils and their biota play a crucial role by mediating ecosystem services that support agricultural productivity. However, a multitude of site-specific environmental factors and management practices interact to affect the ability of soil biota to perform vital functions, confounding the interpretation of results from experimental approaches. Insights can be gained through models, which integrate the physiological, biological and ecological mechanisms underpinning soil functions. We present a powerful modelling approach for predicting how agricultural management practices (pesticide applications and tillage) affect soil functioning through earthworm populations. By combining energy budgets and individual-based simulation models, and integrating key behavioural and ecological drivers, we accurately predict population responses to pesticide applications in different climatic conditions. We use the model to analyse the ecological consequences of different weed management practices. Our results demonstrate that an important link between agricultural management (herbicide applications and zero, reduced and conventional tillage) and earthworms is the maintenance of soil organic matter (SOM). We show how zero and reduced tillage practices can increase crop yields while preserving natural ecosystem functions. This demonstrates how management practices which aim to sustain agricultural productivity should account for their effects on earthworm populations, as their proliferation stimulates agricultural productivity. Synthesis and applications. Our results indicate that conventional tillage practices have longer term effects on soil biota than pesticide control, if the pesticide has a short dissipation time. The risk of earthworm populations becoming exposed to toxic pesticides will be reduced under dry soil conditions. Similarly, an increase in soil organic matter could increase the recovery rate of earthworm populations. However, effects are not necessarily additive and the impact of different management practices on earthworms depends on their timing and the prevailing environmental conditions. Our model can be used to determine which combinations of crop management practices and climatic conditions pose least overall risk to earthworm populations. Linking our model mechanistically to crop yield models would aid the optimization of crop management systems by exploring the trade-off between different ecosystem services.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.

Beverton and Holt equations: spreadsheet functions and uncertainty

This contribution illustrates how modern spreadsheets aid the calculation and visualization of yield models and how the effects of uncertainties may be incorporated using Monte Carlo simulation. It is argued that analogous approaches can be implemented for other assessment models of simple to medium complexity justifying wider use of spreadsheets in fisheries analysis and training.

五种红树植物光合产量模型及对环境因子的响应

以木榄（Bruguiera gymnorriza）、白骨壤（Avicennia marina）、桐花树（Aegiceras corniculata）、秋茄（Kandelia candel）和海漆（Excoecaria agallocha）为对象，以光合作用对环境因子的响应为主线，建立了从叶片水平到群体冠层水平上的光合产量模型，探讨了从器官、个体到群体的光合产量对环境因子响应的定量关系。 将Farquhar提出的单叶片光合作用生理生化模型与气孔导度B-B模型相结合，建立了光合作用－气孔导度耦合模型。模型模拟结果与实际测量结果具有较好的一致性。在温度为25.0℃，光合有效辐射为1000μmol•m-2s-1 的条件下，当外部CO2浓度倍增到720μmol•mol-1时，白骨壤、木榄、桐花树、秋茄、海漆的光合速率分别提高22.56%，17.13%，18.43%，18.63%和18.41%。在大气CO2浓度和光合有效辐射通量密度不变的条件下，光合作用速率对温度的响应呈单峰型曲线，即有一个最适温度，5种红树植物的最适温度值均为26.5℃左右。大气CO2浓度和温度固定不变（分别为350μmol•mol-1和25.0 ℃）时，光合作用对光合有效辐射的响应符合Michaelis-Menten反应曲线，模型在PAR<1800μmol•m-2s-1时模拟精度较高（P<0.01）。 在典型晴天条件下，5种红树植物的光合速率日变化都出现两个极大值（分别在11时和15时左右），中午前后光合速率较低，模型模拟光合速率日变化与实测数值日变化趋势一致。本模型能较好地模拟5种红树植物光合产量以及对环境因子的响应，模拟预测精度较高（P<0.01）。 以Ross和Nilson叶倾角分布模型为基础，分别建立了直接辐射和散射辐射在冠层内传输的子模型。冠层内的消光系数均有明显的日变化，且上午8时之前和下午16时之后随时间变化较大。在典型晴天条件下，单位土地面积日合成干物质总量（折合为CH2O）白骨壤为15.840g•m-2d-1，对于木榄、桐花树、秋茄、海漆其相应的值分别为 22.254 g•m-2d-1， 23.610 g•m-2d-1，24.525 g•m-2d-1和25.996 g•m-2d-1 。

Formação de populações-bases de aves para corte. I. Avaliação de melhoramento genético disponível no mercado

The present work aimed at studying the effect of five doses of zinc (0, 1.0, 2.0, 3.0 and 4.0 ppm) in three levels of soil correction. A Dark-Red Latosol, clay texture, oryginally recovered by cerrado vegetation was used. Soybean was used as test plant, cultivated in green-house conditions. The zinc dose affected dry matter production of aerial parts and grain only in the treatments that received the corrective. The concentration of the element in the soil increased linearly with the dose application, and the critical levels in soil (90% of the maximal yield) were between 0.27 and 0.37 ppm of zinc in the treatments with the equivalent to 2.0 t/ha and 3.2 t/ha of lime, respectively.

