Different ways to model biological relationships between fertility and pH of the semen in rabbits

This work aimed to study the relationship between pH of the semen and fertility (Fert, defined as the success or failure of conception), which is of special interest because pH of the semen can be considered a global marker of the expression of some seminal quality traits. Different methods used to model the relationship between Fert and pH are presented here: 1) ignoring genetic and environmental correlations and including pH either as a covariate or as a cross-classified effect on fertility, 2) a bivariate mixed model, and 3) recursive bivariate mixed models. A total of 653 pH records and 6,365 Fert records after AI were used. Crossbreed does from 2 maternal lines were artificially inseminated with buck semen from a paternal line in a commercial environment. A negative, and almost linear, effect of pH on Fert was detected. The posterior median of pH and Fert heritabilities, and the highest posterior density interval at 95% (in parentheses) were approximately 0.18 (0.05, 0.29) and approximately 0.10 (0.02, 0.20) across all the models, respectively. Genetic correlations between traits were negative, but the highest posterior density interval at 95% included zero [i.e., -0.31 (-0.91, 0.33) in the bivariate mixed model and -0.17 (-0.99, 0.48) and -0.44 (-0.99, 0.10) in the recursive bivariate mixed models including pH as a covariate or as a cross-classified effect, respectively]. All models predicted Fert data reasonably well (i.e., 76 and 62% correct predictions for success and failure, respectively). No differences in the prediction of the EBV for male fertility were encountered between models, showing a good concordance in the animals ranked by their EBV (the correlation between EBV in all models was close to 1). Thus, no differences in results were obtained considering, or not considering, genetic and environmental correlations between pH and Fert and assuming, or not assuming, recursiveness between each trait. This is because the magnitude of the effect of pH on Fert was not large enough; therefore, the same results were obtained even though the models were of different complexity.     

Experimental challenge models for Johne's disease: a review and proposed international guidelines

An international committee of Johne's disease (JD) researchers was convened to develop guidelines for JD challenge studies in multiple animal species. The intent was to develop and propose international standard guidelines for models based on animal species that would gain acceptance worldwide. Parameters essential for the development of long-term and short-term infection models were outlined and harmonized to provide a "best fit" JD challenge model for cattle, goats, sheep, cervids, and mice. These models will be useful to study host-pathogen interactions, host immunity at the local and systemic level, and for evaluating vaccine candidates and therapeutics. The consensus guidelines herein list by animal species strains of Mycobacterium avium subsp. paratuberculosis used, challenge dose, dose frequency, age of challenge, route of challenge, preparation of inoculum, experimental animal selection, quality control, minimal experimental endpoints and other parameters.     

Hydrosedimentology of nested subtropical watersheds with native and eucalyptus forests

Purpose

Information on the effects of eucalyptus forests on hydrosedimentological processes is scarce, particularly at the catchment scale. Monitoring and mathematical modeling are efficient scientific tools used to address the lack of information for natural resource management and the representation and prediction of those processes. This study evaluates the effects of eucalyptus cultivation on hydrosedimentological processes in watersheds and to use the Limburg soil erosion model (LISEM) to represent and predict hydrological processes.

Material and methods

The study was conducted in two forested watersheds: the main watershed (94.46 ha) and a nested sub-watershed (38.86 ha), both cultivated with eucalyptus and residual riparian native forest, located in southern Brazil. Hydrosedimentalogical monitoring was conducted from 16th February 2011 to 31st December 2012, and LISEM model calibrations were performed on the bases of six storms events.

Results and discussion

The sediment yield for 2011 was 41.6 Mg km^?2 and 38.5 Mg km^?2 for the watershed and sub-watershed, respectively. An extreme event in 2012 provided greater sediment yield for the sub-watershed (99.8 Mg km^?2) than that for the watershed (51.7 Mg km^?2). Rainfall events with a greater maximum intensity generated rapid discharge and suspended sediment concentration responses in the sub-watershed due to the smaller drainage area and steeper landscape. In the main watershed, the accumulation of flood waves occurred for most events, with less steep hydrographs, and a later occurrence of the discharge peak after that of the sub-watershed. The LISEM adequately reproduced the peak discharge and runoff for the calibrated events; however, the peak time and the shape of the hydrograph were not adequately represented.

Conclusions

The hydrosedimentological patterns of the watershed and sub-watershed, both cultivated with eucalyptus, was characterized by sedimentographs preceding hydrographs during rainfall–runoff events where scale effects occur, with maximum discharge and specific sediment yield greater in the watershed than that in the sub-watershed. Empirical models based on hydrologic variables may be used for estimating the suspended sediment concentration and sediment yield. Therefore, LISEM may be used for the prediction of hydrological variables in these forested watersheds.     

