Base temperatures affect accuracy of growing degree day model to predict emergence of bermudagrasses

AbstractThe germination of bermudagrass [Cynodon dactylon (L.) Pers.] under different temperature regimes has been extensively investigated, but a discrepancy remains between laboratory studies and field results. Thermal requirements calculated in growing degree days (GDD) have been found to differ within the same species depending on the location of the study. The accumulation of GDD may vary under different thermal conditions from seeding to seedling emergence and could depend on TBASE used in the calculation. The most widely used TBASE for bermudagrass is 5 °C. However, laboratory studies have suggested that a base temperature of 15 °C would more accurately predict seedling emergence. In this field study, we investigated the effect of using TBASE 5 °C vs. TBASE 15 °C on the estimation of GDD required by bermudagrass to emerge. Ten cultivars were seeded in northeastern Italy on three dates between 10 March and the end of April in 2013 and 2014. Number of emerged seedlings was counted weekly and soil temperature at 1‐cm depth was recorded significant differences in seedling emergence between bermudagrass genotypes were found. Results demonstrated that the algorithm used to calculate GDD is strongly influenced by the TBASE used and to include a TBASE of 15 °C explains germination and emergence more accurately than a TBASE of 5 °C

lme4GS: An R-Package for Genomic Selection

Genomic selection (GS) is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). An active area of research is the development of software for fitting LMMs mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. This article describes the new lme4GS package for R, which is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance–covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data

Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data

Background: As a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation. Results: We have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500–690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, − 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction. Conclusions: The proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative

Atrasentan and renal events in patients with type 2 diabetes and chronic kidney disease (SONAR): a double-blind, randomised, placebo-controlled trial

Background: Short-term treatment for people with type 2 diabetes using a low dose of the selective endothelin A receptor antagonist atrasentan reduces albuminuria without causing significant sodium retention. We report the long-term effects of treatment with atrasentan on major renal outcomes. Methods: We did this double-blind, randomised, placebo-controlled trial at 689 sites in 41 countries. We enrolled adults aged 18–85 years with type 2 diabetes, estimated glomerular filtration rate (eGFR)25–75 mL/min per 1·73 m 2 of body surface area, and a urine albumin-to-creatinine ratio (UACR)of 300–5000 mg/g who had received maximum labelled or tolerated renin–angiotensin system inhibition for at least 4 weeks. Participants were given atrasentan 0·75 mg orally daily during an enrichment period before random group assignment. Those with a UACR decrease of at least 30% with no substantial fluid retention during the enrichment period (responders)were included in the double-blind treatment period. Responders were randomly assigned to receive either atrasentan 0·75 mg orally daily or placebo. All patients and investigators were masked to treatment assignment. The primary endpoint was a composite of doubling of serum creatinine (sustained for ≥30 days)or end-stage kidney disease (eGFR <15 mL/min per 1·73 m 2 sustained for ≥90 days, chronic dialysis for ≥90 days, kidney transplantation, or death from kidney failure)in the intention-to-treat population of all responders. Safety was assessed in all patients who received at least one dose of their assigned study treatment. The study is registered with ClinicalTrials.gov, number NCT01858532. Findings: Between May 17, 2013, and July 13, 2017, 11 087 patients were screened; 5117 entered the enrichment period, and 4711 completed the enrichment period. Of these, 2648 patients were responders and were randomly assigned to the atrasentan group (n=1325)or placebo group (n=1323). Median follow-up was 2·2 years (IQR 1·4–2·9). 79 (6·0%)of 1325 patients in the atrasentan group and 105 (7·9%)of 1323 in the placebo group had a primary composite renal endpoint event (hazard ratio [HR]0·65 [95% CI 0·49–0·88]; p=0·0047). Fluid retention and anaemia adverse events, which have been previously attributed to endothelin receptor antagonists, were more frequent in the atrasentan group than in the placebo group. Hospital admission for heart failure occurred in 47 (3·5%)of 1325 patients in the atrasentan group and 34 (2·6%)of 1323 patients in the placebo group (HR 1·33 [95% CI 0·85–2·07]; p=0·208). 58 (4·4%)patients in the atrasentan group and 52 (3·9%)in the placebo group died (HR 1·09 [95% CI 0·75–1·59]; p=0·65). Interpretation: Atrasentan reduced the risk of renal events in patients with diabetes and chronic kidney disease who were selected to optimise efficacy and safety. These data support a potential role for selective endothelin receptor antagonists in protecting renal function in patients with type 2 diabetes at high risk of developing end-stage kidney disease. Funding: AbbVie

Assessing competitiveness of fine fescues (Festuca l. spp.) and tall fescue (schedonorus arundinaceous (schreb.) dumort) established with white clover (trifolium repens l., wc), daisy (bellis perennis l.) and yarrow (achillea millefolium l.)