Modelagem do crescimento e produção em povoamentos desbastados de Eucalyptus grandis

The aim of the study was to develop a system of growth and yield models for thinned stands of Eucalyptus spp.; and to assess the behavior of the growth in scenarios with 10% decrease or increase in rainfall. The probability distribution functions Weibull 2 and 3 parameters and Johnson SB for different methods were fitted. Correlation between the fitted parameters with age was evaluated. Dominant height growth behavior was evaluated to check if thinned stand changes its growth when compared to a non-thinned stands. The stand variables dominant height and basal area were projected and simultaneously predicted and projected, respectively. Individual tree equations were fitted, which were fitted as functions of stand level variables in order to decrease the error propagation. R software was used to fit all the proposed models and consequently all the fitted models were evaluated by their parameters significance (F-test) and graphs of predicted values in relation to the observed values around the 1:1 line. Thus, the prognosis system was made by two ways, first one using the full data set, and for the second one the dataset was restricted at age 7.5. Increase and decrease in 20% of rainfall were assessed by updating the site index function. Method of moments was the most precise to describe the diameter distribution for every age in eucalyptus stands for Johnson SB and Weibull 2 parameters pdfs. When observed for each pdf the correlation for their fitted parameters with age, we noticed that shape parameters for a thinned stand were no longer correlated with age, differently of non-thinned stands. Thus, thinning effect was accounted in the basal area prediction and projection modeling. This result emphasized the necessity of applying the Parameter Recovery method in order to assess differences and capture the right pattern for thinned and non-thinned stands in the future. Dominant height was not influenced by thinning intensity. Therefore the fitted Chapman-Richards model did not account for a stand being thinned or not. All the fitted equations behaved with good precision, no matter using full or precocious dataset. The prognosis system using full and/or precocious date set was evaluated for when using Parameter Recovery method for Sb and Weibull pdfs, and by then, graphical analysis and precision statistics showed appropriated results. Finally, the increase or decrease in rainfall regime were observed for eucalyptus stand yields and we may notice how important is to observe this effect, since the growth pattern is strictly affected by water.

A new probabilistic canopy dynamics model (SLCD) that is suitable for evergreen and deciduous forest ecosystems

There are strong uncertainties regarding LAI dynamics in forest ecosystems in response to climate change. While empirical growth & yield models (G&YMs) provide good estimations of tree growth at the stand level on a yearly to decennial scale, process-based models (PBMs) use LAI dynamics as a key variable for enabling the accurate prediction of tree growth over short time scales. Bridging the gap between PBMs and G&YMs could improve the prediction of forest growth and, therefore, carbon, water and nutrient fluxes by combining modeling approaches at the stand level.Our study aimed to estimate monthly changes of leaf area in response to climate variations from sparse measurements of foliage area and biomass. A leaf population probabilistic model (SLCD) was designed to simulate foliage renewal. The leaf population was distributed in monthly cohorts, and the total population size was limited depending on forest age and productivity. Foliage dynamics were driven by a foliation function and the probabilities ruling leaf aging or fall. Their formulation depends on the forest environment.The model was applied to three tree species growing under contrasting climates and soil types. In tropical Brazilian evergreen broadleaf eucalypt plantations, the phenology was described using 8 parameters. A multi-objective evolutionary algorithm method (MOEA) was used to fit the model parameters on litterfall and LAI data over an entire stand rotation. Field measurements from a second eucalypt stand were used to validate the model. Seasonal LAI changes were accurately rendered for both sites (R-2 = 0.898 adjustment, R-2 = 0.698 validation). Litterfall production was correctly simulated (R-2 = 0.562, R-2 = 0.4018 validation) and may be improved by using additional validation data in future work. In two French temperate deciduous forests (beech and oak), we adapted phenological sub-modules of the CASTANEA model to simulate canopy dynamics, and SLCD was validated using LAI measurements. The phenological patterns were simulated with good accuracy in the two cases studied. However, IA/max was not accurately simulated in the beech forest, and further improvement is required.Our probabilistic approach is expected to contribute to improving predictions of LAI dynamics. The model formalism is general and suitable to broadleaf forests for a large range of ecological conditions. (C) 2014 Elsevier B.V. All rights reserved.