Description of hydrological and erosion processes determined by applying the LISEM model in a rural catchment in southern Brazil

Purpose

Hydrosedimentological modeling is a tool that can be used to understand better important processes occurring at the catchment scale, such as runoff and sediment yield. The aim of this study was to use the Limburg Soil Erosion Model (LISEM) to describe the runoff and sediment yield during rainfall–runoff events in a small rural catchment in southern Brazil.

Materials and methods

The study was conducted in the Lajeado Ferreira Creek catchment (drainage area of 1.19 km²) where intense land use has caused a negative impact on water resources. Thirteen rainfall–runoff events that occurred in 2010 and 2011, including high-magnitude events, were used to model hydrosedimentological processes.

Results and discussion

Results

from the calibration and validation stages indicate that the model had a good performance when representing the hydrograph, including events with greater complexity. The use of a second soil layer in the model increased its efficiency, which is in accordance with the importance of subsurface flow in this catchment and its sensitivity to the physical properties of the soil, which are essential for controlling hydrosedimentological processes at the catchment scale. The simulation of sediment yield was overestimated by the model, constrained by the lack of sensitivity of the model to soil cohesion and the stability of soil aggregates. During the model calibration stage, these parameters had values different from those measured in the field.

Conclusions

The LISEM model performed well in representing runoff for events of different magnitudes. The discretization of the physical–hydrologic properties in the soil profile enabled the evaluation of the effect of subsurface impediment layers on water infiltration and runoff. The simulation was less accurate for suspended sediment concentration than for runoff. This indicates the need for further studies to either identify other factors controlling erosion and sediment yield that have not been identified by the model, or identify if the representation of the physical parameters is inadequate, especially the values of soil cohesion and aggregate stability.     

Dominant discharges for suspended sediment transport in a highly active Pyrenean river

Purpose

Dominant discharges and associated sediment dynamics of the River Isábena, a 445-km² catchment in the central Pyrenees of Spain that is punctuated by badlands, are analysed.

Materials and methods

Calculations of suspended sediment loads are based on continuous records of discharge and turbidity obtained at the basin outlet for the period 2005–2010.

Results and discussion

Dominant discharges for sediment load (i.e. effective discharge) present a bimodal distribution, with one peak falling in the range of low flows and the other associated to less frequent but higher magnitude floods (i.e. bankfull). The highly suspended sediment availability in the badlands, together with the high connectivity between the badlands and the stream network and the important in-channel fine sediment storage, causes both large and small events to remobilize fines. Baseflows, despite their low competence, generate resuspension and massive sediment loads. Thus, effective discharge (i.e. the discharge which transports most of the sediment) is not solely associated with bankfull (i.e. the discharge that dominates channel form), but to a wider range of discharges. Consequently, this river channel is not specifically adjusted to convey most of the sediment load during high floods, as in many other rivers, but instead large volumes of sediment are transferred downstream at an almost constant rate.

Conclusions

Results suggest that dominant discharge may play a lesser role in terms of (suspended) sediment load in non-supply-limited fluvial systems and/or in rivers that permanently work close to, or at, full transport capacity, as is the case of the Isábena.     

Process-based modelling of erosion,sediment transport and reservoir siltation in mesoscale semi-arid catchments

Purpose

To support scientifically sound water management in dryland environments a modelling system has been developed for the quantitative assessment of water and sediment fluxes in catchments, transport in the river system, and retention in reservoirs. The spatial scale of interest is the mesoscale because this is the scale most relevant for management of water and land resources.

Materials and methods

This modelling system comprises process-oriented hydrological components tailored for dryland characteristics coupled with components comprising hillslope erosion, sediment transport and reservoir deposition processes. The spatial discretization is hierarchically designed according to a multi-scale concept to account for particular relevant process scales. The non-linear and partly intermittent run-off generation and sediment dynamics are dealt with by accounting for connectivity phenomena at the intersections of landscape compartments. The modelling system has been developed by means of data from nested research catchments in NE-Spain and in NE-Brazil.

Results and discussion

In the semi-arid NE of Brazil sediment retention along the topography is the main process for sediment retention at all scales, i.e. the sediment delivery is transport limited. This kind of deposition retains roughly 50 to 60 % of eroded sediment, maintaining a similar deposition proportion in all spatial scales investigated. On the other hand, the sediment retained in reservoirs is clearly related to the scale, increasing with catchment area. With increasing area, there are more reservoirs, increasing the possibility of deposition. Furthermore, the area increase also promotes an increase in flow volume, favouring the construction of larger reservoirs, which generally overflow less frequently and retain higher sediment fractions. The second example comprises a highly dynamic Mediterranean catchment in NE-Spain with nested sub-catchments and reveals the full dynamics of hydrological, erosion and deposition features. The run-off modelling performed well with only some overestimation during low-flow periods due to the neglect of water losses along the river. The simulated peaks in sediment flux are reproduced well, while low-flow sediment transport is less well captured, due to the disregard of sediment remobilization in the riverbed during low flow.