Regulatory restrictions on herbicide use for managing turfgrass weeds has prompted the search for alternative control strategies. Fescue (Festuca) species were identified for their potential to interfere with growth of annual and perennial weeds. In a study conducted in 2018 and 2019, six fescue cultivars were tested from five different species for interference with the growth of three common turfgrass weeds: white clover (Trifolium repens L., WC), daisy (Bellis perennis L.) and yarrow (Achillea millefolium L.). Fine (Festuca L. spp.) and tall fescues (Schedonorus arundinaceus (Schreb.) Dumort.) were sown and grown in a field trial for 14 days before overseeding with different weeds. vigor and visual quality of grasses, weed cover, and vegetation cover was recorded regularly for 84 days. Differences in mean temperatures and precipitation between the two years of the study resulted in differences in growth of grasses and weeds, as well as in the extent of weed interference of fescue cultivars. Cultivars Musica (F. rubra L. ssp. commutata Gaudin) and Barpearl (F. rubra L. ssp. littoralis) were least affected by weed growth during both years, but there was overlap with other cultivars for the measured parameters. Melyane (tall fescue) was deemed unsuitable for natural weed suppression because growth and vigor declined after first mowing, ultimately leading to unacceptable visual quality. Turfgrass visual scores were moderately negatively correlated to weed cover in both years. Future research should focus on F. rubra L. ssp. rubra Gaudin and F. rubra L. ssp. littoralis subspecies and identify the mechanisms used to interfere with weed growth

Propiedades del suelo afectadas por el tiempo de descanso en un sistema de roza-tumba-quema

ABSTRACT: The study of traditional agricultural systems performs an option for natural-ecosystems conservation and increase crop production. The slash-and-burn system (RTQ) has been declared obsolete and harmful, but this outlook is inconclusive. The aim of this study is to promote a better understanding of its ecological dynamics, which can guide for take decisions of the rational management of the ecosystems where it is located. A fallow chronosequence of the RTQ was analyzed in a tropical deciduous forest on leptosols, with the objective of describing the relationship of the microbial communities with the fallow period and soil organic matter. It is reported the heterotrophic bacteria functional diversity (Shannon index - H') and its activity, since metabolic profile of the microbial populations using BIOLOG-ECOTM plates, organic matter content, total nitrogen, nitrate, ammonium, extractable phosphorus, texture, bulk density, field capacity and permanent wilting point. The soil physical properties were similar at all over plots. Fallow time affected pH, organic-matter ad nitrates content. No clear relation was found between H' and the fallow time (t) but there were H' significant differences (p < 0.05) between higher and less fallow time.RESUMEN: El estudio de los sistemas agrícolas tradicionales representa una opción ante la necesidad de conservar los ecosistemas naturales e incrementar la producción de alimentos. Una mejor comprensión de la dinámica ecológica de la roza-tumba-quema (RTQ) puede permitir la toma de decisiones encaminadas al manejo racional de los ecosistemas donde esta se practica. Por lo anterior, el objetivo fue describir la relación de las comunidades microbianas con la materia orgánica y la condición nutrimental del suelo. Se calcularon los índices de diversidad de Shannon (H') a partir del perfil metabólico de las poblaciones con el uso de placas BIOLOG-ECOTM , el contenido de materia orgánica (MO), nitrógeno total, nitratos, amonio, fósforo extractable, textura, densidad aparente, capacidad de campo y punto de marchitez permanente del suelo. Las propiedades físicas no fueron significativamente diferentes. El pH, contenido de MO y de nitratos se modificó con el tiempo de descanso. No se encontró relación entre la diversidad microbiana (H') y el tiempo de descanso (t), pero sí diferencias significativas (p < 0.05) entre las parcelas con mayor y menor tiempo de descanso, siendo más diversa esta última

Detection and quantification of broadleaf weeds in turfgrass using close-range multispectral imagery with pixel- and object-based classification

