Geostatistical analysis of the spatial variation of the berry borer and leaf miner in a coffee agroecosystem

The advent of geostatistics and geographical information systems has made it possible to analyze complex spatial patterns of ecological phenomena over large areas in applied insect ecology and pest management. The objective of this study was to use geostatistics to characterize the spatial structure and map the spatial variation of damage caused by the berry borer (Hypothenemus hampei) and leaf miner (Leucoptera coffeella) in a coffee agroecosystem planted with the cultivar Catuai Vermelho IAC-99. Infestations of berry borer and leaf miner were evaluated in fruits and leaves, respectively. The pests were monitored at 67 georeferenced points in an area of 6.6 ha in 2005, 2006 and 2007. Variograms estimated by the method of moments (MoM) and residual maximum likelihood REML were compared. The latter were generally better in terms of the kriging error coefficients. Spherical variograms estimated by REML for berry borer infestation in fruits had ranges of spatial dependence of 34.62–118.4 m and for the leaf miner they were 53.93–133.7 m. For models fitted by weighted ordinary least squares (OLS) to the MoM experimental variogram, the ranges varied between 37.22 and 68.67 m for the berry borer and 100 and 155.4 m for leaf miner infestation. The variogram model parameters were used with the data for ordinary kriging to map the spatial variation of coffee pests for different monitoring periods. If there was no suitable variogram, inverse distance weighting was used to map the variation. The maps enabled visualization of the intensity of infestation of the insect pests for the different periods evaluated.     

Silver-catalyzed C-C bond formation between methane and ethyl diazoacetate in supercritical CO₂

Even in the context of hydrocarbons' general resistance to selective functionalization, methane's volatility and strong bonds pose a particular challenge. We report here that silver complexes bearing perfluorinated indazolylborate ligands catalyze the reaction of methane (CH(4)) with ethyl diazoacetate (N(2)CHCO(2)Et) to yield ethyl propionate (CH(3)CH(2)CO(2)Et). The use of supercritical carbon dioxide (scCO(2)) as the solvent is key to the reaction's success. Although the catalyst is only sparingly soluble in CH(4)/CO(2) mixtures, optimized conditions presently result in a 19% yield of ethyl propionate (based on starting quantity of the diazoester) at 40°C over 14 hours.     

Genetic analysis of live weight of local kids to promote genetic evaluations in the arid areas of Tunisia

Tropical Animal Health and Production - Goat meat production, a widely extended activity in the more arid areas of Tunisia, relies on local breeds. These breeds are well adapted to produce under...     

Fatty acid,chemical, and tissue composition of meat comparing Santa Inês breed sheep and Boer crossbreed goats submitted to different supplementation strategies

Tropical Animal Health and Production - To compare the fatty acid (FA) composition, and chemical and tissue composition of meat, 24 uncastrated males, 12 sheep of the Santa Inês breed, and 12...     

Combination of pelleting and monensin does not affect antioxidant properties and fatty acids in milk of grazing dairy cows supplemented with a concentrate containing soybean seeds

This study was performed with the main objective of evaluating the effect of the combination of pelleting and monensin on fatty acids (FA) composition, the concentration of total polyphenols and flavonoids, and the oxidative stability of milk in cows fed a concentrate containing soybean seeds. Eight Holstein multiparous cows were distributed in a replicated Latin square design. The four supplement treatments consisted of the combination of two factors (pelleting and monensin) and one concentrate as follows: (1) unpelleted concentrate with no monensin (CO); (2) pelleted concentrate with no monensin (PE); (3) unpelleted concentrate with 96 mg of monensin/kg of dry matter, DM (MO); and (4) pelleted concentrate with 96 mg of monensin/kg of DM (PM). There was no interaction between pelleting and monensin for milk production and concentration of milk protein, lactose, total polyphenols, flavonoids, conjugated dienes (CD), and reducing power. Fat and total solids concentration in milk were decreased when cows were fed pelleted (PE and PM) concentrates. Feeding cows with PE and PM concentrates increased the CD concentration in milk. Regarding milk FA concentration, there was no difference among treatments for total saturated, monounsaturated, and polyunsaturated FA. The most prominent result was that pelleting increased the milk concentration of omega-3 FA. Altogether, the present study suggests that the pelleting process can improve the milk fat quality by increasing the omega-3 FA, while the combination of pelleting and monensin in the diet of grazing dairy cows fed soybean-based concentrate adds no further improvements to FA profiles and oxidative stability of milk.