Conclusions

This combined observation and modelling study deepened the understanding of hydro-sedimentological systems characterized by flashy run-off generation and by erosion and sediment transport pulses through the different landscape compartments. The connectivity between the different landscape compartments plays a very relevant role, regarding both the total mass of water and sediment transport and the transport time through the catchment.     

A Weed Species Spectral Detector Based on Neural Networks

A new neural network architecture for classification purposes is proposed. The Self-Organizing Map (SOM) neural network is used in a supervised way for a classification task. The neurons of the SOM become associated with local linear mappings (LLM). Error information obtained during training is used in a novel learning algorithm to train the classifier. The proposed method achieves fast convergence and good generalization. The classification method is then applied in a precision farming application, the classification of crops and different kinds of weeds by using spectral reflectance measurements. The classification performance of the proposed method is proven superior compared to other neural classifiers. Also, the proposed method compares favorably with the results obtained by using an optimal Bayesian classifier.     

Endophytic fungi associated with Mediterranean plants as a source of mycelium-bound lipases

A screening of endophytic fungi isolated from Mediterranean plants rendered a mycelium-bound lipase from a strain of Rhizopus oryzae that catalyzed the esterification of fatty acids in isooctane. The influence of various factors (water content, temperature, and pH) on ester synthesis was investigated. Catalytic activity was inversely correlated with water content. This enzyme was active over the entire pH range studied, from pH 3 to pH 8, and activity was maximal at pH 4 and pH 7. The enzyme was thermostable, with maximal activity at 60 degrees C.     

The role of population and quantitative genetics and modern sequencing technologies to understand evolved herbicide resistance and weed fitness

Evolution of resistance to multiple herbicides with different sites of action and of nontarget site resistance (NTSR) often involves multiple genes. Thus, single‐gene analyses, typical in studies of target site resistance, are not sufficient for understanding the genetic architecture and dynamics of NTSR and multiple resistance. The genetics of weed adaptation to varied agricultural environments is also generally expected to be polygenic. Recent advances in whole‐genome sequencing as well as bioinformatic and statistical tools have made it possible to use population and quantitative genetics methods to expand our understanding of how resistance and other traits important for weed adaptation are genetically controlled at the individual and population levels, and to predict responses to selection pressure by herbicides and other environmental factors. The use of tools such as quantitative trait loci mapping, genome‐wide association studies, and genomic prediction will allow pest management scientists to better explain how pests adapt to control tools and how specific genotypes thrive and spread across agroecosystems and other human‐disturbed systems. The challenge will be to use this knowledge in developing integrated weed management systems that inhibit broad resistance to current and future weed‐control methods. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

Predicting fertility from seminal traits: Performance of several parametric and non-parametric procedures

This research aimed at assessing the efficacy of non-parametric procedures to improve the classification of the ejaculates in the artificial insemination (AI) centers according to their fertility rank predicted from characteristics of the AI doses. A total of 753 ejaculates from 193 bucks were evaluated at three different times from 5 to 9 months of age for 21 seminal variables (related to ejaculate pH and volume, sperm concentration, viability, morphology and acrosome reaction traits, and dose characteristic) and their corresponding fertility score after AI over crossbred females. Fertility rate was categorized into five classes of equal length. Linear Regression (LR), Ordinal Logistic Regression (OLR), Support Vector Regression (SVR), Support Vector Ordinal Regression (SVOR), and Non-deterministic Ordinal Regression (NDOR) were compared in terms of their predictive ability with two base line algorithms: MEAN and MODE which always predict the mean and mode value of the classes observed in the data set, respectively. Predicting ability was measured in terms of rate of erroneous classifications, linear loss (average of the distance between the predicted and the observed classes), the number of predicted classes and the F₁ statistic (which allows comparing procedures taking into account that they can predict different number of classes). The seminal traits with a bigger influence on fertility were established using stepwise regression and a nondeterministic classifier. MEAN, LR and SVR produced a higher percentage of wrong classified cases than MODE (taken as reference for this statistic), whereas it was 6%, 13% and 39% smaller for SVOR, OLR and NDOR, respectively. However, NDOR predicted an average of 2.04 classes instead of one class predicted by the other procedures. All the procedures except MODE showed a similar smaller linear loss than the reference one (MEAN) SVOR being the one with the best performance. The NDOR showed the highest value of the F₁ statistic. Values of linear loss and F₁ statistics were far from their best value indicating that possibly, the variation in fertility explained by this group of semen characteristics is very low. From the total amount of traits included in the full model, 11, 16, 15, 18 and 3 features were kept after performing variable selection with the LR, OLR, SVR, SVOR and NDOR methods, respectively. For all methods, the reduced models showed almost an irrelevant decrease in their predictive abilities compared to the corresponding values obtained with the full models.