Glycerine as a feed supplement for beef and dairy cattle: A meta-analysis on performance,rumen fermentation,blood metabolites and product characteristics

This study aimed to comprehensively evaluate the effects of glycerine supplementation at various concentrations on performance, rumen fermentation, blood metabolites and product characteristics of beef and dairy cattle in vivo by using a quantitative meta-analysis approach. Meta-analysis was performed by integrating a total of 52 studies from 39 articles and 182 treatments into a database. Data were constructed into an intact database and did not distinguish between beef and dairy cattle, except for the parameters of production performance and product characteristics. Data summarized were analysed by using a statistical meta-analysis that employed a fixed effect of glycerine supplementation level and a random effect of various studies for both beef and dairy cattle. Significance of an effect was stated at the probability level of p < .05, and p < .1 was considered as a tendency of significant. Results revealed that there was a linear decrease on dry matter intake (p < .01) and daily gain (p < .05) of beef cattle with the increasing levels of glycerine supplementation. Glycerine supplementation did not decrease milk production of lactating dairy cows. Molar proportion of acetate in the rumen was decreased (p < .001), whereas propionate and butyrate proportions were increased (both at p < .001) by glycerine supplementation. Generally, glycerine did not change nutrient digestibility except that it reduced fibre digestibility (p < .001). Glycerine supplementation linearly lowered triglyceride and NEFA concentrations (both at p < .05) in the blood serum, but not other blood metabolites. Glycerine tended to linearly increase (p < .1) carcass percentage in beef cattle. Increasing dietary glycerine levels decreased milk fat (p < .01) but elevated milk protein (p < .001). Glycerine tended to increase milk lactose (p < .1) by following a quadratic pattern. The proportion of MUFA was increased quadratically by glycerine supplementation (p < .05), whereas glycerine tended to decrease SFA by following a quadratic pattern (p < .1).     

Development of a robust,VdNEP gene-based molecular marker to differentiate between pathotypes of Verticillium dahliae

Verticillium dahliae is a soilborne fungus that causes Verticillium wilt disease in a plethora of crops. Based on symptoms that develop on cotton, olive and okra, V. dahliae isolates are categorized into two pathotypes, namely defoliating and nondefoliating, with the former showing increased virulence and causing severe defoliation. Reliable differentiation between V. dahliae pathotypes is crucial for the management of Verticillium wilt in cotton and olive. In the present study, a polymorphism was detected among isolates of defoliating and nondefoliating pathotypes in Southern blots using the VdNEP gene as a probe. The regions flanking this gene were isolated by inverse PCR and sequence differences in the 3′ untranslated region (3′-UTR) of the VdNEP gene were detected between the two pathotypes. Based on these sequences, primers were designed and assessed to develop a multiplex PCR detection assay. Using this assay, a collection of cotton and olive V. dahliae isolates from Greece and Cyprus was screened, revealing that the defoliating pathotype is present in several regional units of Greece. Thus, this work presents a new, sensitive molecular marker for the differentiation between V. dahliae pathotypes based on the VdNEP gene. Because the 3′-UTR is involved in the phenotypes displayed by the pathotypes, an expression experiment was conducted under conditions simulating the xylem of a host plant. Expression of the VdNEP gene was elevated at all time points in the defoliating compared to the nondefoliating strain, suggesting a possible involvement of VdNEP expression in the defoliation process.     

Predicting on-farm soybean yields in the pampas using CROPGRO-soybean

Soybean is the main rainfed crop in a wide range of latitudes and sowing dates of the Argentine Pampas. It is sown alone or as a second crop after other winter and summer crops. Modelling approaches have proved to be helpful in the decision making process. The on-farm evaluation of CROPGRO is rather difficult since input data are scarce and frequently of worse quality than those from experimental works. Moreover, CROPGRO simulation of water dynamic processes and their relation with biomass production has not been comprehensively evaluated in soybean crops. The aims of this study were (i) to evaluate the CROPGRO-soybean performance, with emphasis on water demand and supply and biomass production under water limited conditions, (ii) to generate a revised CROPGRO model improving those aspects, and (iii) to compare simulations outputs using the original and the revised CROPGRO models, with on-farm crop data set. In the revised model, we multiplied potential evapotranspiration by 1–1.22 when LAI increased from 0 to ≥4.0. We set a root extension rate of 4.0 cm/thermal day and a maximum rooting depth of 2.5 m. Finally, we included a nonlinear equation to simulate the relationship between relative transpiration and relative gross photosynthesis. The ability of the revised CROPGRO-soybean to simulate water content depletion and biomass production was tested against several experiments with an imposed drought period. We also calibrated cultivar parameters using “ad hoc” tests in a range of environments (combinations of sowing dates and locations). The models were evaluated with data from 155 commercial farms. V (%) (root mean square error as percentage of the observed mean) for the total cycle length, vegetative period, and reproductive phase simulations were 7, 13 and 15%, respectively. The revised CROPGRO was more accurate in simulating crop yield, biomass, harvest index and yield numeric components. V (%) values ranged from 11 to 17% (revised version) and from 13 to 22% (original version). Besides, V (%) values for yield were 16% with the revised model versus 32% with the original one, considering only paddocks with higher water stress level. The robust prediction of phenology, biomass and yield components obtained with the revised model across different environmental conditions, support its use in the decision making process of the soybean crop at the farm scale.