The current practice used to evaluate broadleaf weed cover in turfgrass is visual assessment, which is time consuming and often leads to inconsistencies among evaluators. In this study, we investigated the effectiveness of constructing Random Forest models (RF), either pixel-, object-based (OBIA) or a combination of both to detect and quantify broadleaf weed cover. High resolution multispectral images were captured of 136 turfgrass plots, seeded with five species of Festuca L. and overseeded with either clover (Trifolium repens L.), daisy (Bellis perennis L.), yarrow (Achillea millefolium L.), or a mixture of all three weeds. Ground measurements of vegetation cover and bare soil were taken with a point quadrat and digital image analysis. Weeds were detected with 99% accuracy by OBIA, followed by the combined approach (98%) and Pixel-based approach (93%). Accuracy at distinguishing among weed species was somewhat lower (89%, 81% and 90%, respectively), with yarrow contributing most to the decrease in accuracy. The predictions based on ground measurements were further compared to field measurements. For both soil and weed classification, models that used shape features (OBIA and combined) resulted in better agreement with field measurements compared to Pixel- based classifications. Our study suggests that broadleaf weed cover comprised of species such as clover and daisy can be accurately quantified with high resolution multispectral images; however, quantifying yarrow cover remains challenging

A Bayesian Genomic Regression Model with Skew Normal Random Errors

Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be approximated by a normal distribution. However, it is known that the selection process leads to skewed distributions. There is vast statistical literature on skewed distributions, but the skew normal distribution is of particular interest in this research. This distribution includes a third parameter that drives the skewness, so that it generalizes the normal distribution. We propose an extension of the Bayesian whole-genome regression to skew normal distribution data in the context of GS applications, where usually the number of predictors vastly exceeds the sample size. However, it can also be applied when the number of predictors is smaller than the sample size. We used a stochastic representation of a skew normal random variable, which allows the implementation of standard Markov Chain Monte Carlo (MCMC) techniques to efficiently fit the proposed model. The predictive ability and goodness of fit of the proposed model were evaluated using simulated and real data, and the results were compared to those obtained by the Bayesian Ridge Regression model. Results indicate that the proposed model has a better fit and is as good as the conventional Bayesian Ridge Regression model for prediction, based on the DIC criterion and cross-validation, respectively. A computing program coded in the R statistical package and C programming language to fit the proposed model is available as supplementary material

Herencia de la resistencia a Phytophthora parasitica Dastur en Jamaica

To determine the genetics of resistance to Phytophthora parasiticain Jamaica, the generational means of five resistant and five susceptible lines were analyzed to estimate the genetic parameters of resistance in Jamaica crosses. The analysis showed that the additive effects were more important than dominance effects for resistance to P. parasitica. Heritability, broadly speaking, was 37%. The results obtained indicate that a pedigree program can be effective and the most appropriate to increase genetic resistance to P. parasitica.Para determinar la genética de la resistencia a Phytophthora parasitica en jamaica, se analizaron las medias generacionales de cinco líneas resistentes y cinco susceptibles para estimar los parámetros genéticos de la resistencia en cruzas de jamaica. El análisis mostró que los efectos aditivos fueron más importantes que efectos de dominancia para la resistencia a P. parasitica. La heredabilidad, en sentido amplio, fue de 37%. Los resultados obtenidos indican que un programa de pedigree puede ser efectivo y el más adecuado para incrementar la resistencia genética a P. parasitica

Influence of Geographical Orchard Location on the Microbiome from the Progeny of a Pecan Controlled Cross

Carya illinoinensis (Wangenh.) K.Koch production has expanded beyond the native distribution as the genetic diversity of the species, in part, has allowed the trees to grow under broad geographic and climatic ranges. Research in other plant species has demonstrated that the phytobiome enhances their ability to survive and thrive in specific environments and, conversely, is influenced by the prevailing environment and plant genetics, among other factors. We sought to analyze the microbiota of pecan seedlings from the controlled cross ‘Lakota’ × ‘Oaxaca’ that were made in Georgia and Texas, respectively, to determine if the maternal geographical origin influences the microbiome of the resulting progeny. No significant differences in bacterial communities were observed between the seeds obtained from the two different states (p = 0.081). However, seed origin did induce significant differences in leaf fungal composition (p = 0.012). Results suggest that, in addition to some environmental, epigenetics, or host genetic components, ecological processes, such as dispersal mechanisms of the host, differentially impact the pecan microbiome, which may have ramifications for the health of trees grown in different environments. Future studies on the role of the microbiome in plant health and productivity will aid in the development of sustainable agriculture for improved food security