     

Identification of rumen microbial biomarkers linked to methane emission in Holstein dairy cows

Mitigation of greenhouse gas emissions is relevant for reducing the environmental impact of ruminant production. In this study, the rumen microbiome from Holstein cows was characterized through a combination of 16S rRNA gene and shotgun metagenomic sequencing. Methane production (CH₄) and dry matter intake (DMI) were individually measured over 4–6 weeks to calculate the CH₄ yield (CH₄y = CH₄/DMI) per cow. We implemented a combination of clustering, multivariate and mixed model analyses to identify a set of operational taxonomic unit (OTU) jointly associated with CH₄y and the structure of ruminal microbial communities. Three ruminotype clusters (R1, R2 and R3) were identified, and R2 was associated with higher CH₄y. The taxonomic composition on R2 had lower abundance of Succinivibrionaceae and Methanosphaera, and higher abundance of Ruminococcaceae, Christensenellaceae and Lachnospiraceae. Metagenomic data confirmed the lower abundance of Succinivibrionaceae and Methanosphaera in R2 and identified genera (Fibrobacter and unclassified Bacteroidales) not highlighted by metataxonomic analysis. In addition, the functional metagenomic analysis revealed that samples classified in cluster R2 were overrepresented by genes coding for KEGG modules associated with methanogenesis, including a significant relative abundance of the methyl-coenzyme M reductase enzyme. Based on the cluster assignment, we applied a sparse partial least-squares discriminant analysis at the taxonomic and functional levels. In addition, we implemented a sPLS regression model using the phenotypic variation of CH₄y. By combining these two approaches, we identified 86 discriminant bacterial OTUs, notably including families linked to CH₄ emission such as Succinivibrionaceae, Ruminococcaceae, Christensenellaceae, Lachnospiraceae and Rikenellaceae. These selected OTUs explained 24% of the CH₄y phenotypic variance, whereas the host genome contribution was ~14%. In summary, we identified rumen microbial biomarkers associated with the methane production of dairy cows; these biomarkers could be used for targeted methane-reduction selection programmes in the dairy cattle industry provided they are heritable.     

Autoregressive and random regression test-day models for multiple lactations in genetic evaluation of Brazilian Holstein cattle

Autoregressive (AR) and random regression (RR) models were fitted to test-day records from the first three lactations of Brazilian Holstein cattle with the objective of comparing their efficiency for national genetic evaluations. The data comprised 4,142,740 records of milk yield (MY) and somatic cell score (SCS) from 274,335 cows belonging to 2,322 herds. Although heritabilities were similar between models and traits, additive genetic variance estimates using AR were 7.0 (MY) and 22.2% (SCS) higher than those obtained from RR model. On the other hand, residual variances were lower in both traits when estimated through AR model. The rank correlation between EBV obtained from AR and RR models was 0.96 and 0.94 (MY) and 0.97 and 0.95 (SCS), respectively, for bulls (with 10 or more daughters) and cows. Estimated annual genetic gains for bulls (cows) obtained using AR were 46.11 (49.50) kg for MY and −0.019 (−0.025) score for SCS; whereas using RR these values were 47.70 (55.56) kg and −0.022 (−0.028) score. Akaike information criterion was lower for AR in both traits. Although AR model is more parsimonious, RR model assumes genetic correlations different from the unity within and across lactations. Thus, when these correlations are relatively high, these models tend to yield to similar predictions; otherwise, they will differ more and RR model would be theoretically sounder.     

Microsatellite loci reveal distinct populations with high diversity for the pathogenic fungus Pseudocercospora ulei from North-Western Amazonia

European Journal of Plant Pathology - South American Leaf Blight (SALB) is caused by the Ascomycota fungus Pseudocercospora ulei. This disease is recognized as the main threat to Hevea brasiliensis...