Conditional density approximations with mixtures of polynomials

Mixtures of polynomials (MoPs) are a non-parametric density estimation technique especially designed for hybrid Bayesian networks with continuous and discrete variables. Algorithms to learn one- and multi-dimensional (marginal) MoPs from data have recently been proposed. In this paper we introduce two methods for learning MoP approximations of conditional densities from data. Both approaches are based on learning MoP approximations of the joint density and the marginal density of the conditioning variables, but they differ as to how the MoP approximation of the quotient of the two densities is found. We illustrate and study the methods using data sampled from known parametric distributions, and we demonstrate their applicability by learning models based on real neuroscience data. Finally, we compare the performance of the proposed methods with an approach for learning mixtures of truncated basis functions (MoTBFs). The empirical results show that the proposed methods generally yield models that are comparable to or significantly better than those found using the MoTBF-based method.

Yield-factor volatility models

The term structure of interest rates is often summarized using a handful of yield factors that capture shifts in the shape of the yield curve. In this paper, we develop a comprehensive model for volatility dynamics in the level, slope, and curvature of the yield curve that simultaneously includes level and GARCH effects along with regime shifts. We show that the level of the short rate is useful in modeling the volatility of the three yield factors and that there are significant GARCH effects present even after including a level effect. Further, we find that allowing for regime shifts in the factor volatilities dramatically improves the model’s fit and strengthens the level effect. We also show that a regime-switching model with level and GARCH effects provides the best out-of-sample forecasting performance of yield volatility. We argue that the auxiliary models often used to estimate term structure models with simulation-based estimation techniques should be consistent with the main features of the yield curve that are identified by our model.

Maximal structuring of acyclic process models

This article addresses the transformation of a process model with an arbitrary topology into an equivalent structured process model. In particular, this article studies the subclass of process models that have no equivalent well-structured representation but which, nevertheless, can be partially structured into their maximally-structured representation. The transformations are performed under a behavioral equivalence notion that preserves the observed concurrency of tasks in equivalent process models. The article gives a full characterization of the subclass of acyclic process models that have no equivalent well-structured representation, but do have an equivalent maximally-structured one, as well as proposes a complete structuring method. Together with our previous results, this article completes the solution of the process model structuring problem for the class of acyclic process models.

Assessment of functional forms of crop yield loss models of invasive plant species applied in decision support tools and bioeconomic modelling

AbstractObjectives Decision support tools (DSTs) for invasive species management have had limited success in producing convincing results and meeting users' expectations. The problems could be linked to the functional form of model which represents the dynamic relationship between the invasive species and crop yield loss in the DSTs. The objectives of this study were: a) to compile and review the models tested on field experiments and applied to DSTs; and b) to do an empirical evaluation of some popular models and alternatives. Design and methods This study surveyed the literature and documented strengths and weaknesses of the functional forms of yield loss models. Some widely used models (linear, relative yield and hyperbolic models) and two potentially useful models (the double-scaled and density-scaled models) were evaluated for a wide range of weed densities, maximum potential yield loss and maximum yield loss per weed. Results Popular functional forms include hyperbolic, sigmoid, linear, quadratic and inverse models. Many basic models were modified to account for the effect of important factors (weather, tillage and growth stage of crop at weed emergence) influencing weed–crop interaction and to improve prediction accuracy. This limited their applicability for use in DSTs as they became less generalized in nature and often were applicable to a much narrower range of conditions than would be encountered in the use of DSTs. These factors' effects could be better accounted by using other techniques. Among the model empirically assessed, the linear model is a very simple model which appears to work well at sparse weed densities, but it produces unrealistic behaviour at high densities. The relative-yield model exhibits expected behaviour at high densities and high levels of maximum yield loss per weed but probably underestimates yield loss at low to intermediate densities. The hyperbolic model demonstrated reasonable behaviour at lower weed densities, but produced biologically unreasonable behaviour at low rates of loss per weed and high yield loss at the maximum weed density. The density-scaled model is not sensitive to the yield loss at maximum weed density in terms of the number of weeds that will produce a certain proportion of that maximum yield loss. The double-scaled model appeared to produce more robust estimates of the impact of weeds under a wide range of conditions. Conclusions Previously tested functional forms exhibit problems for use in DSTs for crop yield loss modelling. Of the models evaluated, the double-scaled model exhibits desirable qualitative behaviour under most circumstances.

Reconciling yield stability with international fisheries agencies precautionary preferences: the role of non constant discount factors in age structured models

International fisheries agencies recommend exploitation paths that satisfy two features. First, for precautionary reasons exploitation paths should avoid high fishing mortality in those fisheries where the biomass is depleted to a degree that jeopardise the stock's capacity to produce the Maximum Sustainable Yield (MSY). Second, for economic and social reasons, captures should be as stable (smooth) as possible over time. In this article we show that a conflict between these two interests may occur when seeking for optimal exploitation paths using age structured bioeconomic approach. Our results show that this conflict be overtaken by using non constant discount factors that value future stocks considering their relative intertemporal scarcity.